>>From the cube studios in Palo Alto, in Boston, bringing you data-driven insights from the cube and ETR. This is breaking analysis with Dave Vellante >>UI path has always been an unconventional company. You know, it started with humble beginnings. It was essentially a software development shop. And then it caught lightning in a bottle with its computer vision technology. And it's really it's simplification mantra. And it created a very easy to deploy software robot system for bespoke departments. So they could automate mundane tasks. You know, you know, the story, the company grew rapidly was able to go public early this year. Now consistent with its out of the ordinary approach. While other firms are shutting down travel and physical events, UI path is moving ahead with forward for its annual user conference next week with a live audience there at the Bellagio in Las Vegas, it's also fast-forwarding as a company determined to lead the charge beyond RPA and execute on a more all encompassing enterprise automation agenda. Hello everyone. And welcome to this week's Wiki bond Cuban sites powered by ETR in this breaking analysis and a head of forward four we'll update you in the RPA market. >>The progress that UI path has made since its IPO and bringing some ETR customer survey data to contextualize the company's position in the overall market and relative to the competition. Here's a quick rundown of today's agenda. First, I want to tell you the cube is going to be at forward for, at the Bellagio next week, UI paths. This is their big customer event. It's live. It's a physical event. It's primarily outdoors. You have to be vaccinated to attend. Now it's not completely out of the ordinary John furrier and the cube. We're at AWS public sector this past week. And we were at mobile world Congress and one of the first big hybrid events of the year at Barcelona. And we thought that event would kick off the fall event season live event in earnest, but the COVID crisis has caused many tech firms. Most tech firms actually to hit the pause button, not UI path. >>They're moving ahead, they're going forward. And we see a growing trend for smaller VIP events with a virtual component topic, maybe for another day. Now we've talked extensively about the productivity challenges and the automation mandate. The pandemic has thrust upon us. Now we've seen pretty dramatic productivity improvements as remote work kicked in, but it's brought new stresses. For example, according to Qualtrics, 32% of working moms said their mental health has declined since the pandemic hit. 15% of working dads said the same by the way. So one has to question the sustainability of this perpetual Workday, and we're seeing a continuum of automation solutions emerging. And we'll talk about that today. We're seeing tons of MNA, M and a as well, but now in that continuum on the left side of the spectrum, there's Microsoft who in some ways they stand alone and that Azure is becoming ubiquitous as a SAS cloud collaboration and productivity platform. >>Microsoft is everywhere and in virtually every market with their video conferencing security database, cloud CRM, analytics, you name it, Microsoft is pretty much there. And RPA is no different with the acquisition of soft emotive. Last year, Microsoft entered the RTA market in earnest and is penetrating very deeply into the space, particularly as it pertains to personal approach, personal productivity building on its software state. Now in the middle of that spectrum, if you will, we're seeing more M and a, and that's defined really by the big software giants. Think of this domain as integrated software plays SAP, they acquired contexture, uh, uh, they also acquired a company called process insight service now acquired Intella bought Salesforce service trace. We see in for entering the fray. And I, I would put even Pega Pega systems in this camp, software companies focused on integrating RPA into their broader workflows into their software platforms. >>And this is important because these platforms are entrenched. They're walled gardens of sorts and complicated with lots of touchpoints and integration points. And frankly, they're much harder to automate because of their entrenched legacy. Now on the far side of that, spectrum are the horizontal automation players and that's being led by UI path with automate automation anywhere as the number two player in this domain. And I didn't even put blue prism prism in there more M and a recently announced, uh, that Vista is going to acquire them. Vista also owns TIBCO. They're going to merge those two companies, you know, tip goes kind of an integration play. And so again, I'm, I might, I would put them in that, you know, horizontal piece of the spectrum. So with that as background, we're going to look at how UI path has performed since we last covered them at IPO. >>And then we'll bring in some ETR survey data to get the spending view from customers. And then we'll wrap up now just to emphasize the importance of, of automation and the automation mandate mandate. We talk about it all the time in this program, we use this ETR chart. It's a two dimensional view with net score, which is a measure of spending momentum on the vertical axis and market share, which is a proxy for pervasiveness in the dataset. That's on the horizontal axis. Now note that red dotted line at signifies companies with an elevated position on the net score, vertical axis, anything over that is considered pretty good, very good. Now this shows every spending segment within the ETR taxonomy and the four spending categories with the greatest velocity are AI cloud containers and RPA. And they've topped the charts for quite a while. Now they're the only four categories which have sustained above that 40% line consistently throughout the pandemic. >>And even before now, the impressive thing about cloud of course, is it has a spending has both spending momentum on the vertical axis at a very large share of the, of the market share of presence in the dataset. The point is RPA is nascent still. It has an affinity with AI as a means of more intelligently identifying and streamlining process improvements. And so we expect those to, to remain elevated and grow to the right together, UI path pegs it's Tam, total available market at 60 billion. And the reality is that could be understated. Okay. As we reported from the UI path S one analysis, we did pre IPO. The company at that time had an AR annual recurring revenue of $580 million and was growing at 65% annually at nearly 8,000 customers at the time, a thousand of which had an ARR in excess of a hundred K and a net revenue retention, the company had with 145%. >>So let's take a look at the picture six months forward. We mentioned the $60 billion Tam ARR now up over 725 million on its way to a billion ARR holding pretty steady at 60% growth as is an RR net revenue retention, and more than a thousand new customers in 200 more with over a hundred thousand in ARR and a small operating profit, which by the way, exceeded the consensus pretty substantially. Profitability is not shown here and no one seems to care anyway, these days it's all about growing into that Tam. Well, that's a pretty good looking picture. Isn't it? The company had a beat and a raise for the quarter early this month. So looking good, right? Well, you ask how come the stock's not doing better. That's an interesting question. So let's first look at the stocks performance on a relative basis. Here, we show you I pass performance against Pega systems and blue prism. >>The other two publicly traded automation, pure plays, you know, sort of in the case of Pega. So UI path outperformed post its IPO, but since the early summer Pega has been the big winner. Well, UI path slowly decelerated, you see blue prism was the laggard until it was announced. It was in an acquisition talks with a couple of PE firms and the prospects of a bidding war sent that yellow line up. As you can see UI path, as you can see on the inset has a much higher valuation than Pega and way higher than blue prison. Pega. Interestingly is growing revenues nicely at around 40%. And I think what's happening is the street simply wants more, even though UI path beat and raised wall street, still getting comfortable with which is new to the public market game. And the company just needs to demonstrate a track record and build trust. >>There's also some education around billings and multi-year contracts that the company addressed on its last earnings call, but the street was concerned about ARR from new logos. It appears to be slowing down sequentially in a notable decline in billings momentum, which UI pass CEO, CFO addressed on the earnings call saying, look, they don't need to trade margin for prepaid multi-year deals, given the strong cash position while I give anything up. And even though I said, nobody cares about profitability. Well, I guess that's true until you guide for an operating loss. When you've been showing a small profit in recent recent quarters, which you AIPAC did, then all of a sudden people care. So UI path, isn't a bit of an unknown territory to the street and it has a valuation that's pretty rich, very rich, actually at 30 times, a revenue multiple greater than 30 times revenue, multiple. >>So that's why in, in my view, investors are being cautious, but I want to address a dynamic that we've seen with these high growth rocket ship companies, something we talked about with snowflake. And I think you're seeing some of that here with UI paths, different model in the sense that snowflake is pure cloud, but I'm talking about concerns around ARR from new logos and in that growth on a sequential basis. And here's what's happening in my view with UI path, you have a company that started within departments with a small average contract size in ACV, maybe 25,000, maybe 50,000, but not deep six figure deals that wasn't UI paths play it because the company focused so heavily on simplicity and made it really easy to adopt customer saw really fast ROI. I mean breakeven in months. So you very quickly saw expansion into other departments. >>So when ACV started to rise and installations expanded within each customer UI path realized it had to move beyond being a point product. And it started thinking about a platform and making acquisitions like process gold and others, and this marked a much deeper expansion into the customer base. And you can see that here in this UI path, a chart that they shared at their investor deck customers that bought in 2016 and 2017 expanded their they've expanded their spend 15, 13, 15, 18 20 X. So the LTV, the lifetime value of the customer is growing dramatically. And because UI path has focused on simplicity, it has a very facile freemium model, much easier to try before you buy than its competitors. It's CAC, it's customer acquisition costs are likely much lower than some of its peers. And that's a key dynamic. So don't get freaked out by some of those concerns that we raised earlier, because just like snowflake what's happening is the company for sure is gaining new customers. >>Maybe just not at the same rate, but don't miss the forest through the trees. I E they're getting more money from their existing customers, which means retention, loyalty and growth. Speaking of forests, this chart is the dynamic I'm talking about. It's an ETR graphic that shows the components of net score or against spending momentum net score breaks down into five areas that lime green at the top is new additions. Okay? So that's only 11% of the customer mentions by the way, we're talking about more than 125 responses for UI path. So it's meaningful. It's, it's actually larger in this survey, uh, or certainly comparable to Microsoft. So that says something right there. The next bar is the forest green forest. Green is where I want you to focus. That's customer spending 6% or more in the second half of the year, relative to the first half. >>The gray is flat spending, which is quite large, the pink or light red that's spending customer spending 6% or worse. That's a 4% number, but look at the bottom bar. There is no bar that's churn. 0% of the respondents in the survey are churning and churn is the silent killer of SAS companies, 0% defections. So you've got 46% spending, more nobody leaving. That's the dynamic that is powering UI path right now. And I would take this picture any day over a larger lime green and a smaller forest green and a bigger churn number. Okay. So it's pretty good. It's not snowflake good, but it's solid. So how does this picture compare to UI pass peers? Well, let's take a look at that. So this is ETR data, same data showing the granularity net score for Microsoft power, automate UI path automation, anywhere blue prism and Pega. >>So as we said before, Microsoft is ubiquitous. What can we say about that? But UI path is right there with a more robust platform, not to overlook Microsoft. You can't, but UI path, it'll tell you that they don't compete head to head for enterprise automation deals with Microsoft. Now, maybe they will over time. They do however, compete head to head with automation anywhere. And their picture is quite strong. As you can see here, it has this blue Prism's picture and even Pega, although blue prism, automation, anywhere UI path and power automate all have net scores on this chart. As you can see the table in the upper right over 40% Pega does not. But again, we don't see Pega as a pure play RPA vendor. It's a little bit of sort of apples and oranges there, but they do sell RPA and ETR captures in their taxonomy. >>So why not include them also note that UI path has, as I said before, more mentions in the survey than power automate, which is actually quite interesting, given the ubiquity of Microsoft. Now, one other notable notable note is the bright red that's defections and only UI path is showing zero defections. Everybody else has at least even of the slim, some defections. Okay. So take that as you will, but it's another data 0.1. That's powerful, not only for UI path, but really for the entire sector. Now, the last ETR data point that we want to share is our famous two dimensional view. Like the sector chart we showed earlier, this graphic shows net score on the vertical axis. That's against spending velocity and market share or pervasiveness on the horizontal axis. So as we said earlier, UI path actually has greater presence in the survey than the ever-present Microsoft. >>Remember, this is the July survey. We don't have full results from the September, October survey yet. And we can't release them until ETR is out of its quiet period. But I expect the entire sector, like everything is going to be slightly down because as we reported last week, tech spending is moderated slightly in the second half of this year, but we don't expect the picture to change dramatically. UI path and power automate, we think are going to lead and market presence in those two plus automation anywhere are going to show strength and spending momentum as well. Most of the sector. And we'll see who comes in above the 40% line. Okay. What to watch at forward four. So in summary, I'll be looking for a few things. One UI path has hinted toward a big platform announcement that will deepen its capabilities to go beyond being an RPA point tool into much more of an enterprise automation platform rewriting a lot of the code Linux cloud, better automation of the UI. >>You're going to hear all kinds of new product announcements that are coming. So I'll be listening for those details. I want to hear more from customers to further confirm what I've been hearing from them over the last couple of years and get more data, especially on that ROI on that land and expand. I want to understand that dynamic and that true enterprise automation. It's going to be good to get an update face to face and test some of our assumptions here and see where the gaps are and where UI path can improve. Third. I want to talk to ecosystem players to see where they are in participating in the value chain here. What kind of partner has UI path become since it's IPO? Are they investing more in the ecosystem? How to partners fit into that flywheel fourth, I want to hear from UI path management, Daniel DNAs, and other UI path leaders, they're exiting toddler Ville and coming into an adolescent phase or early adulthood. >>And what does that progression look like? How does it feel? What's the vibe at the show. And finally, I'm very excited to participate in a live in-person event to see what's working, see how a hybrid events are evolving. We got a good glimpse at mobile world Congress and this week, and, uh, in DC and public sector summit, here's, you know, the cube has been doing hybrid events for years, and we intend to continue to lead in this regard and bring you the best, real time information as possible. Okay. That's it for today. Remember, these episodes are all available as podcasts, wherever you listen. All you do is search braking analysis podcast. We publish each week on Wiki bond.com and siliconangle.com. And you can always connect on twitter@devolanteoremailmeatdaviddotvolanteatsiliconangle.com. Appreciate the comments on LinkedIn. And don't forget to check out E T r.plus for all the survey data. This is Dave Volante for the cube insights powered by ETR be well, and we'll see you next time.

>> From The Cube studios in Palo Alto in Boston, bringing you data driven insights from The Cube and ETR, this is Breaking Analysis with Dave Vellante. >> UiPath has always been an unconventional company. You know it started with humble beginnings. It's essentially a software development shop. Then it caught lightning in a bottle with its computer vision technology. It's really, it's simplification mantra and it created a very easy to deploy software robot system for bespoke departments so they could automate mundane tasks. You know the story. The company grew rapidly, was able to go public early this year. Now consistent with its out-of-the-ordinary approach, while other firms are shutting down travel and physical events, UiPath is moving ahead with Forward IV, it's annual user conference next week with a live audience there at the Bellagio in Las Vegas. It's also fast forwarding as a company, determined to lead the charge beyond RPA and execute on a more all-encompassing Enterprise automation agenda. Hello everyone and welcome to this week's Wikibond Cube Insights powered by ETR. In this breaking analysis and ahead of Forward IV, we'll update you in the RPA market the progress that UiPath has made since its IPO and bringing some ETR customer survey data that's contextualized the company's position in the overall market and relative to the competition. Here's a quick rundown of today's agenda. First I want to tell you theCube is going to be at Forward IV at the Bellagio next week. UiPath, this is their big customer event. It's live, it's a physical event. It's primarily outdoors. You have to be vaccinated to attend. Now, this not completely out of the ordinary. John Furrier and theCube were at AWS Public Sector this past week and we were at Mobile World Congress in one of the first big hybrid events of the year at Barcelona. We thought that event would kick of the fall event season, live event in earnest but the COVID crisis has caused many tech firms, most tech firms actually, to hit pause button. Not UiPath, they're moving ahead. They're going forward and we see a growing trend for smaller VIP events with a virtual component, topic maybe for another day. Now we've talked extensively about the productivity challenges and the automation mandate the pandemic has thrust upon us. Now, we've seen pretty dramatic productivity improvements as remote work kicked in but its brought new stresses. For example, according to Qualtrics, 32% of working moms said their mental health has declined since the pandemic hit. 15% of working dads said the same by the way. So, one has to question the sustainability of this perpetual workday. And we're seeing a continuum of automation solutions emerging and we'll talk about that today. We're seeing tons of M&A as well but now, in that continuum, on the left-side of the spectrum, there's Microsoft who in some ways, they stand alone and their Azure is becoming ubiquitous as a SaaS-Cloud collaboration and productivity platform. Microsoft is everywhere and in virtually every market, whether video conferencing, security, database, cloud, CRM, analytics, you name it. Microsoft is pretty much there and RPA is no different. With the acquisition of Softomotive last year, Microsoft entered the RTA market in earnest and is penetrating very deeply into the space, particularly as it pertains to personal productivity building on its software stake. Now in the middle of that spectrum if you will, we're seeing more M&A and that's defined really by the big software giants. Think of this domain as integrated software place. SAP, they acquired Contextere. They also acquired a company called Process Insights, Service now acquired Inttellebot. Salesforce acquired Servicetrace, we see Infor entering the frame and I would put even Pega, Pega systems in this camp. Software companies focused on integrating RPA into their broader workflows, into their software platforms and this is important because these platforms are entrenched Their well guardants of thoughts and complicated with lots of touchpoints and integration points and frankly they are much harder to automate because of their entrenched legacy. Now, on the far side of that spectrum, are the horizontal automation players and that's been let by UiPath with automation anywhere as the number two player in this domain. And I even put a blue prism in there more M&A recently announced that Vista is going to acquire them Vista also owns Tibco, they are going to merge those two companies. You know Tibco is come up with the integration play. So again I would put them in that you know, horizontal piece of the spectrum. So with that as background, we're going to look at how UiPath has performed since we last covered them and IPO and I'm going to bring in some ETR survey data to get the spending view from customers and we'll wrap up. Now, just to emphasize the importance of automation and the automation mandate, we talk about it all the time in this program. We use this ETR chart. It's a two dimensional view with net score which is the measure of spending momentum on the vertical axis and market share which is a proxy for pervasiveness in the data set that's on the horizontal axis. Now note that red dotted line, it signifies companies within elevated position on the net score vertical axis anything over that is considered pretty good. Very good. Now this shows every spending segment within the ETR taxonomy. And the four spending categories with the greatest velocity are AI, cloud, containers and RPA. And they have topped the charts for quite a while now. They are the only 4 categories which have sustained above that 40% line consistently throughout the pandemic and even before. Now the impressive thing about cloud of course is it has both spending momentum on the vertical axis and a very large market share or presence in the data set. The point is RPA is nascent still. It has an affinity with AI as a means of more intelligently identifying and streamlining process improvements. And so we expect those two to remain elevated and grow to the right together. UiPath pegs its TAM, total available market at 60 billion. And the reality is that could be understated. Okay, as we reported from the UiPath S1 analysis we did pre IPO, the company at that time had an ARR annual recurring revenue of $580 million and it was growing at 65% annually. And nearly 8000 customers at the time, a 1000 of which had an ARR in excess of a 100k. And the net revenue retention the company had was over 145%. So let's take a look at the pictures 6 months forward. We mentioned the $60 billion TAM, ARR now up over $726.5 million on its way to a billion ARR holding pretty steady at 60% growth as is NRR, net revenue retention and more then a 1000 new customers and 200 more with over a 100000 in ARR and a small operating profit which by the way exceeded the consensuses pretty substantially. Profitability is not shown here and no one seems to care anyway these days. It's all about growing into that TAM. Well that's a pretty good looking picture, isn't it? The company had a beat and a raise for the quarter earlier this month, so looking good right. Well you ask how come the stock is not doing better. That's an interesting question. So let's first look at the stocks performance on a relative basis. Here we show UiPath performance against Pega systems and blue prism, the other two publicly traded automation. Pure plays sort of in the case of Pega. So UiPath outperformed post its IPO but since the early summer Pega is been the big winner while UiPath slowly decelerated. You see Blue prism was at the lag until it was announced that it was in an acquisition talks with a couple of PE firms and the prospects of a bidding war sent that yellow line up as you can see. UiPath as you can see on the inset, has a much higher valuation than Pega and way higher than blue Prism. Pega interestingly is growing revenues nicely at around 40%. And I think what's happening is that the street simply wants more. Even though UiPath beat and raised, Wallstreet is still getting comfortable with management which is new to the public market game and the company just needs to demonstrate a track record and build trust. There's also some education around billings and multi-year contracts that the company addressed on its last earnings call. But the street was concerned about ARR for new logos. It appears to be slowing down sequentially and a notable decline in billings momentum which UiPath CFO addressed on the earnings call saying look they don't need the trade margin for prepaid multi year deals, given the strong cash position. Why give anything up. And even though I said nobody cares about profitability well, I guess that's true until you guide for an operating loss when you've been showing small profit in recent quarters what UiPath did. Then, obviously people start to care. So UiPath is in bit of an unknown territory to the street and it has a valuation, it's pretty rich. Very rich actually at 30 times revenue multiple or greater than 30 times revenue multiple. So that's why in my view, investors are being cautious. But I want to address a dynamic that we have seen with this high growth rocket chip companies. Something we talked about Snowflake and I think you are seeing some of that here with UiPath. Different model in the sense that Snowflake is pure cloud but I'm talking about concerns around ARR and from new logos and that growth in a sequential basis. And here's what's happening in my view with UiPath. You have a company that started within departments with a smaller average contract size, ACV maybe 25000, may be 50000 but not deep six figure deals. That wasn't UiPath's play. And because the company focused so heavily on simplicity and made it really easy to adapt, customers saw really fast ROI. I mean break-even in months. So we very quickly saw expansion into other departments. So when ACV started to rise and installations expanded within each customer, UiPath realized it had to move beyond a point product and it started thing about a platform and making acquisitions like Processgold and others and this marked a much deeper expansion into the customer base. And you can see that here in this UiPath chart that they shared at their investor deck, customers that bought in 2016 and 2017 expanded their spend 13, 15, 18, 20x So the LTV, life time value of the customer is growing dramatically and because UiPath is focused on simplicity, and has a very facile premium model much easier to try before you buy than its competitors it's CAC, Customer acquisition cost are likely much lower than some of its peers. And that's a key dynamic. So don't get freaked out by some of those concerns that we raised earlier because just like Snowflake what's happening is that the company for sure is gaining new customers, may be just not at the same rate but don't miss the forest through the trees I.e getting more money from their existing customers which means retention, loyalty and growth. Now speaking of forest, this chart is the dynamic I'm talking about, its an ETR graphic that shows the components of net score against spending momentum. Net score breaks down into 5 areas. That lime green at the top is new additions. Okay, so that's only 11% of the customer mentions. By the way we are talking about more than a 125 responses for UiPath. So it's meaningful, it's actually larger in this survey or certainly comparable to Microsoft. So that's just something right there. The next bar is the forest green. Forest green is what I want you to focus. That's customer spending 6% or more in the second half of the year relative to the first half. The gray is flat spending which is quite large. The pink or light red, that's spending customers spending 6% or worse, that's a 4% number. But look at the bottom bar. There is no bar, that's churn. 0% of the responders in the survey are churning. And Churn is the silent killer of SaaS companies. 0% defections. So you've got 46% spending more, nobody leaving. That's the dynamic powering UiPath right now and I would take this picture any day over a larger lime green and a smaller forest green and a bigger churn number. Okay, it's pretty good, not Snowflake good but it's solid. So how does this picture compare to UiPath's peers. Let's take a look at that. So this is ETR data, same data showing the granularity net score for Microsoft power automate, UiPath automation anywhere, Blue Prism and Pega. So as we said before, Microsoft is ubiquitous. What can we say about that. But UiPath is right there with a more robust platform. Not to overlook Microsoft, you can't but UiPath will you that the don't compete head to head for enterprise automation deals with Microsoft and may be they will over time. They do however compete head to head with automation anywhere. And their picture is quite strong as you can see here. You know as is Blue Prism's picture and even Pega. Although Blue Prism automation anywhere UiPtah and power automate all have net scores on this chart as you can see the tables in the upper right over 40%, Pega does not. But you can see Pega as a pure play RPA vendor it's a little bit of sort of apples and oranges there but they do sell RPA and ETR captures in their taxonomy so why not include them. Also note that UiPath has as I said before more mentions in the survey than power automate which is actually quite interesting given the ubiquity of Microsoft. Now, one other notable note is the bright red that's defections and only UiPath is showing zero defections Everybody else has at least little of the slims on defections. Okay, so take that as you will but its another data point, the one that is powerful nit only for UiPath but really for the entire sector. Now the last ETR data point that we want to share is the famous two dimensional view. Like the sector chart we showed earlier, this graphic shows the net score on the vertical axis that's against spending velocity and market share or pervasiveness on the horizontal axis. So as we said earlier, UiPath actually has a greater presence in the survey than the ever present Microsoft. Remember, this is the July survey. We don't have full results from the September-October survey yet and we can't release them until ETR is out of its quiet period but I expect the entire sector, like everything is going to be slightly down because as reported last week tech spending is moderated slightly in the second half of this year. But we don't expect the picture to change dramatically UiPath and power automate we think are going to lead in market presence and those two plus automation anywhere is going to show the strength in spending momentum as will most of the sector. We'll see who comes in above the 40% line. Okay, what to watch at Forward IV. So in summary I'll be looking for a few things. One, UiPath has hinted toward a big platform announcement that will deepen its capabilities to beyond being an RPA point tool into much more of an enterprise automation platform, rewriting a lot of the code Linux, cloud, better automation of the UI, you are going to hear all kind of new product announcements that are coming so I'll be listening for those details. I want to hear more from customers that further confirm what I've been hearing from them over the last couple of years and get more data especially on their ROI, on their land and expand, I want to understand that dynamic and that true enterprise automation. It's going to be good to get an update face to face and test some of our assumptions here and see where the gaps are and where UiPath can improve. Third, I want to talk to ecosystem players to see where they are in participating in the value chain here. What kind of partner has UiPath become since its IPO, are they investing more in the ecosystem, how do partners fit into that flywheel. Fourth, I want to hear from UiPath management Daniel Dines and other UiPath leaders, their exiting toddler wheel and coming into an adolescence phase or early adulthood. And what does that progression look like, how does it feel, what's the vibe at the show. And finally I'm very excited to participate in a live in-person event to see what's working, to see how hybrid events are evolving, we got to good glimpse at Mobile congress and this week in DC at public sector summit. As you know theCube is doing hybrid events for years and we intend to continue to lead in this regard and bring you the best real time information as possible. Okay, that's it for today. Remember these episodes are all available as podcasts wherever you listen, all you do is search breaking analysis podcast. We publish each week on Wikibound.com and Siliconangle.com and you can always connect on twitter @dvellante or email me at David.vellante@siliconangle.com Appreciate the comments on LinkedIn and don't forget to check out ETR.plus for all the survey data. This is Dave Vellante for theCube insights powered by ETR. Be well and will see you next time. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe, >>my buddy, We're back. And this is Dave Volante, and we're covering the whole notion of automating data in the Enterprise. And I'm really excited to have Paul Damico here. She's a senior vice president of enterprise data Architecture at Webster Bank. Good to see you. Thanks for coming on. >>Hi. Nice to see you, too. Yes. >>So let's let's start with Let's start with Webster Bank. You guys are kind of a regional. I think New York, New England, uh, leave headquartered out of Connecticut, but tell us a little bit about the bank. >>Yeah, Um, Webster Bank >>is regional Boston And that again, and New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated saying regional bank for this area. They, um, hold, um, quite a few awards for the area for being supportive for the community and, um, are really moving forward. Technology lives. They really want to be a data driven bank, and they want to move into a more robust Bruce. >>Well, we got a lot to talk about. So data driven that is an interesting topic. And your role as data architect. The architecture is really senior vice president data architecture. So you got a big responsibility as it relates to It's kind of transitioning to this digital data driven bank. But tell us a little bit about your role in your organization, >>right? Um, currently, >>today we have, ah, a small group that is just working toward moving into a more futuristic, more data driven data warehouse. That's our first item. And then the other item is to drive new revenue by anticipating what customers do when they go to the bank or when they log into there to be able to give them the best offer. The only way to do that is you >>have uh huh. >>Timely, accurate, complete data on the customer and what's really a great value on off something to offer that or a new product or to help them continue to grow their savings or do and grow their investment. >>Okay. And I really want to get into that. But before we do and I know you're sort of part way through your journey, you got a lot of what they do. But I want to ask you about Cove. It how you guys you're handling that? I mean, you had the government coming down and small business loans and P p p. And huge volume of business and sort of data was at the heart of that. How did you manage through that? >>But we were extremely successful because we have a big, dedicated team that understands where their data is and was able to switch much faster than a larger bank to be able to offer. The TPP longs at to our customers within lightning speeds. And part of that was is we adapted to Salesforce very, for we've had salesforce in house for over 15 years. Um, you know, pretty much, uh, that was the driving vehicle to get our CPP is loans in on and then developing logic quickly. But it was a 24 7 development role in get the data moving, helping our customers fill out the forms. And a lot of that was manual. But it was a It was a large community effort. >>Well, think about that. Think about that too. Is the volume was probably much, much higher the volume of loans to small businesses that you're used to granting. But and then also, the initial guidelines were very opaque. You really didn't know what the rules were, but you were expected to enforce them. And then finally, you got more clarity. So you had to essentially code that logic into the system in real time, right? >>I wasn't >>directly involved, but part of my data movement Team Waas, and we had to change the logic overnight. So it was on a Friday night was released. We've pushed our first set of loans through and then the logic change, Um, from, you know, coming from the government and changed. And we had to re develop our our data movement piece is again and we design them and send them back. So it was It was definitely kind of scary, but we were completely successful. We hit a very high peak and I don't know the exact number, but it was in the thousands of loans from, you know, little loans to very large loans, and not one customer who buy it's not yet what they needed for. Um, you know, that was the right process and filled out the rate and pace. >>That's an amazing story and really great support for the region. New York, Connecticut, the Boston area. So that's that's fantastic. I want to get into the rest of your story. Now let's start with some of the business drivers in banking. I mean, obviously online. I mean, a lot of people have sort of joked that many of the older people who kind of shunned online banking would love to go into the branch and see their friendly teller had no choice, You know, during this pandemic to go to online. So that's obviously a big trend you mentioned. So you know the data driven data warehouse? I wanna understand that. But well, at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change? >>Um, the ability to give the customer what they need at the time when they need it. And what I mean by that is that we have, um, customer interactions in multiple ways, right? >>And I want >>to be able for the customer, too. Walk into a bank, um, or online and see the same the same format and being able to have the same feel, the same look, and also to be able to offer them the next best offer for them. But they're you know, if they want looking for a new a mortgage or looking to refinance or look, you know, whatever it iss, um, that they have that data, we have the data and that they feel comfortable using it. And that's a untethered banker. Um, attitude is, you know, whatever my banker is holding and whatever the person is holding in their phone, that that is the same. And it's comfortable, so they don't feel that they've, you know, walked into the bank and they have to do a lot of different paperwork comparative filling out paperwork on, you know, just doing it on their phone. >>So you actually want the experience to be better. I mean, and it is in many cases now, you weren't able to do this with your existing against mainframe based Enterprise data warehouse. Is is that right? Maybe talk about that a little bit. >>Yeah, we were >>definitely able to do it with what we have today. The technology we're using, but one of the issues is that it's not timely, Um, and and you need a timely process to be able to get the customers to understand what's happening. Um, you want you need a timely process so we can enhance our risk management. We can apply for fraud issues and things like that. >>Yeah, so you're trying to get more real time in the traditional e g W. It's it's sort of a science project. There's a few experts that know how to get it. You consider line up. The demand is tremendous, and often times by the time you get the answer, you know it's outdated. So you're trying to address that problem. So So part of it is really the cycle time, the end end cycle, time that you're pressing. And then there's if I understand it, residual benefits that are pretty substantial from a revenue opportunity. Other other offers that you can you can make to the right customer, Um, that that you, you maybe know through your data. Is that right? >>Exactly. It's drive new customers, Teoh new opportunities. It's enhanced the risk, and it's to optimize the banking process and then obviously, to create new business. Um, and the only way we're going to be able to do that is that we have the ability to look at the data right when the customer walks in the door or right when they open up their app. And, um, by doing, creating more to New York time near real time data for the data warehouse team that's giving the lines of business the ability to to work on the next best offer for that customer. >>Paulo, we're inundated with data sources these days. Are there their data sources that you maybe maybe had access to before? But perhaps the backlog of ingesting and cleaning and cataloging and you know of analyzing. Maybe the backlog was so great that you couldn't perhaps tap some of those data sources. You see the potential to increase the data sources and hence the quality of the data, Or is that sort of premature? >>Oh, no. Um, >>exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of Brennan system that we've had for quite a few years. But now that we're moving to the cloud and off Prem and on France, you know, moving off Prem into like an s three bucket. Where That data king, We can process that data and get that data faster by using real time tools to move that data into a place where, like, snowflake could utilize that data or we can give it out to our market. >>Okay, so we're >>about the way we do. We're in batch mode. Still, so we're doing 24 hours. >>Okay, So when I think about the data pipeline and the people involved, I mean, maybe you could talk a little bit about the organization. I mean, you've got I know you have data. Scientists or statisticians? I'm sure you do. Ah, you got data architects, data engineers, quality engineers, you know, developers, etcetera, etcetera. And oftentimes, practitioners like yourself will will stress about pay. The data's in silos of the data quality is not where we want it to be. We have to manually categorize the data. These are all sort of common data pipeline problems, if you will. Sometimes we use the term data ops, which is kind of a play on Dev Ops applied to the data pipeline. I did. You just sort of described your situation in that context. >>Yeah. Yes. So we have a very large data ops team and everyone that who is working on the data part of Webster's Bay has been there 13 14 years. So they get the data, they understand that they understand the lines of business. Um, so it's right now, um, we could we have data quality issues, just like everybody else does. We have. We have places in him where that gets clans, Um, and we're moving toward. And there was very much silo data. The data scientists are out in the lines of business right now, which is great, cause I think that's where data science belongs. We should give them on. And that's what we're working towards now is giving them more self service, giving them the ability to access the data, um, in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own like tableau dashboards and then pushing the data back out. Um, so they're going to more not, I don't want to say a central repository, but a more of a robust repository that's controlled across multiple avenues where multiple lines of business can access. That said, how >>got it? Yes, and I think that one of the key things that I'm taking away from your last comment is the cultural aspects of this bite having the data. Scientists in the line of business, the line of lines of business, will feel ownership of that data as opposed to pointing fingers, criticizing the data quality they really own that that problem, as opposed to saying, Well, it's it's It's Paulus problem, >>right? Well, I have. My problem >>is, I have a date. Engineers, data architects, they database administrators, right, Um, and then data traditional data forwarding people. Um, and because some customers that I have that our business customers lines of business, they want to just subscribe to a report. They don't want to go out and do any data science work. Um, and we still have to provide that. So we still want to provide them some kind of regimen that they wake up in the morning and they open up their email. And there's the report that they just drive, um, which is great. And it works out really well. And one of the things is why we purchase I o waas. I would have the ability to give the lines of business the ability to do search within the data. And we read the data flows and data redundancy and things like that help me cleanup the data and also, um, to give it to the data. Analysts who say All right, they just asked me. They want this certain report, and it used to take Okay, well, we're gonna four weeks, we're going to go. We're gonna look at the data, and then we'll come back and tell you what we dio. But now with Iot Tahoe, they're able to look at the data and then, in one or two days of being able to go back and say, yes, we have data. This is where it is. This is where we found that this is the data flows that we've found also, which is that what I call it is the birth of a column. It's where the calm was created and where it went live as a teenager. And then it went to, you know, die very archive. Yeah, it's this, you know, cycle of life for a column. And Iot Tahoe helps us do that, and we do. Data lineage has done all the time. Um, and it's just takes a very long time. And that's why we're using something that has AI and machine learning. Um, it's it's accurate. It does it the same way over and over again. If an analyst leads, you're able to utilize talked something like, Oh, to be able to do that work for you. I get that. >>Yes. Oh, got it. So So a couple things there is in in, In researching Iot Tahoe, it seems like one of the strengths of their platform is the ability to visualize data the data structure and actually dig into it. But also see it, um, and that speeds things up and gives everybody additional confidence. And then the other pieces essentially infusing AI or machine intelligence into the data pipeline is really how you're attacking automation, right? And you're saying it's repeatable and and then that helps the data quality, and you have this virtuous cycle. Is there a firm that and add some color? Perhaps >>Exactly. Um, so you're able to let's say that I have I have seven cause lines of business that are asking me questions and one of the questions I'll ask me is. We want to know if this customer is okay to contact, right? And you know, there's different avenues, so you can go online to go. Do not contact me. You can go to the bank and you can say I don't want, um, email, but I'll take tests and I want, you know, phone calls. Um, all that information. So seven different lines of business asked me that question in different ways once said okay to contact the other one says, you know, customer one to pray All these, You know, um, and each project before I got there used to be siloed. So one customer would be 100 hours for them to do that and analytical work, and then another cut. Another analysts would do another 100 hours on the other project. Well, now I can do that all at once, and I can do those type of searches and say, Yes, we already have that documentation. Here it is. And this is where you can find where the customer has said, you know, you don't want I don't want to get access from you by email, or I've subscribed to get emails from you. >>Got it. Okay? Yeah. Okay. And then I want to come back to the cloud a little bit. So you you mentioned those three buckets? So you're moving to the Amazon cloud. At least I'm sure you're gonna get a hybrid situation there. You mentioned Snowflake. Um, you know what was sort of the decision to move to the cloud? Obviously, snowflake is cloud only. There's not an on Prem version there. So what precipitated that? >>Alright, So, from, um, I've been in >>the data I t Information field for the last 35 years. I started in the US Air Force and have moved on from since then. And, um, my experience with off brand waas with Snowflake was working with G McGee capital. And that's where I met up with the team from Iot to house as well. And so it's a proven. So there's a couple of things one is symptomatic of is worldwide. Now to move there, right, Two products, they have the on frame in the offering. Um, I've used the on Prem and off Prem. They're both great and it's very stable and I'm comfortable with other people are very comfortable with this. So we picked. That is our batch data movement. Um, we're moving to her, probably HBR. It's not a decision yet, but we're moving to HP are for real time data which has changed capture data, you know, moves it into the cloud. And then So you're envisioning this right now in Petrit, you're in the S three and you have all the data that you could possibly want. And that's Jason. All that everything is sitting in the S three to be able to move it through into snowflake and snowflake has proven cto have a stability. Um, you only need to learn in train your team with one thing. Um, aws has is completely stable at this 10.2. So all these avenues, if you think about it going through from, um, you know, this is your your data lake, which is I would consider your s three. And even though it's not a traditional data leg like you can touch it like a like a progressive or a dupe and into snowflake and then from snowflake into sandboxes. So your lines of business and your data scientists and just dive right in, Um, that makes a big, big win. and then using Iot. Ta ho! With the data automation and also their search engine, um, I have the ability to give the data scientists and eight analysts the the way of they don't need to talk to i t to get, um, accurate information or completely accurate information from the structure. And we'll be right there. >>Yes, so talking about, you know, snowflake and getting up to speed quickly. I know from talking to customers you get from zero to snowflake, you know, very fast. And then it sounds like the i o Ta ho is sort of the automation cloud for your data pipeline within the cloud. This is is that the right way to think about it? >>I think so. Um, right now I have I o ta >>ho attached to my >>on Prem. And, um, I >>want to attach it to my offering and eventually. So I'm using Iot Tahoe's data automation right now to bring in the data and to start analyzing the data close to make sure that I'm not missing anything and that I'm not bringing over redundant data. Um, the data warehouse that I'm working off is not a It's an on Prem. It's an Oracle database and its 15 years old. So it has extra data in it. It has, um, things that we don't need anymore. And Iot. Tahoe's helping me shake out that, um, extra data that does not need to be moved into my S three. So it's saving me money when I'm moving from offering on Prem. >>And so that was a challenge prior because you couldn't get the lines of business to agree what to delete or what was the issue there. >>Oh, it was more than that. Um, each line of business had their own structure within the warehouse, and then they were copying data between each other and duplicating the data and using that, uh so there might be that could be possibly three tables that have the same data in it. But it's used for different lines of business. And so I had we have identified using Iot Tahoe. I've identified over seven terabytes in the last, um, two months on data that is just been repetitive. Um, it just it's the same exact data just sitting in a different scheme. >>And and that's not >>easy to find. If you only understand one schema that's reporting for that line of business so that >>yeah, more bad news for the storage companies out there. Okay to follow. >>It's HCI. That's what that's what we were telling people you >>don't know and it's true, but you still would rather not waste it. You apply it to, you know, drive more revenue. And and so I guess Let's close on where you see this thing going again. I know you're sort of part way through the journey. May be you could sort of describe, you know, where you see the phase is going and really what you want to get out of this thing, You know, down the road Midterm. Longer term. What's your vision or your your data driven organization? >>Um, I want >>for the bankers to be able to walk around with on iPad in their hands and be able to access data for that customer really fast and be able to give them the best deal that they can get. I want Webster to be right there on top, with being able to add new customers and to be able to serve our existing customers who had bank accounts. Since you were 12 years old there and now our, you know, multi. Whatever. Um, I want them to be able to have the best experience with our our bankers, and >>that's awesome. I mean, that's really what I want is a banking customer. I want my bank to know who I am, anticipate my needs and create a great experience for me. And then let me go on with my life. And so that is a great story. Love your experience, your background and your knowledge. Can't thank you enough for coming on the Cube. >>No, thank you very much. And you guys have a great day. >>Alright, Take care. And thank you for watching everybody keep it right there. We'll take a short break and be right back. >>Yeah, yeah, yeah, yeah.

>>from around the globe. It's the Cube with digital coverage of enterprise data automation an event Siri's brought to you by Iot. Tahoe. >>Okay, we're back. Welcome back to data Automated. A J ahora is CEO of I o Ta ho, JJ. Good to see you. How have things in London? >>Big thing. Well, thinking well, where we're making progress, I could see you hope you're doing well and pleasure being back here on the Cube. >>Yeah, it's always great to talk to. You were talking enterprise data automation. As you know, with within our community, we've been pounding the whole data ops conversation. Little different, though. We're gonna We're gonna dig into that a little bit. But let's start with a J how you've seen the response to Covert and I'm especially interested in the role that data has played in this pandemic. >>Yeah, absolutely. I think everyone's adapting both essentially, um, and and in business, the customers that I speak to on day in, day out that we partner with, um they're busy adapting their businesses to serve their customers. It's very much a game of and showing the week and serve our customers to help their customers um, you know, the adaptation that's happening here is, um, trying to be more agile, kind of the most flexible. Um, a lot of pressure on data. A lot of demand on data and to deliver more value to the business, too. Serve that customer. >>Yeah. I mean, data machine intelligence and cloud, or really three huge factors that have helped organizations in this pandemic. And, you know, the machine intelligence or AI piece? That's what automation is all about. How do you see automation helping organizations evolve maybe faster than they thought they might have to >>Sure. I think the necessity of these times, um, there's there's a says a lot of demand doing something with data data. Uh huh. A lot of a lot of businesses talk about being data driven. Um, so interesting. I sort of look behind that when we work with our customers, and it's all about the customer. You know, the mic is cios invested shareholders. The common theme here is the customer. That customer experience starts and ends with data being able to move from a point that is reacting. So what the customer is expecting and taking it to that step forward where you can be proactive to serve what that customer's expectation to and that's definitely come alive now with they, um, the current time. >>Yes. So, as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline. But talk about enterprise data automation. What is it to you and how is it different from data off? >>Yeah, Great question. Thank you. I am. I think we're all familiar with felt more more awareness around. So as it's applied, Teoh, uh, processes methodologies that have become more mature of the past five years around devil that managing change, managing an application, life cycles, managing software development data about, you know, has been great. But breaking down those silos between different roles functions and bringing people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, landing itself to data with data is exciting. We're excited about that, Andi shifting the focus from being I t versus business users to you know who are the data producers. And here the data consumers in a lot of cases, it concert in many different lines of business. So in data role, those methods those tools and processes well we look to do is build on top of that with data automation. It's the is the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors our R and D and bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is, Is the automation behind the automation we can take? I'll give you an example. Okay, a bank where we did a lot of work to do make move them into accelerating that digital transformation. And what we're finding is that as we're able to automate the jobs related to data a managing that data and serving that data that's going into them as a business automating their processes for their customer. Um, so it's it's definitely having a compound effect. >>Yeah, I mean I think that you did. Data ops for a lot of people is somewhat new to the whole Dev Ops. The data ops thing is is good and it's a nice framework. Good methodology. There is obviously a level of automation in there and collaboration across different roles. But it sounds like you're talking about so supercharging it, if you will, the automation behind the automation. You know, I think organizations talk about being data driven. You hear that? They have thrown around a lot of times. People sit back and say, We don't make decisions without data. Okay? But really, being data driven is there's a lot of aspects there. There's cultural, but it's also putting data at the core of your organization, understanding how it effects monetization. And, as you know, well, silos have been built up, whether it's through M and a, you know, data sprawl outside data sources. So I'm interested in your thoughts on what data driven means and specifically Hi, how Iot Tahoe plays >>there. Yeah, I'm sure we'll be happy. That look that three David, we've We've come a long way in the last four years. We started out with automating some of those simple, um, to codify. Um, I have a high impact on organization across the data, a data warehouse. There's data related tasks that classify data on and a lot of our original pattern. Senai people value that were built up is is very much around. They're automating, classifying data across different sources and then going out to so that for some purpose originally, you know, some of those simpler I'm challenges that we have. Ah, custom itself, um, around data privacy. You know, I've got a huge data lake here. I'm a telecoms business. I've got millions of six subscribers. Um, quite often the chief data office challenges. How do I cover the operational risk? Where, um, I got so much data I need to simplify my approach to automating, classifying that data. Recent is you can't do that manually. We can for people at it. And the the scale of that is is prohibitive, right? Often, if you had to do it manually by the time you got a good picture of it, it's already out of date. Then, starting with those those simple challenges that we've been able to address, we're then going on and build on that to say, What else do we serve? What else do we serve? The chief data officer, Chief marketing officer on the CFO. Within these times, um, where those decision makers are looking for having a lot of choices in the platform options that they say that the tooling they're very much looking for We're that Swiss army. Not being able to do one thing really well is is great, but more more. Where that cost pressure challenge is coming in is about how do we, um, offer more across the organization, bring in those business lines of business activities that depend on data to not just with a T. Okay, >>so we like the cube. Sometimes we like to talk about Okay, what is it? And then how does it work? And what's the business impact? We kind of covered what it is but love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, I wonder if you could tell us and what is the secret sauce behind Iot Tahoe? And if you could take us through this slot. >>Sure. I mean, right there in the middle that the heart of what we do It is the intellectual property. Yeah, that was built up over time. That takes from Petra genius data sources Your Oracle relational database, your your mainframe. If they lay in increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data, classify that data after it's classified them have the ability to form relationships across those different, uh, source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts a contact and meaning around that data. So it's moving it now from bringing data driven on increasingly well. We have really smile, right people in our customer organizations you want do some of those advanced knowledge tasks, data scientists and, uh, quants in some of the banks that we work with. The the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality policies that you apply to that data. I'm putting it in context once you've got the ability to power. A a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the tapestry that fabric across that different systems could be crm air P system such as s AP on some of the newer cloud databases that we work with. Snowflake is a great Well, >>yes. So this is you're describing sort of one of the one of the reasons why there's so many stove pipes and organizations because data is gonna locked in the silos of applications. I also want to point out, you know, previously to do discovery to do that classification that you talked about form those relationship to glean context from data. A lot of that, if not most of that in some cases all that would have been manual. And of course, it's out of date so quickly. Nobody wants to do it because it's so hard. So this again is where automation comes into the the the to the idea of really becoming data driven. >>Sure. I mean the the efforts. If we if I look back, maybe five years ago, we had a prevalence of daily technologies at the cutting edge. Those have said converging me to some of these cloud platforms. So we work with Google and AWS, and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenge at scale. I quickly runs out of steam because once, um, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data estate? It's changed, you know, you've onboard a new customer. You signed up a new partner, Um, customer has no adopted a new product that you just Lawrence and there that that slew of data it's keeps coming. So it's keeping pace with that. The only answer really is is some form of automation. And what we found is if we can tie automation with what I said before the expertise the, um, the subject matter expertise that sometimes goes back many years within an organization's people that augmentation between machine learning ai on and on that knowledge that sits within inside the organization really tends to involve a lot of value in data? >>Yes, So you know Well, a J you can't be is a smaller company, all things to all people. So your ecosystem is critical. You working with AWS? You're working with Google. You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>Yeah, that's that's fundamental. So I mean, when I caimans, we tell her here is the CEO of one of the, um, trends that I wanted us to to be part of was being open, having an open architecture that allowed one thing that was nice to my heart, which is as a CEO, um, a C I O where you've got a budget vision and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using ap eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um, and snowflake here is, um it's those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that, and they're leveraging the value that they've already committed to. >>Okay, so we've talked about kind of what it is and how it works, and I want to get into the business impact. I would say what I would be looking for from from this would be Can you help me lower my operational risk? I've got I've got tasks that I do many year sequential, some who are in parallel. But can you reduce my time to task? And can you help me reduce the labor intensity and ultimately, my labor costs? And I put those resources elsewhere, and ultimately, I want to reduce the end and cycle time because that is going to drive Telephone number R. A. Y So, um, I missing anything? Can you do those things? And maybe you could give us some examples of the tiara y and the business impact. >>Yeah. I mean, the r a y David is is built upon on three things that I mentioned is a combination off leveraging the existing investment with the existing state, whether that's home, Microsoft, Azure or AWS or Google IBM. And I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have you got the automation that is working right down to the level off data, a column level or the file level so we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs, that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome. It could be a customer who wants that experience on a mobile device. A tablet oh, face to face within, within the store. I mean game. Would you provision the right data and enable our customers do that? But their customers, with the right data that they can trust at the right time, just in that real time moment where decision or an action is being expected? That's, um, that's driving the r a y two b in some cases, 20 x but and that's that's really satisfying to see that that kind of impact it is taking years down to months and in many cases, months of work down to days. In some cases, our is the time to value. I'm I'm impressed with how quickly out of the box with very little training a customer and think about, too. And you speak just such a search. They discovery knowledge graph on DM. I don't find duplicates. Onda Redundant data right off the bat within hours. >>Well, it's why investors are interested in this space. I mean, they're looking for a big, total available market. They're looking for a significant return. 10 X is you gotta have 10 x 20 x is better. So so that's exciting and obviously strong management and a strong team. I want to ask you about people and culture. So you got people process technology we've seen with this pandemic that processes you know are really unpredictable. And the technology has to be able to adapt to any process, not the reverse. You can't force your process into some static software, so that's very, very important. But the end of the day you got to get people on board. So I wonder if you could talk about this notion of culture and a data driven culture. >>Yeah, that's that's so important. I mean, current times is forcing the necessity of the moment to adapt. But as we start to work their way through these changes on adapt ah, what with our customers, But that is changing economic times. What? What we're saying here is the ability >>to I >>have, um, the technology Cartman, in a really smart way, what those business uses an I T knowledge workers are looking to achieve together. So I'll give you an example. We have quite often with the data operations teams in the companies that we, um, partnering with, um, I have a lot of inbound enquiries on the day to day level. I really need this set of data they think it can help my data scientists run a particular model? Or that what would happen if we combine these two different silence of data and gets the Richmond going now, those requests you can, sometimes weeks to to realize what we've been able to do with the power is to get those answers being addressed by the business users themselves. And now, without without customers, they're coming to the data. And I t folks saying, Hey, I've now built something in the development environment. Why don't we see how that can scale up with these sets of data? I don't need terabytes of it. I know exactly the columns and the feet in the data that I'm going to use on that gets seller wasted in time, um, angle to innovate. >>Well, that's huge. I mean, the whole notion of self service and the lines of business actually feeling like they have ownership of the data as opposed to, you know, I t or some technology group owning the data because then you've got data quality issues or if it doesn't line up there their agenda, you're gonna get a lot of finger pointing. So so that is a really important. You know a piece of it. I'll give you last word A J. Your final thoughts, if you would. >>Yeah, we're excited to be the only path. And I think we've built great customer examples here where we're having a real impact in in a really fast pace, whether it helping them migrate to the cloud, helping the bean up their legacy, Data lake on and write off there. Now the conversation is around data quality as more of the applications that we enable to a more efficiently could be data are be a very robotic process automation along the AP, eyes that are now available in the cloud platforms. A lot of those they're dependent on data quality on and being able to automate. So business users, um, to take accountability off being able to so look at the trend of their data quality over time and get the signals is is really driving trust. And that trust in data is helping in time. Um, the I T teams, the data operations team, with do more and more quickly that comes back to culture being out, supply this technology in such a way that it's visual insensitive. Andi. How being? Just like Dev Ops tests with with a tty Dave drops putting intelligence in at the data level to drive that collaboration. We're excited, >>you know? You remind me of something. I lied. I don't want to go yet. It's OK, so I know we're tight on time, but you mentioned migration to the cloud. And I'm thinking about conversation with Paula from Webster Webster. Bank migrations. Migrations are, you know, they're they're a nasty word for for organizations. So our and we saw this with Webster. How are you able to help minimize the migration pain and and why is that something that you guys are good at? >>Yeah. I mean, there were many large, successful companies that we've worked with. What's There's a great example where, you know, I'd like to give you the analogy where, um, you've got a lot of people in your teams if you're running a business as a CEO on this bit like a living living grade. But imagine if those different parts of your brain we're not connected, that with, um, so diminish how you're able to perform. So what we're seeing, particularly with migration, is where banks retailers. Manufacturers have grown over the last 10 years through acquisition on through different initiatives, too. Um, drive customer value that sprawl in their data estate hasn't been fully dealt with. It sometimes been a good thing, too. Leave whatever you're fired off the agent incent you a side by side with that legacy mainframe on your oracle, happy and what we're able to do very quickly with that migration challenges shine a light on all the different parts. Oh, data application at the column level or higher level if it's a day late and show an enterprise architect a CDO how everything's connected, where they may not be any documentation. The bright people that created some of those systems long since moved on or retired or been promoted into so in the rose on within days, being out to automatically generate Anke refreshed the states of that data across that man's game on and put it into context, then allows you to look at a migration from a confidence that you did it with the back rather than what we've often seen in the past is teams of consultant and business analysts. Data around this spend months getting an approximation and and a good idea of what it could be in the current state and try their very best to map that to the future Target state. Now, without all hoping out, run those processes within hours of getting started on, um well, that picture visualize that picture and bring it to life. You know, the Yarra. Why, that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on gcb or migration to any other clouds such as AWS or a multi cloud landscape right now with yeah, >>that visibility is key. Teoh sort of reducing operational risks, giving people confidence that they can move forward and being able to do that and update that on an ongoing basis, that means you can scale a J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have >>you. Thank you, David. Look towards smoking in. >>Alright, keep it right there, everybody. We're here with data automated on the Cube. This is Dave Volante and we'll be right back. Short break. >>Yeah, yeah, yeah, yeah

(upbeat music) >> Reporter: From around the globe, it's The Cube with digital coverage of enterprise data automation and event series brought to you by Io-Tahoe. >> Okay, we're back. Focusing on enterprise data automation, we're going to talk about the journey to the cloud. Remember, the hashtag is data automated. We're here with Lester Waters who's the CTO of Io-Tahoe, Lester, good to see you from across the pond on video, wish we were face to face, but it's great to have you on The Cube. >> Also I do, thank you for having me. >> Oh, you're very welcome. Hey, give us a little background on CTO, you got a deep expertise in a lot of different areas, but what do we need to know? >> Well, David, I started my career basically at Microsoft, where I started the Information Security Cryptography Group. They're the very first one that the company had and that led to a career in information security and of course, as you go along with the information security, data is the key element to be protected. So I always had my hands in data and that naturally progressed into a role with Io-Tahoe as their CTO. >> Guys, I have to invite you back, we'll talk crypto all day we'd love to do that but we're here talking about yeah, awesome, right? But we're here talking about the cloud and here we'll talk about the journey to the cloud and accelerate. Everybody's really interested obviously in cloud, even more interested now with the pandemic, but what's that all about? >> Well, moving to the cloud is quite an undertaking for most organizations. First of all, we've got as probably if you're a large enterprise, you probably have thousands of applications, you have hundreds and hundreds of database instances, and trying to shed some light on that, just to plan your move to the cloud is a real challenge. And some organizations try to tackle that manually. Really what Io-Tahoe is bringing is trying to tackle that in an automated version to help you with your journey to the cloud. >> Well, look at migrations are sometimes just an evil word to a lot of organizations, but at the same time, building up technical debt veneer after veneer and year, and year, and year is something that many companies are saying, "Okay, it's got to stop." So what's the prescription for that automation journey and simplifying that migration to the cloud? >> Well, I think the very first thing that's all about is data hygiene. You don't want to pick up your bad habits and take them to the cloud. You've got an opportunity here, so I see the journey to the cloud is an opportunity to really clean house, reorganize things, like moving out. You might move all your boxes, but you're kind of probably cherry pick what you're going to take with you and then you're going to organize it as you end up at your new destination. So from that, I get there's seven key principles that I like to operate by when I advise on the cloud migration. >> Okay. So, where do you start? >> Well, I think the first thing is understanding what you got, so discover and cataloging your data and your applications. If I don't know what I have, I can't move it, I can't improve it, I can't build up on it. And I have to understand there is dependency, so building that data catalog is the very first step. What do I got? >> Now, is that a metadata exercise? Sometimes there's more metadata than there is data. Is metadata part of that first step or? >> In deed, metadata is the first step so the metadata really describes the data you have. So, the metadata is going to tell me I have 2000 tables and maybe of those tables, there's an average of 25 columns each, and so that gives me a sketch if you will, of what I need to move. How big are the boxes I need to pack for my move to the cloud? >> Okay, and you're saying you can automate that data classification, categorization, discovery, correct using math machine intelligence, is that correct? >> Yeah, that's correct. So basically we go, and we will discover all of the schema, if you will, that's the metadata description of your tables and columns in your database in the data types. So we take, we will ingest that in, and we will build some insights around that. And we do that across a variety of platforms because everybody's organization has you've got a one yeah, an Oracle Database here, and you've got a Microsoft SQL Database here, you might have something else there that you need to bring site onto. And part of this journey is going to be about breaking down your data silos and understanding what you've got. >> Okay. So, we've done the audit, we know what we've got, what's next? Where do we go next? >> So the next thing is remediating that data. Where do I have duplicate data? Often times in an organization, data will get duplicated. So, somebody will take a snapshot of a data, and then ended up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer. And you can see where that will go when trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to understand where all your redundant data is. So when you go to the cloud, maybe you have an opportunity here to consolidate that data. >> Yeah, because you like to borrow in an Einstein or apply an Einstein Bromide right. Keep as much data as you can, but no more. >> Correct. >> Okay. So you get to the point to the second step you're kind of a one to reduce costs, then what? You figure out what to get rid of, or actually get rid of it, what's next? >> Yes, that would be the next step. So figuring out what you need and what you don't need often times I've found that there's obsolete columns of data in your databases that you just don't need, or maybe it's been superseded by another, you've got tables that have been superseded by other tables in your database. So you got to understand what's being used and what's not and then from that, you can decide, "I'm going to leave this stuff behind, "or I'm going to archive this stuff "cause I might need it for data retention "or I'm just going to delete it, "I don't need it at all." >> Well, Lester, most organizations, if they've been around a while, and the so-called incumbents, they've got data all over the place, their data marts, data warehouses, there are all kinds of different systems and the data lives in silos. So, how do you kind of deal with that problem? Is that part of the journey? >> That's a great point Dave, because you're right that the data silos happen because this business unit is chartered with this task another business unit has this task and that's how you get those instantiations of the same data occurring in multiple places. So as part of your cloud migration journey, you really want to plan where there's an opportunity to consolidate your data, because that means there'll be less to manage, there'll be less data to secure, and it'll have a smaller footprint, which means reduced costs. >> So, people always talk about a single version of the truth, data quality is a huge issue. I've talked to data practitioners and they've indicated that the quality metrics are in the single digits and they're trying to get to 90% plus, but maybe you could address data quality. Where does that fit in on the journey? >> That's, a very important point. First of all, you don't want to bring your legacy issues with you. As the point I made earlier, if you've got data quality issues, this is a good time to find those and identify and remediate them. But that can be a laborious task. We've had customers that have tried to do this by hand and it's very, very time consuming, cause you imagine if you've got 200 tables, 50,000 columns, imagine, the manual labor involved in doing that. And you could probably accomplish it, but it'll take a lot of work. So the opportunity to use tools here and automate that process is really will help you find those outliers there's that bad data and correct it before you move to the cloud. >> And you're just talking about that automation it's the same thing with data catalog and that one of the earlier steps. Organizations would do this manually or they try to do it manually and that's a lot of reason for the failure. They just, it's like cleaning out your data like you just don't want to do it (laughs). Okay, so then what's next? I think we're plowing through your steps here. What what's next on the journey? >> The next one is, in a nutshell, preserve your data format. Don't boil the ocean here to use a cliche. You want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables on which they sit, the columns and the way they're named. So, some degree you are going to be doing a lift and shift, but it's an intelligent lift and shift using all the insights you've gathered by cataloging the data, looking for data quality issues, looking for duplicate columns, doing planning consolidation. You don't want to also rewrite your application. So, in that aspect, I think it's important to do a bit of lift and shift and preserve those data formats as they sit. >> Okay, so let me follow up on that. That sounds really important to me, because if you're doing a conversion and you're rewriting applications, that means that you're going to have to freeze the existing application, and then you going to be refueling the plane as you're in midair and a lot of times, especially with mission critical systems, you're never going to bring those together and that's a recipe for disaster, isn't it? >> Great analogy unless you're with the air force, you'll (mumbles) (laughs). Now, that's correct. It's you want to have bite-sized steps and that's why it's important to plan your journey, take these steps. You're using automation where you can to make that journey to the cloud much easier and more straightforward. >> All right, I like that. So we're taking a kind of a systems view and end to end view of the data pipeline, if you will. What's next? I think we're through. I think I've counted six. What's the lucky seven? >> Lucky seven, involve your business users. Really, when you think about it, your data is in silos. Part of this migration to the cloud is an opportunity to break down these silos, these silos that naturally occur as part of the business unit. You've got to break these cultural barriers that sometimes exist between business and say, so for example, I always advise, there's an opportunity here to consolidate your sensitive data, your PII, your personally identifiable information, and if three different business units have the same source of truth for that, there's was an opportunity to consolidate that into one as you migrate. That might be a little bit of tweaking to some of the apps that you have that are dependent on it, but in the long run, that's what you really want to do. You want to have a single source of truth, you want to ring fence that sensitive data, and you want all your business users talking together so that you're not reinventing the wheel. >> Well, the reason I think too that's so important is that you're now I would say you're creating a data driven culture. I know that's sort of a buzz word, but what it's true and what that means to me is that your users, your lines of business feel like they actually own the data rather than pointing fingers at the data group, the IT group, the data quality people, data engineers, saying, "Oh, I don't believe it." If the lines of business own the data, they're going to lean in, they're going to maybe bring their own data science resources to the table, and it's going to be a much more collaborative effort as opposed to a non-productive argument. >> Yeah. And that's where we want to get to. DataOps is key, and maybe that's a term that's still evolving. But really, you want the data to drive the business because that's where your insights are, that's where your value is. You want to break down the silos between not only the business units, as I mentioned, but also as you pointed out, the roles of the people that are working with it. A self service data culture is the right way to go with the right security controls, putting on my security hat of course in place so that if I'm a developer and I'm building a new application, I'd love to be able to go to the data catalog, "Oh, there's already a database that has the customer "what the customers have clicked on when shopping." I could use that. I don't have to rebuild that, I'll just use that as for my application. That's the kind of problems you want to be able to solve and that's where your cost reductions come in across the board. >> Yeah. I want to talk a little bit about the business context here. We always talk about data, it's the new source of competitive advantage, I think there's not a lot of debate about that, but it's hard. A lot of companies are struggling to get value out of their data because it's so difficult. All the things we've talked about, the silos, the data quality, et cetera. So, you mentioned the term data apps, data apps is all about streamlining, that data, pipelining, infusing automation and machine intelligence into that pipeline and then ultimately taking a systems view and compressing that time to insights so that you can drive monetization, whether it's cut costs, maybe it's new revenue, drive productivity, but it's that end to end cycle time reduction that successful practitioners talk about as having the biggest business impact. Are you seeing that? >> Absolutely, but it is a journey and it's a huge cultural change for some companies that are. I've worked in many companies that are ticket based IT-driven and just do even the marginalist of change or get insight, raise a ticket, wait a week and then out the other end will pop maybe a change that I needed and it'll take a while for us to get to a culture that truly has a self service data-driven nature where I'm the business owner, and I want to bring in a data scientist because we're losing. For example, a business might be losing to a competitor and they want to find what insights, why is the customer churn, for example, happening every Tuesday? What is it about Tuesday? This is where your data scientist comes in. The last thing you want is to raise a ticket, wait for the snapshot of the data, you want to enable that data scientist to come in, securely connect into the data, and do his analysis, and come back and give you those insights, which will give you that competitive advantage. >> Well, I love your point about churn, maybe it talks about the Andreessen quote that "Software's eating the world," and all companies are our software companies, and SaaS companies, and churn is the killer of SaaS companies. So very, very important point you're making. My last question for you before we summarize is the tech behind all of these. What makes Io-Tahoe unique in its ability to help automate that data pipeline? >> Well, we've done a lot of research, we have I think now maybe 11 pending patent applications, I think one has been approved to be issued (mumbles), but really, it's really about sitting down and doing the right kind of analysis and figuring out how we can optimize this journey. Some of these stuff isn't rocket science. You can read a schema and into an open source solution, but you can't necessarily find the hidden insights. So if I want to find my foreign key dependencies, which aren't always declared in the database, or I want to identify columns by their content, which because the columns might be labeled attribute one, attribute two, attribute three, or I want to find out how my data flows between the various tables in my database. That's the point at which you need to bring in automation, you need to bring in data science solutions, and there's even a degree of machine learning because for example, we might deduce that data is flowing from this table to this table and upon when you present that to the user with a 87% confidence, for example, and the user can go, or the administrator can go. Now, it really goes the other way, it was an invalid collusion and that's the machine learning cycle. So the next time we see that pattern again, in that environment we will be able to make a better recommendation because some things aren't black and white, they need that human intervention loop. >> All right, I just want to summarize with Lester Waters' playbook to moving to the cloud and I'll go through them. Hopefully, I took some notes, hopefully, I got them right. So step one, you want to do that data discovery audit, you want to be fact-based. Two is you want to remediate that data redundancy, and then three identify what you can get rid of. Oftentimes you don't get rid of stuff in IT, or maybe archive it to cheaper media. Four is consolidate those data silos, which is critical, breaking down those data barriers. And then, five is attack the quality issues before you do the migration. Six, which I thought was really intriguing was preserve that data format, you don't want to do the rewrite applications and do that conversion. It's okay to do a little bit of lifting and shifting >> This comes in after the task. >> Yeah, and then finally, and probably the most important is you got to have that relationship with the lines of business, your users, get them involved, begin that cultural shift. So I think great recipe Lester for safe cloud migration. I really appreciate your time. I'll give you the final word if you will bring us home. >> All right. Well, I think the journey to the cloud it's a tough one. You will save money, I have heard people say, you got to the cloud, it's too expensive, it's too this, too that, but really, there is an opportunity for savings. I'll tell you when I run data services as a PaaS service in the cloud, it's wonderful because I can scale up and scale down almost by virtually turning a knob. And so I'll have complete control and visibility of my costs. And so for me, that's very important. Io also, it gives me the opportunity to really ring fence my sensitive data, because let's face it, most organizations like being in a cheese grater when you talk about security, because there's so many ways in and out. So I find that by consolidating and bringing together the crown jewels, if you will. As a security practitioner, it's much more easy to control. But it's very important. You can't get there without some automation and automating this discovery and analysis process. >> Well, great advice. Lester, thanks so much. It's clear that the capex investments on data centers are generally not a good investment for most companies. Lester, really appreciate, Lester waters CTO of Io-Tahoe. Let's watch this short video and we'll come right back. You're watching The Cube, thank you. (upbeat music)

>> Narrator: From around the Globe, it's theCube with digital coverage of Enterprise Data Automation, and event series brought to you by Io-Tahoe. >> Everybody, we're back. And this is Dave Vellante, and we're covering the whole notion of Automated Data in the Enterprise. And I'm really excited to have Paula D'Amico here. Senior Vice President of Enterprise Data Architecture at Webster Bank. Paula, good to see you. Thanks for coming on. >> Hi, nice to see you, too. >> Let's start with Webster bank. You guys are kind of a regional I think New York, New England, believe it's headquartered out of Connecticut. But tell us a little bit about the bank. >> Webster bank is regional Boston, Connecticut, and New York. Very focused on in Westchester and Fairfield County. They are a really highly rated regional bank for this area. They hold quite a few awards for the area for being supportive for the community, and are really moving forward technology wise, they really want to be a data driven bank, and they want to move into a more robust group. >> We got a lot to talk about. So data driven is an interesting topic and your role as Data Architecture, is really Senior Vice President Data Architecture. So you got a big responsibility as it relates to kind of transitioning to this digital data driven bank but tell us a little bit about your role in your Organization. >> Currently, today, we have a small group that is just working toward moving into a more futuristic, more data driven data warehousing. That's our first item. And then the other item is to drive new revenue by anticipating what customers do, when they go to the bank or when they log in to their account, to be able to give them the best offer. And the only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on offer something to offer that, or a new product, or to help them continue to grow their savings, or do and grow their investments. >> Okay, and I really want to get into that. But before we do, and I know you're, sort of partway through your journey, you got a lot to do. But I want to ask you about Covid, how you guys handling that? You had the government coming down and small business loans and PPP, and huge volume of business and sort of data was at the heart of that. How did you manage through that? >> We were extremely successful, because we have a big, dedicated team that understands where their data is and was able to switch much faster than a larger bank, to be able to offer the PPP Long's out to our customers within lightning speed. And part of that was is we adapted to Salesforce very for we've had Salesforce in house for over 15 years. Pretty much that was the driving vehicle to get our PPP loans in, and then developing logic quickly, but it was a 24 seven development role and get the data moving on helping our customers fill out the forms. And a lot of that was manual, but it was a large community effort. >> Think about that too. The volume was probably much higher than the volume of loans to small businesses that you're used to granting and then also the initial guidelines were very opaque. You really didn't know what the rules were, but you were expected to enforce them. And then finally, you got more clarity. So you had to essentially code that logic into the system in real time. >> I wasn't directly involved, but part of my data movement team was, and we had to change the logic overnight. So it was on a Friday night it was released, we pushed our first set of loans through, and then the logic changed from coming from the government, it changed and we had to redevelop our data movement pieces again, and we design them and send them back through. So it was definitely kind of scary, but we were completely successful. We hit a very high peak. Again, I don't know the exact number but it was in the thousands of loans, from little loans to very large loans and not one customer who applied did not get what they needed for, that was the right process and filled out the right amount. >> Well, that is an amazing story and really great support for the region, your Connecticut, the Boston area. So that's fantastic. I want to get into the rest of your story now. Let's start with some of the business drivers in banking. I mean, obviously online. A lot of people have sort of joked that many of the older people, who kind of shunned online banking would love to go into the branch and see their friendly teller had no choice, during this pandemic, to go to online. So that's obviously a big trend you mentioned, the data driven data warehouse, I want to understand that, but what at the top level, what are some of the key business drivers that are catalyzing your desire for change? >> The ability to give a customer, what they need at the time when they need it. And what I mean by that is that we have customer interactions in multiple ways. And I want to be able for the customer to walk into a bank or online and see the same format, and being able to have the same feel the same love, and also to be able to offer them the next best offer for them. But they're if they want looking for a new mortgage or looking to refinance, or whatever it is that they have that data, we have the data and that they feel comfortable using it. And that's an untethered banker. Attitude is, whatever my banker is holding and whatever the person is holding in their phone, that is the same and it's comfortable. So they don't feel that they've walked into the bank and they have to do fill out different paperwork compared to filling out paperwork on just doing it on their phone. >> You actually do want the experience to be better. And it is in many cases. Now you weren't able to do this with your existing I guess mainframe based Enterprise Data Warehouses. Is that right? Maybe talk about that a little bit? >> Yeah, we were definitely able to do it with what we have today the technology we're using. But one of the issues is that it's not timely. And you need a timely process to be able to get the customers to understand what's happening. You need a timely process so we can enhance our risk management. We can apply for fraud issues and things like that. >> Yeah, so you're trying to get more real time. The traditional EDW. It's sort of a science project. There's a few experts that know how to get it. You can so line up, the demand is tremendous. And then oftentimes by the time you get the answer, it's outdated. So you're trying to address that problem. So part of it is really the cycle time the end to end cycle time that you're progressing. And then there's, if I understand it residual benefits that are pretty substantial from a revenue opportunity, other offers that you can make to the right customer, that you maybe know, through your data, is that right? >> Exactly. It's drive new customers to new opportunities. It's enhanced the risk, and it's to optimize the banking process, and then obviously, to create new business. And the only way we're going to be able to do that is if we have the ability to look at the data right when the customer walks in the door or right when they open up their app. And by doing creating more to New York times near real time data, or the data warehouse team that's giving the lines of business the ability to work on the next best offer for that customer as well. >> But Paula, we're inundated with data sources these days. Are there other data sources that maybe had access to before, but perhaps the backlog of ingesting and cleaning in cataloging and analyzing maybe the backlog was so great that you couldn't perhaps tap some of those data sources. Do you see the potential to increase the data sources and hence the quality of the data or is that sort of premature? >> Oh, no. Exactly. Right. So right now, we ingest a lot of flat files and from our mainframe type of front end system, that we've had for quite a few years. But now that we're moving to the cloud and off-prem and on-prem, moving off-prem, into like an S3 Bucket, where that data we can process that data and get that data faster by using real time tools to move that data into a place where, like snowflake could utilize that data, or we can give it out to our market. Right now we're about we do work in batch mode still. So we're doing 24 hours. >> Okay. So when I think about the data pipeline, and the people involved, maybe you could talk a little bit about the organization. You've got, I don't know, if you have data scientists or statisticians, I'm sure you do. You got data architects, data engineers, quality engineers, developers, etc. And oftentimes, practitioners like yourself, will stress about, hey, the data is in silos. The data quality is not where we want it to be. We have to manually categorize the data. These are all sort of common data pipeline problems, if you will. Sometimes we use the term data Ops, which is sort of a play on DevOps applied to the data pipeline. Can you just sort of describe your situation in that context? >> Yeah, so we have a very large data ops team. And everyone that who is working on the data part of Webster's Bank, has been there 13 to 14 years. So they get the data, they understand it, they understand the lines of business. So it's right now. We could the we have data quality issues, just like everybody else does. But we have places in them where that gets cleansed. And we're moving toward and there was very much siloed data. The data scientists are out in the lines of business right now, which is great, because I think that's where data science belongs, we should give them and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own, like Tableau dashboards, and then pushing the data back out. So they're going to more not, I don't want to say, a central repository, but a more of a robust repository, that's controlled across multiple avenues, where multiple lines of business can access that data. Is that help? >> Got it, Yes. And I think that one of the key things that I'm taking away from your last comment, is the cultural aspects of this by having the data scientists in the line of business, the lines of business will feel ownership of that data as opposed to pointing fingers criticizing the data quality. They really own that that problem, as opposed to saying, well, it's Paula's problem. >> Well, I have my problem is I have data engineers, data architects, database administrators, traditional data reporting people. And because some customers that I have that are business customers lines of business, they want to just subscribe to a report, they don't want to go out and do any data science work. And we still have to provide that. So we still want to provide them some kind of regiment that they wake up in the morning, and they open up their email, and there's the report that they subscribe to, which is great, and it works out really well. And one of the things is why we purchased Io-Tahoe was, I would have the ability to give the lines of business, the ability to do search within the data. And we'll read the data flows and data redundancy and things like that, and help me clean up the data. And also, to give it to the data analysts who say, all right, they just asked me they want this certain report. And it used to take okay, four weeks we're going to go and we're going to look at the data and then we'll come back and tell you what we can do. But now with Io-Tahoe, they're able to look at the data, and then in one or two days, they'll be able to go back and say, Yes, we have the data, this is where it is. This is where we found it. This is the data flows that we found also, which is what I call it, is the break of a column. It's where the column was created, and where it went to live as a teenager. (laughs) And then it went to die, where we archive it. And, yeah, it's this cycle of life for a column. And Io-Tahoe helps us do that. And we do data lineage is done all the time. And it's just takes a very long time and that's why we're using something that has AI in it and machine running. It's accurate, it does it the same way over and over again. If an analyst leaves, you're able to utilize something like Io-Tahoe to be able to do that work for you. Is that help? >> Yeah, so got it. So a couple things there, in researching Io-Tahoe, it seems like one of the strengths of their platform is the ability to visualize data, the data structure and actually dig into it, but also see it. And that speeds things up and gives everybody additional confidence. And then the other piece is essentially infusing AI or machine intelligence into the data pipeline, is really how you're attacking automation. And you're saying it repeatable, and then that helps the data quality and you have this virtual cycle. Maybe you could sort of affirm that and add some color, perhaps. >> Exactly. So you're able to let's say that I have seven cars, lines of business that are asking me questions, and one of the questions they'll ask me is, we want to know, if this customer is okay to contact, and there's different avenues so you can go online, do not contact me, you can go to the bank and you can say, I don't want email, but I'll take texts. And I want no phone calls. All that information. So, seven different lines of business asked me that question in different ways. One said, "No okay to contact" the other one says, "Customer 123." All these. In each project before I got there used to be siloed. So one customer would be 100 hours for them to do that analytical work, and then another analyst would do another 100 hours on the other project. Well, now I can do that all at once. And I can do those types of searches and say, Yes, we already have that documentation. Here it is, and this is where you can find where the customer has said, "No, I don't want to get access from you by email or I've subscribed to get emails from you." >> Got it. Okay. Yeah Okay. And then I want to go back to the cloud a little bit. So you mentioned S3 Buckets. So you're moving to the Amazon cloud, at least, I'm sure you're going to get a hybrid situation there. You mentioned snowflake. What was sort of the decision to move to the cloud? Obviously, snowflake is cloud only. There's not an on-prem, version there. So what precipitated that? >> Alright, so from I've been in the data IT information field for the last 35 years. I started in the US Air Force, and have moved on from since then. And my experience with Bob Graham, was with snowflake with working with GE Capital. And that's where I met up with the team from Io-Tahoe as well. And so it's a proven so there's a couple of things one is Informatica, is worldwide known to move data. They have two products, they have the on-prem and the off-prem. I've used the on-prem and off-prem, they're both great. And it's very stable, and I'm comfortable with it. Other people are very comfortable with it. So we picked that as our batch data movement. We're moving toward probably HVR. It's not a total decision yet. But we're moving to HVR for real time data, which is changed capture data, moves it into the cloud. And then, so you're envisioning this right now. In which is you're in the S3, and you have all the data that you could possibly want. And that's JSON, all that everything is sitting in the S3 to be able to move it through into snowflake. And snowflake has proven to have a stability. You only need to learn and train your team with one thing. AWS as is completely stable at this point too. So all these avenues if you think about it, is going through from, this is your data lake, which is I would consider your S3. And even though it's not a traditional data lake like, you can touch it like a Progressive or Hadoop. And then into snowflake and then from snowflake into sandbox and so your lines of business and your data scientists just dive right in. That makes a big win. And then using Io-Tahoe with the data automation, and also their search engine. I have the ability to give the data scientists and data analysts the way of they don't need to talk to IT to get accurate information or completely accurate information from the structure. And we'll be right back. >> Yeah, so talking about snowflake and getting up to speed quickly. I know from talking to customers you can get from zero to snowflake very fast and then it sounds like the Io-Tahoe is sort of the automation cloud for your data pipeline within the cloud. Is that the right way to think about it? >> I think so. Right now I have Io-Tahoe attached to my on-prem. And I want to attach it to my off-prem eventually. So I'm using Io-Tahoe data automation right now, to bring in the data, and to start analyzing the data flows to make sure that I'm not missing anything, and that I'm not bringing over redundant data. The data warehouse that I'm working of, it's an on-prem. It's an Oracle Database, and it's 15 years old. So it has extra data in it. It has things that we don't need anymore, and Io-Tahoe's helping me shake out that extra data that does not need to be moved into my S3. So it's saving me money, when I'm moving from off-prem to on-prem. >> And so that was a challenge prior, because you couldn't get the lines of business to agree what to delete, or what was the issue there? >> Oh, it was more than that. Each line of business had their own structure within the warehouse. And then they were copying data between each other, and duplicating the data and using that. So there could be possibly three tables that have the same data in it, but it's used for different lines of business. We have identified using Io-Tahoe identified over seven terabytes in the last two months on data that has just been repetitive. It's the same exact data just sitting in a different schema. And that's not easy to find, if you only understand one schema, that's reporting for that line of business. >> More bad news for the storage companies out there. (both laughs) So far. >> It's cheap. That's what we were telling people. >> And it's true, but you still would rather not waste it, you'd like to apply it to drive more revenue. And so, I guess, let's close on where you see this thing going. Again, I know you're sort of partway through the journey, maybe you could sort of describe, where you see the phase is going and really what you want to get out of this thing, down the road, mid-term, longer term, what's your vision or your data driven organization. >> I want for the bankers to be able to walk around with an iPad in their hand, and be able to access data for that customer, really fast and be able to give them the best deal that they can get. I want Webster to be right there on top with being able to add new customers, and to be able to serve our existing customers who had bank accounts since they were 12 years old there and now our multi whatever. I want them to be able to have the best experience with our bankers. >> That's awesome. That's really what I want as a banking customer. I want my bank to know who I am, anticipate my needs, and create a great experience for me. And then let me go on with my life. And so that follow. Great story. Love your experience, your background and your knowledge. I can't thank you enough for coming on theCube. >> Now, thank you very much. And you guys have a great day. >> All right, take care. And thank you for watching everybody. Keep right there. We'll take a short break and be right back. (gentle music)

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe, everybody, We're back. We're talking about enterprise data automation. The hashtag is data automated, and we're going to really dig into data migrations, data, migrations. They're risky. They're time consuming, and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So your role is is interesting. We're talking about data migrations. You're gonna head of partnerships. What is your role specifically? And how is it relevant to what we're gonna talk about today? >>Uh, I work with the various businesses such as cloud companies, systems integrators, companies that sell operating systems, middleware, all of whom are often quite well embedded within a company. I t infrastructures and have existing relationships. Because what we do fundamentally makes migrating to the cloud easier on data migration easier. A lot of businesses that are interested in partnering with us. Um, we're interested in parting with, So >>let's set up the problem a little bit. And then I want to get into some of the data. You know, I said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>Uh, I think I mean, all migrations have to start with knowing the facts about your data, and you can try and do this manually. But when that you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. Um, now they're understanding of what they have. Ai's often quite limited because you can try and draw a manual maps, but they're outdated very quickly. Every time that data changes the manual that's out of date on people obviously leave organizations over time, so that kind of tribal knowledge gets built up is limited as well. So you can try a Mackel that manually you might need a db. Hey, thanks. Based analyst or ah, business analyst, and they won't go in and explore the data for you. But doing that manually is very, very time consuming this contract teams of people, months and months. Or you can use automation just like what's the bank with Iot? And they managed to do this with a relatively small team. Are in a timeframe of days. >>Yeah, we talked to Paul from Webster Bank. Awesome discussion. So I want to dig into this migration and let's let's pull up graphic it will talk about. We'll talk about what a typical migration project looks like. So what you see here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this Ah, and then use. If I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones. That blue bar is the time to test so you can see the second step data analysis talking 24 weeks so, you know, very time consuming. And then Let's not get dig into the stuff in the middle of the fine print, but there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom and you can see high is that sort of brown and and you could see a number of data analysis, data staging data prep, the trial, the implementation post implementation fixtures, the transition toe B A B a year, which I think is business as usual. Those are all very labor intensive. So what do you take aways from this typical migration project? What do we need to know yourself? >>I mean, I think the key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the data. So the direction in which the data is going between different data stores and tables, so you start from a position where you have pretty high risk and alleviate that risk. You could be stacking project team of lots and lots of people to do the next base, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis manage. That, in a sense, is, as we all know, on the idea of trying to relate data that's in different those stores relating individual tables and columns. Very, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools, Aziz said earlier. The people who understand some of those systems may have left a while ago. See you even high risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with it, Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data map on the data flow has been generated automatically, much less time and effort and much less cars. Doctor Marley. >>Okay, so I want to bring back that that first chart, and I want to call your attention to the again that area graph the blue bars and then down below that labor intensity. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the So let's go Said Accelerated by Iot, Tom. Okay, great. And we're going to talk about this. But look, what happens to the operational risk. A dramatic reduction in that. That graph. And then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis data staging data prep. Try a lot post implementation fixtures in transition to be a you. All of those went from high labor intensity. So we've now attack that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its Price States catalog, if you like, um, imagine trying to do that manually. You need to go into every individual data store. You need a DB a business analyst, rich data store they need to do in extracted the data table was individually they need to cross reference that with other data school, it stores and schemers and tables. You probably were the mother of all lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems um, take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. Uh, no migrating away from them because they're not able to actually do the work of understanding the data g duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. Go back to the data catalog example. Um, whatever you discover invades, discovery has to persist in a tool like a data catalog. And so we automate data catalog books, including Out Way Cannot be others, but we have our own. The only alternative to this kind of automation is to build out this very large project team or business analysts off db A's project managers processed analysts together with data to understand that the process of gathering data is correct. To put it in the repository to validate it except etcetera, we've got into organizations and we've seen them ramp up teams off 2030 people costs off £234 million a year on a time frame, 15 20 years just to try and get a data catalog done. And that's something that we can typically do in a timeframe of months, if not weeks. And the difference is using automation. And if you do what? I've just described it. In this manual situation, you make migrations to the cloud prohibitively expensive. Whatever saving you might make from shutting down your legacy data stores, we'll get eaten up by the cost of doing it. Unless you go with the more automated approach. >>Okay, so the automated approach reduces risk because you're not gonna, you know you're going to stay on project plan. Ideally, it's all these out of scope expectations that come up with the manual processes that kill you in the rework andan that data data catalog. People are afraid that their their family jewels data is not going to make it through to the other side. So So that's something that you're you're addressing and then you're also not boiling the ocean. You're really taking the pieces that are critical and stuff you don't need. You don't have to pay for >>process. It's a very good point. I mean, one of the other things that we do and we have specific features to do is to automatically and noise data for a duplication at a rover or record level and redundancy on a column level. So, as you say before you go into a migration process. You can then understand. Actually, this stuff it was replicated. We don't need it quite often. If you put data in the cloud you're paying, obviously, the storage based offer compute time. The more data you have in there that's duplicated, that is pure cost. You should take out before you migrate again if you're trying to do that process of understanding what's duplicated manually off tens or hundreds of bases stores. It was 20 months, if not years. Use machine learning to do that in an automatic way on it's much, much quicker. I mean, there's nothing I say. Well, then, that costs and benefits of guitar. Every organization we work with has a lot of money existing, sunk cost in their I t. So have your piece systems like Oracle or Data Lakes, which they've spent a good time and money investing in. But what we do by enabling them to transition everything to the strategic future repositories, is accelerate the value of that investment and the time to value that investment. So we're trying to help people get value out of their existing investments on data estate, close down the things that they don't need to enable them to go to a kind of brighter, more future well, >>and I think as well, you know, once you're able to and this is a journey, we know that. But once you're able to go live on, you're infusing sort of a data mindset, a data oriented culture. I know it's somewhat buzzword, but when you when you see it in organizations, you know it's really and what happens is you dramatically reduce that and cycle time of going from data to actually insights. Data's plentiful, but insights aren't, and that is what's going to drive competitive advantage over the next decade and beyond. >>Yeah, definitely. And you could only really do that if you get your data estate cleaned up in the first place. Um, I worked with the managed teams of data scientists, data engineers, business analysts, people who are pushing out dashboards and trying to build machine learning applications. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data stays in the first place, get rid of duplication. If that pans migrate to cloud store, where things are really accessible on its easy to build connections and to use native machine learning tools, you're well on the way up to date the maturity curve on you can start to use some of those more advanced applications. >>You said. What are some of the pre requisites? Maybe the top few that are two or three that I need to understand as a customer to really be successful here? Is it skill sets? Is it is it mindset leadership by in what I absolutely need to have to make this successful? >>Well, I think leadership is obviously key just to set the vision of people with spiky. One of the great things about Ayatollah, though, is you can use your existing staff to do this work. If you've used on automation, platform is no need to hire expensive people. Alright, I was a no code solution. It works out of the box. You just connect to force on your existing stuff can use. It's very intuitive that has these issues. User interface? >>Um, it >>was only to invest vast amounts with large consultants who may well charging the earth. Um, and you already had a bit of an advantage. If you've got existing staff who are close to the data subject matter experts or use it because they can very easily learn how to use a tool on, then they can go in and they can write their own data quality rules on. They can really make a contribution from day one, when we are go into organizations on way. Can I? It's one of the great things about the whole experience. Veritas is. We can get tangible results back within the day. Um, usually within an hour or two great ones to say Okay, we started to map relationships. Here's the data map of the data that we've analyzed. Harrison thoughts on where the sensitive data is because it's automated because it's running algorithms stater on. That's what they were really to expect. >>Um, >>and and you know this because you're dealing with the ecosystem. We're entering a new era of data and many organizations to your point, they just don't have the resources to do what Google and Amazon and Facebook and Microsoft did over the past decade To become data dominant trillion dollar market cap companies. Incumbents need to rely on technology companies to bring that automation that machine intelligence to them so they can apply it. They don't want to be AI inventors. They want to apply it to their businesses. So and that's what really was so difficult in the early days of so called big data. You have this just too much complexity out there, and now companies like Iot Tahoe or bringing your tooling and platforms that are allowing companies to really become data driven your your final thoughts. Please use it. >>That's a great point, Dave. In a way, it brings us back to where it began. In terms of partnerships and alliances. I completely agree with a really exciting point where we can take applications like Iot. Uh, we can go into enterprises and help them really leverage the value of these type of machine learning algorithms. And and I I we work with all the major cloud providers AWS, Microsoft Azure or Google Cloud Platform, IBM and Red Hat on others, and we we really I think for us. The key thing is that we want to be the best in the world of enterprise data automation. We don't aspire to be a cloud provider or even a workflow provider. But what we want to do is really help customers with their data without automated data functionality in partnership with some of those other businesses so we can leverage the great work they've done in the cloud. The great work they've done on work flows on virtual assistants in other areas. And we help customers leverage those investments as well. But our heart, we really targeted it just being the best, uh, enterprised data automation business in the world. >>Massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Appreciate. All right. And thank you for watching everybody. We'll be right back right after this short break. >>Yeah, yeah, yeah, yeah.

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe Welcome everybody to Enterprise Data Automation. Ah co created digital program on the Cube with support from my hotel. So my name is Dave Volante. And today we're using the hashtag data automated. You know, organizations. They really struggle to get more value out of their data, time to data driven insights that drive cost savings or new revenue opportunities. They simply take too long. So today we're gonna talk about how organizations can streamline their data operations through automation, machine intelligence and really simplifying data migrations to the cloud. We'll be talking to technologists, visionaries, hands on practitioners and experts that are not just talking about streamlining their data pipelines. They're actually doing it. So keep it right there. We'll be back shortly with a J ahora who's the CEO of Iot Tahoe to kick off the program. You're watching the Cube, the leader in digital global coverage. We're right back right after this short break. Innovation impact influence. Welcome to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. High tech digital coverage from around the globe. It's the Cube with digital coverage of enterprise, data, automation and event. Siri's brought to you by Iot. Tahoe. Okay, we're back. Welcome back to Data Automated. A J ahora is CEO of I O ta ho, JJ. Good to see how things in London >>Thanks doing well. Things in, well, customers that I speak to on day in, day out that we partner with, um, they're busy adapting their businesses to serve their customers. It's very much a game of ensuring the week and serve our customers to help their customers. Um, you know, the adaptation that's happening here is, um, trying to be more agile. Got to be more flexible. Um, a lot of pressure on data, a lot of demand on data and to deliver more value to the business, too. So that customers, >>as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline, But talk about enterprise data automation. What is it to you. And how is it different from data off >>Dev Ops, you know, has been great for breaking down those silos between different roles functions and bring people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, lending itself to data with data is exciting. We look to do is build on top of that when data automation, it's the it's the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors, our r and d on bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is is the automation behind new dimension. We've come a long way in the last few years. Boy is, we started out with automating some of those simple, um, to codify, um, I have a high impact on organization across the data a cost effective way house. There's data related tasks that classify data on and a lot of our original pattern certain people value that were built up is is very much around that >>love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, >>sure. I mean right there in the middle that the heart of what we do it is, you know, the intellectual property now that we've built up over time that takes from Hacha genius data sources. Your Oracle Relational database. Short your mainframe. It's a lay and increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data. Classify that data after it's classified. Them have the ability to form relationships across those different source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts of contact and meaning around that data. So it's moving it now from bringing data driven on increasingly where we have really smile, right people in our customer organizations you want I do some of those advanced knowledge tasks data scientists and ah, yeah, quants in some of the banks that we work with, the the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality, the policies that you can apply to that data. I'm putting it in context once you've got the ability to power. Okay, a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the the tapestry that fabric across that different system could be crm air P system such as s AP and some of the newer brown databases that we work with. Snowflake is a great well, if I look back maybe five years ago, we had prevalence of daily technologies at the cutting edge. Those are converging to some of the cloud platforms that we work with Google and AWS and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenges scale quickly runs out of steam because once, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data state? It's changed, You know, you've onboard a new customer. You signed up a new partner. Um, customer has, you know, adopted a new product that you just Lawrence and there that that slew of data keeps coming. So it's keeping pace with that. The only answer really is is some form of automation >>you're working with AWS. You're working with Google, You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>That's fundamental. So, I mean, when I caimans where you tell here is the CEO of one of the, um, trends that I wanted us CIO to be part of was being open, having an open architecture allowed one thing that was close to my heart, which is as a CEO, um, a c i o where you go, a budget vision on and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with the CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using AP eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before. So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um and snowflake here is, um Is those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that. And they're leveraging the value that they've already committed to. >>Yeah, and maybe you could give us some examples of the r A y and the business impact. >>Yeah, I mean, the r a y David is is built upon on three things that I mentioned is a combination off. You're leveraging the existing investment with the existing estate, whether that's on Microsoft Azure or AWS or Google, IBM, and I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have got the automation that is working right down to the level off data, a column level or the file level we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome now without hoping out which run those processes within hours of getting started And, um, Bill that picture, visualize that picture and bring it to life. You know, the PR Oh, I that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on GCB or a migration to any other clouds such as AWS or a multi cloud landscape right off the map. >>A. J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have you. >>Thank you, David. Look who is smoking in >>now. We want to bring in the customer perspective. We have a great conversation with Paul Damico, senior vice president data architecture, Webster Bank. So keep it right there. >>Utah Data automated Improve efficiency, Drive down costs and make your enterprise data work for you. Yeah, we're on a mission to enable our customers to automate the management of data to realise maximum strategic and operational benefits. We envisage a world where data users consume accurate, up to date unified data distilled from many silos to deliver transformational outcomes, activate your data and avoid manual processing. Accelerate data projects by enabling non I t resources and data experts to consolidate categorize and master data. Automate your data operations Power digital transformations by automating a significant portion of data management through human guided machine learning. Yeah, get value from the start. Increase the velocity of business outcomes with complete accurate data curated automatically for data, visualization tours and analytic insights. Improve the security and quality of your data. Data automation improves security by reducing the number of individuals who have access to sensitive data, and it can improve quality. Many companies report double digit era reduction in data entry and other repetitive tasks. Trust the way data works for you. Data automation by our Tahoe learns as it works and can ornament business user behavior. It learns from exception handling and scales up or down is needed to prevent system or application overloads or crashes. It also allows for innate knowledge to be socialized rather than individualized. No longer will your companies struggle when the employee who knows how this report is done, retires or takes another job, the work continues on without the need for detailed information transfer. Continue supporting the digital shift. Perhaps most importantly, data automation allows companies to begin making moves towards a broader, more aspirational transformation, but on a small scale but is easy to implement and manage and delivers quick wins. Digital is the buzzword of the day, but many companies recognized that it is a complex strategy requires time and investment. Once you get started with data automation, the digital transformation initiated and leaders and employees alike become more eager to invest time and effort in a broader digital transformational agenda. Yeah, >>everybody, we're back. And this is Dave Volante, and we're covering the whole notion of automating data in the Enterprise. And I'm really excited to have Paul Damico here. She's a senior vice president of enterprise Data Architecture at Webster Bank. Good to see you. Thanks for coming on. >>Nice to see you too. Yes. >>So let's let's start with Let's start with Webster Bank. You guys are kind of a regional. I think New York, New England, uh, leave headquartered out of Connecticut, but tell us a little bit about the >>bank. Yeah, Webster Bank is regional, Boston. And that again in New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated bank regional bank for this area. They, um, hold, um, quite a few awards for the area for being supportive for the community. And, um, are really moving forward. Technology lives. Currently, today we have, ah, a small group that is just working toward moving into a more futuristic, more data driven data warehouse. That's our first item. And then the other item is to drive new revenue by anticipating what customers do when they go to the bank or when they log into there to be able to give them the best offer. The only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on off something to offer that >>at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change >>the ability to give the customer what they need at the time when they need it? And what I mean by that is that we have, um, customer interactions and multiple weights, right? And I want to be able for the customer, too. Walk into a bank, um, or online and see the same the same format and being able to have the same feel, the same look and also to be able to offer them the next best offer for them. >>Part of it is really the cycle time, the end end cycle, time that you're pressing. And then there's if I understand it, residual benefits that are pretty substantial from a revenue opportunity >>exactly. It's drive new customers, Teoh new opportunities. It's enhanced the risk, and it's to optimize the banking process and then obviously, to create new business. Um, and the only way we're going to be able to do that is that we have the ability to look at the data right when the customer walks in the door or right when they open up their app. >>Do you see the potential to increase the data sources and hence the quality of the data? Or is that sort of premature? >>Oh, no. Um, exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of runnin system that we've had for quite a few years. But now that we're moving to the cloud and off Prem and on France, you know, moving off Prem into, like, an s three bucket Where that data king, we can process that data and get that data faster by using real time tools to move that data into a place where, like, snowflake Good, um, utilize that data or we can give it out to our market. The data scientists are out in the lines of business right now, which is great, cause I think that's where data science belongs. We should give them on, and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own like tableau dashboards and then pushing the data back out. I have eight engineers, data architects, they database administrators, right, um, and then data traditional data forwarding people, Um, and because some customers that I have that our business customers lines of business, they want to just subscribe to a report. They don't want to go out and do any data science work. Um, and we still have to provide that. So we still want to provide them some kind of read regiment that they wake up in the morning and they open up their email. And there's the report that they just drive, um, which is great. And it works out really well. And one of the things. This is why we purchase I o waas. I would have the ability to give the lines of business the ability to do search within the data, and we read the data flows and data redundancy and things like that and help me cleanup the data and also, um, to give it to the data. Analysts who say All right, they just asked me. They want this certain report and it used to take Okay, well, we're gonna four weeks, we're going to go. We're gonna look at the data, and then we'll come back and tell you what we dio. But now with Iot Tahoe, they're able to look at the data and then, in one or two days of being able to go back and say, Yes, we have data. This is where it is. This is where we found that this is the data flows that we've found also, which is what I call it is the birth of a column. It's where the calm was created and where it went live as a teenager. And then it went to, you know, die very archive. >>In researching Iot Tahoe, it seems like one of the strengths of their platform is the ability to visualize data the data structure, and actually dig into it. But also see it, um, and that speeds things up and gives everybody additional confidence. And then the other pieces essentially infusing ai or machine intelligence into the data pipeline is really how you're attacking automation, right? >>Exactly. So you're able to let's say that I have I have seven cause lines of business that are asking me questions. And one of the questions I'll ask me is, um, we want to know if this customer is okay to contact, right? And you know, there's different avenues so you can go online to go. Do not contact me. You can go to the bank And you could say, I don't want, um, email, but I'll take tests and I want, you know, phone calls. Um, all that information. So seven different lines of business asked me that question in different ways once said Okay to contact the other one says, You know, just for one to pray all these, you know, um, and each project before I got there used to be siloed. So one customer would be 100 hours for them to do that and analytical work, and then another cut. Another of analysts would do another 100 hours on the other project. Well, now I can do that all at once, and I can do those type of searches and say yes we already have that documentation. Here it is. And this is where you can find where the customer has said, You know, you don't want I don't want to get access from you by email, or I've subscribed to get emails from you. I'm using Iot typos eight automation right now to bring in the data and to start analyzing the data close to make sure that I'm not missing anything and that I'm not bringing over redundant data. Um, the data warehouse that I'm working off is not, um a It's an on prem. It's an oracle database. Um, and it's 15 years old, so it has extra data in it. It has, um, things that we don't need anymore. And Iot. Tahoe's helping me shake out that, um, extra data that does not need to be moved into my S three. So it's saving me money when I'm moving from offering on Prem. >>What's your vision or your your data driven organization? >>Um, I want for the bankers to be able to walk around with on iPad in their hands and be able to access data for that customer really fast and be able to give them the best deal that they can get. I want Webster to be right there on top, with being able to add new customers and to be able to serve our existing customers who had bank accounts. Since you were 12 years old there and now our, you know, multi. Whatever. Um, I want them to be able to have the best experience with our our bankers. >>That's really what I want is a banking customer. I want my bank to know who I am, anticipate my needs and create a great experience for me. And then let me go on with my life. And so that's a great story. Love your experience, your background and your knowledge. Can't thank you enough for coming on the Cube. >>No, thank you very much. And you guys have a great day. >>Next, we'll talk with Lester Waters, who's the CTO of Iot Toe cluster takes us through the key considerations of moving to the cloud. >>Yeah, right. The entire platform Automated data Discovery data Discovery is the first step to knowing your data auto discover data across any application on any infrastructure and identify all unknown data relationships across the entire siloed data landscape. smart data catalog. Know how everything is connected? Understand everything in context, regained ownership and trust in your data and maintain a single source of truth across cloud platforms, SAS applications, reference data and legacy systems and power business users to quickly discover and understand the data that matters to them with a smart data catalog continuously updated ensuring business teams always have access to the most trusted data available. Automated data mapping and linking automate the identification of unknown relationships within and across data silos throughout the organization. Build your business glossary automatically using in house common business terms, vocabulary and definitions. Discovered relationships appears connections or dependencies between data entities such as customer account, address invoice and these data entities have many discovery properties. At a granular level, data signals dashboards. Get up to date feeds on the health of your data for faster improved data management. See trends, view for history. Compare versions and get accurate and timely visual insights from across the organization. Automated data flows automatically captured every data flow to locate all the dependencies across systems. Visualize how they work together collectively and know who within your organization has access to data. Understand the source and destination for all your business data with comprehensive data lineage constructed automatically during with data discovery phase and continuously load results into the smart Data catalog. Active, geeky automated data quality assessments Powered by active geek You ensure data is fit for consumption that meets the needs of enterprise data users. Keep information about the current data quality state readily available faster Improved decision making Data policy. Governor Automate data governance End to end over the entire data lifecycle with automation, instant transparency and control Automate data policy assessments with glossaries, metadata and policies for sensitive data discovery that automatically tag link and annotate with metadata to provide enterprise wide search for all lines of business self service knowledge graph Digitize and search your enterprise knowledge. Turn multiple siloed data sources into machine Understandable knowledge from a single data canvas searching Explore data content across systems including GRP CRM billing systems, social media to fuel data pipelines >>Yeah, yeah, focusing on enterprise data automation. We're gonna talk about the journey to the cloud Remember, the hashtag is data automate and we're here with Leicester Waters. Who's the CTO of Iot Tahoe? Give us a little background CTO, You've got a deep, deep expertise in a lot of different areas. But what do we need to know? >>Well, David, I started my career basically at Microsoft, uh, where I started the information Security Cryptography group. They're the very 1st 1 that the company had, and that led to a career in information, security. And and, of course, as easy as you go along with information security data is the key element to be protected. Eso I always had my hands and data not naturally progressed into a roll out Iot talk was their CTO. >>What's the prescription for that automation journey and simplifying that migration to the cloud? >>Well, I think the first thing is understanding what you've got. So discover and cataloging your data and your applications. You know, I don't know what I have. I can't move it. I can't. I can't improve it. I can't build upon it. And I have to understand there's dependence. And so building that data catalog is the very first step What I got. Okay, >>so So we've done the audit. We know we've got what's what's next? Where do we go >>next? So the next thing is remediating that data you know, where do I have duplicate data? I may have often times in an organization. Uh, data will get duplicated. So somebody will take a snapshot of the data, you know, and then end up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer, and you can see where that will go. And trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to sort of understand where all your redundant data is? So when you go to the cloud, maybe you have an opportunity here to do you consolidate that that data, >>then what? You figure out what to get rid of our actually get rid of it. What's what's next? >>Yes, yes, that would be the next step. So figure out what you need. What, you don't need you Often times I've found that there's obsolete columns of data in your databases that you just don't need. Or maybe it's been superseded by another. You've got tables have been superseded by other tables in your database, so you got to kind of understand what's being used and what's not. And then from that, you can decide. I'm gonna leave this stuff behind or I'm gonna I'm gonna archive this stuff because I might need it for data retention where I'm just gonna delete it. You don't need it. All were >>plowing through your steps here. What's next on the >>journey? The next one is is in a nutshell. Preserve your data format. Don't. Don't, Don't. Don't boil the ocean here at music Cliche. You know, you you want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables in which they sent the columns and the way they're named. So some degree, you are gonna be doing a lift and ship, but it's an intelligent lift and ship. The >>data lives in silos. So how do you kind of deal with that? Problem? Is that is that part of the journey? >>That's that's great pointed because you're right that the data silos happen because, you know, this business unit is start chartered with this task. Another business unit has this task and that's how you get those in stance creations of the same data occurring in multiple places. So you really want to is part of your cloud migration. You really want a plan where there's an opportunity to consolidate your data because that means it will be less to manage. Would be less data to secure, and it will be. It will have a smaller footprint, which means reduce costs. >>But maybe you could address data quality. Where does that fit in on the >>journey? That's that's a very important point, you know. First of all, you don't want to bring your legacy issues with U. S. As the point I made earlier. If you've got data quality issues, this is a good time to find those and and identify and remediate them. But that could be a laborious task, and you could probably accomplish. It will take a lot of work. So the opportunity used tools you and automate that process is really will help you find those outliers that >>what's next? I think we're through. I think I've counted six. What's the What's the lucky seven >>Lucky seven involved your business users. Really, When you think about it, you're your data is in silos, part of part of this migration to cloud as an opportunity to break down the silos. These silence that naturally occurs are the business. You, uh, you've got to break these cultural barriers that sometimes exists between business and say so. For example, I always advise there's an opportunity year to consolidate your sensitive data. Your P I. I personally identifiable information and and three different business units have the same source of truth From that, there's an opportunity to consolidate that into one. >>Well, great advice, Lester. Thanks so much. I mean, it's clear that the Cap Ex investments on data centers they're generally not a good investment for most companies. Lester really appreciate Lester Water CTO of Iot Tahoe. Let's watch this short video and we'll come right back. >>Use cases. Data migration. Accelerate digitization of business by providing automated data migration work flows that save time in achieving project milestones. Eradicate operational risk and minimize labor intensive manual processes that demand costly overhead data quality. You know the data swamp and re establish trust in the data to enable data signs and Data analytics data governance. Ensure that business and technology understand critical data elements and have control over the enterprise data landscape Data Analytics ENABLEMENT Data Discovery to enable data scientists and Data Analytics teams to identify the right data set through self service for business demands or analytical reporting that advanced too complex regulatory compliance. Government mandated data privacy requirements. GDP Our CCP, A, e, p, R HIPPA and Data Lake Management. Identify late contents cleanup manage ongoing activity. Data mapping and knowledge graph Creates BKG models on business enterprise data with automated mapping to a specific ontology enabling semantic search across all sources in the data estate data ops scale as a foundation to automate data management presences. >>Are you interested in test driving the i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program? Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iot. Top Click on the link and connect with the data engineer to learn more and see Iot Tahoe in action. Everybody, we're back. We're talking about enterprise data automation. The hashtag is data automated and we're going to really dig into data migrations, data migrations. They're risky, they're time consuming and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So let's set up the problem a little bit. And then I want to get into some of the data said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>I think I mean, all migrations have to start with knowing the facts about your data. Uh, and you can try and do this manually. But when you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. >>So I want to dig into this migration and let's let's pull up graphic. It will talk about We'll talk about what a typical migration project looks like. So what you see, here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this, uh and then use if I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones That Blue Bar is the time to test so you can see the second step. Data analysis. It's 24 weeks so very time consuming, and then let's not get dig into the stuff in the middle of the fine print. But there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom, and you can see hi is that sort of brown and and you could see a number of data analysis data staging data prep, the trial, the implementation post implementation fixtures, the transition to be a Blu, which I think is business as usual. >>The key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers, and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the basis of the direction in which the data is going between different data stores and tables. So you start from a position where you have pretty high risk and probably the area that risk you could be. Stack your project team of lots and lots of people to do the next phase, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis, um, manual processes, as we all know, on the layer of trying to relate data that's in different grocery stores relating individual tables and columns, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools. Aziz said earlier the people who understand some of those systems may have left a while ago. CEO even higher risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data met on the data flow has been generated automatically, much less time and effort and much less cars stopped. >>Yeah. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the sort of Cisco said accelerated by Iot, Tom. Okay, great. And we're gonna talk about this, but look, what happens to the operational risk. A dramatic reduction in that, That that graph and then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis, data staging data prep trialling post implementation fixtures in transition to be a you all those went from high labor intensity. So we've now attacked that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its price States catalog. If you like, imagine trying to do that manually, you need to go into every individual data store. You need a DB, a business analyst, reach data store. They need to do an extract of the data. But it on the table was individually they need to cross reference that with other data school, it stores and schemers and tables you probably with the mother of all Lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. I'm not migrating away from them because they're not able to actually do the work of understanding the data, duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data in the first place, get rid of duplication that sounds migrate to cloud store where things are really accessible. It's easy to build connections and to use native machine learning tools. You well, on the way up to the maturity card, you can start to use some of the more advanced applications >>massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Much appreciated. Yeah, yeah, yeah, yeah

>>The Cube Presents UI Path Forward five. Brought to you by UI Path. >>Hi everybody. Welcome back to UI Path Forward at five. You're watching the Cubes coverage. Everybody here is automating everything. Mundane tasks, Enterprisewide Automation Platform Beats product. Dave Nicholson. Dave Ante, Garish Pie is here. He is the Global head of Intelligent Enterprise Automation at Cognizant Global. Si, good to see you. Thanks for coming to the queue. Thank you for having me. Tell us about your role. What are you focused on? So, >>So I lead the enterprise automation practice at Cognizant, and we are focused on three broad segments, right? So we help customers anchor to business outcomes in, in looking at the business outcomes, what we look to do is help them drive transformation at a process level, looking at it from a technology standpoint, and then helping them look at how they're trying to drive change across their entire enterprise and bringing that together, you know, and helping them harmonize both at a technology and at a process level in terms of, you know, the outcomes they're trying to achieve. So >>You guys are a partner, I see your booth over there, and you're also a customer, right? Yes, we are. So are you involved in the both sides? One side, what, what, what's your purpose? >>So we do, so we, we sort of work, So we have a full 360 degree relationship with the i p. So we work with them, you know, in a professional services capacity. They, they support us as a partnership in the marketplace where we go into a number of our customers jointly to drive turnkey transformational engagements from an automation standpoint and second from a, as a, as a, as a customer to UiPath, they've been supporting us, you know, drive a number of automation initiatives across our operations book over the course of the last two years. >>Okay. So tell us more about that. So you started your internal journey, we had you guys on last year. Yep. It were just getting started I think, I think I think you, your head count is what, 60,000 somewhere around? Yeah. 70,000. Yeah, $70,000 growing. I think at the time it was maybe less than 10% of the workforce was kind of automated and the goal was to automate everybody. How are you guys doing along >>The, I think it's starting to in industrialize quite significantly. So over the course of the last year since we last spoke to you, probably, you know, we've doubled the head count in terms of the number of people that are now, you know, officially what we call quote unquote citizen developers. And you know, how they are driving automation at a personal level, we've probably gone about 2.5 x in terms of the number of RS we've saved. So we've done about, I think 450,000 rs, you know, in terms of actual saves at a personal automation level. And look, it's, it's been a great, you know, last 12 months too, right? Because, you know, as we've sort of started to get the message percolated more and more, our teams have started to get energized. They are happy that they are, you know, getting a release in terms of what they're doing on a day to day basis, which is largely repetitive at times, very mundane. And now they have the ability to bring in technology to be able to embrace that and drive that, that you know, much more efficiently. >>Are you talking dozens of bots? Thousands of bots? What's the scope of? >>So I think we've, we've scaled to about 3,500 today in terms of the bots and, and it's, it's a journey that continues to evolve. For me, the number is probably something which I wouldn't anchor to because it's, look, it's end of the day what you end up releasing and what you end up freeing and what the teams are doing. And I think, you know, that's the way we are >>Leading. So you're saying like, we always talk about number of boss, but you're saying it's largely in a relevant metric? Well, and not if it's five versus a thousand. Okay. That's meaningful, right? But, but >>Yeah, I think look a number for me, I think it's not about the number, right? It's about the outcome and it's about what impact you're having in, in terms of, you know, what you're trying to get done at the end of the day, right? Because ultimately you're trying to better, you know, what you do on a day to day basis and you know, whether it's done through 10 or whether that's done through 10,000. >>Yeah. But you pay >>Form, >>Right? Exactly. So, so you better get some value out. Exactly. It's about the value. >>But is there, is there a, is there a curve in terms, you know, an s-curve in terms of scalability though? I mean we, we, we've heard organizations doing, from organizations doing an amazing amount of modernization and automation and they say they've got 15 bots running, you have 3,500. Is there a number where it becomes harder to manage or, or is there scalability involved? >>So look, for me, so let me answer it this way, right? I think, I think there are two aspects to it. I think the, the, the, the more you have, you know, the bigger the challenge in terms of how you run the controls, the governance and the residency in terms of, you know, how you manage the, you know, the, the setup of the bots itself. So I think, yeah, I mean we want to have it to a manageable number, but for us, in the way we've looked at the number of bots, one of the things that we've done is we also look at, you know, what's foundational versus what's nuanced in terms of the kind of use cases that you're trying to deliver. So, so any program of this nature, you need to have a setup, which is, you know, which allows you to sort of orchestrate it in the right manner so that as you sort of scale and you bring more people into that equation, you, it's, you're not just creating bots for the sake of it, but you're actually, you know, trying to look at what you can reuse, what you can orchestrate better. >>And then in the context of that, figuring out where you have the gaps and then hence, you know, sort of taking the delta approach of what else and what more you need to build it. >>So you guys have a big observation space. You work with a lot of customers and, and so what are you seeing as the trends when you look out there? How are you applying it to your own business and your customer's businesses? >>So look, for me, I think the last two years, if anything, the one thing I've taken away is that transformation is now extremely, extremely compressed, right? So, so it's almost, you know, what's true today is probably irrelevant tomorrow. So, which means you have to continually evolve in terms of what needs to be done, right? Second is experiences have become extremely, extremely crucial and critical and experiences of, in, in my mind, you know, two or three kinds, right? One the end customer second from an employee standpoint, and third, in terms of the partner ecosystem that you will have as an enterprise that you have to cater to, right? The other element that you know, which becomes true will always remain true is the whole outcome story in terms of, you know, how we have an anchor to why you're trying to do what you're trying to do. >>And that is, you know, core to what you need to get done. So in the way we've looked at it, as we've said, you know, as you sort of look at how transformation is now evolving and how compressed it's starting to become, the more you are able to orchestrate for what the enterprise is trying to get done in terms of modernization, in terms of digitization, in terms of end goals and end outcomes that they're trying to achieve. And the more you're able to sweat what sits within, you know, the enterprise bring that together as you think about automation is, you know, where the true value lies in terms of being able to create an agile enterprise. >>When you think about digital transformation, digital experiences, if it's, if it's a layer cake, where is automation in that, in that layer? Is it, is it sort of the bottom of the stack? Is it, is it the whole stack? >>So for me it's, I mean it's, it's evolved. If you take today's view, I think what's emerging is a very pervasive view of how you think about automation. It sits across, you know, the entire enterprise. It, it, it, it takes a people process, technology dimension, which is age old. It has to cover, you know, all forms of transformation. You know, whether you're looking at end, how do I say, impact in terms of how you're dealing with customers, whether you're looking at the infrastructure, whether you're looking at the data layer in between, it has to be embedded across the base, right? It, it, you have to take a pervasive approach. And for me, I think automation increasingly in the days ahead is gonna be an enterprise capability. You know, it has to be, you know, all pervasive in the way it needs to be set up. >>The key, the operative word there is pervasive. And that seems to be, you know, the era that we're entering, I don't know what you call it, call it the metaverse, I mean, you know, it's more than cloud and cloud is basically just the infrastructure, right? You're building on top of that, whether it's natural language processing or cryptography or virtual, I mean, there's so many different, you know, technology dimensions, right? But it, but the point about pervasive, okay, it's everywhere. It's sensing, it's anticipatory, it feels like there's this new, you know, construct, emerging of platform that is the basis for digital business, right? And I, and I feel like every 15 years our industry goes through some big transformation. How, how do you see it? You know, do you agree that you, it feels like, okay, something new is happening. It's, it's not gonna be the social media, you know, Facebook's not gonna continue to dominate the world as it does. You already seen some cracks in that armor. We saw Microsoft after the pc, and then of course it came back with cloud Amazon looks, you know, indestructible. But that, that's never the end story, right? In our, in our world, how do you see that? >>No, I think all of what you said, I, I would sort of tend to agree with, for me, look, I don't have a crystal ball to say, you know, what's gonna happen with Facebook or Amazon or >>Otherwise. Yeah. But that's what makes this fun. But >>I, Yeah, but, but I think for me, the, the core is I think you're dictated by, you know, us as end consumers, if you're a B2B or a b2b, b2c, you know, depending upon what business you're in, I think the end customer value dictates, you know, what evolves in terms of, you know, the, the manifestation of, you know, how you will two minutes sort of deliver services, the products that you'll get into. And in that context then, you know, whether you take a, a TikTok view to it or whether you take an Amazon view to it, or whether YouTube becomes relevant in the days ahead, I think it's gonna be dictated by, >>By customer, but it tends to be a technology that's the disruptor, it's the microprocessor or it's the social capability or, or maybe it's ai that, that is the catalyst for that. And then the customer adoption dictates, oh, you're right about that. But there, but the, the match is usually technology. Is that fair or not necessarily? Yeah, >>I still look, I mean you talked about metaverse earlier, right? I think we are, I think we are, it's probably hype more than it is reality right now, at least in my view. And it's, I think we are significantly out in terms of, you know, large scale adoption in terms of what needs to be done. You talk about blockchain, blockchains been around, you know, for at least a decade if not more in, in, right. The way it's being talked about, the adoption, you know, in terms of the, the, the applicability of the, you know, of what is that technology I think is understood, but the actual use cases in terms of how it can be taken into the market and how you can scale it across industries, I think is, you know, is still because >>The economics determine ultimately exactly the outcome. So, Okay, that makes sense. >>Yeah. Now you said you don't have a crystal ball. I, I have one, but when I look into it, it's sort of murky when I try to figure out the answer to the question, Is a platform necessary for this, for automation? I mean, this is really the direction, the question, the existential question in terms of the trajectory of UI path. It seems obvious that automation is critical. It's not as obvious where that automation is going to end up eventually because it's so critical. It feels like it's almost the same as, okay, there's an interface between my keystrokes and filling in a box with text. Well, of course there has to be, there has to be that interface, right? So why wouldn't everyone deliver that by default? So as you gaze into my crystal ball with me, tell me about the things that only a platform can do from your perspective. >>So, >>So, so, so think of it this way, right? I mean, any enterprise probably has hundreds of technologies that they've invested in some platform, some applications that you would've built and evolved over time, which are bespoke custom in nature. So for me, I think when you think about automation, I think it's the balance between the two. What a platform allows you to do is to be able to orchestrate, given the complexity and the, the spa that is any enterprise, you know, that's probably got the burden of, you know, what they've done over the course of the, the previous years. And then in that context then, you know, how do you sort of help get the, the best value out of that in terms of what you want to deliver as the end, end outcomes, if I can call it that, right? So for me, I don't think you can say it's, it's her platform versus the rest. >>I think it's gonna be, it's always gonna be a balance and to the question that you asked earlier. And in terms of saying where does an automation end up at? I think if it's gonna be a pervasive view, look, you know, if, if clients are trying to modernize and get onto the cloud, you can do automation at a cloud level too. Now, you know, do I say then, you know, is it, is it sort of inclusive or it's native to what the cloud providers offer? Or do I then go and say automation needs to be something which I will, you know, sort of overlay on top of what the cloud providers offer. So I think it depends upon what dimension that you come at it. So I don't think you can say it's one or the other. You have a platform, I think it helps you orchestrate quite significantly. But there are gonna be aspects within any enterprise, given the complexity that exists that you will have to balance out, you know, platform versus, you know, how you have to address it maybe in a more individual capacity. >>Garris, gotta go. Thank you so much. Appreciate your perspectives. Good conversation. All right, keep it right there. But trains will back it up. We'll be right back right after this short break. The cube live at UI path forward, five from Las Vegas.