>>from around the globe. It's the >>cube presenting >>active de que intelligent automation for data quality brought to you by Iota Ho >>Welcome to the sixth episode of the I. O. Tahoe data automation series. On the cube. We're gonna start off with a segment on how to accelerate the adoption of snowflake with Glenn Grossman, who is the enterprise account executive from Snowflake and yusef khan, the head of data services from Iota. Gentlemen welcome. >>Good afternoon. Good morning, Good evening. Dave. >>Good to see you. Dave. Good to see you. >>Okay glenn uh let's start with you. I mean the Cube hosted the snowflake data cloud summit in November and we heard from customers and going from love the tagline zero to snowflake, you know, 90 minutes very quickly. And of course you want to make it simple and attractive for enterprises to move data and analytics into the snowflake platform but help us understand once the data is there, how is snowflake helping to achieve savings compared to the data lake? >>Absolutely. dave. It's a great question, you know, it starts off first with the notion and uh kind of, we coined it in the industry or t shirt size pricing. You know, you don't necessarily always need the performance of a high end sports car when you're just trying to go get some groceries and drive down the street 20 mph. The t shirt pricing really aligns to, depending on what your operational workload is to support the business and the value that you need from that business? Not every day. Do you need data? Every second of the moment? Might be once a day, once a week through that t shirt size price and we can align for the performance according to the environmental needs of the business. What those drivers are the key performance indicators to drive that insight to make better decisions, It allows us to control that cost. So to my point, not always do you need the performance of a Ferrari? Maybe you need the performance and gas mileage of the Honda Civic if you would just get and deliver the value of the business but knowing that you have that entire performance landscape at a moments notice and that's really what what allows us to hold and get away from. How much is it going to cost me in a data lake type of environment? >>Got it. Thank you for that yussef. Where does Io Tahoe fit into this equation? I mean what's, what's, what's unique about the approach that you're taking towards this notion of mobilizing data on snowflake? >>Well, Dave in the first instance we profile the data itself at the data level, so not just at the level of metadata and we do that wherever that data lives. So it could be structured data could be semi structured data could be unstructured data and that data could be on premise. It could be in the cloud or it could be on some kind of SAAS platform. And so we profile this data at the source system that is feeding snowflake within snowflake itself within the end applications and the reports that the snowflake environment is serving. So what we've done here is take our machine learning discovery technology and make snowflake itself the repository for knowledge and insights on data. And this is pretty unique. Uh automation in the form of our P. A. Is being applied to the data both before after and within snowflake. And so the ultimate outcome is that business users can have a much greater degree of confidence that the data they're using can be trusted. Um The other thing we do uh which is unique is employee data R. P. A. To proactively detect and recommend fixes the data quality so that removes the manual time and effort and cost it takes to fix those data quality issues. Uh If they're left unchecked and untouched >>so that's key to things their trust, nobody's gonna use the data. It's not trusted. But also context. If you think about it, we've contextualized are operational systems but not our analytic system. So there's a big step forward glen. I wonder if you can tell us how customers are managing data quality when they migrate to snowflake because there's a lot of baggage in in traditional data warehouses and data lakes and and data hubs. Maybe you can talk about why this is a challenge for customers. And like for instance can you proactively address some of those challenges that customers face >>that we certainly can. They have. You know, data quality. Legacy data sources are always inherent with D. Q. Issues whether it's been master data management and data stewardship programs over the last really almost two decades right now, you do have systemic data issues. You have siloed data, you have information operational, data stores data marks. It became a hodgepodge when organizations are starting their journey to migrate to the cloud. One of the things that were first doing is that inspection of data um you know first and foremost even looking to retire legacy data sources that aren't even used across the enterprise but because they were part of the systemic long running operational on premise technology, it stayed there when we start to look at data pipelines as we onboard a customer. You know we want to do that era. We want to do QA and quality assurance so that we can, And our ultimate goal eliminate the garbage in garbage out scenarios that we've been plagued with really over the last 40, 50 years of just data in general. So we have to take an inspection where traditionally it was E. T. L. Now in the world of snowflake, it's really lt we're extracting were loading or inspecting them. We're transforming out to the business so that these routines could be done once and again give great business value back to making decisions around the data instead of spending all this long time. Always re architect ng the data pipeline to serve the business. >>Got it. Thank you. Glenda yourself of course. Snowflakes renowned for customers. Tell me all the time. It's so easy. It's so easy to spin up a data warehouse. It helps with my security. Again it simplifies everything but so you know, getting started is one thing but then adoption is also a key. So I'm interested in the role that that I owe. Tahoe plays in accelerating adoption for new customers. >>Absolutely. David. I mean as Ben said, you know every every migration to Snowflake is going to have a business case. Um uh and that is going to be uh partly about reducing spending legacy I. T. Servers, storage licenses, support all those good things um that see I want to be able to turn off entirely ultimately. And what Ayatollah does is help discover all the legacy undocumented silos that have been built up, as Glenn says on the data estate across a period of time, build intelligence around those silos and help reduce those legacy costs sooner by accelerating that that whole process. Because obviously the quicker that I. T. Um and Cdos can turn off legacy data sources the more funding and resources going to be available to them to manage the new uh Snowflake based data estate on the cloud. And so turning off the old building, the new go hand in hand to make sure those those numbers stack up the program is delivered uh and the benefits are delivered. And so what we're doing here with a Tahoe is improving the customers are y by accelerating their ability to adopt Snowflake. >>Great. And I mean we're talking a lot about data quality here but in a lot of ways that's table stakes like I said, if you don't trust the data, nobody's going to use it. And glenn, I mean I look at Snowflake and I see obviously the ease of use the simplicity you guys are nailing that the data sharing capabilities I think are really exciting because you know everybody talks about sharing data but then we talked about data as an asset, Everyone so high I to hold it. And so sharing is is something that I see as a paradigm shift and you guys are enabling that. So one of the things beyond data quality that are notable that customers are excited about that, maybe you're excited about >>David, I think you just cleared it out. It's it's this massive data sharing play part of the data cloud platform. Uh you know, just as of last year we had a little over about 100 people, 100 vendors in our data marketplace. That number today is well over 450 it is all about democratizing and sharing data in a world that is no longer held back by FTp s and C. S. V. S and then the organization having to take that data and ingested into their systems. You're a snowflake customer. want to subscribe to an S and P data sources an example, go subscribe it to it. It's in your account there was no data engineering, there was no physical lift of data and that becomes the most important thing when we talk about getting broader insights, data quality. Well, the data has already been inspected from your vendor is just available in your account. It's obviously a very simplistic thing to describe behind the scenes is what our founders have created to make it very, very easy for us to democratize not only internal with private sharing of data, but this notion of marketplace ensuring across your customers um marketplace is certainly on the type of all of my customers minds and probably some other areas that might have heard out of a recent cloud summit is the introduction of snow park and being able to do where all this data is going towards us. Am I in an ale, you know, along with our partners at Io Tahoe and R. P. A. Automation is what do we do with all this data? How do we put the algorithms and targets now? We'll be able to run in the future R and python scripts and java libraries directly inside Snowflake, which allows you to even accelerate even faster, Which people found traditionally when we started off eight years ago just as a data warehousing platform. >>Yeah, I think we're on the cusp of just a new way of thinking about data. I mean obviously simplicity is a starting point but but data by its very nature is decentralized. You talk about democratizing data. I like this idea of the global mesh. I mean it's very powerful concept and again it's early days but you know, keep part of this is is automation and trust, yussef you've worked with Snowflake and you're bringing active D. Q. To the market what our customers telling you so far? >>Well David the feedback so far has been great. Which is brilliant. So I mean firstly there's a point about speed and acceleration. Um So that's the speed to incite really. So where you have inherent data quality issues uh whether that's with data that was on premise and being brought into snowflake or on snowflake itself, we're able to show the customer results and help them understand their data quality better Within Day one which is which is a fantastic acceleration. I'm related to that. There's the cost and effort to get that insight is it's a massive productivity gain versus where you're seeing customers who've been struggling sometimes too remediate legacy data and legacy decisions that they've made over the past couple of decades, so that that cost and effort is much lower than it would otherwise have been. Um 3rdly, there's confidence and trust, so you can see Cdos and see IOS got demonstrable results that they've been able to improve data quality across a whole bunch of use cases for business users in marketing and customer services, for commercial teams, for financial teams. So there's that very quick kind of growth in confidence and credibility as the projects get moving. And then finally, I mean really all the use cases for the snowflake depend on data quality, really whether it's data science, uh and and the kind of snow park applications that Glenn has talked about, all those use cases work better when we're able to accelerate the ri for our joint customers by very quickly pushing out these data quality um insights. Um And I think one of the one of the things that the snowflake have recognized is that in order for C. I. O. Is to really adopt enterprise wide, um It's also as well as the great technology with Snowflake offers, it's about cleaning up that legacy data state, freeing up the budget for CIA to spend it on the new modern day to a state that lets them mobilise their data with snowflake. >>So you're seeing the Senate progression. We're simplifying the the the analytics from a tech perspective. You bring in Federated governance which which brings more trust. Then then you bring in the automation of the data quality piece which is fundamental. And now you can really start to, as you guys are saying, democratized and scale uh and share data. Very powerful guys. Thanks so much for coming on the program. Really appreciate your time. >>Thank you. I appreciate as well. Yeah.

>> From theCube studios in Palo Alto in Boston, bringing you data-driven insights from theCube in ETR. This is Breaking Analysis with Dave Vellante. >> New data suggests the tech spending will be higher than we previously thought for 2021. COVID learnings, a faster than expected vaccine rollout, productivity gains in the last 10 months, and broad-based cloud leverage lead us to raise our outlook for next year. We now expect a three to 5% increase in 2021 technology spending, roughly double our previously forecasted growth rate of 2%. Hello everyone and welcome to this week's we keep on Cube Insights powered by ETR. In this breaking analysis, we're going to share new spending data from ETR partners and take a preliminary look at which sectors and which companies are showing momentum heading into next year. Let's get right into it. The data is pointing to a strong 2021 rebound. A latest survey from ETR and the information from theCube Community suggests that the accelerated pace of the vaccine rollout pent up demand for normalcy and learnings from COVID will boost 2021 tech spending higher than previously anticipated. Now a key factor we've cited is that the forced March to digital transformation due to the pandemic created a massive proof of concept for what works and what doesn't in a digital business. CIOs are planning to bet on those sure things to drive continued productivity improvements and new business opportunities. Now, speaking of productivity, nearly 80% of respondents in the latest ETR survey indicate that productivity either stayed the same or improved over the past three months. Now of those, the vast majority, more than 80% cited improvements in productivity. This has been a common theme throughout the year. As well, the expectation among CIOs is that many workers will return to the office in the second half of the year, which we expect will drive new spending in the infrastructure needs of company HQs, which have been neglected over the past 10 months. Now, despite the expectation that many workers will return to the office, 2020 has shown us that working remotely, hey, it's here to stay, and a much larger number of employees are going to be permanently remote working than pre pandemic. ETR survey data shows that that number is going to be approximately double over the longterm. We'll look at some of that specific data. In addition, cloud computing, it became the staple of business viability in 2020. Those that were up the cloud adoption ramp, well, they benefited greatly, those that weren't well, they had to learn fast. Now, along with remote work cloud necessitated new thinking around network security, and as we've reported identity access management, endpoint security and cloud security with the beneficiaries. Companies like Okta, CrowdStrike, Zscaler, a number of others continue to ride this wave. Larger established security companies like Cisco, Palo Alto Networks, F5, Fortunate and others, they have major portions of their business that are benefiting from the tailwinds in the shift and network traffic, as a result of cloud and remote work. Now, despite all the momentum in the market and the expect of improvements in 2021, these tailwinds are not expected to be evenly distributed, far from it. We think Q4 is going to remain soft relative to last year and Q1 2021 is going to be flat, maybe up slightly. Remember the COVID impact was definitely felt in March of this year. So based on the earnings that we saw, there may be some upside in Q1, given that organizations are still being cautious in Q4, and really there's still some uncertainty in Q1. Let's look at some of the survey responses and you'll see why we're more optimistic than we've previously reported. This chart shows the responses to key questions around spending trajectories from the March, June, September, and December surveys of this year. Now it's no surprise that there's been little change in remote workers and limiting business travel. But look at the other categories, seeing a dramatic reduction in hiring freezes. The percentage of companies freezing new IT deployments continues to drop throughout the year. And then conversely, the percentage of companies accelerating new it deployments that's sharply up to 34% from the March low of 12%. And look at the headcount trends. The percentage of companies instituting layoffs. It continues its downward trajectory while accelerated hiring is now up to 17%. So there's a lot to be excited about in these results. Now let's look the remote worker trend. How do CIO see that shift in the near to midterm? This chart shows the work from home data and it's amazingly consistent from the September survey drill down. You can see CIO's is indicate that on average, 15 to 60% of workers were remote prior to the pandemic, and that jumped up to 72 to 73% currently, and is expected to stay in the high fifties until the summer of 2021. Thereafter, organizations expect that the number of employees that work remotely on a permanent basis is going to more than double to 34% long term. By the way, I've talked to a number of executives, CEOs, CIOs, and CFOs that expect that number to be higher than these especially in the technology sector. They expect more than half of their workers to be remote and are looking to consolidate facilities cost to save money. As we've said, cloud computing has been the most significant contributor to business resilience and digital transformation this year. So let's look at cloud strategies and see how CIOs expect those to evolve. This chart shows responses to how organizations see multi-cloud evolving. It's interesting to note the ETR call-out, which concludes that the narrative around multi-cloud multi-cloud is real, and it is. But I want to talk to you about a flip side to this notion in that, as many customers have, or are planning to increasingly concentrate workloads in the cloud. This actually makes some sense. Sure, virtually every major company uses multiple clouds, but more often than not, it concentrate work on a primary cloud. CIO strategies, they're not generally evenly distributed across clouds. The data shows that this is the case for less than 20% of the respondents, rather organizations are typically going to apply an 80, 20 or a 70, 30 rule for their multi-cloud approach. Meaning they pick a primary cloud on which most work is done, and then they use alternative clouds as either a hedge or maybe for specific workloads or maybe even data protection purposes. Now, if you think about it, optimizing on a primary cloud allows organizations to simplify their security and governance and consolidate their skills. At this point in the cloud evolution, it seems CIOs feel there's more value that is going to come from leveraging the cloud to change their operating models, and maybe broadly spreading the wealth to reduce risk or maybe cut costs, or maybe even to tap specialized capabilities. What's more in thinking about AWS and Microsoft respectively. Each can make a very strong case from MANO cloud. AWS has more features than any other cloud, and as such can handle most workloads. Microsoft can make a similar argument for its customers that have an affinity and a largest state of Microsoft software. The key for multi-cloud in our view will be the degree to which technology vendors can abstract the underlying cloud complexity and create a layer that floats above the clouds and adds incremental value. Snowflakes data cloud is one of the best examples of this, and we've covered that pretty extensively. Now, clearly VMware and Red Hat have aspirations at the infrastructure layer in a similar fashion. Pure storage, and NetApp are a couple of the largest storage players with similar visions. And then Qumulo and Clumio are two other examples with promising technologies, but they have a much smaller install base. Take a look at Cisco, Dell, IBM and HPE. They have a lot to gain and a lot to lose in this cloud game. So multi-cloud is an imperative for these leaders, but for them it's much more complicated because of the complexity and vastness of their portfolios. And notably Dell has VMware and IBM of course has Red Hat, which are key assets that can be leveraged for this multi-cloud game. HPE has a channel and a large install base, but all of these firms, they have to spread R&D much more thinly than some of these other companies that we mentioned for example. The bottom line is that multi-cloud has to be more than just plugging into an operating well on any of the clouds. It require... Which is by the way, this is mostly where we are today. It requires an incremental value proposition that solves a clear problem, and at the same time runs efficiently, meaning it takes advantage of cloud native services at scale. What sectors are showing momentum heading into 2021? And who are some of the names that are looking strong? We've reported a lot that cloud containers and container orchestration, machine intelligence and automation are by far the hottest sectors, the biggest areas of investment with the greatest spending momentum. Now we measure this in ETR parlance, remember by net score. But here's the good news, almost every other sector in the ETR taxonomy with the notable exception of IT outsourcing and IT consulting is showing positive spending momentum relative to previous surveys this year. Yeah, maybe not, it's not a shock, but it appears that the tech spending recovery will be broad-based. It's also worth noting that there are several vendors that stand out and we show a number of them here. CrowdStrike, Microsoft has had consistent performance in the dataset throughout this year. Okta, we called out those guys last year and they've clearly performed as you can see in their earnings reports. Pure storage, interestingly, big acceleration and a turnaround from last quarter in the dataset, and of course, snowflake has been off the charts as we reported many times. These guys are all seeing highly accelerated momentum. UiPath just announced its intent to IPO, AWS, Google, Zscaler, SailPoint, ServiceNow, and Elastic, these all continue to trend up. And so, there are some real positives that we're looking for a member of the ETR surveys, they're forward-looking. So we'll see, as we catch up next quarter. Now, before we wrap, I want to say a few words on security, and maybe it's a bit of a non-sequitur here, but I think it's relevant to the trends that we've been discussing, especially as we talk about moving to the cloud. And as you know, we've reported many times on the security space, basically updating you quarterly with our scenarios and the spending and the technology trends and highlighting our four-star companies. Four-star company's insecurity on those with both momentum and significant market presence. And last year we put CrowdStrike, Okta and Zscaler, and some others on the radar. And we've closely track the cyber business of larger companies with a security portfolio like Palo Alto and Cisco, and more recently, VMware has made some acquisitions. Now the government hacked that became news this week. It really underscores the importance of security. It remains the most challenging area for organizations because well, failure's not an option, skills are short, tools are abundant, the adversaries are very well-funded and extremely capable yet failure is common as we saw this week. And there's a misconception that cloud solves the security problem, and it's important to point out that it does not. Cloud is a shared responsibility model, meaning the cloud provider is going to secure the infrastructure for example, but it's up to you as the customer to configure things properly and deal with application security. It's ultimately on you. And the example of S3 is instructive because we've seen a number S3 breaches over the years where the customer didn't properly configure the S3 bucket. We're talking about companies like Honda and Capital One, not just small businesses that don't have the SecOps resources. And generally it was because a non-security person was configuring things. Maybe they were Or developers who are not focused on security, and perhaps permission set too broadly, and access was given to far too many people. Whatever the issue, it took some breaches and subsequent education to increase awareness of this problem and tighten it up. We see some similar trends occurring with new workloads, especially in cloud databases. It's becoming so easy to spin up new data warehouses for example, and we believe that there are exposures out there due the lack of awareness or inconsistent corporate governance being applied to these new data stores. As well, even though important areas like threat intelligence and database security are important, SecOps budgets are stretched thin. And when you ask companies where the priorities are, these fall lower down the list, these areas specifically have taken a back seat, the endpoint, identity and cloud security. And we bring this up because it's a potential blind spot as we saw this week with the US government hack. It was stealthy, it wasn't detected for many, many months. Who knows maybe even years. And not to be a buzzkill, but the point is, cloud enthusiasm has to be concompetent with security vigilant. Enough preaching, let's wrap up here. As we enter 2020, this year, we said the cloud was going to be the force that drove innovation along with data and AI. And as we look in the rear view mirror and put 2020 behind us, I know many of you want to do that, it was the cloud that enabled businesses to not only continue to operate, but to actually increase productivity. Nonetheless, we still see IT spending declines of four to 5% this year with an expectation of a tepid Q4 relative to the last year. We see Q1 slowly rebounding and kind of a swoosh, let me try that again, recovery in the subsequent quarters with tech spending rebounding in 2021 to a positive three to 5%, let's call it 4%. Now supporting us scenario, the pandemic forced a giant Petri dish for digital. And we see some real successes and learnings that organizations will apply in 2021 to bet on sure things. These are cloud, containers, AI, ML, machine intelligence pieces and automation. For sure, along with upticks for virtually every other sector of technology because spending has been so depressed. The two exceptions are outsourcing and IT consulting and related services which continue to be a drag on overall spending. Priorities must be focused on security and governance and further improvements in applying corporate edicts in a cloud world. We also see new data architectures emerging where domain knowledge becomes central to data platforms. We'll be covering this in more detail on top of the work that we've already done in this area. Now, automation is not only an opportunity, it's become a mandate. Yes, RPA, but also broader automation agendas be on point tools. And importantly, we're not talking about paving the cow path here by automating existing processes. Rather we're talking about rethinking processes across the entire organization for a new digital reality where many of these processes are being invented. The work of Erik Brynjolfsson and Andrew McAfee on the second machine age. It was pressured back in 2014 and the conclusions they drew, they're becoming increasingly important in the 2020s, meaning that look machines have always replaced humans throughout time. But for the first time in history, it's happening for cognitive functions, and a huge base of workers is going to be, or as being marginalized, unless they're retrained. Education and public policy that supports this transition is critical. And I for one would like to see a much more productive discussion that goes beyond the cult of break up big tech. Rather I'd like to see governments partner with big tech to truly do good and help drive the re-skilling of workers for the digital age. Now cloud remains the underpinning of the digital business mandate, but the path forward isn't really always crystal clear. This is evidenced by the virtual dead heat between those organizations that are consolidating workloads in a cloud workloads versus those that are hedging bets on a multi-cloud strategy. One thing is clear cloud is the linchpin for our growth scenarios and will continue to be the substrate for innovation in the coming decade. Remember, these episodes, they're all available as podcasts, wherever you listen, all you got to do is search Breaking Analysis podcast, and please subscribe to the series, appreciate that. Check out ETR's website at ETR.plus. We also publish full report every week on wikibond.com and siliconangle.com and get in touch with me at David.vallante, siliconangle.Com, you can DM me at D. Vellante. And please by all means comment on our LinkedIn posts. This is Dave Vellante for theCube Insights powered by ETR. Have a great week everybody, Merry Christmas, happy Hanukkah, happy Kwanzaa, or happy, whatever holiday you celebrate. Stay safe, be well, and we'll see you next time. (upbeat music)

>>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome to cube three 60 fives coverage of the Accenture executive summit. Part of AWS reinvent. I'm your host Rebecca Knight. Today we are joined by a cube alum Karthik NurAin. He is Accenture senior managing director and lead Accenture cloud. First, welcome back to the show Karthik. >>Thank you. Thanks for having me here. >>Always a pleasure. So I want to talk to you. You are an industry veteran, you've been in Silicon Valley for decades. Um, I want to hear from your perspective what the impact of the COVID-19 pandemic has been, what are you hearing from clients? What are they struggling with? What are their challenges that they're facing day to day? >>I think, um, COVID-19 is being a eye-opener from, you know, various facets, you know, um, first and foremost, it's a, it's a head, um, situation that everybody's facing, which is not just, uh, highest economic bearings to it. It has enterprise, um, an organization with bedding to it. And most importantly, it's very personal to people, um, because they themselves and their friends, family near and dear ones are going to this challenge, uh, from various different dimension. But putting that aside, when you come to it from an organization enterprise standpoint, it has changed everything well, the behavior of organizations coming together, working in their campuses, working with each other as friends, family, and, uh, um, near and dear colleagues, all of them are operating differently. So that's what big change to get things done in a completely different way, from how they used to get things done. >>Number two, a lot of things that were planned for normal scenarios, like their global supply chain, how they interact with their client customers, how they coordinate with their partners on how that employees contribute to the success of an organization at all changed. And there are no data models that give them a hint of something like this for them to be prepared for this. So we are seeing organizations, um, that have adapted to this reasonably okay, and are, you know, launching to innovate faster in this. And there are organizations that have started with struggling, but are continuing to struggle. And the gap, uh, between the leaders and legs are widening. So this is creating opportunities in a different way for the leaders, um, with a lot of pivot their business, but it's also creating significant challenge for the lag guides, uh, as we defined in our future systems research that we did a year ago, uh, and those organizations are struggling further. So the gap is actually whitening. >>So you've just talked about the widening gap. I've talked about the tremendous uncertainty that so many companies, even the ones who have adapted reasonably well, uh, in this, in this time, talk a little bit about Accenture cloud first and why, why now? >>I think it's a great question. Um, we believe that for many of our clients COVID-19 has turned, uh, cloud from an experimentation aspiration to an origin mandate. What I mean by that is everybody has been doing something on the other end cloud. There's no company that says we don't believe in cloud. Uh, our, we don't want to do cloud. It was how much they did in cloud. And they were experimenting. They were doing the new things in cloud. Um, but they were operating a lot of their core business outside the cloud or not in the cloud. Those organizations have struggled to operate in this new normal, in a remote fashion as with us, uh, that ability to pivot to all the changes the pandemic has brought to them. But on the other hand, the organizations that had a solid foundation in cloud were able to collect faster and not actually gone into the stage of innovating faster and driving a new behavior in the market, new behavior within their organization. >>So we are seeing that spend to make is actually fast-forwarded something that we always believed was going to happen. This, uh, uh, moving to cloud over the next decade is fast, forwarded it to, uh, happen in the next three to five years. And it's created this moment where it's a once in an era, really replatforming of businesses in the cloud that we are going to see. And we see this moment as a cloud first moment where organizations will use cloud as the, the canvas and the foundation with which they're going to reimagine their business after they were born in the cloud. Uh, and this requires a whole new strategy. Uh, and as Accenture, we are getting a lot in cloud, but we thought that this is the moment where we bring all of that capabilities together because we need a strategy for addressing, moving to cloud are embracing cloud in a holistic fashion. And that's what Accenture cloud first brings together a holistic strategy, a team that's 70,000 plus people that's coming together with rich cloud skills, but investing to tie in all the various capabilities of cloud to Delaware, that holistic strategy to our clients. So I want you to >>Delve into a little bit more about what this strategy actually entails. I mean, it's clearly about embracing change and being willing to experiment and, and having capabilities to innovate. Can you tell us a little bit more about what this strategy entails? >>Yeah. The reason why we say that there's a need for the strategy is, like I said, COVID is not new. There's almost every customer client is doing something with the cloud, but all of them have taken different approaches to cloud and different boundaries to cloud. Some organizations say, I just need to consolidate my multiple data centers to a small data center footprint and move the nest to cloud. Certain other organizations say that well, I'm going to move certain workloads to cloud. Certain other organizations said, well, I'm going to build this Greenfield application or workload in cloud. Certain other said, um, I'm going to use the power of AI ML in the cloud to analyze my data and drive insights. But a cloud first strategy is all of this tied with the corporate strategy of the organization with an industry specific cloud journey to say, if in this current industry, if I were to be reborn in the cloud, would I do it in the exact same passion that I did in the past, which means that the products and services that they offer need to be the matching, how they interact with that customers and partners need to be revisited, how they bird and operate their IP systems need to be the, imagine how they unearthed the data from all the systems under which they attract need to be liberated so that you could drive insights of cloud. >>First strategy. Hans is a corporate wide strategy, and it's a C-suite responsibility. It doesn't take the ownership away from the CIO or CIO, but the CIO is, and CDI was felt that it was just their problem and they were to solve it. And everyone as being a customer, now, the center of gravity is elevated to it becoming a C-suite agenda on everybody's agenda, where probably the CDI is the instrument to execute that that's a holistic cloud-first strategy >>And it, and it's a strategy, but the way you're describing it, it sounds like it's also a mindset and an approach, as you were saying, this idea of being reborn in the cloud. So now how do I think about things? How do I communicate? How do I collaborate? How do I get done? What I need to get done. Talk a little bit about how this has changed, the way you support your clients and how Accenture cloud first is changing your approach to cloud services. >>Wonderful. Um, you know, I did not color one very important aspect in my previous question, but that's exactly what you just asked me now, which is to do all of this. I talked about all of the vehicles, uh, an organization or an enterprise is going to go to, but the good part is they have one constant. And what is that? That is their employees, uh, because you do, the employees are able to embrace this change. If they are able to, uh, change them, says, pivot them says retool and train themselves to be able to operate in this new cloud. First one, the ability to reimagine every function of the business would be happening at speed. And cloud first approach is to do all of this at speed, because innovation is deadly proposed there, do the rate of probability on experimentation. You need to experiment a lot for any kind of experimentation. >>There's a probability of success. Organizations need to have an ability and a mechanism for them to be able to innovate faster for which they need to experiment a lot. The more the experiment and the lower cost at which they experiment is going to help them experiment a lot and experiment demic speed, fail fast, succeed more. And hence, they're going to be able to operate this at speed. So the cloud-first mindset is all about speed. I'm helping the clients fast track that innovation journey, and this is going to happen. Like I said, across the enterprise and every function across every department, I'm the agent of this change is going to be the employee's weapon, race, this change through new skills and new grueling and new mindset that they need to adapt to. >>So Karthik what you're describing it, it sounds so exciting. And yet for a pandemic wary workforce, that's been working remotely that may be dealing with uncertainty if for their kid's school and for so many other aspects of their life, it sounds hard. So how are you helping your clients, employees get onboard with this? And because the change management is, is often the hardest part. >>Yeah, I think it's, again, a great question. A bottle has only so much capacity. Something got to come off for something else to go in. That's what you're saying is absolutely right. And that is again, the power of cloud. The reason why cloud is such a fundamental breakthrough technology and capability for us to succeed in this era, because it helps in various forms. What we talked so far is the power of innovation that could create, but cloud can also simplify the life of the employees in an enterprise. There are several activities and tasks that people do in managing their complex infrastructure, complex ID landscape. They used to do certain jobs and activities in a very difficult, uh, underground about with cloud has simplified. And democratised a lot of these activities. So that things which had to be done in the past, like managing the complexity of the infrastructure, keeping them up all the time, managing the, um, the obsolescence of the capabilities and technologies and infrastructure, all of that could be offloaded to the cloud. >>So that the time that is available for all of these employees can be used to further innovate. Every organization is good to spend almost the same amount of money, but rather than spending activities, by looking at the rear view mirror on keeping the lights on, they're going to spend more money, more time, more energy, and spend their skills on things that are going to add value to their organization. Because you, every innovation that an enterprise can give to their end customer need not come from that enterprise. The word of platform economy is about democratising innovation. And the power of cloud is to get all of these capabilities from outside the four walls of the enterprise, >>It will add value to the organization, but I would imagine also add value to that employee's life because that employee, the employee will be more engaged in his or her job and therefore bring more excitement and energy into her, his or her day-to-day activities too. >>Absolutely. Absolutely. And this is, this is a normal evolution we would have seen everybody would have seen in their lives, that they keep moving up the value chain of what activities that, uh, gets performed buying by those individuals. And there's this, um, you know, no more true than how the United States, uh, as an economy has operated where, um, this is the power of a powerhouse of innovation, where the work that's done inside the country keeps moving up to that. You change. And, um, us leverages the global economy for a lot of things that is required to power the United States and that global economic, uh, phenomenon is very proof for an enterprise as well. There are things that an enterprise needs to do them soon. There are things an employee needs to do themselves. Um, but there are things that they could leverage from the external innovation and the power of innovation that is coming from technologies like cloud. >>So at Accenture, you have long, long, deep Stan, sorry, you have deep and long standing relationships with many cloud service providers, including AWS. How does the Accenture cloud first strategy, how does it affect your relationships with those providers? >>Yeah, we have great relationships with cloud providers like AWS. And in fact, in the cloud world, it was one of the first, um, capability that we started about years ago, uh, when we started developing these capabilities. But five years ago, we hit a very important milestone where the two organizations came together and said that we are forging a pharma partnership with joint investments to build this partnership. And we named that as a Accenture, AWS business group ABG, uh, where we co-invest and brought skills together and develop solutions. And we will continue to do that. And through that investment, we've also made several acquisitions that you would have seen in the recent times, like, uh, an invoice and gecko that we made acquisitions in in Europe. But now we're taking this to the next level. What we are saying is two cloud first and the $3 billion investment that we are bringing in, uh, through cloud first, we are going to make specific investment to create unique joint solution and landing zones foundation, um, cloud packs with which clients can accelerate their innovation or their journey to cloud first. >>And one great example is what we are doing with Takeda, uh, billable, pharmaceutical giant, um, between we've signed a five-year partnership. And it was out in the media just a month ago or so, where we are, the two organizations are coming together. We have created a partnership as a power of three partnership where the three organizations are jointly hoarding hats and taking responsibility for the innovation and the leadership position that Decatur wants to get to with this. We are going to simplify their operating model and organization by providing it flexibility. We're going to provide a lot more insights. Tequila has a 230 year old organization. Imagine the amount of trapped data and intelligence that is there. How about bringing all of that together with the power of AWS and Accenture and Takeda to drive more customer insights, um, come up with breakthrough, uh, R and D uh, accelerate clinical trials and improve the patient experience using AI ML and edge technologies. So all of these things that we will do through this partnership with joint investment from Accenture cloud first, as well as partner like AWS, so that Takeda can realize their gain. And, uh, they're seeing you actually made a statement that five years from now, every ticket an employee will have an AI assistant. That's going to make that beginner employee move up the value chain on how they contribute and add value to the future of tequila with the AI assistant, making them even more equipped and smarter than what they could be otherwise. >>So, one last question to close this out here. What is your future vision for, for Accenture cloud first? What are we going to be talking about at next year's Accenture executive summit? Yeah, the future >>Is going to be, um, evolving, but the part that is exciting to me, and this is, uh, uh, a fundamental belief that we are entering a new era of industrial revolution from industry first, second, and third industry. The third happened probably 20 years ago with the advent of Silicon and computers and all of that stuff that happened here in the Silicon Valley. I think the fourth industrial revolution is going to be in the cross section of, uh, physical, digital and biological boundaries. And there's a great article, um, in what economic forum that, that people, uh, your audience can Google and read about it. Uh, but the reason why this is very, very important is we are seeing a disturbing phenomenon that over the last 10 years, they are seeing a Blackwing of the, um, labor productivity and innovation, which has dropped to about 2.1%. When you see that kind of phenomenon over that longer period of time, there has to be breakthrough innovation that needs to happen to come out of this barrier and get to the next base camp, as I would call it to further this productivity, um, lack that we are seeing, and that is going to happen in the intersection of the physical, digital and biological boundaries. >>And I think cloud is going to be the connective tissue between all of these three, to be able to provide that where it's the edge, especially is going to come closer to the human lives. It's going to come from cloud pick totally in your mind, you can think about cloud as central, either in a private cloud, in a data center or in a public cloud, you know, everywhere. But when you think about edge, it's going to be far reaching and coming close to where we live and maybe work and very, um, get entertained and so on and so forth. And there's going to be, uh, intervention in a positive way in the field of medicine, in the field of entertainment, in the field of, um, manufacturing in the field of, um, uh, you know, mobility. When I say mobility, human mobility, people, transportation, and so on and so forth with all of this stuff, cloud is going to be the connective tissue and the vision of cloud first is going to be, uh, you know, blowing through this big change that is going to happen. And the evolution that is going to happen where, you know, the human grace of mankind, um, our person kind of being very gender neutral in today's world. Um, go first needs to be that beacon of, uh, creating the next generation vision for enterprises to take advantage of that kind of an exciting future. And that's why it, Accenture. We say, let there be change as our, as a purpose. >>I genuinely believe that cloud first is going to be in the forefront of that change agenda, both for Accenture as well as for the rest of the world. Excellent. Let there be change, indeed. Thank you so much for joining us Karthik. A pleasure I'm Rebecca night's stay tuned for more of Q3 60 fives coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS >>Welcome everyone to the Q virtual and our coverage of the Accenture executive summit, which is part of AWS reinvent 2020. I'm your host Rebecca Knight. Today, we are talking about the green, the cloud and joining me is Kishor Dirk. He is Accenture senior managing director cloud first global services lead. Thank you so much for coming on the show. Kishor nice to meet you. So I want to start by asking you what it is that we mean when we say green cloud, we know that sustainability is a business imperative. So many organizations around the world are committing to responsible innovation, lowering carbon emissions, but what's this, what is it? What does it mean when they talk about cloud from a sustainability perspective? I think it's about responsible innovation being cloud is a cloud first approach that has profits and benefit the clients by helping reduce carbon emissions. >>Think about it this way. You have a large number of data centers. Each of these data centers are increasing by 14% every year. And this double digit growth. What you're seeing is these data centers and the consumption is nearly coolant to the kind of them should have a country like Spain. So the magnitude of the problem that is out there and how do we pursue a green approach. If you look at this, our Accenture analysis, in terms of the migration to public cloud, we've seen that we can reduce that by 59 million tons of CO2 per year with just the 5.9% reduction in total ID emissions and equates this to 22 million cars off the road. And the magnitude of reduction can go a long way in meeting climate change commitments, particularly for data sensitive. >>Wow, that's incredible. What the numbers that you're putting forward are, are absolutely mind blowing. So how does it work? Is it a simple cloud migration? So, you know, when companies begin their cloud journey and then they confront, uh, with them a lot of questions, the decision to make, uh, this particular, uh, element sustainable in the solution and benefits they drive and they have to make wise choices, and then they will be unprecedented level of innovation leading to both a greener planet, as well as, uh, a greener balance sheet, I would say, uh, so effectively it's all about ambition data, the ambition, greater the reduction in carbon emissions. So from a cloud migration perspective, we look at it as a, as a simple solution with approaches and sustainability benefits, uh, that vary based on things it's about selecting the right cloud provider, a very carbon thoughtful provider and the first step towards a sustainable cloud journey. >>And here we're looking at cloud operators, obviously they have different corporate commitments towards sustainability, and that determines how they plan, how they build, uh, their, uh, uh, the data centers, how they are consumed and assumptions that operate there and how they, or they retire their data centers. Then, uh, the next element that you want to do is how do you build it ambition, you know, for some of the companies, uh, and average on-prem, uh, drives about 65% energy reduction and the carbon emissions and reduction number was 84%, which is kind of good, I would say. But then if you could go up to 98% by configuring applications to the cloud, that is significant benefit for, uh, for the board. And obviously it's a, a greener cloud that we're talking about. And then the question is, how far can you go? And, uh, you know, the, obviously the companies have to unlock greater financial societal environmental benefits, and Accenture has this cloud based circular operations and sustainable products and services that we bring into play. So it's a, it's a very thoughtful, broader approach that w bringing in, in terms of, uh, just a simple concept of cloud migration, >>We know that in the COVID era, shifting to the cloud has really become a business imperative. How is Accenture working with its clients at a time when all of this movement has been accelerated? How do you partner and what is your approach in terms of helping them with their migration? >>Yeah, I mean, let, let me talk a little bit about the pandemic and the crisis that is there today. And if you really look at that in terms of how we partnered with a lot of our clients in terms of the cloud first approach, I'll give you a couple of examples. We worked with rolls Royce, McLaren, DHL, and others, as part of the ventilator challenge consortium, again, to, uh, coordinate production of medical ventilator surgically needed for the UK health service. Many of these farms I've taken similar initiatives in, in terms of, uh, you know, from a few manufacturers hand sanitizers and to hand sanitizers, and again, leading passionate labels, making PPE, and again, at the UN general assembly, we launched the end-to-end integration guide that helps company essentially to have a sustainable development goals. And that's how we have parking at a very large scale. >>Uh, and, and if you really look at how we work with our clients and what is Accenture's role there, uh, you know, from, in terms of our clients, you know, there are multiple steps that we look at. One is about, uh, planning, building, deploying, and managing an optimal green cloud solution. And Accenture has this concept of, uh, helping clients with a platform to kind of achieve that goal. And here we are having, we are having a platform or a mine app, which has a module called BGR advisor. And this is a capability that helps you provide optimal green cloud, uh, you know, a business case, and obviously a blueprint for each of our clients and right from the start in terms of how do we complete cloud migration recommendation to an improved solution, accurate accuracy to obviously bringing in the end to end perspective, uh, you know, with this green card advisor capability, we're helping our clients capture what we call as a carbon footprint for existing data centers and provide, uh, I would say the current cloud CO2 emission score that, you know, obviously helps them, uh, with carbon credits that can further that green agenda. >>So essentially this is about recommending a green index score, reducing carbon footprint for migration migrating for green cloud. And if we look at how Accenture itself is practicing what we preach, 95% of our applications are in the cloud. And this migration has helped us, uh, to lead to about $14.5 million in benefit. And in the third year and another 3 million analytics costs that are saved through right-sizing a service consumption. So it's a very broad umbrella and a footprint in terms of how we engage societaly with the UN or our clients. And what is it that we exactly bring to our clients in solving a specific problem? >>Accenture isn't is walking the walk, as you say yes. >>So that's that instead of it, we practice what we preach, and that is something that we take it to heart. We want to have a responsible business and we want to practice it. And we want to advise our clients around that >>You are your own use case. And so they can, they know they can take your advice. So talk a little bit about, um, the global, the cooperation that's needed. We know that conquering this pandemic is going to take a coordinated global effort and talk a little bit about the great reset initiative. First of all, what is that? Why don't we, why don't we start there and then we can delve into it a little bit more. >>Okay. So before we get to how we are cooperating, the great reset, uh, initiative is about improving the state of the world. And it's about a group of global stakeholders cooperating to simultaneously manage the direct consequences of their COVID-19 crisis. Uh, and in spirit of this cooperation that we're seeing during COVID-19, uh, which will obviously either to post pandemic, to tackle the world's pressing issues. As I say, uh, we are increasing companies to realize a combined potential of technology and sustainable impact to use enterprise solutions, to address with urgency and scale, and, um, obviously, uh, multiple challenges that are facing our world. One of the ways that you're increasing, uh, companies to reach their readiness cloud with Accenture's cloud core strategy is to build a solid foundation that is resilient and will be able to faster to the current, as well as future times. Now, when you think of cloud as the foundation, uh, that drives the digital transformation, it's about scale speed, streamlining your operations, and obviously reducing costs. >>And as these businesses seize the construct of cloud first, they must remain obviously responsible and trusted. Now think about this, right, as part of our analysis, uh, that profitability can co-exist with responsible and sustainable practices. Let's say that all the data centers, uh, migrated from on-prem to cloud based, we estimate that would reduce carbon emissions globally by 60 million tons per year. Uh, and think about it this way, right? Easier metric would be taking out 22 million cars off the road. Um, the other examples that you've seen, right, in terms of the NHS work that they're doing, uh, in, in UK to build, uh, uh, you know, uh, Microsoft teams in based integration. And, uh, the platform rolled out for 1.2 million in interest users, uh, and got 16,000 users that we were able to secure, uh, instant messages, obviously complete audio video calls and host virtual meetings across India. So, uh, this, this work that we did with NHS is something that we have are collaborating with a lot of tools and powering businesses. >>Well, you're vividly describing the business case for sustainability. What do you see as the future of cloud when thinking about it from this lens of sustainability, and also going back to what you were talking about in terms of how you are helping your, your fostering cooperation within these organizations. >>Yeah, that's a very good question. So if you look at today, right, businesses are obviously environmentally aware and they are expanding efforts to decrease power consumption, carbon emissions, and they want to run a sustainable operational efficiency across all elements of their business. And this is an increasing trend, and there is that option of energy efficient infrastructure in the global market. And this trend is the cloud first thinking. And with the right cloud migration that we've been discussing is about unlocking new opportunity, like clean energy foundations enable enabled by cloud based geographic analysis, material, waste reductions, and better data insights. And this is something that, uh, uh, we'll we'll drive, uh, with obviously faster analytics platform that is out there. Now, the sustainability is actually the future of business, which is companies that are historically different, the financial security or agility benefits to cloud. Now sustainability becomes an imperative for them. And I would on expedience Accenture's experience with cloud migrations, we have seen 30 to 40% total cost of ownership savings. And it's driving a greater workload, flexibility, better service, your obligation, and obviously more energy efficient, uh, public clouds that cost we'll see that, that drive a lot of these enterprise own data centers. So in our view, what we are seeing is that this, this, uh, sustainable cloud position helps, uh, helps companies to, uh, drive a lot of the goals in addition to their financial and other goods. >>So what should organizations who are, who are watching this interview and saying, Hey, I need to know more, what, what do you recommend to them? And what, where should they go to get more information on Greenplum? >>No, if you you're, if you are a business leader and you're thinking about which cloud provider is good, or how, how should applications be modernized to meet our day-to-day needs, which cloud driven innovations should be priorities. Uh, you know, that's why Accenture, uh, formed up the cloud first organization and essentially to provide the full stack of cloud services to help our clients become a cloud first business. Um, you know, it's all about excavation, uh, the digital transformation innovating faster, creating differentiated, uh, and sustainable value for our clients. And we're powering it up at 70,000 cloud professionals, $3 billion investment, and, uh, bringing together and services for our clients in terms of cloud solutions. And obviously the ecosystem partnership that we have that we are seeing today, uh, and the assets that help our clients realize their goals. Um, and again, to do reach out to us, uh, we can help them determine obviously, an optimal, sustainable cloud for solution that meets the business needs and being unprecedented levels of innovation. Our experience will be our advantage. And now more than ever, Rebecca, >>Just closing us out here. Do you have any advice for these companies who are navigating a great deal of uncertainty? We, what, what do you think the next 12 to 24 months? What do you think that should be on the minds of CEOs as they go through? >>So, as CEO's are thinking about rapidly leveraging cloud, migrating to cloud, uh, one of the elements that we want them to be thoughtful about is can they do that, uh, with unprecedent level of innovation, but also build a greener planet and a greener balance sheet, if we can achieve this balance and kind of, uh, have a, have a world which is greener, I think the world will win. And we all along with Accenture clients will win. That's what I would say, uh, >>Optimistic outlook. And I will take it. Thank you so much. Kishor for coming on the show >>That was >>Accenture's Kishor Dirk, I'm Rebecca Knight stay tuned for more of the cube virtuals coverage of the Accenture executive summit >>Around the globe. >>It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cube virtual and our coverage of the Accenture executive summit. Part of AWS reinvent 2020. I'm your host Rebecca Knight. Today, we are talking about the power of three. And what happens when you bring together the scientific know-how of a global bias biopharmaceutical powerhouse in Takeda, a leading cloud services provider in AWS, and Accenture's ability to innovate, execute, and deliver innovation. Joining me to talk about these things. We have Aaron, sorry, Arjun, baby. He is the senior managing director and chairman of Accenture's diamond leadership council. Welcome Arjun Karl hick. He is the chief digital and information officer at Takeda. >>What is your bigger, thank you, Rebecca >>And Brian bowhead, global director, and head of the Accenture AWS business group at Amazon web services. Thanks so much for coming on. Thank you. So, as I said, we're talking today about this relationship between, uh, your three organizations. Carl, I want to talk with you. I know you're at the beginning of your cloud journey. What was the compelling reason? What, what, why, why move to the cloud and why now? >>Yeah, no, thank you for the question. So, you know, as a biopharmaceutical leader, we're committed to bringing better health and a brighter future to our patients. We're doing that by translating science into some really innovative and life transporting therapies, but throughout, you know, we believe that there's a responsible use of technology, of data and of innovation. And those three ingredients are really key to helping us deliver on that promise. And so, you know, while I think, uh, I'll call it, this cloud journey is already always been a part of our strategy. Um, and we've made some pretty steady progress over the last years with a number of I'll call it diverse approaches to the digital and AI. We just weren't seeing the impact at scale that we wanted to see. Um, and I think that, you know, there's a, there's a need ultimately to, you know, accelerate and, uh, broaden that shift. >>And, you know, we were commenting on this earlier, but there's, you know, it's been highlighted by a number of factors. One of those has been certainly a number of the large acquisitions we've made Shire, uh, being the most pressing example, uh, but also the global pandemic, both of those highlight the need for us to move faster, um, at the speed of cloud, ultimately. Uh, and so we started thinking outside of the box because it was taking us too long and we decided to leverage this strategic partner model. Uh, and it's giving us a chance to think about our challenges very differently. We call this the power of three, uh, and ultimately our focus is singularly on our patients. I mean, they're waiting for us. We need to get there faster. It can take years. And so I think that there is a focus on innovation, um, at a rapid speed, so we can move ultimately from treating conditions to keeping people healthy. >>So as you are embarking on this journey, what are some of the insights you want to share about, about what you're seeing so far? >>Yeah, no, it's a great question. So, I mean, look, maybe right before I highlight some of the key insights, uh, I would say that, you know, with cloud now as the, as the launchpad for innovation, you know, our vision all along has been that in less than 10 years, we want every single to kid, uh, associate we're employed to be empowered by an AI assistant. And I think that, you know, that's going to help us make faster, better decisions. That'll help us, uh, fundamentally deliver transformative therapies and better experiences to, to that ecosystem, to our patients, to physicians, to payers, et cetera, much faster than we previously thought possible. Um, and I think that technologies like cloud and edge computing together with a very powerful I'll call it data fabric is going to help us to create this, this real-time, uh, I'll call it the digital ecosystem. >>The data has to flow ultimately seamlessly between our patients and providers or partners or researchers, et cetera. Uh, and so we've been thinking about this, uh, I'll call it legal, hold up, sort of this pyramid, um, that helps us describe our vision. Uh, and a lot of it has to do with ultimately modernizing the foundation, modernizing and rearchitecting, the platforms that drive the company, uh, heightening our focus on data, which means that there's an accelerated shift towards enterprise data platforms and digital products. And then ultimately, uh, uh, P you know, really an engine for innovation sitting at the very top. Um, and so I think with that, you know, there's a few different, uh, I'll call it insights that, you know, are quickly kind of come zooming into focus. I would say one is this need to collaborate very differently. Um, you know, not only internally, but you know, how do we define ultimately, and build a connected digital ecosystem with the right partners and technologies externally? >>I think the second, uh, component that maybe people don't think as much about, but, you know, I find critically important is for us to find ways of really transforming our culture. We have to unlock talent and shift the culture certainly as a large biopharmaceutical very differently. And then lastly, you've touched on it already, which is, you know, innovation at the speed of cloud. How do we re-imagine that, you know, how do ideas go from getting tested and months to kind of getting tested in days? You know, how do we collaborate very differently? Uh, and so I think those are three, uh, perhaps of the larger I'll call it, uh, insights that, you know, the three of us are spending a lot of time thinking about right now. >>So Arjun, I want to bring you into this conversation a little bit. Let's, let's delve into those a bit. Talk first about the collaboration, uh, that Carl was referencing there. How, how have you seen that it is enabling, uh, colleagues and teams to communicate differently and interact in new and different ways? Uh, both internally and externally, as Carl said, >>No, th thank you for that. And, um, I've got to give call a lot of credit, because as we started to think about this journey, it was clear, it was a bold ambition. It was, uh, something that, you know, we had all to do differently. And so the, the concept of the power of three that Carl has constructed has become a label for us as a way to think about what are we going to do to collectively drive this journey forward. And to me, the unique ways of collaboration means three things. The first one is that, um, what is expected is that the three parties are going to come together and it's more than just the sum of our resources. And by that, I mean that we have to bring all of ourselves, all of our collective capabilities, as an example, Amazon has amazing supply chain capabilities. >>They're one of the best at supply chain. So in addition to resources, when we have supply chain innovations, uh, that's something that they're bringing in addition to just, uh, talent and assets, similarly for Accenture, right? We do a lot, uh, in the talent space. So how do we bring our thinking as to how we apply best practices for talent to this partnership? So, um, as we think about this, so that's, that's the first one, the second one is about shared success very early on in this partnership, we started to build some foundations and actually develop seven principles that all of us would look at as the basis for this success shared success model. And we continue to hold that sort of in the forefront, as we think about this collaboration. And maybe the third thing I would say is this one team mindset. So whether it's the three of our CEOs that get together every couple of months to think about, uh, this partnership, or it is the governance model that Carl has put together, which has all three parties in the governance and every level of leadership, we always think about this as a collective group, so that we can keep that front and center. >>And what I think ultimately has enabled us to do is it allowed us to move at speed, be more flexible. And ultimately all we're looking at the target the same way, the North side, the same way. >>Brian, what about you? What have you observed and what are you thinking about in terms of how this is helping teams collaborate differently? >>Yeah, absolutely. And RJ made some, some great points there. And I think if you really think about what he's talking about, it's that, that diversity of talent, diversity of skill and viewpoint and even culture, right? And so we see that in the power of three. And then I think if we drill down into what we see at Takeda, and frankly, Takeda was, was really, I think, pretty visionary and on their way here, right. And taking this kind of cross-functional approach and applying it to how they operate day to day. So moving from a more functional view of the world to more of a product oriented view of the world, right? So when you think about we're going to be organized around a product or a service or a capability that we're going to provide to our customers or our patients or donors in this case, it implies a different structure, although altogether, and a different way of thinking, right? >>Because now you've got technical people and business experts and marketing experts, all working together in this is sort of cross collaboration. And what's great about that is it's really the only way to succeed with cloud, right? Because the old ways of thinking where you've got application people and infrastructure, people in business, people is suboptimal, right? Because we can all access this tool was, and these capabilities and the best way to do that, isn't across kind of a cross collaborative way. And so this is product oriented mindset. It's a keto was already on. I think it's allowed us to move faster in those areas. >>Carl, I want to go back to this idea of unlocking talent and culture. And this is something that both Brian and Arjun have talked about too. People are, are an essential part of their, at the heart of your organization. How will their experience of work change and how are you helping re-imagine and reinforce a strong organizational culture, particularly at this time when so many people are working remotely. >>Yeah. It's a great question. And it's something that, you know, I think we all have to think a lot about, I mean, I think, um, you know, driving this, this call it, this, this digital and data kind of capability building, uh, takes a lot of, a lot of thinking. So, I mean, there's a few different elements in terms of how we're tackling this one is we're recognizing, and it's not just for the technology organization or for those actors that, that we're innovating with, but it's really across all of the Cato where we're working through ways of raising what I'll call the overall digital leaders literacy of the organization, you know, what are the, you know, what are the skills that are needed almost at a baseline level, even for a global bio-pharmaceutical company and how do we deploy, I'll call it those learning resources very broadly. >>And then secondly, I think that, you know, we're, we're very clear that there's a number of areas where there are very specialized skills that are needed. Uh, my organization is one of those. And so, you know, we're fostering ways in which, you know, we're very kind of quickly kind of creating, uh, avenues excitement for, for associates in that space. So one example specifically, as we use, you know, during these very much sort of remote, uh, sort of days, we, we use what we call global it days, and we set a day aside every single month and this last Friday, um, you know, we, we create during that time, it's time for personal development. Um, and we provide active seminars and training on things like, you know, robotic process automation, data analytics cloud, uh, in this last month we've been doing this for months and months now, but in his last month, more than 50% of my organization participated, and there's this huge positive shift, both in terms of access and excitement about really harnessing those new skills and being able to apply them. >>Uh, and so I think that that's, you know, one, one element that, uh, can be considered. And then thirdly, um, of course, every organization to work on, how do you prioritize talent, acquisition and management and competencies that you can't rescale? I mean, there are just some new capabilities that we don't have. And so there's a large focus that I have with our executive team and our CEO and thinking through those critical roles that we need to activate in order to kind of, to, to build on this, uh, this business led cloud transformation. And lastly, probably the hardest one, but the one that I'm most jazzed about is really this focus on changing the mindsets and behaviors. Um, and I think there, you know, this is where the power of three is, is really, uh, kind of coming together nicely. I mean, we're working on things like, you know, how do we create this patient obsessed curiosity, um, and really kind of unlock innovation with a real, kind of a growth mindset. >>Uh, and the level of curiosity that's needed, not to just continue to do the same things, but to really challenge the status quo. So that's one big area of focus we're having the agility to act just faster. I mean, to worry less, I guess I would say about kind of the standard chain of command, but how do you make more speedy, more courageous decisions? And this is places where we can emulate the way that a partner like AWS works, or how do we collaborate across the number of boundaries, you know, and I think, uh, Arjun spoke eloquently to a number of partnerships that we can build. So we can break down some of these barriers and use these networks, um, whether it's within our own internal ecosystem or externally to help, to create value faster. So a lot of energy around ways of working and we'll have to check back in, but I mean, we're early in on this mindset and behavioral shift, um, but a lot of good early momentum. >>Carl you've given me a good segue to talk to Brian about innovation, because you said a lot of the things that I was the customer obsession and this idea of innovating much more quickly. Obviously now the world has its eyes on drug development, and we've all learned a lot about it, uh, in the past few months and accelerating drug development is all, uh, is of great interest to all of us. Brian, how does a transformation like this help a company's, uh, ability to become more agile and more innovative and at a quicker speed to, >>Yeah, no, absolutely. And I think some of the things that Carl talked about just now are critical to that, right? I think where sometimes folks fall short is they think, you know, we're going to roll out the technology and the technology is going to be the silver bullet where we're, in fact it is the culture. It is, is the talent. And it's the focus on that. That's going to be, you know, the determinant of success. And I will say, you know, in this power of three arrangement and Carl talked a little bit about the pyramid, um, talent and culture and that change, and the kind of thinking about that has been a first-class citizen since the very beginning, right. That absolutely is critical for, for being there. Um, and, and so that's been, that's been key. And so we think about innovation at Amazon and AWS, and Carl mentioned some of the things that, you know, partner like AWS can bring to the table is we talk a lot about builders, right? >>So kind of obsessive about builders. Um, and, and we meet what we mean by that is we at Amazon, we hire for builders, we cultivate builders and we like to talk to our customers about it as well. And it also implies a different mindset, right? When you're a builder, you have that, that curiosity, you have that ownership, you have that stake in whatever I'm creating, I'm going to be a co-owner of this product or this service, right. Getting back to that kind of product oriented mindset. And it's not just the technical people or the it people who are builders. It is also the business people as, as Carl talked about. Right. So when we start thinking about, um, innovation again, where we see folks kind of get into a little bit of a innovation pilot paralysis, is that you can focus on the technology, but if you're not focusing on the talent and the culture and the processes and the mechanisms, you're going to be putting out technology, but you're not going to have an organization that's ready to take it and scale it and accelerate it. >>Right. And so that's, that's been absolutely critical. So just a couple of things we've been doing with, with Takeda and Decatur has really been leading the way is, think about a mechanism and a process. And it's really been working backward from the customer, right? In this case, again, the patient and the donor. And that was an easy one because the key value of Decatur is to be a patient focused bio-pharmaceutical right. So that was embedded in their DNA. So that working back from that, that patient, that donor was a key part of that process. And that's really deep in our DNA as well. And Accenture's, and so we were able to bring that together. The other one is, is, is getting used to experimenting and even perhaps failing, right. And being able to iterate and fail fast and experiment and understanding that, you know, some decisions, what we call it at Amazon or two-way doors, meaning you can go through that door, not like what you see and turn around and go back. And cloud really helps there because the costs of experimenting and the cost of failure is so much lower than it's ever been. You can do it much faster and the implications are so much less. So just a couple of things that we've been really driving, uh, with the cadence around innovation, that's been really critical. Carl, where are you already seeing signs of success? >>Yeah, no, it's a great question. And so we chose, you know, uh, with our focus on innovation to try to unleash maybe the power of data digital in, uh, in focusing on what I call sort of a Maven. And so we chose our, our, our plasma derived therapy business, um, and you know, the plasma-derived therapy business unit, it develops critical life-saving therapies for patients with rare and complex diseases. Um, but what we're doing is by bringing kind of our energy together, we're focusing on creating, I'll call it state of the art digitally connected donation centers. And we're really modernizing, you know, the, the, the donor experience right now, we're trying to, uh, improve also I'll call it the overall plasma collection process. And so we've, uh, selected a number of alcohol at a very high speed pilots that we're working through right now, specifically in this, in this area. And we're seeing >>Really great results already. Um, and so that's, that's one specific area of focus are Jen, I want you to close this out here. Any ideas, any best practices advice you would have for other pharmaceutical companies that are, that are at the early stage of their cloud journey? Yes. Sorry. Arjun. >>Yeah, no, I was breaking up a bit. No, I think they, um, the key is what what's sort of been great for me to see is that when people think about cloud, you know, you always think about infrastructure technology. The reality is that the cloud is really the true enabler for innovation and innovating at scale. And, and if you think about that, right, in all the components that you need, uh, ultimately that's where the value is for the company, right? Because yes, you're going to get some cost synergies and that's great, but the true value is in how do we transform the organization in the case of the Qaeda and the life sciences clients, right. We're trying to take a 14 year process of research and development that takes billions of dollars and compress that right. Tremendous amounts of innovation opportunity. You think about the commercial aspect, lots of innovation can come there. The plasma derived therapy is a great example of how we're going to really innovate to change the trajectory of that business. So I think innovation is at the heart of what most organizations need to do. And the formula, the cocktail that Takeda has constructed with this Fuji program really has all the ingredients, um, that are required for that success. >>Great. Well, thank you so much. Arjun, Brian and Carl was really an enlightening conversation. >>Thank you. Yeah, it's been fun. Thanks Rebecca. >>And thank you for tuning into the cube. Virtual is coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cubes coverage of Accenture executive summit here at AWS reinvent. I'm your host Rebecca Knight for this segment? We have two guests. First. We have Helen Davis. She is the senior director of cloud platform services, assistant director for it and digital for the West Midlands police. Thanks so much for coming on the show, Helen, and we also have Matthew lb. He is Accenture health and public service associate director and West Midlands police account lead. Thanks so much for coming on the show. Matthew, thank you for joining us. So we are going to be talking about delivering data-driven insights to the West Midlands police force. Helen, I want to start with >>You. Can you tell us a little bit about the West Midlands police force? How big is the force and also what were some of the challenges that you were grappling with prior to this initiative? >>Yeah, certainly. So Westerners police is the second largest police force in the UK, outside of the metropolitan police in London. Um, we have an excessive, um, 11,000 people work at Westman ins police serving communities, um, through, across the Midlands region. So geographically, we're quite a big area as well, as well as, um, being population, um, density, having that as a, at a high level. Um, so the reason we sort of embarked on the data-driven insights platform and it, which was a huge change for us was for a number of reasons. Um, namely we had a lot of disparate data, um, which was spread across a range of legacy systems that were many, many years old, um, with some duplication of what was being captured and no single view for offices or, um, support staff. Um, some of the access was limited. You have to be in a, in an actual police building on a desktop computer to access it. Um, other information could only reach the offices on the front line, through a telephone call back to one of our enabling services where they would do a manual checkup, um, look at the information, then call the offices back, um, and tell them what they needed to know. So it was a very long laborious, um, process and not very efficient. Um, and we certainly weren't exploiting the data that we had in a very productive way. >>So it sounds like as you're describing, and I'm old clunky system that needed a technological, uh, reimagination. So what was the main motivation for, for doing, for making this shift? >>It was really, um, about making us more efficient and more effective in how we do how we do business. So, um, you know, certainly as a, as an it leader and some of my operational colleagues, we recognize the benefits, um, that data analytics could bring in, uh, in a policing environment, not something that was, um, really done in the UK at the time. You know, we have a lot of data, so we're very data rich and the information that we have, but we needed to turn it into information that was actionable. So that's where we started looking for, um, technology partners and suppliers to help us and sort of help us really with what's the art of the possible, you know, this hasn't been done before. So what could we do in this space? That's appropriate, >>Helen. I love that idea. What is the art of the possible, can you tell us a little bit about why you chose AWS? >>I think really, you know, as with all things and when we're procuring a partner in the public sector that, you know, there are many rules and regulations quite rightly as you would expect that to be because we're spending public money. So we have to be very, very careful and, um, it's, it's a long process and we have to be open to public scrutiny. So, um, we sort of look to everything, everything that was available as part of that process, but we recognize the benefits that Clyde would provide in this space because, you know, we're like moving to a cloud environment. We would literally be replacing something that was legacy with something that was a bit more modern. Um, that's not what we wanted to do. Our ambition was far greater than that. So I think, um, in terms of AWS, really, it was around scalability, interoperability, you know, just us things like the disaster recovery service, the fact that we can scale up and down quickly, we call it dialing up and dialing back. Um, you know, it's it's page go. So it just sort of ticked all the boxes for us. And then we went through the full procurement process, fortunately, um, it came out on top for us. So we were, we were able to move forward, but it just sort of had everything that we were looking for in that space. >>Matthew, I want to bring you into the conversation a little bit here. How are you working with a wet with the West Midlands police, sorry. And helping them implement this cloud-first >>Yeah, so I guess, um, by January the West Midlands police started, um, favorite five years ago now. So, um, we set up a partnership with the fools. I wanted to operate in a way that was very different to a traditional supplier relationship. Um, secretary that the data difference insights program is, is one of many that we've been working with last on, um, over the last five years, um, as having said already, um, cloud gave a number of, uh, advantages certainly from a big data perspective and things that, that enabled us today. Um, I'm from an Accenture perspective that allowed us to bring in a number of the different teams that we have say, cloud teams, security teams, um, and drafted from an insurance perspective, as well as the more traditional services that people would associate with the country. >>I mean, so much of this is about embracing comprehensive change to experiment and innovate and try different things. Matthew, how, how do you help, uh, an entity like West Midlands police think differently when they are, there are these ways of doing things that people are used to, how do you help them think about what is the art of the possible, as Helen said, >>There's a few things to that enable those being critical is trying to co-create solutions together. Yeah. There's no point just turning up with, um, what we think is the right answer, try and say, um, collectively work three, um, the issues that the fullest is seeing and the outcomes they're looking to achieve rather than simply focusing on a long list of requirements, I think was critical and then being really open to working together to create the right solution. Um, rather than just, you know, trying to pick something off the shelf that maybe doesn't fit the forces requirements in the way that it should too, >>Right. It's not always a one size fits all. >>Obviously, you know, today what we believe is critical is making sure that we're creating something that met the forces needs, um, in terms of the outcomes they're looking to achieve the financial envelopes that were available, um, and how we can deliver those in a, uh, iterative agile way, um, rather than spending years and years, um, working towards an outcome, um, that is gonna update before you even get that. >>So Helen, how, how are things different? What kinds of business functions and processes have been re-imagined in, in light of this change and this shift >>It's, it's actually unrecognizable now, um, in certain areas of the business as it was before. So to give you a little bit of, of context, when we, um, started working with essentially an AWS on the data driven insights program, it was very much around providing, um, what was called locally, a wizzy tool for our intelligence analyst to interrogate data, look at data, you know, decide whether they could do anything predictive with it. And it was very much sort of a back office function to sort of tidy things up for us and make us a bit better in that, in that area or a lot better in that area. And it was rolled out to a number of offices, a small number on the front line. Um, and really it was, um, in line with a mobility strategy that we, hardware officers were getting new smartphones for the first time, um, to do sort of a lot of things on, on, um, policing apps and things like that to again, to avoid them, having to keep driving back to police stations, et cetera. >>And the pilot was so successful. Every officer now has access to this data, um, on their mobile devices. So it literally went from a handful of people in an office somewhere using it to do sort of clever whizzbang things to, um, every officer in the force, being able to access that level of data at their fingertips. Literally. So what they were touched we've done before is if they needed to check and address or check details of an individual, um, just as one example, they would either have to, in many cases, go back to a police station to look it up themselves on a desktop computer. Well, they would have to make a call back to a centralized function and speak to an operator, relay the questions, either, wait for the answer or wait for a call back with the answer when those people are doing the data interrogation manually. >>So the biggest change for us is the self-service nature of the data we now have available. So officers can do it themselves on their phone, wherever they might be. So the efficiency savings from that point of view are immense. And I think just parallel to that is the quality of our, because we had a lot of data, but just because you've got a lot of data and a lot of information doesn't mean it's big data and it's valuable necessarily. Um, so again, it was having the single source of truth as we, as we call it. So you know that when you are completing those safe searches and getting the responses back, that it is the most accurate information we hold. And also you're getting it back within minutes, as opposed to, you know, half an hour, an hour or a drive back to a station. So it's making officers more efficient and it's also making them safer. The more efficient they are, the more time they have to spend out with the public doing what they, you know, we all should be doing, >>Seen that kind of return on investment, because what you were just describing with all the steps that we needed to be taken in prior to this, to verify an address say, and those are precious seconds when someone's life is on the line in, in sort of in the course of everyday police work. >>Absolutely. Yeah, absolutely. It's difficult to put a price on it. It's difficult to quantify. Um, but all the, you know, the minutes here and that certainly add up to a significant amount of efficiency savings, and we've certainly been able to demonstrate the officers are spending less time up police stations as a result or more time out on the front frontline also they're safer because they can get information about what may or may not be and address what may or may not have occurred in an area before very, very quickly without having to wait. >>Thank you. I want to hear your observations of working so closely with this West Midlands police. Have you noticed anything about changes in its culture and its operating model in how police officers interact with one another? Have you seen any changes since this technology change? >>What's unique about the Western new misplaces, the buy-in from the top down, the chief and his exact team and Helen as the leader from an IOT perspective, um, the entire force is bought in. So what is a significant change program? Uh, I'm not trickles three. Um, everyone in the organization, um, change is difficult. Um, and there's a lot of time effort. That's been put into both the technical delivery and the business change and adoption aspects around each of the projects. Um, but you can see the step change that is making in each aspect to the organization, uh, and where that's putting West Midlands police as a leader in, um, technology I'm policing in the UK. And I think globally, >>And this is a question for both of you because Matthew, as you said, change is difficult and there is always a certain intransigence in workplaces about this is just the way we've always done things and we're used to this and don't try us to get us. Don't try to get us to do anything new here. It works. How do you get the buy-in that you need to do this kind of digital transformation? >>I think it, it would be wrong to say it was easy. Um, um, we also have to bear in mind that this was one program in a five-year program. So there was a lot of change going on, um, both internally for some of our back office functions, as well as front Tai, uh, frontline offices. So with DDI in particular, I think the stat change occurred when people could see what it could do for them. You know, we had lots of workshops and seminars where we all talk about, you know, big data and it's going to be great and it's data analytics and it's transformational, you know, and quite rightly people that are very busy doing a day job that not necessarily technologists in the main and, you know, are particularly interested quite rightly so in what we are not dealing with the cloud, you know? >>And it was like, yeah, okay. It's one more thing. And then when they started to see on that, on their phones and what teams could do, that's when it started to sell itself. And I think that's when we started to see, you know, to see the stat change, you know, and, and if we, if we have any issues now it's literally, you know, our help desks in meltdown. Cause everyone's like, well, we call it manage without this anymore. And I think that speaks for itself. So it doesn't happen overnight. It's sort of incremental changes and then that's a step change in attitude. And when they see it working and they see the benefits, they want to use it more. And that's how it's become fundamental to all policing by itself, really, without much selling >>You, Helen just made a compelling case for how to get buy in. Have you discovered any other best practices when you are trying to get everyone on board for this kind of thing? >>We've um, we've used a lot of the traditional techniques, things around comms and engagement. We've also used things like, um, the 30 day challenge and nudge theory around how can we gradually encourage people to use things? Um, I think there's a point where all of this around, how do we just keep it simple and keep it user centric from an end user perspective? I think DDI is a great example of where the, the technology is incredibly complex. The solution itself is, um, you know, extremely large and, um, has been very difficult to, um, get delivered. But at the heart of it is a very simple front end for the user to encourage it and take that complexity away from them. Uh, I think that's been critical through the whole piece of DDR. >>One final word from Helen. I want to hear, where do you go from here? What is the longterm vision? I know that this has made productivity, um, productivity savings equivalent to 154 full-time officers. Uh, what's next, >>I think really it's around, um, exploiting what we've got. Um, I use the phrase quite a lot, dialing it up, which drives my technical architects crazy. But so, because it's apparently not that simple, but, um, you know, we've, we've been through significant change in the last five years and we are still continuing to batch all of those changes into everyday, um, operational policing. But what we need to see is we need to exploit and build on the investments that we've made in terms of data and claims specifically, the next step really is about expanding our pool of data and our functions. Um, so that, you know, we keep getting better and better at this. And the more we do, the more data we have, the more refined we can be, the more precise we are with all of our actions. Um, you know, we're always being expected to, again, look after the public purse and do more for less. >>And I think this is certainly an and our cloud journey and, and cloud first by design, which is where we are now, um, is helping us to be future-proofed. So for us, it's very much an investment. And I see now that we have good at embedded in operational policing for me, this is the start of our journey, not the end. So it's really exciting to see where we can go from here. Exciting times. Indeed. Thank you so much. Lily, Helen and Matthew for joining us. I really appreciate it. Thank you. And you are watching the cube stay tuned for more of the cubes coverage of the AWS reinvent Accenture executive summit. I'm Rebecca Knight from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome to the cube virtual coverage of the executive summit at AWS reinvent 2020 virtual. This is the cube virtual. We can't be there in person like we are every year we have to be remote. This executive summit is with special programming supported by Accenture where the cube virtual I'm your host John for a year, we had a great panel here called uncloud first digital transformation from some experts, Stuart driver, the director of it and infrastructure and operates at lion Australia, Douglas Regan, managing director, client account lead at lion for Accenture as a deep Islam associate director application development lead for Centure gentlemen, thanks for coming on the cube virtual that's a mouthful, all that digital, but the bottom line it's cloud transformation. This is a journey that you guys have been on together for over 10 years to be really a digital company. Now, some things have happened in the past year that kind of brings all this together. This is about the next generation organization. So I want to ask Stuart you first, if you can talk about this transformation at lion has undertaken some of the challenges and opportunities and how this year in particular has brought it together because you know, COVID has been the accelerant of digital transformation. Well, if you're 10 years in, I'm sure you're there. You're in the, uh, on that wave right now. Take a minute to explain this transformation journey. >>Yeah, sure. So a number of years back, we, we looked at kind of our infrastructure in our landscape trying to figure out where we >>Wanted to go next. And we were very analog based and stuck in the old it groove of, you know, Capitol reef rash, um, struggling to transform, struggling to get to a digital platform and we needed to change it up so that we could become very different business to the one that we were back then obviously cloud is an accelerant to that. And we had a number of initiatives that needed a platform to build on. And a cloud infrastructure was the way that we started to do that. So we went through a number of transformation programs that we didn't want to do that in the old world. We wanted to do it in a new world. So for us, it was partnering up with a dried organizations that can take you on the journey and, uh, you know, start to deliver bit by bit incremental progress, uh, to get to the, uh, I guess the promise land. >>Um, we're not, not all the way there, but to where we're on the way along. And then when you get to some of the challenges like we've had this year, um, it makes all of the hard work worthwhile because you can actually change pretty quickly, um, provide capacity and, uh, and increase your environments and, you know, do the things that you need to do in a much more dynamic way than we would have been able to previously where we might've been waiting for the hardware vendors, et cetera, to deliver capacity. So for us this year, it's been a pretty strong year from an it perspective and delivering for the business needs >>Before I hit the Douglas. I want to just real quick, a redirect to you and say, you know, if all the people said, Oh yeah, you got to jump on cloud, get in early, you know, a lot of naysayers like, well, wait till to mature a little bit, really, if you got in early and you, you know, paying your dues, if you will taking that medicine with the cloud, you're really kind of peaking at the right time. Is that true? Is that one of the benefits that comes out of this getting in the cloud? Yeah, >>John, this has been an unprecedented year, right. And, um, you know, Australia, we had to live through Bush fires and then we had covert and, and then we actually had to deliver a, um, a project on very nice transformational project, completely remote. And then we also had had some, some cyber challenges, which is public as well. And I don't think if we weren't moved into and enabled through the cloud, we would have been able to achieve that this year. It would have been much different and would have been very difficult to do the backing. We're able to work and partner with Amazon through this year, which is unprecedented and actually come out the other end and we've delivered a brand new digital capability across the entire business. Um, in many, you know, wouldn't have been impossible if we could, I guess, stayed in the old world. The fact that we were moved into the new Naval by the new allowed us to work in this unprecedented year. >>Just quilt. What's your personal view on this? Because I've been saying on the Cuban reporting necessity is the mother of all invention and the word agility has been kicked around as kind of a cliche, Oh, it'd be agile. You know, we're going to get the city, you get a minute on specifically, but from your perspective, uh, Douglas, what does that mean to you? Because there is benefits there for being agile. And >>I mean, I think as Stuart mentioned, right, in a lot of these things we try to do and, you know, typically, you know, hardware and, uh, the last >>To be told and, and, and always on the critical path to be done, we really didn't have that in this case, what we were doing with our projects in our deployments, right. We were able to move quickly able to make decisions in line with the business and really get things going. Right. So you see a lot of times in a traditional world, you have these inhibitors, you have these critical path, it takes weeks and months to get things done as opposed to hours and days, and, and truly allowed us to, we had to, you know, VJ things, move things. And, you know, we were able to do that in this environment with AWS to support and the fact that they can kind of turn things off and on as quickly as we needed. >>Yeah. Cloud-scale is great for speed. So DECA, Gardez get your thoughts on this cloud first mission, you know, it, you know, the dev ops world, they saw this early, that jumping in there, they saw the, the, the agility. Now the theme this year is modern applications with the COVID pandemic pressure, there's real business pressure to make that happen. How did you guys learn to get there fast? And what specifically did you guys do at Accenture and how did it all come together? Can you take us inside kind of how it played out? >>Right. So, yeah, we started off with, as we do in most cases with a much more bigger group, and we worked with lions functional experts and, uh, the lost knowledge that allowed the infrastructure had. Um, we then applied our journey to cloud strategy, which basically revolves around the seminars and, and, uh, you know, the deep three steps from our perspective, uh, assessing the current and bottom and setting up the new cloud environment. And as we go modernizing and, and migrating these applications to the cloud now, you know, one of the key things that, uh, you know, we learned along this journey was that, you know, you can have the best plans, but bottom line that we were dealing with, we often than not have to make changes, uh, what a lot of agility and also work with a lot of collaboration with the, uh, lion team, as well as, uh, uh, AWS. I think the key thing for me was being able to really bring it all together. It's not just, uh, you know, we want to hear it's all of us working together to make this happen. >>What were some of the learnings real quick journey there? >>So I think perspective, the key learnings were that, you know, uh, you know, work, when you look back at, uh, the, the infrastructure that was that we were trying to migrate over to the cloud. A lot of the documentation, et cetera, was not, uh, available. We were having to, uh, figure out a lot of things on the fly. Now that really required us to have, uh, uh, people with deep expertise who could go into those environments and, and work out, uh, you know, the best ways to, to migrate the workloads to the cloud. Uh, I think, you know, the, the biggest thing for me was making sure all the had on that real SMEs across the board globally, that we could leverage across the various technologies, uh, uh, and, and, and, you know, that would really work in our collaborative and agile environment with line. >>Let's do what I got to ask you. How did you address your approach to the cloud and what was your experience? >>Yeah, for me, it's around getting the foundations right. To start with and then building on them. Um, so, you know, you've got to have your, your, your process and you've got to have your, your kind of your infrastructure there and your blueprints ready. Um, AWS do a great job of that, right. Getting the foundations right. And then building upon it, and then, you know, partnering with Accenture allows you to do that very successfully. Um, I think, um, you know, the one thing that was probably surprising to us when we started down this journey and kind of after we got a long way down the track and looking backwards is actually how much you can just turn off. Right? So a lot of stuff that you, uh, you get electric with a legacy in your environment, and when you start to work through it with the types of people that civic just mentioned, you know, the technical expertise working with the business, um, you can really rationalize your environment and, uh, you know, cloud is a good opportunity to do that, to drive that legacy out. >>Um, so you know, a few things there, the other thing is, um, you've got to try and figure out the benefits that you're going to get out of moving here. So there's no point in just taking something that is not delivering a huge amount of value in the traditional world, moving it into the cloud, and guess what is going to deliver the same limited amount of value. So you've got to transform it, and you've got to make sure that you build it for the future and understand exactly what you're trying to gain out of it. So again, you need a strong collaboration. You need a good partners to work with, and you need good engagement from the business as well, because the kind of, uh, you know, digital transformation, cloud transformation, isn't really an it project, I guess, fundamentally it is at the core, but it's a business project that you've got to get the whole business aligned on. You've got to make sure that your investment streams are appropriate and that's, uh, you're able to understand the benefits and the value that say, you're going to drive back towards the business. >>Let's do it. If you don't mind me asking, what was some of the obstacles you encountered or learnings, um, that might different from the expectation we all been there, Hey, you know, we're going to change the world. Here's the sales pitch, here's the outcome. And then obviously things happen, you know, you learn legacy, okay. Let's put some containerization around that cloud native, um, all that rational. You're talking about what are, and you're going to have obstacles. That's how you learn. That's how perfection has developed. How, what obstacles did you come up with and how are they different from your expectations going in? >>Yeah, they're probably no different from other people that have gone down the same journey. If I'm totally honest, the, you know, 70 or 80% of what you do is relatively easy of the known quantity. It's relatively modern architectures and infrastructures, and you can upgrade, migrate, move them into the cloud, whatever it is, rehost, replatform, rearchitect, whatever it is you want to do, it's the other stuff, right? It's the stuff that always gets left behind. And that's the challenge. It's, it's getting that last bit over the line and making sure that you haven't been invested in the future while still carrying all of your legacy costs and complexity within your environment. So, um, to be quite honest, that's probably taken longer and has been more of a challenge than we thought it would be. Um, the other piece I touched on earlier on in terms of what was surprising was actually how much of, uh, your environment is actually not needed anymore. >>When you start to put a critical eye across it and understand, um, uh, ask the tough questions and start to understand exactly what, what it is you're trying to achieve. So if you ask a part of a business, do they still need this application or this service a hundred percent of the time, they will say yes until you start to lay out to them, okay, now I'm going to cost you this to migrate it or this, to run it in the future. And, you know, here's your ongoing costs and, you know, et cetera, et cetera. And then, uh, for a significant amount of those answers, you get a different response when you start to layer on the true value of it. So you start to flush out those hidden costs within the business, and you start to make some critical decisions as a company based on, uh, based on that. So that was a little tougher than we first thought and probably broader than we thought there was more of that than we anticipated, um, which actually results in a much cleaner environment, post post migration, >>You know, the old expression, if it moves automated, you know, it's kind of a joke on government, how they want to tax everything, you know, you want to automate, that's a key thing in cloud, and you've got to discover those opportunities to create value Stuart and Siddique. Mainly if you can weigh in on this love to know the percentage of total cloud that you have now, versus when you started, because as you start to uncover whether it's by design for purpose, or you discover opportunity to innovate, like you guys have, I'm sure it kind of, you took on some territory inside Lyon, what percentage of cloud now versus start? >>Yeah. And at the start it was minimal, right. You know, close to zero, right. Single and single digits. Right. It was mainly SAS environments that we had, uh, sitting in clouds when we, uh, when we started, um, Doug mentioned earlier on a really significant transformation project, um, that we've undertaken and recently gone live on a multi-year one. Um, you know, that's all stood up on AWS and is a significant portion of our environment, um, in terms of what we can move to cloud. Uh, we're probably at about 80 or 90% now. And the balance bit is, um, legacy infrastructure that is just going to retire as we go through the cycle rather than migrate to the cloud. Um, so we are significantly cloud-based and, uh, you know, we're reaping the benefits of it in a year, like 2020, and makes you glad that you did all of the hard yards in the previous years when you started that business challenges thrown out as, >>So do you any common reaction still the cloud percentage penetration? >>Sorry, I didn't, I didn't guys don't, but I, I was going to say it was, I think it's like the 80 20 rule, right? We, we, we worked really hard in the, you know, I think 2018, 19 to get any person off, uh, after getting onto the cloud and, or the last year is the 20% that we have been migrating. And Stuart said like a non-athlete that is also, that's going to be the diet. And I think our next big step is going to be obviously, you know, the icing on the cake, which is to decommission all these apps as well. Right. So, you know, to get the real benefits out of, uh, the whole conservation program from a, uh, from a >>Douglas and Stewart, can you guys talk about the decision around the cloud because you guys have had success with AWS, why AWS how's that decision made? Can you guys give some insight into some of those thoughts? >>I can, I can start, start off. I think back when the decision was made and it was, Oh, it was a while back, um, you know, there's some clear advantages of moving relay, Ws, a lot of alignment with some of the significant projects and, uh, the trend, that particular one big transformation project that we've alluded to as well. Um, you know, we needed some, um, some very robust and, um, just future proof and, um, proven technology. And AWS gave that to us. We needed a lot of those blueprints to help us move down the path. We didn't want to reinvent everything. So, um, you know, having a lot of that legwork done for us and an AWS gives you that, right. And particularly when you partner up with, uh, with a company like Accenture as well, you get combinations of the technology and the skills and the knowledge to, to move you forward in that direction. >>So, um, you know, for us, it was a, uh, uh, it was a decision based on, you know, best of breed, um, you know, looking forward and, and trying to predict the future needs and, and, and kind of the environmental that we might need. Um, and, you know, partnering up with organizations that can take you on the journey. Yeah. And just to build on it. So obviously, you know, lion's like an NWS, but, you know, we knew it was a very good choice given that, um, uh, the skills and the capability that we had, as well as the assets and tools we had to get the most out of, um, out of AWS. And obviously our, our CEO globally is just spending, you know, announcement about a huge investment that we're making in cloud. Um, but you know, we've, we've worked very well. AWS, we've done some joint workshops and joint investments, um, some joint POC. So yeah, w we have a very good working relationship, AWS, and I think, um, one incident to reflect upon whether it's cyber it's and again, where we actually jointly, you know, dove in with, um, with Amazon and some of their security experts and our experts. And we're able to actually work through that with mine quite successful. So, um, you know, really good behaviors as an organization, but also really good capabilities. >>Yeah. As you guys, you're essential cloud outcomes, research shown, it's the cycle of innovation with the cloud. That's creating a lot of benefits, knowing what you guys know now, looking back certainly COVID is impacted a lot of people kind of going through the same process, knowing what you guys know now, would you advocate people to jump on this transformation journey? If so, how, and what tweaks they make, which changes, what would you advise? >>Uh, I might take that one to start with. Um, I hate to think where we would have been when, uh, COVID kicked off here in Australia and, you know, we were all sent home, literally were at work on the Friday, and then over the weekend. And then Monday, we were told not to come back into the office and all of a sudden, um, our capacity in terms of remote access and I quadrupled, or more four, five X, what we had on the Friday we needed on the Monday. And we were able to stand that up during the day Monday into Tuesday, because we were cloud-based and, uh, you know, we just spun up your instances and, uh, you know, sort of our licensing, et cetera. And we had all of our people working remotely, um, within, uh, you know, effectively one business day. Um, I know peers of mine in other organizations and industries that are relying on kind of a traditional wise and getting hardware, et cetera, that were weeks and months before they could get there the right hardware to be able to deliver to their user base. >>So, um, you know, one example where you're able to scale and, uh, um, get, uh, get value out of this platform beyond probably what was anticipated at the time you talk about, um, you know, less the, in all of these kinds of things. And you can also think of a few scenarios, but real world ones where you're getting your business back up and running in that period of time is, is just phenomenal. There's other stuff, right? There's these programs that we've rolled out, you do your sizing, um, and in the traditional world, you would just go out and buy more servers than you need. And, you know, probably never realize the full value of those, you know, the capability of those servers over the life cycle of them. Whereas, you know, in a cloud world, you put in what you think is right. And if it's not right, you pump it up a little bit when, when all of your metrics and so on, tell you that you need to bump it up. And conversely you scale it down at the same rate. So for us, with the types of challenges and programs and, uh, uh, and just business need, that's come at as this year, uh, we wouldn't have been able to do it without a strong cloud base, uh, to, uh, to move forward. >>You know, Douglas, one of the things I talked to, a lot of people on the right side of history who have been on the right wave with cloud, with the pandemic, and they're happy, they're like, and they're humble. Like, well, we're just lucky, you know, luck is preparation meets opportunity. And this is really about you guys getting in early and being prepared and readiness. This is kind of important as people realize, then you gotta be ready. I mean, it's not just, you don't get lucky by being in the right place, the right time. And there were a lot of companies were on the wrong side of history here who might get washed away. This is a super important, I think, >>To echo and kind of building on what Stewart said. I think that the reason that we've had success and I guess the momentum is we didn't just do it in isolation within it and technology. It was actually linked to broader business changes, you know, creating basically a digital platform for the entire business, moving the business, where are they going to be able to come back stronger after COVID, when they're actually set up for growth, um, and actually allows, you know, a line to achievements growth objectives, and also its ambitions as far as what it wants to do, uh, with growth in whatever they make, do with acquiring other companies and moving into different markets and launching new products. So we've actually done it in a way that is, you know, real and direct business benefit, uh, that actually enables line to grow >>General. I really appreciate you coming. I have one final question. If you can wrap up here, uh, Stuart and Douglas, you don't mind weighing in what's the priorities for the future. What's next for lion in a century >>Christmas holidays, I'll start Christmas holidays. I spent a good year and then a, and then a reset, obviously, right? So, um, you know, it's, it's figuring out, uh, transform what we've already transformed, if that makes sense. So God, a huge proportion of our services sitting in the cloud. Um, but we know we're not done even with the stuff that is in there. We need to take those next steps. We need more and more automation and orchestration. We need to, um, our environment is more future proof. We need to be able to work with the business and understand what's coming at them so that we can, um, you know, build that into, into our environment. So again, it's really transformation on top of transformation is the way that I'll describe it. And it's really an open book, right? Once you get it in and you've got the capabilities and the evolving tool sets that AWS continue to bring to the market based, um, you know, working with the partners to, to figure out how we unlock that value, um, you know, drive our costs down efficiency, uh, all of those kind of, you know, standard metrics. >>Um, but you know, we're looking for the next things to transform and showed value back out to our customer base, um, that, uh, that we continue to, you know, sell our products to and work with and understand how we can better meet their needs. Yeah, I think just to echo that, I think it's really leveraging this and then did you capability they have and getting the most out of that investment. And then I think it's also moving to, uh, and adopting more new ways of working as far as, you know, the speed of the business, um, is getting up to speed in the market is changing. So being able to launch and do things quickly and also, um, competitive and efficient operating costs, uh, now that they're in the cloud, right? So I think it's really leveraging the most out of the platform and then, you know, being efficient in launching things. So putting them with >>Siddique, any word from you on your priorities by you see this year in folding, >>There's got to say like e-learning squares, right, for me around, you know, just journey. This is a journey to the cloud, right? >>And, uh, you know, as well dug into sort of Saturday, it's getting all, you know, different parts of the organization along the journey business to it, to your, uh, product lenders, et cetera. Right. And it takes time. It is tough, but, uh, uh, you know, you got to get started on it. And, you know, once we, once we finish off, uh, it's the realization of the benefits now that, you know, looking forward, I think for, from Alliance perspective, it is, uh, you know, once we migrate all the workloads to the cloud, it is leveraging, uh, all stack drive. And as I think Stewart said earlier, uh, with, uh, you know, the latest and greatest stuff that AWS it's basically working to see how we can really, uh, achieve more better operational excellence, uh, from a, uh, from a cloud perspective. >>Well, Stewart, thanks for coming on with a and sharing your environment and what's going on and your journey you're on the right wave. Did the work you're in, it's all coming together with faster, congratulations for your success, and, uh, really appreciate Douglas with Steve for coming on as well from essential. Thank you for coming on. Thanks, John. Okay. Just the cubes coverage of executive summit at AWS reinvent. This is where all the thought leaders share their best practices, their journeys, and of course, special programming with Accenture and the cube. I'm Sean ferry, your host, thanks for watching from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cube virtuals coverage of the Accenture executive summit. Part of AWS reinvent 2020. I'm your host Rebecca Knight. We are talking today about reinventing the energy data platform. We have two guests joining us. First. We have Johan Krebbers. He is the GM digital emerging technologies and VP of it. Innovation at shell. Thank you so much for coming on the show, Johan you're welcome. And next we have Liz Dennett. She is the lead solution architect for O S D U on AWS. Thank you so much, Liz, maybe here. So I want to start our conversation by talking about OSD. You like so many great innovations. It started with a problem. Johann, what was the problem you were trying to solve at shell? We go back a couple of years, we started summer 2017, where we had a meeting with the guys from exploration in shell, and the main problem they had, of course, they got lots of lots of data, but are unable to find the right data. They need to work from all over the place and told him >>To, and we'll probably try to solve is how that person working exploration could find their proper date, not just a day, but also the date you really needed that we did probably talked about is summer 2017. And we said, okay, the only way ABC is moving forward is to start pulling that data into a single data platform. And that, that was at the time that we called it as the, you, the subsurface data universe in there was about the shell name was so in, in January, 2018, we started a project with Amazon to start grating a co fricking that building, that Stu environment, that the, the universe, so that single data level to put all your exploration and Wells data into that single environment that was intent. And every cent, um, already in March of that same year, we said, well, from Michele point of view, we will be far better off if we could make this an industry solution and not just a shelf solution, because Shelby, Shelby, if you can make an industry solution, but people are developing applications for it. >>It also is far better than for shell to say we haven't shell special solution because we don't make money out of how we start a day that we can make money out of it. We have access to the data, we can explore the data. So storing the data we should do as efficiently possibly can. So we monitor, we reach out to about eight or nine other last, uh, or I guess operators like the economics, like the tutorials, like the shepherds of this world and say, Hey, we inshallah doing this. Do you want to join this effort? And to our surprise, they all said, yes. And then in September, 2018, we had our kickoff meeting with your open group where we said, we said, okay, if you want to work together and lots of other companies, we also need to look at, okay, how, how we organize that. >>Or if you started working with lots of large companies, you need to have some legal framework around some framework around it. So that's why we went to the open group and say, okay, let's, let's form the old forum as we call it at the time. So it's September, 2080, where I did a Galleria in Houston, but the kickoff meeting for the OT four with about 10 members at the time. So that's just over two years ago, we started an exercise for me called ODU. They kicked it off. Uh, and so that's really them will be coming from and how we've got there. Also >>The origin story. Um, what, so what digging a little deeper there? What were some of the things you were trying to achieve with the OSU? >>Well, a couple of things we've tried to achieve with you, um, first is really separating data from applications for what is, what is the biggest problem we have in the subsurface space that the data and applications are all interlinked or tied together. And if, if you have them and a new company coming along and say, I have this new application and he's access to the data that is not possible because the data often interlinked with the application. So the first thing we did is really breaking the link between the application, the data as those levels, the first thing we did, secondly, put all the data to a single data platform, take the silos out what was happening in the sub-service space. They got all the data in what we call silos in small little islands out there. So what we're trying to do is first break the link to great, great. >>They put the data single day, the bathroom, and the third part, put a standard layer on top of that, it's an API layer on top to equate a platform. So we could create an ecosystem out of companies to start a valving Schoff application on top of dev data platform across you might have a data platform, but you're only successful if have a rich ecosystem of people start developing applications on top of that. And then you can export the data like small companies, last company, university, you name it, we're getting after create an ecosystem out here. So the three things were first break the link between application data, just break it and put data at the center and also make sure that data, this data structure would not be managed by one company, but it would only be met. It would be managed the data structures by the ODI forum. Secondly, then put a, the data, a single data platform certainly then has an API layer on top and then create an ecosystem. Really go for people, say, please start developing applications, because now you had access to the data. I've got the data no longer linked to somebody whose application was all freely available, but an API layer that was, that was all September, 2018, more or less. >>And hear a little bit. Can you talk a little bit about some of the imperatives from the AWS standpoint in terms of what you were trying to achieve with this? Yeah, absolutely. And this whole thing is Johann said started with a challenge that was really brought out at shell. The challenges that geoscientists spend up to 70% of their time looking for data. I'm a geologist I've spent more than 70% of my time trying to find data in these silos. And from there, instead of just figuring out how we could address that one problem, we worked together to really understand the root cause of these challenges and working backwards from that use case OSU and OSU on AWS has really enabled customers to create solutions that span, not just this in particular problem, but can really scale to be inclusive of the entire energy value chain and deliver value from these use cases to the energy industry and beyond. Thank you, Lee, uh, Johann. So talk a little bit about Accenture's cloud first approach and how it has, uh, helped shell work faster and better with speed. >>Well, of course, access a cloud first approach only works together. It's been an Amazon environment, AWS environment. So we're really looking at, uh, at, at Accenture and others altogether helping shell in this space. Now the combination of the two is what we're really looking at, uh, where access of course can be recent knowledge student to that environment operates support knowledge, do an environment. And of course, Amazon will be doing that to today's environment that underpinning their services, et cetera. So, uh, we would expect a combination, a lot of goods when we started rolling out and put in production, the old you are three and bug because we are anus. Then when the release feed comes to the market in Q1, next year of ODU have already started going to Audi production inside shell. But as the first release, which is ready for prime time production across an enterprise will be released just before Christmas, last year when he's still in may of this year. But really three is the first release we want to use for full scale production deployment inside shell, and also the operators around the world. And there is one Amazon, sorry, at that one. Um, extensive can play a role in the ongoing, in the, in deployment building up, but also support environment. >>So one of the other things that we talk a lot about here on the cube is sustainability. And this is a big imperative at so many organizations around the world in particular energy companies. How does this move to OSD you, uh, help organizations become, how is this a greener solution for companies? >>Well, first we make it's a greatest solution because you start making a much more efficient use of your resources, which is already an important one. The second thing we're doing is also, we started ODU in framers, in the oil and gas space in the expert development space. We've grown, uh, OTU in our strategy of growth. I was, you know, also do an alternative energy sociology. We'll all start supporting next year. Things like solar farms, wind farms, uh, the, the dermatomal environment hydration. So it becomes an and an open energy data platform, not just what I want to get into sleep. That's what new industry, any type of energy industry. So our focus is to create, bring the data of all those various energy data sources to get me to a single data platform you can to use AI and other technologies on top of that, to exploit the data, to meet again into a single data platform. >>Liz, I want to ask you about security because security is, is, is such a big concern when it comes to data. How secure is the data on OSD? You, um, actually, can I talk, can I do a follow up on this sustainability talking? Oh, absolutely. By all means. I mean, I want to interject though security is absolutely our top priority. I don't mean to move away from that, but with sustainability, in addition to the benefits of the OSU data platform, when a company moves from on-prem to the cloud, they're also able to leverage the benefits of scale. Now, AWS is committed to running our business in the most environmentally friendly way possible. And our scale allows us to achieve higher resource utilization and energy efficiency than a typical data center. >>Now, a recent study by four 51 research found that AWS is infrastructure is 3.6 times more energy efficient than the median of surveyed enterprise data centers. Two thirds of that advantage is due to higher, um, server utilization and a more energy efficient server population. But when you factor in the carbon intensity of consumed electricity and renewable energy purchases for 51 found that AWS performs the same task with an 88% lower carbon footprint. Now that's just another way that AWS and OSU are working to support our customers is they seek to better understand their workflows and make their legacy businesses less carbon intensive. >>That's that's incorrect. Those are those statistics are incredible. Do you want to talk a little bit now about security? Absolutely. And security will always be AWS is top priority. In fact, AWS has been architected to be the most flexible and secure cloud computing environment available today. Our core infrastructure is built to satisfy. There are the security requirements for the military, local banks and other high sensitivity organizations. And in fact, AWS uses the same secure hardware and software to build and operate each of our regions. So that customers benefit from the only commercial cloud that's hat hits service offerings and associated supply chain vetted and deemed secure enough for top secret workloads. That's backed by a deep set of cloud security tools with more than 200 security compliance and governmental service and key features as well as an ecosystem of partners like Accenture, that can really help our customers to make sure that their environments for their data meet and or exceed their security requirements. Johann, I want you to talk a little bit about how OSD you can be used today. Does it only handle subsurface data? >>Uh, today it's Honda's subserves or Wells data, we go to add to that production around the middle of next year. That means that the whole upstate business. So we've got goes from exploration all the way to production. You've made it together into a single data platform. So production will be added around Q3 of next year. Then a principal. We have a difficult, the elder data that single environment, and we want to extend them to other data sources or energy sources like solar farms, wind farms, uh, hydrogen, hydro, et cetera. So we're going to add a whore, a whole list of audit day energy source to them and be all the data together into a single data club. So we move from a falling guest data platform to an aniseed data platform. That's really what our objective is because the whole industry, if you look it over, look at our companies are all moving in. That same two acts of quantity of course, are very strong in oil and gas, but also increased the, got into the other energy sources like, like solar, like wind, like th like highly attended, et cetera. So we would be moving exactly. But that same method that, that, that the whole OSU can't really support at home. And as a spectrum of energy sources, >>Of course, and Liz and Johan. I want you to close us out here by just giving us a look into your crystal balls and talking about the five and 10 year plan for OSD. You we'll start with you, Liz. What do you, what do you see as the future holding for this platform? Um, honestly, the incredibly cool thing about working at AWS is you never know where the innovation and the journey is going to take you. I personally am looking forward to work with our customers, wherever their OSU journeys, take them, whether it's enabling new energy solutions or continuing to expand, to support use cases throughout the energy value chain and beyond, but really looking forward to continuing to partner as we innovate to slay tomorrow's challenges, Johann first, nobody can look at any more nowadays, especially 10 years own objective is really in the next five years, you will become the key backbone for energy companies for storing your data. You are efficient intelligence and optimize the whole supply energy supply chain in this world down here, you'll uncovers Liz Dennett. Thank you so much for coming on the cube virtual I'm Rebecca Knight stay tuned for more of our coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cubes coverage of the Accenture executive summit. Part of AWS reinvent. I'm your host Rebecca Knight today we're welcoming back to Kubila. We have Kishor Dirk. He is the Accenture senior managing director cloud first global services lead. Welcome back to the show Kishore. Thank you very much. Nice to meet again. And, uh, Tristan moral horse set. He is the managing director, Accenture cloud first North America growth. Welcome back to you to trust and great to be back in grapes here again, Rebecca. Exactly. Even in this virtual format, it is good to see your faces. Um, today we're going to be talking about my nav and green cloud advisor capability. Kishor I want to start with you. So my nav is a platform that is really celebrating its first year in existence. Uh, November, 2019 is when Accenture introduced it. Uh, but it's, it has new relevance in light of this global pandemic that we are all enduring and suffering through. Tell us a little bit about the lineup platform, what it is that cloud platform to help our clients navigate the complexity of cloud and cloud decisions to make it faster. And obviously, you know, we have in the cloud, uh, you know, with >>The increased relevance and all the, especially over the last few months with the impact of COVID crisis and exhibition of digital transformation, you know, we are seeing the transformation or the acceleration to cloud much faster. This platform that you're talking about has enabled and 40 clients globally across different industries. You identify the right cloud solution, navigate the complexity, provide a cloud specific solution simulate for our clients to meet the strategy business needs, and the clients are loving it. >>I want to go to you now trust and tell us a little bit about how mine nav works and how it helps companies make good cloud choice. >>Yeah, so Rebecca, we we've talked about cloud is, is more than just infrastructure and that's what mine app tries to solve for it. It really looks at a variety of variables, including infrastructure operating model and fundamentally what client's business outcomes, um, uh, our clients are, are looking for and, and identifies the optimal solution for what they need. And we assign this to accelerate and we mentioned the pandemic. One of the big focus now is to accelerate. And so we worked through a three-step process. The first is scanning and assessing our client's infrastructure, their data landscape, their application. Second, we use our automated artificial intelligence engine to interact with. We have a wide variety and library of a collective plot expertise. And we look to recommend what is the enterprise architecture and solution. And then third, before we aligned with our clients, we look to simulate and test this scaled up model. And the simulation gives our clients a way to see what cloud is going to look like, feel like and how it's going to transform their business before they go there. >>Tell us a little bit about that in real life. Now as a company, so many of people are working remotely having to collaborate, uh, not in real life. How is that helping them right now? >>So, um, the, the pandemic has put a tremendous strain on systems, uh, because of the demand on those systems. And so we talk about resiliency. We also now need to collaborate across data across people. Um, I think all of us are calling from a variety of different places where our last year we were all at the VA cube itself. Um, and, and cloud technologies such as teams, zoom that we're we're leveraging now has fundamentally accelerated and clients are looking to onboard this for their capabilities. They're trying to accelerate their journey. They realize that now the cloud is what is going to become important for them to differentiate. Once we come out of the pandemic and the ability to collaborate with their employees, their partners, and their clients through these systems is becoming a true business differentiator for our clients. >>Keisha, I want to talk with you now about my navs multiple capabilities, um, and helping clients design and navigate their cloud journeys. Tell us a little bit about the green cloud advisor capability and its significance, particularly as so many companies are thinking more deeply and thoughtfully about sustainability. >>Yes. So since the launch of my lab, we continue to enhance, uh, capabilities for our clients. One of the significant, uh, capabilities that we have enabled is the being taught advisor today. You know, Rebecca, a lot of the businesses are more environmentally aware and are expanding efforts to decrease power consumption, uh, and obviously carbon emissions and, uh, and run a sustainable operations across every aspect of the enterprise. Uh, as a result, you're seeing an increasing trend in adoption of energy, efficient infrastructure in the global market. And one of the things that we did a lot of research we found out is that there's an ability to influence our client's carbon footprint through a better cloud solution. And that's what the internet brings to us, uh, in, in terms of a lot of the client connotation that you're seeing in Europe, North America and others, lot of our clients are accelerating to a green cloud strategy to unlock beta financial, societal and environmental benefit, uh, through obviously cloud-based circular, operational, sustainable products and services. That is something that we are enhancing my now, and we are having active client discussions at this point of time. >>So Tristan, tell us a little bit about how this capability helps clients make greener decisions. >>Yeah. Um, well, let's start about the investments from the cloud providers in renewable and sustainable energy. Um, they have most of the hyperscalers today, um, have been investing significantly on data centers that are run on renewable energy, some incredibly creative constructs on the how to do that. And sustainability is there for a key, um, key item of importance for the hyperscalers and also for our clients who now are looking for sustainable energy. And it turns out this marriage is now possible. I can, we marry the, the green capabilities of the comm providers with a sustainability agenda of our clients. And so what we look into the way the mine EF works is it looks at industry benchmarks and evaluates our current clients, um, capabilities and carpet footprint leveraging their existing data centers. We then look to model from an end-to-end perspective, how the, their journey to the cloud leveraging sustainable and, um, and data centers with renewable energy. We look at how their solution will look like and, and quantify carbon tax credits, um, improve a green index score and provide quantifiable, um, green cloud capabilities and measurable outcomes to our clients, shareholders, stakeholders, clients, and customers. Um, and our green plot advisers sustainability solutions already been implemented at three clients. And in many cases in two cases has helped them reduce the carbon footprint by up to 400% through migration from their existing data center to green cloud. Very, very, >>That is remarkable. Now tell us a little bit about the kinds of clients. Is this, is this more interesting to clients in Europe? Would you say that it's catching on in the United States? Where, what is the breakdown that you're seeing right now? >>Sustainability is becoming such a global agenda and we're seeing our clients, um, uh, tie this and put this at board level, um, uh, agenda and requirements across the globe. Um, Europe has specific constraints around data sovereignty, right, where they need their data in country, but from a green, a sustainability agenda, we see clients across all our markets, North America, Europe, and our growth markets adopt this. And we have seen case studies and all three months. >>Keisha, I want to bring you back into the conversation. Talk a little bit about how MindUP ties into Accenture's cloud first strategy, your Accenture's CEO, Julie Sweet has talked about post COVID leadership requiring every business to become a cloud first business. Tell us a little bit about how this ethos is in Accenture and how you're sort of looking outward with it too. >>So Rebecca mine is the launch pad, uh, to a cloud first transformation for our clients. Uh, Accenture, see your jewelry suite, uh, you know, shared the Accenture cloud first and our substantial investment demonstrate our commitment and is delivering greater value for our clients when they need it the most. And with the digital transformation requiring cloud at scale, you know, we're seeing that in the post COVID leadership, it requires that every business should become a cloud business. And my nap helps them get there by evaluating the cloud landscape, navigating the complexity, modeling architecting and simulating an optimal cloud solution for our clients. And as Justin was sharing a greener cloud. >>So Tristan, talk a little bit more about some of the real life use cases in terms of what are we, what are clients seeing? What are the results that they're having? >>Yes. Thank you, Rebecca. I would say two key things right around my neck. The first is the iterative process. Clients don't want to wait, um, until they get started, they want to get started and see what their journey is going to look like. And the second is fundamental acceleration, dependent make, as we talked about, has accelerated the need to move to cloud very quickly. And my nav is there to do that. So how do we do that? First is generating the business cases. Clients need to know in many cases that they have a business case by business case, we talk about the financial benefits, as well as the business outcomes, the green, green clot impact sustainability impacts with minus. We can build initial recommendations using a basic understanding of their environment and benchmarks in weeks versus months with indicative value savings in the millions of dollars arranges. >>So for example, very recently, we worked with a global oil and gas company, and in only two weeks, we're able to provide an indicative savings for $27 million over five years. This enabled the client to get started, knowing that there is a business case benefit and then iterate on it. And this iteration is, I would say the second point that is particularly important with my nav that we've seen in bank, the clients, which is, um, any journey starts with an understanding of what is the application landscape and what are we trying to do with those, these initial assessments that used to take six to eight weeks are now taking anywhere from two to four weeks. So we're seeing a 40 to 50% reduction in the initial assessment, which gets clients started in their journey. And then finally we've had discussions with all of the hyperscalers to help partner with Accenture and leverage mine after prepared their detailed business case module as they're going to clients. And as they're accelerating the client's journey, so real results, real acceleration. And is there a journey? Do I have a business case and furthermore accelerating the journey once we are by giving the ability to work in iterative approach. >>I mean, it sounds as though that the company that clients and and employees are sort of saying, this is an amazing time savings look at what I can do here in, in so much in a condensed amount of time, but in terms of getting everyone on board, one of the things we talked about last time we met, uh, Tristan was just how much, uh, how one of the obstacles is getting people to sign on and the new technologies and new platforms. Those are often the obstacles and struggles that companies face. Have you found that at all? Or what is sort of the feedback that you're getting from employers? >>Sorry. Yes. We clearly, there are always obstacles to a cloud journey. If there were an obstacles, all our clients would be, uh, already fully in the cloud. What man I gives the ability is to navigate through those, to start quickly. And then as we identify obstacles, we can simulate what things are going to look like. We can continue with certain parts of the journey while we deal with that obstacle. And it's a fundamental accelerator. Whereas in the past one, obstacle would prevent a class from starting. We can now start to address the obstacles one at a time while continuing and accelerating the contrary. That is the fundamental difference. >>Kishor I want to give you the final word here. Tell us a little bit about what is next for Accenture might have and what we'll be discussing next year at the Accenture executive summit >>Sort of echo, we are continuously evolving with our client needs and reinventing, reinventing for the future. For mine, as I've been taught advisor, our plan is to help our clients reduce carbon footprint and again, migrate to a green cloud. Uh, and additionally, we're looking at, you know, two capabilities, uh, which include sovereign cloud advisor, uh, with clients, especially in, in Europe and others are under pressure to meet, uh, stringent data norms that Kristen was talking about. And the sovereign cloud advisor health organization to create an architecture cloud architecture that complies with the green. Uh, I would say the data sovereignty norms that is out there. The other element is around data to cloud. We are seeing massive migration, uh, for, uh, for a lot of the data to cloud. And there's a lot of migration hurdles that come within that. Uh, we have expanded mine app to support assessment capabilities, uh, for, uh, assessing applications, infrastructure, but also covering the entire state, including data and the code level to determine the right cloud solution. So we are, we are pushing the boundaries on what mine app can do with mine. Have you created the ability to take the guesswork out of cloud navigate the complexity? We are roaring risks costs, and we are, you know, achieving client's static business objectives while building a sustainable alerts with being cloud >>Any platform that can take some of the guesswork out of the future. I'm I'm onboard with. Thank you so much, Tristin and Kishore. This has been a great conversation. >>Thank you. >>Stay tuned for more of the cubes coverage of the Accenture executive summit. I'm Rebecca Knight from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Hey, welcome back to the cubes coverage of 80 us reinvent 2020 virtual centric executive summit. The two great guests here to break down the analysis of the relationship with cloud and essential Brian bowhead director ahead of a century 80. It was business group at Amazon web services. And Andy T a B G the M is essentially Amazon business group lead managing director at Accenture. Uh, I'm sure you're super busy and dealing with all the action, Brian. Great to see you. Thanks for coming on. So thank you. You guys essentially has been in the spotlight this week and all through the conference around this whole digital transformation, essentially as business group is celebrating its fifth anniversary. What's new, obviously the emphasis of next gen post COVID generation, highly digital transformation, a lot happening. You got your five-year anniversary, what's new. >>Yeah, it, you know, so if you look back, it's exciting. Um, you know, so it was five years ago. Uh, it was actually October where we, where we launched the Accenture AWS business group. And if we think back five years, I think we're still at the point where a lot of customers were making that transition from, you know, should I move to cloud to how do I move to cloud? Right? And so that was one of the reasons why we launched the business group. And since, since then, certainly we've seen that transition, right? Our conversations today are very much around how do I move to cloud, help me move, help me figure out the business case and then pull together all the different pieces so I can move more quickly, uh, you know, with less risk and really achieve my business outcomes. And I would say, you know, one of the things too, that's, that's really changed over the five years. >>And what we're seeing now is when we started, right, we were focused on migration data and IOT as the big three pillars that we launched with. And those are still incredibly important to us, but just the breadth of capability and frankly, the, the, the breadth of need that we're seeing from customers. And obviously as AWS has matured over the years and launched our new capabilities, we're Eva with Accenture and in the business group, we've broadened our capabilities and deepened our capabilities over the, over the last five years as well. For instance, this year with, with COVID, especially, it's really forced our customers to think differently about their own customers or their citizens, and how do they service those citizens? So we've seen a huge acceleration around customer engagement, right? And we powered that with Accenture customer engagement platform powered by ADA, Amazon connect. And so that's been a really big trend this year. And then, you know, that broadens our capability from just a technical discussion to one where we're now really reaching out and, and, um, and helping transform and modernize that customer and citizen experience as well, which has been exciting to see. >>Yeah, Andy, I want to get your thoughts here. We've been reporting and covering essentially for years. It's not like it's new to you guys. I mean, five years is a great anniversary. You know, check is good relationship, but you guys have been doing the work you've been on the trend line. And then this hits and Andy said on his keynote and I thought he said it beautifully. And he even said it to me in my one-on-one interview with them was it's on full display right now, the whole digital transformation, everything about it is on full display and you're either were prepared for it or you kind of word, and you can see who's there. You guys have been prepared. This is not new. So give us the update from your perspective, how you're taking advantage of this, of this massive shift, highly accelerated digital transformation. >>Well, I think, I think you can be prepared, but you've also got to be prepared to always sort of, I think what we're seeing in, in, um, in, in, in, in recent times and particularly 20 w what is it I think today there are, um, full sense of the enterprise workloads, the cloud, um, you know, that leaves 96 percentile now for him. Um, and I, over the next four to >>Five years, um, we're going to see that sort of, uh, acceleration to the, to the cloud pick up, um, this year is, as Andy touched on, I think, uh, uh, on Tuesday in his, I think the pandemic is a forcing function, uh, for companies to, to really pause and think about everything from, from, you know, how they, um, manage that technology to infrastructure, to just to carotenoids where the data sets to what insights and intelligence that getting from that data. And then eventually even to, to the talent, the talent they have in the organization and how they can be competitive, um, their culture, their culture of innovation, of invention and reinvention. And so I think, I think, you know, when you, when you think of companies out there faced with these challenges, it, it forces us, it forces AWS, it forces AEG to come together and think through how can we help create value for them? How can we help help them move from sort of just causing and rethinking to having real plans in an action and that taking them, uh, into, into implementation. And so that's, that's what we're working on. Um, I think over the next five years, we're looking to just continue to come together and help these, these companies get to the cloud and get the value from the cloud because it's beyond just getting to the cloud attached to them and living in the cloud and, and getting the value from it. >>It's interesting. Andy was saying, don't just put your toe in the water. You got to go beyond the toe in the water kind of approach. Um, I want to get to that large scale cause that's the big pickup this week that I kind of walked away with was it's large scale. Acceleration's not just toe in the water experimentation. Can you guys share, what's causing this large scale end to end enterprise transformation? And what are some of the success criteria have you seen for the folks who have done that? >>Yeah. And I'll, I'll, I'll start. And at the end you can buy a lawn. So, you know, it's interesting if I look back a year ago at re-invent and when I did the cube interview, then we were talking about how the ABG, we were starting to see this shift of customers. You know, we've been working with customers for years on a single of what I'll call a single-threaded programs, right. We can do a migration, we could do SAP, we can do a data program. And then even last year, we were really starting to see customers ask. The question is like, what kind of synergies and what kind of economies of scale do I get when I start bringing these different threads together, and also realizing that it's, you know, to innovate for the business and build new applications, new capabilities. Well, that then is going to inform what data you need to, to hydrate those applications, right? Which then informs your data strategy while a lot of that data is then also embedded in your underlying applications that sit on premises. So you should be thinking through how do you get those applications into the cloud? So you need to draw that line through all of those layers. And that was already starting last year. And so last year we launched the joint transformation program with AEG. And then, so we were ready when this year happened and then it was just an acceleration. So things have been happening faster than we anticipated, >>But we knew this was going to be happening. And luckily we've been in a really good position to help some of our customers really think through all those different layers of kind of pyramid as we've been calling it along with the talent and change pieces, which are also so important as you make this transformation to cloud >>Andy, what's the success factors. Andy Jassy came on stage during the partner day, a surprise fireside chat with Doug Hume and talking about this is really an opportunity for partners to, to change the business landscape with enablement from Amazon. You guys are in a pole position to do that in the marketplace. What's the success factors that you see, >>Um, really from three, three fronts, I'd say, um, w one is the people. Um, and, and I, I, again, I think Andy touched on sort of eight, uh, success factors, uh, early in the week. And for me, it's these three areas that it sort of boils down to these three areas. Um, one is the, the, the, the people, uh, from the leaders that it's really important to set those big, bold visions point the way. And then, and then, you know, set top down goals. How are we going to measure Z almost do get what you measure, um, to be, you know, beyond the leaders, to, to the right people in the right position across the company. We we're finding a key success factor for these end to end transformations is not just the leaders, but you haven't poached across the company, working in a, in a collaborative, shared, shared success model, um, and people who are not afraid to, to invent and fail. >>And so that takes me to perhaps the second point, which is the culture, um, it's important, uh, with finding for the right conditions to be set in the company that enabled, uh, people to move at pace, move at speed, be able to fail fast, um, keep things very, very simple and just keep iterating and that sort of culture of iteration and improvement versus seeking perfection is, is super important for, for success. And then the third part of maybe touch on is, is partners. Um, I think, you know, as we move forward over the next five years, we're going to see an increasing number of players in the ecosystem in the enterprise and state. Um, you're going to see more and more SAS providers. And so it's important for companies and our joint clients out there to pick partners like, um, like AWS or, or Accenture or others, but to pick partners who have all worked together and you have built solutions together, and that allows them to get speed to value quicker. It allows them to bring in pre-assembled solutions, um, and really just drive that transformation in a quicker, it sorts of manner. >>Yeah, that's a great point worth calling out, having that partnership model that's additive and has synergy in the cloud, because one of the things that came out of this this week, this year is reinvented, is there's new things going on in the public cloud, even though hybrid is an operating model, outpost and super relevant. There, there are benefits for being in the cloud and you've got partners API, for instance, and have microservices working together. This is all new, but I got, I got to ask that on that thread, Andy, where did you see your customers going? Because I think, you know, as you work backwards from the customers, you guys do, what's their needs, how do you see them? W you know, where's the puck going? Where can they skate where the puck's going, because you can almost look forward and say, okay, I've got to build modern apps. I got to do the digital transformation. Everything is a service. I get that, but what are they, what solutions are you building for them right now to get there? >>Yeah. And, and of course, with, with, you know, industries blurring and multiple companies, it's always hard to boil down to the exact situations, but you could probably look at it from a sort of a thematic lens. And what we're seeing is as the cloud transformation journey picks up, um, from us perspective, we've seen a material shift in the solutions and problems that we're trying to address with clients that they are asking for us, uh, to, to help, uh, address is no longer just the back office, where you're sort of looking at cost and efficiency and, um, uh, driving gains from that perspective. It's beyond that, it's now materially the top line. It's, how'd you get the driving to the, you know, speed to insights, how'd you get them decomposing, uh, their application set in order to derive those insights. Um, how'd you get them, um, to, to, um, uh, sort of adopt leading edge industry solutions that give them that jump start, uh, and that accelerant to winning the customers, winning the eyeballs. >>Um, and then, and then how'd, you help drive the customer experience. We're seeing a lot of push from clients, um, or ask for help on how do I optimize my customer experience in order to retain my eyeballs. And then how do I make sure I've got a soft self-learning ecosystem of play, um, where, uh, you know, it's not just a practical experience that I can sort of keep learning and iterating, um, how I treat my, my customers, um, and a lot of that, um, that still self-learning, that comes from, you know, putting in intelligence into your, into your systems, getting an AI and ML in there. And so, as a result of that work, we're seeing a lot of push and a lot of what we're doing, uh, is pouring investment into those areas. And then finally, maybe beyond the bottom line, and the top line is how do you harden that and protect that with, um, security and resilience? So I'll probably say those are the three areas. John, >>You know, the business model side, obviously the enablement is what Amazon has. Um, we see things like SAS factory coming on board and the partner network, obviously a century is a big, huge partner of you guys. Um, the business models there, you've got I, as, as doing great with chips, you have this data modeling this data opportunity to enable these modern apps. We heard about the partner strategy for me and D um, talking to me now about how can partners within even Accenture, w w what's the business model, um, side on your side that you're enabling this. Can you just share your thoughts on that? >>Yeah, yeah. And so it's, it's interesting. I think I'm going to build it and then build a little bit on some of the things that Andy really talked about there, right? And that we, if you think of that from the partnership, we are absolutely helping our customers with kind of that it modernization piece. And we're investing a lot and there's hard work that needs to get done there. And we're investing a lot as a partnership around the tools, the assets and the methodology. So in AWS and Accenture show up together as AEG, we are executing office single blueprint with a single set of assets, so we can move fast. So we're going to continue to do that with all the hybrid announcements from this past week, those get baked into that, that migration modernization theme, but the other really important piece here as we go up the stack, Andy mentioned it, right? >>The data piece, like so much of what we're talking about here is around data and insights. Right? I did a cube interview last week with, uh, Carl hick. Um, who's the CIO from Takeda. And if you hear Christophe Weber from Takeda talk, he talks about Takeda being a data company, data and insights company. So how do we, as a partnership, again, build the capabilities and the platforms like with Accenture's applied insights platform so that we can bootstrap and really accelerate our client's journey. And then finally, on the innovation on the business front, and Andy was touching on some of these, we are investing in industry solutions and accelerators, right? Because we know that at the end of the day, a lot of these are very similar. We're talking about ingesting data, using machine learning to provide insights and then taking action. So for instance, the cognitive insurance platform that we're working together on with Accenture, if they give out property and casualty claims and think about how do we enable touchless claims using machine learning and computer vision that can assess based on an image damage, and then be able to triage that and process it accordingly, right? >>Using all the latest machine learning capabilities from AWS with that deep, um, AI machine learning data science capability from Accenture, who knows all those algorithms that need to get built and build that library by doing that, we can really help these insurance companies accelerate their transformation around how they think about claims and how they can speed those claims on behalf of their policy holder. So that's an example of a, kind of like a bottom to top, uh, view of what we're doing in the partnership to address these new needs. >>That's awesome. Andy, I want to get back to your point about culture. You mentioned it twice now. Um, talent is a big part of the game here. Andy Jassy referenced Lambda. The next generation developers were using Lambda. He talked about CIO stories around, they didn't move fast enough. They lost three years. A new person came in and made it go faster. This is a new, this is a time for a certain kind of, um, uh, professional and individual, um, to, to be part of, um, this next generation. What's the talent strategy you guys have to attract and attain the best and retain the people. How do you do it? >>Um, you know, it's, it's, um, it's an interesting one. It's, it's, it's oftentimes a, it's, it's a significant point and often overlooked. Um, you know, people, people really matter and getting the right people, um, in not just in AWS or it, but then in our customers is super important. We often find that much of our discussions with, with our clients is centered around that. And it's really a key ingredient. As you touched on, you need people who are willing to embrace change, but also people who are willing to create new, um, to invent new, to reinvent, um, and to, to keep it very simple. Um, w we're we're we're seeing increasingly that you need people that have a sort of deep learning and a deep, uh, or deep desire to keep learning and to be very curious as, as they go along. Most of all, though, I find that, um, having people who are not willing or not afraid to fail is critical, absolutely critical. Um, and I think that that's, that's, uh, a necessary ingredient that we're seeing, um, our clients needing more off, um, because if you can't start and, and, and you can't iterate, um, you know, for fear of failure, you're in trouble. And, and I think Andy touched on that you, you know, where that CIO, that you referred to last three years, um, and so you really do need people who are willing to start not afraid to start, uh, and, uh, and not afraid to lead >>Was a gut check there. I just say, you guys have a great team over there. Everyone at the center I've interviewed strong, talented, and not afraid to lean in and, and into the trends. Um, I got to ask on that front cloud first was something that was a big strategic focus for Accenture. How does that fit into your business group? That's an Amazon focused, obviously they're cloud, and now hybrid everywhere, as I say, um, how does that all work it out? >>We're super excited about our cloud first initiative, and I think it fits it, um, really, uh, perfectly it's it's, it's what we needed. It's, it's, it's a, it's another accelerant. Um, if you think of count first, what we're doing is we're, we're putting together, um, uh, you know, capability set that will help enable him to and transformations as Brian touched on, you know, help companies move from just, you know, migrating to, to, to modernizing, to driving insights, to bringing in change, um, and, and, and helping on that, on that talent side. So that's sort of component number one is how does Accenture bring the best, uh, end to end transformation capabilities to our clients? Number two is perhaps, you know, how do we, um, uh, bring together pre-assembled as Brian touched on pre-assembled industry offerings to help as an accelerant, uh, for our, for our customers three years, as we touched on earlier is, is that sort of partnership with the ecosystem. >>We're going to see an increasing number of SAS providers in an estate, in the enterprise of snakes out there. And so, you know, panto wild cloud first, and our ABG strategy is to increase our touch points in our integrations and our solutions and our offerings with the ecosystem partners out there, the ISP partners out, then the SAS providers out there. And then number four is really about, you know, how do we, um, extend the definition of the cloud? I think oftentimes people thought of the cloud just as sort of on-prem and prem. Um, but, but as Andy touched on earlier this week, you know, you've, you've got this concept of hybrid cloud and that in itself, um, uh, is, is, is, you know, being redefined as well. You know, when you've got the intelligent edge and you've got various forms of the edge. Um, so that's the fourth part of, of, uh, of occupied for strategy. And for us was super excited because all of that is highly relevant for ABG, as we look to build those capabilities as industry solutions and others, and as when to enable our customers, but also how we, you know, as we, as we look to extend how we go to market, I'll join tele PS, uh, in, uh, in our respective skews and products. >>Well, what's clear now is that people now realize that if you contain that complexity, the upside is massive. And that's great opportunity for you guys. We got to get to the final question for you guys to weigh in on, as we wrap up next five years, Brian, Andy weigh in, how do you see that playing out? What do you see this exciting, um, for the partnership and the cloud first cloud, everywhere cloud opportunities share some perspective. >>Yeah, I, I think, you know, just kinda building on that cloud first, right? What cloud first, and we were super excited when cloud first was announced and you know, what it signals to the market and what we're seeing in our customers, which has cloud really permeates everything that we're doing now. Um, and so all aspects of the business will get infused with cloud in some ways, you know, it, it touches on, on all pieces. And I think what we're going to see is just a continued acceleration and getting much more efficient about pulling together the disparate, what had been disparate pieces of these transformations, and then using automation using machine learning to go faster. Right? And so, as we started thinking about the stack, right, well, we're going to get, I know we are, as a partnership is we're already investing there and getting better and more efficient every day as the migration pieces and the moving the assets to the cloud are just going to continue to get more automated, more efficient. And those will become the economic engines that allow us to fund the differentiated, innovative activities up the stack. So I'm excited to see us kind of invest to make those, those, um, those bets accelerated for customers so that we can free up capital and resources to invest where it's going to drive the most outcome for their end customers. And I think that's going to be a big focus and that's going to have the industry, um, you know, focus. It's going to be making sure that we can >>Consume the latest and greatest of AWS as capabilities and, you know, in the areas of machine learning and analytics, but then Andy's also touched on it bringing in ecosystem partners, right? I mean, one of the most exciting wins we had this year, and this year of COVID is looking at the universe, looking at Massachusetts, the COVID track and trace solution that we put in place is a partnership between Accenture, AWS, and Salesforce, right? So again, bringing together three really leading partners who can deliver value for our customers. I think we're going to see a lot more of that as customers look to partnerships like this, to help them figure out how to bring together the best of the ecosystem to drive solutions. So I think we're going to see more of that as well. >>All right, Andy final word, your take >>Thinks of innovation is, is picking up, um, dismiss things are just going faster and faster. I'm just super excited and looking forward to the next five years as, as you know, the technology invention, um, comes out and continues to sort of set new standards from AWS. Um, and as we, as Accenture wringing, our industry capabilities, we marry the two. We, we go and help our customers super exciting time. >>Well, congratulations on the partnership. I want to say thank you to you guys, because I've reported a few times some stories around real successes around this COVID pandemic that you guys worked together on with Amazon that really changed people's lives. Uh, so congratulations on that too as well. I want to call that out. Thanks for coming >>Up. Thank you. Thanks for coming on. >>Okay. This is the cubes coverage, essentially. AWS partnership, part of a century executive summit at Atrius reinvent 2020 I'm John for your host. Thanks. >>You're watching from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Hello, and welcome back to the cubes coverage of AWS reinvent 2020. This is special programming for the century executive summit, where all the thought leaders going to extract the signal from the nose to share with you their perspective of this year's reinvent conference, as it respects the customers' digital transformation. Brian Bohan is the director and head of a center. ADA was business group at Amazon web services. Brian, great to see you. And Chris Wegman is the, uh, center, uh, Amazon business group technology lead at Accenture. Um, guys, this is about technology vision, this, this conversation, um, Chris, I want to start with you because you, Andy Jackson's keynote, you heard about the strategy of digital transformation, how you gotta lean into it. You gotta have the guts to go for it, and you got to decompose. He went everywhere. So what, what did you hear? What was striking about the keynote? Because he covered a lot of topics. Yeah. You know, it >>Was Epic, uh, as always for Mandy, a lot of topics, a lot to cover in the three hours. Uh, there was a couple of things that stood out for me, first of all, hybrid, uh, the concept, the new concept of hybrid and how Andy talked about it, you know, uh, bringing the compute and the power to all parts of the enterprise, uh, whether it be at the edge or are in the big public cloud, uh, whether it be in an outpost or wherever it might be right with containerization now, uh, you know, being able to do, uh, Amazon containerization in my data center and that that's, that's awesome. I think that's gonna make a big difference, all that being underneath the Amazon, uh, console and billing and things like that, which is great. Uh, I'll also say the, the chips, right. And I know compute is always something that, you know, we always kind of take for granted, but I think again, this year, uh, Amazon and Andy really focused on what they're doing with the chips and PR and compute, and the compute is still at the heart of everything in cloud. And that continued advancement is, is making an impact and will make a continue to make a big impact. >>Yeah, I would agree. I think one of the things that really, I mean, the container thing was, I think really kind of a nuanced point when you got Deepak sing on the opening day with Andy Jassy and he's, he runs a container group over there, you know, small little team he's on the front and front stage. That really is the key to the hybrid. And I think this showcases this new layer and taking advantage of the graviton two chips that, which I thought was huge. Brian, this is really a key part of the platform change, not change, but the continuation of AWS higher level servers building blocks that provide more capabilities, heavy lifting as they say, but the new services that are coming on top really speaks to hybrid and speaks to the edge. >>It does. Yeah. And it, it, you know, I think like Andy talks about, and we talk about, I, you know, we really want to provide choice to our customers, uh, first and foremost, and you can see that and they re uh, services. We have, we can see it in the, the hybrid options that Chris talked about, being able to run your containers through ECS or EKS anywhere I just get to the customer's choice. And one of the things that I'm excited about as you talk about going up the stack and on the edge are things will certainly outpost. Um, right. So now I'll post those launched last year, but then with the new form factors, uh, and then you look at services like Panorama, right? Being able to take computer vision and embed machine learning and computer vision, and do that as a managed capability at the edge, um, for customers. >>And so we see this across a number of industries. And so what we're really thinking about is customers no longer have to make trade-offs and have to think about those, those choices that they can really deploy, uh, natively in the cloud. And then they can take those capabilities, train those models, and then deploy them where they need to, whether that's on premises or at the edge, you know, whether it be in a factory or retail environment. When we start, I think we're really well positioned when, um, you know, hopefully next year we started seeing the travel industry rebound, um, and the, the need, you know, more than ever really to, uh, to kind of rethink about how we kind of monitor and make those environments safe. Having this kind of capability at the edge is really going to help our customers as, as we come out of this year and hopefully rebound next year. >>Yeah. Chris, I want to go back to you for a second. It's hard to hard to pick your favorite innovation from the keynote, because, you know, just reminded me that Brian just reminded me of some things I forgot happened. It was like a buffet of innovation. Some keynotes have one or two, it was like 20, you got the industrial piece that was huge. Computer vision machine learning. That's just a game changer. The connect thing came out of nowhere, in my opinion, I mean, it's a call center technology. This is boring as hell. What are you gonna do with that? It turns out it's a game changer. It's not about the calls with the contact and that's discern intermediating, um, in the stack as well. So again, a feature that looks old is actually new and relevant. What's your, what was your favorite, um, innovation? >>Uh, it it's, it's, it's hard to say. I will say my personal favorite was the, the maca last. I, I just, I think that is a phenomenal, um, uh, just addition, right? And the fact that AWS is, has worked with Apple to integrate the Nitra chip into, into, uh, you know, the iMac and offer that out. Um, you know, a lot of people are doing development, uh, on for ILS and that stuff. And that there's just gonna be a huge benefit, uh, for the development teams. But, you know, I will say, I'll come back to connect you. You mentioned it. Um, you know, but you're right. It was a, it's a boring area, but it's an area that we've seen huge success with since, since connect was launched and the additional features and the Amazon continues to bring, you know, um, obviously with, with the pandemic and now that, you know, customer engagement through the phone, uh, through omni-channel has just been critical for companies, right. >>And to be able to have those agents at home, working from home versus being in the office, it was a huge, huge advantage for, for several customers that are using connect. You know, we, we did some great stuff with some different customers, but the continue technology, like you said, the, you know, the call translation and during a call to be able to pop up those key words and have a, have a supervisor, listen is awesome. And a lot of that was some of that was already being done, but we were stitching multiple services together. Now that's right out of the box. Um, and that Google's location is only going to make that go faster and make us to be able to innovate faster for that piece of the business. >>It's interesting, you know, not to get all nerdy and, and business school life, but you've got systems of records, systems of engagement. If you look at the call center and the connect thing, what got my attention was not only the model of disintermediating, that part of the engagement in the stack, but what actually cloud does to something that's a feature or something that could be an element, like say, call center, you old days of, you know, calling an 800 number, getting some support you got in chip, you have machine learning, you actually have stuff in the, in the stack that actually makes that different now. So you w you know, the thing that impressed me was Andy was saying, you could have machine learning, detect pauses, voice inflections. So now you have technology making that more relevant and better and different. So a lot going on, this is just one example of many things that are happening from a disruption innovation standpoint. W what do you guys, what do you guys think about that? And is that like getting it right? Can you share it? >>I think, I think, I think you are right. And I think what's implied there and what you're saying, and even in the, you know, the macro S example is the ability if we're talking about features, right. Which by themselves, you're saying, Oh, wow, what's, what's so unique about that, but because it's on AWS and now, because whether you're a developer working on, you know, w with Mac iOS and you have access to the 175 plus services, that you can then weave into your new applications, talk about the connect scenario. Now we're embedding that kind of inference and machine learning to do what you say, but then your data Lake is also most likely running in AWS, right? And then the other channels, whether they be mobile channels or web channels, or in store physical channels, that data can be captured in that same machine learning could be applied there to get that full picture across the spectrum. Right? So that's the, that's the power of bringing together on AWS to access to all those different capabilities of services, and then also the where the data is, and pulling all that together, that for that end to end view, okay, >>You guys give some examples of work you've done together. I know this stuff we've reported on. Um, in the last session we talked about some of the connect stuff, but that kind of encapsulates where this, where this is all going with respect to the tech. >>Yeah. I think one of the, you know, it was called out on Doug's partner summit was, you know, is there a, uh, an SAP data Lake accelerator, right? Almost every enterprise has SAP, right. And SAP getting data out of SAP has always been a challenge, right. Um, whether it be through, you know, data warehouses and AWS, sorry, SAP BW, you know, what we've focused on is, is getting that data when you're on have SAP on AWS getting that data into the data Lake, right. And getting it into, into a model that you can pull the value out of the customers can pull the value out, use those AI models. Um, so that was one thing we worked on in the last 12 months, super excited about seeing great success with customers. Um, you know, a lot of customers had ideas. They want to do this. They had different models. What we've done is, is made it very, uh, simplified, uh, framework that allows customers to do it very quickly, get the data out there and start getting value out of it and iterating on that data. Um, we saw customers are spending way too much time trying to stitch it all together and trying to get it to work technically. Uh, and we've now cut all that out and they can immediately start getting down to, to the data and taking advantage of those, those different, um, services are out there by AWS. >>Brian, you want to weigh in as things you see as relevant, um, builds that you guys done together that kind of tease out the future and connect the dots to what's coming. >>Uh, I, you know, I'm going to use a customer example. Uh, we worked with, um, and it just came out with, with Unilever around their blue air connected, smart air purifier. And what I think is interesting about that, I think it touches on some of the themes we're talking about, as well as some of the themes we talked about in the last session, which is we started that program before the pandemic. Um, and, but, you know, Unilever recognized that they needed to differentiate their product in the marketplace, move to more of a services oriented business, which we're seeing as a trend. We, uh, we enabled this capability. So now it's a smart air purifier that can be remote manage. And now in the pandemic head, they are in a really good position, obviously with a very relevant product and capability, um, to be used. And so that data then, as we were talking about is going to reside on the cloud. And so the learning that can now happen about usage and about, you know, filter changes, et cetera, can find its way back into future iterations of that valve, that product. And I think that's, that's keeping with, you know, uh, Chris was talking about where we might be systems of record, like in SAP, how do we bring those in and then start learning from that data so that we can get better on our future iterations? >>Hey, Chris, on the last segment we did on the business mission, um, session, Andy Taylor from your team, uh, talked about partnerships within a century and working with other folks. I want to take that now on the technical side, because one of the things that we heard from, um, Doug's, um, keynote and that during the partner day was integrations and data were two big themes. When you're in the cloud, technically the integrations are different. You're going to get unique things in the public cloud that you're just not going to get on premise access to other cloud native technologies and companies. How has that, how do you see the partnering of Accenture with people within your ecosystem and how the data and the integration play together? What's your vision? >>Yeah, I think there's two parts of it. You know, one there's from a commercial standpoint, right? So marketplace, you know, you, you heard Dave talk about that in the, in the partner summit, right? That marketplace is now bringing together this ecosystem, uh, in a very easy way to consume by the customers, uh, and by the users and bringing multiple partners together. And we're working with our ecosystem to put more products out in the marketplace that are integrated together, uh, already. Um, you know, I think one from a technical perspective though, you know, if you look at Salesforce, you know, we talked a little earlier about connect another good example, technically underneath the covers, how we've integrated connect and Salesforce, some of it being prebuilt by AWS and Salesforce, other things that we've added on top of it, um, I think are good examples. And I think as these ecosystems, these IFCs put their products out there and start exposing more and more API APIs, uh, on the Amazon platform, make opening it up, having those, those prebuilt network connections there between, you know, the different VPCs and the different areas within, within a customer's network. >>Um, and having them, having that all opened up and connected and having all that networking done underneath the covers. You know, it's one thing to call the API APIs. It's one thing to have access to those. And that's been a big focus of a lot of, you know, ISBNs and customers to build those API APIs and expose them, but having that network infrastructure and being able to stay within the cloud within AWS to make those connections, the past that data, we always talk about scale, right? It's one thing if I just need to pass like a, you know, a simple user ID back and forth, right? That's, that's fine. We're not talking massive data sets, whether it be seismic data or whatever it be passing those of those large, those large data sets between customers across the Amazon network is going to, is going to open up the world. >>Yeah. I see huge possibilities there and love to keep on this story. I think it's going to be important and something to keep track of. I'm sure you guys will be on top of it. You know, one of the things I want to, um, dig into with you guys now is Andy had kind of this philosophy philosophical thing in his keynote, talk about societal change and how tough the pandemic is. Everything's on full display. Um, and this kind of brings out kind of like where we are and the truth. You look at the truth, it's a virtual event. I mean, it's a website and you got some sessions out there with doing remote best weekend. Um, and you've got software and you've got technology and, you know, the concept of a mechanism it's software, it does something, it does a purpose. Essentially. You guys have a concept called living systems where growth strategy powered by technology. How do you take the concept of a, of a living organism or a system and replace the mechanism, staleness of computing and software. And this is kind of an interesting, because we're on the cusp of a, of a major inflection point post COVID. I get the digital transformation being slow that's yes, that's happening. There's other things going on in society. What do you guys think about this living systems concept? >>Yeah, so I, you know, I'll start, but, you know, I think the living system concept, um, you know, it started out very much thinking about how do you rapidly change the system, right? And, and because of cloud, because of, of dev ops, because of, you know, all these software technologies and processes that we've created, you know, that's where it started it, making it much easier to make it a much faster being able to change rapidly, but you're right. I think as you now bring in more technologies, the AI technology self-healing technologies, again, you're hurting Indian in his keynote, talk about, you know, the, the systems and services they're building to the tech problems and, and, and, and give, uh, resolve those problems. Right. Obviously automation is a big part of that living systems, you know, being able to bring that all together and to be able to react in real time to either what a customer, you know, asks, um, you know, either through the AI models that have been generated and turning those AI models around much faster, um, and being able to get all the information that came came in in the last 20 minutes, right. >>You know, society's moving fast and changing fast. And, you know, even in one part of the world, if, um, something, you know, in 10 minutes can change and being able to have systems to react to that, learn from that and be able to pass that on to the next country, especially in this world with COVID and, you know, things changing very quickly with quickly and, and, and, um, diagnosis and, and, um, medical response, all that so quickly to be able to react to that and have systems pass that information learned from that information is going to be critical. >>That's awesome. Brian, one of the things that comes up every year is, Oh, the cloud scalable this year. I think, you know, we've, we've talked on the cube before, uh, years ago, certainly with the censure and Amazon, I think it was like three or four years ago. Yeah. The clouds horizontally scalable, but vertically specialized at the application layer. But if you look at the data Lake stuff that you guys have been doing, where you have machine learning, the data's horizontally scalable, and then you got the specialization in the app changes that changes the whole vertical thing. Like you don't need to have a whole vertical solution or do you, so how has this year's um, cloud news impacted vertical industries because it used to be, Oh, the oil and gas financial services. They've got a team for that. We've got a stack for that. Not anymore. Is it going away? What's changing. Wow. >>I, you know, I think it's a really good question. And I don't think, I think what we're saying, and I was just on a call this morning talking about banking and capital markets. And I do think the, you know, the, the challenges are still pretty sector specific. Um, but what we do see is the, the kind of commonality, when we start looking at the, and we talked about it as the industry solutions that we're building as a partnership, most of them follow the pattern of ingesting data, analyzing that data, and then being able to, uh, provide insights and an actions. Right. So if you think about creating that yeah. That kind of common chassis of that ingest the data Lake and then the machine learning, can you talk about, you know, the announces around SageMaker and being able to manage these models, what changes then really are the very specific industries algorithms that you're, you're, you're writing right within that framework. And so we're doing a lot in connect is a good example of this too, where you look at it. Yeah. Customer service is a horizontal capability that we're building out, but then when you stop it into insurance or retail banking or utilities, there are nuances then that we then extend and build so that we meet the unique needs of those, those industries. And that's usually around those, those models. >>Yeah. And I think this year was the first reinvented. I saw real products coming out that actually solve that problem. And that was their last year SageMaker was kinda moving up the stack, but now you have apps embedding machine learning directly in, and users don't even know it's in there. I mean, Christmas is kind of where it's going. Right. I mean, >>Yeah. Announcements. Right. How many, how many announcements where machine learning is just embedded in? I mean, so, you know, code guru, uh, dev ops guru Panorama, we talked about, it's just, it's just there. >>Yeah. I mean, having that knowledge about the linguistics and the metadata, knowing the, the business logic, those are important specific use cases for the vertical and you can get to it faster. Right. Chris, how is this changing on the tech side, your perspective? Yeah. >>You know, I keep coming back to, you know, AWS and cloud makes it easier, right? None of this stuff, you know, all of this stuff can be done, uh, and has some of it has been, but you know, what Amazon continues to do is make it easier to consume by the developer, by the, by the customer and to actually embedded into applications much easier than it would be if I had to go set up the stack and build it all on that and, and, and, uh, embed it. Right. So it's, shortcutting that process. And again, as these products continue to mature, right. And some of the stuff is embedded, um, it makes that process so much faster. Uh, it makes it reduces the amount of work required by the developers, uh, the engineers to get there. So I I'm expecting, you're going to see more of this. >>Right. I think you're going to see more and more of these multi connected services by AWS that has a lot of the AIML, um, pre-configured data lakes, all that kind of stuff embedded in those services. So you don't have to do it yourself and continue to go up the stack. And we was talking about, Amazon's built for builders, right. But, you know, builders, you know, um, have been super specialized in, or we're becoming, you know, as engineers, we're being asked to be bigger and bigger and to be, you know, uh, be able to do more stuff. And I think, you know, these kinds of integrated services are gonna help us do that >>And certainly needed more. Now, when you have hybrid edge that are going to be operating with microservices on a cloud model, and with all those advantages that are going to come around the corner for being in the cloud, I mean, there's going to be, I think there's going to be a whole clarity around benefits in the cloud with all these capabilities and benefits cloud guru. Thanks my favorite this year, because it just points to why that could happen. I mean, that happens because of the cloud data. If you're on premise, you may not have a little cloud guru, you got to got to get more data. So, but they're all different edge certainly will come into your vision on the edge. Chris, how do you see that evolving for customers? Because that could be complex new stuff. How is it going to get easier? >>Yeah. It's super complex now, right? I mean, you gotta design for, you know, all the different, uh, edge 5g, uh, protocols are out there and, and, and solutions. Right. You know, Amazon's simplifying that again, to come back to simplification. Right. I can, I can build an app that, that works on any 5g network that's been integrated with AWS. Right. I don't have to set up all the different layers to get back to my cloud or back to my, my bigger data side. And I was kind of choking. I don't even know where to call the cloud anymore, big cloud, which is a central and I go down and then I've got a cloud at the edge. Right. So what do I call that? >>Exactly. So, you know, again, I think it is this next generation of technology with the edge comes, right. And we put more and more data at the edge. We're asking for more and more compute at the edge, right? Whether it be industrial or, you know, for personal use or consumer use, um, you know, that processing is gonna get more and more intense, uh, to be able to manage and under a single console, under a single platform and be able to move the code that I develop across that entire platform, whether I have to go all the way down to the, you know, to the very edge, uh, at the, at the 5g level, right? Or all the way into the bigger cloud and how that process, isn't there be able to do that. Seamlessly is going to be allow the speed of development that's needed. >>Well, you guys done a great job and no better time to be a techie or interested in technology or computer science or social science for that matter. This is a really perfect storm, a lot of problems to solve a lot of things, a lot of change happening, positive change opportunities, a lot of great stuff. Uh, final question guys, five years working together now on this partnership with AWS and Accenture, um, congratulations, you guys are in pole position for the next wave coming. Um, what's exciting. You guys, Chris, what's on your mind, Brian. What's, what's getting you guys pumped up >>Again. I come back to G you know, Andy mentioned it in his keynote, right? We're seeing customers move now, right. We're seeing, you know, five years ago we knew customers were going to get a new, this. We built a partnership to enable these enterprise customers to make that, that journey. Right. But now, you know, even more, we're seeing them move at such great speed. Right. Which is super excites me. Right. Because I can see, you know, being in this for a long time, now I can see the value on the other end. And I really, we've been wanting to push our customers as fast as they can through the journey. And now they're moving out of, they're getting, they're getting the religion, they're getting there. They see, they need to do it to change your business. So that's what excites me is just the excites me. >>It's just the speed at which we're, we're in a single movement. Yeah, yeah. I'd agree with, yeah, I'd agree with that. I mean, so, you know, obviously getting, getting customers to the cloud is super important work, and we're obviously doing that and helping accelerate that, it's it, it's what we've been talking about when we're there, all the possibilities that become available right. Through the common data capabilities, the access to the 175 some-odd AWS services. And I also think, and this is, this is kind of permeated through this week at re-invent is the opportunity, especially in those industries that do have an industrial aspect, a manufacturing aspect, or a really strong physical aspect of bringing together it and operational technology and the business with all these capabilities, then I think edge and pushing machine learning down to the edge and analytics at the edge is really going to help us do that. And so I'm super excited by all that possibility is I feel like we're just scratching the surface there, >>Great time to be building out. And you know, this is the time for re reconstruction. Re-invention big themes. So many storylines in the keynote, in the events. It's going to keep us busy here. It's looking at angle in the cube for the next year. Gentlemen, thank you for coming out. I really appreciate it. Thanks. Thank you. All right. Great conversation. You're getting technical. We could've go on another 30 minutes. Lot to talk about a lot of storylines here at AWS. Reinvent 2020 at the Centure executive summit. I'm John furrier. Thanks for watching.

>>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome to cube three 60 fives coverage of the Accenture executive summit. Part of AWS reinvent. I'm your host Rebecca Knight. Today we are joined by a cube alum, Karthik, Lorraine. He is Accenture senior managing director and lead Accenture cloud. First, welcome back to the show Karthik. >>Thank you. Thanks for having me here. >>Always a pleasure. So I want to talk to you. You are an industry veteran, you've been in Silicon Valley for decades. Um, I want to hear from your perspective what the impact of the COVID-19 pandemic has been, what are you hearing from clients? What are they struggling with? What are their challenges that they're facing day to day? >>I think, um, COVID-19 is being a eye-opener from, you know, various facets, you know, um, first and foremost, it's a, it's a hell, um, situation that everybody's facing, which is not just, uh, highest economic bearings to it. It has enterprise, um, an organization with bedding to it. And most importantly, it's very personal to people, um, because they themselves and their friends, family near and dear ones are going through this challenge, uh, from various different dimension. But putting that aside, when you come to it from an organization enterprise standpoint, it has changed everything well, the behavior of organizations coming together, working in their campuses, working with each other as friends, family, and, uh, um, near and dear colleagues, all of them are operating differently. So that's what big change to get things done in a completely different way, from how they used to get things done. >>Number two, a lot of things that were planned for normal scenarios, like their global supply chain, how they interact with their client customers, how they go innovate with their partners on how that employees contribute to the success of an organization at all changed. And there are no data models that give them a hint of something like this for them to be prepared for this. So we are seeing organizations, um, that have adapted to this reasonably okay, and are, you know, launching to innovate faster in this. And there are organizations that have started with struggling, but are continuing to struggle. And the gap between the leaders and legs are widening. So this is creating opportunities in a different way for the leaders, um, with a lot of pivot their business, but it's also creating significant challenge for the lag guides, uh, as we defined in our future systems research that we did a year ago, uh, and those organizations are struggling further. So the gap is actually widening. >>So you just talked about the widening gap. I've talked about the tremendous uncertainty that so many companies, even the ones who have adapted reasonably well, uh, in this, in this time, talk a little bit about Accenture cloud first and why, why now? >>I think it's a great question. Um, we believe that for many of our clients COVID-19 has turned, uh, cloud from an experimentation aspiration to an origin mandate. What I mean by that is everybody has been doing something on the other end cloud. There's no company that says we don't believe in cloud are, we don't want to do cloud. It was how much they did in cloud. And they were experimenting. They were doing the new things in cloud, but they were operating a lot of their core business outside the cloud or not in the cloud. Those organizations have struggled to operate in this new normal, in a remote fashion, as well as, uh, their ability to pivot to all the changes the pandemic has brought to them. But on the other hand, the organizations that had a solid foundation in cloud were able to collect faster and not actually gone into the stage of innovating faster and driving a new behavior in the market, new behavior within their organization. >>So we are seeing that spend to make is actually fast-forwarded something that we always believed was going to happen. This, uh, uh, moving to cloud over the next decade is fast forward it to happen in the next three to five years. And it's created this moment where it's a once in an era, really replatforming of businesses in the cloud that we are going to see. And we see this moment as a cloud first moment where organizations will use cloud as the, the, the canvas and the foundation with which they're going to reimagine their business after they were born in the cloud. Uh, and this requires a whole new strategy. Uh, and as Accenture, we are getting a lot in cloud, but we thought that this is the moment where we bring all of that, gave him a piece together because we need a strategy for addressing, moving to cloud are embracing cloud in a holistic fashion. And that's what Accenture cloud first brings together a holistic strategy, a team that's 70,000 plus people that's coming together with rich cloud skills, but investing to tie in all the various capabilities of cloud to Delaware, that holistic strategy to our clients. So I want you to >>Delve into a little bit more about what this strategy actually entails. I mean, it's clearly about embracing change and being willing to experiment and having capabilities to innovate. Can you tell us a little bit more about what this strategy entails? >>Yeah. The reason why we say that as a need for strategy is like I said, cloud is not new. There's almost every customer client is doing something with the cloud, but all of them have taken different approaches to cloud and different boundaries to cloud. Some organizations say, I just need to consolidate my multiple data centers to a small data center footprint and move the nest to cloud. Certain other organizations say that well, I'm going to move certain workloads to cloud. Certain other organizations said, well, I'm going to build this Greenfield application or workload in cloud. Certain other said, um, I'm going to use the power of AI ML in the cloud to analyze my data and drive insights. But a cloud first strategy is all of this tied with the corporate strategy of the organization with an industry specific cloud journey to say, if in this current industry, if I were to be reborn in the cloud, would I do it in the exact same passion that I did in the past, which means that the products and services that they offer need to be the matching, how they interact with that customers and partners need to be revisited, how they bird and operate their IP systems need to be the, imagine how they unearthed the data from all of the systems under which they attract need to be liberated so that you could drive insights of cloud. >>First strategy hands is a corporate wide strategy, and it's a C-suite responsibility. It doesn't take the ownership away from the CIO or CIO, but the CIO is, and CDI was felt that it was just their problem and they were to solve it. And everyone as being a customer, now, the center of gravity is elevated to it becoming a C-suite agenda on everybody's agenda, where probably the CDI is the instrument to execute that that's a holistic cloud-first strategy >>And it, and it's a strategy, but the way you're describing it, it sounds like it's also a mindset and an approach, as you were saying, this idea of being reborn in the cloud. So now how do I think about things? How do I communicate? How do I collaborate? How do I get done? What I need to get done. Talk a little bit about how this has changed, the way you support your clients and how Accenture cloud first is changing your approach to cloud services. >>Wonderful. Um, you know, I did not color one very important aspect in my previous question, but that's exactly what you just asked me now, which is to do all of this. I talked about all of the variables, uh, an organization or an enterprise is going to go through, but the good part is they have one constant. And what is that? That is their employees, uh, because you do, the employees are able to embrace this change. If they are able to, uh, change them, says, pivot them says retool and train themselves to be able to operate in this new cloud. First one, the ability to reimagine every function of the business would be happening at speed. And cloud first approach is to do all of this at speed, because innovation is deadly proposed there, do the rate of probability on experimentation. You need to experiment a lot for any kind of experimentation. >>There's a probability of success. Organizations need to have an ability and a mechanism for them to be able to innovate faster for which they need to experiment a lot, the more the experiment and the lower cost at which they experiment is going to help them experiment a lot. And they experiment demic speed, fail fast, succeed more. And hence, they're going to be able to operate this at speed. So the cloud-first mindset is all about speed. I'm helping the clients fast track that innovation journey, and this is going to happen. Like I said, across the enterprise and every function across every department, I'm the agent of this change is going to be the employees or weapon, race, this change through new skills and new grueling and new mindset that they need to adapt to. >>So Karthik what you're describing it, it sounds so exciting. And yet for a pandemic wary workforce, that's been working remotely that may be dealing with uncertainty if for their kid's school and for so many other aspects of their life, it sounds hard. So how are you helping your clients, employees get onboard with this? And because the change management is, is often the hardest part. >>Yeah, I think it's, again, a great question. A bottle has only so much capacity. Something got to come off for something else to go in. That's what you're saying is absolutely right. And that is again, the power of cloud. The reason why cloud is such a fundamental breakthrough technology and capability for us to succeed in this era, because it helps in various forms. What we talked so far is the power of innovation that can create, but cloud can also simplify the life of the employees in an enterprise. There are several activities and tasks that people do in managing that complex infrastructure, complex ID landscape. They used to do certain jobs and activities in a very difficult underground about with cloud has simplified. And democratised a lot of these activities. So that things which had to be done in the past, like managing the complexity of the infrastructure, keeping them up all the time, managing the, um, the obsolescence of the capabilities and technologies and infrastructure, all of that could be offloaded to the cloud. >>So that the time that is available for all of these employees can be used to further innovate. Every organization is going to spend almost the same amount of money, but rather than spending activities, by looking at the rear view mirror on keeping the lights on, they're going to spend more money, more time, more energy, and spend their skills on things that are going to add value to their organization. Because you, every innovation that an enterprise can give to their end customer need not come from that enterprise. The word of platform economy is about democratising innovation. And the power of cloud is to get all of these capabilities from outside the four walls of the enterprise, >>It will add value to the organization, but I would imagine also add value to that employee's life because that employee, the employee will be more engaged in his or her job and therefore bring more excitement and energy into her, his or her day-to-day activities too. >>Absolutely. Absolutely. And this is, this is a normal evolution we would have seen everybody would have seen in their lives, that they keep moving up the value chain of what activities that, uh, gets performed buying by those individuals. And this is, um, you know, no more true than how the United States, uh, as an economy has operated where, um, this is the power of a powerhouse of innovation, where the work that's done inside the country keeps moving up to value chain. And, um, us leverage is the global economy for a lot of things that is required to power the United States and that global economic, uh, phenomenon is very proof for an enterprise as well. There are things that an enterprise needs to do them soon. There are things an employee needs to do themselves. Um, but there are things that they could leverage from the external innovation and the power of innovation that is coming from technologies like cloud. >>So at Accenture, you have long, long, deep Stan, sorry, you have deep and long-standing relationships with many cloud service providers, including AWS. How does the Accenture cloud first strategy, how does it affect your relationships with those providers? >>Yeah, we have great relationships with cloud providers like AWS. And in fact, in the cloud world, it was one of the first, um, capability that we started about years ago, uh, when we started developing these capabilities. But five years ago, we hit a very important milestone where the two organizations came together and said that we are forging a pharma partnership with joint investments to build this partnership. And we named that as a Accenture, AWS business group ABG, uh, where we co-invest and brought skills together and develop solutions. And we will continue to do that. And through that investment, we've also made several acquisitions that you would have seen in the recent times, like, uh, an invoice and gecko that we made acquisitions in in Europe. But now we're taking this to the next level. What we are saying is two cloud first and the $3 billion investment that we are bringing in, uh, through cloud-first. >>We are going to make specific investment to create unique joint solution and landing zones foundation, um, cloud packs with which clients can accelerate their innovation or their journey to cloud first. And one great example is what we are doing with Takeda, uh, billable, pharmaceutical giant, um, between we've signed a five-year partnership. And it was out in the media just a month ago or so, where we are, the two organizations are coming together. We have created a partnership as a power of three partnership, where the three organizations are jointly hoarding hats and taking responsibility for the innovation and the leadership position that Takeda wants to get to with this. We are going to simplify their operating model and organization by providing and flexibility. We're going to provide a lot more insights. Tequila has a 230 year old organization. Imagine the amount of trapped data and intelligence that is there. >>How about bringing all of that together with the power of AWS and Accenture and Takeda to drive more customer insights, um, come up with breakthrough R and D uh, accelerate clinical trials and improve the patient experience using AI ML and edge technologies. So all of these things that we will do through this partnership with joined investment from Accenture cloud first, as well as partner like AWS, so that Takeda can realize their gain. And, uh, their senior actually made a statement that five years from now, every ticket an employee will have an AI assistant. That's going to make that beginner employee move up the value chain on how they contribute and add value to the future of tequila with the AI assistant, making them even more equipped and smarter than what they could be otherwise. >>So, one last question to close this out here. What is your future vision for, for Accenture cloud first? What are we going to be talking about at next year's Accenture executive summit? Yeah, the future >>Is going to be, um, evolving, but the part that is exciting to me, and this is, uh, uh, a fundamental belief that we are entering a new era of industrial revolution from industry first, second, and third industry. The third happened probably 20 years ago with the advent of Silicon and computers and all of that stuff that happened here in the Silicon Valley. I think the fourth industrial revolution is going to be in the cross section of, uh, physical, digital and biological boundaries. And there's a great article, um, in one economic forum that people, uh, your audience can Google and read about it. Uh, but the reason why this is very, very important is we are seeing a disturbing phenomenon that over the last 10 years are seeing a Blackwing of the, um, labor productivity and innovation, which has dropped to about 2.1%. When you see that kind of phenomenon over that longer period of time, there has to be breakthrough innovation that needs to happen to come out of this barrier and get to the next, you know, base camp, as I would call it to further this productivity, um, lack that we are seeing, and that is going to happen in the intersection of the physical, digital and biological boundaries. >>And I think cloud is going to be the connective tissue between all of these three, to be able to provide that where it's the edge, especially is good to come closer to the human lives. It's going to come from cloud. Yeah. Pick totally in your mind, you can think about cloud as central, either in a private cloud, in a data center or in a public cloud, you know, everywhere. But when you think about edge, it's going to be far reaching and coming close to where we live and maybe work and very, um, get entertained and so on and so forth. And there's good to be, uh, intervention in a positive way in the field of medicine, in the field of entertainment, in the field of, um, manufacturing in the field of, um, you know, mobility. When I say mobility, human mobility, people, transportation, and so on and so forth with all of this stuff, cloud is going to be the connective tissue and the vision of cloud first is going to be, uh, you know, blowing through this big change that is going to happen. And the evolution that is going to happen where, you know, the human grace of mankind, um, our person kind of being very gender neutral in today's world. Um, go first needs to be that beacon of, uh, creating the next generation vision for enterprises to take advantage of that kind of an exciting future. And that's why it, Accenture, are we saying that there'll be change as our, as our purpose? >>I genuinely believe that cloud first is going to be in the forefront of that change agenda, both for Accenture as well as for the rest of the work. Excellent. Let there be change, indeed. Thank you so much for joining us Karthik. A pleasure I'm Rebecca nights stay tuned for more of Q3 60 fives coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS >>Welcome everyone to the Q virtual and our coverage of the Accenture executive summit, which is part of AWS reinvent 2020. I'm your host Rebecca Knight. Today, we are talking about the green cloud and joining me is Kishor Dirk. He is Accenture senior managing director cloud first global services lead. Thank you so much for coming on the show. Kishor nice to meet you. So I want to start by asking you what it is that we mean when we say green cloud, we know the sustainability is a business imperative. So many organizations around the world are committing to responsible innovation, lowering carbon emissions. But what is this? What is it? What does it mean when they talk about cloud from a sustainability perspective? I think it's about responsible innovation being cloud is a cloud first approach that has benefit the clients by helping reduce carbon emissions. Think about it this way. >>You have a large number of data centers. Each of these data centers are increasing by 14% every year. And this double digit growth. What you're seeing is these data centers and the consumption is nearly coolant to the kind of them should have a country like Spain. So the magnitude of the problem that is out there and how do we pursue a green approach. If you look at this, our Accenture analysis, in terms of the migration to public cloud, we've seen that we can reduce that by 59 million tons of CO2 per year with just the 5.9% reduction in total emissions and equates this to 22 million cars off the road. And the magnitude of reduction can go a long way in meeting climate change commitments, particularly for data sensitive. Wow, that's incredible. The numbers that you're putting forward are, are absolutely mind blowing. So how does it work? Is it a simple cloud migration? So, you know, when companies begin their cloud journey and then they confront, uh, with >>Them a lot of questions, the decision to make, uh, this particular, uh, element sustainable in the solution and benefits they drive and they have to make wise choices, and then they will gain unprecedented level of innovation leading to both a greener planet, as well as, uh, a greener balance sheet, I would say, uh, so effectively it's all about ambition, data ambition, greater the reduction in carbon emissions. So from a cloud migration perspective, we look at it as a, as a simple solution with approaches and sustainability benefits, uh, that vary based on things it's about selecting the right cloud provider, a very carbon thoughtful provider and the first step towards a sustainable cloud journey. And here we're looking at cloud operators know, obviously they have different corporate commitments towards sustainability, and that determines how they plan, how they build, uh, their, uh, uh, the data centers, how they are consumed and assumptions that operate there and how they, or they retire their data centers. >>Then, uh, the next element that you want to do is how do you build it ambition, you know, for some of the companies, uh, and average on-prem, uh, drives about 65% energy reduction and the carbon emission reduction number was 84%, which is kind of good, I would say. But then if you could go up to 98% by configuring applications to the cloud, that is significant benefit for, uh, for the board. And obviously it's a, a greener cloud that we're talking about. And then the question is, how far can you go? And, uh, you know, the, obviously the companies have to unlock greater financial societal environmental benefits, and Accenture has this cloud based circular operations and sustainable products and services that we bring into play. So it's a, it's a very thoughtful, broader approach that w bringing in, in terms of, uh, just a simple concept of cloud migration. >>So we know that in the COVID era, shifting to the cloud has really become a business imperative. How is Accenture working with its clients at a time when all of this movement has been accelerated? How do you partner and what is your approach in terms of helping them with their migrations? >>Yeah, I mean, let, let me talk a little bit about the pandemic and the crisis that is that today. And if you really look at that in terms of how we partnered with a lot of our clients in terms of the cloud first approach, I'll give you a couple of examples. We worked with rolls, Royce, MacLaren, DHL, and others, as part of the ventilator, a UK challenge consortium, again, to, uh, coordinate production of medical ventilator surgically needed for the UK health service. Many of these farms I've taken similar initiatives in, in terms of, uh, you know, from a few manufacturers hand sanitizers, and to answer it as us and again, leading passionate labels, making PPE, and again, at the UN general assembly, we launched the end-to-end integration guide that helps company is essentially to have a sustainable development goals. And that's how we are parking at a very large scale. >>Uh, and, and if you really look at how we work with our clients and what is Accenture's role there, uh, you know, from, in terms of our clients, you know, there are multiple steps that we look at. One is about planning, building, deploying, and managing an optimal green cloud solution. And Accenture has this concept of, uh, helping clients with a platform to kind of achieve that goal. And here we are having, we are having a platform or a mine app, which has a module called BGR advisor. And this is a capability that helps you provide optimal green cloud, uh, you know, a business case, and obviously a blueprint for each of our clients and right from the start in terms of how do we complete cloud migration recommendation to an improved solution, accurate accuracy to obviously bringing in the end to end perspective, uh, you know, with this green card advisor capability, we're helping our clients capture what we call as a carbon footprint for existing data centers and provide, uh, I would say the current cloud CO2 emission score that, you know, obviously helps them, uh, with carbon credits that can further that green agenda. >>So essentially this is about recommending a green index score, reducing carbon footprint for migration migrating for green cloud. And if we look at how Accenture itself is practicing what we preach, 95% of our applications are in the cloud. And this migration has helped us, uh, to lead to about $14.5 million in benefit. And in the third year and another 3 million analytics costs that are saved through right-sizing a service consumption. So it's a very broad umbrella and a footprint in terms of how we engage societaly with the UN or our clients. And what is it that we exactly bring to our clients in solving a specific problem? >>Accenture isn't is walking the walk, as you say, >>Instead of it, we practice what we preach, and that is something that we take it to heart. We want to have a responsible business and we want to practice it. And we want to advise our clients around that >>You are your own use case. And so they can, they know they can take your advice. So talk a little bit about, um, the global, the cooperation that's needed. We know that conquering this pandemic is going to take a coordinated global effort and talk a little bit about the great reset initiative. First of all, what is that? Why don't we, why don't we start there and then we can delve into it a little bit more. >>Okay. So before we get to how we are cooperating, the great reset, uh, initiative is about improving the state of the world. And it's about a group of global stakeholders cooperating to simultaneously manage the direct consequences of their COVID-19 crisis. Uh, and in spirit of this cooperation that we're seeing during COVID-19, uh, which will obviously either to post pandemic, to tackle the world's pressing issues. As I say, uh, we are increasing companies to realize a combined potential of technology and sustainable impact to use enterprise solutions, to address with urgency and scale, and, um, obviously, uh, multiple challenges that are facing our world. One of the ways that are increasing, uh, companies to reach their readiness cloud with Accenture's cloud strategy is to build a solid foundation that is resilient and will be able to faster to the current, as well as future times. Now, when you think of cloud as the foundation, uh, that drives the digital transformation, it's about scale speed, streamlining your operations, and obviously reducing costs. >>And as these businesses seize the construct of cloud first, they must remain obviously responsible and trusted. Now think about this, right, as part of our analysis, uh, that profitability can co-exist with responsible and sustainable practices. Let's say that all the data centers, uh, migrated from on-prem to cloud based, we estimate that would reduce carbon emissions globally by 60 million tons per year. Uh, and think about it this way, right? Easier metric would be taking out 22 million cars off the road. Um, the other examples that you've seen, right, in terms of the NHS work that they're doing, uh, in, in UK to build, uh, uh, you know, uh, Microsoft teams in based integration. And, uh, the platform rolled out for 1.2 million users, uh, and got 16,000 users that we were able to secure, uh, instant messages, obviously complete audio video calls and host virtual meetings across India. So, uh, this, this work that we did with NHS is, is something that we have, we are collaborating with a lot of tools and powering businesses. >>Well, you're vividly describing the business case for sustainability. What do you see as the future of cloud when thinking about it from this lens of sustainability, and also going back to what you were talking about in terms of how you are helping your, your fostering cooperation within these organizations. >>Yeah, that's a very good question. So if you look at today, right, businesses are obviously environmentally aware and they are expanding efforts to decrease power consumption, carbon emissions, and they want to run a sustainable operational efficiency across all elements of their business. And this is an increasing trend, and there is that option of energy efficient infrastructure in the global market. And this trend is the cloud first thinking. And with the right cloud migration that we've been discussing is about unlocking new opportunity, like clean energy foundations enable enabled by cloud based geographic analysis, material, waste reductions, and better data insights. And this is something that, uh, uh, will drive, uh, with obviously faster analytics platform that is out there. Now, the sustainability is actually the future of business, which is companies that are historically different, the financial security or agility benefits to cloud. Now sustainability becomes an imperative for them. And I would own experience Accenture's experience with cloud migrations. We have seen 30 to 40% total cost of ownership savings, and it's driving a greater workload, flexibility, better service, your obligation, and obviously more energy efficient, uh, public clouds that cost, uh, we'll see that, that drive a lot of these enterprise own data centers. So in our view, what we are seeing is that this, this, uh, sustainable cloud position helps, uh, helps companies to, uh, drive a lot of the goals in addition to their financial and other goods. >>So what should organizations who are, who are watching this interview and saying, Hey, I need to know more, what, what do you recommend to them? And what, where should they go to get more information on Greenplum? >>Yeah. If you wanna, if you are a business leader and you're thinking about which cloud provider is good, or how, how should applications be modernized to meet our day-to-day needs, which cloud driven innovations should be priorities. Uh, you know, that's why Accenture, uh, formed up the cloud first organization and essentially to provide the full stack of cloud services to help our clients become a cloud first business. Um, you know, it's all about excavation, uh, the digital transformation innovating faster, creating differentiated, uh, and sustainable value for our clients. And we are powering it up at 70,000 cloud professionals, $3 billion investment, and, uh, bringing together and services for our clients in terms of cloud solutions. And obviously the ecosystem partnership that we have that we are seeing today, uh, and, and the assets that help our clients realize their goals. Um, and again, to do reach out to us, uh, we can help them determine obviously, an optimal, sustainable cloud for solution that meets the business needs and being unprecedented levels of innovation. Our experience, uh, will be our advantage. And, uh, now more than ever Rebecca, >>Just closing us out here. Do you have any advice for these companies who are navigating a great deal of uncertainty? We, what, what do you think the next 12 to 24 months? What do you think that should be on the minds of CEOs as they go through? >>So, as CEO's are thinking about rapidly leveraging cloud, migrating to cloud, uh, one of the elements that we want them to be thoughtful about is can they do that, uh, with unprecedent level of innovation, but also build a greener planet and a greener balance sheet, if we can achieve this balance and kind of, uh, have a, have a world which is greener, I think the world will win. And we all along with Accenture clients will win. That's what I would say, uh, >>Optimistic outlook, and I will take it. Thank you so much. Kishor for coming on the show >>That was >>Accenture's Kishor Dirk, I'm Rebecca Knight stay tuned for more of the cube virtuals coverage of the Accenture executive summit >>Around the globe. >>It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cube virtual and our coverage of the Accenture executive summit. Part of AWS reinvent 2020. I'm your host Rebecca Knight. Today, we are talking about the power of three. And what happens when you bring together the scientific know-how of a global bias biopharmaceutical powerhouse in Takeda, a leading cloud services provider in AWS, and Accenture's ability to innovate, execute, and deliver innovation. Joining me to talk about these things. We have Aaron, sorry, Arjun, baby. He is the senior managing director and chairman of Accenture's diamond leadership council. Welcome Arjun, Karl hick. He is the chief digital and information officer at Takeda. What is your bigger, thank you, Rebecca and Brian bowhead, global director, and head of the Accenture AWS business group at Amazon web services. Thanks so much for coming up. So, as I said, we're talking today about this relationship between, uh, your three organizations. Carl, I want to talk with you. I know you're at the beginning of your cloud journey. What was the compelling reason? What w why, why move to the cloud and why now? >>Yeah, no, thank you for the question. So, you know, as a biopharmaceutical leader, we're committed to bringing better health and a brighter future to our patients. We're doing that by translating science into some really innovative and life transporting therapies, but throughout, you know, we believe that there's a responsible use of technology, of data and of innovation. And those three ingredients are really key to helping us deliver on that promise. And so, you know, while I think, uh, I'll call it, this cloud journey is already always been a part of our strategy. Um, and we've made some pretty steady progress over the last years with a number of I'll call it diverse approaches to the digital and AI. We just weren't seeing the impact at scale that we wanted to see. Um, and I think that, you know, there's a, there's a need ultimately to, you know, accelerate and, uh, broaden that shift. >>And, you know, we were commenting on this earlier, but there's, you know, it's been highlighted by a number of factors. One of those has been certainly a number of the large acquisitions we've made Shire, uh, being the most pressing example, uh, but also the global pandemic, both of those highlight the need for us to move faster, um, at the speed of cloud, ultimately. Uh, and so we started thinking outside of the box because it was taking us too long and we decided to leverage the strategic partner model. Uh, and it's giving us a chance to think about our challenges very differently. We call this the power of three, uh, and ultimately our focus is singularly on our patients. I mean, they're waiting for us. We need to get there faster. It can take years. And so I think that there is a focus on innovation, um, at a rapid speed, so we can move ultimately from treating conditions to keeping people healthy. >>So, as you are embarking on this journey, what are some of the insights you want to share about, about what you're seeing so far? >>Yeah, no, it's a great question. So, I mean, look, maybe right before I highlight some of the key insights, uh, I would say that, you know, with cloud now as the, as the launchpad for innovation, you know, our vision all along has been that in less than 10 years, we want every single to kid, uh, associate we're employed to be empowered by an AI assistant. And I think that, you know, that's going to help us make faster, better decisions. It'll help us, uh, fundamentally deliver transformative therapies and better experiences to, to that ecosystem, to our patients, to physicians, to payers, et cetera, much faster than we previously thought possible. Um, and I think that technologies like cloud and edge computing together with a very powerful I'll call it data fabric is going to help us to create this, this real-time, uh, I'll call it the digital ecosystem. >>The data has to flow ultimately seamlessly between our patients and providers or partners or researchers, et cetera. Uh, and so we've been thinking about this, uh, I'll call it, we call it sort of this pyramid, um, that helps us describe our vision. Uh, and a lot of it has to do with ultimately modernizing the foundation, modernizing and rearchitecting, the platforms that drive the company, uh, heightening our focus on data, which means that there's an accelerated shift towards, uh, enterprise data platforms and digital products. And then ultimately, uh, uh, P you know, really an engine for innovation sitting at the very top. Um, and so I think with that, you know, there's a few different, I'll call it insights that, you know, are quickly kind of come zooming into focus. I would say one is this need to collaborate very differently. Um, you know, not only internally, but you know, how do we define ultimately, and build a connected digital ecosystem with the right partners and technologies externally? >>I think the second component that maybe people don't think as much about, but, you know, I find critically important is for us to find ways of really transforming our culture. We have to unlock talent and shift the culture certainly as a large biopharmaceutical very differently. And then lastly, you've touched on it already, which is, you know, innovation at the speed of cloud. How do we re-imagine that, you know, how do ideas go from getting tested and months to kind of getting tested in days? You know, how do we collaborate very differently? Uh, and so I think those are three, uh, perhaps of the larger I'll call it, uh, insights that, you know, the three of us are spending a lot of time thinking about right now. >>So Arjun, I want to bring you into this conversation a little bit, let let's delve into those a bit. Talk first about the collaboration, uh, that Carl was referencing there. How, how have you seen that? It is enabling, uh, colleagues and teams to communicate differently and interact in new and different ways? Uh, both internally and externally, as Carl said, >>No, th thank you for that. And, um, I've got to give call a lot of credit, because as we started to think about this journey, it was clear, it was a bold ambition. It was, uh, something that, you know, we had all to do differently. And so the, the concept of the power of three that Carl has constructed has become a label for us as a way to think about what are we going to do to collectively drive this journey forward. And to me, the unique ways of collaboration means three things. The first one is that, um, what is expected is that the three parties are going to come together and it's more than just the sum of our resources. And by that, I mean that we have to bring all of ourselves, all of our collective capabilities, as an example, Amazon has amazing supply chain capabilities. >>They're one of the best at supply chain. So in addition to resources, when we have supply chain innovations, uh, that's something that they're bringing in addition to just, uh, talent and assets, similarly for Accenture, right? We do a lot, uh, in the talent space. So how do we bring our thinking as to how we apply best practices for talent to this partnership? So, um, as we think about this, so that's, that's the first one, the second one is about shared success very early on in this partnership, we started to build some foundations and actually develop seven principles that all of us would look at as the basis for this success shared success model. And we continue to hold that sort of in the forefront, as we think about this collaboration. And maybe the third thing I would say is this one team mindset. So whether it's the three of our CEOs that get together every couple of months to think about, uh, this partnership, or it is the governance model that Carl has put together, which has all three parties in the governance and every level of leadership. We always think about this as a collective group, so that we can keep that front and center. And what I think ultimately has enabled us to do is it allowed us to move at speed, be more flexible. And ultimately all we're looking at the target the same way, the North side, the same way. >>Brian, what about you? What have you observed? And are you thinking about in terms of how this is helping teams collaborate differently, >>Lillian and Arjun made some, some great points there. And I think if you really think about what he's talking about, it's that, that diversity of talent, diversity of scale and viewpoint and even culture, right? And so we see that in the power of three. And then I think if we drill down into what we see at Takeda, and frankly, Takeda was, was really, I think, pretty visionary and on their way here, right? And taking this kind of cross functional approach and applying it to how they operate day to day. So moving from a more functional view of the world to more of a product oriented view of the world, right? So when you think about we're going to be organized around a product or a service or a capability that we're going to provide to our customers or our patients or donors in this case, it implies a different structure, although altogether, and a different way of thinking, right? >>Because now you've got technical people and business experts and marketing experts, all working together in this is sort of cross collaboration. And what's great about that is it's really the only way to succeed with cloud, right? Because the old ways of thinking where you've got application people and infrastructure, people in business, people is suboptimal, right? Because we can all access this tool as these capabilities and the best way to do that. Isn't across kind of a cross-collaborative way. And so this is product oriented mindset. It's a keto was already on. I think it's allowed us to move faster in those areas. >>Carl, I want to go back to this idea of unlocking talent and culture. And this is something that both Brian and Arjun have talked about too. People are an essential part of their, at the heart of your organization. How will their experience of work change and how are you helping re-imagine and reinforce a strong organizational culture, particularly at this time when so many people are working remotely. >>Yeah. It's a great question. And it's something that, you know, I think we all have to think a lot about, I mean, I think, um, you know, driving this, this call it, this, this digital and data kind of capability building, uh, takes a lot of, a lot of thinking. So, I mean, there's a few different elements in terms of how we're tackling this one is we're recognizing, and it's not just for the technology organization or for those actors that, that we're innovating with, but it's really across all of the Cato where we're working through ways of raising what I'll call the overall digital leaders literacy of the organization, you know, what are the, you know, what are the skills that are needed almost at a baseline level, even for a global bio-pharmaceutical company and how do we deploy, I'll call it those learning resources very broadly. >>And then secondly, I think that, you know, we're, we're very clear that there's a number of areas where there are very specialized skills that are needed. Uh, my organization is one of those. And so, you know, we're fostering ways in which, you know, we're very kind of quickly kind of creating, uh, avenues excitement for, for associates in that space. So one example specifically, as we use, you know, during these very much sort of remote, uh, sort of days, we, we use what we call global it meet days, and we set a day aside every single month and this last Friday, um, you know, we, we create during that time, it's time for personal development. Um, and we provide active seminars and training on things like, you know, robotic process automation, data analytics cloud, uh, in this last month we've been doing this for months and months now, but in his last month, more than 50% of my organization participated, and there's this huge positive shift, both in terms of access and excitement about really harnessing those new skills and being able to apply them. >>Uh, and so I think that that's, you know, one, one element that, uh, can be considered. And then thirdly, um, of course, every organization to work on, how do you prioritize talent, acquisition and management and competencies that you can't rescale? I mean, there are just some new capabilities that we don't have. And so there's a large focus that I have with our executive team and our CEO and thinking through those critical roles that we need to activate in order to kind of, to, to build on this, uh, this business led cloud transformation. And lastly, probably the hardest one, but the one that I'm most jazzed about is really this focus on changing the mindsets and behaviors. Um, and I think there, you know, this is where the power of three is, is really, uh, kind of coming together nicely. I mean, we're working on things like, you know, how do we create this patient obsessed curiosity, um, and really kind of unlock innovation with a real, kind of a growth mindset. >>Uh, and the level of curiosity that's needed, not to just continue to do the same things, but to really challenge the status quo. So that's one big area of focus we're having the agility to act just faster. I mean, to worry less, I guess I would say about kind of the standard chain of command, but how do you make more speedy, more courageous decisions? And this is places where we can emulate the way that a partner like AWS works, or how do we collaborate across the number of boundaries, you know, and I think, uh, Arjun spoke eloquently to a number of partnerships that we can build. So we can break down some of these barriers and use these networks, um, whether it's within our own internal ecosystem or externally to help, to create value faster. So a lot of energy around ways of working and we'll have to check back in, but I mean, we're early in on this mindset and behavioral shift, um, but a lot of good early momentum. >>Carl you've given me a good segue to talk to Brian about innovation, because you said a lot of the things that I was the customer obsession and this idea of innovating much more quickly. Obviously now the world has its eyes on drug development, and we've all learned a lot about it, uh, in the past few months and accelerating drug development is all, uh, is of great interest to all of us. Brian, how does a transformation like this help a company's, uh, ability to become more agile and more innovative and add a quicker speed to, >>Yeah, no, absolutely. And I think some of the things that Carl talked about just now are critical to that, right? I think where sometimes folks fall short is they think, you know, we're going to roll out the technology and the technology is going to be the silver bullet where in fact it is the culture, it is, is the talent. And it's the focus on that. That's going to be, you know, the determinant of success. And I will say, you know, in this power of three arrangement and Carl talked a little bit about the pyramid, um, talent and culture and that change, and that kind of thinking about that has been a first-class citizen since the very beginning, right. That absolutely is critical for, for being there. Um, and, and so that's been, that's been key. And so we think about innovation at Amazon and AWS, and Carl mentioned some of the things that, you know, partner like AWS can bring to the table is we talk a lot about builders, right? >>So kind of obsessive about builders. Um, and, and we meet what we mean by that is we at Amazon, we hire for builders, we cultivate builders and we like to talk to our customers about it as well. And it also implies a different mindset, right? When you're a builder, you have that, that curiosity, you have that ownership, you have that stake and whatever I'm creating, I'm going to be a co-owner of this product or this service, right. Getting back to that kind of product oriented mindset. And it's not just the technical people or the it people who are builders. It is also the business people as, as Carl talked about. Right. So when we start thinking about, um, innovation again, where we see folks kind of get into a little bit of a innovation pilot paralysis, is that you can focus on the technology, but if you're not focusing on the talent and the culture and the processes and the mechanisms, you're going to be putting out technology, but you're not going to have an organization that's ready to take it and scale it and accelerate it. >>Right. And so that's, that's been absolutely critical. So just a couple of things we've been doing with, with Takeda and Decatur has really been leading the way is, think about a mechanism and a process. And it's really been working backward from the customer, right? In this case, again, the patient and the donor. And that was an easy one because the key value of Decatur is to be a patient focused bio-pharmaceutical right. So that was embedded in their DNA. So that working back from that, that patient, that donor was a key part of that process. And that's really deep in our DNA as well. And Accenture's, and so we were able to bring that together. The other one is, is, is getting used to experimenting and even perhaps failing, right. And being able to iterate and fail fast and experiment and understanding that, you know, some decisions, what we call it at Amazon are two two-way doors, meaning you can go through that door, not like what you see and turn around and go back. And cloud really helps there because the costs of experimenting and the cost of failure is so much lower than it's ever been. You can do it much faster and the implications are so much less. So just a couple of things that we've been really driving, uh, with the cadence around innovation, that's been really critical. Carl, where are you already seeing signs of success? >>Yeah, no, it's a great question. And so we chose, you know, uh, with our focus on innovation to try to unleash maybe the power of data digital in, uh, in focusing on what I call sort of a nave. And so we chose our, our, our plasma derived therapy business, um, and you know, the plasma-derived therapy business unit, it develops critical life-saving therapies for patients with rare and complex diseases. Um, but what we're doing is by bringing kind of our energy together, we're focusing on creating, I'll call it state of the art digitally connected donation centers. And we're really modernizing, you know, the, the, the donor experience right now, we're trying to, uh, improve also I'll call it the overall plasma collection process. And so we've, uh, selected a number of alcohol at a very high speed pilots that we're working through right now, specifically in this, in this area. And we're seeing >>Really great results already. Um, and so that's, that's one specific area of focus are Jen, I want you to close this out here. Any ideas, any best practices advice you would have for other pharmaceutical companies that are, that are at the early stage of their cloud journey? Sorry. Was that for me? Yes. Sorry. Origin. Yeah, no, I was breaking up a bit. No, I think they, um, the key is what's sort of been great for me to see is that when people think about cloud, you know, you always think about infrastructure technology. The reality is that the cloud is really the true enabler for innovation and innovating at scale. And, and if you think about that, right, and all the components that you need, ultimately, that's where the value is for the company, right? Because yes, you're going to get some cost synergies and that's great, but the true value is in how do we transform the organization in the case of the Qaeda and our life sciences clients, right. >>We're trying to take a 14 year process of research and development that takes billions of dollars and compress that right. Tremendous amounts of innovation opportunity. You think about the commercial aspect, lots of innovation can come there. The plasma derived therapy is a great example of how we're going to really innovate to change the trajectory of that business. So I think innovation is at the heart of what most organizations need to do. And the formula, the cocktail that the Qaeda has constructed with this footie program really has all the ingredients, um, that are required for that success. Great. Well, thank you so much. Arjun, Brian and Carl was really an enlightening conversation. Thank you. It's been a lot of, thank you. Yeah, it's been fun. Thanks Rebecca. And thank you for tuning into the cube. Virtual has coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cubes coverage of Accenture executive summit here at AWS reinvent. I'm your host Rebecca Knight for this segment? We have two guests. First. We have Helen Davis. She is the senior director of cloud platform services, assistant director for it and digital for the West Midlands police. Thanks so much for coming on the show, Helen, and we also have Matthew pound. He is Accenture health and public service associate director and West Midlands police account lead. Thanks so much for coming on the show. Matthew, thank you for having us. So we are going to be talking about delivering data-driven insights to the West Midlands police force. Helen, I want to start with >>You. Can you tell us a little bit about the West Midlands police force? How big is the force and also what were some of the challenges that you were grappling with prior to this initiative? >>Yeah, certainly. So Westerners police is the second largest police force in the UK, outside of the metropolitan police in London. Um, we have an excessive, um, 11,000 people work at Westman ins police serving communities, um, through, across the Midlands region. So geographically, we're quite a big area as well, as well as, um, being population, um, density, having that as a, at a high level. Um, so the reason we sort of embarked on the data-driven insights platform and it, which was a huge change for us was for a number of reasons. Um, namely we had a lot of disparate data, um, which was spread across a range of legacy systems that were many, many years old, um, with some duplication of what was being captured and no single view for offices or, um, support staff. Um, some of the access was limited. You have to be in a, in an actual police building on a desktop computer to access it. Um, other information could only reach the offices on the frontline through a telephone call back to one of our enabling services where they would do a manual checkup, um, look at the information, then call the offices back, um, and tell them what they needed to know. So it was a very long laborious, um, process and not very efficient. Um, and we certainly weren't exploiting the data that we had in a very productive way. >>So it sounds like as you're describing and an old clunky system that needed a technological, uh, reimagination, so what was the main motivation for, for doing, for making this shift? >>It was really, um, about making us more efficient and more effective in how we do how we do business. So, um, you know, certainly as a, as an it leader and sort of my operational colleagues, we recognize the benefits, um, that data and analytics could bring in, uh, in a policing environment, not something that was, um, really done in the UK at the time. You know, we have a lot of data, so we're very data rich and the information that we have, but we needed to turn it into information that was actionable. So that's where we started looking for, um, technology partners and suppliers to help us and sort of help us really with what's the art of the possible, you know, this hasn't been done before. So what could we do in this space that's appropriate for policing? >>I love that idea. What is the art of the possible, can you tell us a little bit about why you chose AWS? >>I think really, you know, as with all things and when we're procuring a partner in the public sector that, you know, there are many rules and regulations, uh, quite rightly as you would expect that to be because we're spending public money. So we have to be very, very careful and, um, it's, it's a long process and we have to be open to public scrutiny. So, um, we sort of look to everything, everything that was available as part of that process, but we recognize the benefits that Clyde would provide in this space because, you know, without moving to a cloud environment, we would literally be replacing something that was legacy with something that was a bit more modern. Um, that's not what we wanted to do. Our ambition was far greater than that. So I think, um, in terms of AWS, really, it was around the scalability, interoperability, you know, disaster things like the disaster recovery service, the fact that we can scale up and down quickly, we call it dialing up and dialing back. Um, you know, it's it's page go. So it just sort of ticked all the boxes for us. And then we went through the full procurement process, fortunately, um, it came out on top for us. So we were, we were able to move forward, but it just sort of had everything that we were looking for in that space. >>Matthew, I want to bring you into the conversation a little bit here. How are you working with a wet with the West Midlands police, sorry. And helping them implement this cloud-first journey? >>Yeah, so I guess, um, by January the West Midlands police started, um, favorite five years ago now. So, um, we set up a partnership with the force. I wanted to operate in a way that it was very different to a traditional supplier relationship. Um, secretary that the data difference insights program is, is one of many that we've been working with last nights on, um, over the last five years. Um, as having said already, um, cloud gave a number of, uh, advantages certainly from a big data perspective and the things that that enabled us today, um, I'm from an Accenture perspective that allowed us to bring in a number of the different themes that we have say, cloud teams, security teams, um, and drafted from an insurance perspective, as well as more traditional services that people would associate with the country. >>I mean, so much of this is about embracing comprehensive change to experiment and innovate and try different things. Matthew, how, how do you help, uh, an entity like West Midlands police think differently when they are, there are these ways of doing things that people are used to, how do you help them think about what is the art of the possible, as Helen said, >>There's a few things to that enable those being critical is trying to co-create solutions together. Yeah. There's no point just turning up with, um, what we think is the right answer, try and say, um, collectively work three, um, the issues that the fullest is seeing and the outcomes they're looking to achieve rather than simply focusing on a long list of requirements, I think was critical and then being really open to working together to create the right solution. Um, rather than just, you know, trying to pick something off the shelf that maybe doesn't fit the forces requirements in the way that it should too, >>Right. It's not always a one size fits all. >>Absolutely not. You know, what we believe is critical is making sure that we're creating something that met the forces needs, um, in terms of the outcomes they're looking to achieve the financial envelopes that were available, um, and how we can deliver those in a, uh, iterative agile way, um, rather than spending years and years, um, working towards an outcome, um, that is gonna update before you even get that. >>So Helen, how, how are things different? What kinds of business functions and processes have been re-imagined in, in light of this change and this shift >>It's, it's actually unrecognizable now, um, in certain areas of the business as it was before. So to give you a little bit of, of context, when we, um, started working with essentially in AWS on the data driven insights program, it was very much around providing, um, what was called locally, a wizzy tool for our intelligence analysts to interrogate data, look at data, you know, decide whether they could do anything predictive with it. And it was very much sort of a back office function to sort of tidy things up for us and make us a bit better in that, in that area or a lot better in that area. And it was rolled out to a number of offices, a small number on the front line. Um, I'm really, it was, um, in line with a mobility strategy that we, hardware officers were getting new smartphones for the first time, um, to do sort of a lot of things on, on, um, policing apps and things like that to again, to avoid them, having to keep driving back to police stations, et cetera. >>And the pilot was so successful. Every officer now has access to this data, um, on their mobile devices. So it literally went from a handful of people in an office somewhere using it to do sort of clever bang things to, um, every officer in the force, being able to access that level of data at their fingertips. Literally. So what they were touched with done before is if they needed to check and address or check details of an individual, um, just as one example, they would either have to, in many cases, go back to a police station to look it up themselves on a desktop computer. Well, they would have to make a call back to a centralized function and speak to an operator, relay the questions, either, wait for the answer or wait for a call back with the answer when those people are doing the data interrogation manually. >>So the biggest change for us is the self-service nature of the data we now have available. So officers can do it themselves on their phone, wherever they might be. So the efficiency savings from that point of view are immense. And I think just parallel to that is the quality of our, because we had a lot of data, but just because you've got a lot of data and a lot of information doesn't mean it's big data and it's valuable necessarily. Um, so again, it was having the single source of truth as we, as we call it. So you know that when you are completing those safe searches and getting the responses back, that it is the most accurate information we hold. And also you're getting it back within minutes, as opposed to, you know, half an hour, an hour or a drive back to a station. So it's making officers more efficient and it's also making them safer. The more efficient they are, the more time they have to spend out with the public doing what they, you know, we all should be doing >>That kind of return on investment because what you were just describing with all the steps that we needed to be taken in prior to this, to verify an address say, and those are precious seconds when someone's life is on the line in, in sort of in the course of everyday police work. >>Absolutely. Yeah, absolutely. It's difficult to put a price on it. It's difficult to quantify. Um, but all the, you know, the minutes here and there certainly add up to a significant amount of efficiency savings, and we've certainly been able to demonstrate the officers are spending less time up police stations as a result or more time out on the front line. Also they're safer because they can get information about what may or may not be and address what may or may not have occurred in an area before very, very quickly without having to wait. >>I do, I want to hear your observations of working so closely with this West Midlands police. Have you noticed anything about changes in its culture and its operating model in how police officers interact with one another? Have you seen any changes since this technology change? >>What's unique about the Western displaces, the buy-in from the top down, the chief and his exact team and Helen as the leader from an IOT perspective, um, the entire force is bought in. So what is a significant change program? Uh, I'm not trickles three. Um, everyone in the organization, um, change is difficult. Um, and there's a lot of time effort that's been put in to bake the technical delivery and the business change and adoption aspects around each of the projects. Um, but you can see the step change that is making in each aspect to the organization, uh, and where that's putting West Midlands police as a leader in, um, technology I'm policing in the UK. And I think globally, >>And this is a question for both of you because Matthew, as you said, change is difficult and there is always a certain intransigence in workplaces about this is just the way we've always done things and we're used to this and don't try us to get us. Don't try to get us to do anything new here. It works. How do you get the buy-in that you need to do this kind of digital transformation? >>I think it would be wrong to say it was easy. Um, um, we also have to bear in mind that this was one program in a five-year program. So there was a lot of change going on, um, both internally for some of our back office functions, as well as front tie, uh, frontline offices. So with DDI in particular, I think the stack change occurred when people could see what it could do for them. You know, we had lots of workshops and seminars where we all talk about, you know, big data and it's going to be great and it's data analytics and it's transformational, you know, and quite rightly people that are very busy doing a day job, but not necessarily technologists in the main and, you know, are particularly interested quite rightly so in what we are not dealing with the cloud, you know? And it was like, yeah, okay. >>It's one more thing. And then when they started to see on that, on their phones and what teams could do, that's when it started to sell itself. And I think that's when we started to see, you know, to see the stat change, you know, and, and if we, if we have any issues now it's literally, you know, our help desks in meltdown. Cause everyone's like, well, we call it manage without this anymore. And I think that speaks for itself. So it doesn't happen overnight. It's sort of incremental changes and then that's a step change in attitude. And when they see it working and they see the benefits, they want to use it more. And that's how it's become fundamental to all policing by itself, really, without much selling >>You, Helen just made a compelling case for how to get buy in. Have you discovered any other best practices when you are trying to get everyone on board for this kind of thing? >>We've um, we've used a lot of the traditional techniques, things around comms and engagement. We've also used things like, um, the 30 day challenge and nudge theory around how can we gradually encourage people to use things? Um, I think there's a point where all of this around, how do we just keep it simple and keep it user centric from an end user perspective? I think DDI is a great example of where the, the technology is incredibly complex. The solution itself is, um, you know, extremely large and, um, has been very difficult to, um, get delivered. But at the heart of it is a very simple front end for the user to encourage it and take that complexity away from them. Uh, I think that's been critical through the whole piece of DDR. >>One final word from Helen. I want to hear, where do you go from here? What is the longterm vision? I know that this has made productivity, um, productivity savings equivalent to 154 full-time officers. Uh, what's next, >>I think really it's around, um, exploiting what we've got. Um, I use the phrase quite a lot, dialing it up, which drives my technical architects crazy, but because it's apparently not that simple, but, um, you know, we've, we've been through significant change in the last five years and we are still continuing to batch all of those changes into everyday, um, operational policing. But what we need to see is we need to exploit and build on the investments that we've made in terms of data and claims specifically, the next step really is about expanding our pool of data and our functions. Um, so that, you know, we keep getting better and better at this. Um, the more we do, the more data we have, the more refined we can be, the more precise we are with all of our actions. Um, you know, we're always being expected to, again, look after the public purse and do more for less. And I think this is certainly an and our cloud journey and cloud first by design, which is where we are now, um, is helping us to be future-proofed. So for us, it's very much an investment. And I see now that we have good at embedded in operational policing for me, this is the start of our journey, not the end. So it's really exciting to see where we can go from here. >>Exciting times. Indeed. Thank you so much. Lily, Helen and Matthew for joining us. I really appreciate it. Thank you. And you are watching the cube stay tuned for more of the cubes coverage of the AWS reinvent Accenture executive summit. I'm Rebecca Knight from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Hi, everyone. Welcome to the cube virtual coverage of the executive summit at AWS reinvent 2020 virtual. This is the cube virtual. We can't be there in person like we are every year we have to be remote. This executive summit is with special programming supported by Accenture where the cube virtual I'm your host John for a year, we had a great panel here called uncloud first digital transformation from some experts, Stuart driver, the director of it and infrastructure and operates at lion Australia, Douglas Regan, managing director, client account lead at lion for Accenture as a deep Islam associate director application development lead for Accenture gentlemen, thanks for coming on the cube virtual that's a mouthful, all that digital, but the bottom line it's cloud transformation. This is a journey that you guys have been on together for over 10 years to be really a digital company. Now, some things have happened in the past year that kind of brings all this together. This is about the next generation organization. So I want to ask Stuart you first, if you can talk about this transformation at lion has undertaken some of the challenges and opportunities and how this year in particular has brought it together because you know, COVID has been the accelerant of digital transformation. Well, if you're 10 years in, I'm sure you're there. You're in the, uh, on that wave right now. Take a minute to explain this transformation journey. >>Yeah, sure. So number of years back, we looked at kind of our infrastructure and our landscape trying to figure out where we >>Wanted to go next. And we were very analog based and stuck in the old it groove of, you know, Capitol reef rash, um, struggling to transform, struggling to get to a digital platform and we needed to change it up so that we could become very different business to the one that we were back then obviously cloud is an accelerant to that. And we had a number of initiatives that needed a platform to build on. And a cloud infrastructure was the way that we started to do that. So we went through a number of transformation programs that we didn't want to do that in the old world. We wanted to do it in a new world. So for us, it was partnering up with a dried organizations that can take you on the journey and, uh, you know, start to deliver bit by bit incremental progress, uh, to get to the, uh, I guess the promise land. >>Um, we're not, not all the way there, but to where we're on the way along. And then when you get to some of the challenges like we've had this year, um, it makes all of the hard work worthwhile because you can actually change pretty quickly, um, provide capacity and, uh, and increase your environments and, you know, do the things that you need to do in a much more dynamic way than we would have been able to previously where we might've been waiting for the hardware vendors, et cetera, to deliver capacity. So for us this year, it's been a pretty strong year from an it perspective and delivering for the business needs >>Before I hit the Douglas. I want to just real quick, a redirect to you and say, you know, if all the people said, Oh yeah, you got to jump on cloud, get in early, you know, a lot of naysayers like, well, wait till to mature a little bit, really, if you got in early and you, you know, paying your dues, if you will taking that medicine with the cloud, you're really kind of peaking at the right time. Is that true? Is that one of the benefits that comes out of this getting in the cloud? Yeah, >>John, this has been an unprecedented year, right. And, um, you know, Australia, we had to live through Bush fires and then we had covert and, and then we actually had to deliver a, um, a project on very large transformational project, completely remote. And then we also had had some, some cyber challenges, which is public as well. And I don't think if we weren't moved into and enabled through the cloud, we would have been able to achieve that this year. It would have been much different, would have been very difficult to do the backing. We're able to work and partner with Amazon through this year, which is unprecedented and actually come out the other end. Then we've delivered a brand new digital capability across the entire business. Um, in many, you know, wouldn't have been impossible if we could, I guess, state in the old world, the fact that we were moved into the new Naval by the new allowed us to work in this unprecedented year. >>Just quick, what's your personal view on this? Because I've been saying on the Cuban reporting necessity is the mother of all invention and the word agility has been kicked around as kind of a cliche, Oh, it'd be agile. You know, we're going to get the city, you get a minute on specifically, but from your perspective, uh, Douglas, what does that mean to you? Because there is benefits there for being agile. And >>I mean, I think as Stuart mentioned, right, in a lot of these things we try to do and, you know, typically, you know, hardware and of the last >>To be told and, and, and always on the critical path to be done, we really didn't have that in this case, what we were doing with our projects in our deployments, right. We were able to move quickly able to make decisions in line with the business and really get things going. Right. So you see a lot of times in a traditional world, you have these inhibitors, you have these critical path, it takes weeks and months to get things done as opposed to hours and days, and truly allowed us to, we had to, you know, VJ things, move things. And, you know, we were able to do that in this environment with AWS support and the fact that we can kind of turn things off and on as quickly as we need it. >>Yeah. Cloud-scale is great for speed. So DECA, Gardez get your thoughts on this cloud first mission, you know, it, you know, the dev ops world, they saw this early that jumping in there, they saw the, the, the agility. Now the theme this year is modern applications with the COVID pandemic pressure, there's real business pressure to make that happen. How did you guys learn to get there fast? And what specifically did you guys do at Accenture and how did it all come together? Can you take us inside kind of how it played out? >>Oh, right. So yeah, we started off with, as we do in most cases with a much more bigger group, and we worked with lions functional experts and, uh, the lost knowledge that allowed the infrastructure being had. Um, we then applied our journey to cloud strategy, which basically revolves around the seminars and, and, uh, you know, the deep three steps from our perspective, uh, assessing the current environment, setting up the new cloud environment. And as we go modernizing and, and migrating these applications to the cloud now, you know, one of the key things that, uh, you know, we learned along this journey was that, you know, you can have the best plans, but bottom line that we were dealing with, we often than not have to make changes. Uh, what a lot of agility and also work with a lot of collaboration with the, uh, Lyon team, as well as, uh, uh, AWS. I think the key thing for me was being able to really bring it all together. It's not just, uh, you know, essentially mobilize it's all of us working together to make this happen. >>What were some of the learnings real quick journeys? >>So I think so the perspective of the key learnings that, you know, uh, you know, when you look back at, uh, the, the infrastructure that was that we were trying to migrate over to the cloud, a lot of the documentation, et cetera, was not available. We were having to, uh, figure out a lot of things on the fly. Now that really required us to have, uh, uh, people with deep expertise who could go into those environments and, and work out, uh, you know, the best ways to, to migrate the workloads to the cloud. Uh, I think, you know, the, the biggest thing for me was making sure all the had on that real SMEs across the board globally, that we could leverage across the various technologies, uh, uh, and, and, and, you know, that would really work in our collaborative and agile environment with line. >>Let's do what I got to ask you. How did you address your approach to the cloud and what was your experience? >>Yeah, for me, it's around getting the foundations right. To start with and then building on them. Um, so, you know, you've gotta have your, your, your process and you've got to have your, your kind of your infrastructure there and your blueprints ready. Um, AWS do a great job of that, right. Getting the foundations right. And then building upon it, and then, you know, partnering with Accenture allows you to do that very successfully. Um, I think, um, you know, the one thing that was probably surprising to us when we started down this journey and kind of after we got a long way down the track and looking backwards is actually how much you can just turn off. Right? So a lot of stuff that you, uh, you get left with a legacy in your environment, and when you start to work through it with the types of people that civic just mentioned, you know, the technical expertise working with the business, um, you can really rationalize your environment and, uh, you know, cloud is a good opportunity to do that, to drive that legacy out. >>Um, so you know, a few things there, the other thing is, um, you've got to try and figure out the benefits that you're going to get out of moving here. So there's no point just taking something that is not delivering a huge amount of value in the traditional world, moving it into the cloud, and guess what is going to deliver the same limited amount of value. So you've got to transform it, and you've got to make sure that you build it for the future and understand exactly what you're trying to gain out of it. So again, you need a strong collaboration. You need a good partners to work with, and you need good engagement from the business as well, because the kind of, uh, you know, digital transformation, cloud transformation, isn't really an it project, I guess, fundamentally it is at the core, but it's a business project that you've got to get the whole business aligned on. You've got to make sure that your investment streams are appropriate and that you're able to understand the benefits and the value that, so you're going to drive back towards the business. >>Let's do it. If you don't mind me asking, what was some of the obstacles you encountered or learnings, um, that might different from the expectation we all been there, Hey, you know, we're going to change the world. Here's the sales pitch, here's the outcome. And then obviously things happen, you know, you learn legacy, okay. Let's put some containerization around that cloud native, um, all that rational. You're talking about what are, and you're going to have obstacles. That's how you learn. That's how perfection has developed. How, what obstacles did you come up with and how are they different from your expectations going in? >>Yeah, they're probably no different from other people that have gone down the same journey. If I'm totally honest, the, you know, 70 or 80% of what you do is relatively easy of the known quantity. It's relatively modern architectures and infrastructures, and you can upgrade, migrate, move them into the cloud, whatever it is, rehost, replatform, rearchitect, whatever it is you want to do, it's the other stuff, right? It's the stuff that always gets left behind. And that's the challenge. It's, it's getting that last bit over the line and making sure that you haven't invested in the future while still carrying all of your legacy costs and complexity within your environment. So, um, to be quite honest, that's probably taken longer and has been more of a challenge than we thought it would be. Um, the other piece I touched on earlier on in terms of what was surprising was actually how much of, uh, your environment is actually not needed anymore. >>When you start to put a critical eye across it and understand, um, uh, ask the tough questions and start to understand exactly what, what it is you're trying to achieve. So if you ask a part of a business, do they still need this application or this service a hundred percent of the time, they will say yes until you start to lay out to them, okay, now I'm going to cost you this to migrate it or this, to run it in the future. And, you know, here's your ongoing costs and, you know, et cetera, et cetera. And then, uh, for a significant amount of those answers, you get a different response when you start to layer on the true value of it. So you start to flush out those hidden costs within the business, and you start to make some critical decisions as a company based on, uh, based on that. So that was a little tougher than we first thought and probably broader than we thought there was more of that than we anticipated, um, which actually results in a much cleaner environment post and post migration. >>You know, the old expression, if it moves automated, you know, it's kind of a joke on government, how they want to tax everything, you know, you want to automate, that's a key thing in cloud, and you've got to discover those opportunities to create value Stuart and Sadiq. Mainly if you can weigh in on this love to know the percentage of total cloud that you have now, versus when you started, because as you start to uncover whether it's by design for purpose, or you discover opportunities to innovate, like you guys have, I'm sure it kind of, you took on some territory inside Lyon, what percentage of cloud now versus stark? >>Yeah. At the start, it was minimal, right. You know, close to zero, right. Single and single digits. Right. It was mainly SAS environments that we had, uh, sitting in clouds when we, uh, when we started, um, Doug mentioned earlier on a really significant transformation project, um, that we've undertaken and recently gone live on a multi-year one. Um, you know, that's all stood up on AWS and is a significant portion of our environment, um, in terms of what we can move to cloud. Uh, we're probably at about 80 or 90% now. And the balanced bit is, um, legacy infrastructure that is just gonna retire as we go through the cycle rather than migrate to the cloud. Um, so we are significantly cloud-based and, uh, you know, we're reaping the benefits of it. I know you like 20, 20, I'm actually glad that you did all the hard yards in the previous years when you started that business challenges thrown out as, >>So do you any common reaction to the cloud percentage penetration? >>I mean, guys don't, but I was going to say was, I think it's like the 80 20 rule, right? We, we, we worked really hard in the, you know, I think 2018, 19 to get any person off, uh, after getting a loan, the cloud and, or the last year is the 20% that we have been migrating. And Stuart said like, uh, not that is also, that's going to be a good diet. And I think our next big step is going to be obviously, you know, the icing on the tape, which is to decommission all these apps as well. Right. So, you know, to get the real benefits out of, uh, the whole conservation program from a, uh, from a >>Douglas and Stewart, can you guys talk about the decision around the cloud because you guys have had success with AWS, why AWS how's that decision made? Can you guys give some insight into some of those thoughts? >>I can stop, start off. I think back when the decision was made and it was, it was a while back, um, you know, there's some clear advantages of moving relay, Ws, a lot of alignment with some of the significant projects and, uh, the trend, that particular one big transformation project that we've alluded to as well. Um, you know, we needed some, uh, some very robust and, um, just future proof and, um, proven technology. And they Ws gave that to us. We needed a lot of those blueprints to help us move down the path. We didn't want to reinvent everything. So, um, you know, having a lot of that legwork done for us and AWS gives you that, right. And, and particularly when you partner up with, uh, with a company like Accenture as well, you get combinations of the technology and the skills and the knowledge to, to move you forward in that direction. >>So, um, you know, for us, it was a, uh, uh, it was a decision based on, you know, best of breed, um, you know, looking forward and, and trying to predict the future needs and, and, and kind of the environmental that we might need. Um, and, you know, partnering up with organizations that can then take you on the journey. Yeah. And just to build on it. So obviously, you know, lion's like an AWS, but, you know, we knew it was a very good choice given that, um, uh, the skills and the capability that we had, as well as the assets and tools we had to get the most out of, um, AWS and obviously our, our CEO globally, you know, announcement about a huge investment that we're making in cloud. Um, but you know, we've, we've worked very well DWS, we've done some joint workshops and joint investments, um, some joint POC. So yeah, w we have a very good working relationship, AWS, and I think, um, one incident to reflect upon whether it's cyber it's and again, where we actually jointly, you know, dove in with, um, with Amazon and some of their security experts and our experts. And we're able to actually work through that with mine quite successfully. So, um, you know, really good behaviors as an organization, but also really good capabilities. >>Yeah. As you guys, you're essential cloud outcomes, research shown, it's the cycle of innovation with the cloud. That's creating a lot of benefits, knowing what you guys know now, looking back certainly COVID is impacted a lot of people kind of going through the same process, knowing what you guys know now, would you advocate people to jump on this transformation journey? If so, how, and what tweaks they make, which changes, what would you advise? >>Uh, I might take that one to start with. Um, I hate to think where we would have been when, uh, COVID kicked off here in Australia and, you know, we were all sent home, literally were at work on the Friday, and then over the weekend. And then Monday, we were told not to come back into the office and all of a sudden, um, our capacity in terms of remote access and I quadrupled, or more four, five X, uh, what we had on the Friday we needed on the Monday. And we were able to stand that up during the day Monday and into Tuesday, because we were cloud-based. And, uh, you know, we just found up your instances and, uh, you know, sort of our licensing, et cetera. And we had all of our people working remotely, um, within, uh, you know, effectively one business day. >>Um, I know peers of mine in other organizations and industries that are relying on kind of a traditional wise and getting hardware, et cetera, that were weeks and months before they could get their, the right hardware to be able to deliver to their user base. So, um, you know, one example where you're able to scale and, uh, uh, get, uh, get value out of this platform beyond probably what was anticipated at the time you talk about, um, you know, less the, in all of these kinds of things. And you can also think of a few scenarios, but real world ones where you're getting your business back up and running in that period of time is, is just phenomenal. There's other stuff, right? There's these programs that we've rolled out, you do your sizing, um, and in the traditional world, you would just go out and buy more servers than you need. >>And, you know, probably never realize the full value of those, you know, the capability of those servers over the life cycle of them. Whereas you're in a cloud world, you put in what you think is right. And if it's not right, you pump it up a little bit when, when all of your metrics and so on, tell you that you need to bump it up. And conversely you scale it down at the same rate. So for us, with the types of challenges and programs and, uh, uh, and just business need, that's come at as this year, uh, we wouldn't have been able to do it without a strong cloud base, uh, to, uh, to move forward >>Know Douglas. One of the things that I talked to, a lot of people on the right side of history who have been on the right wave with cloud, with the pandemic, and they're happy, they're like, and they're humble. Like, well, we're just lucky, you know, luck is preparation meets opportunity. And this is really about you guys getting in early and being prepared and readiness. This is kind of important as people realize, then you gotta be ready. I mean, it's not just, you don't get lucky by being in the right place, the right time. And there were a lot of companies were on the wrong side of history here who might get washed away. This is a super important, I think, >>To echo and kind of build on what Stewart said. I think that the reason that we've had success and I guess the momentum is we, we didn't just do it in isolation within it and technology. It was actually linked to broader business changes, you know, creating basically a digital platform for the entire business, moving the business, where are they going to be able to come back stronger after COVID, when they're actually set up for growth, um, and actually allows, you know, lying to achievements growth objectives, and also its ambitions as far as what it wants to do, uh, with growth in whatever they make, do with acquiring other companies and moving into different markets and launching new products. So we've actually done it in a way that is, you know, real and direct business benefit, uh, that actually enables line to grow >>General. I really appreciate you coming. I have one final question. If you can wrap up here, uh, Stuart and Douglas, you don't mind weighing in what's the priorities for the future. What's next for lion in a century >>Christmas holidays, I'll start Christmas holidays been a big deal and then a, and then a reset, obviously, right? So, um, you know, it's, it's figuring out, uh, transform what we've already transformed, if that makes sense. So God, a huge proportion of our services sitting in the cloud. Um, but we know we're not done even with the stuff that is in there. We need to take those next steps. We need more and more automation and orchestration. We need to, um, our environment, there's more future growth. We need to be able to work with the business and understand what's coming at them so that we can, um, you know, build that into, into our environment. So again, it's really transformation on top of transformation is the way that I'll describe it. And it's really an open book, right? Once you get it in and you've got the capabilities and the evolving tool sets that, uh, AWS continue to bring to the market, um, you know, working with the partners to, to figure out how we unlock that value, um, you know, drive our costs down efficiency, uh, all of those kind of, you know, standard metrics. >>Um, but you know, we're looking for the next things to transform and show value back out to our customer base, um, that, uh, that we continue to, you know, sell our products to and work with and understand how we can better meet their needs. Yeah, I think just to echo that, I think it's really leveraging this and then did you capability they have and getting the most out of that investment. And then I think it's also moving to, uh, and adopting more new ways of working as far as, you know, the speed of the business, um, is getting up the speed of the market is changing. So being able to launch and do things quickly and also, um, competitive and efficient operating costs, uh, now that they're in the cloud, right? So I think it's really leveraging the most out of the platform and then, you know, being efficient in launching things. So putting them with the business, >>Any word from you on your priorities by you see this year in folding, >>There's got to say like e-learning squares, right, for me around, you know, just journey. This is a journey to the cloud, right. >>And, uh, you know, as well, the sort of Saturday, it's getting all, you know, different parts of the organization along the journey business to it, to your, uh, product lenders, et cetera. Right. And it takes time. It is tough, but, uh, uh, you know, you got to get started on it. And, you know, once we, once we finish off, uh, it's the realization of the benefits now that, you know, looking forward, I think for, from Alliance perspective, it is, uh, you know, once we migrate all the workloads to the cloud, it is leveraging, uh, all staff, right. And as I think students said earlier, uh, with, uh, you know, the latest and greatest stuff that AWS is basically working to see how we can really, uh, achieve more better operational excellence, uh, from a, uh, from a cloud perspective. >>Well, Stewart, thanks for coming on with a and sharing your environment and what's going on and your journey you're on the right wave. Did the work you're in, it's all coming together with faster, congratulations for your success, and, uh, really appreciate Douglas with Steve for coming on as well from Accenture. Thank you for coming on. Thanks, John. Okay. Just the cubes coverage of executive summit at AWS reinvent. This is where all the thought leaders share their best practices, their journeys, and of course, special programming with Accenture and the cube. I'm Sean ferry, your host, thanks for watching from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cube virtuals coverage of the Accenture executive summit. Part of AWS reinvent 2020. I'm your host Rebecca Knight. We are talking today about reinventing the energy data platform. We have two guests joining us. First. We have Johan Krebbers. He is the GM digital emerging technologies and VP of it. Innovation at shell. Thank you so much for coming on the show, Johan you're welcome. And next we have Liz Dennett. She is the lead solution architect for O S D U on AWS. Thank you so much Liz to be here. So I want to start our conversation by talking about OSD. You like so many great innovations. It started with a problem Johan. What was the problem you were trying to solve at shell? >>Yeah, the ethical back a couple of years, we started shoving 2017 where we had a meeting with the deg, the gas exploration in shell, and the main problem they had. Of course, they got lots of lots of data, but are unable to find the right data. They need to work from all over the place. And totally >>Went to real, probably tried to solve is how that person working exploration could find their proper date, not just a day, but also the date you really needed that we did probably talked about his summer 2017. And we said, okay, they don't maybe see this moving forward is to start pulling that data into a single data platform. And that, that was at the time that we called it as the, you, the subsurface data universe in there was about the shell name was so in, in January, 2018, we started a project with Amazon to start grating a co fricking that building, that Stu environment that subserve the universe, so that single data level to put all your exploration and Wells data into that single environment that was intent. And every cent, um, already in March of that same year, we said, well, from Michelle point of view, we will be far better off if we could make this an industry solution and not just a shelf sluice, because Shelby, Shelby, if you can make an industry solution where people are developing applications for it, it also is far better than for shell to say we haven't shell special solution because we don't make money out of how we start a day that we can make money out of it. >>We have access to the data, we can explore the data. So storing the data we should do as efficiently possibly can. So we monitor, we reach out to about eight or nine other large, uh, or I guess operators like the economics, like the tutorials, like the chefs of this world and say, Hey, we inshallah doing this. Do you want to join this effort? And to our surprise, they all said, yes. And then in September, 2018, we had our kickoff meeting with your open group where we said, we said, okay, if you want to work together with lots of other companies, we also need to look at okay, how, how we organize that. Or if you started working with lots of large companies, you need to have some legal framework around some framework around it. So that's why we went to the open group and say, okay, let's, let's form the old forum as we call it at the time. So it's September, 2080, where I did a Galleria in Houston, but the kickoff meeting for the OT four with about 10 members at the time. So there's just over two years ago, we started an exercise for me called ODU, uh, kicked it off. Uh, and so that's really them will be coming from and how we've got there. Also >>The origin story. Um, what, so what digging a little deeper there? What were some of the things you were trying to achieve with the OSU? >>Well, a couple of things we've tried to achieve with you, um, first is really separating data from applications for what is, what is the biggest problem we have in the subsurface space that the data and applications are all interlinked tied together. And if, if you have them and a new company coming along and say, I have this new application and is access to the data that is not possible because the data often interlinked with the application. So the first thing we did is really breaking the link between the application, the data out as those levels, the first thing we did, secondly, put all the data to a single data platform, take the silos out what was happening in the sub-service space and know they got all the data in what we call silos in small little islands out there. So what we're trying to do is first break the link to great, great. >>They put the data single day, the bathroom, and the third part, put a standard layer on top of that, it's an API layer on top to create a platform. So we could create an ecosystem out of companies to start a valving shop application on top of dev data platform across you might have a data platform, but you're only successful. If you have a rich ecosystem of people start developing applications on top of that. And then you can export the data like small companies, last company, university, you name it, we're getting after create an ecosystem out there. So the three things were as was first break, the link between application data, just break it and put data at the center and also make sure that data, this data structure would not be managed by one company. It would only be met. It will be managed the data structures by the ODI forum. Secondly, then put a data, a single data platform certainly then has an API layer on top and then create an ecosystem. Really go for people, say, please start developing applications because now you have access to the data or the data no longer linked to somebody whose application was all freely available, but an API layer that was, that was all September, 2018, more or less >>To hear a little bit. Can you talk a little bit about some of the imperatives from the AWS standpoint in terms of what you were trying to achieve with this? Yeah, absolutely. And this whole thing is Johann said started with a challenge that was really brought out at shell. The challenges that geoscientists spend up to 70% of their time looking for data. I'm a geologist I've spent more than 70% of my time trying to find data in these silos. And from there, instead of just figuring out how we could address that one problem, we worked together to really understand the root cause of these challenges and working backwards from that use case OSU and OSU on AWS has really enabled customers to create solutions that span, not just this in particular problem, but can really scale to be inclusive of the entire energy value chain and deliver value from these use cases to the energy industry and beyond. >>Thank you, Lee, >>Uh, Johann. So talk a little bit about Accenture's cloud first approach and how it has, uh, helped shell work faster and better with it. >>Well, of course, access a cloud first approach only works together. It's been an Amazon environment, AWS environment. So we really look at, uh, at, at Accenture and others up together helping shell in this space. Now the combination of the two is where we're really looking at, uh, where access of course can be increased knowledge student to that environment operates support knowledge to do an environment. And of course, Amazon will be doing that to this environment that underpinning their services, et cetera. So, uh, we would expect a combination, a lot of goods when we started rolling out and put in production, the old you are three and four because we are anus. Then when release feed comes to the market in Q1 next year of ODU, when he started going to Audi production inside shell, but as the first release, which is ready for prime time production across an enterprise will be released just before Christmas, last year when he's still in may of this year. But really three is the first release we want to use for full scale production deployment inside shell, and also all the operators around the world. And there is one Amazon, sorry, at that one. Um, extensive can play a role in the ongoing, in the, in deployment building up, but also support environment. >>So one of the other things that we talk a lot about here on the cube is sustainability. And this is a big imperative at so many organizations around the world in particular energy companies. How does this move to OSD you, uh, help organizations become, how is this a greener solution for companies? >>Well, first he make it's a greatest solution because you start making a much more efficient use of your resources. is already an important one. The second thing we're doing is also, we started with ODU in framers, in the oil and gas space in the expert development space. We've grown, uh, OTU in our strategy, we've grown. I was, you know, also do an alternative energy sociology. We'll all start supporting next year. Things like solar farms, wind farms, uh, the, the dermatomal environment hydration. So it becomes an and, and an open energy data platform, not just what I want to get into steep that's for new industry, any type of energy industry. So our focus is to create, bring the data of all those various energy data sources to get me to a single data platform you can to use AI and other technology on top of that, to exploit the data, to beat again into a single data platform. >>Liz, I want to ask you about security because security is, is, is such a big concern when it comes to data. How secure is the data on OSD? You, um, actually, can I talk, can I do a follow up on this sustainability talking? Oh, absolutely. By all means. I mean, I want to interject though security is absolutely our top priority. I don't mean to move away from that, but with sustainability, in addition to the benefits of the OSU data platform, when a company moves from on-prem to the cloud, they're also able to leverage the benefits of scale. Now, AWS is committed to running our business in the most environmentally friendly way possible. And our scale allows us to achieve higher resource utilization and energy efficiency than a typical data center. Now, a recent study by four 51 research found that AWS is infrastructure is 3.6 times more energy efficient than the median of surveyed enterprise data centers. Two thirds of that advantage is due to higher, um, server utilization and a more energy efficient server population. But when you factor in the carbon intensity of consumed electricity and renewable energy purchases for 51 found that AWS performs the same task with an 88% lower carbon footprint. Now that's just another way that AWS and OSU are working to support our customers is they seek to better understand their workflows and make their legacy businesses less carbon intensive. >>That's that's incorrect. Those are those statistics are incredible. Do you want to talk a little bit now about security? Absolutely. Security will always be AWS is top priority. In fact, AWS has been architected to be the most flexible and secure cloud computing environment available today. Our core infrastructure is built to satisfy. There are the security requirements for the military global banks and other high sensitivity organizations. And in fact, AWS uses the same secure hardware and software to build an operate each of our regions. So that customers benefit from the only commercial cloud that's hat hits service offerings and associated supply chain vetted and deemed secure enough for top secret workloads. That's backed by a deep set of cloud security tools with more than 200 security compliance and governmental service and key features as well as an ecosystem of partners like Accenture, that can really help our customers to make sure that their environments for their data meet and or exceed their security requirements. Johann, I want you to talk a little bit about how OSD you can be used today. Does it only handle subsurface data? >>Uh, today it's Honda's subserves or Wells data. We got to add to that production around the middle of next year. That means that the whole upstate business. So we've got goes from exploration all the way to production. You've made it together into a single data platform. So production will be added around Q3 of next year. Then a principal. We have a difficult, the elder data that single environment, and we want to extend it then to other data sources or energy sources like solar farms, wind farms, uh, hydrogen, hydro, et cetera. So we're going to add a whore, a whole list of audit day energy source to them and be all the data together into a single data club. So we move from an all in guest data platform to an entity data platform. That's really what our objective is because the whole industry, if you look it over, look at our competition or moving in that same two acts of quantity of course, are very strong in oil and gas, but also increased the, got into other energy sources like, like solar, like wind, like th like highly attended, et cetera. So we would be moving exactly what it's saying, method that, that, that, that the whole OSU can't really support at home. And as a spectrum of energy sources, >>Of course, and Liz and Johan. I want you to close this out here by just giving us a look into your crystal balls and talking about the five and 10 year plan for OSD. We'll start with you, Liz, what do you, what do you see as the future holding for this platform? Um, honestly, the incredibly cool thing about working at AWS is you never know where the innovation and the journey is going to take you. I personally am looking forward to work with our customers, wherever their OSU journeys, take them, whether it's enabling new energy solutions or continuing to expand, to support use cases throughout the energy value chain and beyond, but really looking forward to continuing to partner as we innovate to slay tomorrow's challenges, Johann first, nobody can look at any more nowadays, especially 10 years, but our objective is really in the next five years, you will become the key backbone for energy companies for store your data intelligence and optimize the whole supply energy supply chain, uh, in this world Johan Krebbers Liz Dennett. Thank you so much for coming on the cube virtual. Thank you. I'm Rebecca Knight stay tuned for more of our coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cubes coverage of the Accenture executive summit. Part of AWS reinvent. I'm your host Rebecca Knight today we're welcoming back to Cuba alum. We have Kishor Dirk. He is the Accenture senior managing director cloud first global services lead. Welcome back to the show Kishore. Thank you very much. Nice to meet again. And, uh, Tristan moral horse set. He is the managing director, Accenture cloud first North American growth. Welcome back to you to Tristin. Great to be back in grapes here again, Rebecca. Exactly. Even in this virtual format, it is good to see your faces. Um, today we're going to be talking about my NAB and green cloud advisor capability. Kishor I want to start with you. So my NAB is a platform that is really celebrating its first year in existence. Uh, November, 2019 is when Accenture introduced it. Uh, but it's, it has new relevance in light of this global pandemic that we are all enduring and suffering through. Tell us a little bit about the lineup platform, what it is that cloud platform to help our clients navigate the complexity of cloud and cloud decisions and to make it faster. And obviously, you know, we have in the cloud, uh, you know, with >>The increased relevance and all the, especially over the last few months with the impact of COVID crisis and exhibition of digital transformation, you know, we are seeing the transformation of the exhibition to cloud much faster. This platform that you're talking about has enabled hardened 40 clients globally across different industries. You identify the right cloud solution, navigate the complexity, provide a cloud specific solution simulate for our clients to meet that strategy business needs. And the clients are loving it. >>I want to go to you now trust and tell us a little bit about how my nav works and how it helps companies make good cloud choice. >>Yeah, so Rebecca, we we've talked about cloud is, is more than just infrastructure and that's what mine app tries to solve for it. It really looks at a variety of variables, including infrastructure operating model and fundamentally what clients' business outcomes, um, uh, our clients are, are looking for and, and identifies the optimal solution for what they need. And we assign this to accelerate. And we mentioned that the pandemic, one of the big focus now is to accelerate. And so we worked through a three-step process. The first is scanning and assessing our client's infrastructure, their data landscape, their application. Second, we use our automated artificial intelligence engine to interact with. We have a wide variety and library of, uh, collective plot expertise. And we look to recommend what is the enterprise architecture and solution. And then third, before we live with our clients, we look to simulate and test this scaled up model. And the simulation gives our clients a way to see what cloud is going to look like, feel like and how it's going to transform their business before they go there. >>Tell us a little bit about that in real life. Now as a company, so many of people are working remotely having to collaborate, uh, not in real life. How is that helping them right now? >>So, um, the, the pandemic has put a tremendous strain on systems, uh, because of the demand on those systems. And so we talk about resiliency. We also now need to collaborate across data across people. Um, I think all of us are calling from a variety of different places where our last year we were all at the VA cube itself. Um, and, and cloud technologies such as teams, zoom that we're we're leveraging now has fundamentally accelerated and clients are looking to onboard this for their capabilities. They're trying to accelerate their journey. They realize that now the cloud is what is going to become important for them to differentiate. Once we come out of the pandemic and the ability to collaborate with their employees, their partners, and their clients through these systems is becoming a true business differentiator for our clients. >>Keisha, I want to talk with you now about my navs multiple capabilities, um, and helping clients design and navigate their cloud journeys. Tell us a little bit about the green cloud advisor capability and its significance, particularly as so many companies are thinking more deeply and thoughtfully about sustainability. >>Yes. So since the launch of my NAB, we continue to enhance capabilities for our clients. One of the significant, uh, capabilities that we have enabled is the being or advisor today. You know, Rebecca, a lot of the businesses are more environmentally aware and are expanding efforts to decrease power consumption, uh, and obviously carbon emissions and, uh, and run a sustainable operations across every aspect of the enterprise. Uh, as a result, you're seeing an increasing trend in adoption of energy, efficient infrastructure in the global market. And one of the things that we did, a lot of research we found out is that there's an ability to influence our client's carbon footprint through a better cloud solution. And that's what we internalize, uh, brings to us, uh, in, in terms of a lot of the client connotation that you're seeing in Europe, North America and others. Lot of our clients are accelerating to a green cloud strategy to unlock greater financial societal and environmental benefit, uh, through obviously cloud-based circular, operational, sustainable products and services. That is something that we are enhancing my now, and we are having active client discussions at this point of time. >>So Tristan, tell us a little bit about how this capability helps clients make greener decisions. >>Yeah. Um, well, let's start about the investments from the cloud providers in renewable and sustainable energy. Um, they have most of the hyperscalers today, um, have been investing significantly on data centers that are run on renewable energy, some incredibly creative constructs on the, how, how to do that. And sustainability is there for a key, um, key item of importance for the hyperscalers and also for our clients who now are looking for sustainable energy. And it turns out this marriage is now possible. I can, we marry the, the green capabilities of the cloud providers with a sustainability agenda of our clients. And so what we look into the way the mind works is it looks at industry benchmarks and evaluates our current clients, um, capabilities and carpet footprint leveraging their existing data centers. We then look to model from an end-to-end perspective, how the, their journey to the cloud leveraging sustainable and, um, and data centers with renewable energy. We look at how their solution will look like and, and quantify carbon tax credits, um, improve a green index score and provide quantifiable, um, green cloud capabilities and measurable outcomes to our clients, shareholders, stakeholders, clients, and customers. Um, and our green plot advisers sustainability solutions already been implemented at three clients. And in many cases in two cases has helped them reduce the carbon footprint by up to 400% through migration from their existing data center to green cloud. Very, very, >>That is remarkable. Now tell us a little bit about the kinds of clients. Is this, is this more interesting to clients in Europe? Would you say that it's catching on in the United States? Where, what is the breakdown that you're seeing right now? >>Sustainability is becoming such a global agenda and we're seeing our clients, um, uh, tie this and put this at board level, um, uh, agenda and requirements across the globe. Um, Europe has specific constraints around data sovereignty, right, where they need their data in country, but from a green, a sustainability agenda, we see clients across all our markets, North America, Europe in our growth markets adopt this. And we have seen case studies and all three months, >>Kesha. I want to bring you back into the conversation. Talk a little bit about how MindUP ties into Accenture's cloud first strategy, your Accenture's CEO, Julie Sweet, um, has talked about post COVID leadership, requiring every business to become a cloud first business. Tell us a little bit about how this ethos is in Accenture and how you're sort of looking outward with it too. >>So Rebecca mine is the launch pad, uh, to a cloud first transformation for our clients. Uh, Accenture, see your jewelry suite, uh, shared the Accenture cloud first and our substantial investment demonstrate our commitment and is delivering greater value for our clients when they need it the most. And with the digital transformation requiring cloud at scale, you know, we're seeing that in the post COVID leadership, it requires that every business should become a cloud business. And my nap helps them get there by evaluating the cloud landscape, navigating the complexity, modeling architecting and simulating an optimal cloud solution for our clients. And as Justin was sharing a greener cloud. >>So Tristan, talk a little bit more about some of the real life use cases in terms of what are we, what are clients seeing? What are the results that they're having? >>Yes. Thank you, Rebecca. I would say two key things right around my notes. The first is the iterative process. Clients don't want to wait, um, until they get started, they want to get started and see what their journey is going to look like. And the second is fundamental acceleration, dependent make, as we talked about, has accelerated the need to move to cloud very quickly. And my nav is there to do that. So how do we do that? First is generating the business cases. Clients need to know in many cases that they have a business case by business case, we talk about the financial benefits, as well as the business outcomes, the green, green clot impact sustainability impacts with minus. We can build initial recommendations using a basic understanding of their environment and benchmarks in weeks versus months with indicative value savings in the millions of dollars arranges. >>So for example, very recently, we worked with a global oil and gas company, and in only two weeks, we're able to provide an indicative savings where $27 million over five years, this enabled the client to get started, knowing that there is a business case benefit and then iterate on it. And this iteration is, I would say the second point that is particularly important with my nav that we've seen in bank of clients, which is, um, any journey starts with an understanding of what is the application landscape and what are we trying to do with those, these initial assessments that used to take six to eight weeks are now taking anywhere from two to four weeks. So we're seeing a 40 to 50% reduction in the initial assessment, which gets clients started in their journey. And then finally we've had discussions with all of the hyperscalers to help partner with Accenture and leverage mine after prepared their detailed business case module as they're going to clients. And as they're accelerating the client's journey, so real results, real acceleration. And is there a journey? Do I have a business case and furthermore accelerating the journey once we are by giving the ability to work in iterative approach. >>I mean, it sounds as though that the company that clients and and employees are sort of saying, this is an amazing time savings look at what I can do here in, in so much in a condensed amount of time, but in terms of getting everyone on board, one of the things we talked about last time we met, uh, Tristin was just how much, uh, how one of the obstacles is getting people to sign on and the new technologies and new platforms. Those are often the obstacles and struggles that companies face. Have you found that at all? Or what is sort of the feedback that you're getting? >>Yeah, sorry. Yes. We clearly, there are always obstacles to a cloud journey. If there were an obstacles, all our clients would be, uh, already fully in the cloud. What man I gives the ability is to navigate through those, to start quickly. And then as we identify obstacles, we can simulate what things are going to look like. We can continue with certain parts of the journey while we deal with that obstacle. And it's a fundamental accelerator. Whereas in the past one, obstacle would prevent a class from starting. We can now start to address the obstacles one at a time while continuing and accelerating the contrary. That is the fundamental difference. >>Kishor I want to give you the final word here. Tell us a little bit about what is next for Accenture might have and what we'll be discussing next year at the Accenture executive summit, >>Rebecca, we are continuously evolving with our client needs and reinventing reinventing for the future. Well, mine has been toward advisor. Our plan is to help our clients reduce carbon footprint and again, migrate to a green cloud. Uh, and additionally, we're looking at, you know, two capabilities, uh, which include sovereign cloud advisor, uh, with clients, especially in, in Europe and others are under pressure to meet, uh, stringent data norms that Kristen was talking about. And the sovereign cloud advisor helps organization to create an architecture cloud architecture that complies with the green. Uh, I would say the data sovereignty norms that is out there. The other element is around data to cloud. We are seeing massive migration, uh, for, uh, for a lot of the data to cloud. And there's a lot of migration hurdles that come within that. Uh, we have expanded mine app to support assessment capabilities, uh, for, uh, assessing applications, infrastructure, but also covering the entire state, including data and the code level to determine the right cloud solution. So we are, we are pushing the boundaries on what mine app can do with mine. Have you created the ability to take the guesswork out of cloud, navigate the complexity? We are rolling risks costs, and we are, you know, achieving client's static business objectives while building a sustainable alerts with being cloud, >>Any platform that can take some of the guesswork out of the future. I am I'm on board with thank you so much, Tristin and Kishore. This has been a great conversation. Stay tuned for more of the cubes coverage of the Accenture executive summit. I'm Rebecca Knight.

>> Narrator: From around the globe, it's the CUBE, with digital coverage of Dell Technologies world. Digital experience brought to you by Dell Technologies. >> Welcome to the CUBE'S coverage of Dell Technologies World 2020, The Digital Experience. I'm Lisa Martin joined by a couple of guys from Dell Technology. Please welcome Martin Glynn, the senior director for product management for PowerMax Martin good morning. >> Good morning. >> Nice to see you. And joining Martin is Matthew Paul, the senior director of product management for PowerFlex at Dell Technologies. Matthew, nice to see you. >> Nice to see you thanks for having us Lisa. >> So our virtual cube this year can't be with you guys in person or the 14,000 other folks that usually attend at Dell Technologies World but a lot of opportunities to engage customers and partners and present analysts digitally, which is great. So Matthew, let's go ahead and start with you. Talk to us about what's new with PowerFlex, this was the kind of the end of the rebrand under the power portfolio that Dell Technologies undertook the last couple of years formerly the VXFlex excuse me, from Scale IO, what's new with PowerFlex? >> Yeah, that's a spot on. So really the idea of us aligning the full power portfolio is kind of a big deal, right? Part of the winning roadmap to at IO, kind of assigned to our customers and our field and everyone that software defined storage is a critical part of the Dell Technologies strategy. If you think about PowerFlex, just to kind of level set, it's really a software defined infrastructure kind of system that brings you the best of traditional three tier infrastructure and the best of HCI infrastructure while being able to make that experience really simple in the enterprise while still delivering exemplary really great performance and scale. In terms of new things, well, just real quick, in terms of kind of new things, we brought interesting topics like native Async replication, secure snapshots, some end to end lifecycle management pieces. So a lot of great innovation in the last year. >> And that was some of the recent announcements. Tell me Matthew, from a customer perspective since you've announced Asynchronous replication snapshots, what's the customer adoption, customer feedback been like? >> Yeah, it's been fantastic. We continue to grow this market really strong, you know, we're focusing on high end large enterprise customers working towards, bringing down also into kind of enterprise and commercial customers, so it'll make things easier to use. But very strong adoption and great investments here at Dell with this product. >> All right, so PowerFlex, Martin, let's go to you PowerMax, talk to us about PowerMax. And then also how it kind of fits into the whole power portfolio. >> Sure, yeah, so thanks Lisa. The PowerMax products, I think was the first product other than of course, the server products to be powered up in the storage portfolio, PowerMax is the sort of flagship sort of derived product that we've had now for, you know, a few decades really been a leader in mission critical data centers. But I think that pace of innovation over the last year just like Matt describing the PowerFlex side has been a really phenomenal. Just about a year ago he came out with a storage class memory, we did fiber channel Endymion over fiber channel, and more recently brought in a few really interesting new technologies, like support for replication, with VVols, cloud mobility, and now, efficient encryption. So the set of things we're enabling our customers to do with their you know, sort of traditional three tier SAN infrastructure is really just unmatched. >> So Matt talk to me about the last six seven months, where are these enterprise customers in terms of leveraging PowerMax for example, when everything just changed dramatically almost overnight. Enterprises in every industry had to suddenly remote workforce. How did PowerMax help your customers pivot and ensure that their digital transformation could support this business surviving? >> Yeah, well, like everybody we were a little worried at the outset, you know lot of uncertainty about how things would play out and the response from our customers has been amazing. You know, they've all sort of really doubled down on using our technology to support their businesses through this new model. So, you know, the business has been really amazing really incredible, and it's been great to partner with our customers that help them continue to deliver the services that they need you know, in this new model. So that part's been, been really wonderful, and as we work really closely with them, some of the things we just came out with, you know, they've helped us to design and deliver in a way that they can best take advantage of so, you know, for example the new cloud mobility functionality that's letting them take information directly off of their mission, critical sort of bedrock sand infrastructure and push it up to an object store. And that could be a local private object store, it could be a public object store like AWS. And so that's you know, it's enabling them to take advantage of some new models and a new approach to doing things. And I think ultimately that's going to help them work through this you know, new normal, we're all participating in. >> Yeah, we want to help those businesses not just survive this time, but be able to thrive, especially as we don't know how much of this remote scattered workforce is going to remain. We're hearing estimates from some of the big technology leaders at all. 50% percent of the workforce is going to remain at home so really helping organizations to maneuver and navigate these challenging landscapes is a big priority I know for Dell Technologies we talked about that with some other guests. Matthew, over to you talk to me about PowerFlex from a workloads perspective, so we can get a good idea for the workloads that it's really ideally best suited for. >> Yeah, I think wanted to just take a quick second on the COVID piece, because we have a couple of really big customers that we had to enable really quickly for curbside checkout and, you know, they were trying to run things, they were putting it on their existing infrastructure, their existing systems, and it just wasn't fast enough, it wasn't keeping up. And by working closely with the customer and designing a system with PowerFlex as the core, allowed us to enable them really quickly to turn from a customer who didn't have this idea of curbside checkout to enabling curbside checkout. So I think working and partnering closely with our customers is a critical part of how Dell Tech is successful and enabling them to kind of work through these tough times. With workloads, Yeah, oh, go ahead sorry. >> That's okay go ahead. >> I was going to say with workloads in general, the way that we have to think about them with enterprise quality or enterprise requirements is really in kind of a scheme of looking at performance, understanding scalability, ensuring we have enterprise class availability, and then last but definitely not least is like how we manage that and how we make it easier for customers to work through those. And when I think about Flex there's two or three key areas that we try to go after, if you, one of the key differentiation pieces around Flex is the fact that we can deploy it in multiple manners. So you can deploy it in an HCI mode, where you have the compute and networking together, or you can go deploy it in a dis-aggregated mode where you have compute and networking, I mean, compute and storage separate. And if those are separate that allows you to scale those independently work really, really well for key database workloads, key workloads like, let's say even like Honda, where you maybe have really high compute but little less storage requirements. So that really allows customers to dial up and down what makes the most sense for them right? The other angle that we're seeing pretty big adoption is around this idea of re-platform or realigning the data center with transformation with software defined scale all block storage. So think about deploying Powerflex in an environment and then being able to use that in a virtual environment in a physical environment, in a container environment being able to have your traditional applications like SQL or Oracle, right along really cool new applications like the ELK Stack or Mongo DB or other things, because of the way that we design our layout, it's really aligned towards being able to re-platform and align in a software defined infrastructure. So customers are using to kind of align those pieces meaning platforms, re-platforming and then also aligning specific applications that require high performance. >> I heard a lot in that and one word that pops up is no, that's good. >> No, I can tell you're passionate about it. >> I love it, yeah. >> And also the customer influence is absolutely critical. I think this is a time you mentioned the curbs I check in, and then I was reading a few months ago about some of the huge brands that were filing for chapter 11 and companies like big retailers that simply couldn't pivot, couldn't digitally transform to even offer curbside check in so that factor alone since us consumers are so demanding was table stakes a few months ago. It still is, but getting an organization able to pivot so quickly is key. Martin let's go over to you, PowerMax, workloads. Talk to me about some differentiators as well. >> Yeah Aatually, if I could I'll start with sort of some similar examples that Matt laid out there, you know, just like we have customers who chose PowerFlex you know, were in environments that made sense for them. We had customers who chose PowerMax to meet similar new demands with the whole, you know pandemic. So we had some really big customers just so okay, now we have sort of line of sight and, you know, across both products, I think the thing that our customers value most is you know, the quality of the experience, the performance of the experience, some of the things Matt mentioned already. But they really pull forward, you know, huge numbers of systems and business, and be able to support you know, where they saw things going. So that was really great to partner with them on that and be ready to help support them and provide a product that they felt really good about making such huge investments in, you know, it was great to see their trust in us and be able to deliver for them. So, that was, I think a big part of the first half of the year, that sort of new, you know, new workloads and new use cases for us on the PowerMax side really revolve around giving our customers new capabilities that can deliver new services for their end users. So one of those is our new support for VVols remote replication. And this really lets us tie together the way that the infrastructure is managed at the VMware level, much more closely to the way that the storage infrastructure is managed. And the result is that our, our customers can do more granular operations for their end users, they can simplify the whole process, and now they can do it on top of our remote replication solution, which, you know going on 20 plus years now, it's really been sort of the gold standard in which they've come to rely on so much. So that's really exciting to be able to offer that to them now, to have it be part of the whole VMware stack that they're deploying and let them use you know, new things like, you know the way VVols works with our cyber site recovery manager, to let them automate you know, the testing, I feel always in the actual fail over. There's an interesting example of how I think our customers are going to take advantage of some of these new technologies as we go forward. >> You mentioned giving customers the ability with the right infrastructure to offer new services. And that's another critical component as we've seen in 2020 is businesses needing to pivot continuously and come up with new creative ideas, products, and services and new ways of delivering those to their existing customers holding onto them and hopefully growing their customer base. And that ability to leverage technology, to deliver new services is also one of the key kind of foundations that will allow businesses to be the winners of tomorrow. Matthew, to you talk to me when you're in customer situations, customers have choice, we know this, ding into me, give me the top three differentiators when you're talking to customers, why PowerFlex is the ideal solution for them? >> That's a great question. I'm glad you asked. (laughs) So I think, you know, as part of being a product guy it's really cool when the intellectual property within your product is software that your company owns and hardware, your company owns. So we're able to do some really cool stuff together to deliver innovative solutions for our customers. But, you know, when I think about my product I think first and foremost, around performance and scale right? You know, several million, IO'S a sub-millisecond response time and anytime someone wants more performance they just add another server, right? So this idea that we scale literally is a key differentiator for the product. A second key differentiator is this idea that I talked a little bit about before that we, you can kind of multi-platform this. So when you roll this out, you can deploy to use it with virtual environments, whether it's VMware or Hyper-V or other virtual environments. You can have bare metal deployment. So if you want to run this with Linux and use software defined storage in the bare metal, we can support that. Or we can go directly to containers. So you can use containers, bare metal or virtual. And so this idea of choice is a huge differentiator. And then the last one is anchored around this idea that when you scale and you get the benefit of management, you don't have to scale everything at the same time. So in traditional software defined infrastructure on the HCI side you have to scale compute and storage together. So every time you add a node you add compute power and storage power. With power flex, we've been able to effectively split those two pieces off, so a customer could actually only scale what they need. And in fact, if they only want to buy storage side of the solution, you can just buy storage side solution and then you can have existing infrastructure connect to that and it behaves just like a traditional three tier model. So those are, I think are the key things that I think differentiate the product and kind of make it special here at Dell and for our customers. >> Matthew, sticking with you, are there any, I think of things like compliance and healthcare and financial services, especially right now, what are some of the key benefits that PowerFlex delivers, say for some of those essential industries right now? >> Yeah, I think, you know it's interesting 'cause those are two of our largest space and financial is probably our largest space. And really for them, it comes down to, you talked about compliance, you talk about scale and then you talk about management. So we said some really interesting requirements because of scale so large, for example, in our last release we're able to start to do rack level firmware and software updates. So when you look at other solutions they might be doing system at a time, doing updates taking them offline and then running those around. But in our scenario, since we kind of own the SDS layer and the compute side, we can actually do update these for an entire rack in one shot. Dramatically reducing the complexity, dramatically reducing the amount of time it takes to do updates. So that's a real big deal in financial space. And then in terms of healthcare, for example we're the only software defined solution product that can run all of Epic healthcare, all pieces of Epic within our product. All other products run out of bandwidth, run out of performance. So they end up not being able to run all sides of the requirement, whether it's the database back end, or the VDI front end, we're the only one on the market that can do all of that. >> It seems to really be a big differentiator in healthcare as a lot of organizations run on Epic or try to, to help with patient care and care delivery. Martin, last question for you. Give me a snapshot of the partner's perspective over the last couple of years with the rebrand under Dell Technologies, with the power portfolio, how have your partners embraced the simplification? >> So, you know, I think that the overall, this gave them clearer understanding of where and what to sell and what made sense for power max in particular, you know, I think it let them anchor on, you know the flagship product of the legendary performance and reliability of that platform and, you know, gave them an easy way to think about where to position that with, you know, our end customers and, you know, in what ways that the products would benefit their customers the most. So, you know, as Matt described on the PowerFlex side, it starts with our performance and reliability and then ultimately, you know enabling them to do whatever they need to do, so across all the different data services and we got to talk ready about some of the new ones you know, but we also have a lot that we've you know, refined over the years and, you know making it sort of official and sort of the PowerMax envelope what everyone really just sort of simplify how they would consume it all. So, you know, I think, you know maybe one of the thing, you know, worth mentioning in all these new use cases and environments and, you know, all the different applications that our customers are trying to operate and deliver on is, you know, security, you know, so we developed a new capability that we call end-to-end efficient encryption. And this really lets customers do encryption all the way from the host through to the storage. And, you know I think ultimately that's going to help them sleep better at night and also, you know help them avoid some of the things that you've seen crop up now. Now that the world is so digital and all the different threats that our customers face. So we're keeping our finger on the pulse of a lot of different needs you know, whether it's flexibility, performance reliability, but all these new new technologies as well to make sure that we set our customers up to be successful as possible. >> That's exactly what they want to be, successful. Martin, Matthew, thank you so much for joining me on the Cube, sharing the updates for PowerMax, PowerFlex, the differentiators. We appreciate your time. >> Thank you, Lisa. >> Yeah, thank you Lisa this was fun. Alright from my guests, I'm Lisa Martin. You've been watching the cubes coverage, Dell Technologies World at 2020, the digital experience. (gentle music)

>>from the Silicon Angle Media Office in Boston, Massachusetts. It's the cue. Here's your host Still, Minutemen >>Hi and welcome to the Boston area studio. Happy to welcome back two of our Cube alumni, both from Active e o Brian Regan, the C M O of the company. And it took Rommel. Who's the vice president and general manager of Cloud? Gentlemen, thanks so much for joining us. >>Happy New Year's too great to be here. >>Yeah, 2020 way we're talking about. We don't all have flying cars and some of these things, but there are a lot of exciting things and ever changing in the tech world. We're gonna talk a lot about N. C. Which, of course, is active use announcement. If I heard the sea, it's about clouds, about containers and about copy data management. With course, you know we know act as always quite well, Brian. Let's start with a company update first. Of course, you know, copy data management is where activity really created a category, but all of these new waves of technology that activity is fitting into Well, 2000 >>19 was an incredible year for us, you know, continued accelerating our growth in the market in the enterprise particularly, You know that the secular trends around hybrid and multi cloud really played well to our existing strengths. And 10 c really builds on those strengths will talk more about that. I know in a moment we also saw continued, you know, as digital transformation as as application modernization initiatives to cold. In just about every enterprise, our database capabilities really played again a cz a strength that we could capitalize on to land significant enterprise accounts, get started with them and then really start to expand overall data platform data management platform in those accounts >>s Oh, sure, before we get into the 10 see stuff specifically. But Brian, Brian teed up some of those cloud trends and how I think about data protection. Data management absolutely has changed. You know, I remember a couple years ago we said, Oh, well, you know, people are adopting all these clouds. All of these concerns still exist. You know. It doesn't go away. It's not magically Oh, I did office 3 65 I don't need to think about all the things that I thought about without. Look, when I do public cloud and build new applications. Oh, wait. You know, somebody needs to take care of that data. So bring us inside your customers. The team that's building these products and some of those big trends should >>happen. You're still so happy to be back in the Cube. So 2019 really defined. There were a lot of for enterprises really started moving. Production will look to the cloud multi cloud become a reality for active field way. We're running production workloads on seven o'clock platforms. So the key elements off being infrastructure agnostic wherein active you can do everything in all clark platforms. Basically, infrastructure neutral was a key element. On the other element was a single pane of glass. You could have an Oracle worker running on prime with the logic application running in azure and not know the difference. S o. The seamless mobility of data was the key element. That lot of our enterprises took advantage from elective standpoint on a lot of the 10 see capabilities adds onto those capabilities and you see more of these adoptions happening in 2020. So I think 10 seat eases up absolutely perfectly for that market. >>Yeah, let's talk a little bit about activities, place in the market, that differentiation there, that direct connection with the application and the partner's eyes. Real big piece of it. >>It's a huge piece and something we really not just double triple down on in 2019. Certainly for us our database capabilities, which we believe are really second to none in the industry, we continue to expand and enrich the capabilities, including ASAP Hana obviously already Oracle and sequel server D B two, as well as the linen space databases, the new and no sequel databases. We also understood, and as our customers were talking to us about their application modernization, they were moving Maur of their front and capabilities two containers, and they wanted that the data to come with it a t east temporarily on. So that was a big focus for us as well was making sure that we could bring the data whether it was into a V M, into a container into a physical server into any number of clouds in order to support that application. At that time, it was a critical part of our differentiation. For two dozen 1 19 >>I'd love just a little more on the database piece because you go to Amazon, reinvent and you know, the migrations of databases to the cloud, of course, is a major conversation. You look at Amazon, they have a whole number of their offerings as well, as if you want to use any database out there, they'll let you use it. Course Oracle might charge him or if you're doing it on the Amazon, the Amazon partner. The azure partnership with Oracle was big news in the back and 1/2 of 2019. So when you're working with their customers, you know, databases still central to you know how they run their business and one of the bigger expenses on the books, they're So you know what we look at 2020. You know, what is the landscape specifically from a database? Well, we continue >>to see and in most of our large enterprise accounts that Oracle and sequel servers continue to dominate the majority of the payload of databases. We don't see that changing, although we do see net new applications being built on new database platforms. Thio complement the oracle and sequel server back end. So we are seeing a rise of the bongos and the new and no Sequels out there. We're also seeing Maur consideration of building in the cloud, as opposed to starting on Prem and then potentially leveraging the cloud sort of post facto and in terms of the application architecture's. So our ability to support both the the legacy big iron database platforms as well as the new generation platforms, regardless of application architectural, regardless of the geometry of the application, is a big part of our differentiation >>going forward. >>All right, so let let's Wave hinted about it. But 10 c major announcement. Let's get into how that extends what we've been talking about. >>Absolutely so you know, we've made a lot of the new databases, particularly the no sequel databases, the Mongols and Hannah's first class citizens intensity, which means we understand not just the database. He also he also the ecosystem that the database lives. We all know Hannah's a fairly big database in terms of the number of machines that consumes number off, you know, applications that you use it and toe capture and actually provide value for Hannah. You need to understand where the Honda database lifts and so some of the capabilities we've added in 10 C's to kind of figure out this ecosystem, and when you migrate, you might need the ecosystem, not just the holiday. The peace because you know that is that is a key element. On the second aspect is the containers that that Brian touched on. Now we're seeing legacy data being presented into containers, and there's a bridge too quiet for that. Now. How do you present that bridge containers could be brought up, but they're lifeless unless you give them data. So the actors of bridge ready and you bring up the container using communities of whatever framework you have and be married the data into the container framework. So most organizations, you know, as they evolved from yesterday's architecture to today's architect. And they need this bridge, which helps them navigate that that my creation process and an active field being the data normalization platform is helping them live on both segments, Right? Nobody does us turn the switch off of the old one and move to the new That'll be co exist. That is the key element >>way spent a lot of time over the last couple of years hearing about cloud native architectures and that discussion of data, it is kind of something you need to kind of dig in to understand. I'm glad to hear you talking about, You know, when you talk about storage and container ization, you know where that fits today? Because originally it was only stateless. But now we know we could do state full environment here. But while container ization is, you know, growing at huge leaps and bounds, customers aren't taking their Oracle database and shoving Brian A lot of discussion about the partnerships. I think it was seven. You know, major cloud providers. That activity is there talk a little bit about the common native. The relationships with some >>of those partners? Absolutely. I mean, way made great strides from a go to market standpoint with our cloud partners this past year. Google Cloud is probably our most significant go to market partner. From a cloud standpoint, we've done a lot of joint engineering works in order to support both our existing, uh, software platform as well as our SAS control plane in the Google Cloud. We have landed many significant deals with with Google this past year on dhe. They have been as they continue to really increase their focus on enterprise accounts and both hybrid as well as public cloud sort of architectures. We are hand in glove with them as their backup in D R partner for those club >>workloads. >>Great eso We talked quite a bit about the database peace, but in general, back into the cloud archive in the cloud. What is 10 see specifically an active you, in general, enhance in those environments >>so tense he bring It brings in you know, the key elements of the recovery orchestration. So if I have to bring up, let's say, 500 machines in any club platform, how did I do it? Well, I can go and bring up one machine at a time and take two days to bring it up or with active fuels. Resiliency. Director. You can create a recovery plan and a push pardon Recovery happens, so we've seen a lot of customers adopt that, particularly customers that want to leverage the Google platform for its infrastructure capabilities. Wants an orchestration, that is, that is, that understands the applications that are coming up, so there is a significant benefit from a PR standpoint of the recovery orchestrations will be invested a lot of time and tuning the performance and understanding Google and Amazon and Azure to make sure this was built, right. The other big push we're seeing for the clock platforms ASAP, ASAP, as an enterprise has taken a mission to say, there's no more data centers. Everything is going to the cloud. So an escapee workloads are not the easiest were close to manage. And so they did the the intersection point of S A P and the cloud is very active. Field becomes really valuable because, though, did this data sets by definition or large, their complex and there were distributed. And the D artists of paramount importance because these air crown jewels So so those segments of the R orchestration forward with, you know ASAP and Hannah, which is to get our strength of databases. It's kind of their tense. He really hits, hits, hits a home run >>when we're talking to users in the discussion of multi Cloud in general, one of the challenges is Yoon hee. Different skill sets across. One of those powerful things I've heard from active use really is a normalization across any cloud or even in a cloud. Oh, wait. I was gonna stuck six up again in an archive. That means I'm never going to touch it again. Ingress and egress fees. You know, I have to figure these out or I need toe dedicated engineer to those kind of environments. So it seems that just fundamentally the architecture that you built it active eo is toe help customers really get their arms around those multi cloud >>environments? Absolutely. And I think there are two additional components that really one of which has lived with activity from the very beginning of the company, which is a p a p I. First, the cloud is very much an AP I centric type of operating model on with active fio We don't change the management system were operating model. But in fact we incorporate in eso all of this orchestration that it shook talked about can be actuated via a P I. The second piece, which we really started in 2017 with our eight Dato platform release, is the the consumption and the intelligent consumption of object with 10 see, we've continued to advance our object capabilities. In fact, we published a paper with the SG in late 2019 that talked about mounting 50 terabyte Oracle databases directly out of object with actually increased performance versus the production block >>storage behind it. >>So we have really with 10 C, actually added cashing to even further performance optimized object workloads, which speaks to both the flexibility but also the economic flexibility of being able. Thio contemplate running workloads in the cloud out of object at a lower cost platform without necessarily the compromise of performance that you would normally expect >>absolutely. And like you said, the skill set required. Do I need to put it in object to any reported in block? We can eliminate that right. Be neutralized that to say you want to leverage the cloud, give us your cost point and you can dial the cost up or down, depending on what you see for performance, and we will be the day that back and forth, so that flexibility is enormous for customers. >>That's greater if you talk to anybody that's been in the storage industry for a while, and you want to make them squirm, say the word migration s O. We know how painful it has been if you go talk to any of the triple vendors, they have so many tools and so many service is to help do that in a cloud era. It should be a little bit easier, but it sounds like that's another key piece. Intensity? >>Absolutely, absolutely. I mean, 10 See, you know, hits the home. I think with the A P. I integration. So the other element 2019 Saul, was the scale of deployment effective. You know, when you have to manage hundreds of thousands of machines across different geo's, that is a scale that comes to the data protection that you know, people. Really? You have a seat to actually build for it and and work with it and be sorry in 2019 and 10 See, incorporates a lot of that capabilities as well, making it ask Cloud needed as possible. So basically, around these applications globally. All >>right, uh, I was wondering if you might have a customer example toe really highlight the impact that NBC's having understand if you can't name them specifically, but, uh, yeah, >>well, actually, shook has already talked about 11 customer slash partner. Who is I think still the world's largest software company in the world based out of Germany. And they are powering their enterprise cloud on the data management data protection. Beneath that enterprise cloud across four different hyper scale er's using, active you on. I think they're on record in a weapon. Our earlier in December, talking about their evaluation of pretty much every technology out there on the one that could really deliver on performance at scale across clouds was activity >>on. The key element was they wanted a single platform with a single pane of glass across all platforms, and an active feel was the solution to each other. So >>and certainly I think we credit them and are the rest of our enterprise customers for pushing us to make 10 see more powerful and more a capable across any clout, you know, Ultimately, an inter enterprise is going to make a decision that they've probably already made the decision to incorporate cloud into their enterprise architecture. What we give them is the freedom and the flexibility to choose any cloud. And, by the way, any cloud today that might change tomorrow and having the ability to seamlessly migrate and or convert from cloud eight o'clock be. Is something that active powers as well? >>Yeah, just make sure we're clear as to what's happening there. It's great that you've got flexibility there when we're talking about data and data gravity. Of course, we're not talking about just lifting an entire database land, you know, ignoring the laws of physics there. But it's the flexibility of using a ll These various things, any way Talk about A S, A P, of course, needs to live across all these clouds. But when you talk about an enterprise, you know what is kind of that? That killer use case? Because we said we're not at a point where cloud is not a utility. I don't wake up in the morning and look at the sheet and say, Oh, I'm gonna, you know, use Cloud a versus cloud be s o. You know what is? You know the importance of that flexibility for us >>today. The majority of our business starts with company saying I need to deliver my data faster to my developers or my tester's, or even increasingly, my data scientists and analysts and my data sets have become so large that it's becoming increasingly difficult for me to do that with regularity. So the currency of the data is starting to suffer. That is the first use case for us and that that powering that enterprise transformational initiative around a new application or an updated application based on a historical app using those enterprise databases delivering that seamlessly quickly, regardless of how big the data is still remains our first use case. And then, increasingly, those customers air realizing that they can start to achieve the other benefits of active eo, including I can start to back that up to the cloud. Aiken actually orchestrate recoveries in the cloud. Not just bulk sort of transfer, but actually the entire application stack. And bring that up in the cloud. I can start Thio, take those those data sets and actually amount them into containers for my next generation application. So that starting point of give me my data as quickly as possible, regardless of how big it is, starts to become universal in terms of its applicability for all use cases. >>Yeah, I guess I shook. The last thing I wanna understand from you is in 2019. We saw a lot of large providers putting out their vision for how I manage in this multi cloud environment. You were at the Google Cloud event where Anthros was unveiled. I was at Microsoft ignite when as your ark was unveiled. VM wear has things like tans you out there. So this moldy cloud environment how do I manage across these disperse environments? What? What What are all those move mean to active you on how you look at things. >>And I think you know, the Tennessee release and with the core architecture that if you had in place, which was multiple already and a P I ready. So those are the two elements that are kind of building blocks that you can tie into any one of those construct you talked about. All right, so we've had we have customers, innovated us with Antos. If customers get up service now we have customers doing Vieira with us, right? So there are many, many integration platforms. The latest I saw was an Alexa app, but we were mounting an oracle database on a voice command. So So you know, there's endless possibilities as thes equal systems evolve because active feel stays behind the cowards powering the data delivering the data available if needed on the target. So that is the key element in the neighbor that we see that helps all these other platforms become super successful. >>So, Brian, it sounds very much a hell wind. The big trends that we're seeing here keep partnerships and, you know, meeting your customers where they need to >>pay. Absolutely. We continue Thio play in the enterprise market, where these thes are absolutely top of mind of every CEO and top of their agenda. Onda, we are working hand in glove with them to make sure that our platform not only anticipates their needs but delivers on their current state of needs as well. >>Brian, thank you so much. Congratulations on the 10 sea launch Cloud containers. Copy data management. Look forward to watching your customers and your continued Thanks. As always, Very much. All right, I'm still Minutemen. Lots more coverage here in 2020. Check out the cube dot net for all of it. And thank you for watching the Cube

>> Narrator: Live from San Francisco celebrating 10 years of high tech coverage, It's the Cube! Covering VM World 2019 Brought to you by VMware and its ecosystem partners >> Hello everyone and welcome to the Cube's coverage here in San Francisco, California of the VMWorld 2019. I'm John Furrier with my cohost Dave Vellante Dave, 10 years covering VMWorld since 2010, it's been quite a ride, lot of changes. >> Dave: Sure has. >> John: We're going to do a Power Panel our format we normally do it remote guests in our Palo Alto and Boston studios in person because we're here. Why not do it? Of course, Keith Townsend, CTO Advisor friend of the Cube, Cube host sometimes and Sarbjeet Johal, cloud architect cloud expert, friends on Twitter. We're always jammin' on Twitter. So we'll have to take it to the video. Guys, thanks for joining us on the Power Panel. >> Good to see you, Gents. >> Good seein' ya. >> Good to be here. >> Yeah, I, I hope we don't come to blows, Sarbjeet. I mean we've had some passionate conversations over the past couple months. >> Yeah, Santoro, yes, yes. >> John: The activity has been at an all time high. I mean, snark aside, there's real things to talk about. >> Yes. >> I mean we are talking about VMware a software company, staying with their roots. We know what happened in 2016 The Amazon relationship cleared the air so to speak, pun intended. Vcloud air kind of goes it's way stock prices go up and to the right Yeah, fluctuations happening but still financially doing well. >> Keith: Yeah. >> Customers have clarity. They're an operate. They run, they target operators not developers. We're living in a DevOps world we talk about this all the time dev and ops this is the cloud world that they want Michael Dell was on the Cube Dell Technologies owns VMware they put Pivotal on VMware moves are being made. Keith, how do you make sense of it? What's your take? You've been on the inside. >> Well, you know, VMware has a tough time. Pat came in, 2013, we remember it. He said we are going to double down on virtualization. He is literally paying the cost for that hockey stick movement VMware has had this reputation of being an operator based company Infrastructure based, you go into accounts, you're stuck in this IT Infrastructure cells movement. VMware has done awesome over the past year. Few years, I had to eat a little crow and say that the move to eject Pivotal was the right thing for the Stock but for the reputation, VMware is stuck so Pat, what, tallied up 5 billion dollars in sales, in purchases last week to get out of this motion of being stuck in the IT Infrastructure realm Will it pay off? I think it's going to be a good conversation because they're going to need those Pivotal guys to push this PKS vision of theirs. This PKS and Kubernetes vision that they have >> Well they got to figure it out but certainly it's a software world and one of the things that's interesting we were talking before we started is, they are stuck in that operator world but it's part of DevOps, Dev and Ops. This is the world that they operate in Google's cloud shows how to do it. You got SRE's run things and developers this program infrastructure is code. This is the promise of this new generation. Sarbjeet, we talk about it all the time on Twitter developers coding away not dealing with the infrastructure, that's the goal >> Yeah, traditionally, developers never sort of mucked around with infrastructure. Gradually we are moving into where developers have to take care of infrastructure themselves the teams are like two person teams we hear that all the time. They are responsible for running the show from beginning to the end. Operations are under them, it's Dev and Ops are put together, right? But I'll speak from my own personal experience with working at VMware in the past that from all the companies which are operations focused, that's HP, IBM, and Oracle to a certain extent. So portfolio and all that. And BMC, and CA, those are pure companies in the operations space, right? I think VMware is one of those which values software a lot. So it's a purely, inside the VMware it's purely software driven. But to the outside, what they produce what they have produced in the past that's all operations, right? So I think they can move that switch because of the culture and then with Pivotal acquisition I think it will make it much easier because there's some following of the Pivotal stack, if you will the only caveat I think on that side is it is kind of a little bit of interlocking-ish, right? That is one of the fears I have. >> Who's not, even RedHat these days is, locking you in. >> Yeah, you know, I pulled some interesting stat metadata from a blog post from Paul Fazzone announcing the Pivotal acquisition. He mentioned Kubernetes 22 times. He mentioned Pivotal Cloud Foundry once. So VMware is all in on this open-shift type movement I think VMware is looking at the Red shift I mean Red OpenShift acquisition by IBM and thinking, "Man, I wish we didn't have this "Sense of relationship with Pivotal "So we could have went out and bought RedHat." >> Well that's a good point about Kubernetes, I think you're right on that. And remember, we've been covering Open Stack up until about a year ago, and they changed the name it's now something else, but I remember when Open Shift wasn't doing well. >> Keith: I do too! >> And what really was a tipping point for them was they had all the elements, but it was Kubernetes that really put them in a position to take advantage of what they were trying to do and I think you're right, I think VMware sees that, now that IBM owns RedHat and Open Shift, it's clear. But I think the vSphere deal with Project Pacific points out that they want to use Kubernetes as a distraction layer for developers, and have a developer interface to vSphere. So they get the operators with vSphere, they put Kubernetes in there and they say, "Hey developers, use us." Now I think that's a hedge also against Pivotal 'cause if that horse doesn't come across the track to the finish line, you know... >> It's definitely a hedge on Containers just a finer point of what you were saying there was a slight difference in the cash outlay for RedHat, 34 billion versus the cash outlay for Pivotal was 800 million. So they picked up an 800 million dollar asset or a 4 billion dollar asset for 2.7 billion. >> Hold on, explain that because 2.7 billion was the number we reported you're saying that VMware put out only 800 million in cash, which, what's that mean? >> That's correct. So they put out 800 million in cash to the existing shareholders of Pivotal, which is a minority of the shareholders. Michael Dell owns 70% of it, VMware owns 15% of it. So they take the public shareholders get the 800 million >> John: They get taken out, yep. >> Michael Dell gets more VMware stock, so now he owns more of VMware. VMware already owns 15% of Pivotal, so for 800 million, they get Pivotal. >> So, the VMware independent shareholders get... they get diluted. >> Right. >> Did they lose out in the deal is the question and I think the thing that most people are missing in this conversation is that Pivotal has a army of developers. Regardless of whether developers focus on PCF or Kubernetes is irrelevant. VMware has a army, a services army now that they can point towards the industry and say, "We have the chops to have "The conversation around why you should "Come to us for developing." >> So I want to come back to that but just, a good question is, Do the VMware shareholders get screwed? Near term, the stock drops, right? Which is what happens, right? Pivotal was up 77% on the day that the Dow dropped 800 points. Here's where I think it makes sense, and there are some external risks. Pivotal plus Carbon Black, the combination they shelled out 2.7 billion in cash. They're going to add a billion dollars to VMware's subscription business next year. VMware trades at 5x revenue multiple, so the shareholders will, in theory, get back 5 billion. In year two, it's going to be 3 billion that they're going to add to the subscription revenue so in theory, that's 15 billion of value added. I think that goes into the thinking, so, now, are people going to flock to VMware? Are Kubernetes developers going to flock to VMware? I mean to your point, that to me, that's the value of Pivotal is they can get VMware into the developer community. 'Cause where is VMware with developers? Nobody, no developers in this audience. >> That's true. >> What are your guys' thoughts on that? >> Yeah, I think that we have to dissect the workload of applications at the enterprise level, right? There are a variety of applications, right, from SAPs Oracles of the world those are two heavyweights in the application space. And then there's a long trail of ISVs, right. And then there's homegrown applications I think where Pivotal plays a big role is the homegrown applications. When you're shipping a lot as an ISV or within your enterprise, you're writing software you're shipping applications to the user base. It could be internal for partners, for customers, right, I think that's where Pivotal plays Pivotal is pivotal, if you will. >> I think that's a good bet too, one of the things we've been pulling the CESoEs data for when we got reinforced we started pulling CESoEs in our network, and it's interesting. They're under the gun to produce security solutions and manage the vendors and do all that stuff they're all telling us, the majority of them are telling us that they're building their own stacks internally to handle the crisis and the challenge of security, which I think's a leading indicator versus the kind of slow, slower CIO which LOVES multi-anything. Multi-vendor, control, a deal with contracts CESoEs, they don't have the DOGMA because they can't have the DOGMA. They got to deliver and they're saying, "We're going to build a stack "On one cloud. "Have a backup cloud, "I want all my developer resources "On this cloud, not fork my team "And I'm going to build a stack "And then I'm going to ship APIs "And say to my suppliers, in the RFP process, "If you support these APIs, "You could do business with us." >> Keith: So, if you don't -- >> That's kind of a cutting edge. If you don't, you can't, you can't. And that's the new normal. We're seeing it with the Jedi deal with Oracle not getting, playing 'cause they're not certified at the level that Amazon is, and you're going to start to see these new requirements emerging this is a huge point. I think that's where Pivotal could really shine not being the, quote, developer channel for VMware. I think it's more of really writing apps >> And John, I think people aren't even going to question that model. Capital One is probably the poster child for that model they actually went out and acquired a start-up, a security, a container security start up, integrated them into their operations and they still failed. Security in the cloud is hard. I think we'll get into a multi-cloud discussion this is one of the reasons why I'm not a big fan of multi-cloud from an architecture perspective, but from a practical challenge, security is one of the number one challenges. >> That's a great point on Capital One in fact, that's a great example. In fact, I love to argue this point. On Twitter, I was heavily arguing this point which is, yeah, they had a breach. But that was a very low-level it's like the equivalent of a S3 bucket not being configured, right? I mean it was so trivial of a problem but still, it takes one whole-- (hearty laughing) One, one entry point for malware to get in. One entry point to get into any network where it's IOT This is the huge challenge. So the question there is, automation. Do you do the, so, again, these are the, that's a solvable problem with Capital One. What we don't know is, what has Capital One done that we don't know that they've solved? So, again, I look at that breech as pretty, obviously, major, but it was a freakin' misconfigured firewall. >> So, come back to your comments on multi-cloud. I'm inferring from what you said, and I'd love to get your opinion, Sarbjeet. That multi-cloud is not an architectural strategy. I've said this. It's kind of a symptom of multiple vendors playing but so, can multi-cloud become, because certainly VMware IBM RedHat, Google with Anthos, maybe a little bit less Microsoft but those three-- >> Dell Technologies. >> Cisco, Cisco and certainly Dell all talking about multi-cloud is the clear strategy that's where CIOs are going, you're not buying it. Will it ever become a clear strategy from an architectural standpoint? >> Multi-cloud is the NSX and I don't mean NSX in VMware NSX it's the Acura NSX of enterprise IT. The idea of owning the NSX is great it brings me into the showroom, but I am going to buy, I'm going to go over to the Honda side or I'm going to go buy the MDX or something more reasonable. Multi-cloud, the idea, sure it's possible. It's possible for me to own a NSX sports car. But it's more practical for me to be able to shop around I can go to Google via cloud simple I mean I can go via cloud simple to Azure, GCP or I can go BMC, I have options to where I land, but to say that I am going to operate across all three? That's the NSX. >> If you had a NSX sports car, by the way, to use the analogy in my mind is great one, the roads aren't open yet. So, yeah, okay great. (hearty laughing) >> Or you go to Germany and you're in California. So, the transport, and again in the applications you could build tech for good applications all you want, and they're talking about tech for good here but if it's insecure, those apps are going to create more entry points. Again, for cyber threats, for malware, so again, the security equation, and you're right is super important, and they don't have it. >> Dave: What's your thought on all (mumble)? >> Sarbjeet: I think on multi-cloud you are, when you are going to use multi-cloud you going to expand the threat surface if you will 'cause you're putting stuff at different places. But I don't think it, like as you said Dave, the multi-cloud is not more of an architectural choice, it's more like a risk mitigation strategy from the vendor point of view. Like, Amazon, who they don't compete with or who they won't compete with in the future we don't know, right? So... >> You mean within the industry. >> Yeah, within the industry right-- >> Autos or healthcare or... >> Sarbjeet: Yeah, they will, they are talking about that, right? So if you put all, all sort of all your bets on that or Azure, let's say even Azure, right? They are not in that kind of category, but still if you go with one vendor, and that's mission critical and something happens like government breaks them up or they go under, sideways, whatever, right? And then your business is stuck with them and another thing is that the whole US business, if you think about it at a global scale, like where US stands and all that stuff and even global companies are using these hourglass providers based in US, these companies are becoming like they're becoming too big to fail, right? If you put everything on one company, right, and then something happens will we bail them out? Right, will the government bail them out? Like stuff like that. Like banks became too big to fail, I think. I think from that point of view, bigger companies will shift to multi-cloud for, to hedge, right, >> Risk Mitigation >> Risk mitigation. >> Yeah, that's, okay, that's fair. >> I mean, I believe in multi-cloud in one definition only. I think, for now, the nirvana of having different workload management across utility bases, that's fantasy. >> Keith: Yeah, that's fantasy. >> I think you could probably engineer it, but there might not be a workload for that or maybe data analytics I could see moving around as a use case, certainly, but I think-- >> D-R! >> The reality is, is that all companies will probably have multiple clouds, clearly like, if you're going to run Office 365, and it's going to be on Azure, you're an Azure customer, okay. You have Azure cloud. If you're building your security stack on Amazon, and got a development team, you're on Amazon. You got two clouds. You add Google in there, big tables, great for certain things you know, Big Query, you got Google. You might even have Alibaba if you're operating in China So, again, you going to have multiple clouds the question is, the workloads define cloud selection. So, I've been on this thing, if you got a workload, an app, that app should choose its best infrastructure possible that maximizes what the outcome is. >> And John, I think what people fail to realize, that users, when you give them a set of tools, they're going to do what users do, which is, be productive. Just like users went out and took credit cards swiped it and got Amazon. If you, if in your environment you have Amazon you have GCP, you have Azure, you have Salesforce, O-365, and a user has access to all five platforms, whether or not you built a multi-cloud application a user's going to find a way to get their work done with all five, and you're going to have multi-cloud fallout because users will build data sets and workloads across that, even if IT isn't the one that designed it. >> All right, guys, final question of the Power Panel Dave, I want to include this for you too, and I'll weigh in as well. Take a minute to share what you're thinking right now is on the industry. What's taking up your attention? What's dominating your Twittershpere right now? What's the bee in your bonnet? What's the hot-button issue that you're kicking the tires on, learning about, or promoting? Sarbjeet, we'll start with you. What's on top of the mind for you these days? >> I think with talk about multi-cloud all the time, that's in discussions all the time and then Blockchain is another like slow-moving train, if you will, I think it's arriving now, and we will see some solutions coming down the pike from different, like a platformization of the Blockchain, if you will, that's happening, I think those are two actually things I keep my eyes on and how developers going to move, which side to take and then how the AWSs dominance is challenged by Microsoft and Google there's one thing I usually talk about on Twittersphere, is that there's a data gravity and there's a scales gravity, right? So people who are getting trained on Amazon, they will tend to stay with them 'cause that's, at the end of the day, it's people using technology, right? So, moving from one to another is a challenge. Whoever throws in a lot of education at the developers and operators, they will win. >> Keith, what are you gettin' excited about? >> So, CTO advisor has this theory about the data framework, or data infrastructure. Multi-cloud is the conversation about workloads going here, there, irrelevant, it's all about the data. How do I have a consistent data policy? A data protection policy, data management policy across SAS, O-365, Sales Force Workday, my IAF providers, my PATH providers, and OMPRIM, how do I move that data and make sure another data management backup company won Best of VMWorld this year. This is like the third or fourth year and a reason it's not because of backup. It's because CIOs, CDOs are concerned about this data challenge, and as much as we want to talk about multi-cloud, I think well, the industry will discover the problem isn't in Kubernetes the solution isn't in Kubernetes it's going to be one of these cool start-ups or one of these legacy vendors such as NetAp, Dell, EMC that solves that data management layer. >> All right, great stuff. My hot button is cloud 2.0 as everyone knows, I think there's new requirements that are coming out, and what got my attention is this enterprise action of VMware, the CIA deal at Amazon, the Jedi deal show that there are new requirements that our customers are driving that the vendors don't have, and that's a function that cloud providers are going to provide, and I think that's that's the canary in the coal mine. >> I've got to chime in. I've got to chime in. Sorry, Lenard, but it's the combination what excites me is the combination of data plus machine intelligence and cloud scale. A new scenario of disruption moving beyond a remote set of cloud services to a ubiquitous set of digital services powered by data that are going to disrupt every industry. That's what I get excited about. >> Guys, great Power Panel. We'll pick this up online. We'll actually get the Power Panels working out of our Palo Alto studio. If you haven't seen the Power Panels, check them out. Search Power Panels the Cube on Google, you'll see the videos. We talk about an issue, we get experts it's an editorial product. You'll see more of that online. More coverage here at VMWorld 2019 after this short break. (lively techno music)

live from San Francisco it's the cube covering micron insight 2018 brought to you by micron welcome back to the San Francisco Bay everybody we saw the Sun rise in the bay this morning of an hour so we're gonna see the Sun set this gorgeous setting here at Pier 27 Nob Hills up there the Golden Gate Bridge over there and of course we have this gorgeous view of the bay you're watching the cube the leader in live tech coverage we're covering micron insight 2018 ai accelerating intelligence a lot of talk on on on memory and storage but a lot more talk around the future of AI so we got a great discussion here on the auto business and how AI is powering that business Jeff Bader is here is the corporate vice president and general manager of the embedded business unit at micron good to see you again Jeff thanks for coming on and Simon and rigor is the vice president BMW and he's also joined by Vijay Nadkarni who was the global head of AI and augmented reality at Visteon which is a supplier to Automobile Manufacturers gentlemen welcome to the cube thanks so much for coming on thank you so you guys had a panel earlier today which was pretty extensive and just a lot of talk about AI how AI will be a platform for interacting with the vehicle the consumer the driver interacting with the vehicle also talked a lot about autonomous vehicles but Simon watch you kick it off your role at BMW let's let's just start there it will do the same for Vijay and then get into it research portion that we do globally in which is represented here in North America and so obviously we're working on autonomous vehicles as well as integrating assistance into the car and basically what we're trying to do is to get use AI as much as possible in all of the behavioral parts of the vehicle that uses have an expectations towards being more personalized and having a personalized experience whereas we have a solid portion of the vehicle is going to be as a deterministic anesthetic as we have it before like all of the safety aspects for example and that is what we're working on here right now Vijay Visteon is a supplier to BMW and other auto manufacturers yes we are a tier 1 supplier so we basically don't make cars but we supply auto manufacturers of which BMW is one and my role is essentially AI technology adversity on and also augmented reality so in AI there are basically two segments that we cater to and one of them is that almost driving which is fully our biggest segment and the second one is infotainment and in that the whole idea is to give the driver a better experience in the car by way of recommendations or productivity improvements and such so that is so my team basically develops the technology and then we centrally integrate that into our products so so not necessarily self-driving it's really more about the experience inside the vehicle that is the and then on the autonomous driving side we of course very much are involved with the autonomous driving technology which is tested with detecting objects are also making the proper maneuvers for the Waker and we're definitely going to talk about that now Jeff you sell to the embedded industry of fooding automobile manufacturers we hear that cars have I forget the number of microprocessors but there's also a lot of memory and storage associate yeah I mean if you follow the chain you have our simon representing the OEMs Vijay represented the Tier one suppliers were supplier to those Tier one suppliers in essence right so so we're providing memory and storage that then goes in to the car in as you said across all of the different sort of control and engine drone and computing units within the car in particular into that infotainment application and increasingly into the a TAS or advanced driver assistance systems that are leading toward autonomous driving so there's a lot of AI or some AI anyway in vehicles today right presumably yeah affected David who did a wonderful job on the panel he was outstanding but he kind of got caught up in having multiple systems like a like an apple carplay your own system I actually have a bit about kind of a BMW have a mini because I'm afraid it's gonna be self-driving cars and I just want to drive a drive on car for this take it away from me though but but you push a button if you want to talk to a Syrian yeah push another button if you want to talk to the mini I mean it's it's gonna use it for different use cases right exactly may I is also about adaption and is also about integrating so AI is is is coming with you with the devices that you have with you anyway right so your might be an Alexa user rather than a Google assistant user and you would have that expectation to be able to ask to chat with your Alexa in your car as well that's why we have them in the vehicle also we have an own voice assistant that we recently launched in Paris Motorshow which augments the experience that you have with your own assistants because it factors in all of the things you can do with the car so you can say there is a solid portion of AI already in the vehicle it's mainly visible in the infotainment section right and of course I remember the first time I'm sure you guys experienced to that the the car braked on my behalf and then kind of freaked me out but then I kind of liked it too and that's another form of machine intelligence well that out well that counts for you that had not that has not necessarily been done by AI because in in in let's say self-driving there is a portion of pretty deterministic rule based behavior and exactly that one like hitting an object at parking you don't need AI to determine to hit the right there is no portion or of AI necessary in order to improve that behavior whereas predicting the best driving strategy for your 20-mile ride on the highway this is where AI is really beneficial in fact I was at a conference last week in Orlando it's the Splunk show and it was a speaker from BMW talking about what you're doing in that regard yeah it's all about the data right learning about it and and in turning data into insights into better behavior yes into better expected behavior from whatever the customer wants so Vijay you were saying before that you actually provide technology for autonomous vehicles all right I got a question for you could it autonomous - could today's state of autonomous vehicles pass a driver's test no no would you let it take one no it depends I mean there are certain companies like way mo for example that do a lot but I still don't think way mo can take a proper driver's test as of today but it is of course trying to get there but what we are essentially doing is taking baby steps first and I think you may be aware of the SAE levels so level 1 level 2 level 3 level 4 SF and a 5 so we and most of the companies in the industry right now are really focusing more on the level 2 through level 4 and a few companies like Google or WAV or other and uber and such are focusing on the level 5 we actually believe that the level 2 through 4 is the market would be ready for that essentially in the shorter term whereas the level 5 will take a little while to get that so everybody Christmas and everyone we're gonna have autonomous because I'm not gonna ask you that question because there's such a spectrum of self-driving but I want to ask you the question differently and I ask each of you when do you think that driving your own car will become the exception rather than than the rule well I'd rather prefer actually to rephrase the question maybe to where not when because we're on a highway setting this question can be answered precisely in roughly two to three years the the functionality will kick in and then it's going to be the renewal of the vehicles so if you answer if you if you ask where then there is an answer within the next five years definitely if we talk about an urban downtown scenario the question when is hard to answer yeah well so my question is more of a social question it is a technology question because I'm not giving up my stick shift high example getting my 17 year old to get his permit was like kicking a bird out of the nest I did drive his permanent driver on staff basically with me right so why but I mean when I was a kid that was freedom 16 years old you racing out and there is a large generational group growing up right now that doesn't necessarily see it as a necessity right so not driving your own car I think car share services right share who bore the so and so forth are absolutely going to solve a large portion of the technology of the transportation challenge for a large portion of the population I think but I agree with the the earlier answers of it's gonna be where you're not driving as opposed to necessarily win and I think we heard today of course the you know talking about I think the number is 40,000 fatalities on the roadways in the u.s. in the u.s. yeah everybody talks about how autonomous vehicles are going to help attack that problem um but it strikes me talk about autonomous cars it why don't we have autonomous carts like in a hospital or even autonomous robots that aren't relying on lines or stripes or beacons you one would think that that would come before in our autonomous vehicle am I missing something are there are there there there systems out there that that I just haven't seen well I don't know if you've ever seen videos of Amazon distribution centers yeah but they're there they're going to school on lines and beacons and they are they're not really autonomous yeah that's fair that's fair yeah so will we see autonomous carts before we see autonomous cars I think it's a question what problem that solves necessarily yeah it's just as easy for them to know where something is yeah you think about microns fabs every one of our fabs is is completely automated as a material handling system that runs up and down around the ceilings handling all the wafers and all the cartridges the wafers moving it from one tool to the next tool to the next tool there's not people anymore carrying that around or even robots on the floor right but it's a guided track system that only can go to certain you know certain places well the last speaker today ii was talking about it I remember when robots couldn't climb stairs and now they can do backflips and you know you think about the list of things that humans can do that computers can't do it let's get smaller and smaller every year so it's kind of scary to think about one hand is that does the does the concept of Byzantine fault-tolerance you guys familiar with that does that does that come into play here you guys know what that's about I don't know what it is exactly so that's a problem and I first read about it with it's the Byzantine general problem if you have nine generals for one Oh attack for one retreat and the ninth sends a message to half to retreat or not and then you don't have the full force of the attack so the concept is if you're in a self-driving boat within the vehicle and within the ecosystem around the city then you're collectively solving the problem so there these are challenging math that need to be worked out and and I'm not saying I'm a skeptic but I just wanted more I read about it the more hurdles we have there's some isolated examples of where AI I think fits really well and is gonna solve problems today but this singularity of vehicle seems to be we have a highly regulated environment obviously public transportation or public roads right are a highly regulated environment so it's like it's different than curating playlists or whatever right this is not so much regulated traffic and legislation isn't there yet so especially and it's it's designed for humans right traffic cars roads are designed for human to use them and so the adoption to they the design of any legislation any public infrastructure would be completely different if we didn't drive as humans but we have it we have machines drive them so why are robots and carts not coming because the infrastructure really is designed for humans and so I think that's what's going to be the ultimate slow down is how fast we as a society that comes up with legislation with acceptance of behavioral aspects that are driven by AI on how fast we adopt it technically I think it can happen faster than yeah yeah it's not a technology problem as much as it is the public policy insurance companies think about one of the eventually you can think of from from let's say even level four capable car on a highway is platooning yeah right instead of having X number of car lengths to the turn fryer you just stack them up and they're all going on in a row that sounds great until Joe Blow with their 20 year old Honda you know starts to pull into that Lane right so you either say this Lane is not allowed for that or you create special infrastructure essentially that isn't designed for humans there is more designed specifically for the for the machine driven car right how big is this market it's it feels like it's enormous I don't know how do you look at the tan we can talk to the memory I can talk the memory storage part of it right but today memory and storage all of memory storage for automotive is about a two and a half billion dollar market that is gonna triple in the next three years and probably beyond that my visibility is not so good maybe yours is better for sure but it then really driven by adoption rate and how fast that starts to penetrate through the car of OAM lines and across the different car in vijay your firm is when were you formed how long you've been around or vistas be around basically since around 2001 okay we were part of relatively old spun out whiskey on that at work right okay so so alright so that's been around forever yeah for this Greenfield for you for your your group right where's the aw this is transitional right so is it is it is it you try not to get disrupted or you trying to be the disrupter or is it just all sort of incremental as a 101 year old company obviously people think about you as being ripe for disruption and I think we do quite well in terms of renewing ourselves coming from aeroplane business to a motorcycle business to garbage and so I think the answer is are we fast enough I'll be fast enough in adoption and on the other hand it's fair to say that BMW with all of its brands is part of a premium thing and so it's not into the mass transportation so everything that's going to be eaten up by something like multi occupancy vehicle mass transportation in a smaller effort right this is probably not going to hurt the premium brand so much as a typical econo type of boxy car exciting time so thanks so much for coming on the cube you got a run appreciate thank you so much okay thanks for watching everybody we are out from San Francisco you've watched the cube micron inside 2018 check out Silicon angle comm for all the published research the cube dotnet as well you'll find these videos will keep on calm for all the research thanks for watching everybody we'll see you next time you

>> Hi, I'm Peter Burris. Welcome to Wikibon's Action Item. On Action Item, every week I assemble the core of the Wikibon research time here in our theCUBE Palo Alto studios, as well as remotely, to discuss a seminal topic that's facing the technology industry, and business overall, as we navigate this complex transition of digital business. Here in the studio with me this week, I have David Floyer. David, welcome. >> Thank you. >> And then remotely, we have George Gilbert, Neil Raden, Jim Kobielus, and Ralph Finos. Guys, thank you very much for joining today. >> Hi, how are you doing? >> Great to be here. >> This week, we're going to discuss something that's a challenge to talk about in a small format, but we're going to do our best, and that is, given that the industry is maneuvering through this significant transformation from a product orientation to a services orientation, what's that going to mean for business models? Now this is not a small question, because there are some very, very big players that the technology industry has been extremely dependent upon to drive forward invention, and innovation, and new ideas, and customers, that are entirely dependent upon this ongoing stream of product revenue. On the other hand, we've got companies like AWS, and others that are much more dependent upon the notion of services revenue, where the delivery of the value is in a continuous service orientation. And we conclude most of the SaaS players in that as well, like sales force, etc. So how are those crucial companies, that have been so central to the development of the technology industry, and still are essential to the future of the technology industry, going to navigate this transition? Similarly, how are the services companies, for those circumstances in which the customer does want a private asset that they can utilize as a basis for performing their core business, how are they going to introduce a product orientation? What's that mix, what's that match going to be? And that's what we're going to talk about today. So David, I've kind of laid it out, but really, where are we in this notion of product to service in some of these business model changes? >> It's an early stage, but it's very, very profound changes going on. We can see it from the amount of business of the cloud business supplies are providing. You can see that Amazon, Google, IBM, and Microsoft Azure, all of those are putting very large resources into creating services to be provided to the business itself. But equally, we are aware that services themselves need to be on premise as well, so we're seeing the movement of true private cloud, for example, which is going to be provided as a service as well, so if we take some examples, like for example, Oracle, the customer, they're a cloud customer, they're providing exactly the same service on premise as they provide in the cloud. >> And by service, you mean in how the customer utilizes the technologies. >> Correct. >> The asset arrangement may be very different, but the proposition of what the customer gets out of the assets are essentially the same. >> Yes, the previous model was, we provide you with a product, you buy a number of those products, you put them together, you service it, you look after it. The new model, here coming in with TPC, with the single throat to choke, is that the vendor will look after the maintenance of everything, putting in new releases, bringing things up to date, and they will have a smaller set of things that they will support, and as a result, it's win-win. It's win for the customer, because he's costs are lower, and he can concentrate on differentiated services. >> And secure and privatize his assets. >> Right, and the vendor wins because they have economies of scale, they can provide it at a much lower cost as well. And even more important to both sides is that the time to value of new releases is much, much quicker, and time to security exposures, time to a whole number of other things, improve with this new model. >> So Jim, when we think about this notion of a services orientation, ultimately, it starts to change the relationships between the customer and the vendor. And the consequence of that is, not surprisingly, that a number of different considerations, whether they be metrics, or other elements, become more important. Specifically we start thinking about the experience that the customer has of using something. Walk us through this kind of transition to an experience-oriented approach to conceiving of whether or not the business model's being successful. >> Right, your customer will now perceive the experience in the context of an entire engagement that is multi-channel, multi-touch point, multi-device, multi-application, and so forth, where they're expecting the same experience, the same value, the same repeatable package of goodies, whatever it is they get from you, regardless of the channel through which you're touching them or they're touching you. That channel may be provided through a private, on-premises implementation of your stack, or through a public cloud implementation of your capability, or most likely through all of the above, combined into a hybrid true private cloud. Regardless of the packaging, and the delivery of that value in the context of the engagement the customer expects it to be, self-service increasingly, predictable, managed by the solution provider, guaranteed with a fast continuous release in update cycle. So, fundamentally it's an experience economy, because the customer has many other options to go to, of providers that can provide them with a good or better experience, in terms of the life cycle of things that you're doing for them. So bottom line, the whole notion of a TPC really gets to that notion that the experience is the most important thing, the cloud experience, that can be delivered on-prem, or can be delivered in the public environment. And that's really the new world. With a multi-cloud is that master sort of a matrix of the seamless cross-channel experience. >> We like to think of the notion of a business model as worrying about three fundamental questions. How are you going to create value? How are you going to deliver value? And how are you going to capture value? Where the creation is how shared it's going to be, it's going to be a network of providers, you're going to have to work with OEMs. The delivery, is it going to be online, is it going to be on-prem? Those types of questions, but this notion of value capture is a key feature, David, of how this is changing. And George, I want to ask you a question. The historical norm is that value capture took place in the form of, I give you a product, you give me cash. But when we start moving to a services-orientation, where the services is perhaps being operated and delivered by the supplier, it introduces softer types of exchange mechanisms, like, how are you going to use my data? Are you going to improve the fidelity of the system by pooling me with a lot of other customers? Am I losing my differentiation? My understanding of customers, is that being appropriated and munged with others to create models? Take us through this soft value capture challenge that a service provider has, and what specifically, I guess actually the real challenge that the customer has as they try to privatize their assets, George. >> So, it's a big question that you're asking, and let me use an example to help sort of make concrete the elaboration, or an explanation. So now we're not just selling software, but we might be selling sort of analytic data services. Let's say, a vendor like IBM works with Airbus to build data services where the aircraft that Airbus sells to its airline customers, that provides feedback data that both IBM has access to, to improve its models about how the aircraft work, as well as that data would also go back to Airbus. Now, Airbus then can use that data service to help its customers with prescriptions about how to operate better on certain routes, how to do maintenance better, not just predictive maintenance, but how to do it more just in time with less huge manuals. The key here is that since it's a data service that's being embedded with the product, multiple vendors can benefit from that data service. And the customer of the traditional software company, so in this case, Airbus being the customer of IBM, has to negotiate to make sure its IP is protected to some extent, but at the same time, they want IBM to continue working with that data feedback because it makes their models richer, the models that Airbus gets access to richer over time. >> But presumably that has to be factored into the contractual obligations of both parties enter into, to make sure that those soft dollars are properly commensurated in the agreements. That's not something that we're seeing a lot in the industry, but the model of how we work closely with our clients and our customers is an important one. And it's likely to change the way that IT thinks about itself as a provider of services. Neil, what kinds of behaviors are IT likely to start exhibiting as it finds itself, if not competing, at least trying to mimic the classes of behaviors that we're seeing from service providers inside their own businesses? >> Yeah, well, IT organizations grew over the last, I dunno, 50 years or so, organically, and it was actually amazing how similar their habits and processes, and ways of doing things were the same across industries, and locations, and so forth. But the problem was that everything they had to deal with, whether they were the computers, or the storage, or the networks, and so forth, were all really expensive. So they were always in a process of managing from scarcity. The business wanted more and more from them, and they had lower and lower budgets, because they had to maintain what they had, so it created a lot of tension between IT and organizations, and because of that, whenever a conversation happened between other groups within the business and IT, IT always seemed to have the last word, no, or okay. Whatever the decision was, it was really IT's. And what I see happening here is, when the IT business becomes less insular, I think a lot of this tension between IT and the rest of the organization will start to dissipate. And that's what I'm hoping will happen, because they started this concept of IT vs the business, but if you went out in an organization and asked 100 people what they did, not one of them would say, "I'm the business," right? They have a function, but IT created this us vs them thing, to protect themselves, and I think that once they're able to utilize external services for hardware, for software, for whatever else they have to do, they become more like a commercial operation, like supply-side, or procurement, or something, and managing those relationships, and getting the services that they're paying for, and I think ultimately that could really help organizations, by breaking down those walls in IT. >> So it used to be that an IT decision to make an investment would have uncertain returns, but certain costs, and there are multiple reasons why those returns would be uncertain, or those benefits would be uncertain. Usually it was because some other function would see the benefits under their umbrella, you know, marketing might see increased productivity, or finance would see increased productivity as a consequence of those investments, but the costs always ended up in IT. And that's one of the reasons why we yet find ourself in this nasty cycle of constantly trying to push costs down, because the benefits always showed up somewhere else, the costs always showed up inside IT. But it does raise this question ultimately of, does this notion of an ongoing services orientation, is it just another way of saying, we're letting a lock in back in the door in a big way? Because we're now moving from a relationship, a sourcing relationship that's procurement oriented, buy it, spend as little money as possible, get value out of it, as opposed to a services orientation, which is effectively, move responsibility for this part of the function off into some other service provider, perpetually. And that's going to have a significant implication, ultimately, on the question of whether or not we buy services, default to services. Ralph, what do you think, where are businesses going to end up on this, are we just going to see everything end up being a set of services, or is there going to be some model that we might use, and I'll ask the team this, some model that we might use to conceive when it should be a purchase, and when it should be a service? What do you think, Ralph? >> Yeah, I think the industry's gravitating towards a service model, and I think it's a function of differentiation. You know, if you're an enterprise, and you're running a hundred different workloads, and 15 of them are things that really don't differentiate you from your competition, or create value that's differentiable in some kind of way, it doesn't make any sense to own that kind of functionality. And I think, in the long run, more and more aspects, or a higher percentage of workload is going to be in that category. There will always be differentiation workloads, there will always be workloads requiring unique kinds of security, especially around transactions. But in the net, the slow march of service makes a lot of sense to me. >> What do you think, guys? Are we going to see, uh, do we agree with Ralph, number one? And number two, what about those exceptions? Is there a framework that we can start to utilize to start helping folks imagine what are the exceptions to that rule, what do you think David? >> Sure, I think that there are circumstances when... >> Well first, do we generally agree with the march? >> Absolutely, absolutely. >> I agree too. >> Yes, fully agree that more and more services are going to be purchased, and a smaller percentage of the IT budget from an enterprise will go into specific purchases of assets. But there are some circumstances where you will want to make sure that you have those assets on premise, that there is no other call on those assets, either from the court, or from difference of priority between what you need and what a service provider needs. So in both those circumstances, they may well choose to purchase it, or to have the asset on the premise so that it's clearly theirs, and clearly their priority of when to use it, and how to use it. So yes, clearly, an example might be, for example, if you are a bank, and you need to guarantee that all of that information is yours, because you need to know what assets are owned by who, and if you give it to a service provider, there are circumstances where there could be a legal claim on that service provider, which would mean that you'll essentially go out of business. So there are very clear examples of where that could happen, but in general, I agree. There's one other thing I'd like to add to this conversation. The interesting thing from an IT point of view, an enterprise IT, is that you'll have fewer people to do business with, you'll be buying a package of services. So that means many of the traditional people that you did business with, both software and hardware, will not be your customers anymore, and they will have to change their business models to deal with this. So for example, Permabit has become an OEM supplier of capabilities of data management inside. And Kaminario has just announced that it's becoming a software vendor. >> Nutanix. >> Nutanix is becoming a software vendor, and is either allowing other people to take the single throat to choke, or putting together particular packages where it will be the single throat to choke. >> Even NetAct, which is a pretty consequential business, has been been around for a long time, is moving in this direction. >> Yes, a small movement in that direction, but I think a key question for many of these vendors are, do I become an OEM supplier to the... >> Customer owner. >> The customer owner. Or what's my business model going to be? Should I become the OEM supplier, or should I try and market something directly in some sort of way to the vendors? >> Now this is a very important point, David, because one of the reasons, for a long time, why the OEM model ran into some challenges, is precisely over customer ownership. But when data from operations of the product, or of the service is capable of flowing, not only to the customer engagement originator, but also to the OEM supplier, the supplier has pretty significant, the OEM company has pretty significant visibility, ultimately, into what is going on with their product. And they can use that to continuously improve their product, while at the same time, reducing some of the costs associated with engagement. So the flowing of data, the whole notion of digital business allows a single data about operation to go to multiple parties, and as a consequence, all those parties now have viable business models, if they do it right. >> Yeah, absolutely. And Kaminario will be be a case in point. They need metadata about the whole system, as a whole, to help them know how to apply the best patches to their piece of software, and the same is true for other suppliers of software, the Permabit, or whoever those are, and it's the responsibility of that owner or the customer to make sure that all of those people can work in that OEM environment effectively, and improve their product as well. >> Yeah, so great conversation guys. This is a very, very rich and fertile domain, and I think it's one that we're going to come back to, if not directly, at least in talking about how different vendors are doing things, or how customers have to, or IT organizations have to adjust their behaviors to move from a procurement to a strategic sourcing set of relationships, etc. But what I'd like to do now, as we try to do every week, is getting to the Action Item round, and I'm going to ask each of you guys to give me, give our audience, give our users, the action item, what do they do differently on next Monday as a consequence of this conversation? And George Gilbert, I'm going to start with you. George, action item. >> Okay, so mine is really an extension of what we were talking about when I was raising my example, which is your OEM supplier, let's say IBM, or a company we just talked to recently, C3 IoT, is building essentially what are application data services that would accompany your products that you, who used to be a customer, are selling a supply chain master, say. So really trying to boil that down is, there is a model of your product or service could be the digital twin, and as your vendor keeps improving it, and you offer it to your customers, you need to make sure that as the vendor improves it, that there is a version that is backward compatible with what you are using. So there's the IP protection part, but then there's also the compatibility protection part. >> Alright, so George, your action item would be, don't focus narrowly on the dollars being spent, factor those soft dollars as well, both from a value perspective, as well an ongoing operational compatibility perspective. Alright, Jim Kobielus, action item. >> Action item's for IT professionals to take a quick inventory of what of your assets in computing you should be outsourcing to the cloud as services, it's almost everything. And also, to inventory, what of your assets must remain in the form of hard discreet tangible goods or products, and my contention is that, I would argue that the edge, the OT, the operational technology, the IOT, sensors and actuators that are embedded in your machine tools and everything else, that you're running the business on, are the last bastion of products in this new marketplace, where everything else becomes a service. Because the actual physical devices upon which you've built your OT are essentially going to remain hard tangible products forevermore, of necessity, and you'll probably want to own those, because those are the very physical fabric of your operation. >> So Jim, your action item is, start factoring the edge into your consideration of the arrangements of your assets, as you think about product vs services. >> Yes. >> Neil Raden, action item. >> Well, I want to draw a distinction between actually, sorry, between actually, ah damn, sorry. (laughs) >> Jim: I like your fan, Neil. >> Peter: Action item, get your monitor right. >> You know. I want to draw the distinction between actually moving to a service, as opposed to just doing something that's a funding operate. Suppose we have 500 Oracle applications in our company running on 35 or 40 Oracle instances, and we have this whole army of Oracle DBAs, and programmers, and instance tuners, and we say well, we're going to give all the servers to the Salvation Army, and we're going to move everything to the Oracle cloud. We haven't really changed anything in the way the IT organization works. So if we're really looking for change in culture and operation, and everything else, we have to make sure we're thinking about how we're changing, reading the way things get done and managed in the organization. And I think just moving to the cloud is very often just a budgetary thing. >> So your action item would be, as you go through this process, you're going to re-institutionalize the way you work, get ready to do it. Ralph Finos, action item. >> Yeah, I think if you're a vendor, if you're an IT industry vendor, you kind of want to begin to look a lot like, say, a Honda or Toyota in terms of selling the hardware to get the service in the long term relationship in the lock-in. I think that's really where the hardware vendors, as one group of providers, is going to want to go. And I think you want, as a user and an enterprise, I think you're going to want to drive your vendors in that direction. >> So your action item would be, for a user anyway, move from a procurement orientation that's focused on cost, to a vendor management orientation that's focused on co-development, co-evolution of the value that's being delivered by the service. David Floyer, action item. >> So my action item is for vendors, a whole number of smaller vendors. They have to decide whether they're going to invest in the single most expensive thing that they can do, which is an enterprise sales force, for direct selling of their products to enterprise IT, and-or whether they're going to take an OEM type model, and provide services to a subset, for example, to focus on the cloud service providers, which Kaminario are doing, or focus on selling indirectly to all of the, the vendors who are owning the relationship with the enterprise. So that, to me, is a key decision, very important decision as the number of vendors will decline over the next five years. >> Certainly, what we have, visibility to what we have right now, so your action item is, as a small vendor, choose whose sales force you're going to use, yours or somebody else's. >> Correct. >> Alright. So great conversation guys. Let me kind of summarize this a bit. This week, we talked about the evolving business models in the industry, and the basic notion, or the reason why this has become such an important consideration, is because we're moving from an era where the types of applications that we were building were entirely being used internally, and were therefore effectively entirely private, vs increasingly trying to extend even those high-volume transaction processing applications into other types of applications that deliver things out to customers. So the consequence of the move to greater integration, greater external delivery of things within the business, has catalyzed this movement to the cloud. And as a consequence, this significant reformation, from a product to a services orientation, is gripping the industry, and that's going to have significant implications on how both buyers and users of technology, and sellers and providers of technology are going to behave. We believe that the fundamental question is going to come down to, what process are you going to use to create value, with partnerships, go it alone? How are you going to deliver that value, through an OEM sales force, through a network of providers? And how are you going to capture value out of that process, through money, through capturing of data, and more of an advertising model? These are not just questions that feature in the consumer world, they're questions that feature significantly in the B2B world as well. Our expectations, over the next few years, we expect to see a number of changes start to manifest themselves. We expect to see, for example, a greater drive towards experience of the customer as a dominant consideration. And today, it's the cloud experience that's driving many of these changes. Can we get the cloud experience, both the public cloud, and on premise, for example? Secondly, our expectations that we're going to see a lot of emphasis on how soft exchanges of value take place, and how we privatize those exchanges. Hard dollars are always going to flow back and forth, even if they take on subscription, as opposed to a purchase orientation, but what about that data that comes out of the operations? Who owns that, and who gets to lay claim to future revenue streams as a consequence of having that data? Similarly, we expect to see that we will have a new model that IT can use to start focusing its efforts on more business orientation, and therefore not treating IT as the managers of hardware assets, but rather managers of business services that have to remain private to the business. And then finally, our expectation is that this march is going to continue. There will be significant and ongoing drive to increase the role that a service's business model plays in how value is delivered, and how value is captured. Partly because of the increasing dominant role that data's playing as an asset in digital business. But we do believe that there are some concrete formulas and frameworks that can be applied to best understand how to arrange those assets, how to institutionalize and work around those assets, and that's a key feature of how we're working with our customers today. Alright, once again, team, thank you very much for this week's Action Item. From theCUBE studios in beautiful Palo Alto, I want to thank David Floyer, George Gilbert, Jim Kobielus, Neil Raden, and Ralph Finos, this has been Action Item.

>> Announcer: Live, from Las Vegas, it's the Cube, covering AWS Re:invent 2017 presented by AWS, Intel, and our ecosystem of partners. >> Welcome back everyone, we're here live in Las Vegas for 45,000 tech industry folks and customers with Amazon re:Invent 2017. This is the Cube's exclusive coverage, I'm John Furrier, with my co-host Justin Warren this segment. Our next guest, Bill Magro, is the Chief Technologist for Intel covering HPC high performance computing. Bill, welcome to the Cube. >> Thank you. >> John: Thanks for coming on. You guys, your booth's behind us, I don't if they can see it in the wide shot, but Intel is really taking advantage of the I don't want to say Intel inside the Cloud 'cause that's really what you guys are doing, but you got so much compute, this is your wheelhouse. Compute is what Intel is. >> Bill: Right. >> Andy Jassy at AWS, talking with their customers, they want more compute, edge of the network, so HPC, high performance computing's been around for awhile. What's the state of the art and how should people think about HPC versus the Cloud, are they the same, what's the relationship? >> Intel actually thinks of HPC or high performance computing more in terms of the activity and the workloads than the infrastructure that it runs on. So very early in the days of Cloud computing, there were a lot of people who said that the Cloud was kind of the opposite of HPC and therefore, they could never go together. But we think of Cloud as a delivery vehicle, a way to get access to compute storage networking and HPC is what you're doing. And so then, if you think about HPC as kind of a range of workloads, you can start to think about which ones are a good fit for the Cloud, and which ones aren't. So we talk a little bit about the high performance computing and tailored infrastructure for the most extreme cases of HPC. That's where you see the biggest differences with Cloud, 'cause they're at opposite ends of the spectrum. >> But you see holistically the Cloud is interplaying with HPC. >> Yeah. >> They're not mutually exclusive. >> Absolutely, we see Cloud as a way to deliver HPC capabilities. So if you think of the most demanding HPC problems, the ones that are used in national security, that are used to design commercial airplanes, and so on, those are some of the hardest problems. Predicting the climate change, predicting the weather, paths of hurricanes, those are what we call grand challenge problems. Those are not running in the Cloud. Those are running on dedicated, tailored, infrastructure built for high performance computing at that extreme. And those systems have a lot of characteristics such as very high performance networks, different from ethernet, custom topologies and are designed with software to really minimize variation because it's one large problem that has to move forward. The Cloud is kinda the opposite in a sense. It started as taking a large amount of resources and making it possible to carve them up, right. It's the opposite of aggregating resources. And so that's where a lot of the early thoughts of Cloud and HPC being at odds with each other. >> It seems to be a dream scenario because I mean, in the old days, in 80s and 90s when I was breaking into the business. If you were a database guy or a compute guy, you were a specialist, it was high end kind of computing. Moore's Law, certainly Intel, you guys took advantage of it. But now, you see so much, it's cool to do more compute. So like, it's been democratized. Databases and compute, certainly in all the conversations, for everybody, not just, the technologists. >> Right, where that's where Cloud fits in for HPC. So if you think of HPC in terms of the characteristics of the workload, it's something that's really demanding computationally. The product of the computation is like an intellectual insight. You can design a better airplane wing, a safer car, you can figure out where that hurricane is going and tell which people to evacuate. There's an intellectual product to the compute. And then the last characteristic is when you apply more compute power appropriately, you get a more valuable result. So it could be better prediction of that hurricane path, it could be a safer car because you have more time, you have more capability and were able to build a better design ahead of that deadline to get that model year of the car out. And so, if you think about that, there's a lot, there's a broad spectrum and I talked about some of those most extreme problems, but even in something like designing an airplane, there might be 16, 20, a hundred different small design variations you want to explore. Well those can actually be great for The Cloud, 'cause they're small calculations and you run many of them at the same time. And the elastic capability of The Cloud augments the supercomputer that you might be using to run your hardest problems. >> So the aperture of problem-solving is huge now. >> Bill: That's right. >> You can do more. I mean we had Thorn on yesterday. Thorn was a company that partners with Intel to do, you know, find missing and exploited children. AI for good, so everything's possible. >> Yeah even AI we think of as an example of a high performance computing workload because what does it do? It gives you insights that you didn't have otherwise, it's compute intensive, and it does better when you apply more resources. So that fits our definition. So AI is definitely under the umbrella of high performance computing. >> One of the things, one of the great benefits of Cloud is the elasticity which you mentioned before. It's like, and some of the, we know that Amazon's just brought out the C5 Instances which is a specific instance site, which would be quite useful for HPC. But what is it about the bursting workloads or that elasticity that specifically works well for HPC do you think? >> Well, there's a couple use cases that we think are particularly relevant. One of them is an existing company. Just imagine some Fortune 50 manufacturer. They have a lot of stuff that they really need to build their own supercomputer for, their own high performance computing system, but their usage, even though they keep that system busy all the time, there is some variability and they have opportunity costs of an engineer sitting while their job is in the queue, 'cause you're paying that engineer but you're not giving them insights, right. And so the Cloud can augment that, but we have a lot of examples of large Fortune 500, Fortune 50 companies augmenting their on-premise with Cloud as a way to push those workloads that can run on the Cloud there, to free up those on-prem resources which are much more tailored, much more expensive and get more value out of them. >> Okay, and what's Intel doing to help customers figure out which of those workloads is best suited for Cloud and which ones are better suited for something which is running on site? >> Well, it's mostly through our influencer sales force who engages with many, many major companies and provides consulting, because Intel doesn't sell computers directly to anyone, so it's more of a knowledge, our knowledge and sharing that with people. And what we're trying to help enterprises understand is what workloads need to stay on premise, which ones can go to the Cloud and how this, the elasticity of the Cloud can augment those on-premise resources and thus, you know, go back and forth. >> It's the classic mission for Intel, make the apps go faster, faster, smaller, cheaper, right. >> And get 'em land in the right place. So really, the two biggest considerations we find in deciding whether a workload goes into the Cloud or stays on-premise in high performance computing are the following, one, is really the sensitivity of the IP. There's a lot of workloads that could run in the Cloud and people simply want to keep it on-premise 'cause they're more comfortable knowing that their IP is sitting inside their own firewall. Though the reality is, more and more companies are getting comfortable with Cloud security as they see data breaches. And realize that some of the big Cloud providers, like Amazon, maybe have better access to the security talent than they do. >> I think Goldman-Sachs just announced they're going all in. That's Goldman-Sachs, they never do a testimonial. >> So the privacy and the sensitivity of the data is king, you know, you have to be willing to put it in the Cloud. Then the second question is, is it a technical fit? And that's where this spectrum of workloads comes in. The bigger a workload goes and the more you want to speed it up but keep the workload the same size, that's what we call strong scaling and that starts to stress the network, and stress the system. And that's where these tailored systems come in. And so, you have to look at where things fall on the spectrum. A good example of workloads that would fit is these design space explorations, anything we would call pleasingly parallel or embarrassingly parallel in the industry where the communication does happen, but it's not the limiter of the calculation. So screening for a drug candidates, for personalized medicine, lot of life sciences applications, financial services is a good fit, in manufacturing a design space exploration maybe for different designs and materials for a dashboard or a component of a car. >> Bill, when you were at your Thanksgiving dinner and your family or wherever, you're moving around in your personal life, you're a technologist. How do explain the phenomenon of Amazon Web Services and the Cloud action right now? Because, you know, you're in it every day. You're close to all the action. But I get asked all the time, what's the hub-bub about AWS and it's hard to explain the phenomenon. How would describe the, I mean you're talking about tailored systems, elasticity, I mean it's a tech dream. I mean, how do you explain it to like a normal person? >> The conversation's usually pretty short because my family involves a historian, an English major, an accountant and people who really couldn't have, a musician, a singer, people who really don't have the slightest interest in technology. >> It's hard to talk about lambda, when you're. >> So I'm really the only technologist in my family so I just avoid it, but the question does come up with my parents. You know, parents like to brag on their kids so they like to know what you do, and every year my mom asks me what I do and I try to explain high performance computing to her and she says, oh, I don't get it. But when you explain it in terms of things like climate modeling and being able to support the nuclear test ban that's worldwide, that's done with high performance computing. Safing cars, finding missing children, better quality of life through all the AI that we're now experiencing. >> John: Analytics is a great use case. >> Then people say, oh, you know, they can understand the use cases. The elasticity of the Cloud, really is not something that I discuss with family, but even coworkers, I think, that's what the conversation focuses on. Recognizing that high performance computing is a range of workloads. >> Okay, so I'll rephrase it differently. What's your perspective on, what observations that you get excited about that are enabled now by these new use cases? 'Cause there's new things now that are possible. The number of computations, you got analytics, you mentioned a few of them. What jumps out at you, wow, that's really awesome, we can do that now? >> You know, this is gonna sound a little odd, and maybe not what you expected, but I'm not actually a technology enthusiast, believe it or not, despite. I think technology's cool, I like what it does, but I don't get super excited about technology. One of the things that I'm excited about with the Cloud is probably at the opposite extreme of what you would expect which is, back to, how does the elasticity of the Cloud fit? There's so many companies in this world who could benefit from high performance computing and don't today. A recent study showed that 95 percent of U.S. small medium manufacturers which is over 300,000 are not using HPC today. And so, as they're part of this supply chain, whether it's into a Boeing or and Airbus or a Lockheed Martin or a Honda or a Toyota, there's this whole supply chain. HPC's being used at the top, it's not being used at the bottom, so I think the Cloud is actually really, really exciting because it allows somebody to get over those initial hurdles, the cap-ex, getting access to pay as you go, prove the value proposition, because a small medium business actually has to take a risk to use HPC. They have to divert capital and divert resources. And they could lose a contract. >> So do you see a lot more companies starting to take advantage of some of this high performance computing capability just because it's now, you can rent it by the hour and try it out, give it a bit of a whirl, and then see, actually this is going to be really valuable for us, and then deploy a lot more of it. >> Exactly and that's one of the key things we're promoting is 'cause we want to bring more people into the world of high performance computing. So, AWS provides all the building blocks. Compute, elastic storage and so on. But high performance computing applications really expect a specific type of platform that they can run on, and that platform aggregates the resources so there's a number of companies Rescale is one, Psycho-Computing, and others who are actually providing that platform layer. And then once you've got the platform layer, all the, I'll call it the, geeky stuff that they do, AWS has abstracted away. Now the applications can run and that's that's what's bringing new users in. >> Bill, final question for you. AWS launched its C5 Instances. What's that about, what's it mean for customers? Can you explain a little bit more on that one piece? >> Sure, we're delighted to see Amazon deploying the C5 Instances. It's based on our latest technology in the Xeon product family. We call that the Intel Xeon scalable processor family. It includes, it's based on what we call Skylake technology or code name Skylake. There's a lot of innovations in that processor and that platform that are specifically driven by the needs of high performance computing. There's something called AVX 512, which is a doubling of the vector width. Means that every core can actually do 32 floating point, double-precision floating point operations per clock. That's tremendous, tremendous compute capability, in a 2X over the previous generation. On the memory bandwidth side, which is another huge factor for high performance computing applications, like 66 percent increase in memory bandwidth. So it's a balanced platform, and we're seeing improvements in high performance computing apps of anywhere from 1.7x sometimes almost up to 5x improvement in going from the C4 to C5 Instances on a per note basis. >> This is really going to enable a lot of action. IOT, tons of great stuff. >> Absolutely and as I talked about that range of HPC and you know, what fits and what doesn't fit in the Cloud, every generation of technology, what fits in the Cloud is growing, and C5 is another important step in that direction. >> Bill, thanks for coming on the Cube. Bill Magro, Chief Technologist at Intel, HPC, high performance computing. The Cloud is one big high performance machine in the sky, wherever you want to look at it, really great opportunity at enabling all new use cases, doing things for society benefits, and customers. Great stuff here, Cloud impact is significant. IOT to the Cloud. This is the Cube, doing our share here at AWS in Las Vegas. We'll be right back with more coverage after this short break. (electronic music)

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.

>> Narrator: From the Fairmont Hotel in the heart of Silicon Valley it's theCUBE covering when IoT met AI, The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back here everybody Jeff Frick here with theCUBE. We're in San Jose, California at the Fairmont Hotel, at the when IoT met AI show, it's all about the intelligence of things. A lot of really interesting start ups here, we're still so early days in most of this technology. Facial recognition gets a lot of play, iris recognition, got to get rid of these stupid passwords. We're really excited to have our next guest, he's Modar Alaoui, he's the CEO and founder of Eyeris. And it says here Modar that you guys are into face analytics and emotion recognition. First off welcome. >> Thank you so much for having me. >> So face analytics, I'm a clear customer I love going to clear at the airport, I put my two fingers down, I think they have my iris, they have different things but what's special about the face compared to some of these other biometric options that people have? >> We go beyond just the biometrics, we do pretty much the entire suites of face analytics. Anything from eye openness, face, gender, emotion recognition, head bows, gaze estimation, et cetera et cetera. So it is pretty much anything and everything you can derive from the face including non verbal clues, yawning, head nod, head shake, et cetera. >> That was a huge range of things, so clearly just the face recognition to know that I am me probably relatively straight forward. A couple anchor points, does everything measure up and match the prior? But emotion that's a whole different thing, not only are there lots of different emotions, but the way I express my emotion might be different than the way you express the very same emotion. Right, everybody has a different smile. So how do you start to figure out the algorithms to sort through this? >> Right, so you're right. There are some nuances between cultures, ages, genders, ethnicities and things like that. Generally they've been universalized for the past three and a half decades by the scholars the psychologists et cetera. So what they actually have a consensus on is that there are only seven or six universal emotions plus neutral. >> Six, what are the six? >> Joy, surprise, anger, disgust, fear, sadness, and neutral. >> Okay and everything is some derivation of that, you can kind of put everything into little buckets. >> That is correct so think of them as seven universal colors or seven primary colors and then everything else is a derivative of that. The other thing is that emotions are hard wired into our brain they happen in a 1/15th or a 1/25th of a second, particularly micro expressions. And they can generally give up a lot of information as to whether a person has suppressed the certain emotion or not or whether they are thinking about something negatively before they could respond positively, et cetera. >> Okay so now you've got the data, you know how I'm feeling, what are you doing with it? It must tie back to all types of different applications I would assume. >> That's right there are a number of applications. Initially when we created this, what we call, enabling technology we wanted to focus on two things. One, is what type of application could have the biggest impact but also the quickest adoption in terms of volumes. Today we focus on driver monitoring AI as well as occupants monitoring AI so we focus on Autonomous and semi autonomous vehicles. And a second application is social robotics, but in essence if you think of a car it's also another robot except that social robotics are those potentially AI engines, or even AI engines in form of an actual robot that communicates with humans. Therefore, the word social. >> Right, so I can see a kind of semi autonomous vehicle or even a not autonomous vehicle you want to know if I'm dosing off. And some of those things have been around in a basic form for a little while. But what about in an autonomous vehicle is impacted by my emotion as a passenger, not necessarily a driver if it's a level five? >> That's right, so when we talk about an autonomous vehicle I think what you're referring to is level five autonomy where a vehicle does not actually have a steering wheel or gas pedal or anything like that. And we don't foresee that those will be on a road for at least another 10 years or more. The focus today is on level two, three, and four, and that's semi autonomy. Even for autonomous, fully autonomous vehicles, you would see them come out with vision sensors or vision AI inside the vehicle. So that these sensors could, together with the software that could analyze everything that's happening inside, cater to the services towards what is going to be the ridership economy. Once the car drives itself autonomously, the focus shifts from the driver to the occupants. As a matter of a fact it's the occupants that would be riding in these vehicles or buying them or sharing them, not the driver. And therefore all these services will revolve around who is inside the vehicle like age, gender emotion, activity, et cetera. >> Interesting, so all these things the age, gender emotion, activity, what is the most important do you think in terms of your business and kind of where as you say you can have a big impact. >> We can group them into two categories, the first one is safety obviously, eye openness, head bows, blinking, yawning, and all these things are utmost importance especially focused on the driver at this point. But then there is a number of applications that relates to comfort and personalization. And so those could potentially take advantage of the emotions and the rest of the analytics. >> Okay, so then where are you guys, Eyeris as a company? Where do have some installations I assume out there? Are you still early days kind of? Where are you in terms of the development of the company? >> We have quite a mature product, what I can disclose is we have plans to go into mass production starting 2018. Some plans for Q4 2017 have been pushed out. So we'll probably start seeing some of those in Q1, Q2 2018. >> Okay. >> We made some announcements earlier this year at CS with Toyota and Honda. But then we'll be seeing some mass volume starting 2019 and beyond. >> Okay, and I assume you're a cloud based solution. >> We do have that as well, but we are particularly a local processing solution. >> Jeff: Oh you are? >> Yes so think of it as an edge computing type of solution. >> Okay and then you work with other peoples sensors and existing systems or are you more of a software component that plugs in? Or you provide the whole system in terms of the, I assume, cameras to watch the people? >> So we're a software company only, we however, are hardware processor camera diagnostic. And of course for everything to succeed there will have to be some components of sensor fusion. And therefore we can work and do work with other sensor companies in order to provide higher confidence level of all the analytics that we provide. >> Pretty exciting, so is it commercially available you're GA now or not quite yet? >> We'll be commercially available, you'll start seeing it on the roads or in the market sometime early next year. >> Sometime early next year? Alright well we will look forward to it. >> Thank you so much. >> Very exciting times, alright, he's Modar Alaoui. And he's going to be paying attention to you to make sure you're paying attention to the roads. So you don't fall asleep, or doze off and go to sleep. So I'm Jeff Frick, you're watching theCUBE at IoT met AI, The Intelligence of Things. San Jose, California, we'll be right back after this short break, thanks for watching. (bright techno music)

>> Announcer: It's The Cube. Covering HPE Big Data Conference 2016. Now here are your hosts, Dave Vellante and Paul Gillin. >> Welcome back to Boston, everybody, this is The Cube, we're here live at HP's big data conference, hashtag seize the data. Steve Spear is here, he's an author, MIT professor, author of The High Velocity Edge, welcome to The Cube, thanks for coming on. >> Oh, thanks for having me. >> I got to tell you, following Phil Black, you were coming onstage, I have never heard you speak before, I said, "Oh, this poor guy," and you did awesome, you were great, you held the audience, so congratulations, you were very dynamic and he was unbelievable and you were fantastic, so. >> Today was second-worst speaking setup, one time I was on a panel where it was three admirals, a general, and then the other guy wearing a suit, I said, "Well at least another schmo in a suit," and his opening lines were, "You know, this reminds me, "when I was on the space shuttle and we were flying "to the Hubble," and I'm like, "A flipping astronaut, "I got to follow an astronaut?" So anyway, this was only a SEAL, there were a lot of them, there were far fewer astronauts, so that was easy. >> What I really liked about your talk is, first of all, you told the story of Toyota, which I didn't know, you may. >> No, my experience with Toyota was in the early '70s, I remember the Toyota sort of sweeping into the market but you talked about 20 years before it when they were first entering and how this really was a company that had a lot of quality problems and it was perceived as not being very competitive. >> Yeah, Toyota now people look at as almost, they just take for granted the quality, the productivity, they assume good labor relations and that kind of thing, it's non-unionized, not because the unions haven't tried to unionize, but the employees don't feel the need. And again, in the '50s, Toyota was absolutely an abysmal auto-maker, their product was terrible, their productivity was awful and they didn't have particularly good relations with the workforce either. I mean, it's a profound transformation. >> And you gave this test, in the 50s, I forget what it was, it was one-tenth the productivity of the sort of average automobile manufacturer and then they reached parity in '62, by '68 they were 2X, and by '73, they were off the charts. >> Right, right, right. >> Right, so amazing transformation and then you try to figure out how they did it and they couldn't answer, but they said, "We can show you," right? And that sort of led to your research and your book. >> Yeah, so the quick background is in some regards, this fellow Kenneth Bowen, who was my mentor and advisor when I was doing my doctorate, he could argue we were late to the game because people started recognizing Toyota as this paragon of virtue, high quality at low cost, and so that in the 1980s prompted this whole investigation and the term lean manufacturing came out of the realization that on any given day, Toyota and suppliers were making basically twice the product with half the effort and so you had this period of '85 to about '95 where there was this intense attempt to study Toyota, document Toyota, imitate Toyota, General Motors had a joint venture with Toyota, and then you have the mid-'90s and there's no second Toyota, despite all this investment, so we go to the Toyota guys and say, "Look, clearly if everyone is studying you, imitating you, "copying you, and they haven't replicated you, "they've missed something, so what is it?" And they say, "I'm sorry, but we can't tell you." And we said, "Well you got to be kidding, I mean, "you have a joint venture with your biggest competitor, "General Motors," and they said, "No, no, it's not that we wouldn't tell you, "we just actually don't know how to explain what we do "'cause most of us learn it in this very immersive setting, "but if you'd like to learn it, "you can learn it the way we do." I didn't realize at the time that it would be this Karate Kid wax-on, wax-off, paint-up, paint-down experience, which took years and years to learn and there are some funny anecdotes about it but even at the end, their inability to say what it is, so I went years trying to capture what they were doing and realizing I was wrong 'cause different things wouldn't work quite right, and I can tell you, I was on the Shinkansen with the guy who was my Toyota mentor and I finally said, "Mr. Oba, I think I finally "figured it out, it all boils down to these basic "approaches to seeing and solving problems." And he's looking over my cartoons and stuff and he says, "Well, I don't see anything wrong with this." (laughs) >> That was as good as it got. >> That was as good as it got, I was like, "Score, nothing wrong that he can see!" So anyway. >> But so if you talk about productivity, reliability, you made huge gains there, and the speed of product cycles, were the three knobs that Toyota was turning much more significantly than anybody else and then fuel efficiency came. >> Right, so if you start looking at Toyota and I think this is where people first got the attraction and then sort of the dismissive of, we don't make cars, so the initial hook was the affordable reliability, they could deliver a much higher-quality car, much more affordable based on their productivity. And so that's what triggered attention which then manifest itself as this lean manufacturing and its production control tools. What then sort of started to fall off people's radar is that Toyota not only stayed ahead on those dimensions but they added to the dimensionality of the game, so they started introducing new product faster than anybody else and then they introduced new brand more successfully so all the Japanese, Nissan, Honda, Toyota, all came out with a luxury version, but no one came out with Lexus other than Toyota. The Affinity and the Acura, I mean, it's nice cars, but it didn't become this dominant brand like the Lexus. And then in trying to hit the youth market, everyone tried to come up with, like Honda had the Element but nothing like the Scion, so then Toyota's, and that's much further upstream, a much more big an undertaking than just productivity in a factory. And then when it came time to this issue around fuel efficiency, that's a big technology play of trying to figure out how you get these hybridized technologies with a very very complex software engineering overlay to coordinate power flow in this thing and that, and everyone has their version of hybrid, but no one has it through six generations, 21 platforms, and millions of copies sold. So it didn't matter where you were, Toyota figured out how to compete on this value to market with speed and ease which no one else in their industry was replicating. >> You're talking about, this has nothing to do with operational efficiency, when you talk about the Scion for example, you're talking about tapping into a customer, into an emotional connection with your customer and being able to actually anticipate what they will want before they even know, how do you operationalize that? >> So I think, again, Toyota made such an impression on people with operational efficiency that a lot of their genius went unrecognized, so what I was trying to elaborate on this morning is that Toyota's operational efficiency is not the consequence of just more clever design of operations, like you have an algorithm which I lack and so you get to a better answer than I do, it was this very intense almost empathetic approach to improving existing operations, so you're working on something and it's difficult so we're perceptive of that difficulty and try to understand the source of that difficulty and resolve it, and just do that relentlessly about everything all the time, and it's that empathy to understand your difficulty which then becomes the trigger for making things better, so as far as the Scion comes in, what you see is the same notion of empathic design apply to the needs of the youth market. And the youth market unlike the folks who are, let's say at the time, middle-aged, was less about reliable affordability, but these were people who were coming of age during the Bannatyne era where, very fast mass customization or the iPod era, which was common Chassis but very fast, inexpensive personalization and the folks at Toyota said, "You know what, "the youth market, we don't really understand that, "we've been really successful for this older mid-market, "so let's try to understand the problems that the youth "are trying to solve with their acquisitions," and it turned out personalization. And so if you look at the Scion, it wasn't necessarily a technically or technologically sophisticated quote-unquote sexy product, what it did was it leant itself towards very diverse personalization, which was the problem that the youth market was trying to solve. And you actually see, if I can go on this notion of empathic design, so you see this with the Lexus, so I think the conventional wisdom about luxury cars was Uber technology and bling it, throw chrome and leather and wood and when Toyota tried that initially, they took what was I guess now the Avalon, full-sized car, and they blinged it up and it was contradictory 'cause if you're looking for a luxury car, you don't go to a Toyota dealer, and if you go to a Toyota dealer and you see something with chrome and leather and wood veneer, you're like, you have dissonance. So they tried to understand what luxury meant from the American consumer perspective and again, it wasn't, you always wish you'd get this job, but they sent an engineering team to live in Beverly Hills for some months. (laughs) It's like, ooh, twist my arm on that one, right? But what they found was that luxury wasn't just the physical product, it was the respectful service around it, like when you came back to your hotel room, you walked in, people remembered your name or remembered that, oh we noticed that you used a lot of bath towels so we made sure there were extra in your room, that sort of thing, and if you look at the Lexus, and people were dismissive of the Lexus, saying, "It looks like slightly fancier Toyota, "but what's the big deal, it's not a Beamer or Mercedes." But that wasn't the point, it was the experience you got when you went for sales and service, which was, you got treated so nice, and again, not like hoity toity but you got treated respectfully, so anyway, it all comes back to this empathic design around what problem is the customer or someone inside a plan trying to solve. >> So Toyota and Volkswagen trying to vie for top market share but Toyota, as you say, has got this brand and this empathy that Volkswagen doesn't. You must get a lot of questions about Tesla. Thoughts on Tesla. >> Yeah, cool product, cool technology and time will tell if they're actually solving a real problem. And I don't mean to be dismissive, it's just not an area where I've spent a lot of time. >> And we don't really know, I mean, it's amazing and a software-defined automobile and autonomous, very difficult to predict, we're very tight on time. >> All the cool people seem to drive them though. >> Yeah, that's true. Last question I have is, what the heck does this have to do with analytics at a conference like this? >> Right, so you start thinking about the Toyota model, really, it's not that you can sit down and design something right, it's that you design things which you know deep-rooted in your DNA is that what you've designed is wrong, and that in order to get it right and actually much righter than anything else in the marketplace, what you need to do is understand what's wrong about it and so the experience of the user will help inform what's wrong, the worker rounds they do, the inconveniences they experience, the coping, the compensation they do, and that you can not only use that to help inform what's wrong, but then help shape your understanding of how to get to right, and so where all this fits in is that when you start thinking about data, well first of all, these are gigantic systems, right, which it's probably well-informed to think in terms of these systems are being designed by flawed human beings so the systems themselves have flaws, so it's good to be attentive to the flaws that are designed in it so you can fix them and make them more usable by your intended clientele. But the other thing is that these systems can help you gain much greater precision, granularity, frequency of sampling and understanding of where things are misfiring sooner than later, smaller than larger, so you can adjust and adapt and be more agile in shaping the experience. >> Well Steve, great work, thanks very much for coming on The Cube and sharing and great to meet you. >> Yeah likewise, thanks for having me. >> You're welcome. Alright, keep it right there, everybody, Paul and I will be back with our next guest, we're live from Boston, this is The Cube, we'll be right back. (upbeat music)