>>From around the globe, but it's the cube presenting active DQ, intelligent automation for data quality brought to you by IO Tahoe. >>Now we're going to look at the role automation plays in mobilizing your data on snowflake. Let's welcome. And Duncan Turnbull who's partner sales engineer at snowflake and AIG Vihara is back CEO of IO. Tahoe is going to share his insight. Gentlemen. Welcome. >>Thank you, David. Good to have you back. Yeah, it's great to have you back >>A J uh, and it's really good to CIO Tao expanding the ecosystem so important. Um, now of course bringing snowflake and it looks like you're really starting to build momentum. I mean, there's progress that we've seen every month, month by month, over the past 12, 14 months, your seed investors, they gotta be happy. >>They are all that happy. And then I can see that we run into a nice phase of expansion here and new customers signing up. And now you're ready to go out and raise that next round of funding. I think, um, maybe think of a slight snowflake five years ago. So we're definitely on track with that. A lot of interest from investors and, um, we're right now trying to focus in on those investors that can partner with us, understand AI data and, and automation. >>So personally, I mean, you've managed a number of early stage VC funds. I think four of them, uh, you've taken several comp, uh, software companies through many funding rounds and growth and all the way to exit. So, you know how it works, you have to get product market fit, you know, you gotta make sure you get your KPIs, right. And you gotta hire the right salespeople, but, but what's different this time around, >>Uh, well, you know, the fundamentals that you mentioned though, those are never change. And, um, what we can say, what I can say that's different, that's shifted, uh, this time around is three things. One in that they used to be this kind of choice of, do we go open source or do we go proprietary? Um, now that has turned into, um, a nice hybrid model where we've really keyed into, um, you know, red hat doing something similar with Santos. And the idea here is that there is a core capability of technology that independence a platform, but it's the ability to then build an ecosystem around that made a pervade community. And that community may include customers, uh, technology partners, other tech vendors, and enabling the platform adoption so that all of those folks in that community can build and contribute, um, while still maintaining the core architecture and platform integrity, uh, at the core of it. >>And that's one thing that's changed was fitting a lot of that type of software company, um, emerge into that model, which is different from five years ago. Um, and then leveraging the cloud, um, every cloud snowflake cloud being one of them here in order to make use of what customers, uh, and customers and enterprise software are moving towards. Uh, every CIO is now in some configuration of a hybrid. Um, it is state whether those cloud multi-cloud on prem. That's just the reality. The other piece is in dealing with the CIO is legacy. So the past 15, 20 years they've purchased many different platforms, technologies, and some of those are still established and still, how do you, um, enable that CIO to make purchase while still preserving and in some cases building on and extending the, the legacy, um, material technology. So they've invested their people's time and training and financial investment into solving a problem, customer pain point, uh, with technology, but, uh, never goes out of fashion >>That never changes. You have to focus like a laser on that. And of course, uh, speaking of companies who are focused on solving problems, don't can turn bill from snowflake. You guys have really done a great job and really brilliantly addressing pain points, particularly around data warehousing, simplified that you're providing this new capability around data sharing, uh, really quite amazing. Um, Dunkin AAJ talks about data quality and customer pain points, uh, in, in enterprise. It, why is data quality been such a problem historically? >>Oh, sorry. One of the biggest challenges that's really affected by it in the past is that because to address everyone's need for using data, they've evolved all these kinds of different places to store all these different silos or data marts or all this kind of clarification of places where data lives and all of those end up with slightly different schedules to bringing data in and out. They end up with slightly different rules for transforming that data and formatting it and getting it ready and slightly different quality checks for making use of it. And this then becomes like a big problem in that these different teams are then going to have slightly different or even radically different ounces to the same kinds of questions, which makes it very hard for teams to work together, uh, on their different data problems that exist inside the business, depending on which of these silos they end up looking at and what you can do. If you have a single kind of scalable system for putting all of your data into it, you can kind of sidestep along to this complexity and you can address the data quality issues in a, in a single and a single way. >>Now, of course, we're seeing this huge trend in the market towards robotic process automation, RPA, that adoption is accelerating. Uh, you see, in UI paths, I IPO, you know, 35 plus billion dollars, uh, valuation, you know, snowflake like numbers, nice cops there for sure. Uh, agent you've coined the phrase data RPA, what is that in simple terms? >>Yeah, I mean, it was born out of, uh, seeing how in our ecosystem concern community developers and customers, uh, general business users for wanting to adopt and deploy a tar hose technology. And we could see that, um, I mean, there's not monkeying out PA we're not trying to automate that piece, but wherever there is a process that was tied into some form of a manual overhead with handovers and so on. Um, that process is something that we were able to automate with, with our ties technology and, and the deployment of AI and machine learning technologies specifically to those data processes almost as a precursor to getting into financial automation that, um, that's really where we're seeing the momentum pick up, especially in the last six months. And we've kept it really simple with snowflake. We've kind of stepped back and said, well, you know, the resource that a snowflake can leverage here is, is the metadata. So how could we turn snowflake into that repository of being the data catalog? And by the way, if you're a CIO looking to purchase a data catalog tool stop, there's no need to, um, working with snowflake, we've enable that intelligence to be gathered automatically and to be put, to use within snowflake. So reducing that manual effort, and I'm putting that data to work. And, um, and that's where we've packaged this with, uh, AI machine learning specific to those data tasks. Um, and it made sense that's, what's resonated with, with our customers. >>You know, what's interesting here, just a quick aside, as you know, I've been watching snowflake now for awhile and, and you know, of course the, the competitors come out and maybe criticize why they don't have this feature. They don't have that feature. And it's snowflake seems to have an answer. And the answer oftentimes is, well, its ecosystem ecosystem is going to bring that because we have a platform that's so easy to work with though. So I'm interested Duncan in what kind of collaborations you are enabling with high quality data. And of course, you know, your data sharing capability. >>Yeah. So I think, uh, you know, the ability to work on, on datasets, isn't just limited to inside the business itself or even between different business units. And we were kind of discussing maybe with their silos. Therefore, when looking at this idea of collaboration, we have these where we want to be >>Able to exploit data to the greatest degree possible, but we need to maintain the security, the safety, the privacy, and governance of that data. It could be quite valuable. It could be quite personal depending on the application involved. One of these novel applications that we see between organizations of data sharing is this idea of data clean rooms. And these data clean rooms are safe, collaborative spaces, which allow multiple companies or even divisions inside a company where they have particular, uh, privacy requirements to bring two or more data sets together for analysis. But without having to actually share the whole unprotected data set with each other, and this lets you to, you know, when you do this inside of snowflake, you can collaborate using standard tool sets. You can use all of our SQL ecosystem. You can use all of the data science ecosystem that works with snowflake. >>You can use all of the BI ecosystem that works with snowflake, but you can do that in a way that keeps the confidentiality that needs to be presented inside the data intact. And you can only really do these kinds of, uh, collaborations, especially across organization, but even inside large enterprises, when you have good reliable data to work with, otherwise your analysis just isn't going to really work properly. A good example of this is one of our large gaming customers. Who's an advertiser. They were able to build targeting ads to acquire customers and measure the campaign impact in revenue, but they were able to keep their data safe and secure while doing that while working with advertising partners, uh, the business impact of that was they're able to get a lifted 20 to 25% in campaign effectiveness through better targeting and actually, uh, pull through into that of a reduction in customer acquisition costs because they just didn't have to spend as much on the forms of media that weren't working for them. >>So, ha I wonder, I mean, you know, with, with the way public policy shaping out, you know, obviously GDPR started it in the States, you know, California, consumer privacy act, and people are sort of taking the best of those. And, and, and there's a lot of differentiation, but what are you seeing just in terms of, you know, the government's really driving this, this move to privacy, >>Um, government public sector, we're seeing a huge wake up an activity and, uh, across the whole piece that, um, part of it has been data privacy. Um, the other part of it is being more joined up and more digital rather than paper or form based. Um, we've all got stories of waiting in line, holding a form, taking that form to the front of the line and handing it over a desk. Now government and public sector is really looking to transform their services into being online, to show self service. Um, and that whole shift is then driving the need to, um, emulate a lot of what the commercial sector is doing, um, to automate their processes and to unlock the data from silos to put through into those, uh, those processes. Um, and another thing I can say about this is they, the need for data quality is as a Dunkin mentions underpins all of these processes, government pharmaceuticals, utilities, banking, insurance, the ability for a chief marketing officer to drive a, a loyalty campaign. >>They, the ability for a CFO to reconcile accounts at the end of the month. So do a, a, uh, a quick, accurate financial close. Um, also the, the ability of a customer operations to make sure that the customer has the right details about themselves in the right, uh, application that they can sell. So from all of that is underpinned by data and is effective or not based on the quality of that data. So whilst we're mobilizing data to snowflake cloud, the ability to then drive analytics, prediction, business processes off that cloud, um, succeeds or fails on the quality of that data. >>I mean it, and, you know, I would say, I mean, it really is table stakes. If you don't trust the data, you're not gonna use the data. The problem is it always takes so long to get to the data quality. There's all these endless debates about it. So we've been doing a fair amount of work and thinking around this idea of decentralized data, data by its very nature is decentralized, but the fault domains of traditional big data is that everything is just monolithic and the organizations monolithic technology's monolithic, the roles are very, you know, hyper specialized. And so you're hearing a lot more these days about this notion of a data fabric or what calls a data mesh. Uh, and we've kind of been leaning in to that and the ability to, to connect various data capabilities, whether it's a data warehouse or a data hub or a data Lake that those assets are discoverable, they're shareable through API APIs and they're governed on a federated basis. And you're using now bringing in a machine intelligence to improve data quality. You know, I wonder Duncan, if you could talk a little bit about Snowflake's approach to this topic. >>Sure. So I'd say that, you know, making use of all of your data, is there a key kind of driver behind these ideas that they can mesh into the data fabrics? And the idea is that you want to bring together not just your kind of strategic data, but also your legacy data and everything that you have inside the enterprise. I think I'd also like to kind of expand upon what a lot of people view as all of the data. And I think that a lot of people kind of miss that there's this whole other world of data they could be having access to, which is things like data from their business partners, their customers, their suppliers, and even stuff that's more in the public domain, whether that's, you know, demographic data or geographic or all these kinds of other types of data sources. And what I'd say to some extent is that the data cloud really facilitates the ability to share and gain access to this both kind of between organizations inside organizations. >>And you don't have to, you know, make lots of copies of the data and kind of worry about the storage and this federated, um, you know, idea of governance and all these things that it's quite complex to kind of manage this. Uh, you know, the snowflake approach really enables you to share data with your ecosystem all the world, without any latency with full control over what's shared without having to introduce new complexities or having complex attractions with APIs or software integration. The simple approach that we provide allows a relentless focus on creating the right data product to meet the challenges facing your business today. >>So, Andrea, the key here is to don't get to talking about it in my mind. Anyway, my cake takeaway is to simplicity. If you can take the complexity out of the equation, we're going to get more adoption. It really is that simple. >>Yeah, absolutely. Do you think that that whole journey, maybe five, six years ago, the adoption of data lakes was, was a stepping stone. Uh, however, the Achilles heel there was, you know, the complexity that it shifted towards consuming that data from a data Lake where there were many, many sets of data, um, to, to be able to cure rate and to, um, to consume, uh, whereas actually, you know, the simplicity of being able to go to the data that you need to do your role, whether you're in tax compliance or in customer services is, is key. And, you know, listen for snowflake by auto. One thing we know for sure is that our customers are super small and they're very capable. They're they're data savvy and know, want to use whichever tool and embrace whichever, um, cloud platform that is gonna reduce the barriers to solving. What's complex about that data, simplifying that and using, um, good old fashioned SQL, um, to access data and to build products from it to exploit that data. So, um, simplicity is, is key to it to allow people to, to, to make use of that data. And CIO is recognize that >>So Duncan, the cloud obviously brought in this notion of dev ops, um, and new methodologies and things like agile that brought that's brought in the notion of data ops, which is a very hot topic right now. Um, basically dev ops applies to data about how D how does snowflake think about this? How do you facilitate that methodology? >>Yeah, sorry. I agree with you absolutely. That they drops takes these ideas of agile development of >>Agile delivery and of the kind of dev ops world that we've seen just rise and rise, and it applies them to the data pipeline, which is somewhere where it kind of traditionally hasn't happened. And it's the same kinds of messages as we see in the development world, it's about delivering faster development, having better repeatability and really getting towards that dream of the data-driven enterprise, you know, where you can answer people's data questions, they can make better business decisions. And we have some really great architectural advantages that allow us to do things like allow cloning of data sets without having to copy them, allows us to do things like time travel so we can see what data looked like at some point in the past. And this lets you kind of set up both your own kind of little data playpen as a clone without really having to copy all of that data. >>So it's quick and easy, and you can also, again, with our separation of storage and compute, you can provision your own virtual warehouse for dev usage. So you're not interfering with anything to do with people's production usage of this data. So the, these ideas, the scalability, it just makes it easy to make changes, test them, see what the effect of those changes are. And we've actually seen this. You were talking a lot about partner ecosystems earlier. Uh, the partner ecosystem has taken these ideas that are inside snowflake and they've extended them. They've integrated them with, uh, dev ops and data ops tooling. So things like version control and get an infrastructure automation and things like Terraform. And they've kind of built that out into more of a data ops products that, that you can, you can make yourself so we can see there's a huge impact of, of these ideas coming into the data world. >>We think we're really well-placed to take advantage to them. The partner ecosystem is doing a great job with doing that. And it really allows us to kind of change that operating model for data so that we don't have as much emphasis on like hierarchy and change windows and all these kinds of things that are maybe use as a lot of fashioned. And we kind of taking the shift from this batch data integration into, you know, streaming continuous data pipelines in the cloud. And this kind of gets you away from like a once a week or once a month change window, if you're really unlucky to, you know, pushing changes, uh, in a much more rapid fashion as the needs of the business change. >>I mean, those hierarchical organizational structures, uh, w when we apply those to begin to that, what it actually creates the silos. So if you're going to be a silo Buster, which aji look at you guys in silo busters, you've got to put data in the hands of the domain experts, the business people, they know what data they want, if they have to go through and beg and borrow for a new data sets, et cetera. And so that's where automation becomes so key. And frankly, the technology should be an implementation detail, not the dictating factor. I wonder if you could comment on this. >>Yeah, absolutely. I think, um, making the, the technologies more accessible to the general business users >>Or those specialists business teams that, um, that's the key to unlocking is it is interesting to see is as people move from organization to organization where they've had those experiences operating in a hierarchical sense, I want to break free from that and, um, or have been exposed to, um, automation, continuous workflows, um, change is continuous in it. It's continuous in business, the market's continuously changing. So having that flow across the organization of work, using key components, such as get hub, similar to what you drive process Terraform to build in, um, code into the process, um, and automation and with a high Tahoe leveraging all the metadata from across those fragmented sources is, is, is good to say how those things are coming together. And watching people move from organization to organization say, Hey, okay, I've got a new start. I've got my first hundred days to impress my, my new manager. >>Uh, what kind of an impact can I, um, bring to this? And quite often we're seeing that as, you know, let me take away the good learnings from how to do it, or how not to do it from my previous role. And this is an opportunity for me to, to bring in automation. And I'll give you an example, David, you know, recently started working with a, a client in financial services. Who's an asset manager, uh, managing financial assets. They've grown over the course of the last 10 years through M and a, and each of those acquisitions have bought with it tactical data. It's saying instead of data of multiple CRM systems now multiple databases, multiple bespoke in-house created applications. And when the new CIO came in and had a look at those well, you know, yes, I want to mobilize my data. Yes, I need to modernize my data state because my CEO is now looking at these crypto assets that are on the horizon and the new funds that are emerging that around digital assets and crypto assets. >>But in order to get to that where absolutely data underpins and is the core asset, um, cleaning up that, that legacy situation mobilizing the relevant data into the Safelite cloud platform, um, is where we're giving time back, you know, that is now taking a few weeks, whereas that transitioned to mobilize that data, start with that, that new clean slate to build upon a new business as a, a digital crypto asset manager, as well as the legacy, traditional financial assets, bonds stocks, and fixed income assets, you name it, uh, is where we're starting to see a lot of innovation. >>Yeah. Tons of innovation. I love the crypto examples and FTS are exploding and, you know, let's face it, traditional banks are getting disrupted. Uh, and so I also love this notion of data RPA. I, especially because I've done a lot of work in the RPA space. And, and I want to, what I would observe is that the, the early days of RPA, I call it paving the cow path, taking existing processes and applying scripts, get letting software robots, you know, do its thing. And that was good because it reduced, you know, mundane tasks, but really where it's evolved is a much broader automation agenda. People are discovering new, new ways to completely transform their processes. And I see a similar, uh, analogy for data, the data operating model. So I'm wonder whenever you think about that, how a customer really gets started bringing this to their ecosystem, their data life cycles. >>Sure. Yeah. So step one is always the same is figuring out for the CIO, the chief data officer, what data do I have, um, and that's increasingly something that they want towards a mate, so we can help them there and, and do that automated data discovery, whether that is documents in the file, share backup archive in a relational data store, in a mainframe really quickly hydrating that and bringing that intelligence, the forefront of, of what do I have, and then it's the next step of, well, okay. Now I want to continually monitor and curate that intelligence with the platform that I've chosen. Let's say snowflake, um, in order such that I can then build applications on top of that platform to serve my, my internal, external customer needs and the automation around classifying data reconciliation across different fragmented data silos, building that in those insights into snowflake. >>Um, as you say, a little later on where we're talking about data quality, active DQ, allowing us to reconcile data from different sources, as well as look at the integrity of that data. Um, so they can go on to remediation, you know, I, I wanna, um, harness and leverage, um, techniques around traditional RPA. Um, but to get to that stage, I need to fix the data. So remediating publishing the data in snowflake, uh, allowing analysis to be formed performance snowflake. Th those are the key steps that we see and just shrinking that timeline into weeks, giving the organization that time back means they're spending more time on their customer and solving their customer's problem, which is where we want them to be. >>This is the brilliance of snowflake actually, you know, Duncan is, I've talked to him, then what does your view about this and your other co-founders and it's really that focus on simplicity. So, I mean, that's, you, you picked a good company to join my opinion. So, um, I wonder if you could, you know, talk about some of the industry sectors that are, again, going to gain the most from, from data RPA, I mean, traditional RPA, if I can use that term, you know, a lot of it was back office, a lot of, you know, financial w what are the practical applications where data RPA is going to impact, you know, businesses and, and the outcomes that we can expect. >>Yes, sir. So our drive is, is really to, to make that, um, business general user's experience of RPA simpler and, and using no code to do that, uh, where they've also chosen snowflake to build that their cloud platform. They've got the combination then of using a relatively simple script scripting techniques, such as SQL, uh, without no code approach. And the, the answer to your question is whichever sector is looking to mobilize their data. Uh, it seems like a cop-out, but to give you some specific examples, David, um, in banking where, uh, customers are looking to modernize their banking systems and enable better customer experience through, through applications and digital apps. That's where we're, we're seeing a lot of traction, uh, and this approach to, to pay RPA to data, um, health care, where there's a huge amount of work to do to standardize data sets across providers, payers, patients, uh, and it's an ongoing, um, process there for, for retail, um, helping to, to build that immersive customer experience. >>So recommending next best actions, um, providing an experience that is going to drive loyalty and retention, that's, that's dependent on understanding what that customer's needs intent, uh, being out to provide them with the content or the outfit at that point in time, or all data dependent utilities is another one great overlap there with, with snowflake where, you know, helping utilities, telecoms energy, water providers to build services on that data. And this is where the ecosystem just continues to, to expand. If we, if we're helping our customers turn their data into services for, for their ecosystem, that's, that's exciting. And they were more so exciting than insurance, which we always used to, um, think back to, uh, when insurance used to be very dull and mundane, actually, that's where we're seeing a huge amounts of innovation to create new flexible products that are priced to the day to the situation and, and risk models being adaptive when the data changes, uh, on, on events or circumstances. So across all those sectors that they're all mobilizing that data, they're all moving in some way, shape or form to a, a multi-cloud, um, set up with their it. And I think with, with snowflake and without Tahoe, being able to accelerate that and make that journey simple and as complex is, uh, is why we found such a good partner here. >>All right. Thanks for that. And then thank you guys. Both. We gotta leave it there. Uh, really appreciate Duncan you coming on and Aja best of luck with the fundraising. >>We'll keep you posted. Thanks, David. All right. Great. >>Okay. Now let's take a look at a short video. That's going to help you understand how to reduce the steps around your data ops. Let's watch.

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

>>The data Lake concept was intriguing when first introduced in 2010, but people quickly realized that shoving data into a data Lake may data Lake stagnant, repositories that were essentially storage bins that were less expensive than traditional data warehouses. This is Dave Vellante joined me for IO. Tahoe's latest installment of the data automation series, active DQ, intelligent automation for data quality management. We'll talk to experts from snowflake about their data assessment utility from within the snowflake platform and how it scales to the demands of business. While also controlling costs. I have Tahoe CEO, AIG Hora will explain how IO Tahoe and snowflake together are bringing active DQ to market. And what the customers are saying about it. Save the date Thursday, April 29th for IO Tahoes data automation series active DQ, intelligent automation for data quality show streams promptly at 11:00 AM Eastern on the cube, the >>In high tech coverage.

(bright upbeat music) >> Well, hello everybody. John Walls is here on "the CUBE". Great to have you with us as we continue our series here at the AWS Executive Summit sponsored by Accenture. And today we're talking about public service and not just a little slice of public service but probably the largest public sector offering in the UK and for with us or with us. Now to talk about that is Lianne Anderton, who is in with the Intelligent Automation Garage Delivery Lead at the UK Department of Work and Pension. Lianne, good to see you today. Thanks for joining us here on "the CUBE". >> Hi, thanks for having me. >> And also with this us is Amar Narayan, who is a Manager Director at Accenture the AWS Business Group for the Lead in Health and Public Sector, also UK and Ireland. And Amar, I think, you and Lianne, are in the same location, Newcastle, I believe in the UK, is that right? >> Yeah, absolutely. Yep, yeah, we're, here in the northeast of UK. >> Well, thank you for being with us. I appreciate the time. Lianne, let's talk about what you do, the Department of Work and Pension, the famous DWP in England. You have influence or certainly touchpoints with a huge amount of the British population. In what respects, what are you doing for the working class in England and what does technology have to do with all that? >> Sure, so for the Department for Work and Pensions I think the pensions bit is fairly self explanatory so anybody who is over state pension age within the UK. for the work part of that we also deal with people of working age. So, these are people who are either in employment and need additional help through various benefits we offer in the UK. Those people who are out of work. And we also deal with health related benefits as well. And we are currently serving over 20 million claimants every year at this moment in time. So, we're aware of a huge part of the UK government. >> All right, so say that number again. How many? >> 20 million claimants every year. >> Million with an M, right? >> Yeah. >> So, and that's individuals. And so how many transactions, if you will, how many do you think you process in a month? How, much traffic basically, are you seeing? >> An extraordinary amount? I'm not even, I don't think I even know that number. (Lianne laughing) >> Mind blowing, right? So, it's- >> A huge, huge amount. >> Mind blowing. >> Yeah, so, basically the we kind of keep the country going. So, you know, if the department for Work and Pensions kind of didn't exist anymore then actually it would cause an infinite number of problems in society. We, kind of help and support the people who need that. And, yeah, so we play a really vital role in kind of you know, social care and kind of public service. >> So, what was your journey to Accenture then? What, eventually led you to them? What problem were you having and how have you collaborated to solve that? >> So, in terms of how we work with Accenture. So, we had in around 2017 DWP was looking at a projected number of transactions growing by about 210 million which was, you know, an extraordinary amount. And, you know, I think as we've kind of covered everything that we do is on a massive scale. So, we as DWP as an organization we had absolutely no idea how we were going to be able to handle such a massive increase in the transactions. And actually, you know, after kind of various kind of paths and ideas of how we were going to do that, automation, was actually the answer. But the problem that we have with that is that we have, like many governments around the world, we have really older legacy systems. So, each of these benefits that we deal with are on legacy systems. So, whatever we were going to develop had to, you know, connect to all of these, it had to ingest and then process all of these pieces of data some of which, you know, given the fact that a lot of these systems have a lot of manual input you have data issues there that you have to solve and whatever we did, you know, as we've talked about in terms of volumes has to scale instantly as well. So, it has to be able to scale up and down to meet demand and, you know, and that down scaling is also equally as important. So yeah, you've got to be able to scale up to meet the volumes but also you've got to be able to downscale when when it's not needed. But we had nothing that was like that kind of helped us to meet that demand. So, we built our own automation platform, The Intelligent Automation Garage and we did that with Accenture. >> So Amar, I'd like you to chime in here then. So, you're looking at this client who has this massive footprint and obviously vital services, right? So, that's paramount that you have to keep that in mind and the legacy systems that Lianne was just talking about. So, now you're trying to get 'em in the next gen but also respecting that they have a serious investment already in a lot of technology. How do you approach that kind of problem solving, those dynamics and how in this case did you get them to automation as the solution? >> Sure, so I think I think one of the interesting things, yeah as Lianne has sort of described it, right? It's effectively like, you know the department has to have be running all of the time, right? They can't, you know, they can't effectively stop and then do a bunch of IT transformation, you know it's effectively like, you know, changing the wheels of a jumbo jet whilst it's taking off, right? And you've got to do all of that all in one go. But what I think we really, really liked about the situation that we were in and the client relationship we had was that we knew we had to it wasn't just a technology play, we couldn't just go, "All right, let's just put some new technology in." What we also needed to do was really sort of create a culture, an innovation culture, and go, "Well how do we think about the problems that we currently have and how do we think about solving them differently and in collaboration, right?" So, not just the, "Let's just outsource a bunch of technology for to, you know, to Accenture and build a bunch of stuff." So, we very carefully thought about, well actually, the unique situation that they're in the demands that the citizens have on the services that the department provide. And as Lianne mentioned, that technology didn't exist. So, we fundamentally looked at this in a different way. So, we worked really closely with the department. We said, Look, actually what we ultimately need is the equivalent of a virtual workforce. Something where if you already, you know all of a sudden had a hundred thousand pension claims that needed to be processed in a week that you could click your fingers and, you know in a physical world you'd have another building all of your kits, a whole bunch of trained staff that would be able to process that work. And if in the following week you didn't need that you no longer needed that building that stuff or the machinery. And we wanted to replicate that in the virtual world. So, we started designing a platform we utilized and focused on using AWS because it had the scalability. And we thought about, how were we going to connect something as new as AWS to all of these legacy systems. How are we going to make that work in the modern world? How are we going to integrate it? How we going to make sure it's secure? And frankly, we're really honest with the client we said, "Look, this hasn't been done before. Like, nowhere in Accenture has done it. No one's done it in the industry. We've got some smart people, I think we can do it." And, we've prototyped and we've built and we were able to prove that we can do that. And that in itself just created an environment of solving tricky problems and being innovative but most importantly not doing sort of proof of concepts that didn't go anywhere but building something that actually scaled. And I think that was really the real the start of what was has been the Garage. >> So, And Lianne, you mentioned this and you just referred to it Amar, about The Garage, right? The Intelligent Automation Garage. What exactly is it? I mean, we talked about it, what the needs are all this and that, but Lianne, I'll let you jump in first and Amar, certainly compliment her remarks, but what is the IAG, what's the... >> So, you know, I think exactly what kind of Amar, has said from a from a kind of a development point of view I think it started off, you know, really, really small. And the idea is that this is DWP, intelligent automation center of excellence. So, you know, it's aims are that, you know, it makes sure that it scopes out kind of the problems that DWP are are facing properly. So, we really understand what the crux of the problem is. In large organizations It's very easy, I think to think you understand what the problem is where actually, you know, it is really about kind of delving into what that is. And actually we have a dedicated design team that really kind of get under the bonnet of what these issues really are. It then kind of architects what the solutions need to look like using as Amar said, all the exciting new technology that we kind of have available to us. That kind of sensible solution as to what that should look like. We then build that sensible solution and we then, you know as part of that, we make sure that it scales to demand. So, something that might start out with, I dunno, you know a few hundred claimants or kind of cases going through it can quite often, you know, once that's that's been successful scale really, really quickly because as you know, we have 20 million claimants that come through us every year. So, these types of things can grow and expand but also a really key function of what we do is that we have a fully supported in-house service as well. So, all of those automations that we build are then maintained and you know, so any changes that kind of needed to be need to be made to them, we have all that and we have that control and we have our kind of arms wrapped around all of those. But also what that allows us to do is it allows us to be very kind of self-sufficient in making sure that we are as sufficient, sorry, as efficient as possible. And what I mean by that is looking at, you know as new technologies come around and they can allow us to do things more effectively. So, it allows us to kind of almost do that that kind of continuous improvement ourselves. So, that's a huge part of what we do as well. And you know, I think from a size point of view I said this started off really small as in the idea was this was a kind of center of excellence but actually as automation, I think as Amar alluded to is kind of really started to embed in DWP culture what we've started to kind of see is the a massive expansion in the types of of work that people want us to do and the volume of work that we are doing. So, I think we're currently running at around around a hundred people at the moment and I think, you know we started off with a scrum, a couple of scrum teams under Amar, so yeah, it's really grown. But you know, I think this is here to stay within DWP. >> Yeah, well when we talk about automation, you know virtual and robotics and all this I like to kind of keep the human element in mind here too. And Amar, maybe you can touch on that in certain terms of the human factors in this equation. 'Cause people think about, you know, robots it means different things to different people. In your mind, how does automation intersect with the human element here and in terms of the kinds of things Lianne wants to do down the road, you know, is a road for people basically? >> Oh yeah, absolutely. I think fundamentally what the department does is support people and therefore the solutions that we designed and built had to factor that in mind right? We were trying to best support and provide the best service we possibly can. And not only do we need to support the citizens that it supports. The department itself is a big organization, right? We're up to, we're talking between sort of 70 and 80,000 employees. So, how do we embed automation but also make the lives of the, of the DWP agents better as well? And that's what we thought about. So we said, "Well look, we think we can design solutions that do both." So, a lot of our automations go through a design process and we work closely with our operations team and we go, well actually, you know in processing and benefit, there are some aspects of that processing that benefit that are copy and paste, right? It doesn't require much thought around it, but it just requires capturing data and there's elements of that solution or that process that requires actual thought and understanding and really empathy around going, "Well how do I best support this citizen?" And what we tended to do is we took all of the things that were sort of laborious and took a lot of time and would slow down the overall process and we automated those and then we really focused on making sure that the elements that required the human, the human input was made as user friendly and centric as we possibly could. So, if there's a really complex case that needs to be processed, we were able to present the information in a really digestible and understandable way for the agents so that they could make a informed and sensible decision based around a citizen. And what that enabled us to do is essentially meet the demands of the volumes and the peaks that came in but also maintain the quality and if not improve, you know the accuracy of the claims processing that we had. >> So, how do you know, and maybe Lianne, you can address this. How do you know that it's successful on both sides of that equation? And, 'cause Amar raised a very good point. You have 70 to 80,000 employees that you're trying to make their work life much more efficient, much simpler and hopefully make them better at their jobs at the end of the day. But you're also taking care of 20 million clients on the, your side too. So, how do you, what's your measurement for success and what kind of like raw feedback do you get that says, "Okay, this has worked for both of our client bases, both our citizens and our employees?" >> Yeah, so we can look at this both from a a quantitative and a qualitative point of view as well. So, I think from a let take the kind figures first. So we are really hot on making sure that whatever automations we put in place we are there to measure how that automation is working what it's kind of doing and the impact that it's having from an operational point of view. So I think, you know, I think the proof of the fact that the Intelligent Automation Garage is working is that, you know, in the, in its lifetime, we've processed over 20 million items and cases so far. We have 65 scaled and transitioned automations and we've saved over 2 million operational hours. I was going to say that again that's 2 million operational hours. And what that allows us to do as an organization those 2 million hours have allowed us to rather than people as Amar, said, cutting and pasting and doing work that that is essentially very time consuming and repetitive. That 2 million hours we've been able to use on actual decision making. So, the stuff that you need as sentient human being to make judgment calls on and you know and kind of make those decisions that's what it's allowed us as an organization to do. And then I think from a quality point of view I think the feedback that we have from our operational teams is, you know is equally as as great. So, we have that kind of feedback from, you know all the way up from to the director level about, you know how it's kind of like I said that freeing up that time but actually making the operational, you know they don't have an easy job and it's making that an awful lot easier on a day to day basis. It has a real day to day impact. But also, you know, there are other things that kind of the knock on effects in terms of accuracy. So for example, robot will do is exactly as it's told it doesn't make any mistakes, it doesn't have sick days, you know, it does what it says on the tin and actually that kind of impact. So, it's not necessarily, you know, counting your numbers it's the fact that then doesn't generate a call from a customer that kind of says, "Well you, I think you've got this wrong." So, it's all that kind of, these kind of ripple effects that go out. I think is how we measure the fact that A, the garage is working and b, it's delivering the value that we needed to deliver. >> Robots, probably ask better questions too so yeah... (Lianne laughing) So, real quick, just real quick before you head out. So, the big challenge next, eureka, this works, right? Amar, you put together this fantastic system it's in great practice at the DWP, now what do we do? So, it's just in 30 seconds, Amar, maybe if you can look at, be the headlights down the road here for DWP and say, "This is where I think we can jump to next." >> Yeah, so I think, what we've been able to prove as I say is that is scaled innovation and having the return and the value that it creates is here to stay, right? So, I think the next things for us are a continuous expand the stuff that we're doing. Keeping hold of that culture, right? That culture of constantly solving difficult problems and being able to innovate and scale them. So, we are now doing a lot more automations across the department, you know, across different benefits across the digital agenda. I think we're also now becoming almost a bit of the fabric of enabling some of the digital transformation that big organizations look at, right? So moving to a world where you can have a venture driven architectures and being able to sort of scale that. I also think the natural sort of expansion of the team and the type of work that we're going to do is probably also going to expand into sort of the analytics side of it and understanding and seeing how we can take the data from the cases that we're processing to overall have a smoother journey across for our citizens. But it's looking, you know, the future's looking bright. I think we've got a number of different backlogs of items to work on. >> Well, you've got a great story to tell and thank you for sharing it with us here on "the CUBE", talking about DWP, the Department of Work and Pensions in the UK and the great work that Accenture's doing to make 20 million lives plus, a lot simpler for our friends in England. You've been watching ""the CUBE"" the AWS Executive Summit sponsored by Accenture. (bright upbeat music)

(bright upbeat music) >> Well, hello everybody. John Walls is here on "the CUBE". Great to have you with us as we continue our series here at the AWS Executive Summit sponsored by Accenture. And today we're talking about public service and not just a little slice of public service but probably the largest public sector offering in the UK and for with us or with us. Now to talk about that is Lianne Anderton, who is in with the Intelligent Automation Garage Delivery Lead at the UK Department of Work and Pension. Lianne, good to see you today. Thanks for joining us here on "the CUBE". >> Hi, thanks for having me. >> And also with this us is Amar Narayan, who is a Manager Director at Accenture the AWS Business Group for the Lead in Health and Public Sector, also UK and Ireland. And Amar, I think, you and Lianne, are in the same location, Newcastle, I believe in the UK, is that right? >> Yeah, absolutely. Yep, yeah, we're, here in the northeast of UK. >> Well, thank you for being with us. I appreciate the time. Lianne, let's talk about what you do, the Department of Work and Pension, the famous DWP in England. You have influence or certainly touchpoints with a huge amount of the British population. In what respects, what are you doing for the working class in England and what does technology have to do with all that? >> Sure, so for the Department for Work and Pensions I think the pensions bit is fairly self explanatory so anybody who is over state pension age within the UK. for the work part of that we also deal with people of working age. So, these are people who are either in employment and need additional help through various benefits we offer in the UK. Those people who are out of work. And we also deal with health related benefits as well. And we are currently serving over 20 million claimants every year at this moment in time. So, we're aware of a huge part of the UK government. >> All right, so say that number again. How many? >> 20 million claimants every year. >> Million with an M, right? >> Yeah. >> So, and that's individuals. And so how many transactions, if you will, how many do you think you process in a month? How, much traffic basically, are you seeing? >> An extraordinary amount? I'm not even, I don't think I even know that number. (Lianne laughing) >> Mind blowing, right? So, it's- >> A huge, huge amount. >> Mind blowing. >> Yeah, so, basically the we kind of keep the country going. So, you know, if the department for Work and Pensions kind of didn't exist anymore then actually it would cause an infinite number of problems in society. We, kind of help and support the people who need that. And, yeah, so we play a really vital role in kind of you know, social care and kind of public service. >> So, what was your journey to Accenture then? What, eventually led you to them? What problem were you having and how have you collaborated to solve that? >> So, in terms of how we work with Accenture. So, we had in around 2017 DWP was looking at a projected number of transactions growing by about 210 million which was, you know, an extraordinary amount. And, you know, I think as we've kind of covered everything that we do is on a massive scale. So, we as DWP as an organization we had absolutely no idea how we were going to be able to handle such a massive increase in the transactions. And actually, you know, after kind of various kind of paths and ideas of how we were going to do that, automation, was actually the answer. But the problem that we have with that is that we have, like many governments around the world, we have really older legacy systems. So, each of these benefits that we deal with are on legacy systems. So, whatever we were going to develop had to, you know, connect to all of these, it had to ingest and then process all of these pieces of data some of which, you know, given the fact that a lot of these systems have a lot of manual input you have data issues there that you have to solve and whatever we did, you know, as we've talked about in terms of volumes has to scale instantly as well. So, it has to be able to scale up and down to meet demand and, you know, and that down scaling is also equally as important. So yeah, you've got to be able to scale up to meet the volumes but also you've got to be able to downscale when when it's not needed. But we had nothing that was like that kind of helped us to meet that demand. So, we built our own automation platform, The Intelligent Automation Garage and we did that with Accenture. >> So Amar, I'd like you to chime in here then. So, you're looking at this client who has this massive footprint and obviously vital services, right? So, that's paramount that you have to keep that in mind and the legacy systems that Lianne was just talking about. So, now you're trying to get 'em in the next gen but also respecting that they have a serious investment already in a lot of technology. How do you approach that kind of problem solving, those dynamics and how in this case did you get them to automation as the solution? >> Sure, so I think I think one of the interesting things, yeah as Lianne has sort of described it, right? It's effectively like, you know the department has to have be running all of the time, right? They can't, you know, they can't effectively stop and then do a bunch of IT transformation, you know it's effectively like, you know, changing the wheels of a jumbo jet whilst it's taking off, right? And you've got to do all of that all in one go. But what I think we really, really liked about the situation that we were in and the client relationship we had was that we knew we had to it wasn't just a technology play, we couldn't just go, "All right, let's just put some new technology in." What we also needed to do was really sort of create a culture, an innovation culture, and go, "Well how do we think about the problems that we currently have and how do we think about solving them differently and in collaboration, right?" So, not just the, "Let's just outsource a bunch of technology for to, you know, to Accenture and build a bunch of stuff." So, we very carefully thought about, well actually, the unique situation that they're in the demands that the citizens have on the services that the department provide. And as Lianne mentioned, that technology didn't exist. So, we fundamentally looked at this in a different way. So, we worked really closely with the department. We said, Look, actually what we ultimately need is the equivalent of a virtual workforce. Something where if you already, you know all of a sudden had a hundred thousand pension claims that needed to be processed in a week that you could click your fingers and, you know in a physical world you'd have another building all of your kits, a whole bunch of trained staff that would be able to process that work. And if in the following week you didn't need that you no longer needed that building that stuff or the machinery. And we wanted to replicate that in the virtual world. So, we started designing a platform we utilized and focused on using AWS because it had the scalability. And we thought about, how were we going to connect something as new as AWS to all of these legacy systems. How are we going to make that work in the modern world? How are we going to integrate it? How we going to make sure it's secure? And frankly, we're really honest with the client we said, "Look, this hasn't been done before. Like, nowhere in Accenture has done it. No one's done it in the industry. We've got some smart people, I think we can do it." And, we've prototyped and we've built and we were able to prove that we can do that. And that in itself just created an environment of solving tricky problems and being innovative but most importantly not doing sort of proof of concepts that didn't go anywhere but building something that actually scaled. And I think that was really the real the start of what was has been the Garage. >> So, And Lianne, you mentioned this and you just referred to it Amar, about The Garage, right? The Intelligent Automation Garage. What exactly is it? I mean, we talked about it, what the needs are all this and that, but Lianne, I'll let you jump in first and Amar, certainly compliment her remarks, but what is the IAG, what's the... >> So, you know, I think exactly what kind of Amar, has said from a from a kind of a development point of view I think it started off, you know, really, really small. And the idea is that this is DWP, intelligent automation center of excellence. So, you know, it's aims are that, you know, it makes sure that it scopes out kind of the problems that DWP are are facing properly. So, we really understand what the crux of the problem is. In large organizations It's very easy, I think to think you understand what the problem is where actually, you know, it is really about kind of delving into what that is. And actually we have a dedicated design team that really kind of get under the bonnet of what these issues really are. It then kind of architects what the solutions need to look like using as Amar said, all the exciting new technology that we kind of have available to us. That kind of sensible solution as to what that should look like. We then build that sensible solution and we then, you know as part of that, we make sure that it scales to demand. So, something that might start out with, I dunno, you know a few hundred claimants or kind of cases going through it can quite often, you know, once that's that's been successful scale really, really quickly because as you know, we have 20 million claimants that come through us every year. So, these types of things can grow and expand but also a really key function of what we do is that we have a fully supported in-house service as well. So, all of those automations that we build are then maintained and you know, so any changes that kind of needed to be need to be made to them, we have all that and we have that control and we have our kind of arms wrapped around all of those. But also what that allows us to do is it allows us to be very kind of self-sufficient in making sure that we are as sufficient, sorry, as efficient as possible. And what I mean by that is looking at, you know as new technologies come around and they can allow us to do things more effectively. So, it allows us to kind of almost do that that kind of continuous improvement ourselves. So, that's a huge part of what we do as well. And you know, I think from a size point of view I said this started off really small as in the idea was this was a kind of center of excellence but actually as automation, I think as Amar alluded to is kind of really started to embed in DWP culture what we've started to kind of see is the a massive expansion in the types of of work that people want us to do and the volume of work that we are doing. So, I think we're currently running at around around a hundred people at the moment and I think, you know we started off with a scrum, a couple of scrum teams under Amar, so yeah, it's really grown. But you know, I think this is here to stay within DWP. >> Yeah, well when we talk about automation, you know virtual and robotics and all this I like to kind of keep the human element in mind here too. And Amar, maybe you can touch on that in certain terms of the human factors in this equation. 'Cause people think about, you know, robots it means different things to different people. In your mind, how does automation intersect with the human element here and in terms of the kinds of things Lianne wants to do down the road, you know, is a road for people basically? >> Oh yeah, absolutely. I think fundamentally what the department does is support people and therefore the solutions that we designed and built had to factor that in mind right? We were trying to best support and provide the best service we possibly can. And not only do we need to support the citizens that it supports. The department itself is a big organization, right? We're up to, we're talking between sort of 70 and 80,000 employees. So, how do we embed automation but also make the lives of the, of the DWP agents better as well? And that's what we thought about. So we said, "Well look, we think we can design solutions that do both." So, a lot of our automations go through a design process and we work closely with our operations team and we go, well actually, you know in processing and benefit, there are some aspects of that processing that benefit that are copy and paste, right? It doesn't require much thought around it, but it just requires capturing data and there's elements of that solution or that process that requires actual thought and understanding and really empathy around going, "Well how do I best support this citizen?" And what we tended to do is we took all of the things that were sort of laborious and took a lot of time and would slow down the overall process and we automated those and then we really focused on making sure that the elements that required the human, the human input was made as user friendly and centric as we possibly could. So, if there's a really complex case that needs to be processed, we were able to present the information in a really digestible and understandable way for the agents so that they could make a informed and sensible decision based around a citizen. And what that enabled us to do is essentially meet the demands of the volumes and the peaks that came in but also maintain the quality and if not improve, you know the accuracy of the claims processing that we had. >> So, how do you know, and maybe Lianne, you can address this. How do you know that it's successful on both sides of that equation? And, 'cause Amar raised a very good point. You have 70 to 80,000 employees that you're trying to make their work life much more efficient, much simpler and hopefully make them better at their jobs at the end of the day. But you're also taking care of 20 million clients on the, your side too. So, how do you, what's your measurement for success and what kind of like raw feedback do you get that says, "Okay, this has worked for both of our client bases, both our citizens and our employees?" >> Yeah, so we can look at this both from a a quantitative and a qualitative point of view as well. So, I think from a let take the kind figures first. So we are really hot on making sure that whatever automations we put in place we are there to measure how that automation is working what it's kind of doing and the impact that it's having from an operational point of view. So I think, you know, I think the proof of the fact that the Intelligent Automation Garage is working is that, you know, in the, in its lifetime, we've processed over 20 million items and cases so far. We have 65 scaled and transitioned automations and we've saved over 2 million operational hours. I was going to say that again that's 2 million operational hours. And what that allows us to do as an organization those 2 million hours have allowed us to rather than people as Amar, said, cutting and pasting and doing work that that is essentially very time consuming and repetitive. That 2 million hours we've been able to use on actual decision making. So, the stuff that you need as sentient human being to make judgment calls on and you know and kind of make those decisions that's what it's allowed us as an organization to do. And then I think from a quality point of view I think the feedback that we have from our operational teams is, you know is equally as as great. So, we have that kind of feedback from, you know all the way up from to the director level about, you know how it's kind of like I said that freeing up that time but actually making the operational, you know they don't have an easy job and it's making that an awful lot easier on a day to day basis. It has a real day to day impact. But also, you know, there are other things that kind of the knock on effects in terms of accuracy. So for example, robot will do is exactly as it's told it doesn't make any mistakes, it doesn't have sick days, you know, it does what it says on the tin and actually that kind of impact. So, it's not necessarily, you know, counting your numbers it's the fact that then doesn't generate a call from a customer that kind of says, "Well you, I think you've got this wrong." So, it's all that kind of, these kind of ripple effects that go out. I think is how we measure the fact that A, the garage is working and b, it's delivering the value that we needed to deliver. >> Robots, probably ask better questions too so yeah... (Lianne laughing) So, real quick, just real quick before you head out. So, the big challenge next, eureka, this works, right? Amar, you put together this fantastic system it's in great practice at the DWP, now what do we do? So, it's just in 30 seconds, Amar, maybe if you can look at, be the headlights down the road here for DWP and say, "This is where I think we can jump to next." >> Yeah, so I think, what we've been able to prove as I say is that is scaled innovation and having the return and the value that it creates is here to stay, right? So, I think the next things for us are a continuous expand the stuff that we're doing. Keeping hold of that culture, right? That culture of constantly solving difficult problems and being able to innovate and scale them. So, we are now doing a lot more automations across the department, you know, across different benefits across the digital agenda. I think we're also now becoming almost a bit of the fabric of enabling some of the digital transformation that big organizations look at, right? So moving to a world where you can have a venture driven architectures and being able to sort of scale that. I also think the natural sort of expansion of the team and the type of work that we're going to do is probably also going to expand into sort of the analytics side of it and understanding and seeing how we can take the data from the cases that we're processing to overall have a smoother journey across for our citizens. But it's looking, you know, the future's looking bright. I think we've got a number of different backlogs of items to work on. >> Well, you've got a great story to tell and thank you for sharing it with us here on "the CUBE", talking about DWP, the Department of Work and Pensions in the UK and the great work that Accenture's doing to make 20 million lives plus, a lot simpler for our friends in England. You've been watching ""the CUBE"" the AWS Executive Summit sponsored by Accenture. (bright upbeat music)

>>The Cube presents UI Path Forward five. Brought to you by UI Path. >>Hi everybody. Welcome back to Las Vegas. You're watching the Cube's coverage of UI Path Forward. Five. We reach cruising altitude on day two. Christina Seacrest is here. She's the process Artificial intelligence and automation GPS automation leader at ey. And Bob PCIs, executive director for Intelligent Automation for the state of Tennessee. Folks, welcome to the cube. Thank you for Adam. >>Good >>To have you. Okay, I don't know if I messed up that title, Christina, but it's kind of interesting. You got process, you got ai, you got automation, you got gps. What's your role? >>I have a lot of rules, so thank you for that. Yeah, so my focus is first and foremost automation. So how do you get things like UI path into our clients, but also I focus specifically in our government and public sector clients. So sled specifically. So state local education. So that's why I'm here with the state of Tennessee. And then we also like to take it beyond automation. So how do you bring an artificial intelligence and all the technologies that come with that. So really full end to end spectrum of >>Automation. So Bob, when you think about the sort of the, the factors that are driving your organization of, how did you describe that, Those sort of external factors that inform your strategy. What, what's, what are the catalysts for how you determine to deploy technology? >>Well, it was primarily that we know tendency has a tendency to provide good customer service, but we want to get to a great status best in class, if you will. And we had an external advisory review where it said, Hey, you know, we could make automation to improve our customer experience. And so that was like a directive of the, the state leaders to go across the board and automate all processes statewide, starting with the 23 executive agencies. >>So where's the focus from that standpoint? Is it on just providing better interfaces to your constituents, your customers? Is it cutting costs or you actually have more budget to invest? Kind of a combination of >>Those? Yeah, so it's, it's really both qualitative and quantitative, right? So quantitative is where we're able to reduce hours and therefore we can redirect people to more less mundane work, if you will. And then qualitative is where we're able to reduce the errors, improve data quality, reduce cycle time for our citizens, you know, when they're making requests, et cetera. So it's, I think it's a combination of both of those quantitative and qualitative metrics that we are mandated in, in micromanaged, quite frankly to, to bring, make those >>Numbers. So I'm from Massachusetts, when I go to a a mass.gov website, I say, all this was done in the 1990s and you could just see where the different stovepipes were, were. But then every now and then you'll hit one and you'll say, Wow, okay, this is up to, it's such a great experience. And then the flip side of that is you want your employees to be happy and not have to do all this mundane work so you can retain the best people. You don't have to. So you're living that in, in state and, and local. So where did you start your automation journey? What role did EY play? Let's go. Yeah, >>Sure. So I, I, I think the thought for process automation was probably three or four years ago, but then we started the program about 18 months ago and there was a lot of, let's say behind the scenes work before we could bring EY in, you know, like what resources was I gonna have in, in the state that were gonna help me address all of the agency simultaneously, right? Cuz normally you'll see a project that'll do be more siloed across the state and say, we're gonna do this agency, we're gonna do this division. Well, you have 40 other agencies that are, you know, the momentum is it's just gonna fall, it wayside. So how we looked at it was let's blanket it and go across all 23 agencies at the same time, you know, identify common processes that are used across 40 divisions, for example, right? >>So, so what we basically did is we procured the software, you know, did the contracts, and then it was really about, I designed, I'm gonna say a multistream approach where they were, we could run multiple work streams, independent define all the architectures, required dev tests, production, the disaster recovery at the same time in parallel developed the center of excellence, the operation model, the processes, methodologies. And the third one was, let's go out to a few divisions, business administration, health, you know, health, human resources, and be able to do a process inventory to see what was there. And then based on that, there's all this theory of well let's do a proof of concept. Let's do a proof of technology, let's do apply. Well, the bottom line is rpa technology's been around for a long time. It's proven there's nothing to prove. But really what was important to prove before we decided to go, you know, full tilt was, you know, develop a proof of perceived business value. >>Are we gonna bring in the, the business value, the hours and the qu qualitative metrics that is expected by our ex executive team, The leadership, we were able to do that, you know, with the help of help of ey, we built out the prototypes and we got the green light to go forward, got ey to start, and then we just basically went pedal to the metal. We had our foundation already defined. We built up the architecture in less than one to two months. Now, in, in a public sector or private sector, it's just not heard of, right? But we have a tendency with EYs technical team, myself, we look around the, the road around the rock instead, the rock in the road, right? So we ended up coming up with a very unique, very easy to easy to handle architecture that was very scalable. And then were able to hit the ground running and deploy in production by December where head of >>Was EY involved in the whole, you know, dev test production, dr. Center of excellence, the, the process inventory or did you bring them in? Did you kind of do that internally then bring EY in for the proof of >>Value? EY was actually awarded the contract for soup to nuts, basically the first phase, which was those four work streams I told you about. And they worked with myself and the state of Tennessee infrastructure architecture teams. We needed to get these things defined and signed off the architecture so we could expedite getting them built out. And then they, and they basically ran all four work streams, you know, the process, inventory, the prototype, the, the proof of perceived business value, the building out the center of excellence, working with myself. And, and this wasn't just us in a, a vacuum, we ended up having to, I mean, I could do the strategy, I could do the technology and I could said the roadmap and all the good stuff, but we had to actually meet with a lot of the state or tendency organizations on change management. How do we end up putting this process or an automation in the middle of the, the normal traditional process, right? So there was a lot of interaction there and getting their feedback and then tweaking our operational model based on feedback from the state of Tennessee. So it was all very collective collaborative. I think that would be the keyword is collaborative and then building out everything. So then, and then we ended up going to the next way where they knew so much and we were, we had such a tight timeframe that we continued with ey. >>So Christina, Bob mentioned center of excellence a couple of times in the state of Tennessee, but then beyond state of Tennessee, other organizations you've worked with in this space, what's the relationship between center of excellence and this thing we've been hearing about over the last couple of days, the citizen developer has that been, has, has, has that been leveraged in the state of Tennessee? Bob, have you seen that leveraged in other places? Christina? What's that relationship look like? >>Yeah, so we don't leverage that, that model yet we have centralized model and there's reasons for that. So we don't end up having maverick's, runoff runoffs have one off, have, you know, have a a UI path version or down this division or have another RPA tool in another division, right? So then all of a sudden we're, we have a maintenance nightmare. Manageability nightmare. So we basically, you know, I I I negotiate an ELA with UI path, so therefore if anyone wants to go do another automation on another division, or they would basically follow our model, our design, our coe, our quality gates. We we're the gatekeepers to bring into production. >>Got it. Now, yeah. Now Christina, what's your perspective? Because I can imagine Nashville and Memphis might have very different ideas about a lot of things. Yeah. Little Tennessee reference there, but what, what, what about what, what about other places are you, are you seeing the citizen developer leveraged in, in some kinds of places more than others or >>What? Yeah. Yeah. And that's part of, because of the foundation we're building. Yeah. So we laid, you know, when, when Bob talks about the first phase of eight weeks, that was amazingly fast, even in that's ridiculous. Spoke about it to say you're gonna lay these four foundations. I was excited, like, I was like, wow, this, this is a very serious client. They wanna go fast and they wanna get that momentum, but the AUM was laid out so we could propel ourselves. So we are at 40 automations right now. We're in the works of creating 80 more automations in this next year. We'll be at 120 really quickly. The AUM is critical. And I will say at a client, I've, I've worked with over 50 clients on automation programs. The way state of Tennessee treats the aom and they abide by it, it is the living document of how you go and go fast. Got it. And the one thing I would say is it's also allowed us to have such immense quality. So I always talk about you put in forward, you put in another 80, we're at 98% uptime on all our automations, meaning they don't go down. And that's because of the AOM we set up. And the natural progression is going to be how do you take it to citizen developer? How do you take it to, we call, you know, process automation plus, >>But methodically, methodically, not just throwing it out at the beginning and, and hoping the chaos >>Works. Exactly. Exactly. And >>The ratio of of bots to automations, is that one to one or you have automation? Oh no, the single bot is doing multiple. So how many bots are you talking about? >>We're doing, Bob, you're gonna answer this better than I will, but the efficiency is amazing. We've been pushing that. >>So our ratio now, cause we have a high density architecture we put in is four bots, excuse me, four processes. The one bot and four bots, The one virtual machine EC two server. Right? So it's four to one, four to one. Now what we're going to get by next summer, we'll do more analysis. We'll probably get the six to one, six to one that's made serious shrinkage of our footprint from a machine, you know, management perspective from 60 down to seven right now we're gonna add the next chunk. We add another 80 automations in FIS gear 24. We're only gonna add two more bot, two more servers. Right? So that's only 10 running like close to 200 bucks. >>And, and is doing this on prem in the cloud? >>No, our, the architecture's fully >>Oh, cloud based >>Ct. Yeah. So we use UiPath SAS model. Yeah. Right. So that handles the orchestrator, the attended bots, all the other tooling you need automation hub, process minor et etc. Etc. Cetera. And then on the state side in aws we have, we use unattended bots, cert bots that have to go down into the legacy systems, et cetera. And they're sitting on EC two instances. >>Was there, was there a security not hole that you had to get through internally? What was that like? >>No, actually we, we, we were lock and step with the security team on this. I mean, there are some standards and templates and you know, what we had to follow, you know, but they're doing an assessment every single release, they do assessments on little bots, what systems it's activating or are accessing, et cetera. The data, because you have fedra data of FTI data, you know, in the public sector to make sure we're not touching it. >>Do you guys golf? >>I do, yeah. Not Well, yes, >>If you mean I I like golf but not don't golf well, but so you know what, what a mulligan is. If you had a Mulligan right, for the state of Tennessee, what'd you learn? What would you do differently? You know, what are some of the gotchas you see maybe Christina in, in other customers and then maybe specifically state of Tennessee, >>Right? I would say, you know, it is the intangibles. So when we talk about our clients that go fast and go big, like state of Tennessee, it's because that, that we call it phase zero that gets done that Bob did. It's about making sure you've got the sponsorship. So we've got executive sponsorship all the way up. You've got amazing stakeholder engagement. So you're communicating the value of what we're trying to do. And you're, you're showing them the value. We have been really focused on the return on investment and we'll talk a little bit about that, but it's how do you make sure that when you do, you know, states are different with those agencies, you have such an opportunity to maximize return on investment if you do it right, because you're not talking about automation in one agency, you're talking it across multiple agencies. We call that the multiplier effect. And that's huge. And if you understand that and how to actually apply that, the value you get is amazing. So I, I don't, I can't say there's a mulligan here, Bob, you may think of some, I know on other clients, if you don't line up your stakeholders and you don't set the expectations early on, you meander and you may get five, six automations in over the year. You know, when I go to clients and say, we're doing 40, we're doing 80, they're like, >>Wow, that's the, but that's the bottom line. Gotcha. Is if you, if you want to have an operational impact and have multiple zeros, you gotta go through that process that you said up front. >>Exactly. A >>Anything you do differently, Bob? >>Well, I I what I do differently, I mean, I think, I mean we, we did get executive sponsorship, you know, and in one area, but we still have to go out to all the 23 agencies and get, and bring awareness and kind of like set the hook to bring 'em in, right? Bring 'em to the, to the, to the lake. Right. And, and I think if, if it was more of a blanket top down, getting every agency to agree to, you know, in investigate automation, it would've been a lot easier. So we're, we're, we're getting it done. We've gone through 13 agencies already and less than a year, all of our releases are sprinkling across multiple agencies. So it's not like a silo. I'll look at that. Everyone at every agency is being impacted. So I think that's great. But I, I think our, our Mueller now is just trying to make sure we have enough backlog to do the next sprints. >>Is it, you know, the ROI on these initiatives is, is, is so clear and so fast. Is it self-funding? Is there gain sharing or do you just give business, give money back to the state and have to scramble for more? Do you get to, you know, get a lick off that cone? >>Unfortunately we don't, but I, I, I try to see if we could get some property like, nah, we don't do that. It's all cost, cost based. But, but our ROI is very attractive, I think for, for doing a whole state, you know, transformation. I think our ROI is three and a half to four years. Right. And that's pretty mind blowing. Even if you look at private sector or, I, I think some of the, the key things which people are noticing, even though we're in public sector, we're we are very nimble. This project is extremely nimble. We've had people come in, exactly, we need this, so we're gonna get penalized. Okay, knock it out in four hours, four days. Right? So it's that nimbleness that you just don't hear of even in private sector or public sector. And we're just able to do that for all the collaboration we do across ey, across myself and across all the other organizations that I, that I kind of drag along or what have, >>What do you, what do you, do you see any limits to the opportunities here? I mean, is this a decade long opportunity? Is you have that much runway >>Or that's just not my dna, so we're gonna, we're gonna probably do it like in four years, but Well, when >>You say do it, I mean, will you be done at that point? Or do you see the weight, >>Look at, you know, we could boil the ocean and I think this is one of the reasons why we're successful is we could boil the ocean and and be, it will be 10 attended 20 year program. Yeah. Okay. Or we looked at it, we had some of EY guys look at it and say, I said, what's the 25 80 rule? Meaning, you know, give me, So if we had 500 processes, tell me how many processes will gimme 80% of the hours. And it was 125, it was a 25 80 rule. I said, that's what we're doing it, we're doing, we're gonna do the 80% of the hours quantifiably. Now when we're done with that pass, then we'll have those other ones that are bringing 20% of the hours, that's when we might be bringing citizens in. That's what we're bringing state workers in. But at that same time, we will be going back in the wave and doing advanced ai. Right. Or advance ia, in other words. So right now we do rpa, ocr, icr, but you know, there's NL ml nps, there's virtual agents and stuff. So that's like the wave we're gonna do through the ones we've already gone through. Got it. Right. So it'll probably be a two or three wave or iterations. >>Cool. Guys, thanks so much for coming into the cube. Great story. Really appreciate you taking us through it. Thank you so much for having us. You're very welcome. All right, keep it right there. Dave Nicholson. The Dave ante. We back at UI path forward five from the Venetian in Las Vegas. Keep it right there.

>>The Cube Presents UI Path Forward five. Brought to you by UI Path, >>Everybody. We're back in Las Vegas. The cube's coverage we're day one at UI Path forward. Five. Pascal Borne is here. He's an expert and bestselling author in the topic of AI and automation and the book Intelligent Automation. Welcome to the world of Hyper Automation, the first book on the topic. And of course, Ray Wong is back on the cube. He's the founder, chairman and principal analyst, Constellation Reese, also bestselling author of Everybody Wants To Rule the World. Guys, thanks so much for coming on The Cubes. Always a pleasure. Ray Pascal, First time on the Cube, I believe. >>Yes, thank you. Thanks for the invitation. Thank you. >>So what is artificial about artificial intelligence, >>For sure, not people. >>So, okay, so you guys are both speaking at the conference, Ray today. I think you're interviewing the co CEOs. What do you make of that? What's, what are you gonna, what are you gonna probe with these guys? Like, how they're gonna divide their divide and conquer, and why do you think the, the company Danielle in particular, decided to bring in Rob Sland? >>Well, you know what I mean, Like, you know, these companies are now at a different stage of growth, right? There's that early battle between RPA vendors. Now we're actually talking something different, right? We're talking about where does automation go? How do we get the decisioning? What's the next best action? That's gonna be the next step. And to take where UI path is today to somewhere else, You really want someone with that enterprise cred and experience the sales motions, the packages, the partnership capabilities, and who else better than Roblin? He, that's, he's done, he can do that in his sleep, but now he's gotta do that in a new space, taking whole category to another level. Now, Daniel on the other hand, right, I mean, he's the visionary founder. He put this thing from nothing to where he is today, right? I mean, at that point you want your founder thinking about the next set of ideas, right? So you get this interesting dynamic that we've seen for a while with co CEOs, those that are doing the operations, getting the stuff out the door, and then letting the founders get a chance to go back and rethink, take a look at the perspective, and hopefully get a chance to build the next idea or take the next idea back into the organization. >>Right? Very well said. Pascal, why did you write your book on intelligent automation and, and hyper automation, and what's changed since you've written that book? >>So, I, I wrote this book, An Intelligent Automation, two years ago. At that time, it was really a new topic. It was really about the key, the, the key, the key content of the, of the book is really about combining different technologies to automate the most complex end to end business processes in companies. And when I say capabilities, it's, we, we hear a lot about up here, especially here, robotic process automation. But up here alone, if you just trying to transform a company with only up here, you just fall short. Okay? A lot of those processes need more than execution. They need language, they need the capacity to view, to see, they need the capacity to understand and to, and to create insights. So by combining process automation with ai, natural language processing, computer vision, you give this capability to create impact by automating end to end processes in companies. >>I, I like the test, what I hear in the keynote with independent experts like yourself. So we're hearing that that intelligent automation or automation is a fundamental component of digital transformation. Is it? Or is it more sort of a back office sort of hidden in inside plumbing Ray? What do you think? >>Well, you start by understanding what's going on in the process phase. And that's where you see discover become very important in that keynote, right? And that's where process mining's playing a role. Then you gotta automate stuff. But when you get to operations, that's really where the change is going to happen, right? We actually think that, you know, when you're doing the digital transformation pieces, right? Analytics, automation and AI are coming together to create a concept we call decision velocity. You and I make a quick decision, boom, how long does it take to get out? Management committee could free forever, right? A week, two months, never. But if you're thinking about competing with the automation, right? These decisions are actually being done a hundred times per second by machine, even a thousand times per second. That asymmetry is really what people are facing at the moment. >>And the companies that are gonna be able to do that and start automating decisions are gonna be operating at another level. Back to what Pascal's book talking about, right? And there are four questions everyone has to ask you, like, when do you fully intelligently automate? And that happens right in the background when you augment the machine with a human. So we can find why did you make an exception? Why did you break a roll? Why didn't you follow this protocol so we can get it down to a higher level confidence? When do you augment the human with the machine so we can give you the information so you can act quickly. And the last one is, when do you wanna insert a human in the process? That's gonna be the biggest question. Order to cash, incident or resolution, Hire to retire, procure to pay. It doesn't matter. When do you want to put a human in the process? When do you want a man in the middle, person in the middle? And more importantly, when do you want insert friction? >>So Pascal, you wrote your book in the middle of the, the pandemic. Yes. And, and so, you know, pre pandemic digital transformation was kind of a buzzword. A lot of people gave it lip service, eh, not on my watch, I don't have to worry about that. But then it became sort of, you're not a digital business, you're out of business. So, so what have you seen as the catalyst for adoption of automation? Was it the, the pandemic? Was it sort of good runway before that? What's changed? You know, pre isolation, post isolation economy. >>You, you make me think about a joke. Who, who did your best digital transformation over the last years? The ceo, C H R O, the Covid. >>It's a big record ball, right? Yeah. >>Right. And that's exactly true. You know, before pandemic digital transformation was a competitive advantage. >>Companies that went into it had an opportunity to get a bit better than their, their competitors during the pandemic. Things have changed completely. Companies that were not digitalized and automated could not survive. And we've seen so many companies just burning out and, and, and those companies that have been able to capitalize on intelligent automation, digital transformations during the pandemic have been able not only to survive, but to, to thrive, to really create their place on the market. So that's, that has been a catalyst, definitely a catalyst for that. That explains the success of the book, basically. Yeah. >>Okay. Okay. >>So you're familiar with the concept of Stew the food, right? So Stew by definition is something that's delicious to eat. Stew isn't simply taking one of every ingredient from the pantry and throwing it in the pot and stirring it around. When we start talking about intelligent automation, artificial intelligence, augmented intelligence, it starts getting a bit overwhelming. My spy sense goes off and I start thinking, this sounds like mush. It doesn't sound like Stew. So I wanna hear from each of you, what is the methodical process that, that people need to go through when they're going through digital trans transmission, digital transformation, so that you get delicious stew instead of a mush that's just confused everything in your business. So you, Ray, you want, you want to, you wanna answer that first? >>Yeah. You know, I mean, we've been talking about digital transformation since 2010, right? And part of it was really getting the business model, right? What are you trying to achieve? Is that a new type of offering? Are you changing the way you monetize something? Are you taking existing process and applying it to a new set of technologies? And what do you wanna accomplish, right? Once you start there, then it becomes a whole lot of operational stuff. And it's more than st right? I mean, it, it could be like, well, I can't use those words there. But the point being is it could be a complete like, operational exercise. It could be a complete revenue exercise, it could be a regulatory exercise, it could be something about where you want to take growth into the next level. And each one of those processes, some of it is automation, right? There's a big component of it today. But most of it is really rethinking about what you want things to do, right? How do you actually make things to be successful, right? Do I reorganize a process? Do I insert a place to do monetization? Where do I put engagement in place? How do I collect data along the way so I can build better feedback loop? What can I do to build the business graph so that I have that knowledge for the future so I can go forward doing that so I can be successful. >>The Pascal should, should, should the directive be first ia, then ai? Or are these, are these things going to happen in parallel naturally? What's your position on that? Is it first, >>So it, so, >>So AI is part of IA because that's, it's, it's part of the big umbrella. And very often I got the question. So how do you differentiate AI in, I a, I like to say that AI is only the brain. So think of ai cuz I'm consider, I consider AI as machine learning, Okay? Think of AI in a, like a brain near jar that only can think, create, insight, learn, but doesn't do anything, doesn't have any arms, doesn't have any eyes, doesn't not have any mouth and ears can't talk, can't understand with ia, you, you give those capabilities to ai. You, you basically, you create a cap, the capability, technological capability that is able to do more than just thinking, learning and, and create insight, but also acting, speaking, understanding the environment, viewing it, interacting with it. So basically performing these, those end to end processes that are performed currently by people in companies. >>Yeah, we're gonna get to a point where we get to what we call a dynamic scenario generation. You're talking to me, you get excited, well, I changed the story because something else shows up, or you're talking to me and you're really upset. We're gonna have to actually ch, you know, address that issue right away. Well, we want the ability to have that sense and respond capability so that the next best action is served. So your data, your process, the journey, all the analytics on the top end, that's all gonna be served up and changed along the way. As we go from 2D journeys to 3D scenarios in the metaverse, if we think about what happens from a decentralized world to decentralized, and we think about what's happening from web two to web three, we're gonna make those types of shifts so that things are moving along. Everything's a choose your end venture journey. >>So I hope I remember this correctly from your book. You talked about disruption scenarios within industries and within companies. And I go back to the early days of, of our industry and East coast Prime, Wang, dg, they're all gone. And then, but, but you look at companies like Microsoft, you know, they were, they were able to, you know, get through that novel. Yeah. Ibm, you know, I call it survived. Intel is now going through their, you know, their challenge. So, so maybe it's inevitable, but how do you see the future in terms of disruption with an industry, Forget our industry for a second, all industry across, whether it's healthcare, financial services, manufacturing, automobiles, et cetera. How do you see the disruption scenario? I'm pretty sure you talked about this in your book, it's been a while since I read it, but I wonder if you could talk about that disruption scenario and, and the role that automation is going to play, either as the disruptor or as the protector of the incumbents. >>Let's take healthcare and auto as an example. Healthcare is a great example. If we think about what's going on, not enough nurses, massive shortage, right? What are we doing at the moment? We're setting five foot nine robots to do non-patient care. We're trying to capture enough information off, you know, patient analytics like this watch is gonna capture vitals from a going forward. We're doing a lot what we can do in the ambient level so that information and data is automatically captured and decisions are being rendered against that. Maybe you're gonna change your diet along the way, maybe you're gonna walk an extra 10 minutes. All those things are gonna be provided in that level of automation. Take the car business. It's not about selling cars. Tesla's a great example. We talk about this all the time. What Tesla's doing, they're basically gonna be an insurance company with all the data they have. They have better data than the insurance companies. They can do better underwriting, they've got better mapping information and insights they can actually suggest next best action do collision avoidance, right? Those are all the things that are actually happening today. And automation plays a big role, not just in the collection of that, that information insight, but also in the ability to make recommendations, to do predictions and to help you prevent things from going wrong. >>So, you know, it's interesting. It's like you talk about Tesla as the, the disrupting the insurance companies. It's almost like the over the top vendors have all the data relative to the telcos and mopped them up for lunch. Pascal, I wanna ask you, you know, the topic of future of work kind of was a bromide before, but, but now I feel like, you know, post pandemic, it, it actually has substance. How do you see the future of work? Can you even summarize what it's gonna look like? It's, it's, Or are we here? >>It's, yeah, it's, and definitely it's, it's more and more important topic currently. And you, you all heard about the great resignation and how employee experience is more and more important for companies according to have a business review. The companies that take care of their employee experience are four times more profitable that those that don't. So it's a, it's a, it's an issue for CEOs and, and shareholders. Now, how do we get there? How, how do we, how do we improve the, the quality of the employee experience, understanding the people, getting information from them, educating them. I'm talking about educating them on those new technologies and how they can benefit from those empowering them. And, and I think we've talked a lot about this, about the democratization local type of, of technologies that democratize the access to those technologies. Everyone can be empowered today to change their work, improve their work, and finally, incentivization. I think it's a very important point where companies that, yeah, I >>Give that. What's gonna be the key message of your talk tomorrow. Give us the bumper sticker, >>If you will. Oh, I'm gonna talk, It's a little bit different. I'm gonna talk for the IT community in this, in the context of the IT summit. And I'm gonna talk about the future of intelligent automation. So basically how new technologies will impact beyond what we see today, The future of work. >>Well, I always love having you on the cube, so articulate and, and and crisp. What's, what's exciting you these days, you know, in your world, I know you're traveling around a lot, but what's, what's hot? >>Yeah, I think one of the coolest thing that's going on right now is the fact that we're trying to figure out do we go to work or do we not go to work? Back to your other point, I mean, I don't know, work, work is, I mean, for me, work has been everywhere, right? And we're starting to figure out what that means. I think the second thing though is this notion around mission and purpose. And everyone's trying to figure out what does that mean for themselves? And that's really, I don't know if it's a great, great resignation. We call it great refactoring, right? Where you work, when you work, how we work, why you work, that's changing. But more importantly, the business models are changing. The monetization models are changing macro dynamics that are happening. Us versus China, G seven versus bricks, right? War on the dollar. All these things are happening around us at this moment and, and I think it's gonna really reshape us the way that we came out of the seventies into the eighties. >>Guys, always a pleasure having folks like yourself on, Thank you, Pascal. Been great to see you again. All right, Dave Nicholson, Dave Ante, keep it right there. Forward five from Las Vegas. You're watching the cue.

>>The Cube presents UI Path Forward. Five. Brought to you by UI Path. >>Welcome back to The Cube's coverage of UI Path Forward. Five from Las Vegas. We're inside. The formerly was The Sands, now it's the Venetian Convention Center. Dave Nicholson. David, Deb. I've never seen it set up like this before. UI Path's. Very cool company. So of course the setup has to be cool, not like tons of concrete. James Furlong is here, the Vice President of Supply Chain Management and projects at Puma. And Derek Weimer is the CEO of Robo, who's an implementation partner, expert at Intelligent Automation. Folks, welcome to the Cube. Good to see you. Great to have you on. >>Thank you. It's a pleasure. >>So what's happening at Puma these days? I love your sneakers, but you guys probably do more than that, but let's tell us about, give us the update on Puma. >>Yeah, absolutely. Puma's one of the world's leading sports, sports brands. So we encompass all things sports. We do footwear, we do apparel, we do accessories. Cobra, Puma golf is underneath our umbrella as well. So we get the added benefit of having that category as well. And yeah, trade, trade all over the world and it's an exciting, exciting brand to be with. >>And di Robo Atlanta based really specialists in intelligent automation. That's pretty much all you do, is that right? >>Yeah, we are a pure play intelligence automation professional services firm. That's all we do. We're the world's largest firm that focuses only on automation headquarter in Germany, but with a large presence here in Americas. >>So we hear from a lot of customers. We've heard from like with the journey it started, you know, mid last decade, Puma James is just getting started. We April you mentioned. So take us through that. What was the catalyst as you're exiting the, the pandemic, the isolation economy we call it? Yeah. What was the catalyst tell, take us through the sort of business case for automation. >>Sure, absolutely. So Puma, our mission is forever faster. It's, it's our mantra and something we live and breathe. So naturally we have an intense focus on innovation and, and automation. So with that mindset, the way this all kicked off is that I had the opportunity to go into some of our distribution facility and I was unbelievably impressed with the automation that I saw there. So how automation augmented the employee workforce. And it was just very impressive to see that some of our state of the art technology and automation at the same time. Then I went back to the office with that excitement and that passion and I saw that we had the opportunity to take that to our employee base as well. We sort of lacked that same intense focus on how do we take automation and technology like I saw at the distribution facilities and bring it to our employees because picture a large workforce of talented, dedicated employees and they just couldn't keep up with the explosive growth who's seen explosive growth over the last couple of years and they just couldn't keep up with it. So I said that that's it. We need to, to take that same passion and innovation and enter in hyper hyper automation. So we went to the leadership team and no surprise they were all in. We went with them with the idea of bringing hyper automation, starting with RPA to, to our office employees. And they were in, they support innovation and they said, Great, what do you need? Really? Go for it. >>The first question wasn't how much, >>Actually the first question I will say that the funny part is, is they said, Well I like this, it sounds too good to be true. And because it, it really does. If you're new to it like we were and I'm pitching all the benefits that RPA could bring, it does sound too good to me. True. So they said, All right, you know, we trust you and, and go for it. What do you need? Resources, just let us know. So sure enough, I had a proof of concept, I had an idea, but now what? I didn't know where to go from there. So that's where we did some intensive research into software suppliers, but also implementation partners because now we knew what we wanted to do. We had excitement, we had leadership buy-in now, now what do I do? So this is when we entered our partnership to figure out, okay, help Puma on this journey. >>How'd you guys find each other? You know, >>Just intensive research and spoke with a lot of people here. Is there a lot of great organizations? But at the end of the day, they really supported everything that Houma stood for, what we're looking to do and had a lot of trust in the beginning and Dirk and his team and how he could help us on this journey. Yeah. >>Now James, your, your job title system for supply chain management. It is, but I understand that you have had a variety of roles within the organization. Now if we're talking about another domain, artificial intelligence, machine learning. Yeah. There's always this concept of domain expertise. Yeah. And how when you're trying to automate things in that realm, domain expertise is critical. Yeah. You have domain expertise outside of your job title. Yeah. So has that helped you with this journey looking at automation, being able to, being able to have insight into those other organizations? >>Yeah, absolutely. And I think when we were pitching it to the leadership team in the beginning, that enabled me to look at each one sitting at the table and saying, alright, and on the sales, on a commercial side, I was a head of sales for one of the trade channels. I could speak directly to him in the benefits it could have with not with tribal knowledge and with an expertise. So it wasn't something that, it was just, oh, that's supply chain. I could sit, you know, with the, our CFO and talk to him about the, the benefits for his group merchandising and legal so on. I was really able to kind of speak to each one of them and how it would support, because I had that knowledge from being blessed of 15 years experience at, at Puma. So yeah, I was able to take all of that and figure out how do I make sure not just supply chain benefits from rpa, but how does the whole organization benefit from not only RPA but the hyper automation strategy. >>So what's an engagement look like? You start, I presume you, you gotta do some type of assessment and, and you know, of some upfront planning work. Yeah. What does that look like? How, what's the starting point? Take us through that >>Journey. Yeah, so exactly. So the, the key when you're trying to get value from Intel automation is finding the right opportunities, right? And you can automate a lot of things, but which are the things that are gonna drive the most value and, and the value that actually matters to the company, right? So where are you trying to get to from a strategic level, your objectives and how do you actually use automation to help you get to there? So the first thing is, what are the opportunities gonna help you do that? And then once you identify, what we recommend is start with something that's gonna be, you know, accessible, small, You're gonna get a quick win. Cuz then the important thing is once you get that out there, you build the momentum and excitement in the organization that then leads to more and more. And then you build a proper pipeline and you and you get that the, the engagement. >>So what was that discovery like? Was it you fly up there and do a, a chalk talk? Or did you already know James, like where you wanted to focus? >>Yeah, I knew I had a solid proof of concept with the disruptions in supply chain we couldn't keep up with, with all the changes and supply. So right away I knew that I have a very substantial impact on the organization and it would be a solid proof of concept. It was something that not only would supply chain steal, but our customers would feel that we would be servicing them better. Our sales team, the commercial team, marketing impacted everybody. But at the same time it was tangible. I saw two people that just physically couldn't get their, their work done despite how talented and hardworking they were. So I, I was in on that proof of concept and then I just took that idea with some strong advice from Dirk and and his team on, okay, well how do I take that? But then also use that to evangelize through the organization. What are some pitfalls to avoid? Because as a proof of concept, they just told me it's too good to be true. I believe in it. So it was so important to me that it >>Was successful. >>It get your neck out. Oh, I sure was. Which is a little scary, but I had confidence that we would >>Do it. But your poc you had to have a systems view. Yes. Right? Cuz you were trying to, I think you, I'm inferring that you had two people working really hard, but they couldn't get their job done. Yeah, for sure. They were just sitting on their hands. Right. Waiting. Okay. So you kind of knew where the bottlenecks were. Yes. And that's what you attacked and or you helped James and her the team think through that or, >>Yeah, exactly. So, so a couple points you were asking about her domain model of knowledge earlier, and I think that's really key to the puma's success with it, is that they've come at it from a business point of view, what matters to the business. And at the point, you know, supply chain challenges, how do we use automation to address that? And then, you know, and then it's gonna, it's actually gonna, you know, pick opportunities that are gonna matter to the business. Yeah, >>Yeah. At the same time, we, we knew this could be a scary thing, right? If it's not done right, you know, automation definitely can, can take a, a wrong path. So what we relied on them for is tell us how to make this successful. We wanted structure, we wanted oversight, we wanted to balance that with speed and really, you know, developing our pipeline, but at the same time, tell us how to do this right? How do we set up a center, our first ever center of excellence? They help us set that up. Our steerco, our process definition documents are like, they really helped us add that structure to how to make this successful, sustainable and make sure that we were standing things up the right way versus launching into a strong proof, proof of concept. But then it's not gonna be scalable if we didn't really take their strong advice on how to make this something, you know, that had the right oversight, the right investment. So that was, that was key as >>Well for us. So when you looked at the POC and James was saying there were potential pitfalls, what were those pitfalls? Like what did you tell Puma, Hey, watch out for this, watch out for that. What was sort of the best advice there? >>Yeah, so I think one is understanding complexity, right? So a lot of opportunities sound good, but you want to make sure that it's, it's feasible with the right tool set. And also that you're not bit off too much in the beginning is really important. And so some of that is that bringing that expertise to say, Okay, yeah, look, that does something, a good process. You're gonna get value out. It's not gonna be overly complicated. It's a good place to start. And then also, I guess the thing too to mention is it's more than just a technology project. And that's the thing that we also really focus on is it's actually as much about the change management, it's much about, you know, what is the right story, the business case around it, the technology actually in a way is the easy part and it's all the stuff around it that really makes the POC effective, >>Obviously the process. Yeah. Been the people I presume getting to adopt, >>Right? And I think, again, with our, our brand mantra forever faster, we, we get that support that the buy-in from the top is is there from, from the beginning. So that's a benefit that some companies don't, they don't have, right? They have a little resistance maybe from the top. We're trying to get everyone's buy in it. And we had that. So we had, you know, the buy-in the engagement, we were ready to go. So now we just needed someone to kind of help us. >>One more if I may. Yeah, yeah. Gabe, six months in. Yes. That's the business impact that, can >>You tell you? That was tremendous. Yeah. >>Really already six months. Wow. >>Yeah, >>Absolutely. Cfo, CFO's dream. Yeah. >>And again, and, and we had a CFO change mid, mid project. So the new CFO comes in, not new to Puma, the same thing. Super, super smart guy. And I had to sit and again pitch, you know, pitch what it is and the support that I needed by way of investment. And he saw the results and he was all in, you know, what do you need, what's next? And instantly was challenging his departments, Why don't he got competitive, right? We're a competitive bunch, so why don't you know, you should have more in the pipeline. And he was, he was bought in. So there was that fear of a new CFO coming in and how do you show value? Because some of it is, it's very easy to show right away, You know, we were able to refocus those two full-time employees on, on higher value chain activity and you know, they're doing a tremendous job and they're, you know, they have the, the bot and the automation supporting them. So he saw that right away. And we can show him that. But he also understands, as does the whole leadership team, the concept of downstream impacts that you can't necessarily, you know, touch and, and put on paper. So he sees some, but then he also recognizes all the other upstream and downstream impacts that it's had and he's all in and supports whatever, whatever we need. >>Yeah. New CFOs like George Seaford taking over for bill walls. >>Yeah, exactly. Exactly. We >>Have, we have to keep showing results and it has to be sustainable. So that's, again, we'll rely on our partnership to say, okay, this is the beginning, you know, what's next? Keep us, you know, honest on oversight and, and any pitfalls that we should avoid because he's excited. But at the same time, we need to make sure that we sustain those results and, and show what's next. Now they all gotta taste to the apple and they're very eager to see what's next in, in, in this hyper automation journey. >>Well, Dirk, you've partnered on this journey, this specific journey with, with, with Puma. But from your perspective in the broader marketplace, what would be the perfect low hanging fruit opportunity that you would like to have somebody call you and say, Hey, we've got, we've got this perspective engagement with a client. What would be the, what would be the like, Oh yeah, that's easy, that's huge roi really quickly, What does that look like? >>Yeah, I think there's, there's a few areas, right? You know, one task automation RPA is a, is a really good entry point, right? Because it's, it's, it's not overly complex. It doesn't involve a lot of complicated technologies. And I'd say the, the usual starting areas, you know, you, you finance back office, you know, shared service, invoice processing, you know, payables is a very good opportunity area. HR is also an area I would look at, you know, in new, new employee onboarding process or you know, payroll, et cetera. And then supply chain is actually becoming more and more, more common, right? So those would be I guess, top three areas I would mention. And >>Then, and then kind of follow onto that, what's the tip of this sphere? What's the sort of emerging market Yeah. >>For >>This kind of technology? >>I think there's two things. One, it's taking a holistic into end view and leveraging multiple, you know, technology, you know, beyond just rpa, right? You know, intelligent document processing, iml, you know, bringing all this to bear to actually do a true digital transformation. That's, that's number one. And then I'd say the second is going from focusing on cost and efficiency to actually getting into the front office and how do you, how do you actually increase revenue? How do you increase margin? How do you actually, you know, help with that, that top line growth. I think that's really, and that's where you're leveraging technologies, you know, like the, the AI as an example to really help you understand how do you optimize. >>So James, that's, that becomes then an enterprise wide initiative. Yeah. That's, that's, is that your vision? Maybe maybe lay that out for >>Us a bit. Yeah, ab absolutely. The, the vision is now that we've seen what, what it can do, how do we take it from being managed by just, you know, supply chain and this proof of concept cuz I manage projects, but now it's bigger than just a supply chain project. And how do we sort of evangelize that through the whole organization And you know, they mentioned on main stage this, the creation of new jobs and, and roles and how a, a company might set out their strategic directive now is, is changing and evolving. So you know that that's our idea now and that what we'll need support next is how should we structure now for success. And so that it's across the whole enterprise. But that's, that's the vision for >>Sure. What worries you do, you worried about it like taking off and getting outta control and not being governed and so you have to be a little bit careful there. >>Yeah, for sure. That was really important to us. And we actually got to leverage a lot of heavy lifting that Puma Global had done at the same time that we were coming up and, and thinking of the idea of rpa. They were having the same thoughts and they did a lot of heavy lifting again, about not only the software providers but also what does the structure look like, the oversight, a center of excellence globally. So we were able to really leverage a lot of best practices and SOPs that they had set out and we were able to kind of leverage those, bring those to Puma North America so that we didn't face that fear cuz that would be a limiting factor for us. So because we were so disciplined and we could leverage the work that they had done, that fear wasn't, wasn't there. Now we have to stay, you know, on top of it. And as people get excited, how do you kind of mirror the excitement and with it at the same time that the oversight and not getting, you know, too, too big, too fast. So that's the balance that we'll, we'll work through now. It's a good problem to have. >>Well, exactly. It is super exciting. Great story. Congratulations on, on the success and good luck. Thank you. Yeah, you very much for coming to the, Yeah. Thank you. Thank you. All right. And thank you for watching. Keep it right there. Dave Nicholson Andante right back, the cube live from Las Vegas UI path forward. Five.

(upbeat music) >> Live, from Las Vegas, it's theCUBE, covering UiPath Forward Americas 2019. Brought to you by UiPath. >> Welcome back to Las Vegas, 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 of UiPath Forward III, the third North American conference that UiPath-- The rocket ship that is UiPath. Clemmie Malley is here. She's the Enterprise RPA Center of Excellence Lead at NextEra Energy. Welcome. Great to have you. And Mariesa Coughanour, who is the Managing Principal of Intelligent Automation and Technology at Cognizant. Nice to see you guys. >> Nice seeing you. >> Nice to see you. >> Thanks for coming on. How's the show going for you? >> It's been great so far. >> Yes. >> It's been awesome. >> Have you been to multiple... >> This is my third. >> Yep. >> Really? Okay, great. How does this compare? >> It has changed significantly in three years, so. It was very small in New York in 2017 and even last year grew, but now it's a two-year event taking over. >> Yeah, last year Miami was-- >> I don't know. >> It was nice. >> Definitely smaller than this, but it was happening. Kind of hip vibe. We're here in Vegas, everybody loves to be in Vegas. CUBE comes to Vegas a lot. So tell me more about your role at NextEra Energy. But let's start with the company. You guys are multi billion, many, many, tens of billions, probably close to $20 billion energy firm. Really dynamic industry. >> Yeah, so NextEra Energy is actually an awesome company, right? So we're the world's largest in clean renewable energy. So with wind and solar, really, and we also have Florida Power and Light, which is one of the child companies to NextEra as a parent, which is headquartered out of Florida. So it's usually the regulated side of power in the state of Florida. >> We know those guys. We've actually done some work with Florida Power and Light. Cool people down there. And we heard, one of the keynotes today, Craig LeClaire, was saying, "Yeah, the Center of Excellence, "that's actually maybe asking too much." But there are a lot of folks here that are sort of involved in a COE and that's kind of your role. But I was surprised to hear him say that. I don't know if you were in the keynote this morning, but was it a challenge to get a Center of Excellence? What is that all about? >> So I think there's a little bit of caution around doing it initially. People are very aggressive. And we actually learned from this story. So when we started, it was more about showing value, building as many automations as possible. We didn't really care about having a COE. The COE just happened to form. >> Okay. >> Because we found out we needed some level of governance and control around what we were doing. But now that I look back on it, it's really instrumental to making sure we have the success. So whether you do a hybrid development to automation, which you can have citizen development, or you're fully centralized, I think having the strong COE to have that core governance model and control and process is important. >> Mariesa, so your title is not, there's not RPA in your title, right? RPA is too narrow, right? >> Yeah. >> In your business you're trying to help transform companies, it's all about automation. But maybe explain a little bit about your practice and your role. >> Sure, so Cognizant's been on the automation journey now for three years. We started back in 2014 and right out the gate it was all about intelligent automation, just not RPA. Because we knew to be able to do end-to-end solutions you would need multiple technologies to really get the job done and get the outcomes they wanted. So we sit now, over 2,500 folks at our practice, going out, working cross-industry, cross-regions to be able to work with people like Clemmie to put in their program. And we've even added some stuff recently. A lot of it actually inspired by NextEra. And we have an advisory team now. And our whole job is to go in and help people unstuck their programs, for lack of a better way to say it. Help them think about, how do you put that foundation? Get a little bit stronger and actually enable scale, and putting in all this technology to get outcomes? Versus just focusing on just the pure play RPA, which a lot of people struggle to gain the benefits from. >> So Clemmie, what leads you to the decision to bring in an outside firm like Cognizant? What's that discussion like internally? >> So, I'll just give you a little bit of backstory, because I think that's interesting, as well. When we started playing with RPA in late 2016, early 2017, we knew that we wanted to do a lot of things in-house, but in order to have a flex model and really develop automations across the company, we needed to have a partner. And we wanted them to focus more on delivery, so developing, and then partner with us to give us some best practices, things that we could do better. When we founded the COE we knew what we wanted to do. So we actually had two other partners before we went with Cognizant, and that was a huge challenge for us. We found we were reworking a lot of the code that they gave us. They weren't there to be our partners. They wanted to come and actually do the work for us, instead of enabling us to be successful. And we actually said, "We don't want a partner." And then Cognizant came in and they actually were like, "Let's give you somebody." So we wanted somebody around delivery, because we said, "Okay, now that we centralize, "we have a good foundation, a good model, "we're going to need to focus on scale. "So how do we do that? "We need a flex model." So Cognizant came in and they said, "Well, we're going to offer you a delivery lead "to help focus on making sure "you get the automations out the door." Well, Mariesa actually showed up, which was one of the best hidden surprises that we received. And she really just came in, learned the company, learned our culture, and was able to say, "Okay, here's some guidance. "What can you instill? "What can you bring?" Tracking, and start capturing the outcomes that she's mentioned. And I know that was a little bit more, but it's been quite a journey. >> No, it's really good, back up. So Mariesa, I'm hearing from Clemmie that you were willing to teach these guys how to fish, as opposed to just perpetual, hourly, daily rate billing. >> Yep. And that's really what our belief is. We can go in, and yes, we can augment, from resourcing perspective, help them deliver, develop, support everything, which we do. And we work with Clemmie and others to do that. But what's really important to get to scale was how do we teach them how to go do this? Because if you're going to really embed this type of automation culture and mindset, you have to teach people how to do it. It's not about just leaning on me. I needed to help Clemmie. I need to help her team, and also their leadership and their employees. On how do you identify opportunities, and how then do you make these things actually work and run? >> So you really understand the organization. Clemmie was saying you learned the culture. >> Yeah. >> So you're not just a salesperson going in and hanging out in theCUBE. So you're kind of an extension, really, of the staff. So, either of you, if you can explain to me sort of, where RPA fits into this broader vision. That would really be helpful. >> Sure, so maybe I can kick a little bit off from what I'm seeing from clients like Clemmie, and also other customers. So what you'll find is RPA tends to be like this gateway. It's the stepping stone to all things automation. Because folks in the business, they really understand it. It's rule-based, right? It's a game of Simon Says, in some ways, when you first get this going. And then after that, it's enabling the other technology and looking at, "Look, if I want to go end-to-end, "what do I need to get the job done? "What do I need around data intake? "How do I have the right framework "to pick the right OCR tool, "or put analytics on, "or machine learning?" Because there's so much out there today and you need to have the stuff that's right-fit to come in. And so it's really about looking at what's that company strategy? And then looking at this as a tool set. And how to use these tools to go and get the job done. And that's what we were doing a lot with Clemmie and team when we sat down. They have a steering committee that's chaired by their CIO, Chief Accounting Officer, and senior leaders from every business unit across their enterprise. >> So you mentioned scaling. >> Yep. >> We heard today in the predictions segment that we're going to move from snowflake to snowball. And so I would think for scaling it's important to identify reusable components. And so how have you, how has that played out for you? And how's the scaling going? >> Yeah, so that's been one really cool component that we've built out in the COE. So I had my team actually vote on a name and we said, "We want to go after reusable components." They decided to call them Microbots. So it's a cool little term that we coined. >> That's cool. >> And our CIO and CAO actually talk about them frequently. "How are our Microbots? "How many do we have? "What are they doing?" So it's pretty catchy. But what it's really enabled us is to build these reusable snippets of code that are specific to how we perform as a company that we can plug and play and reduce our cycle time. So we've actually reduced our cycle time by over 50%. And reusable components is one of the major key components. >> So how do you share those components? Are they available in some kind of internal marketplace? And how do you train people to actually know what to apply where? >> Right. So because we're centralized, it's a little bit easier, right? We have a stored repository, where they're available. We document them-- >> And it's the COE-- Sorry to interrupt. It's the COE's responsibility, and-- >> Exactly. So the COE has it. We're actually working with Cognizant right now to figure out how can we document those further, right? And UiPath. There's a lot of cool tools that were introduced this week. So I think we're definitely going to be leveraging from them. But the ability to really show what they are, make them available, and we're doing all of that internally right now. Probably a little manual. So it'll be great to have that available. >> So Amazon has this cool concept they call working backwards documents. I don't know if you ever heard this. But what they do is they basically write the press release, thinking five years in advance. This is how they started AWS, they actually wrote. This is what we want, and then they work backwards from there. So my question is around engineering outcomes. Can you engineer outcomes, and is that how you were thinking about this? Or is it just too many unknown parts of the process that you can't predict? >> So I think one of the things that we did was we did think about, "What do we want to achieve with this?" So one of the big programs that Clemmie and the team have is also around accelerate. And their key initiatives to drive, whether it's improve customer experience, more efficiencies of certain processes across the company. And so we looked at that first, and said, "Okay, how do we enable that?" That's a top strategy driven by their CEO. And even when we prioritize all the work, we actually build a model for them. So that it's objective. So if any opportunities that come in align to those key outcomes that the company's striving for, they can prioritize first to be worked on. I actually also think this is where this is all going. Everyone focuses today on these automation COEs and automation teams, but what you will see, and this is happening at NextEra, and all the places we're starting to see this scale, is you end up with this outcomes management office. This is a core nucleus of a team that is automation, there's IT at the table, there's this lean quality mindset at the table, and they're actually looking at opportunities and saying, "All right, this one's yours. "This one's yours and then I'll pick up from you." And it's driving, then, the right outcomes for the organization versus just saying, "I have a hammer, I'm going to go find a nail," which sometimes happens. >> Right, oh, for sure. And it may be a fine nail to hit, but it might not be the most strategic-- >> Exactly. >> Or the most valuable. So what are some examples of areas that you're most excited about? Where you've applied automation and have given a business outcome that's been successful? >> Yeah, so we are an energy company. And we've had a lot of really awesome brainstorming sessions that we've held with UiPath and Cognizant. And a couple of key ones that have come out of it, really around storm season is big for us in the state of Florida. And making sure that our critical infrastructure is available. So our nursing homes, our hospitals, and so on. So we've actually built automations that help us to ping and make sure that they're available, so that we can stay proactive, right? There's also a cool use-case around, really, the intelligent automations space. So our linemen in their trucks are saying, "Hey, we spend a lot of time having to log on the computer, "log our tickets, "and then we have to turn our computers off, "drive to the next site, "and we're not able to restore as much power "or resolve issues as quickly as possible." So we said, "How can we enable them?" Speech recognition, where they can talk to it, it can log a ticket for them on their behalf. So it's pretty exciting. >> So that's kind of an interesting example. Where RPA, in and of itself's not going to solve that problem, right, but speech recognition-- >> It's a combination. >> So you got to bring in other technology, so using, what, some NLP capability, or? >> Yeah, so that's one we're currently working on. But yes, you would need some type of cognitive speech recognition, and. >> So you sort of playing around with that in R&D right now? The speech [Mumbles]. >> Yeah. >> Which, as you know, is not perfect, right? >> It is not. >> Talk to us. We know about it all. Because we transcribe every word that's said on theCUBE. And so, there's some good ones and there's some not so good ones. And they're getting better, though. They're getting better. And that's going to be kind of commodity shortly. You really need just good enough, right? I mean, is that true? Or do you need near perfect? >> So I think there's a happy medium. It depends on what you're trying to do. In this case we're logging tickets, so there might be some variability that you can have. But I will say, so NextEra is really focused on energy, but they're also trying to set themselves apart. So they're trying to focus on innovation, as well. So this is a lot of the areas that they're focusing on: the machine learning, and the processing, and we even have chat bots that they're coining and branding internally, so it's pretty exciting. >> So NextEra is, are you entirely new energy? Is that right? No fossil fuels, or? >> So it's all clean energy, yes. Across the enterprise. >> Awesome. How's that going? Obviously you guys are very successful, but, I mean, what's kind of happening in the energy business today? You're sort of seeing a resurgence in oil, right, but? >> Yeah, so I think we had a really good boom. A couple years ago there were a lot of tax credits that we were able to grow that side of our company. And it enabled us to really pivot to be the clean energy that we are. >> I mean, that's key, right? I mean, United States, we want to lead in clean energy. And I'm not sure we are. I mean, like you say, there was tax incentives and credits that sort of drove a lot of innovation, but am I correct? You see countries outside the U.S., really, maybe leaning in harder. I mean, obviously we got NextEra, but. >> I mean, I think there's definitely competition out there. We're focused on trying to be, maybe not the best, but compete with the best. We're also trying to focus on what's next, right? So be proactive, and grow the company in a multitude of ways. Maybe even outside the energy sector, just to make sure that we can compete. But really what we're focused on is the clean renewables, so. >> That's awesome. I mean, as a country we need this, and it's great to have organizations like yours. Mariesa, I'll give you the final word. Kind of, the landscape of automation. What inning are we in? Baseball analogy. Or how far can this thing go? And what's your sort of, as you pull out the binoculars, maybe not the telescope, but the binoculars, where do you see it going? >> I think there's a lot of runway left. So if you look at a lot of the research out there today, I heard today, 10% was quoted by one person. I heard 13% quoted from HFS around where are we at on scale from an RPA perspective? And that's just RPA. >> Yeah. >> So that means there's still so much out there to still go and look at and be able to make an impact. But if you look, there's also a lot of runway on this intelligent automation. And that's where, I think, we have to shift the focus. You're seeing it now, at these conferences. That you're starting to see people talk about, "How do I integrate? "How do I actually think about connecting the dots "to get bigger and broader outcomes for an organization?" and I think that's where we're going to shift to, is talking about how do we bring together multiple technologies to be able to go and get these end-to-end solutions for customers? And ultimately go, what we were talking a little bit about before, on outcome-focused for an organization. Not talking about just, "How do I go do AI? "How do I go put a bot in?" But, "I want to choose this outcome for my customer. "I need to grow the top line. "I'm getting this feedback." Or even internally, "I want to get more efficient so I can deliver." And focus there, and then what we'll do is find the right tools to be able to move all that forward. >> It's interesting. We're out of time, but you think about, it's somewhat surprising when people hear what you just said, Mariesa, because people think, "Wow, we've had all this technology for 50 years. "Haven't we automated everything?" Well, Daniel Dines, last night, put forth the premise that all this technology's actually creating inefficiencies and somewhat creating the problem. So technology's kind of got us into the problem. We'll see if technology can get us out. All right? Thanks, you guys, for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you for having us. >> You're welcome. >> Thanks. >> All right, keep it right there, everybody. We'll be right back with our next guest right after this short break. UiPath Forward III from Las Vegas. You're watching theCUBE. (electronic music)