>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all, as we know, data is changing the world, and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to the Cube's presentation of "Democratizing Analytics Across the Enterprise," made possible by Alteryx. An Alteryx-commissioned IDC InfoBrief entitled, Four Ways to Unlock Transformative Business Outcomes From Analytics Investments, found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special Cube presentation, Jason Klein, Product Marketing Director of Alteryx, will join me to share key findings from the new Alteryx-commissioned IDC Brief, and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, Chief Data and Analytics Officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then, in our final segment, Paula Hansen, who is the President and Chief Revenue Officer of Alteryx, and Jacqui Van der Leij-Greyling, who is the Global Head of Tax Technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, Product Marketing Director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research which spoke with about 1500 leaders? What nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees. And this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity, and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics. And we're able to focus on the behaviors driving higher ROI. >> So the InfoBrief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the InfoBrief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack what's driving this demand, this need for analytics across organizations? >> Sure, well, first, there's more data than ever before. The data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins, and to improve customer experiences. And analytics, along with automation and AI, is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> Yet not all analytics spending is resulting in the same ROI. So, what are some of the discrepancies that the InfoBrief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow, and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead, they're relying on outdated spreadsheet technology. Nine out of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone, regardless of skill level, should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically then, what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value >> from their data and analytics and achieved more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics, across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture, and this begins with people. But we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources compared to only 67% among the ROI laggards. >> So interesting that you mentioned people. I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand. We know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right. So analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also, among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well, compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively, and letting them do so cross-functionally >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side, and is expected to spend more on analytics than other IT. What risks does this present to the overall organization? If IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this is because the lines of business have recognized the value of analytics and plan to invest accordingly. But a lack of alignment between IT and business, this will negatively impact governance, which ultimately impedes democratization and hence, ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more, you know, on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up an Alteryx environment. But also to take a look at your analytics stack, and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process and technologies. Jason, thank you so much for joining me today, unpacking the IDC InfoBrief and the great nuggets in there. Lots that organizations can learn, and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you. It's been a pleasure. >> In a moment, Alan Jacobson, who's the Chief Data and Analytics Officer at Alteryx, is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching the Cube, the leader in tech enterprise coverage. (gentle music)

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all, as we know, data is changing the world, and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to the Cube's presentation of "Democratizing Analytics Across the Enterprise," made possible by Alteryx. An Alteryx-commissioned IDC InfoBrief entitled, Four Ways to Unlock Transformative Business Outcomes From Analytics Investments, found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special Cube presentation, Jason Klein, Product Marketing Director of Alteryx, will join me to share key findings from the new Alteryx-commissioned IDC Brief, and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, Chief Data and Analytics Officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then, in our final segment, Paula Hansen, who is the President and Chief Revenue Officer of Alteryx, and Jacqui Van der Leij-Greyling, who is the Global Head of Tax Technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, Product Marketing Director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research which spoke with about 1500 leaders? What nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees. And this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity, and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics. And we're able to focus on the behaviors driving higher ROI. >> So the InfoBrief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the InfoBrief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack what's driving this demand, this need for analytics across organizations? >> Sure, well, first, there's more data than ever before. The data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins, and to improve customer experiences. And analytics, along with automation and AI, is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> One of the things that the study also showed was that not all analytics spending is resulting in the same ROI. What are some of the discrepancies that the InfoBrief uncovered with respect to the the changes in ROI that organizations are achieving? >> Our research with IDC revealed significant roadblocks across people, processes, and technologies. They're preventing companies from reaping greater benefits from their investments. So for example, on the people side, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy, as compared to the technology itself. And next, while data is everywhere, most organizations, 63%, from our survey, are still not using the full breadth of data types available. Yet, data's never been this prolific. It's going to continue to grow, and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytics tools to help everyone unlock the power of data. They instead rely on outdated spreadsheet technology. In our survey, 9 out of 10 respondents said less than half of their knowledge workers are active users of analytics software beyond spreadsheets. But true analytic transformation can't happen for an organization in a few select pockets or silos. We believe everyone, regardless of skill level, should be able to participate in the data and analytics process and be driving value. >> Should we retake that, since I started talking over Jason accidentally? >> Yep, absolutely, you can do so. Yep, we'll go back to Lisa's question. Let's retake the question and the answer. >> That'll be not all analog spending results in the same ROI. What are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we can get that clean question and answer. >> Okay. >> Thank you for that. on your ISO, we're still speeding, Lisa. So give it a beat in your head, and then on you. >> Yet not all analytics spending is resulting in the same ROI. So, what are some of the discrepancies that the InfoBrief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow, and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead, they're relying on outdated spreadsheet technology. Nine out of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone, regardless of skill level, should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically then, what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value from their data and analytics and achieved more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics, across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So very strategic investments. Did the survey uncover any specific areas where most companies are falling short, like any black holes that organizations need to be aware of at the outset? >> It did. It did. So organizations, they need to build a data-centric culture. And this begins with people. But what the survey told us is that the people aspect of analytics is the most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone in the organization has access to the data and analytics technology they need. And then the organizations also have to align their investments with upskilling in data literacy to enjoy that higher ROI. Companies who did so experience higher ROI than companies who underinvested in analytics literacy. So among the high ROI achievers, 78% have a good or great alignment between analytics investment and workforce upskilling compared to only 64% among those without positive ROI. And as more orgs adopt cloud data warehouses or cloud data lakes, in order to manage the massively increasing workloads. Can I start that one over? Can I redo this one? >> Sure. >> Yeah >> Of course. Stand by. >> Tongue tied. >> Yep. No worries. >> One second. >> If we could get, if we could do the same, Lisa, just have a clean break. We'll go to your question. Yep. >> Yeah. >> On you Lisa. Just give that a count and whenever you're ready, here, I'm going to give us a little break. On you Lisa. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture, and this begins with people. But we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources compared to only 67% among the ROI laggards. >> So interesting that you mentioned people. I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand. We know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right. So analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also, among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well, compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively, and letting them do so cross-functionally >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side, and is expected to spend more on analytics than other IT. What risks does this present to the overall organization? If IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this is because the lines of business have recognized the value of analytics and plan to invest accordingly. But a lack of alignment between IT and business, this will negatively impact governance, which ultimately impedes democratization and hence, ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more, you know, on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up an Alteryx environment. But also to take a look at your analytics stack, and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process and technologies. Jason, thank you so much for joining me today, unpacking the IDC InfoBrief and the great nuggets in there. Lots that organizations can learn, and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you. It's been a pleasure. >> In a moment, Alan Jacobson, who's the Chief Data and Analytics Officer at Alteryx, is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching the Cube, the leader in tech enterprise coverage. (gentle 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 Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. (futuristic music) >> Welcome back to Cambridge, Massachusetts everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events and we extract the signal from the noise. This is day two, we're sort of wrapping up the Chief Data Officer event. It's MIT CDOIQ, it started as an information quality event and with the ascendancy of big data the CDO emerged and really took center stage here. And it's interesting to know that it's kind of come full circle back to information quality. People are realizing all this data we have, you know the old saying, garbage in, garbage out. So the information quality worlds and this chief data officer world have really come colliding together. Robert Abate is here, he's the Vice President and CDO of Global IDS and also the co-chair of next year's, the 14th annual MIT CDOIQ. Robert, thanks for coming on. >> Oh, well thank you. >> Now you're a CDO by background, give us a little history of your career. >> Sure, sure. Well I started out with an Electrical Engineering degree and went into applications development. By 2000, I was leading the Ralph Lauren's IT, and I realized when Ralph Lauren hired me, he was getting ready to go public. And his problem was he had hired eight different accounting firms to do eight different divisions. And each of those eight divisions were reporting a number, but the big number didn't add up, so he couldn't go public. So he searched the industry to find somebody who could figure out the problem. Now I was, at the time, working in applications and had built this system called Service Oriented Architectures, a way of integrating applications. And I said, "Well I don't know if I could solve the problem, "but I'll give it a shot." And what I did was, just by taking each silo as it's own problem, which was what EID Accounting Firm had done, I was able to figure out that one of Ralph Lauren's policies was if you buy a garment, you can return it anytime, anywhere, forever, however long you own it. And he didn't think about that, but what that meant is somebody could go to a Bloomingdale's, buy a garment and then go to his outlet store and return it. Well, the cross channels were different systems. So the outlet stores were his own business, retail was a different business, there was a completely different, each one had their own AS/400, their own data. So what I quickly learned was, the problem wasn't the systems, the problem was the data. And it took me about two months to figure it out and he offered me a job, he said well, I was a consultant at the time, he says, "I'm offering you a job, you're going to run my IT." >> Great user experience but hard to count. >> (laughs) Hard to count. So that's when I, probably 1999 was when that happened. I went into data and started researching-- >> Sorry, so how long did it take you to figure that out? You said a couple of months? >> A couple of months, I think it was about two months. >> 'Cause jeez, it took Oracle what, 10 years to build Fusion with SOA? That's pretty good. (laughs) >> This was a little bit of luck. When we started integrating the applications we learned that the messages that we were sending back and forth didn't match, and we said, "Well that's impossible, it can't not match." But what didn't match was it was coming from one channel and being returned in another channel, and the returns showed here didn't balance with the returns on this side. So it was a data problem. >> So a forensics showdown. So what did you do after? >> After that I went into ICICI Bank which was a large bank in India who was trying to integrate their systems, and again, this was a data problem. But they heard me giving a talk at a conference on how SOA had solved the data challenge, and they said, "We're a bank with a wholesale, a retail, "and other divisions, "and we can't integrate the systems, can you?" I said, "Well yeah, I'd build a website "and make them web services and now what'll happen is "each of those'll kind of communicate." And I was at ICICI Bank for about six months in Mumbai, and finished that which was a success, came back and started consulting because now a lot of companies were really interested in this concept of Service Oriented Architectures. Back then when we first published on it, myself, Peter Aiken, and a gentleman named Joseph Burke published on it in 1996. The publisher didn't accept the book, it was a really interesting thing. We wrote the book called, "Services Based Architectures: A Way to Integrate Systems." And the way Wiley & Sons, or most publishers work is, they'll have three industry experts read your book and if they don't think what you're saying has any value, they, forget about it. So one guy said this is brilliant, one guy says, "These guys don't know what they're talking about," and the third guy says, "I don't even think what they're talking about is feasible." So they decided not to publish. Four years later it came back and said, "We want to publish the book," and Peter said, "You know what, they lost their chance." We were ahead of them by four years, they didn't understand the technology. So that was kind of cool. So from there I went into consulting, eventually took a position as the Head of Enterprise and Director of Enterprise Information Architecture with Walmart. And Walmart, as you know, is a huge entity, almost the size of the federal government. So to build an architecture that integrates Walmart would've been a challenge, a behemoth challenge, and I took it on with a phenomenal team. >> And when was this, like what timeframe? >> This was 2010, and by the end of 2010 we had presented an architecture to the CIO and the rest of the organization, and they came back to me about a week later and said, "Look, everybody agrees what you did was brilliant, "but nobody knows how to implement it. "So we're taking you away, "you're no longer Director of Information Architecture, "you're now Director of Enterprise Information Management. "Build it. "Prove that what you say you could do, you could do." So we built something called the Data CAFE, and CAFE was an acronym, it stood for: Collaborative Analytics Facility for the Enterprise. What we did was we took data from one of the divisions, because you didn't want to take on the whole beast, boil the ocean. We picked Sam's Club and we worked with their CFO, and because we had information about customers we were able to build a room with seven 80 inch monitors that surrounded anyone in the room. And in the center was the Cisco telecommunications so you could be a part of a meeting. >> The TelePresence. >> TelePresence. And we built one room in one facility, and one room in another facility, and we labeled the monitors, one red, one blue, one green, and we said, "There's got to be a way where we can build "data science so it's interactive, so somebody, "an executive could walk into the room, "touch the screen, and drill into features. "And in another room "the features would be changing simultaneously." And that's what we built. The room was brought up on Black Friday of 2013, and we were able to see the trends of sales on the East Coast that we quickly, the executives in the room, and these are the CEO of Walmart and the heads of Sam's Club and the like, they were able to change the distribution in the Mountain Time Zone and west time zones because of the sales on the East Coast gave them the idea, well these things are going to sell, and these things aren't. And they saw a tremendous increase in productivity. We received the 2014, my team received the 2014 Walmart Innovation Project of the Year. >> And that's no slouch. Walmart has always been heavily data-oriented. I don't know if it's urban legend or not, but the famous story in the '80s of the beer and the diapers, right? Walmart would position beer next to diapers, why would they do that? Well the father goes in to buy the diapers for the baby, picks up a six pack while he's on the way, so they just move those proximate to each other. (laughs) >> In terms of data, Walmart really learned that there's an advantage to understanding how to place items in places that, a path that you might take in a store, and knowing that path, they actually have a term for it, I believe it's called, I'm sorry, I forgot the name but it's-- >> Selling more stuff. (laughs) >> Yeah, it's selling more stuff. It's the way you position items on a shelf. And Walmart had the brilliance, or at least I thought it was brilliant, that they would make their vendors the data champion. So the vendor, let's say Procter & Gamble's a vendor, and they sell this one product the most. They would then be the champion for that aisle. Oh, it's called planogramming. So the planogramming, the way the shelves were organized, would be set up by Procter & Gamble for that entire area, working with all their other vendors. And so Walmart would give the data to them and say, "You do it." And what I was purporting was, well, we shouldn't just be giving the data away, we should be using that data. And that was the advent of that. From there I moved to Kimberly-Clark, I became Global Director of Enterprise Data Management and Analytics. Their challenge was they had different teams, there were four different instances of SAP around the globe. One for Latin America, one for North America called the Enterprise Edition, one for EMEA, Europe, Middle East, and Africa, and one for Asia-Pacific. Well when you have four different instances of SAP, that means your master data doesn't exist because the same thing that happens in this facility is different here. And every company faces this challenge. If they implement more than one of a system the specialty fields get used by different companies in different ways. >> The gold standard, the gold version. >> The golden version. So I built a team by bringing together all the different international teams, and created one team that was able to integrate best practices and standards around data governance, data quality. Built BI teams for each of the regions, and then a data science and advanced analytics team. >> Wow, so okay, so that makes you uniquely qualified to coach here at the conference. >> Oh, I don't know about that. (laughs) There are some real, there are some geniuses here. >> No but, I say that because these are your peeps. >> Yes, they are, they are. >> And so, you're a practitioner, this conference is all about practitioners talking to practitioners, it's content-heavy, There's not a lot of fluff. Lunches aren't sponsored, there's no lanyard sponsor and it's not like, you know, there's very subtle sponsor desks, you have to have sponsors 'cause otherwise the conference's not enabled, and you've got costs associated with it. But it's a very intimate event and I think you guys want to keep it that way. >> And I really believe you're dead-on. When you go to most industry conferences, the industry conferences, the sponsors, you know, change the format or are heavily into the format. Here you have industry thought leaders from all over the globe. CDOs of major Fortune 500 companies who are working with their peers and exchanging ideas. I've had conversations with a number of CDOs and the thought leadership at this conference, I've never seen this type of thought leadership in any conference. >> Yeah, I mean the percentage of presentations by practitioners, even when there's a vendor name, they have a practitioner, you know, internal practitioner presenting so it's 99.9% which is why people attend. We're moving venues next year, I understand. Just did a little tour of the new venue, so, going to be able to accommodate more attendees, so that's great. >> Yeah it is. >> So what are your objectives in thinking ahead a year from now? >> Well, you know, I'm taking over from my current peer, Dr. Arka Mukherjee, who just did a phenomenal job of finding speakers. People who are in the industry, who are presenting challenges, and allowing others to interact. So I hope could do a similar thing which is, find with my peers people who have real world challenges, bring them to the forum so they can be debated. On top of that, there are some amazing, you know, technology change is just so fast. One of the areas like big data I remember only five years ago the chart of big data vendors maybe had 50 people on it, now you would need the table to put all the vendors. >> Who's not a data vendor, you know? >> Who's not a data vendor? (laughs) So I would think the best thing we could do is, is find, just get all the CDOs and CDO-types into a room, and let us debate and talk about these points and issues. I've seen just some tremendous interactions, great questions, people giving advice to others. I've learned a lot here. >> And how about long term, where do you see this going? How many CDOs are there in the world, do you know? Is that a number that's known? >> That's a really interesting point because, you know, only five years ago there weren't that many CDOs to be called. And then Gartner four years ago or so put out an article saying, "Every company really should have a CDO." Not just for the purpose of advancing your data, and to Doug Laney's point that data is being monetized, there's a need to have someone responsible for information 'cause we're in the Information Age. And a CIO really is focused on infrastructure, making sure I've got my PCs, making sure I've got a LAN, I've got websites. The focus on data has really, because of the Information Age, has turned data into an asset. So organizations realize, if you utilize that asset, let me reverse this, if you don't use data as an asset, you will be out of business. I heard a quote, I don't know if it's true, "Only 10 years ago, 250 of the Fortune 10 no longer exists." >> Yeah, something like that, the turnover's amazing. >> Many of those companies were companies that decided not to make the change to be data-enabled, to make data decision processing. Companies still use data warehouses, they're always going to use them, and a warehouse is a rear-view mirror, it tells you what happened last week, last month, last year. But today's businesses work forward-looking. And just like driving a car, it'd be really hard to drive your car through a rear-view mirror. So what companies are doing today are saying, "Okay, let's start looking at this as forward-looking, "a prescriptive and predictive analytics, "rather than just what happened in the past." I'll give you an example. In a major company that is a supplier of consumer products, they were leading in the industry and their sales started to drop, and they didn't know why. Well, with a data science team, we were able to determine by pulling in data from the CDC, now these are sources that only 20 years ago nobody ever used to bring in data in the enterprise, now 60% of your data is external. So we brought in data from the CDC, we brought in data on maternal births from the national government, we brought in data from the Census Bureau, we brought in data from sources of advertising and targeted marketing towards mothers. Pulled all that data together and said, "Why are diaper sales down?" Well they were targeting the large regions of the country and putting ads in TV stations in New York and California, big population centers. Birth rates in population centers have declined. Birth rates in certain other regions, like the south, and the Bible Belt, if I can call it that, have increased. So by changing the marketing, their product sales went up. >> Advertising to Texas. >> Well, you know, and that brings to one of the points, I heard a lecture today about ethics. We made it a point at Walmart that if you ran a query that reduced a result to less than five people, we wouldn't allow you to see the result. Because, think about it, I could say, "What is my neighbor buying? "What are you buying?" So there's an ethical component to this as well. But that, you know, data is not political. Data is not chauvinistic. It doesn't discriminate, it just gives you facts. It's the interpretation of that that is hard CDOs, because we have to say to someone, "Look, this is the fact, and your 25 years "of experience in the business, "granted, is tremendous and it's needed, "but the facts are saying this, "and that would mean that the business "would have to change its direction." And it's hard for people to do, so it requires that. >> So whether it's called the chief data officer, whatever the data czar rubric is, the head of analytics, there's obviously the data quality component there whatever that is, this is the conference for, as I called them, your peeps, for that role in the organization. People often ask, "Will that role be around?" I think it's clear, it's solidifying. Yes, you see the chief digital officer emerging and there's a lot of tailwinds there, but the information quality component, the data architecture component, it's here to stay. And this is the premiere conference, the premiere event, that I know of anyway. There are a couple of others, perhaps, but it's great to see all the success. When I first came here in 2013 there were probably about 130 folks here. Today, I think there were 500 people registered almost. Next year, I think 600 is kind of the target, and I think it's very reasonable with the new space. So congratulations on all the success, and thank you for stepping up to the co-chair role, I really appreciate it. >> Well, let me tell you I thank you guys. You provide a voice at these IT conferences that we really need, and that is the ability to get the message out. That people do think and care, the industry is not thoughtless and heartless. With all the data breaches and everything going on there's a lot of fear, fear, loathing, and anticipation. But having your voice, kind of like ESPN and a sports show, gives the technology community, which is getting larger and larger by the day, a voice and we need that so, thank you. >> Well thank you, Robert. We appreciate that, it was great to have you on. Appreciate the time. >> Great to be here, thank you. >> All right, and thank you for watching. We'll be right back with out next guest as we wrap up day two of MIT CDOIQ. You're watching theCUBE. (futuristic music)

>> From Cambridge, Massachusetts, it's theCUBE covering MIT Chief Data Officer and Information Quality Symposium 2019 brought to you by SiliconANGLE Media. (upbeat techno music) >> Hi everybody, welcome back to Cambridge, Massachusetts. You're watching theCUBE, the leader in tech coverage. We go out to the events. We extract the signal from the noise, and we're here at the MIT CDOIQ Conference, Chief Data Officer Information Quality Conference. It is the 13th year here at the Tang building. We've outgrown this building and have to move next year. It's fire marshal full. Gokula Mishra is here. He is the Senior Director of Global Data and Analytics and Supply Chain-- >> Formerly. Former, former Senior Director. >> Former! I'm sorry. It's former Senior Director of Global Data Analytics and Supply Chain at McDonald's. Oh, I didn't know that. I apologize my friend. Well, welcome back to theCUBE. We met when you were at Oracle doing data. So you've left that, you're on to your next big thing. >> Yes, thinking through it. >> Fantastic, now let's start with your career. You've had, so you just recently left McDonald's. I met you when you were at Oracle, so you cut over to the dark side for a while, and then before that, I mean, you've been a practitioner all your life, so take us through sort of your background. >> Yeah, I mean my beginning was really with a company called Tata Burroughs. Those days we did not have a lot of work getting done in India. We used to send people to U.S. so I was one of the pioneers of the whole industry, coming here and working on very interesting projects. But I was lucky to be working on mostly data analytics related work, joined a great company called CS Associates. I did my Master's at Northwestern. In fact, my thesis was intelligent databases. So, building AI into the databases and from there on I have been with Booz Allen, Oracle, HP, TransUnion, I also run my own company, and Sierra Atlantic, which is part of Hitachi, and McDonald's. >> Awesome, so let's talk about use of data. It's evolved dramatically as we know. One of the themes in this conference over the years has been sort of, I said yesterday, the Chief Data Officer role emerged from the ashes of sort of governance, kind of back office information quality compliance, and then ascended with the tailwind of the Big Data meme, and it's kind of come full circle. People are realizing actually to get value out of data, you have to have information quality. So those two worlds have collided together, and you've also seen the ascendancy of the Chief Digital Officer who has really taken a front and center role in some of the more strategic and revenue generating initiatives, and in some ways the Chief Data Officer has been a supporting role to that, providing the quality, providing the compliance, the governance, and the data modeling and analytics, and a component of it. First of all, is that a fair assessment? How do you see the way in which the use of data has evolved over the last 10 years? >> So to me, primarily, the use of data was, in my mind, mostly around financial reporting. So, anything that companies needed to run their company, any metrics they needed, any data they needed. So, if you look at all the reporting that used to happen it's primarily around metrics that are financials, whether it's around finances around operations, finances around marketing effort, finances around reporting if it's a public company reporting to the market. That's where the focus was, and so therefore a lot of the data that was not needed for financial reporting was what we call nowadays dark data. This is data we collect but don't do anything with it. Then, as the capability of the computing, and the storage, and new technologies, and new techniques evolve, and are able to handle more variety and more volume of data, then people quickly realize how much potential they have in the other data outside of the financial reporting data that they can utilize too. So, some of the pioneers leverage that and actually improved a lot in their efficiency of operations, came out with innovation. You know, GE comes to mind as one of the companies that actually leverage data early on, and number of other companies. Obviously, you look at today data has been, it's defining some of the multi-billion dollar company and all they have is data. >> Well, Facebook, Google, Amazon, Microsoft. >> Exactly. >> Apple, I mean Apple obviously makes stuff, but those other companies, they're data companies. I mean largely, and those five companies have the highest market value on the U.S. stock exchange. They've surpassed all the other big leaders, even Berkshire Hathaway. >> So now, what is happening is because the market changes, the forces that are changing the behavior of our consumers and customers, which I talked about which is everyone now is digitally engaging with each other. What that does is all the experiences now are being captured digitally, all the services are being captured digitally, all the products are creating a lot of digital exhaust of data and so now companies have to pay attention to engage with their customers and partners digitally. Therefore, they have to make sure that they're leveraging data and analytics in doing so. The other thing that has changed is the time to decision to the time to act on the data inside that you get is shrinking, and shrinking, and shrinking, so a lot more decision-making is now going real time. Therefore, you have a situation now, you have the capability, you have the technology, you have the data now, you have to make sure that you convert that in what I call programmatic kind of data decision-making. Obviously, there are people involved in more strategic decision-making. So, that's more manual, but at the operational level, it's going more programmatic decision-making. >> Okay, I want to talk, By the way, I've seen a stat, I don't know if you can confirm this, that 80% of the data that's out there today is dark data or it's data that's behind a firewall or not searchable, not open to Google's crawlers. So, there's a lot of value there-- >> So, I would say that percent is declining over time as companies have realized the value of data. So, more and more companies are removing the silos, bringing those dark data out. I think the key to that is companies being able to value their data, and as soon as they are able to value their data, they are able to leverage a lot of the data. I still believe there's a large percent still not used or accessed in companies. >> Well, and of course you talked a lot about data monetization. Doug Laney, who's an expert in that topic, we had Doug on a couple years ago when he, just after, he wrote Infonomics. He was on yesterday. He's got a very detailed prescription as to, he makes strong cases as to why data should be valued like an asset. I don't think anybody really disagrees with that, but then he gave kind of a how-to-do-it, which will, somewhat, make your eyes bleed, but it was really well thought out, as you know. But you talked a lot about data monetization, you talked about a number of ways in which data can contribute to monetization. Revenue, cost reduction, efficiency, risk, and innovation. Revenue and cost is obvious. I mean, that's where the starting point is. Efficiency is interesting. I look at efficiency as kind of a doing more with less but it's sort of a cost reduction, but explain why it's not in the cost bucket, it's different. >> So, it is first starts with doing what we do today cheaper, better, faster, and doing more comes after that because if you don't understand, and data is the way to understand how your current processes work, you will not take the first step. So, to take the first step is to understand how can I do this process faster, and then you focus on cheaper, and then you focus on better. Of course, faster is because of some of the market forces and customer behavior that's driving you to do that process faster. >> Okay, and then the other one was risk reduction. I think that makes a lot of sense here. Actually, let me go back. So, one of the key pieces of it, of efficiency is time to value. So, if you can compress the time, or accelerate the time and you get the value that means more cash in house faster, whether it's cost reduction or-- >> And the other aspect you look at is, can you automate more of the processes, and in that way it can be faster. >> And that hits the income statement as well because you're reducing headcount cost of your, maybe not reducing headcount cost, but you're getting more out of different, out ahead you're reallocating them to more strategic initiatives. Everybody says that but the reality is you hire less people because you just automated. And then, risk reduction, so the degree to which you can lower your expected loss. That's just instead thinking in insurance terms, that's tangible value so certainly to large corporations, but even midsize and small corporations. Innovation, I thought was a good one, but maybe you could use an example of, give us an example of how in your career you've seen data contribute to innovation. >> So, I'll give an example of oil and gas industry. If you look at speed of innovation in the oil and gas industry, they were all paper-based. I don't know how much you know about drilling. A lot of the assets that goes into figuring out where to drill, how to drill, and actually drilling and then taking the oil or gas out, and of course selling it to make money. All of those processes were paper based. So, if you can imagine trying to optimize a paper-based innovation, it's very hard. Not only that, it's very, very by itself because it's on paper, it's in someone's drawer or file. So, it's siloed by design and so one thing that the industry has gone through, they recognize that they have to optimize the processes to be better, to innovate, to find, for example, shale gas was a result output of digitizing the processes because otherwise you can't drill faster, cheaper, better to leverage the shale gas drilling that they did. So, the industry went through actually digitizing a lot of the paper assets. So, they went from not having data to knowingly creating the data that they can use to optimize the process and then in the process they're innovating new ways to drill the oil well cheaper, better, faster. >> In the early days of oil exploration in the U.S. go back to the Osage Indian tribe in northern Oklahoma, and they brilliantly, when they got shuttled around, they pushed him out of Kansas and they negotiated with the U.S. government that they maintain the mineral rights and so they became very, very wealthy. In fact, at one point they were the wealthiest per capita individuals in the entire world, and they used to hold auctions for various drilling rights. So, it was all gut feel, all the oil barons would train in, and they would have an auction, and it was, again, it was gut feel as to which areas were the best, and then of course they evolved, you remember it used to be you drill a little hole, no oil, drill a hole, no oil, drill a hole. >> You know how much that cost? >> Yeah, the expense is enormous right? >> It can vary from 10 to 20 million dollars. >> Just a giant expense. So, now today fast-forward to this century, and you're seeing much more sophisticated-- >> Yeah, I can give you another example in pharmaceutical. They develop new drugs, it's a long process. So, one of the initial process is to figure out what molecules this would be exploring in the next step, and you could have thousand different combination of molecules that could treat a particular condition, and now they with digitization and data analytics, they're able to do this in a virtual world, kind of creating a virtual lab where they can test out thousands of molecules. And then, once they can bring it down to a fewer, then the physical aspect of that starts. Think about innovation really shrinking their processes. >> All right, well I want to say this about clouds. You made the statement in your keynote that how many people out there think cloud is cheaper, or maybe you even said cheap, but cheaper I inferred cheaper than an on-prem, and so it was a loaded question so nobody put their hand up they're afraid, but I put my hand up because we don't have any IT. We used to have IT. It was a nightmare. So, for us it's better but in your experience, I think I'm inferring correctly that you had meant cheaper than on-prem, and certainly we talked to many practitioners who have large systems that when they lift and shift to the cloud, they don't change their operating model, they don't really change anything, they get a bill at the end of the month, and they go "What did this really do for us?" And I think that's what you mean-- >> So what I mean, let me make it clear, is that there are certain use cases that cloud is and, as you saw, that people did raise their hand saying "Yeah, I have use cases where cloud is cheaper." I think you need to look at the whole thing. Cost is one aspect. The flexibility and agility of being able to do things is another aspect. For example, if you have a situation where your stakeholder want to do something for three weeks, and they need five times the computing power, and the data that they are buying from outside to do that experiment. Now, imagine doing that in a physical war. It's going to take a long time just to procure and get the physical boxes, and then you'll be able to do it. In cloud, you can enable that, you can get GPUs depending on what problem we are trying to solve. That's another benefit. You can get the fit for purpose computing environment to that and so there are a lot of flexibility, agility all of that. It's a new way of managing it so people need to pay attention to the cost because it will add to the cost. The other thing I will point out is that if you go to the public cloud, because they make it cheaper, because they have hundreds and thousands of this canned CPU. This much computing power, this much memory, this much disk, this much connectivity, and they build thousands of them, and that's why it's cheaper. Well, if your need is something that's very unique and they don't have it, that's when it becomes a problem. Either you need more of those and the cost will be higher. So, now we are getting to the IOT war. The volume of data is growing so much, and the type of processing that you need to do is becoming more real-time, and you can't just move all this bulk of data, and then bring it back, and move the data back and forth. You need a special type of computing, which is at the, what Amazon calls it, adds computing. And the industry is kind of trying to design it. So, that is an example of hybrid computing evolving out of a cloud or out of the necessity that you need special purpose computing environment to deal with new situations, and all of it can't be in the cloud. >> I mean, I would argue, well I guess Microsoft with Azure Stack was kind of the first, although not really. Now, they're there but I would say Oracle, your former company, was the first one to say "Okay, we're going to put the exact same infrastructure on prem as we have in the public cloud." Oracle, I would say, was the first to truly do that-- >> They were doing hybrid computing. >> You now see Amazon with outposts has done the same, Google kind of has similar approach as Azure, and so it's clear that hybrid is here to stay, at least for some period of time. I think the cloud guys probably believe that ultimately it's all going to go to the cloud. We'll see it's going to be a long, long time before that happens. Okay! I'll give you last thoughts on this conference. You've been here before? Or is this your first one? >> This is my first one. >> Okay, so your takeaways, your thoughts, things you might-- >> I am very impressed. I'm a practitioner and finding so many practitioners coming from so many different backgrounds and industries. It's very, very enlightening to listen to their journey, their story, their learnings in terms of what works and what doesn't work. It is really invaluable. >> Yeah, I tell you this, it's always a highlight of our season and Gokula, thank you very much for coming on theCUBE. It was great to see you. >> Thank you. >> You're welcome. All right, keep it right there everybody. We'll be back with our next guest, Dave Vellante. Paul Gillin is in the house. You're watching theCUBE from MIT. Be right back! (upbeat techno music)

>> Live from San Francisco, California, it's the theCUBE, covering the IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM Chief Data Officer Summit, 10th anniversary. Seth Dobrin is here, he's the Vice President and Chief Data Officer of the IBM Analytics Group. Seth, always a pleasure to have you on. Good to see you again. >> Yeah, thanks for having me back Dave. >> You're very welcome. So I love these events you get a chance to interact with chief data officers, guys like yourself. We've been talking a lot today about IBM's internal transformation, how IBM itself is operationalizing AI and maybe we can talk about that, but I'm most interested in how you're pointing that at customers. What have you learned from your internal experiences and what are you bringing to customers? >> Yeah, so, you know, I was hired at IBM to lead part of our internal transformation, so I spent a lot of time doing that. >> Right. >> I've also, you know, when I came over to IBM I had just left Monsanto where I led part of their transformation. So I spent the better part of the first year or so at IBM not only focusing on our internal efforts, but helping our clients transform. And out of that I found that many of our clients needed help and guidance on how to do this. And so I started a team we call, The Data Science an AI Elite Team, and really what we do is we sit down with clients, we share not only our experience, but the methodology that we use internally at IBM so leveraging things like design thinking, DevOps, Agile, and how you implement that in the context of data science and AI. >> I've got a question, so Monsanto, obviously completely different business than IBM-- >> Yeah. >> But when we talk about digital transformation and then talk about the difference between a business and a digital business, it comes down to the data. And you've seen a lot of examples where you see companies traversing industries which never used to happen before. You know, Apple getting into music, there are many, many examples, and the theory is, well, it's 'cause it's data. So when you think about your experiences of a completely different industry bringing now the expertise to IBM, were there similarities that you're able to draw upon, or was it a completely different experience? >> No, I think there's tons of similarities which is, which is part of why I was excited about this and I think IBM was excited to have me. >> Because the chances for success were quite high in your mind? >> Yeah, yeah, because the chance for success were quite high, and also, you know, if you think about it there's on the, how you implement, how you execute, the differences are really cultural more than they're anything to do with the business, right? So it's, the whole role of a Chief Data Officer, or Chief Digital Officer, or a Chief Analytics Officer, is to drive fundamental change in the business, right? So it's how do you manage that cultural change, how do you build bridges, how do you make people, how do you make people a little uncomfortable, but at the same time get them excited about how to leverage things like data, and analytics, and AI, to change how they do business. And really this concept of a digital transformation is about moving away from traditional products and services, more towards outcome-based services and not selling things, but selling, as a Service, right? And it's the same whether it's IBM, you know, moving away from fully transactional to Cloud and subscription-based offerings. Or it's a bank reimagining how they interact with their customers, or it's oil and gas company, or it's a company like Monsanto really thinking about how do we provide outcomes. >> But how do you make sure that every, as a Service, is not a snowflake and it can scale so that you can actually, you know, make it a business? >> So underneath the, as a Service, is a few things. One is, data, one is, machine learning and AI, the other is really understanding your customer, right, because truly digital companies do everything through the eyes of their customer and so every company has many, many versions of their customer until they go through an exercise of creating a single version, right, a customer or a Client 360, if you will, and we went through that exercise at IBM. And those are all very consistent things, right? They're all pieces that kind of happen the same way in every company regardless of the industry and then you get into understanding what the desires of your customer are to do business with you differently. >> So you were talking before about the Chief Digital Officer, a Chief Data Officer, Chief Analytics Officer, as a change agent making people feel a little bit uncomfortable, explore that a little bit what's that, asking them questions that intuitively they, they know they need to have the answer to, but they don't through data? What did you mean by that? >> Yeah so here's the conversations that usually happen, right? You go and you talk to you peers in the organization and you start having conversations with them about what decisions are they trying to make, right? And you're the Chief Data Officer, you're responsible for that, and inevitably the conversation goes something like this, and I'm going to paraphrase. Give me the data I need to support my preconceived notions. >> (laughing) Yeah. >> Right? >> Right. >> And that's what they want to (voice covers voice). >> Here's the answer give me the data that-- >> That's right. So I want a Dashboard that helps me support this. And the uncomfortableness comes in a couple of things in that. It's getting them to let go of that and allow the data to provide some inkling of things that they didn't know were going on, that's one piece. The other is, then you start leveraging machine learning, or AI, to actually help start driving some decisions, so limiting the scope from infinity down to two or three things and surfacing those two or three things and telling people in your business your choices are one of these three things, right? That starts to make people feel uncomfortable and really is a challenge for that cultural change getting people used to trusting the machine, or in some instances even, trusting the machine to make the decision for you, or part of the decision for you. >> That's got to be one of the biggest cultural challenges because you've got somebody who's, let's say they run a big business, it's a profitable business, it's the engine of cashflow at the company, and you're saying, well, that's not what the data says. And you're, say okay, here's a future path-- >> Yeah. >> For success, but it's going to be disruptive, there's going to be a change and I can see people not wanting to go there. >> Yeah, and if you look at, to the point about, even businesses that are making the most money, or parts of a business that are making the most money, if you look at what the business journals say you start leveraging data and AI, you get double-digit increases in your productivity, in your, you know, in differentiation from your competitors. That happens inside of businesses too. So the conversation even with the most profitable parts of the business, or highly, contributing the most revenue is really what we could do better, right? You could get better margins on this revenue you're driving, you could, you know, that's the whole point is to get better leveraging data and AI to increase your margins, increase your revenue, all through data and AI. And then things like moving to, as a Service, from single point to transaction, that's a whole different business model and that leads from once every two or three or five years, getting revenue, to you get revenue every month, right? That's highly profitable for companies because you don't have to go in and send your sales force in every time to sell something, they buy something once, and they continue to pay as long as you keep 'em happy. >> But I can see that scaring people because if the incentives don't shift to go from a, you know, pay all up front, right, there's so many parts of the organization that have to align with that in order for that culture to actually occur. So can you give some examples of how you've, I mean obviously you ran through that at IBM, you saw-- >> Yeah. >> I'm sure a lot of that, got a lot of learnings and then took that to clients. Maybe some examples of client successes that you've had, or even not so successes that you've learned from. >> Yeah, so in terms of client success, I think many of our clients are just beginning this journey, certainly the ones I work with are beginning their journey so it's hard for me to say, client X has successfully done this. But I can certainly talk about how we've gone in, and some of the use cases we've done-- >> Great. >> With certain clients to think about how they transformed their business. So maybe the biggest bang for the buck one is in the oil and gas industry. So ExxonMobile was on stage with me at, Think, talking about-- >> Great. >> Some of the work that we've done with them in their upstream business, right? So every time they drop a well it costs them not thousands of dollars, but hundreds of millions of dollars. And in the oil and gas industry you're talking massive data, right, tens or hundreds of petabytes of data that constantly changes. And no one in that industry really had a data platform that could handle this dynamically. And it takes them months to get, to even start to be able to make a decision. So they really want us to help them figure out, well, how do we build a data platform on this massive scale that enables us to be able to make decisions more rapidly? And so the aim was really to cut this down from 90 days to less than a month. And through leveraging some of our tools, as well as some open-source technology, and teaching them new ways of working, we were able to lay down this foundation. Now this is before, we haven't even started thinking about helping them with AI, oil and gas industry has been doing this type of thing for decades, but they really were struggling with this platform. So that's a big success where, at least for the pilot, which was a small subset of their fields, we were able to help them reduce that timeframe by a lot to be able to start making a decision. >> So an example of a decision might be where to drill next? >> That's exactly the decision they're trying to make. >> Because for years, in that industry, it was boop, oh, no oil, boop, oh, no oil. >> Yeah, well. >> And they got more sophisticated, they started to use data, but I think what you're saying is, the time it took for that analysis was quite long. >> So the time it took to even overlay things like seismic data, topography data, what's happened in wells, and core as they've drilled around that, was really protracted just to pull the data together, right? And then once they got the data together there were some really, really smart people looking at it going, well, my experience says here, and it was driven by the data, but it was not driven by an algorithm. >> A little bit of art. >> True, a lot of art, right, and it still is. So now they want some AI, or some machine learning, to help guide those geophysicists to help determine where, based on the data, they should be dropping wells. And these are hundred million and billion dollar decisions they're making so it's really about how do we help them. >> And that's just one example, I mean-- >> Yeah. >> Every industry has it's own use cases, or-- >> Yeah, and so that's on the front end, right, about the data foundation, and then if you go to a company that was really advanced in leveraging analytics, or machine learning, JPMorgan Chase, in their, they have a division, and also they were on stage with me at, Think, that they had, basically everything is driven by a model, so they give traders a series of models and they make decisions. And now they need to monitor those models, those hundreds of models they have for misuse of those models, right? And so they needed to build a series of models to manage, to monitor their models. >> Right. >> And this was a tremendous deep-learning use case and they had just bought a power AI box from us so they wanted to start leveraging GPUs. And we really helped them figure out how do you navigate and what's the difference between building a model leveraging GPUs, compared to CPUs? How do you use it to accelerate the output, and again, this was really a cost-avoidance play because if people misuse these models they can get in a lot of trouble. But they also need to make these decisions very quickly because a trader goes to make a trade they need to make a decision, was this used properly or not before that trade is kicked off and milliseconds make a difference in the stock market so they needed a model. And one of the things about, you know, when you start leveraging GPUs and deep learning is sometimes you need these GPUs to do training and sometimes you need 'em to do training and scoring. And this was a case where you need to also build a pipeline that can leverage the GPUs for scoring as well which is actually quite complicated and not as straight forward as you might think. In near real time, in real time. >> Pretty close to real time. >> You can't get much more real time then those things, potentially to stop a trade before it occurs to protect the firm. >> Yeah. >> Right, or RELug it. >> Yeah, and don't quote, I think this is right, I think they actually don't do trades until it's confirmed and so-- >> Right. >> Or that's the desire as to not (voice covers voice). >> Well, and then now you're in a competitive situation where, you know. >> Yeah, I mean people put these trading floors as close to the stock exchange as they can-- >> Physically. >> Physically to (voice covers voice)-- >> To the speed of light right? >> Right, so every millisecond counts. >> Yeah, read Flash Boys-- >> Right, yeah. >> So, what's the biggest challenge you're finding, both at IBM and in your clients, in terms of operationalizing AI. Is it technology? Is it culture? Is it process? Is it-- >> Yeah, so culture is always hard, but I think as we start getting to really think about integrating AI and data into our operations, right? As you look at what software development did with this whole concept of DevOps, right, and really rapidly iterating, but getting things into a production-ready pipeline, looking at continuous integration, continuous development, what does that mean for data and AI? And these concept of DataOps and AIOps, right? And I think DataOps is very similar to DevOps in that things don't change that rapidly, right? You build your data pipeline, you build your data assets, you integrate them. They may change on the weeks, or months timeframe, but they're not changing on the hours, or days timeframe. As you get into some of these AI models some of them need to be retrained within a day, right, because the data changes, they fall out of parameters, or the parameters are very narrow and you need to keep 'em in there, what does that mean? How do you integrate this for your, into your CI/CD pipeline? How do you know when you need to do regression testing on the whole thing again? Does your data science and AI pipeline even allow for you to integrate into your current CI/CD pipeline? So this is actually an IBM-wide effort that my team is leading to start thinking about, how do we incorporate what we're doing into people's CI/CD pipeline so we can enable AIOps, if you will, or MLOps, and really, really IBM is the only company that's positioned to do that for so many reasons. One is, we're the only one with an end-to-end toolchain. So we do everything from data, feature development, feature engineering, generating models, whether selecting models, whether it's auto AI, or hand coding or visual modeling into things like trust and transparency. And so we're the only one with that entire toolchain. Secondly, we've got IBM research, we've got decades of industry experience, we've got our IBM Services Organization, all of us have been tackling with this with large enterprises so we're uniquely positioned to really be able to tackle this in a very enterprised-grade manner. >> Well, and the leverage that you can get within IBM and for your customers. >> And leveraging our clients, right? >> It's off the charts. >> We have six clients that are our most advanced clients that are working with us on this so it's not just us in a box, it's us with our clients working on this. >> So what are you hoping to have happen today? We're just about to get started with the keynotes. >> Yeah. >> We're going to take a break and then come back after the keynotes and we've got some great guests, but what are you hoping to get out of today? >> Yeah, so I've been with IBM for 2 1/2 years and I, and this is my eighth CEO Summit, so I've been to many more of these than I've been at IBM. And I went to these religiously before I joined IBM really for two reasons. One, there's no sales pitch, right, it's not a trade show. The second is it's the only place where I get the opportunity to listen to my peers and really have open and candid conversations about the challenges they're facing and how they're addressing them and really giving me insights into what other industries are doing and being able to benchmark me and my organization against the leading edge of what's going on in this space. >> I love it and that's why I love coming to these events. It's practitioners talking to practitioners. Seth Dobrin thanks so much for coming to theCUBE. >> Yeah, thanks always, Dave. >> Always a pleasure. All right, keep it right there everybody we'll be right back right after this short break. You're watching, theCUBE, live from San Francisco. Be right back.

>> Live from Las Vegas; it's the Cube. Covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back everyone to the Cube's live coverage of the AWS executive summit here at the Venetian in Las Vegas Nevada. I'm your host Rebecca Knight. We have two guests for this segment. We have Hitoshi Ienaka the CEO of ARISE Analytics and Takuya Kudo the Chief Sciences Officer at ARISE. Thank you both so much for coming on the program. >> Thank you. >> So I want to start by having you tell our viewers a little bit more about ARISE analytics. >> Well ARISE analytics is a joint venture between KDDI and Accenture. Well last, well last year we established a company yeah. That's family. >> Right and that's you know kind of we provide like tying the capabilities and the KDDI is kind of number two mobile network operator in Japan, has 50 million subscribers, massive data. So that's there a lot of room to cook but they don't have enough capability to support that. So that's why we kind of married together. >> And it helps companies leverage a wealth of knowledge resources and data between firms to bring about digital transformation. >> Right. >> That's what you're doing. So talk a little bit about what you've seen so far. >> Well so we have two assets, KDDI has, well big data and well Accenture has, well a lot of analytic skills. So using this well these assets, we built our integrated analytics platform hosted on a eda-brais. And what our first challenge was to deduce, channel out to the other operators and were which caused a challenge risk to well more than 40 million subscribers and by digging into that data and using machine learning origin and our data includes (mumbles) and life style service usage. And well, we optimize customer channels and contact timing and well to target customers efficiently. And well we well we tried art of well, other event well art of >> (mumbles) >> Yeah yeah. >> Yeah (mumbles) marketing. >> Okay. >> Yeah and we can get a good result and well it was not only due to our activities but only last year, only KDDI well could increase the market share among three network operators in Japan. That is our our achievements yeah. >> That's very impressive! So can you talk a little bit about the initial pilot in particular what you saw. Taku, do want to? >> Right so like as he mentioned like we have two work stream gigantic work stream. One is for consumer facing right. So customer chai and the you know out of on three marketing's or like recommendation engines based upon this stream data because we have massive like this is a consumption data too. Not just about like you know one handset data. In another work stream is a B2B, a business domain which is sounds like not related to mobile network operators but they have massive network to sell to B2B customer. So we utilize those gigantic data, combine those maybe I can mention but data but combine those data creating new service model. So that's quite a new IOT initiatives for B2B layers and consumer initiatives you know to support ongoing current business. >> And you're using this in a variety of sectors in particular I wanted you to ask you about one that you're doing with Toyota and a taxi service. >> Right so (mumbles) so yeah that that one is like five years like example because a, unless otherwise, I don't think that new business model to compete with Uber never happened right? So KDDI provide like Maura Handu said like location data over like you indigenous subscribers creating some, you know demand side riders for (mumbles) right? Over there, on top of that Toyota's transact log, which is technically like kinematics data provide like supply side which is cause, right? Focusing model and taxi also provide like meters, where customer riders get in and get off and combine those three completely different cable and data sets. >> But also with things like weather and those kinds of other >> Exactly yeah. >> outside. >> Open data too. And combine those data sets. We in, we provide, Accenture provides like talents and creating completely new forecasting model it's called AI taxi dispatch model. So now if you go to Tokyo, majority of taxi has our algorithm like Arizona takes in, you know KDDI and Accenture provide it. >> So that's very cool! Can you talk a little bit about what you've learned, about, in terms of when the weather is like this, taxis happen this >> Yeah, so it's of course weather has massive impact over, like if it's mornings specifically lane, it boosts like demands and also events. We have also events data. Maybe I don't know concerts, some famous singer, celebrities came and it's you know boost like riders demands. So that's actually significant impact of our demand focused model. Rather than using pushing like Uber, you strike you know app, mobile app. we actually treated as (mumbles) like taxi actually go because taxi driver and I can see where is a hot spot to pick up riders. And that's what we try to do. So based up on those, you know people don't even have like maybe like my father's age right, that don't have a smartphone they can get the benefit universality right. So that's the base concepts to provide Universal model to those you know without these >> So even people lacking technology >> Right exactly. >> Can still reap the benefits of this kind of approach. >> (mumbles) is universality so that's also our business strategy. Yeah. >> So you're also using this approach in a manufacturing environment. >> Yeah that's right. We are also working with some manufacturing factory. On the factory field were experienced workers can detect machine breakdown before they occur. But well how can that not be passed on to less experienced employees? So we created a live predictive maintenance which alerts companies ahead of time to pre potential breakdowns. Sensors (mumbles) about things like vibrations, temperature and electrical current. The collected data is analyzed by the AI system. So in this way the prediction of machine (mumbles) can be performed by almost anyone. Well it used to be others by only experienced employees before, yeah. >> So it not only helps the company know when a machine is going to fail, it also empowers the employee to fix it him or herself. >> Right it's a preventive way and so it's up and running over the ad-abreis. We use kinesis in late shift you know, learn the functions and over EC2. So that's completely free stock over ad-abreis capability too. >> So what you're describing sounds like it requires a lot of collaboration, a lot of deep relationship building between not only Accenture and KDDI but also the clients that you're working with. Can you describe how you all work together? >> Right. So maybe I'm going to provide that information. So like of course like KDDI's employee has specific domain knowledge and we provide like you know like data science capabilities and also like maybe through the interview right, found workers or like taxi, they have specific domain knowledge So combine those collaboration. It's called two in the box and we collaboratively paired each employees and you know supply the knowledge each other so that's it. Just one is not enough but as a team integrated over database and created a very strong team and that's a you know we try to cherish and that's culture. And the two boost the data science, data driven companies decision-making process. >> So i think our viewers are pretty amazed and impressed with what's going on. But in this era of 5G and IOT, what's next, what are you working at? It's a relatively new partnership. What are what are some of the most exciting things in the pipeline? >> So the (laughs) the very strategic so we strategizing right now in terms of 5G in IOT. But definitely one of the pieces could be like deep learning right? And also about your realities which nobody has done before. So that's where we try to collaborate with other sectors, industries, to create a new. And to do so we need a massive like computation power like GPU servers and we have to rely on the ad-abreis because otherwise we cannot achieve those goals and specifically 5G maybe changing in the game. Maybe like you know low latency and you know wireless connectivity, you know we don't need connections so maybe the factory lining assembly lines. You know completely change the way crispy like edge computing no more. Maybe like for computing, right, in between like Saba and edge because of the 5G. I don't know but we are strategizing now in a very exciting moment. We are doing right now. >> Indeed it is. >> Yeah. >> Well Hitoshi, Taku, thank you so much for coming on the Cube. This was a lot of fun. >> Thank you very much. I'm Rebecca Knight. Stay tuned for more of the Cube's live coverage of the AWS Executive Summit coming up just after this. (Uptempo music)

>> Live from the MIT campus in Cambridge, Massachusetts, it's theCUBE, covering the 12th Annual MIT Chief Data Officer and Information Quality Symposium. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE's coverage of MIT CDOIQ here in Cambridge, Massachusetts. I'm your host Rebecca Knight along with my cohost Peter Burris. We have two guests on this segment. We have Cortnie Abercrombie, she is the founder of the nonprofit AI Truth, and Carl Gerber, who is the managing partner at Global Data Analytics Leaders. Thanks so much for coming on theCUBE Cortnie and Carl. >> Thank you. >> Thank you. >> So I want to start by just having you introduce yourselves to our viewers, what you do. So tell us a little bit about AI Truth, Cortnie. >> So this was born out of a passion. As I, the last gig I had at IBM, everybody knows me for chief data officer and what I did with that, but the more recent role that I had was developing custom offerings for Fortune 500 in the AI solutions area, so as I would go meet and see different clients, and talk with them and start to look at different processes for how you implement AI solutions, it became very clear that not everybody is attuned, just because they're the ones funding the project or even initiating the purpose of the project, the business leaders don't necessarily know how these things work or run or what can go wrong with them. And on the flip side of that, we have very ambitious up-and-comer-type data scientists who are just trying to fulfill the mission, you know, the talent at hand, and they get really swept up in it. To the point where you can even see that data's getting bartered back and forth with any real governance over it or policies in place to say, "Hey, is that right? Should we have gotten that kind of information?" Which leads us into things like the creepy factor. Like, you know target (laughs) and some of these cases that are well-known. And so, as I saw some of these mistakes happening that were costing brand reputation, our return on investment, or possibly even creating opportunities for risk for the companies and for the business leaders, I felt like someone's got to take one for the team here and go out and start educating people on how this stuff actually works, what the issues can be and how to prevent those issues, and then also what do you do when things do go wrong, how do you fix it? So that's the mission of AI Truth and I have a book. Yes, power to the people, but you know really my main concern was concerned individuals, because I think we've all been affected when we've sent and email and all of a sudden we get a weird ad, and we're like, "Hey, what, they should not, is somebody reading my email?" You know, and we feel this, just, offense-- >> And the answer is yes. >> Yes, and they are, they are. So I mean, we, but we need to know because the only way we can empower ourselves to do something is to actually know how it works. So, that's what my missions is to try and do. So, for the concerned individuals out there, I am writing a book to kind of encapsulate all the experiences that I had so people know where to look and what they can actually do, because you'll be less fearful if you know, "Hey, I can download DuckDuckGo for my browser, or my search engine I mean, and Epic for my browser, and some private, you know, private offerings instead of the typical free offerings. There's not an answer for Facebook yet though. >> So, (laughs) we'll get there. Carl, tell us a little bit about Global Data Analytics Leaders. >> So, I launched Analytics Leaders and CDO Coach after a long career in corporate America. I started building an executive information system when I was in the military for a four-star commander, and I've really done a lot in data analytics throughout my career. Most recently, starting a CDO function at two large multinational companies in leading global transformation programs. And, what I've experienced is even though the industries may vary a little bit, the challenges are the same and the patterns of behavior are the same, both the good and bad behavior, bad habits around the data. And, through the course of my career, I've developed these frameworks and playbooks and just ways to get a repeatable outcome and bring these new technologies like machine learning to bear to really overcome the challenges that I've seen. And what I've seen is a lot of the current thinking is we're solving these data management problems manually. You know, we all hear the complaints about the people who are analysts and data scientists spending 70, 80% of their time being a data gatherer and not really generating insight from the data itself and making it actionable. Well, that's why we have computer systems, right? But that large-scale technology in automation hasn't really served us well, because we think in silos, right? We fund these projects based on departments and divisions. We acquire companies through mergers and acquisitions. And the CDO role has emerged because we need to think about this, all the data that an enterprise uses, horizontally. And with that, I bring a high degree of automation, things like machine learning, to solve those problems. So, I'm now bottling that and advising my clients. And at the same time, the CDO role is where the CIO role was 20 years ago. We're really in it's infancy, and so you see companies define it differently, have different expectations. People are filling the roles that may have not done this before, and so I provide the coaching services there. It's like a professional golfer who has a swing coach. So I come in and I help the data executives with upping their game. >> Well, it's interesting, I actually said the CIO role 40 years ago. But, here's why. If we look back in the 1970s, hardcore financial systems were made possible by the technology which allowed us to run businesses like a portfolio: Jack Welch, the GE model. That was not possible if you didn't have a common asset management system, if you didn't have a common cached management system, etc. And so, when we started creating those common systems, we needed someone that could describe how that shared asset was going to be used within the organization. And we went from the DP manager in HR, the DP manager within finance, to the CIO. And in many respects, we're doing the same thing, right? We're talking about data in a lot of different places and now the business is saying, "We can bring this data together in new and interesting ways into more a shared asset, and we need someone that can help administer that process, and you know, navigate between different groups and different needs and whatnot." Is that kind of what you guys are seeing? >> Oh yeah. >> Yeah. >> Well you know once I get to talking (laughs). For me, I can going right back to the newer technologies like AI and IOT that are coming from externally into your organization, and then also the fact that we're seeing bartering at an unprec... of data at an unprecedented level before. And yet, what the chief data officer role originally did was look at data internally, and structured data mostly. But now, we're asking them to step out of their comfort zone and start looking at all these unknown, niche data broker firms that may or may not be ethical in how they're... I mean, I... look I tell people, "If you hear the word scrape, you run." No scraping, we don't want scraped data, no, no, no (laugh). But I mean, but that's what we're talking about-- >> Well, what do you mean by scraped data, 'cause that's important? >> Well, this is a well-known data science practice. And it's not that... nobody's being malicious here, nobody's trying to have a malintent, but I think it's just data scientists are just scruffy, they roll up their sleeves and they get data however they can. And so, the practice emerged. Look, they're built off of open-source software and everything's free, right, for them, for the most part? So they just start reading in screens and things that are available that you could see, they can optical character read it in, or they can do it however without having to have a subscription to any of that data, without having to have permission to any of that data. It's, "I can see it, so it's mine." But you know, that doesn't work in candy stores. We can't just go, or jewelry stores in my case, I mean, you can't just say, "I like that diamond earring, or whatever, I'm just going to take it because I can see it." (laughs) So, I mean, yeah we got to... that's scraping though. >> And the implications of that are suddenly now you've got a great new business initiative and somebody finds out that you used their private data in that initiative, and now they've got a claim on that asset. >> Right. And this is where things start to get super hairy, and you just want to make sure that you're being on the up-and-up with your data practices and you data ethics, because, in my opinion, 90% of what's gone wrong in AI or the fear factor of AI is that your privacy's getting violated and then you're labeled with data that you may or may not know even exists half the time. I mean. >> So, what's the answer? I mean as you were talking about these data scientists are scrappy, scruffy, roll-up-your-sleeves kind of people, and they are coming up with new ideas, new innovations that sometimes are good-- >> Oh yes, they are. >> So what, so what is the answer? Is this this code of ethics? Is it a... sort of similar to a Hippocratic Oath? I mean how would you, what do you think? >> So, it's a multidimensional problem. Cortnie and I were talking earlier that you have to have more transparency into the models you're creating, and that means a significant validation process. And that's where the chief data officer partners with folks in risk and other areas and the data science team around getting more transparency and visibility into what's the data that's feeding into it? Is it really the authoritative data of the company? And as Cortnie points out, do we even have the rights to that data that's feeding our models? And so, by bringing that transparency and a little more validation before you actually start making key, bet-the-business decisions on the outcomes of these models, you need to look at how you're vetting them. >> And the vetting process is part technology, part culture, part process, it goes back to that people process technology trying. >> Yeah, absolutely, know where your data came from. Why are you doing this model? What are you doing to do with the outcomes? Are you actually going to do something with it or are you going to ignore it? Under what conditions will you empower a decision-maker to use the information that is the output of the model? A lot of these things, you have to think through when you want to operationalize it. It's not just, "I'm going to go get a bunch of data wherever I can, I put a model together. Here, don't you like the results?" >> But this is Silicon Valley way, right? An MVP for everything and you just let it run until... you can't. >> That's a great point Cortnie (laughs) I've always believed, and I want to test this with you, we talk about people process technology about information, we never talk about people process technology and information of information. There's a manner of respects what we're talking about is making explicit the information about... information, the metadata, and how we manage that and how we treat that, and how we defuse that, and how we turn that, the metadata itself, into models to try to govern and guide utilization of this. That's especially important in AI world, isn't it? >> I start with this. For me, it's simple, I mean, but everything he said was true. But, I try to keep it to this: it's about free will. If I said you can do that with my data, to me it's always my data. I don't care if it's on Facebook, I don't care where it is and I don't care if it's free or not, it's still my data. Even if it's X23andMe, or 23andMe, sorry, and they've taken the swab, or whether it's Facebook or I did a google search, I don't care, it's still my data. So if you ask me if it's okay to do a certain type of thing, then maybe I will consent to that. But I should at least be given an option. And no, be given the transparency. So it's all about free will. So in my mind, as long as you're always providing some sort of free will (laughs), the ability for me to having a decision to say, "Yes, I want to participate in that," or, "Yes, you can label me as whatever label I'm getting, Trump or a pro-Hillary or Obam-whatever, name whatever issue of the day is," then I'm okay with that as long as I get a choice. >> Let's go back to it, I want to build on that if I can, because, and then I want to ask you a question about it Carl, the issue of free will presupposes that both sides know exactly what's going into the data. So for example, if I have a medical procedure, I can sit down on that form and I can say, "Whatever happens is my responsibility." But if bad things happen because of malfeasance, guess what? That piece of paper's worthless and I can sue. Because the doctor and the medical provider is supposed to know more about what's going on than I do. >> Right. >> Does the same thing exist? You talked earlier about governance and some of the culture imperatives and transparency, doesn't that same thing exist? And I'm going to ask you a question: is that part of your nonprofit is to try to raise the bar for everybody? But doesn't that same notion exist, that at the end of the day, you don't... You do have information asymmetries, both sides don't know how the data's being used because of the nature of data? >> Right. That's why you're seeing the emergence of all these data privacy laws. And so what I'm advising executives and the board and my clients is we need to step back and think bigger about this. We need to think about as not just GDPR, the European scope, it's global data privacy. And if we look at the motivation, why are we doing this? Are we doing it just because we have to be regulatory-compliant 'cause there's a law in the books, or should we reframe it and say, "This is really about the user experience, the customer experience." This is a touchpoint that my customers have with my company. How transparent should I be with what data I have about you, how I'm using it, how I'm sharing it, and is there a way that I can turn this into a positive instead of it's just, "I'm doing this because I have to for regulatory-compliance." And so, I believe if you really examine the motivation and look at it from more of the carrot and less of the stick, you're going to find that you're more motivated to do it, you're going to be more transparent with your customers, and you're going to share, and you're ultimately going to protect that data more closely because you want to build that trust with your customers. And then lastly, let's face it, this is the data we want to analyze, right? This is the authenticated data we want to give to the data scientists, so I just flip that whole thing on its head. We do for these reasons and we increase the transparency and trust. >> So Cortnie, let me bring it back to you. >> Okay. >> That presupposes, again, an up-leveling of knowledge about data privacy not just for the executive but also for the consumer. How are you going to do that? >> Personally, I'm going to come back to free will again, and I'm also going to add: harm impacts. We need to start thinking impact assessments instead of governance, quite frankly. We need to start looking at if I, you know, start using a FICO score as a proxy for another piece of information, like a crime record in a certain district of whatever, as a way to understand how responsible you are and whether or not your car is going to get broken into, and now you have to pay more. Well, you're... if you always use a FICO score, for example, as a proxy for responsibility which, let's face it, once a data scientist latches onto something, they share it with everybody 'cause that's how they are, right? They love that and I love that about them, quite frankly. But, what I don't like is it propagates, and then before you know it, the people who are of lesser financial means, it's getting propagated because now they're going to be... Every AI pricing model is going to use FICO score as a-- >> And they're priced out of the market. >> And they're priced out of the market and how is that fair? And there's a whole group, I think you know about the Fairness Accountability Transparency group that, you know, kind of watch dogs this stuff. But I think business leaders as a whole don't really think through to that level like, "If I do this, then this this and this could incur--" >> So what would be the one thing you could say if, corporate America's listening. >> Let's do impact. Let's do impact assessments. If you're going to cost someone their livelihood, or you're going to cost them thousands of dollars, then let's put more scrutiny, let's put more government validation. To your point, let's put some... 'cause not everything needs the nth level. Like, if I present you with a blue sweater instead of a red sweater on google or whatever, (laughs) You know, that's not going to harm you. But it will harm you if I give you a teacher assessment that's based on something that you have no control over, and now you're fired because you've been laid off 'cause your rating was bad. >> This is a great conversation. Let me... Let me add something different, 'cause... Or say it a different way, and tell me if you agree. In many respects, it's: Does this practice increase inclusion or does this practice decrease inclusion? This is not some goofy, social thing, this is: Are you making your market bigger or are you making your market smaller? Because the last thing you want is that the participation by people ends with: You can't play because of some algorithmic response we had. So maybe the question of inclusion becomes a key issue. Would you agree with that? >> I do agree with it, and I still think there's levels even to inclusion. >> Of course. >> Like, you know, being a part of the blue sweater club versus the (laughs) versus, "I don't want to be a convict," you know, suddenly because of some record you found, or association with someone else. And let's just face it, a lot of these algorithmic models do do these kinds of things where they... They use n+1, you know, a lot... you know what I'm saying. And so you're associated naturally with the next person closest to you, and that's not always the right thing to do, right? So, in some ways, and so I'm positing just little bit of a new idea here, you're creating some policies, whether you're being, and we were just talking about this, but whether you're being implicit about them or explicit, more likely you're being implicit because you're just you're summarily deciding. Well, okay, I have just decided in the credit score example, that if you don't have a good credit threshold... But where in your policies and your corporate policy did it ever say that people of lesser financial means should be excluded from being able to have good car insurance for... 'cause now, the same goes with like Facebook. Some people feel like they're going to have to opt of of life, I mean, if they don't-- >> (laughs) Opt out of life. >> I mean like, seriously, when you think about grandparents who are excluded, you know, out in whatever Timbuktu place they live, and all their families are somewhere else, and the only way that they get to see is, you know, on Facebook. >> Go back to the issue you raised earlier about "Somebody read my email," I can tell you, as a person with a couple of more elderly grandparents, they inadvertently shared some information with me on Facebook about a health condition that they had. You know how grotesque the response of Facebook was to that? And, it affected me to because they had my name in it. They didn't know any better. >> Sometimes there's a stigma. Sometimes things become a stigma as well. There's an emotional response. When I put the article out about why I left IBM to start this new AI Truth nonprofit, the responses I got back that were so immediate were emotional responses about how this stuff affects people. That they're scared of what this means. Can people come after my kids or my grandkids? And if you think about how genetic information can get used, you're not just hosing yourself. I mean, breast cancer genes, I believe, aren't they, like... They run through families, so, I-- >> And they're pretty well-understood. >> If someone swabs my, and uses it and swaps it with other data, you know, people, all of a sudden, not just me is affected, but my whole entire lineage, I mean... It's hard to think of that, but... it's true (laughs). >> These are real life and death... these are-- >> Not just today, but for the future. And in many respects, it's that notion of inclusion... Going back to it, now I'm making something up, but not entirely, but going back to some of the stuff that you were talking about, Carl, the decisions we make about data today, we want to ensure that we know that there's value in the options for how we use that data in the future. So, the issue of inclusion is not just about people, but it's also about other activities, or other things that we might be able to do with data because of the nature of data. I think we always have to have an options approach to thinking about... as we make data decisions. Would you agree with that? Yes, because you know, data's not absolute. So, you can measure something and you can look at the data quality, you can look at the inputs to a model, whatever, but you still have to have that human element of, "Are you we doing the right thing?" You know, the data should guide us in our decisions, but I don't think it's ever an absolute. It's a range of options, and we chose this options for this reason. >> Right, so are we doing the right thing and do no harm too? Carl, Cortnie, we could talk all day, this has been a really fun conversation. >> Oh yeah, and we have. (laughter) >> But we're out of time. I'm Rebecca Knight for Peter Burris, we will have more from MIT CDOIQ in just a little bit. (upbeat music)

>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, its theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer event in San Francisco, Spring, 2018. About 100 people, predominantly practitioners, which is a pretty unique event. Not a lot of vendors, a couple of them around, but really a lot of people that are out in the wild doing this work. We're really excited to have a return guest. We last saw him at Spark Summit East 2017. Can you believe I keep all these shows straight? I do not. Alfred Essa, he is the VP, Analytics and R&D at McGraw-Hill Education. Alfred, great to see you again. >> Great being here, thank you. >> Absolutely, so last time we were talking it was Spark Summit, it was all about data in motion and data on the fly, and real-time analytics. You talked a lot about trying to apply these types of new-edge technologies and cutting-edge things to actually education. What a concept, to use artificial intelligence, a machine learning for people learning. Give us a quick update on that journey, how's it been progressing? >> Yeah, the journey progresses. We recently have a new CEO come on board, started two weeks ago. Nana Banerjee, very interesting background. PhD in mathematics and his area of expertise is Data Analytics. It just confirms the direction of McGraw-Hill Education that our future is deeply embedded in data and analytics. >> Right. It's funny, there's a often quoted kind of fact that if somebody came from a time machine from, let's just pick 1849, here in San Francisco, everything would look different except for Market Street and the schools. The way we get around is different. >> Right. >> The things we do to earn a living are different. The way we get around is different, but the schools are just slow to change. Education, ironically, has been slow to adopt new technology. You guys are trying to really change that paradigm and bring the best and latest in cutting edge to help people learn better. Why do you think it's taken education so long and must just see nothing but opportunity ahead for you. >> Yeah, I think the... It was sort of a paradox in the 70s and 80s when it came to IT. I think we have something similar going on. Economists noticed that we were investing lots and lots of money, billions of dollars, in information technology, but there were no productivity gains. So this was somewhat of a paradox. When, and why are we not seeing productivity gains based on those investments? It turned out that the productivity gains did appear and trail, and it was because just investment in technology in itself is not sufficient. You have to also have business process transformation. >> Jeff Frick: Right. >> So I think what we're seeing is, we are at that cusp where people recognize that technology can make a difference, but it's not technology alone. Faculty have to teach differently, students have to understand what they need to do. It's a similar business transformation in education that I think we're starting to see now occur. >> Yeah it's great, 'cause I think the old way is clearly not the way for the way forward. That's, I think, pretty clear. Let's dig into some of these topics, 'cause you're a super smart guy. One thing's talk about is this algorithmic transparency. A lot of stuff in the news going on, of course we have all the stuff with self-driving cars where there's these black box machine learning algorithms, and artificial intelligence, or augmented intelligence, bunch of stuff goes in and out pops either a chihuahua or a blueberry muffin. Sometimes it's hard to tell the difference. Really, it's important to open up the black box. To open up so you can at least explain to some level of, what was the method that took these inputs and derived this outpout. People don't necessarily want to open up the black box, so kind of what is the state that you're seeing? >> Yeah, so I think this is an area where not only is it necessary that we have algorithmic transparency, but I think those companies and organizations that are transparent, I think that will become a competitive advantage. That's how we view algorithms. Specifically, I think in the world of machine learning and artificial intelligence, there's skepticism, and that skepticism is justified. What are these machines? They're making decisions, making judgments. Just because it's a machine, doesn't mean it can't be biased. We know it can be. >> Right, right. >> I think there are techniques. For example, in the case of machine learning, what the machines learns, it learns the algorithm, and those rules are embedded in parameters. I sort of think of it as gears in the black box, or in the box. >> Jeff Frick: Right. >> What we should be able to do is allow our customers, academic researchers, users, to understand at whatever level they need to understand and want to understand >> Right. >> What the gears do and how they work. >> Jeff Frick: Right. >> Fundamental, I think for us, is we believe that the smarter our customers are and the smarter our users are, and one of the ways in which they can become smarter is understanding how these algorithms work. >> Jeff Frick: Right. >> We think that that will allow us to gain a greater market share. So what we see is that our customers are becoming smarter. They're asking more questions and I think this is just the beginning. >> Jeff Frick: Right. >> We definitely see this as an area that we want to distinguish ourselves. >> So how do you draw lines, right? Because there's a lot of big science underneath those algorithms. To different degrees, some of it might be relatively easy to explain as a simple formula, other stuff maybe is going into some crazy, statistical process that most layman, or business, or stakeholders may or may not understand. Is there a way you slice it? Is there kind of wars of magnitude in how much you expose, and the way you expose within that box? >> Yeah, I think there is a tension. The tension traditionally, I think organizations think of algorithms like they think of everything else, as intellectual property. We want to lock down our intellectual property, we don't want to expose that to our competitors. I think... I think that's... We do need to have intellectual property, however, I think many organizations get locked into a mental model, which I don't think is just the right one. I think we can, and we want our customers to understand how our algorithm works. We also collaborate quite a bit with academic researchers. We want validation from the academic research community that yeah, the stuff that you're building is in fact based on learning science. That it has warrant. That when you make claims that it works, yes, we can validate that. Now, where I think... Based on the research that we do, things that we publish, our collaboration with researchers, we are exposing and letting the world know how we do things. At the same time, it's very, very difficult to build an engineer, an architect, scalable solutions that implement those algorithms for millions of users. That's not trivial. >> Right, right, right. >> Even if we give away quite a bit of our secret sauce, it's not easy to implement that. >> Jeff Frick: Right. >> At the same time, I believe and we believe, that it's good to be chased by our competition. We're just going to go faster. Being more open also creates excitement and an ecosystem around our products and solutions, and it just makes us go faster. >> Right, which gives to another transition point, which would you talk about kind of the old mental model of closed IP systems, and we're seeing that just get crushed with open source. Not only open source movements around specific applications, and like, we saw you at Spark Summit, which is an open source project. Even within what you would think for sure has got to be core IP, like Facebook opening up their hardware spec for their data centers, again. I think what's interesting, 'cause you said the mental model. I love that because the ethos of open source, by rule, is that all the smartest people are not inside your four walls. >> Exactly. >> There's more of them outside the four walls regardless of how big your four walls are, so it's more of a significant mental shift to embrace, adopt, and engage that community from a much bigger accumulative brain power than trying to just trying to hire the smartest, and keep it all inside. How is that impacting your world, how's that impacting education, how can you bring that power to bear within your products? >> Yeah, I think... You were in effect quoting, I think it was Bill Joy saying, one of the founders of Sun Microsystems, they're always, you have smart people in your organization, there are always more smarter people outside your organization, right? How can we entice, lure, and collaborate with the best and the brightest? One of the ways we're doing that is around analytics, and data, and learning science. We've put together a advisory board of learning science researchers. These are the best and brightest learning science researcher, data scientists, learning scientists, they're on our advisory board and they help and set, give us guidance on our research portfolio. That research portfolio is, it's not blue sky research, we're on Google and Facebook, but it's very much applied research. We try to take the no-knowns in learning science and we go through a very quick iterative, innovative pipeline where we do research, move a subset of those to product validation, and then another subset of that to product development. This is under the guidance, and advice, and collaboration with the academic research community. >> Right, right. You guys are at an interesting spot, because people learn one way, and you've mentioned a couple times this interview, using good learning science is the way that people learn. Machines learn a completely different way because of the way they're built and what they do well, and what they don't do so well. Again, I joked before about the chihuahua and the blueberry muffin, which is still one of my favorite pictures, if you haven't seen it, go find it on the internet. You'll laugh and smile I promise. You guys are really trying to bring together the latter to really help the former. Where do those things intersect, where do they clash, how do you meld those two methodologies together? >> Yeah, it's a very interesting question. I think where they do overlap quite a bit is... in many ways machines learn the way we learn. What do I mean by that? Machine learning and deep learning, the way machines learn is... By making errors. There's something, a technical concept in machine learning called a loss function, or a cost function. It's basically the difference between your predicted output and ground truth, and then there's some sort of optimizer that says "Okay, you didn't quite get it right. "Try again." Make this adjustment. >> Get a little closer. >> That's how machines learn, they're making lots and lots of errors, and there's something behind the scenes called the optimizer, which is giving the machine feedback. That's how humans learn. It's by making errors and getting lots and lots of feedback. That's one of the things that's been absent in traditional schooling. You have a lecture mode, and then a test. >> Jeff Frick: Right. >> So what we're trying to do is incorporate what's called formative assessment, this is just feedback. Make errors, practice. You're not going to learn something, especially something that's complicated, the first time. You need to practice, practice, practice. Need lots and lots of feedback. That's very much how we learn and how machines learn. Now, the differences are, technologically and state of knowledge, machines can now do many things really well but there's still some things and many things, that humans are really good at. What we're trying to do is not have machines replace humans, but have augmented intelligence. Unify things that machines can do really well, bring that to bear in the case of learning, also insights that we provide. Instructors, advisors. I think this is the great promise now of combining the best of machine intelligence and human intelligence. >> Right, which is great. We had Gary Kasparov on and it comes up time and time again. The machine is not better than a person, but a machine and a person together are better than a person or a machine to really add that context. >> Yeah, and that dynamics of, how do you set up the context so that both are working in tandem in the combination. >> Right, right. Alright Alfred, I think we'll leave it there 'cause I think there's not a better lesson that we could extract from our time together. I thank you for taking a few minutes out of your day, and great to catch up again. >> Thank you very much. >> Alright, he's Alfred, I'm Jeff. You're watching theCUBE from the Corinium Chief Analytics Officer event in downtown San Francisco. Thanks for watching. (energetic music)

(upbeat music) >> From the Corinium Chief Analytics Officer Conference, Spring, San Francisco. It's theCUBE! (computerized thrum) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer event in Spring 2018. Really, a ton of practitioners for such a very small event. Super, super intimate, super, super customer stories and practitioners, so we're really excited to have our next guest. She's Kirtida Parikh, she's the Head of Enterprise Business Analytics for Silicon Valley Bank. Welcome. >> Thank you. Good to be here. >> So, what do you think of the show? It's kind of an interesting little event. >> I personally do think that they do an amazing job of organizing this particular event, and out of all the events throughout the year I try to choose and come to this event. >> Right, very good. So, you were just on a panel. >> Kirtida: Yes. >> With a bunch of practitioners. For the folks that didn't attend the panel, what were some of the interesting things that came out of it? Some surprises? >> I think one of the main surprises that I had as one of the panel members is the audience, and the audience actually did say that not 99% of the people have issues working with other virtual teams within the bank, or within their own organization. And many people have tried to figure out how to work together, and that was a very pleasant surprise to me. >> And they're working better together. >> Absolutely. >> From what you said before we turned on the cameras. >> It's a higher productivity when you try to work things out together. >> What's going to happen to shadow IT if the IT department is suddenly easier to work with? >> (laughing) Well, I don't think it is either the department or a person that is difficult to work with. It's, I think, more of a clash of cultures between the two groups. And IT does need, for their own right reasons, to have a process in place and go by the rules so that they can keep the company safe from compliance and regulation perspective. >> Right. >> Whereas analytics, by nature, needs to be creative and has to focus on time to market. And they have to be agile and work really fast enough, and so they can't have the bandwidth to follow the process. So it's more of a clash of two cultures. >> Jeff: Right. >> And I think we need to open up the boundaries and think about virtual efforts to be able to get something done. >> That's interesting, because we always talk about people, process, and tech. And they're called "tech conferences," they're not called "process tech conferences." >> Yeah. >> And so there's a lot of focus on the technology and the new shiny object. >> Mm-hmm (affirmative). >> Whether it's Hadoop, or big data, or Spark, or, you know, all this fun stuff. But as you just said, really, the harder part is the people and the process. >> People. >> And as you just said, culture really is derived from the processes and the responsibilities that you have under your jurisdiction, I guess, so. >> Absolutely. And I personally feel technology is not an end by itself. It's a means to an end. >> Right, right. >> And so the success of a company is how you embrace. How people embrace technology leads to results. >> Right. >> It's neither technology nor people on their own, it's how they embrace technology is what leads to success. >> So I wonder if you can share some insight from your experience at Silicon Valley Bank? You're the head of the analytics group. You know, banks are interesting to me because banks have been data-driven forever, right? >> They have to be. >> There isn't really any money in a room somewhere. It's numbers on a page and numbers on a database. >> Kirtida: Mm-hmm (affirmative). >> And all your products are pretty digital, so, when you start to bring more advanced analytics and you try to change the culture a little bit and run it through the, overused, "digital transformation." What are some of the things you're looking at? How are they transformational? What's kind of the acceptance in the broader team, as you said, when there can be some culture clash, and you have regulation and you're a regulated industry and there's real issues and barriers that you have to overcome? >> Right. So, barriers are always there in any organization, in any industry, particularly when you are introducing a totally new way of making decisions. And when the company is very successful based on making intuition-based decisions, it's hard for you to sell the idea that, no, I can give you information, and that will expedite your decision-making process. So, I think when I joined the bank, I didn't realize, but 99% of my job was to be the change agent. (laughing) >> (laughing) Not an easy job. >> And a storyteller. >> Right, right. >> Because unless you tell the story and sell the idea, you are not able to bring the change. >> Jeff: Right. >> So, yes, there are barriers, and there are always going to be barriers. But I personally like challenges, so I embrace the challenges and try to overcome. So what I ended up doing is, I started thinking about where can I have IT add value, and where are the opportunities where I can value them? So instead of me going to the business and talking to them about what we can do together, I brought that team member along with me. So that visibility and transparency made them feel valued, and they were more than willing to partner with me, and so that changed the landscape to work with IT. But on the other hand, from the business side, I personally think that unless you have one or two examples, and one of my first examples was a business process. And it used to take a number of hours, and I reduced it to leave it only 10% of that time. And they said, oh, wow, that does make sense. What can we do more? Can we partner on this? So initially, first quarter, I had 20 questions and requests, and the second quarter... First whole year we had only twenty questions and requests, and the following quarter we had 200 of them. >> Wow. So when you're looking for an opportunity to apply your skills, your knowledge to bring some change to your organization, how much of it is you kind of searching for inefficiencies, say in the internal business process, versus maybe a business stakeholder saying, wow, you know, if we could only do X. Or I have this problem, can you help me find the root cause? Silicon Valley Bank's such a unique institution, because it's got a couple of segments that it really focuses on. >> Kirtida: Mm-hmm (affirmative). >> Obviously in tech, a little-known wine business. I think you guys do a lot of investing there. >> Yes. >> Because tech guys like to open wineries. >> Tech banking. >> (laughing) So you've got some really small specialty segments. So how did you find some of those early opportunities? >> You see, when you do something and it's successful, it's a two-edged sword. Things keep coming, and the demand grows exponentially fast, it's an exponential growth rate. So what we had to do was really focus on what matters the most, and that came only from two-way communication with the business as well as with the executive team. So if the executive team, we realize that this is the revenue-generating opportunities, here is where we can make a difference, we focus on it and show them the value. Or, if it is a process that really needed some attention, and we could benefit from cost effectiveness, so there was kind of an RY framework where we focus on it. But, to be very honest, we didn't have to look far to look for opportunities, just because revenue is the main focus for business as well as executives. >> Right, right, right. >> So it was a two-way communication that helped us really identify, but I didn't have to hunt for opportunities because, you know, that's where your experience come into play. >> Right, right. So, I'm just curious on the revenue side, the question always comes up, how do I get started, how do we get started, how do we get early wins to build momentum in my company? So was it customer retention, was it cross-selling? I mean, what were some of the things that you saw that were revenue-tied, and everybody likes being tied to revenue, where you thought you could have some success? >> So, my idea of really making a difference is very simple. What does the business focus on? How does a bank operate? They have to get new clients, and increase the size of the cake, or the size of the clientele that they have. So, acquisition is one area. >> Jeff: Okay. >> The second is, once you have them, how can you have them deepen their relationship with you so that the switching cost to another bank is higher? >> Jeff: Right. >> And the third is, once they're with you, you also want to retain them in many different ways by increasing client satisfaction. And then, of course, cost effectiveness. How do you plan your staffing needs and capacity? So, I started in each of those areas at least taking up one or two business questions and showing them the value. And now it's covering all those spectrum of businesses. >> That's great. So now you've got more inbound opportunities for places to apply your analytics than you probably have people to apply them. (laughing) >> (laughing) Yes. That's a good problem to have. >> That's a good problem to have. Well, I'd just love to get your take, too, on kind of the higher level view of the democratization of the data. Of the data itself, of the tools to operate the data, and then, of course, hopefully if you've democratized the access and the tools, hopefully when somebody finds something, they actually have the power to implement it. So how have you seen that environment change, not specifically at Silicon Valley Bank, but generically over the last couple years within your career? >> Well, I personally think that, in my career, in different organizations, democratization is a necessity. It's no longer a topic of discussion. It is something you have to do. Because analytics in general is an enabler community, and you can have as many enablers as you have the people who are users. So, how do you really create analytic center of excellence by giving them the ropes and tools to fish for themselves, or to find their own insights and create their own stories. >> Jeff: Right. >> So what I did, and this worked really well, is create a virtual team of analytic center of excellence where it's not only my team members, but it's some other pockets of analytics teams, but at the same time, the users themselves. >> Jeff: Right. >> And they become the advocates of what you do, and as far as tools are concerned, you know, we used to have an era where you have IT control tools to be able to democratize and give the insights, and now it is user-driven tools. So we did move from one end of the spectrum to the other end of the spectrum, so that it becomes easy for the user to actually grasp the insights. >> Right, right. And still maintain control and governance and all that kind of stuff, yeah. >> Oh, yeah. Security, information security control is a big one, and we can maintain that. >> Right, right. >> And as far as the governance and the data, I mean, they're not pulling their own definitions and other things. It's based off of information foundation, which is solid and scalable. >> Which is solid. Okay, so, going to give you the last word. You've said the word "story" at least four times. >> Uh-huh. (laughing) >> Maybe more since we sat down, we'll have to check the transcript. I wonder if you could expand a little bit on how valuable storytelling is in this whole process. I think it gets left off a lot, right? >> Mm-hmm (affirmative). >> People want to focus on the math and focus on the technology, and focus on the wiz-bang and the flashing lights and the datacenter, but you keep saying "story." Why do you keep saying story? Why is story so important? >> You have multiple stakeholders. First thing is the executive team, they do not have the time. I mean, they are focusing on so many different aspects that they don't have the time enough for anybody to go through the whole textbook, or whole chapter. So if you can tell them story in 30 seconds in an elevator, or three minutes in a hallway, and then request for 30 minutes, you are bound to get some time with them. And in that short time, would you rather show them the value that you can bring to the table, or would you show them how the sausage is being made? >> Jeff: Right. >> And so that's where one type of storytelling is important, to sell the idea. The second is the working team, who we are working with. And I have seen that unless you tell your story and sell the story, you can't get their buy-in, and the virtual team effort that I was talking about fails miserably. So that's another area where you need to tell the story. >> Jeff: Right. >> And the third is, once you have an analytic product, then how do you get adopters? So to tell the adopter what is in there for them is a storytelling too. >> Right, right. Small detail. >> Yeah. >> Actually getting people to use it for their benefit. >> (laughing) >> All right, well I think this is so important, because as you mentioned a number of times, it's about people, and people working together, teams working together in this collaborative effort to make it happen. As somebody else said, it's a team sport. >> And you know, the interesting that I have seen is now that I come to these conferences, there are five people, at least, in different five companies, they said they've hired a journalist on their team because they realized the storytelling is so important. >> Jeff: Really? >> Yeah, so the hybrid function analytics, we say, requires data engineers, data scientists, statisticians, communicators, storyweavers and tellers, which is a journalist, and then a change agent and project manager. >> That's why they bring theCUBE. >> (laughing) >> Trying to tell the story. So, thank you for sharing your story. >> Thank you so much. >> We really appreciate the time. All right. >> Kirtida: Take care. >> You're watching theCUBE from the Corinium Chief Analytics Officer Summit in San Francisco. Thanks for watching. (computerized music)

>> From the Corinium Chief Analytics Officer Conference Spring San Francisco, it's The Cube >> Hey welcome back, Jeff Frick with The Cube We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring event. We go to Chief Data Officer, this is Chief Analytics Officer. There's so much activity around big data and analytics and this one is really focused on the practitioners. Relatively small event, and we're excited to have another practitioner here today and it's Kevin Bates. He's the VP of Enterprise Data Strategy Execution for Fannie Mae. Kevin, welcome. >> It's a mouthful. Thank you. >> You've got it all. You've got strategy, which is good, and then you've got execution. And you've been at a big Fannie Mae for 15 years according to your LinkedIn, so you've seen a lot of changes. Give us kind of your perspective as this train keeps rolling down the tracks. >> OK. Yeah, so it's been a wild ride I've been there, like you say, for 15 years. When I started off there I was writing code, working on their underwriting systems. And I've been in different divisions including the credit loss division, which had a pretty exciting couple of years back around 2008. >> More exciting than you care to - >> Well, there was certainly a lot going on. Data's been sort of a consistent theme throughout my career, so the data, Fannie Mae not unlike most companies, is really the blood that keeps the entire organism functioning. So over the past few years I've actually moved into the Enterprise Data Division of the company where I have responsibility for delivery, operations, platforms, the whole 9 yards. And that's really given me the unique view of what the company does. It's given me the opportunity to touch most of the different business areas and learn a lot about what we need to do better. >> So how is the perspective changed around the data? Before data was almost a liability because you had to store it, keep it, manage it, and take good care of it. Now it's a core asset and we see the valuations up and down. One on one probably the driver of some of the crazy valuations that you see in a lot of the companies. So how has that added to change and what have you done to take advantage of that shift in attitude? >> Sure, it's a great question. So I think the data has always been the life blood and key ingredient to success for the company, but the techniques of managing the data have changed for sure, and with that the culture has to change and how you think about the data has to change. If you go back 10 years ago all of our data was stored in our data center, which means that we had to pay for all of those servers, and every time data kept getting bigger we had to buy more servers and it almost became like a bad thing. >> That's what I said, almost like a liability >> That's right And as we've certainly started adopting the cloud and technologies associated with the cloud you may step into that thinking "OK, now I don't have to manage my own data center I'll let Amazon or whoever do it for me." But it's much more fundamental than that because as you start embracing the cloud and now storage is no longer a limitation and compute is no longer a limitation the numbers of tools that you use is no longer really a limitation. So as an organization you have to change your way of thinking from "I'm going to limit the number of business intelligence tools that my users can take advantage of" to "How can I support them to use whatever tools they want?" So the mentality around the data I think really goes to how can I make sure the right data is available at the right time with the right quality checks so that everybody can say "yep, I can hang my hat on that data" but then get out of the way and let them self serve from there. It's very challenging, there's a lot of new tools and technologies involved. >> And that's a huge piece of the old innovation game to have the right data for the right people with the right tools and let more people play with it. But you've got this other pesky thing like governance. You've got a lot of legal restrictions and regulations and compliances. So how do you fold that into opening up the goodies, if you will. >> So I think one effort we have is we're building a platform we call the Enterprise Data Infrastructure so for that 85 percent of data at Fannie Mae what we do is loans, we create securities from the loans. And there's liabilities. There's a pretty finite set of data areas that are pretty much consistent at Fannie Mae and everybody uses those data sets. So taking those and calling them enterprise data sets that will be centralized they will be presented to our customers in a uniform way with all of the data quality checks in place. That's the big effort. It means that you're standardizing your data. You're performing a consistent data quality approach on that data and then you're making it available through any number of consumption patterns so that can be applications needed, so I'm integrating applications. It could be warehousing analytics. But it's the same data and it comes from that promise that we've tagged it enterprise data and we've done that good stuff to make sure that it's good, that it's healthy. That we know where we stand so if it's not a good data set we know how to tag it and make it such. For all the other data around we have to let our business partners be accountable for how they're enriching that data and innovating and so forth. But governance is not a - I think in the past another part of your question, governance used to be more of a, slow everybody down but if we can incorporate governance and have implied governance in the platform and then allow the customers to self serve off of that platform, governance becomes really that universal good. That thing that allows you to be confident that you can take the data and innovate with that data. >> So I'm curious how much of the value add now comes from the not enterprise data. The outside the core which you've had forever. What's the increasing importance and overlay of that exterior data to your enterprise data to drive more value out of your enterprise data? >> So that enterprise data like I say may be the 85%, it's just the facts. These are the loans we brought in. Here's how we can aggregate risk or how we can aggregate what we call UPB, or the value of our loans. That is pretty generic and it's intended to be. The third party data sets that our business partners may bring in that they bump up against that data can give them strategic advantages. Also the data that those businesses generate our business lines generate within their local applications which we would not call enterprise data, that's very much their special sauce. That's something that the broader organization doesn't need. Those things are all really what our data scientists and our business people combine to create the value added reports that they use for decisioning and so forth. >> And then I'm curious how the big data and the analytics environment has changed from the old day where you had some PHds and some super bright guys that ran super hard algorithms and it was on Mahogany Row and you put in the request and maybe from down high someday you'll get your request versus really trying to enable a broader set of analysts to have access to that data with a much broader set of tools, enabling a bunch of tools versus picking the one or two winners that are very expensive, you got to limit the seats et cetera. How has that changed the culture of the company as well as the way that you are able to deliver products and deliver new applications if you will? >> So I think that's a work in progress. We still have all the PHds and they still really call the shots. They're the ones that get the call from the Executive Vice President and they want to see something today that tells them what decision they should make. We have to enable them. They were enabled in the past by having people basically hustle to get them what they need. The big change we're trying to make now is to present the data in a common platform where they really can take it and run with it so there is a change in how we're delivering our systems to make sure we have the lowest level of granularity. That we have real time data. there's no longer waiting. And the technology tools that have come out in the past 10 years have enabled that. It's not just about implementing that, making it available to all those Phds. There's another population of analysts that is now empowered where they were not before. The guys that suffered just using excel or access databases that were I would call them not the power users but the empowered analysts. The ones who know the data, know how to query data but they're not hard core quants and they're not developers. Those guys have access to a plethora of tools now that were never available before that allow them to wrangle data from 20 different data sets, align it, ask questions of it. And they're really focused on operations and running our systems in a smoother, lower cost way. So I think the granularity, the timing, and support for that explosion of tools we'll still have the big, heavy SAS and R users that are the quants. I think that's the combination everything has to be supported and we'll support it better with higher quality, with more recent data, but the culture change isn't going to happen even in a few years. It will be a longer term path for larger organizations to really see maybe possibilities where they can restructure themselves based on technology. Right now the technologies are early enough and young enough that I think they're going to wait and see. >> Obviously you have a ton of legacy systems, you have all these tools. You have that core set, your enterprise data that doesn't really change that much. What's the objective down the road? Are you looking to expand on that core set? Is it such a fixture that you can't do anything with it in terms of flexibility? Where do you go from here? if we were to sit down three years from now what are we going to be talking about? >> So two things. One, I hope I'll be looking back with excitement at my huge success at transforming those legacy systems. In particular we have what we call the legacy warehouses that have been around well over 20 years that are limited and have not been updated because we've been trying to retire them for many years. Folding all of that into my core enterprise data infrastructure that will be fully aligned on terminology, on near-real time, all those things. That will be a huge success, I'll be looking back and glowing about how we did that and how we've empowered the business with that core data set that is uniquely available on this platform. They don't need to go anywhere else to find it. The other thing I think we'll see is enabling analysts to utilize cloud-based assets and really be successful working both with our on-premises data center, our own data center-supported applications but also starting to move their heavy running quantitative modeling and all the sorts of things they do into the data lake which will be cloud based and really enabling that as a true kind of empowerment for them so they can use a different sent of tools. They can move all that heavy lifting and the servers they sometimes bring down right now move it into an environment where they can really manage their own performance. I think those are going to be the two big changes three years from now that will feel like we're in the next generation. >> All right. Kevin Bates, projecting the future so we look forward to that day. Thanks for taking a few minutes out of your day. >> Thank you. >> All right, thanks. He's Kevin, I'm Jeff. You're watching The Cube from the Corinium Chief Analytics Officer Event in San Francisco. Thanks for watching. (music)

>> Announcer: From the Corinium Chief Analytics Officer Conference Spring, San Francisco It's theCUBE. >> Hey welcome back, everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring Event about a hundred CAO's as opposed to CDO's talking about big data, transformation and analytics and the role of analytics and a lot of practitioners are really excited to have our next guest. He's up from Mexico City, it's Jose Murillo. He's the chief analytics officer from Banorte. Jose, great to see you. >> Thank you for having me, Jeff. >> Absolutely, so for people that aren't familiar with Banorte give us a quick overview. >> Banorte's the second largest financial group in Mexico. We, for the last, during the last three years were able to leapfrog city bank. >> Congratulations, and as we were talking before we turned the cameras on, you and your project had a big part of that. So before we get in it, you are a chief analytics officer. How did you come in, what's the reporting structure, how do you work within the broader spectrum of the bank? >> Well I moved to Banorte like about five years ago from, I was working at the central bank where I spent about 10 years in the MPC, the Monitor Policy Committee, and I was invited by initially by the president of the board and when the new chief operating officer was named he invited me to, to lead a new analytics business unit that he wanted to create. And that's the way that I arrived there. >> Okay so you report in to the COO. >> He's the COO/CFO, so he's not only a very smart guy but a very powerful guy running the organization. >> And does the CIO also report to him? >> The CIO, the CDO, the CMO report to him. >> Okay so you have a CDO as well Chief Data Officer. >> We have a CDO who I work very close with him. >> We could go for a long time I might not let you leave for lunch. So I'm just curious on the relationship between the CDO and the CAO, the data officer and the analytics officer. We often hear one or the other, it's very seldom that I've heard both. So how do you guys divide and conquer your responsibilities? How do you parse that out? >> I guess he provides the foundation that we need to find analytics projects that are going to transform the financial group and he has been a very good partner in providing the data that we need and basically what we do as the CAO we find those opportunities to improve the efficiency, to bring the customer to the center, and be able to deliver value to our stakeholders. >> Right, so he's really kind of giving you the infrastructure if you will, of making that data available, getting it to you from all various sources, et cetera, that then you can use for your analytics magic on top. >> Exactly >> Okay, so that's very good, so when we sat down you said an exciting report has come out from, I believe it was HBR, about the tremendous ROI that you guys have realized. So you tell the story better than I, what did they find in your recent article? >> Well in the recent article from the Harvard Business Review is how Banorte has made its analytics business unit pay off. And what we have found in the past two and a half years is we've been able to deliver massive value and by now we have surpassed a billion dollars in net income creation. From analytics projects made on cost saving strategies and revenue generating projects. >> So you paid for yourself just barely >> Yeah. >> No I mean that's such a great story, just barely 'cause it's so it's so important. So as you said, that billion dollars have been realized both in cost savings but more importantly on incremental revenue and that's really the most important thing. >> Exactly >> So how are you measuring that ROI? >> So basically the way we measure it is on cost saving strategies that are related to a risk operational and financial cost. It's the contemporary news effect. And that can be audited. And on the other side, on revenue generating projects, the way we do it is we estimate the customer lifetime value, which is nothing else than the net present value of the relationship with our customers, so we need to estimate survival rates plus the depth of the relationship with our customers. >> So I just love, so you're doing all kinds of projects, you're measuring the value of the projects. What are some of the projects that had a high ROI that you would've never guessed that you guys applied some analytics to and said wow, terrific value relative to what we expected. >> Let me tell you about two types of projects. The first project that we started on was on cost of risk cutting strategies. And we delivered massive value and very quickly. So that helped us gain credibility. And the way we do it, we did it, is like to analyze a dicing of the data where we had excessive cost of risk. And in the first year, actually, that was the first quarter of Operations, we yielded about a 25% incremental value to the credit card business. And after that, we start to work with them and started the discovery data process. And from there, we were able to optimize analytically the cross cell process. And that's a project that has already a three year maturity. And by this time, we are able to sell, without having any bricks or mortars, about 25% of the credit cards sold by the financial group. If we were a territory within the financial group, we would be the largest one with 400 basis points lower on cost of risk, 30% more on activation rates. And it's no surprise that the acquisition cost is 30% less, vis-a-vis our most efficient channel. >> Right, I just want to keep digging down into this, Jose, there's a lot of this stuff to go. I mean, you've been issuing cards forever. So was it just a better way to score customers, was it a better way to avoid the big fraud customers, was it a better way to steal customers maybe from a competitor with a competitive rate that you can afford, I mean, what are some of the factors that allowed you to grow this business in such a big way? >> I guess it's something that has been improving during the first three years. The first thing is that we made like, a very simple cascade on seeing why we were not that efficient cross cell process. And we kind of fixed every part of it. Like on the income estimation models that we had, and we partner with the risk department to improve them. Up to the information that we had on our customers to contact them, and we partner with data governance to improve those. And finally, on the delivery process and all the engaging process with the customers. And it seemed that we were going to find something that was going to be more costly, but it was something that we had at the center of the customers so that it was more likely for them to go and pick up the card and we deliver it to their homes. And finally, that process was much more efficient and the gains that we had, we shared them with our customers. And after three years, we've done things with artificial intelligence to have much better scripts so that we are better able to serve our customers. We do a lot of experimentation, experimentation that we didn't do before. And we use some concepts from behavioral economics to try to explain much better the value proposition to our customers. >> So I just, I love this point, is that it was a bunch of small, it was optimizing lots of little steps and little pieces of the pie that added up to such a significant thing, it wasn't like this magic AI pixie dust. >> Initially, it as a big bang, and then it has been something incremental that has since, it's a project that at the end of the day, we own, and it's something that we are tracking. We are willing to put all the effort to have all the incremental efficiency within the process. >> So people, process, and technology, we talk about, those are the three pieces always to drive organizational change. And usually, the technology is the easy part, the hard part is the people and the process. So as you and your team have started to work with the various lines of businesses for all these different pieces. Promotional piece, customary attention piece, risk and governance piece, cross sale pice, how has their attitude towards your group changed over time as you've started to deliver insight and all this incremental deltas into their business. >> I guess you are hitting just on the spot. Building the models is the easy part. The hard part is to build the consensus around, to change a process that has run for 20 years, there's a lot of inertia. >> Right, right. >> And there are a lot of silos within organizations. So initially, I guess, the credibility that we gained initially helped us move faster. And at the end of the day, I think what happens is the way that we are set up is that the incentives are very well aligned within the different units that need to interact in the sense that we are a unit that is sponsored by the, corporately sponsored, and we make it easier for our partners to attain their goals. So that's, and they don't share the cost of us, so that helps. >> And those are the goals they already had. So you're basically helping them achieve their objectives that they already had better and more efficiently. >> Yeah, and you are pointing out correctly, it's the people, and besides the math, it's a highly, you could say diplomatic or political position in the sense that you need to have all the different partners and stakeholders aligned to change something that has been running for 20 years. >> Right, right. And i just love it, it's a ton of little marginal improvements across a wide variety of tough points, it's so impactful. So as you look forward now, is there another big bang out there, or do you just see kind of this constant march of incremental improvement, and, or are you just going to start getting into more different businesses or kind of different areas in the bank to apply the same process, where do you go next? >> Well, we started with the credit card business, but we moved toward the verticals within the financial group. From mortgages, auto loans, payroll loans, to we are working with the insurance company, the long term savings company. So we've increased the scope of the group. And we moved not only from cost to revenue generating projects. And so far, it has been, we have been on an exponential increase of our impact, I guess that's the big question. The first, we were able to do 46 times our cost. The second year, we made 106 times our cost, the third year, we are close to 200 times our cost with an incremental base. And so far, we've been on this increasing slide. At some point, it's, I guess, we are going to decelerate, but so far, we haven't hit the point. >> Right, the law of big numbers, eventually, you got to, eventually, you'll slow down a little bit. All right, well Jose, I'll give you the last word before we sign off here. Kind of tips and tricks that you would share with a peer if we're sitting around on a Friday afternoon on a back porch. You know, as you've gone through this journey, three and a half years and really sold you and your vision into the company, what would you share with a peer that's kind of starting this journey or starting to run into some of the early hurdles to get past. >> I guess there are two things that I could share. And once you have built a group like this and you have already, the incentives aligned and you have support from the top in the sense that they know that there's no other way they want really to compete and be successful, and suppose that you have all these preconditions set up and suddenly, you have a bunch of really smart people that are coming to a company, so you need to focus on ROI, high ROI projects. I;s very easy to get distracted on non-impactful projects. And I guess, the most important thing is that you have to learn to say no to a lot of things. >> Speaking my language, I love it. Learn to say no, it's the most important thing you'll ever, all right, well Jose, thanks for spending a few minutes and congratulations on all your success, what a great story. >> Thank you for having me, Jeff. >> Absolutely, he's Jose, I'm Jeff, you're watching theCUBE from the Corinium Chief Analytics Officer Summit in downtown San Francisco. (electronic music)

>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, it's theCUBE. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer Symposium or Summit in San Francisco at the Parc 55 Hotel. We came up here last year. It's a really small event, very intimate, but a lot of practitioners sharing best practices and we're excited to have a really data-driven company represented, see Scott Zoldi, Chief Analytics Officer from FICO, Scott, great to see you. >> It's great to be here, thanks Jim. >> Absolutely. So, before we jump into it, I was just kind of curious. One of the things that comes up all the time, when we do Chief Data Officer and there's this whole structuring of how do people integrate data organizationally? Does it report to the CIO, the CEO? So, how have you guys done it, where do you report into in the FICO? >> So at FICO, when we work with data, it's generally going up through our CIO, but as part of that we have both the Chief Analytics Officer and the Chief Technology Officer that are also part of that responsibility of ensuring that we organize the data correctly, we have the proper governance in place, right, and the proper sort of concerns around privacy and security in place. >> Right, so you guys have been in the data business forever, I mean, data is your business, so when you hear all this talk about digital transformation and becoming more data-driven as a company, how does that impact a company like FICO? You guys have been doing this forever. What kind of opportunities are there to take, kind of, analytics to the next level? >> For us, I think it's really exciting. So, you're right, we've been at it for 60 years, right? And analytics is at the core of our business, and operationalizing out the data and around bringing better analytics into play. And now there's this new term, you know, Operationalizing Analytics. And so as we look at digital, we look at all the different types of data that are available to decisions and all the computation power that we have available today, it's really exciting now, to see the types of decisions that can be made with all the data and different types of analytics that are available today. >> Right, so what are some of those nuanced decisions? 'Cause, you know, from the outside world looking in, we see, kind of binary decisions, you know either I get approved for the card or not, or I get the unfortunate, you know you card didn't get through, we had a fraud event, I got to call and tell them please turn my card back on. Seems very binary, so as you get beyond the really simple binary, what are some of the things that you guys have been able to do with the business, having a much more obviously nuanced and rich set of data from which to work? >> So one of the things that we focus on is really around having a profile of each and every customer so we can make a better behavioral decision. So we're trying to understand behavior, ultimately, and that behavior can be manifested in terms of making a fraud decision, or a credit decision. But it's really around personalized analytics, essentially like an analytics of one, that allows us to understand that customer very, very well to make a decision around, what is the next sort of opportunity from a business perspective, a retention perspective, or improving that customer experience. Right, and then how much is it is your driving, could you talk about the operationalizing this? So there's operationalizing it inside the computers and the machines that are making judgements, and scoring things, and passing out decisions, versus more the human factor, the human touch. How do you divide which goes where? And how do you prioritize so that more people get more data from which to work with and make decisions, versus just the ones that are driven inside of an algorithm, inside of a machine? >> Yeah, it's a great point, because a lot of times organizations want to apply analytics to the data they have, but they haven't given a thought to the entire operization of that. So we generally look at it in four parts. One is around data, what is the data we need to make a decision, 'cause decisions always come first, business decisions. Where is that data, how do we gather it and then make it available? Next stage, what are the analytics that we want to apply? And that involves the time that we need to make a decision and how to make that decision over time. And then comes the people part, right? What is the process to work with that score, record the use of, let's say, an analytic, what was the outcome, was it more positive or based on using that analytic, right? And incorporating that back to make a change to the business over time, make actions over time in terms of improving that process, and that's a continual sort of process that you have to have when you operationalize analytics. Otherwise, this could be a one-off sort of analytic adventure, but not part of the core business. >> Right, and you don't want that. Now what about the other data, you know third-party data that you've brought in that isn't kind of part your guys' core? Obviously you have a huge corpus of your own internal data and through your partner financial institutions, but have you started to pull in more kind of third-party data, social data, other types of things to help you build that behavioral model? >> It kind of depends on the business that we're in and the region that we're in. Some regions, for example, outside the United States they're taking much more advantage of social data and social media, and even mobile data to make, let's say, credit decisions. But we generally are finding that most organizations aren't even looking that up, they already have it housed appropriately and to the maximum extent, and so that's usually where our focus is. Right, so to shift gears about the inside, and there's an interesting term, explainable AI, I've never heard that phrase, so what exactly, when you guys talk about explainable AI, what does that mean? Yeah, so machine-learning is kind of a very, very hot topic today and it's one that is focused on development of machine-learning models that learn relationships in data. And it means that you can leverage algorithms to make decisions based on collecting all this information. Now, the challenge is that these algorithms are much more intelligent than a human being, they're superhuman, but generally they're very difficult to understand how they made the decision, and how they came up with a score. So, explainable AI is around deconstructing and analyzing that model so we can provide examples and reasons for why the model scored the way it did. And that's actually paramount, because today we need to provide explanations as part of regulatory concerns around the use of these models, and so it's a very core part of that fact that as we operationalize analytics, and we use things like machine-learning and artificial intelligence, that explainability, the ability to say why did this model score me this way, is at front and center so we can have that dialogue with a customer and they can understand the reasons, and maybe improve the outcome in the future. >> Right, and was that driven primarily by regulations or because it just makes sense to be able to pull back the onion? On the other hand, as you said, the way machines learn and the way machines operate is very different than the way humans calculate, so maybe, I don't know if there's just some stuff in there that's just not going to make sense to a person. So how do you kind of square that circle? >> So, for us our journey to explainable AI started in the early 90s, so it's always been core to our business because, as you say, it makes common sense that you need to be able to explain that score, and if you're going to have a conversation with the customer. You know, since that time, machine-learning's become much more mainstream. There's over 2,000 start-up companies today all trying to apply machine-learning and AI. >> Right. >> And that's where regulation is coming in, because in the early days we used explainable AI to make sure we understood what the model did, how to explain it to our governance teams, how to explain it to our customers, and the customers explain it to their clients, right? Today, it's around having regulation to make sure that machine-learning and artificial intelligence is used responsibly in business. >> Yeah, it's pretty amazing, and that's why I think we hear so much about augmented intelligence as opposed to artificial intelligence, there's nothing artificial about it. It's very different, but it really is trying to add to, you know, provide a little bit more data, a little bit more structure, more context to people that are trying to make decisions. >> And that's critically important because, you know, very often, the AI or machine-learning will make a decision differently than we will, so it can add some level of insight to us, but we always need that human factor in there to kind of validate the reasons, the explanations, and then make sure that we have that kind of human judgment that's running alongside. >> Right, right. So I can't believe I'm going to sit here and say that it's, whatever it is, May 15th today, the year's almost halfway over. But what are some of your priorities for the balance of the year, what are some of the things you are working on as you look forward? Obviously, FICO's a big data-driven company, you guys have a ton of data, you're in a ton of transactions so you've got kind of a front edge of this whole process. What are you looking at, what are some of your short-term priorities, mid-term priorities, as you move through the balance of the year and into next year? >> So number one is around explainable AI, right? And really helping organizations get that ability to explain their models. We're also focused very much around bringing more of the unsupervised analytic technologies to the market. So, very often when you build a model, you have a set of data and a set of outcomes, and you train that model, and you have a model that makes prediction. But more and more, we have parts of our businesses today that where unsupervised analytic models are much more important, in areas like-- >> What does that mean, unsupervised analytics models? >> So, essentially what it means is we're trying to look for patterns that are not normal, unlike any other customers. So if you think about a money launderer, there's going to be very few people that will behave like a money launderer, or an insider, or something along those lines. And so, by building really, really good models of predicting normal behavior any deviation or a mis-prediction from that model could point to something that's very abnormal, and something that should be investigated. And very often, we use those in areas of cyber-security crimes, blatant money laundering, insider fraud, in areas like that where you're not going to have a lot of outcome data, of data to train on, but you need to still make the decisions. >> Wow. Which is really hard for a computer, right? That's the opposite of the types of problems that they like. They like a lot of, a lot of, of revs. >> Correct, so that's why the focus is on understanding good behavior really, really well. And anything different than what it thinks is good could be potentially valuable. >> Alright, Scott, well keep track of all of our scores, we all depend on it. (laughs) >> Scott: We all do. >> Thanks for taking a few minutes out of your day. >> Scott: Appreciate it. >> Alright, he's Scott, I'm Jeff, you are watching theCUBE from San Francisco. Thanks for watching. (upbeat electronic music)

>> Announcer: From the Corinium Chief Analytics Officer Conference. Spring, San Francisco, it's theCUBE! >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring event 2018. About 100 people, really intimate, a lot of practitioners sharing best practices about how they got started, how are they really leveraging data and becoming digitally transformed, analytically driven, data driven. We're excited to have Vishal Morde. He's the VP of Data Science at Barclays, welcome. >> Glad to be here, yeah. >> Absolutely. So we were just talking about Philly, you're back in Delaware, and you actually had a session yesterday talking about Barclays journey. So I was wondering if you could share some of the highlights of that story with us. >> Absolutely, so I had a talk, I opened the conference with data science journey at Barclays. And, we have been on this journey for five years now where we transform our data and analytics practices and really harness the power of Big Data, Machine Learning, and advanced analytics. And the whole idea was to use this power of, newly found power that we have, to make the customer journey better. Better through predictive models, better through deeper and richer consumer insights and better through more personalized customer experience. So that is the sole bet. >> Now it's interesting because we think of financial services as being a data driven, organization already. You guys are way ahead Obviously Wall Street's trading on microseconds. What was different about this digital transformation than what you've been doing for the past? >> I think the key was, we do have all the data in the world. If you think about it, banks know everything about you, right? We have our demographic data, behaviors data. From very granular credit card transactions data, we have your attitudal data, but what we quickly found out that we did not have a strategy to use that data well. To improve our our productivity, profitability of a business and make the customer experience better. So what we did was step one was developing a comprehensive data strategy and that was all about organizing, democratizing, and monetizing our data assets. And step towards, then we went about the monetization part in a very disciplined way. We built a data science lab where we can quickly do a lot of rapid prototyping, look at any idea in machine learning data science, incubate it, validate it, and finally, it was ready for production. >> So I'm curious on that first stage, so you've got all this data, you've been collecting it forever, suddenly now you're going to take an organized approach to it. What'd you find in that first step when you actually tried to put a little synthesis and process around what you already had? >> Well the biggest challenge was, the data came from different sources. So we do have a lot of internal data assets, but we are in the business where we do have to get a lot of external data. Think about credit bureau's, right? Also we have a co-brand business, where we work with partners like Uber, imagine the kind of data we get from them, we have data from American Airlines. So our idea was to create a data governance structure of, we formed a Chief Data Office, the officer forum, we got all the people across our organization to understand the value of data. We are a data driven company as you said but, it took us a while to take that approach and importance of data, and then, data analytics need to be embedded in the organizational DNA, and that's what we're going to focus on first. Data awareness of importance of data, importance of governance as well, and then we could think about democratizing and monetizing, organization's the key for us. >> Right, right, well so how did you organize, how has the Chief Data Officer, what did he or she, who did he or she report to, how did you organize? >> Right, so it was directly reporting to our CEO. >> Jeff: Into the CEO, not into the CIO? >> Not into the CIO. We had a technology office, we do kind of, have a line-of-sight or adopted line with technology, and we made sure that that office has a lot of high-level organization buy-in, they are given budgets to make sure the data governance was in place, key was to get data ownership going. We were using a lot of data, but there was no data ownership. And that was the key, once we know that, who actually owned this data, then you can establish a governance framework, then you can establish how you use this data, and then, how to be monetized. >> So who owned it before you went through this exercise, just kind of, it was just kind of there? >> Yeah, there wasn't a clear ownership, and that's the key for us. Once you establish ownership, then it becomes an asset, we were not treating data as an asset, so there was a change in, kind of mindset, that we had to go through, that data is an asset, and it was used as a means to an end, rather than an asset. >> Right, well what about the conflict with the governance people, I'm sure there was a lot of wait, wait, wait, we just can't open this up to anybody, I'm sure it's a pretty interesting discussion because you have to open it up to more people, but you still have to obviously follow the regs. >> Right, and that's where there are a lot of interesting advancement in data science, where, in the area of data governance, there are new tools out there which lets you track who's actually accessing your data. Once we had that infrastructure, then you can start figuring out okay, how do we allow access, how do we actually proliferate that data across different levels of the organization? Because data needs to be in the hands of decision makers, no matter who they are, could be our CEO, to somebody who's taking our phone calls. So that democratization piece became so important, then we can think about how do you-- you can't directly jump into monetization phase before you get your, all the ducks in order. >> So what was the hardest part, the biggest challenge, of that first phase in organizing the data? >> Creating that 360 degree view on our customers, we had a lot of interesting internal data assets, but we were missing big pieces of the puzzles, where we're looking at, you're trying to create a 360 degree view on a customer, it does take a while to get that right, and that's where the data, setting up the data governance piece, setting up the CDO office, those are the more painful, more difficult challenges, but they lay the foundation for all the the work that we wanted to do, and it allowed to us to kind of think through more methodically about our problems and establish a foundation that we can now, we can take any idea and use it, and monetize it for you. >> So it's interesting you, you said you've been on this journey for five years, so, from zero to a hundred, where are you on your journey do you think? >> Right, I think we're just barely scratching the surface, (both laughing) - I knew you were going to say that >> Because I do feel that, the data science field itself is evolving, I look at data science as like ever-evolving, ever-mutating kind of beast, right? And we just started our journey, I think we are off to a good start, we have really good use-cases, we have starting using the data well, we have established importance of data, and now we are operationalized on the machine learning data science projects as well. So that's been great, but I do feel there's a lot of untapped potential in this, and I think it'll only get better. >> What about on the democratization, we just, in the keynote today there was a very large retailer, I think he said he had 50 PhDs on staff and 150 data centers this is a multi-billion dollar retailer. How do you guys deal with resource constraints of your own data science team versus PhDs, and trying to democratize the decision making out to a much broader set of people? >> So I think the way we've thought about this is think big, but start small. And what we did was, created a data science lab, so what it allowed is to kind of, and it was the cross-functional team of data scientists, data engineers, software developers kind of working together, and that is a primary group. And they were equally supported by your info-sec guys, or data governance folks, so, they're a good support group as well. And with that cross-functional team, now we are able to move from generating an idea, to incubating it, making sure it has a true commercial value and once we establish that, then we'll even move forward operationalization, so it was more surgical approach rather than spending millions and millions of dollars on something that we're not really sure about. So that did help us to manage a resource constraint now, only the successful concepts were actually taken through operationalization, and we before, we truly knew the bottom line impact, we could know that, here's what it means for us, and for consumers, so that's the approach that we took. >> So, we're going to leave it there, but I want to give you the last word, what advice would give for a peer, not in the financial services industry, they're not watching this. (both laugh) But you know, in terms of doing this journey, 'cause it's obviously, it's a big investment, you've been at it for five years, you're saying you barely are getting started, you're in financial services, which is at it's base, basically an information technology industry. What advice do you give your peers, how do they get started, what do they do in the dark days, what's the biggest challenge? >> Yeah, I feel like my strong belief is, data science is a team sport, right? A lot of people come and ask me: how do we find these unicorn data scientist, and my answer always being that, they don't exist, they're figments of imagination. So it's much better to take cross-functional team, with a complimentary kind of skill set, and get them work together, how do you fit different pieces of the puzzle together, will determine the success of the program. Rather than trying to go really big into something, so that's, the team sport is the key concept here, and if I can get the word out across, that'll be really valuable. >> Alright, well thanks for sharin' that, very useful piece of insight! >> Vishal: Absolutely! >> Alright thanks Vishal, I'm Jeff Frick, you are watching theCUBE, from the Corinium Chief Analytic Officer summit, San Francisco, 2018, at the Parc 55, thanks for watching! (bubbly music plays)

(techno music) >> Announcer: From the Corinium Chief Analytics Officer Conference Spring San Francisco. It's theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the Parc 55 Hotel at the Corinium Chief Analytics Officer Spring 2018 event, about 100 people, pretty intimate affair. A lot of practitioners here talking about the challenges of Big Data and the challenges of Analytics. We're really excited to have a very special Cube guest. I think he was the first guy to launch his company on theCUBE. It was Big Data New York City 2013. I remember it distinctly. It's Prakash Nanduri, the co-founder and CEO of Paxata. Great to see you. >> Great seeing you. Thank you for having me back. >> Absolutely. You know we got so much mileage out of that clip. We put it on all of our promotional materials. You going to launch your company? Launch your company on theCUBE. >> You know it seems just like yesterday but it's been a long ride and it's been a fantastic ride. >> So give us just a quick general update on the company, where you guys are now, how things are going. >> Things are going fantastic. We continue to grow. If you recall, when we launched, we launched the whole notion of democratization of information in the enterprise with self service data prep. We have gone onto now delivered real value to some of the largest brands in the world. We're very proud that 2017 was the year when massive amount of adoption of Paxata's adaptive information platform was taken across multiple industries, financial services, retail, CPG, high tech, in the OIT space. So, we just keep growing and it's the usual challenges of managing growth and managing, you know, the change in the company as you, as you grow from being a small start-up to know being a real company. >> Right, right. There's good problems and bad problems. Those are the good problems. >> Yes, yes. >> So, you know, we do so many shows and there's two big themes over and over and over like digital transformation which gets way over used and then innovation and how do you find a culture of innovation. In doing literally thousands of these interviews, to me it seems pretty simple. It is about democratization. If you give more people the data, more people the tools to work with the data, and more people the power to do something once they find something in the data, and open that up to a broader set of people, they're going to find innovations, simply the fact of doing it. But the reality is those three simple steps aren't necessarily very easy to execute. >> You're spot on, you're spot on. I like to say that when we talk about digital transformation the real focus should be on the deed . And it really centers around data and it centers around the whole notion of democratization, right? The challenge always in large enterprises is democratization without governance becomes chaos. And we always need to focus on democratization. We need to focus on data because as we all know data is the new oil, all of that, and governance becomes a critical piece too. But as you recall, when we launched Paxata, the entire vision from day one has been while the entire focus around digitization covers many things right? It covers people processes. It covers applications. It's a very large topic, the whole digital transformation of enterprise. But the core foundation to digital transformation, data democratization governance, but the key issue is the companies that are going to succeed are the companies that turn data into information that's relevant for every digital transformation effort. >> Right, right. >> Because if you do not turn raw data into information, you're just dealing with raw data which is not useful >> Jeff: Right >> And it will not be democratized. >> Jeff: Right >> Because the business will only consume the information that is contextual to their need, the information that's complete and the information that is clean. >> Right, right. >> So that's really what we're driving towards. >> And that's interesting 'cause the data, there's so many more sources of data, right? There's data that you control. There's structured data, unstructured data. You know, I used to joke, just the first question when you'd ask people "Where's your data?", half the time they couldn't even, they couldn't even get beyond that step. And that's before you start talking about cleaning it and making it ready and making it available. Before you even start to get into governance and rights and access so it's a really complicated puzzle to solve on the backend. >> I think it starts with first focusing on what are the business outcomes we are driving with digital transformation. When you double-click on digital transformation and then you start focusing on data and information, there's a few things that come to fore. First of all, how do I leverage information to improve productivity in my company? There's multiple areas, whether it is marketing or supply chain or whatever. The second notion is how do I ensure that I can actually transform the culture in my company and attract the brightest and the best by giving them the the environment where democratization of information is actually reality, where people feel like they're empowered to access data and turn it into information and then be able to do really interesting things. Because people are not interested on being subservient to somebody who gives them the data. They want to be saying "Give it to me. "I'm smart enough. "I know analytics. "I think analytically and I want to drive my career forward." So the second thing is the cultural aspect to it. And the last thing, which is really important is every company, regardless of whether you're making toothpicks or turbines, you are looking to monetize data. So it's about productivity. It's about cultural change and attracting of talent. And it's about monetization. And when it comes to monetization of data, you cannot be satisfied with only covering enterprise data which is sitting in my enterprise systems. You have to be able to focus on, oh, how can I leverage the IOT data that's being generated from my products or widgets. How can I generate social immobile? How can I consume that? How can I bring all of this together and get the most complete insight that I need for my decision-making process? >> Right. So, I'm just curious, how do you see it your customers? So this is the chief analytics officer, we go to chief data officer, I mean, there's all these chief something officers that want to get involved in data and marketing is much more involved with it. Forget about manufacturing. So when you see successful cultural change, what drives that? Who are the people that are successful and what is the secret to driving the cultural change that we are going to be data-driven, we are going to give you the tools, we are going to make the investment to turn data which historically was even arguably a liability 'cause it had to buy a bunch o' servers to stick it on, into that now being an asset that drives actionable outcomes? >> You know, recently I was having this exact discussion with the CEO of one of the largest financial institutions in the world. This gentleman is running a very large financial services firm, is dealing with all the potential disruption where they're seeing completely new type of PINTEC products coming in, the whole notion of blockchain et cetera coming in. Everything is changing. Everything looks very dramatic. And what we started talking about is the first thing as the CEO that we always focus on is do we have the right people? And do we have the people that are motivated and driven to basically go and disrupt and change? For those people, you need to be able to give them the right kind of tools, the right kind of environment to empower them. This doesn't start with lip service. It doesn't start about us saying "We're going to be on a digital transformation journey" but at the same time, your data is completely in silos. It's locked up. There is 15,000 checks and balances before I can even access a simple piece of data and third, even when I get access to it, it's too little, too late or it's garbage in, garbage out. And that's not the culture. So first, it needs to be CEO drive, top down. We are going to go through digital transformation which means we are going to go through a democratization effort which means we are going to look at data and information as an asset and that means we are not only going to be able to harness these assets, but we're also going to monetize these assets. How are we going to do it? It depends very much on the business you're in, the vertical industry you play in, and your strengths and weaknesses. So each company has to look at it from their perspective. There's no one size fits all for everyone. >> Jeff: Right. >> There are some companies that have fantastic cultures of empowerment and openness but they may not have the right innovation or the right kind of product innovation skills in place. So it's about looking at data across the board. First from your culture and your empowerment, second about democratization of information which is where a company like Paxata comes in, and third, along with democratization, you have to focus on governance because we are for-profit companies. We have a fiducial responsibility to our customers and our regulators and therefore we cannot have democratization without governance. >> Right, right >> And that's really what our biggest differentiation is. >> And then what about just in terms of the political play inside the company. You know, on one hand, used to be if you held the information, you had the power. And now that's changed really 'cause there's so much information. It's really, if you are the conduit of information to help people make better decisions, that's actually a better position to be. But I'm sure there's got to be some conflicts going through digital transformation where I, you know, I was the keeper of the kingdom and now you want to open that up. Conversely, it must just be transformational for the people on the front lines that finally get the data that they've been looking for to run the analysis that they want to rather than waiting for the weekly reports to come down from on high. >> You bet. You know what I like to say is that if you've been in a company for 10, 15 years and if you felt like a particular aspect, purely selfishly, you felt a particular aspect was job security, that is exactly what's going to likely make you lose your job today. What you thought 10 years ago was your job security, that's exactly what's going to make you lose your job today. So if you do not disrupt yourself, somebody else will. So it's either transform yourself or not. Now this whole notion of politics and you know, struggle within the company, it's been there for as long as, humans generally go towards entropy. So, if you have three humans, you have all sort of issues. >> Jeff: Right, right. >> The issue starts frankly with leadership. It starts with the CEO coming down and not only putting an edict down on how things will be done but actually walking the walk with talking the talk. If, as a CEO, you're not transparent, it you're not trusting your people, if you're not sharing information which could be confidential, but you mention that it's confidential but you have to keep this confidential. If you trust your people, you give them the ability to, I think it's a culture change thing. And the second thing is incentivisation. You have to be able to focus on giving people the ability to say "by sharing my data, "I actually become a hero." >> Right, right. >> By giving them the actual credit for actually delivering the data to achieve an outcome. And that takes a lot of work. But if you do not actually drive the cultural change, you will not drive the digital transformation and you will not drive the democratization of information. >> And have you seen people try to do it without making the commitment? Have you seen 'em pay the lip service, spend a few bucks, start a project but then ultimately they, they hamstring themselves 'cause they're not actually behind it? >> Look, I mean, there's many instances where companies start on digital transformation or they start jumping into cool terms like AI or machine-learning, and there's a small group of people who are kind of the elites that go in and do this. And they're given all the kind of attention et cetera. Two things happen. Because these people who are quote, unquote, the elite team, either they are smart but they're not able to scale across the organization or many times, they're so good, they leave. So that transformation doesn't really get democratized. So it is really important from day one to start a culture where you're not going to have a small group of exclusive data scientists. You can have those people but you need to have a broader democratization focus. So what I have seen is many of the siloed, small, tight, mini science projects end up failing. They fail because number one, either the business outcome is not clearly identified early on or two, it's not scalable across the enterprise. >> Jeff: Right. >> And a majority of these exercises fail because the whole information foundation that is taking raw data turning it into clean, complete, potential consumable information, to feed across the organization, not just for one siloed group, not just one data science team. But how do you do that across the company? That's what you need to think from day one. When you do these siloed things, these departmental things, a lot of times they can fail. Now, it's important to say "I will start with a couple of test cases" >> Jeff: Right, right. >> "But I'm going to expand it across "from the beginning to think through that." >> So I'm just curious, your perspective, is there some departments that are the ripest for being that leading edge of the digital transformation in terms of, they've got the data, they've got the right attitude, they're just a short step away. Where have you seen the great place to succeed when you're starting on kind of a smaller PLC, I don't know if you'd say PLC, project or department level? >> So, it's funny but you will hear this, it's not rocket science. Always they say, follow the money. So, in a business, there are three incentives, making more money, saving money, or staying out of jail. (laughs) >> Those are good. I don't know if I'd put them in that order but >> Exactly, and you know what? Depending on who are you are, you may have a different order but staying out of jail if pretty high on my list. >> Jeff: I'm with you on that one. >> So, what are the ambiants? Risk and compliance. Right? >> Jeff: Right, right. >> That's one of those things where you absolutely have to deliver. You absolutely have to do it. It's significantly high cost. It's very data and analytic centric and if you find a smart way to do it, you can dramatically reduce your cost. You can significantly increase your quality and you can significantly increase the volume of your insights and your reporting, thereby achieving all the risk and compliance requirements but doing it in a smarter way and a less expensive way. >> Right. >> That's where incentives have really been high. Second, in making money, it always comes down to sales and marketing and customer success. Those are the three things, sales, marketing, and customer success. So most of our customers who have been widely successful, are the ones who have basically been able to go and say "You know what? "It used to take us eight months "to be able to even figure out a customer list "for a particular region. "Now it takes us two days because of Paxata "and because of the data prep capabilities "and the governance aspects." That's the power that you can deliver today. And when you see one person who's a line of business person who says "Oh my God. "What used to take me eight months, "now it's done in half a day". Or "What use to take me 22 days to create a report, "is now done in 45 minutes." All of a sudden, you will not have a small kind of trickle down, you will have a tsunami of democratization with governance. That's what we've seen in our customers. >> Right, right. I love it. And this is just so classic too. I always like to joke, you know, back in the day, you would run your business based on reports from old data. Now we want to run your business with stuff you can actually take action on now. >> Exactly. I mean, this is public, Shameek Kundu, the chief data officer of Standard Chartered Bank and Michael Gorriz who's the global CIO of Standard Chartered Bank, they have embraced the notion that information democratization in the bank is a foundational element to the digital transformation of Standard Chartered. They are very forward thinking and they're looking at how do I democratize information for all our 87,500 employees while we maintain governance? And another major thing that they are looking at is they know that the data that they need to manipulate and turn into information is not sitting only on premise. >> Right, right. >> It's sitting across a multi-cloud world and that's why they've embraced the Paxata information platform to be their information fabric for a multi-cloud hybrid world. And this is where we see successes and we're seeing more and more of this, because it starts with the people. It starts with the line of business outcomes and then it starts with looking at it from scale. >> Alright, Prakash, well always great to catch up and enjoy really watching the success of the company grow since you launched it many moons ago in New York City >> yes Fantastic. Always a pleasure to come back here. Thank you so much. >> Alright. Thank you. He's Prakash, I'm Jeff Frick. You're watching theCUBE from downtown San Francisco. Thanks for watching. (techno music)

(music playing) >> Narrator: Live, from downtown San Francisco It's the Cube. Covering IBM Chief Data Officer Startegy Summit 2018. Brought to you by: IBM >> Welcome back to San Francisco everybody we're at the Parc 55 in Union Square. My name is Dave Vellante, and you're watching the Cube. The leader in live tech coverage and this is our exclusive coverage of IBM's Chief Data Officer Strategy Summit. They hold these both in San Francisco and in Boston. It's an intimate event, about 150 Chief Data Officers really absorbing what IBM has done internally and IBM transferring knowledge to its clients. Steven Eluk is here. He is one of those internal practitioners at IBM. He's the Vice President of Deep Learning and the Global Chief Data Office at IBM. We just heard from him and some of his strategies and used cases. He's joined by Sumit Gupta, a Cube alum. Who is the Vice President of Machine Learning and deep learning within IBM's cognitive systems group. Sumit. >> Thank you. >> Good to see you, welcome back Steven, lets get into it. So, I was um paying close attention when Bob Picciano took over the cognitive systems group. I said, "Hmm, that's interesting". Recently a software guy, of course I know he's got some hardware expertise. But bringing in someone who's deep into software and machine learning, and deep learning, and AI, and cognitive systems into a systems organization. So you guys specifically set out to develop solutions to solve problems like Steven's trying to solve. Right, explain that. >> Yeah, so I think ugh there's a revolution going on in the market the computing market where we have all these new machine learning, and deep learning technologies that are having meaningful impact or promise of having meaningful impact. But these new technologies, are actually significantly I would say complex and they require very complex and high performance computing systems. You know I think Bob and I think in particular IBM saw the opportunity and realized that we really need to architect a new class of infrastructure. Both software and hardware to address what data scientist like Steve are trying to do in the space, right? The open source software that's out there: Denzoflo, Cafe, Torch - These things are truly game changing. But they also require GPU accelerators. They also require multiple systems like... In fact interestingly enough you know some of the super computers that we've been building for the scientific computing world, those same technologies are now coming into the AI world and the enterprise. >> So, the infrastructure for AI, if I can use that term? It's got to be flexible, Steven we were sort of talking about that elastic versus I'm even extending it to plastic. As Sumit you just said, it's got to have that tooling, got to have that modern tooling, you've got to accommodate alternative processor capabilities um, and so, that forms what you've used Steven to sort of create new capabilities new business capabilities within IBM. I wanted to, we didn't touch upon this before, but we touched upon your data strategy before but tie it back to the line of business. You essentially are a presume a liaison between the line of business and the chief data office >> Steven: Yeah. >> Officer office. How did that all work out, and shake out? Did you defining the business outcomes, the requirements, how did you go about that? >> Well, actually, surprisingly, we have very little new use cases that we're generating internally from my organization. Because there's so many to pick from already throughout the organization, right? There's all these business units coming to us and saying, "Hey, now the data is in the data lake and now we know there's more data, now we want to do this. How do we do it?" You know, so that's where we come in, that's where we start touching and massaging and enabling them. And that's the main efforts that we have. We do have some derivative works that have come out, that have been like new offerings that you'll see here. But mostly we already have so many use cases that from those businesses units that we're really trying to heighten and bring extra value to those domains first. >> So, a lot of organizations sounds like IBM was similar you created the data lake you know, things like "a doop" made a lower cost to just put stuff in the data lake. But then, it's like "okay, now what?" >> Steven: Yeah. >> So is that right? So you've got the data and this bog of data and you're trying to make more sense out of it but get more value out of it? >> Steven: Absolutely. >> That's what they were pushing you to do? >> Yeah, absolutely. And with that, with more data you need more computational power. And actually Sumit and I go pretty far back and I can tell you from my previous roles I heightened to him many years ago some of the deficiencies in the current architecture in X86 etc and I said, "If you hit these points, I will buy these products." And what they went back and they did is they, they addressed all of the issues that I had. Like there's certain issues... >> That's when you were, sorry to interrupt, that's when you were a customer, right? >> Steven: That's when I was... >> An external customer >> Outside. I'm still an internal customer, so I've always been a customer I guess in that role right? >> Yep, yep. >> But, I need to get data to the computational device as quickly as possible. And with certain older gen technologies, like PTI Gen3 and certain issues around um x86. I couldn't get that data there for like high fidelity imaging for autonomous vehicles for ya know, high fidelity image analysis. But, with certain technologies in power we have like envy link and directly to the CPU. And we also have PTI Gen4, right? So, so these are big enablers for me so that I can really keep the utilization of those very expensive compute devices higher. Because they're not starved for data. >> And you've also put a lot of emphasis on IO, right? I mean that's... >> Yeah, you know if I may break it down right there's actually I would say three different pieces to the puzzle here right? The highest level from Steve's perspective, from Steven's teams perspective or any data scientist perspective is they need to just do their data science and not worry about the infrastructure, right? They actually don't want to know that there's an infrastructure. They want to say, "launch job" - right? That's the level of grand clarity we want, right? In the background, they want our schedulers, our software, our hardware to just seamlessly use either one system or scale to 100 systems, right? To use one GPU or to use 1,000 GPUs, right? So that's where our offerings come in, right. We went and built this offering called Powder and Powder essentially is open source software like TensorFlow, like Efi, like Torch. But performace and capabilities add it to make it much easier to use. So for example, we have an extremely terrific scheduling software that manages jobs called Spectrum Conductor for Spark. So as the name suggests, it uses Apache Spark. But again the data scientist doesn't know that. They say, "launch job". And the software actually goes and scales that job across tens of servers or hundreds of servers. The IT team can determine how many servers their going to allocate for data scientist. They can have all kinds of user management, data management, model management software. We take the open source software, we package it. You know surprisingly ugh most people don't realize this, the open source software like TensorFlow has primarily been built on a (mumbles). And most of our enterprise clients, including Steven, are on Redhat. So we, we engineered Redhat to be able to manage TensorFlow. And you know I chose those words carefully, there was a little bit of engineering both on Redhat and on TensorFlow to make that whole thing work together. Sounds trivial, took several months and huge value proposition to the enterprise clients. And then the last piece I think that Steven was referencing too, is we also trying to go and make the eye more accessible for non data scientist or I would say even data engineers. So we for example, have a software called Powder Vision. This takes images and videos, and automatically creates a trained deep learning model for them, right. So we analyze the images, you of course have to tell us in these images, for these hundred images here are the most important things. For example, you've identified: here are people, here are cars, here are traffic signs. But if you give us some of that labeled data, we automatically do the work that a data scientist would have done, and create this pre trained AI model for you. This really enables many rapid prototyping for a lot of clients who either kind of fought to have data scientists or don't want to have data scientists. >> So just to summarize that, the three pieces: It's making it simpler for the data scientists, just run the job - Um, the backend piece which is the schedulers, the hardware, the software doing its thing - and then its making that data science capability more accessible. >> Right, right, right. >> Those are the three layers. >> So you know, I'll resay it in my words maybe >> Yeah please. >> Ease of use right, hardware software optimized for performance and capability, and point and click AI, right. AI for non data scientists, right. It's like the three levels that I think of when I'm engaging with data scientists and clients. >> And essentially it's embedded AI right? I've been making the point today that a lot of the AI is going to be purchased from companies like IBM, and I'm just going to apply it. I'm not going to try to go build my own, own AI right? I mean, is that... >> No absolutely. >> Is that the right way to think about it as a practitioner >> I think, I think we talked about it a little bit about it on the panel earlier but if we can, if we can leverage these pre built models and just apply a little bit of training data it makes it so much easier for the organizations and so much cheaper. They don't have to invest in a crazy amount of infrastructure, all the labeling of data, they don't have to do that. So, I think it's definitely steering that way. It's going to take a little bit of time, we have some of them there. But as we as we iterate, we are going to get more and more of these types of you know, commodity type models that people could utilize. >> I'll give you an example, so we have a software called Intelligent Analytics at IBM. It's very good at taking any surveillance data and for example recognizing anomalies or you know if people aren't suppose to be in a zone. Ugh and we had a client who wanted to do worker safety compliance. So they want to make sure workers are wearing their safety jackets and their helmets when they're in a construction site. So we use surveillance data created a new AI model using Powder AI vision. We were then able to plug into this IVA - Intelligence Analytic Software. So they have the nice gooey base software for the dashboards and the alerts, yet we were able to do incremental training on their specific use case, which by the way, with their specific you know equipment and jackets and stuff like that. And create a new AI model, very quickly. For them to be able to apply and make sure their workers are actually complaint to all of the safety requirements they have on the construction site. >> Hmm interesting. So when I, Sometimes it's like a new form of capture says identify "all the pictures with bridges", right that's the kind of thing you're capable to do with these video analytics. >> That's exactly right. You, every, clients will have all kinds of uses I was at a, talking to a client, who's a major car manufacturer in the world and he was saying it would be great if I could identify the make and model of what cars people are driving into my dealership. Because I bet I can draw a ugh corelation between what they drive into and what they going to drive out of, right. Marketing insights, right. And, ugh, so there's a lot of things that people want to do with which would really be spoke in their use cases. And build on top of existing AI models that we have already. >> And you mentioned, X86 before. And not to start a food fight but um >> Steven: And we use both internally too, right. >> So lets talk about that a little bit, I mean where do you use X86 where do you use IBM Cognitive and Power Systems? >> I have a mix of both, >> Why, how do you decide? >> There's certain of work loads. I will delegate that over to Power, just because ya know they're data starved and we are noticing a complication is being impacted by it. Um, but because we deal with so many different organizations certain organizations optimize for X86 and some of them optimize for power and I can't pick, I have to have everything. Just like I mentioned earlier, I also have to support cloud on prim, I can't pick just to be on prim right, it so. >> I imagine the big cloud providers are in the same boat which I know some are your customers. You're betting on data, you're betting on digital and it's a good bet. >> Steven: Yeah, 100 percent. >> We're betting on data and AI, right. So I think data, you got to do something with the data, right? And analytics and AI is what people are doing with that data we have an advantage both at the hardware level and at the software level in these two I would say workloads or segments - which is data and AI, right. And we fundamentally have invested in the processor architecture to improve the performance and capabilities, right. You could offer a much larger AI models on a power system that you use than you can on an X86 system that you use. Right, that's one advantage. You can train and AI model four times faster on a power system than you can on an Intel Based System. So the clients who have a lot of data, who care about how fast their training runs, are the ones who are committing to power systems today. >> Mmm.Hmm. >> Latency requirements, things like that, really really big deal. >> So what that means for you as a practitioner is you can do more with less or is it I mean >> I can definitely do more with less, but the real value is that I'm able to get an outcome quicker. Everyone says, "Okay, you can just roll our more GPU's more GPU's, but run more experiments run more experiments". No no that's not actually it. I want to reduce the time for a an experiment Get it done as quickly as possible so I get that insight. 'Cause then what I can do I can get possibly cancel out a bunch of those jobs that are already running cause I already have the insight, knowing that that model is not doing anything. Alright, so it's very important to get the time down. Jeff Dean said it a few years ago, he uses the same slide often. But, you know, when things are taking months you know that's what happened basically from the 80's up until you know 2010. >> Right >> We didn't have the computation we didn't have the data. Once we were able to get that experimentation time down, we're able to iterate very very quickly on this. >> And throwing GPU's at the problem doesn't solve it because it's too much complexity or? >> It it helps the problem, there's no question. But when my GPU utilization goes from 95% down to 60% ya know I'm getting only a two-thirds return on investment there. It's a really really big deal, yeah. >> Sumit: I mean the key here I think Steven, and I'll draw it out again is this time to insight. Because time to insight actually is time to dollars, right. People are using AI either to make more money, right by providing better customer products, better products to the customers, giving better recommendations. Or they're saving on their operational costs right, they're improving their efficiencies. Maybe their routing their trucks in the right way, their routing their inventory in the right place, they're reducing the amount of inventory that they need. So in all cases you can actually coordinate AI to a revenue outcome or a dollar outcome. So the faster you can do that, you know, I tell most people that I engage with the hardware and software they get from us pays for itself very quickly. Because they make that much more money or they save that much more money, using power systems. >> We, we even see this internally I've heard stories and all that, Sumit kind of commented on this but - There's actually sales people that take this software & hardware out and they're able to get an outcome sometimes in certain situations where they just take the clients data and they're sales people they're not data scientists they train it it's so simple to use then they present the client with the outcomes the next day and the client is just like blown away. This isn't just a one time occurrence, like sales people are actually using this right. So it's getting to the area that it's so simple to use you're able to get those outcomes that we're even seeing it you know deals close quicker. >> Yeah, that's powerful. And Sumit to your point, the business case is actually really easy to make. You can say, "Okay, this initiative that you're driving what's your forecast for how much revenue?" Now lets make an assumption for how much faster we're going to be able to deliver it. And if I can show them a one day turn around, on a corpus of data, okay lets say two months times whatever, my time to break. I can run the business case very easily and communicate to the CFO or whomever the line of business head so. >> That's right. I mean just, I was at a retailer, at a grocery store a local grocery store in the bay area recently and he was telling me how In California we've passed legislation that does not allow plastic bags anymore. You have to pay for it. So people are bringing their own bags. But that's actually increased theft for them. Because people bring their own bag, put stuff in it and walk out. And he didn't want to have an analytic system that can detect if someone puts something in a bag and then did not buy it at purchase. So it's, in many ways they want to use the existing camera systems they have but automatically be able to detect fraudulent behavior or you know anomalies. And it's actually quite easy to do with a lot of the software we have around Power AI Vision, around video analytics from IBM right. And that's what we were talking about right? Take existing trained AI models on vision and enhance them for your specific use case and the scenarios you're looking for. >> Excellent. Guys we got to go. Thanks Steven, thanks Sumit for coming back on and appreciate the insights. >> Thank you >> Glad to be here >> You're welcome. Alright, keep it right there buddy we'll be back with our next guest. 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