>>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)

(bright upbeat music) >> Welcome back to theCube's coverage of HPE's Green Lake announcement. We've been following the caves of Green Lake's announcement for several quarters now, and even years. And we're going to look at cloud adoption and frameworks to help facilitate cloud adoptions. You know, in 2020, the world was on a forced march to digital and there was a lot that they didn't know. Big part of that was how to automate, how to reduce your reliance on physically, manually and plugging things in. And so, customers need an adoption framework to better understand and how to de-risk that journey to the cloud. And with me to talk about that are Alexia Clements, who's the Vice President at Worldwide go to market for GreenLake cloud services at HPE and Alexei Gerasimov who's the vice president of Hybrid Cloud Delivery advisory and professional services at Hewlett Packard Enterprise. Folks, welcome to theCube. >> Alexia: Thanks so much for having us. >> You're very welcome. So, Alexei, what is a cloud adoption framework? How does that all work? >> Gerasimov: Yeah, thanks Dave. So the framework is a structured approach to elevate the conversation, to help our customers get outcomes. So we've been helping customers adopt the benefits in the most of IT for a decade. And we've noticed that they basically focus on eight key areas as they transform to cloud-like capabilities. It's a strategy and governance, it's innovation, people, a dev ops applications, operations security, and data. So we've structured our framework around those core components to help our customers get value. Because end of the day, it's all about changing the way they operate. To get the advantage of all of it. >> Yes. So you can't just pave the cow path and kind of plug your existing process. There's a lot that's unknown, as I said up front. So, so Alexia, maybe you could talk a little bit more about some of the real problems that you're solving with customers that you see in the field. >> Alexey: Yeah, absolutely. So most customers are going through some form of digital transformation and these transformations are difficult and they need a structured approach to help them through that journey. I kind of like to think of it as a recipe to make a meal. So you need to know what ingredients to buy and what are the steps to perform to make that meal. >> Okay. So when you talk to customers, what do you, what do you tell them? That's in it for them after the, after you've actually successfully helped them deploy? What are they telling you? >> Yeah, well, they're telling they now have reached their business outcomes and they're, you know, they're a more agile organization. >> What's the experience look like when you, when you go through one of these journeys and you, you apply the adoption framework, can you sort of paint a picture for us? >> Yeah, absolutely. So every customer is in some sort of transformation, like Alexia said, that transformation implies you've got to know where you start and again, know where you're going. So the experience traditionally is customers need to understand what are my current hybrid cloud capabilities? What do I have, what am I missing? What's lacking and then determine where do you want to go? And in order to get from point A to point B, they have to get a prescriptive approach. So the framework sort of breaks down their path from where they are to their desired maturity. And it takes them in the very prescriptive path to get there. >> So you start with an assessment, you do a gap analysis based on their skill sets. I presume you identify what's possible, help them understand, you know, best practice, which they may not achieve, but this is kind of their north star. Right? And then do you help? How do you help them fill those gaps? Because are skills gaps. Everybody talks about that today. You guys presumably can provide additional services to do that, but so can you add a little bit color to that scope? >> Yeah, yeah, absolutely. And so to your point, the first is a maturity level. So once you figure out the maturity level, you understand what needs to be done. So if you look at our domain, the eight domains that I mentioned and the framework, people is a big one, right? Most of the folks are struggling with people's skills and organizational capabilities. And it's so because it's an operating model change, right? And people are the key component to this operating model change. So we help our customers figure out how do we achieve that optimal operating level and operating a model maturity. And that could be on-prem that could be on public cloud. That could be hybrid. That could be at the edge. And yeah, we, if we can HP, the framework, by the way is pretty, pretty open and pretty objective. If we can help our customers address and achieve their sales gaps great. If we can not directly, then we can have a partner that can help them, you know, plug in something that we don't have. >> Are you finding that, that in terms of the maturity that most people have some kind of experience with, with cloud, but they're struggling to bring that cloud experience to their on-premise state. They don't want to just shove everything into the cloud. Right. So, what does that kind of typical journey look like for folks? I know there's--it's a wide spectrum, or you've got people that are maybe more mature. Maybe some of the folks in financial services got more resources, but can you sort of give us a sense as to what the typical, the average. >> Oh yeah yeah yeah, absolutely. By the way. So that give you a customer example, perfect example of a large North American integrated energy company. They decided to go cloud fresh, like a lot of companies. that wants to do cloud first. And why? The reason was agility. So they started going to the cloud and they realized in order to get agility, you can't just go to you, pick your public CSP, you got to change the way to operate. So they brought us in and they asked, could you help me figure out how we can change the organization? So we actually operate on the proper level of maturity. So we brought our team in. We help them figure out what do we need to look at? We need to look at operations. We need to look at people. We need to look at applications, and we need to figure out what gives you the best value. So when all said and done, they realized that their initial desire of, you know, public first or cloud first, wasn't really public cloud first. It's a way to operate. So now the customer is in three different public CSPs. They're on-prem, there are at edge and everywhere. So that's the focus. Yeah. >> Is the scope predominantly the technical organization. How deep does it go into the, to the business? Is it obviously the application development team is involved, but how deep into the business does this go? The framework. >> Right, and it's absolutely not a technology focused, the whole concept areas, it's outcomes based, and it's a results based. So if you look at the framework, there's really not a single element of the framework that says tech, like storage or compute. No, it's its people, its data, it's business value, strategy and governance, because the goal for us is being objective is we're just trying to help them address the outcomes. Not necessarily to give them more tech. >> So Alexia, I like that answer because it's a wider scope as, I mean, if we just focused on the tech and that's the swim lane, it'd be a lot easier. But as we all know, it's the people in the process that are really the hard part. So that, that makes the challenge for customers greater. You're hurting more cats. So what are the, some of the obstacles that potentially you help customers before they dive in understand. >> Yeah. So we're giving them a roadmap on where they need to go. So we're like I mentioned that recipe, so we're really trying to identify what is their strategy and where do they, what are the outcomes that they're trying to drive and help them on a street, you know, with that path to meet those outcomes. So some of those, I mean, every customer's a little bit different. I mean, we had one customer, which was a, one of the largest hospitals in north America and they, they would needed to, they wanted to go to the cloud, but they realized they couldn't put all of their patient data on the cloud. So what we did was we helped them in changing their operating model and really look to see how does that, how do they need to what's that end game for them, and actually help redo their operating model to have some in the cloud and some on-prem and, and really identify, you know, where they needed to go for their roadmap. So that was an obstacle that they had, hey, we can't put all this stuff out there. How does that now need to work in this new world? >> I would think the data model is a big deal here. I mean, you just gave an example where there's a, there's a, there's a governance and compliance aspect to it. So thinking about that example, did they have to change the way in which they provided federated governance was that presumably identify whose whose responsibility that was to adjudicate, but also had to get the, the implementers to follow that's the, how does that all work? Is it just the deep conversations? And then you figure out how to codify it or. >> No. So what so we have, so through those eight domains that Alexia mentioned, we go through, step-by-step how they need to think about it. And within mind, what are their business outcomes and goals that they're trying to achieve? So really identifying how they need to change that operating model to meet those business outcomes. >> So what's the output, it's a plan, right. That's tailored to the customer. Is that, is that correct? And, and then sort of assistance in implementing downstream or what do they get? >> Yeah, yeah, absolutely. Just to piggyback to what Alexia said, the alignment, the early alignment, the strategy and governance, as you mentioned, this is probably the most important thing, because everybody says we want to be cloud first, but what does that mean? Cloud first means different things to everyone. So we said, give him a plan. The first we'll help with figure out is what does that mean for you? Because at the end of the day, you're not going to the cloud for the sake of cloud, or anywhere you go into the cloud to get some sort of value. So what's that alignment. So the plan is supposed to help you on your road to that value, right? So we'll help them figure out what I want to do, why, for what purpose, what's going to actually address my business value. So yes, they will get a plan as part of it. But more importantly, they get, they get a set of activities, communication plans, which by the way, another block that you got to address. >> Dave: Huge. >> Yeah. >> Yeah. I mean, a lot of executives tell me, look, if you don't change your operating model and go to the cloud, yeah. You're talking, you know, nickels and dimes. If you want to get telephone numbers, you know, big companies, you want to get into bees with billions, you have to change the operating model. And the problem that they tell me is a lot of times the corner offices, okay, we're doing this, but everybody in the fat middle says, what are we doing? >> Right. And now more than ever, I mean, customers need to look at that model like a more modern operating model to realize the benefits of cloud capabilities, whether that be at the edge, their data centers, their colos cloud. So they really need to look at that. And what we've seen is with our framework, we're really helping customers accelerate their business outcomes. De-risk their transformation, and really optimize that cloud operating model. >> It's that alignment you reducing friction within the organization, confusing confusion. When people don't know which direction they're going, they'll just going to go wherever they're pointed. Right. Right. >> And you back to the alignment. So you've got alignment and you mentioned communication. You have to communicate up and down and left and right across the organization because that's one of the most probably ignored elements of any transformation lots of people don't know. So you got to communicate. And then you have to actually measure and report on how they, you know, how the transformation is happening. So we can help in all three of those. >> Especially when everybody's remote. Yeah. Right. And then I said, hey, these digital transformations, there's so much, that's unknown. >> Alexia: Right. It's difficult. >> It's a lot of new. And so you also have to, I presume part of the plan is, Hey, you're not, it's not going to be a hundred percent perfect. So you have to have. >> Alexia: Right. And you're constantly iterating on that plan. >> What does this have to do with GreenLake? >> Alexia: Yeah. So, I mean, GreenLake is HPE's you know, cloud everywhere. And what we're really doing is this framework is helping customers with that path to get that cloud-like experience and as a service model. And so the framework is really helping clients understand where do they need to go and what GreenLake solutions can help them get there. >> So the fundamental assumption of not every cloud player necessarily bad, I would say most hyperscalers is, hey, ultimately, all the data and the workloads are going to go to the cloud, that's their operating premise. So they all have an operating framework to facilitate that. >> Alexia: Right. >> It's, it's tongue in cheek, but it's true. So, but everybody has one of these. How was yours different? >> Yeah. So like, like you said, there's lots of different, you know, frameworks out there, but what we're really focused on is meeting those business goals and outcomes for clients. So we didn't focus on the technology. Like we mentioned what we were really focusing around. I mean, we kind of learned early on that every customer has technical capabilities, applications, data in multiple clouds, on-prem in colos and at the edge. So we didn't focus on like just the technology. So it's really driving business outcomes and their goals and, and the tech, all those frameworks that we just mentioned, they're really specifically driving a particular technology tool or vendor implementing a particular technology or vendor. >> So we've talked about outcomes a lot, but I wonder if we could peel the onion on that. So, you know, the highest level outcome is I want to increase revenue, cut costs, drop to the bottom line, increase shareholder value, improve employee experiences and retention, make customers happier, grow my business. I mean, those are, I mean, I, I don't know a lot of businesses that don't... >> Alexia: Right. >> want to do that, So. Okay. That's cool. But then I'm imagining you really start to peel the layers and say, okay, this is how we're going to get there. And you get down to specific objectives as to the, how is that sort of how this works? >> Right, and that's due to echo at Alexia. So that's exactly why ours is different. We're not focusing on how to adopt Microsoft or AWS or Alibaba with focusing on how we can deliver the customer experience or a better revenue, you know, or, you know, increase the value for the consumer for whatever the company will help him. So the framework we'll look at that and figure out how do we actually address it, whether it's on public cloud, whether it's on prem, whether it's at the edge. >> You mentioned Alexia, that something, hey, if we don't have the skills, we can get a partner who does, a big company. You got a huge partner network. So for example, if you might not have necessarily a deep industry expertise, that's where you might lean on a partner or is that, is that a good example or is there a better one? >> Yes and we know. We're not going to just like you mentioned AWS or Microsoft, Alibaba thing that everything will go to public cloud. I don't believe so, but at the same time we know not everything will stay on-prem. So the combination of on-prem, the edge, you know, private cloud and public cloud is what the customers are after. So our partners could be either third party, system integrator that can help us implement something or even the public CSPs, because we know our customers have capabilities everywhere. So the question becomes, how can we holistically address their needs, whether it's on-prem, whether it's in public cloud. >> Great. Guys, thanks so much. >> Alexia: Thank you. Thanks for having us. Appreciate it. >> My pleasure and thank you for watching everybody's as theCube's continuous coverage of HPE's GreenLake announcement, keep it right there for more great content. (bright upbeat music)

(upbeat music) >> Thank you and good morning or afternoon, everyone, depending on where you're coming to us from and welcome to today's breakout session, Fast Data, a retail industry business imperative. My name is Brent Biddulph, Global Managing Director of Retail and Super Bids here at Cloudera and today's hosts. Joining me today is our feature speaker Brian Kilcourse, Managing Partner from RSR. We'll be sharing insights and implications from recently completed research across retailers of all sizes in empirical segments. At the end of today's session I'll share a brief overview on what I personally learned from retailers and how Cloudera continues to support retail data analytic requirements, and specifically around streaming data ingest, analytics, automation for customers around the world. There really is the next step up in terms of what's happening with data analytics today. So let's get started. So I thought it'd be helpful to provide some background first on how Cloudera is supporting retail industry leaders specifically how they're leveraging Cloudera for leading practice data analytics use cases, primarily across four key business pillars and these will be very familiar to those in the industry. Personalize interactions of course plays heavily into e-commerce and marketing, whether that's developing customer profiles, understanding the omni-channel journey, moving into the merchandising line of business, focused on localizing sorbet, promotional planning, forecasting, demand forecast accuracy, then into supply chain where inventory visibility is becoming more and more critical today, whether it's around fulfillment or just understanding where your stuff is from a customer perspective. And obviously in and outbound route optimization, right now as retailers are taking control of actual delivery, whether it's to a physical store location or to the consumer. And then finally, which is pretty exciting to me as a former store operator, what's happening with physical brick and mortar right now, especially for traditional retailers. The whole re-imagining of stores right now is on fire in a lot of focus because frankly this is where fulfillment is happening, this is where customers steal 80% of revenue is driven through retail through physical brick and mortar. So right now store operations is getting more focused and I would say it probably is had in decades and a lot of it has to do of course with IoT data and analytics in the new technologies that really help drive benefits for retailers from a brick and mortars standpoint. And then finally, to wrap up before handing off to Brian, as you'll see, all of these lines of businesses are rogue, really experiencing the need for speed, fast data. So we're moving beyond just discovery analytics, things that happened five, six years ago with big data, et cetera and we're really moving into real time capabilities because that's really where the difference makers are, that's where the competitive differentiation is across all of these lines of business and these four key pillars within retail. The dependency on fast data is evident, it's something that we all read in terms of those that are students of the industry if you will, that we're all focused on in terms of bringing value to the individual lines of business but more importantly to the overall enterprise. So without further ado, I really want to have Brian speak here as a third party analyst. He's close in touch with what's going on retail talking to all the solution providers, all the key retailers about what's important, what's on their plate, what are they focusing on right now in terms of fast data and how that could potentially make a difference for them going forward. So Brian off to you. >> Well, thanks, Brent. I appreciate the introduction. And I was thinking as you were talking, what is fast data? Well, fast data is fast data, it's stuff that comes at you very quickly. When I think about the decision cycles in retail, they were time phased and there was a time when we could only make a decision perhaps once a month and then met once a week and then once a day, and then intraday. Fast data is data that's coming at you in something approaching real time and we'll explain why that's important in just a second. But first I want to share with you just a little bit about RSR. We've been in business now for 14 years and what we do is we studied the business use cases that drive the adoption of technology in retail. We come from the retail industry. I was a retail technologist my entire working life and so we started this company. So I have a built-in bias of course, and that is that the difference between the winners in the retail world and in fact in the entire business world and everybody else is how they value the strategic importance of information, and really that's where the battle is being fought today. We'll talk a little bit about that. So anyway, one other thing about RSR Research, our research is free to the entire world. We don't have a paywall that you have to get behind, all you have to do is sign into our website, identify yourself and all of our research, including these two reports that we're showing on the screen now are available to you and we'd love to hear your comments. So when we talk about data, there's a lot of business implications to what we're trying to do with fast data and is being driven by the real world. We saw a lot of evidence of that during the COVID pandemic in 2020, when people had to make many decisions very, very quickly, for example, a simple one, do I redirect my replenishments to store B because store A is impacted by the pandemic, those kinds of things. These two drawings are actually from a book that came out in 1997 and it was a really important book for me personally is by a guy named Steven Hegel and the name of the book was "The Adaptive Enterprise." When you think about your business model and you think about the retail business model, most of those businesses are what you see on the left. First of all, the mission of the business doesn't change much at all, it changes once in a generation or maybe once in a lifetime, but it's established quite early. And then from that point on, it's basically a wash, rinse and repeat cycle. You do the things that you do over and over and over again, year in and year out, season in and season out and the most important pieces of information that you have is the transaction data from the last cycle. So Brent knows this from his experience as a retailer, the baseline for next year's forecast is last year's performance. And this is transactional in nature, it's typically pulled from your ERP or from your best of breed solution set. On the right is where the world is really going, and before we get into the details of this, I'll just use a real example. I'm sure like me, you've watched the path of hurricanes as they go up to the Florida Coast. And one of the things you might've noticed is that there are several different possible paths. These are models and you'll hear a lot about models when you talk to people in the AI world. These are models based on lots and lots of information that they're getting from Noah and from the oceanographic people and all those kinds of folks to understand the likely path of the hurricane. Based on their analysis, the people who watch these things will choose the most likely paths and they will warn communities to lock down and do whatever they need to do. And then they see as the real hurricane progresses, they will see if it's following that path or if it's varying, it's going down a different path and based on that they will adapt to a new model. And that is what I'm talking about here. Not everything is of course is life and death as a hurricane but it's basically the same concept. What's happening is you have your internal data that you've had since this command and control model that we've mentioned on the left and you're taking an external data from the world around you and you're using that to make snap decisions or quick decisions based on what you see, what's observable on the outside. Back to my COVID example, when people were tracking the path of the pandemic through communities, they learned that customers or consumers would favor certain stores to pick up what they needed to get. So they would avoid some stores and they would favor other stores and that would cause smart retailers to redirect the replenishments on very fast cycles to those stores where the consumers are most likely to be. They also did the same thing for employees, they wanted to know where they could get their employees to service these customers, how far away were they, were they in a community that was impacted or were they relatively safe. These are the decisions that were being made in real time based on the information that they were getting from the marketplace around them. So first of all, there's a context for these decisions, there's a purpose and the bounds of the adaptive structure, and then there's a coordination of capabilities in real time and that creates an internal feedback loop, but there's also an external feedback loop. This is more of an ecosystem view and based on those two inputs what's happening internally, where your performance is internally and how your community around you is reacting to what you're providing. You make adjustments as necessary and this is the essence of the adaptive enterprise. Engineers might call this a sense and respond model, and that's where retail is going. But what's essential to that is information and information, not just about the products that you sell or the stores that you sell it in or the employees that you have on the sales floor or the number of market baskets you've completed in the day, but something much, much more. If you will, a twin, a digital twin of the physical assets of your business, all of your physical assets, the people, the products, the customers, the buildings, the rolling stock, everything, everything. And if you can create a digital equivalent of a physical thing, you can then analyze it. And if you can analyze it, you can make decisions much, much more quickly. So this is what's happening with the predict pivot based on what you see and then because it's an intrinsically more complicated model to automate decision-making where it makes sense to do so. That's pretty complicated and I talk about new data and as I said earlier, the old data is all transactional in nature, mostly about sales. Retail has been a wash in sales data for as long as I can remember, they throw most of it away but they do keep enough to create the forecast for the next business cycle. But there's all kinds of new information that they need to be thinking about and a lot of this is from the outside world and a lot of this is non-transactional in nature. So let's just take a look at some of them. Competitive information. Retailers are always interested in what the competitor is up to, what are they promoting? How well are they doing? Where are they? What kind of traffic are they generating? Sudden and significant changes in customer behaviors and sentiment, COVID is a perfect example of something that would cause this, consumers changing their behaviors very quickly. And we have the ability to observe this because in a great majority of cases nowadays, retailers have observed that customers start their shopping journey in the digital space. As a matter of fact, Google recently came out and said that 63% of all sales transactions begin in the digital domain, even if many of them end up in the store. So we have the ability to observe changes in consumer behavior, what are they looking at? When are they looking at it? How long do they spend looking at it? What else are they looking at while they're doing that? What is the outcome of them looking? Market metrics certainly, what's going on in the marketplace around you? A good example of this might be something related to a sporting event. If you've planned based on normal demand and for your store and there's a big sporting event, like a football match or a baseball game, suddenly you're going to see a spike in demand, so understanding what's going on in the market is really important. Location, demographics and psychographics. Demographics have always been important to retailers, but now we're talking about dynamic demographics. What customers or what consumers are in your market in something approaching real time. Psychographics has more to do with their attitudes, what kind of folks are in a particular marketplace, what do they think about, what do they favor, and all those kinds of interesting details. Real time environmental and social incidents, of course, I mentioned hurricanes and so that's fairly self-evident. Disruptive events, sporting events, et cetera, these are all real. And then we get the real time Internet-of-Things, these are RFID sensors, beacons, video, et cetera. There's all kinds of stuff. And this is where it really gets interesting, this is where the supply chain people will start talking about the digital twin to their physical world. If you can't say something you can't manage it and retailers want to be able to manage things in real time. So IoT along with AI analytics and the data that's generated is really, really important for them going forward. Community health, we've been talking a lot about that, the progression of the flu, et cetera, et cetera. Business schedules, commute patterns, school schedules, and weather, these are all external data that are interesting to retailers and can help them to make better operational decisions in something approaching real time. I mentioned the automation of decision-making, this is a chart from Gardner and I'd love to share with you. It's a really good one because it describes very simply what we're talking about and it also describes where the inflection of new technology happens. If you look on the left there's data, we have lots and lots of data, we're getting more data all the time. Retailers for a long time now since certainly since the seventies or eighties have been using data to describe what happened, this is the retrospective analysis that we're all very familiar with, data cubes and those kinds of things. And based on that, the human makes some decisions about what they're going to do going forward. Sometime in the not-too-distant past this data was started to be used to make diagnostic decisions, not only what happened but why did it happen? And we might think of this as, for example, if sales were depressed and for a certain product, was it because we had another product on sale that day, that's a good example of fairly straightforward diagnostics. We then move forward to what we might think of as predictive analytics and this was based on what happened in the past and why it happened in the past. This is what's likely to happen in the future. You might think of this as, for example, halo effect or the cannibalization effect of your category plans if you happen to be a grocer. And based on that, the human will make a decision as to what they need to do next. Then came along AI, and I don't want to oversell AI here. AI is a new way for us to examine lots and lots of data, particularly unstructured data. AI if I could simplify it to the next maximum extent, it essentially is a data tool that allows you to see patterns in data which might be interesting. It's very good at sifting through huge data sets of unstructured data and detecting statistically significant patterns. It gets deeper than that of course, because it uses math instead of rules. So instead of an if then or else statement that we might've used with our structured data, we use the math to detect these patterns in unstructured data and based on those we can make some models. For example, my guy in my (chuckles) just turned 70. My 70 year old man, I'm a white guy, I live in California, I have a certain income and a certain educational level. I'm likely to behave in this way based on a model, that's pretty simplistic but based on that, you can see that when another person who meets my psychographics, my demographics, my age group, my income level and all the rest, they might be expected to make a certain action. And so this is where prescriptive really comes into play. AI makes that possible. And then finally, when you start to think about moving closer to the customer or something approaching a personalized level, a one-to-one level, you suddenly find yourself in the situation of having to make not thousands of decisions but tens of millions of decisions and that's when the automation of decision-making really gets to be pretty important. So this is all interesting stuff, and I don't want to oversell it. It's exciting and it's new, it's just the latest turn of the technology screw and it allows us to use this new data to basically automate decision-making in the business in something approaching real time so that we can be much, much more responsive to real-time conditions in the marketplace. Very exciting. So I hope this is interesting. This is a piece of data from one of our recent pieces of research. This happens to be from a location analytics study we just published last week, and we asked retailers, what are the big challenges? What's been going on in the last 12 months for them, and what's likely to be happening for them in the next few years and it's just fascinating because it speaks to the need for faster decision-making. The challenges in the last 12 months are all related to COVID. First of all, fulfilling growing online demand, this is a very real time issue that we all had to deal with. But the next one was keeping forecasts in sync with changing demand and this is one of those areas where retailers are now finding themselves needing to look at that exogenous or that external data that I mentioned to you. Last year sales were not a good predictor of next year sales, they needed to look at sentiment, they needed to look at the path of the disease, they needed to look at the availability of products, alternate sourcing, global political issues, all of these things get to be pretty important and they affect the forecast. And then finally, managing the movement of the supply through the supply chain so that they could identify bottlenecks. Now, point to one of them which we can all laugh at now because it's kind of funny, it wasn't funny at the time. We ran out of toilet paper (laughs) toilet paper was a big problem. Now there is nothing quite as predictable as toilet paper, it's tied directly to the size of the population and yet we ran out. And the thing we didn't expect when the COVID pandemic hit was that people would panic and when people panic they do funny things. One of the things I do is buy up all the available toilet paper, I'm not quite sure why that happen but it did happen and it drained the supply chain. So retailers needed to be able to see that, they needed to be able to find alternative sources, they needed to be able to do those kinds of things. This gets to the issue of visibility, real-time data, fast data. Tomorrow's challenge is kind of interesting because one of the things that retailers put at the top of their list is improve inventory productivity. The reason that they are interested in this is because they will never spend as much money on anything as they will on inventory and they want the inventory to be targeted to those places where it is most likely to be consumed and not to places where it's least likely to be consumed. So this is trying to solve the issue of getting the right product at the right place at the right time to the right consumer and retailers want to improve this because the dollars are just so big. But in this complex, fast moving world that we live in today is this requires something approaching real-time visibility. They want to be able to monitor the supply chain, the DCs and the warehouses and their picking capacity. We're talking about Echo's, we're talking about Echo's level of decision-making about what's flowing through the supply chain all the way from the manufacturing door to the manufacturer through to consumption. There's two sides of the supply chain and retailers want to look at it. You'll hear retailers and people like me talk about the digital twin, this is where this really becomes important. And again, the digital twin is enabled by IoT and AI analytics. And finally, they need to increase their profitability for online fulfillment. This is a huge issue, for some grocers the volume of online orders went from less than 10% to somewhere north of 40%. And retailers did in 2020 what they needed to do to fulfill those customer orders in the year of the pandemic, that now the expectation that consumers have have been raised significantly. They now expect those features to be available to them all the time and many people really like them. Now retailers need to find out how to do it profitably and one of the first things they need to do is they need to be able to observe the process so that they can find places to optimize. This is out of our recent research and I encourage you to read it. Now when we think about the hard one wisdom that retailers have come up with we think about these things, better visibility has led to better understanding which increases their reaction time which increases their profitability. So what are the opportunities? This is the first place that you'll see something that's very common and in our research, we separate over-performers, who we call retail winners from everybody else, average and under-performers. And we've noticed throughout the life of our company that retail winners don't just do all the same things that others do, they tend to do other things and this shows up in this particular graph. This again is from the same study. So what are the opportunities to address these challenges I mentioned to you in the last slide? First of all, strategic placement of inventory throughout the supply chain to better fulfill customer needs. This is all about being able to observe the supply chain, get the inventory into a position where it can be moved quickly to fast changing demand on the consumer side. A better understanding and reacting to unplanned events that can drive a dramatic change in customer behavior. Again, this is about studying the data, analyzing the data and reacting to the data that comes before the sales transaction. So this is observing the path to purchase, observing things that are happening in the marketplace around the retailer so that they can respond very quickly, a better understanding of the dramatic changes in customer preference and path to purchase as they engage with us. One of the things we all know about consumers now is that they are in control and literally the entire planet is the assortment that's available to them. If they don't like the way they're interacting with you, they will drop you like a hot potato and go to somebody else. And what retailers fear justifiably is the default response to that is to just see if they can find it on Amazon. You don't want this to happen if you're a retailer. So we want to observe how we are interacting with consumers and how well we are meeting their needs. Optimizing omni-channel order fulfillment to improve profitability. We've already mentioned this, retailers did what they needed to do to offer new fulfillment options to consumers. Things like buy online pickup curbside, buy online pickup in-store, buy online pick up at a locker, a direct to consumer, all of those things. Retailers offer those in 2020 because the consumers demand it and needed it. So when retailers are trying to do now is to understand how to do that profitably. And finally, this is important and never goes away is the reduction of waste, shrink within the supply chain. I'm embarrassed to say that when I was a retail executive in the nineties, we were no more certain of consumer demand than anybody else was but we wanted to commit to very high service levels for some of our key categories somewhere approaching 95% and we found the best way to do that was to flood the supply chain with inventory. It sounds irresponsible now, but in those days that was a sure-fire way to make sure that the customer had what she was looking for when she looked for it. You can't do that in today's world, money is too tight and we can't have that inventory sitting around and move to the right places once we discover what the right places. We have to be able to predict, observe, and respond in something much closer to real time. Onto the next slide, the simple message here, again a difference between winners and everybody else. The messages, if you can't see it you can't manage it. And so we asked retailers to identify to what extent an AI enabled supply chain can help their company address some issues. Look at the differences here, they're shocking. Identifying network bottlenecks, this is the toilet paper story I told you about. Over half of retail winners feel that that's very important, only 19% of average and under-performers, no surprise that they're average and under-performers. Visibility into available to sell inventory anywhere within the enterprise, 58% of winners and only 32% of everybody else. And you can go on down the list but you get the just, retail winners understand that they need to be able to see their assets and something approaching real time so that they can make the best decisions possible going forward in something approaching real time. This is the world that we live in today and in order to do that you need to be able to number one, see it and number two, you need to be able to analyze it, and number three, you have to be able to make decisions based on what you saw. Just some closing observations and I hope this was interesting for you. I love talking about this stuff, you can probably tell I'm very passionate about it. But the rapid pace of change in the world today is really underscoring the importance, for example, of location intelligence as a key component of helping businesses to achieve sustainable growth, greater operational effectiveness and resilience, and ultimately your success. So this is really, really critical for retailers to understand and successfully evolving businesses need to accommodate these new consumer shopping behaviors and changes and how products are brought to the market. And in order to do that they need to be able to see people, they need to be able to see their assets, and they need to be able to see their processes in something approaching real time, and then they need to analyze it and based on what they've uncovered, they need to be able to make strategic and operational decision making very quickly. This is the new world we live in, it's a real-time world, it's a sense and respond world and it's the way forward. So Brent, I hope that was interesting for you. I really enjoyed talking about this as I said, we'd love to hear a little bit more. >> Hey, Brian, that was excellent. I always love hearing from RSR because you're so close to what retailers are talking about and the research that your company pulls together. One of the higher level research articles around fast data frankly, is the whole notion of IoT, right? Now many does a lot of work in this space. What I find fascinating based off the recent research is believe it or not, there's $1.2 trillion at stake in retail per year between now and 2025. Now, how's that possible? Well, part of it is because of the Kinsey captures not only traditional retail but also QSRs and entertainment venues, et cetera, that's considered all of retail. But it's a staggering number and it really plays to the effect that real time can have on individual enterprises, in this case we're talking of course about retail. So a staggering number and if you think about it, from streaming video to sensors, to beacons, RFID, robotics, autonomous vehicles retailers are asking today, even pizza delivery and autonomous vehicles. If you think about it, it shouldn't be that shocking, but when they were looking at 12 different industries, retail became like the number three out of 12 and there's a lot of other big industries that will be leveraging IoT in the next four years. So retailers in the past have been traditionally a little stodgy about their spend in data and analytics. I think retailers in general have got the religion that this is what it's going to take to compete in today's world, especially in a global economy and IoT really is the next frontier, which is kind of the definition of fast data. So I just wanted to share just a few examples or exemplars of retailers that are leveraging the Cloudera technology today. So now they pay for advertisement at the end of this, right? So what is Cloudera bringing to market here? So across all retail verticals, if we look at, for example, a well-known global mass virtual retailer, they're leveraging Cloudera data flow which is our solution to move data from point to point in wicked fast space. So it's open source technology that was originally developed by the NSA. So it is best to class movement of data from an ingest standpoint, but we're also able to help the round trip. So we'll pull up sensor data off all the refrigeration units for this particular retailer, they'll hit it up against the product lifecycle table, they'll understand temperature fluctuations of 10, 20 degrees based on fresh food products that are in the store, what adjustments might need to be made because frankly store operators, they'll never know refrigeration, they'll know if a cooler goes down and they'll have to react quickly, but they won't know that 10, 20 degree temperature changes have happened overnight. So this particular customer leverages further data flow to understand temperature fluctuations, the impact on the product life cycle and the roundtrip communication back to the individual department manager, let's say a produce department manager, deli manager, meat manager. Hey, you had a 20 degree drop in temperature, we suggest you lower the price on these products that we know are in that cooler for the next couple of days by 20%. So you don't have to worry about freshness issues and or potential shrink. The grocery with fresh product, if you don't sell it, you smell it, you throw it away, it's cost to the bottom line. So critically important and tremendous ROI opportunity that we're helping to enable there. From a leading global drugstore retailer, so this is more about data processing and we're excited of the recent partnership with the Nvidia. So fast data isn't always at the edge with IoT, it's also about workloads. And in retail, if you are processing your customer profiles or segmentation like intra day, you will never achieve personalization, you will never achieve one-on-one communications with retailers or with customers, and why is that? Because customers in many cases are touching your brand several times a week. So if taking you a week or longer to process your segmentation schemes, you've already lost and you'll never achieve personalization, in fact, you may offend customers by offers you might push out based on what they just bought yesterday you had no idea of it. So that's what we're really excited about, again with the computation speed that Nvidia brings to Cloudera. We're already doing this today, we've already been providing levels of exponential speed and processing data, but when Nvidia brings to the party is course GPUs right, which is another exponential improvement to processing workloads like demand forecast, customer profiles. These things need to happen behind the scenes in the back office much faster than retailers have been doing in the past. That's just the world we all live in today. And then finally, from a proximity marketing standpoint or just from an in-store operations standpoint, retailers are leveraging Cloudera today, not only data flow but also of course our compute and storage platform and ML, et cetera, to understand what's happening in store. It's almost like the metrics that we used to look at in the past in terms of conversion and traffic, all those metrics are now moving into the physical world. If you can leverage computer vision in streaming video, to understand how customers are traversing your store, how much time they're standing in front of the display, how much time they're standing in checkout line, you can now start to understand how to better merchandise the store, where the hotspots are, how to in real time improve your customer service. And from a proximity marketing standpoint, understand how to engage with the customer for right at the moment of truth, right, when they're right there in front of the particular department or category, upward leveraging mobile device. So that's the world of fast data in retail and just kind of a summary in just a few examples of how folks are leveraging Cloudera today. From an overall platform standpoint of course, Cloudera is an enterprise data platform, right? So we're helping to enable the entire data life cycle, so we're not a data warehouse, we're much more than that. So we have solutions to ingest data from the Edge, from IoT, leading practice solutions to bring it in. We also have experiences to help leverage the analytic capabilities of data engineering, data science, analytics and reporting. We're not encroaching upon the legacy solutions that many retailers have today, we're providing a platform that's open source that helps weave all this mess together that existed retail today from legacy systems because no retailer frankly is going to rip and replace a lot of stuff that they have today. Right. And the other thing the Cloudera brings to market is this whole notion of on-prem hybrid cloud and multicloud, right. So our whole culture has been built around open source technology as the company that provides most of the source code to the Apache network around all these open source technologies. We're kind of religious about open source and lack of vendor lock-in, maybe to our fault, but as a company we pull that together from a data platform standpoint so it's not a rip or replace situation. It's like helping to connect legacy systems, data and analytics, weaving that whole story together to be able to solve this whole data life cycle from beginning to end. And then finally, I want to thank everyone for joining today's session, I hope you found it informative. I can't thank Brian Kilcourse enough, like he's my trusted friend in terms of what's going on in the industry. He has much broader reach of course in talking to a lot of our partners in other technology companies out there as well. But I really appreciate everyone joining the session, and Brian, I'm going to kind of leave it open to you to any closing comments that you might have based on what we're talking about today in terms of fast data and retail. >> First of all, thank you, Brent. And this is an exciting time to be in this industry. And I'll just leave it with this. The reason that we are talking about these things is because we can, the technology has advanced remarkably in the last five years. Some of this data has been out there for a lot longer than that and it frankly wasn't even usable. But what we're really talking about is increasing the cycle time for decisions, making them go faster and faster so that we can respond to consumer expectations and delight them in ways that make us a trusted provider of their lifestyle needs. So this is really a good time to be a retailer, a real great time to be servicing the retail technology community and I'm glad to be a part of it and I'm glad to be working with you. So thank you, Brent. >> Yeah, of course, Brian. And one of the exciting things for me too, I've being in the industry as long as I have and being a former retailer is it's really exciting for me to see retailers actually spending money on data and IT for a change, right? (Brian laughs) They've all kind of come to this final pinnacle of this is what it's going to take to compete. You and I talked to a lot of colleagues, even salespeople within Cloudera, like, oh, retail, very stodgy, slow to move. That's not the case anymore. >> No. >> Everyone gets the religion of data and analytics and the value of that. And what's exciting for me to see as all this infusion of immense talent within the industry that we couldn't see years ago, Brian. I mean, retailers are like pulling people from some of the greatest tech companies out there, right? From a data science, data engineering standpoint, application developers. Retail is really getting its legs right now in terms of go to market and the leverage of data and analytics, which to me is very exciting. >> Well, you're right. I mean, I became a CIO around the time that point of sale and data warehouses were starting to happen, data cubes and all those kinds of things. And I never thought I would see a change that dramatic as the industry experience back in those days, 1989, 1990, this changed doors that, but the good news is again, as the technology is capable, we're talking about making technology and information available to retail decision-makers that consumers carry around in their purses and pockets as they're right now today. So the question is, are you going to utilize it to win or are you going to get beaten? That's really what it boils down to. >> Yeah, for sure. Hey, thanks everyone. We'll wrap up, I know we ran a little bit long, but appreciate everyone hanging in here with us. We hope you enjoyed the session. Our contact information is right there on the screen, feel free to reach out to either Brian and I. You can go to cloudera.com, we even have joint sponsored papers with RSR, you can download there as well as other eBooks, other assets that are available if you're interested. So thanks again, everyone for joining and really appreciate you taking the time today.

(bright upbeat music) >> Announcer: We are entering an age of insight where data moves freely between environments to work together powerfully, from wherever it lives. A new era driven by next generation cloud services. It's freedom that accelerates innovation and digital transformation, but it's only for those who dare to propel their business toward a new future that pushes beyond the usual barriers. To a place that unites all information under a fluid yet consistent operating model, across all your applications and data. To a place called HPE GreenLake. HPE GreenLake pushes beyond the obstacles and limitations found in today's infrastructure because application entanglements, data gravity, security, compliance, and cost issues simply aren't solved by current cloud options. Instead, HPE GreenLake is the cloud that comes to you, bringing with it, increased agility, broad visibility, and open governance across your entire enterprise. This is digital transformation unlocked, incompatibility solved, data decentralized, and insights amplified. For those thinkers, makers and doers who want to create on the fly scale up or down with a single click, stand up new ideas without risk, and view it all as a single agile system of systems. HPE GreenLake is here and all are invited. >> The definition of cloud is evolving and now clearly comprises hybrid and on-prem cloud. These trends are top of mind for every CIO and the space is heating up as every major vendor has been talking about as-a-Service models and making moves to better accommodate customer needs. HPE was the first to market with its GreenLake brand, and continues to make new announcements designed to bring the cloud experience to far more customers. Come here from HPE and its partners about the momentum that they're seeing with this trend and what actions you can take to stay ahead of the competition in this fast moving market. (bright soft music) Okay, we're with Keith White, Senior Vice President and General Manager for GreenLake at HPE, and George Hope, who's the Worldwide Head of Partner Sales at Hewlett Packard Enterprise. Welcome gentlemen, good to see you. >> Awesome to be here. >> Yeah. Thanks so much. >> You're welcome, Keith, last we spoke, we talked about how you guys were enabling high performance computing workloads to get green-late right for enterprise markets. And you got some news today, which we're going to get to but you guys, you put out a pretty bold position with GreenLake, basically staking a claim if you will, the edge, cloud as-a-Service all in. How are you thinking about its impacts for your customers so far? >> You know, the impact's been amazing and, you know, in essence, I think the pandemic has really brought forward this real need to accelerate our customer's digital transformation, their modernization efforts, and you know, frankly help them solve what was amounting to a bunch of new business problems. And so, you know, this manifests itself in a set of workloads, set of solutions, and across all industries, across all customer types. And as you mentioned, you know GreenLake is really bringing that value to them. It brings the cloud to the customer in their data center, in their colo, or at the edge. And so frankly, being able to do that with that full cloud experience. All is a pay per use, you know, fully consumption-based scenario, all managed for them so they get that as I mentioned, true cloud experience. It's really sort of landing really well with customers and we continue to see accelerated growth. We're adding new customers, we're adding new technology. And we're adding a whole new set of partner ecosystem folks as well that we'll talk about. >> Well, you know, it's interesting you mentioned that just cause as a quick aside it's, the definition of cloud is evolving and it's because customers, it's the way customers look at it. It's not just vendor marketing. It's what customers want, that experience across cloud, edge, you know, multiclouds, on-prem. So George, what's your take? Anything you'd add to Keith's response? >> I would, you've heard Antonio Neri say it several times and you probably saying it for yourself. The cloud is an experience, it's not a destination. The digital transformation is pushing new business models and that demands more flexible IT. And the first round of digital transformation focused on a cloud first strategy. For our customers we're looking to get more agility. As Keith mentioned, the next phase of transformation will be characterized by bringing the cloud speed and agility to all apps and data, regardless of where they live, According to IDC, by the end of 2021, 80% of the businesses will have some mechanism in place to shift the cloud centric, infrastructure and apps and twice as fast as before the pandemic. So the pandemic has actually accelerated the impact of the digital divide, specifically, in the small and medium companies which are adapting to technology change even faster and emerging stronger as a result. You know, the analysts agree cloud computing and digitalization will be key differentiators for small and medium business in years to come. And speed and automation will be pivotal as well. And by 2022, at least 30% of the lagging SMBs will accelerate digitalization. But the fair focus will be on internal processes and operations. The digital leaders, however, will differentiate by delivering their customers, a dynamic experience. And with our partner ecosystem, we're helping our customers embrace our as-a-Service vision and stand out wherever they are. on their transformation journey. >> Well, thanks for those stats, I always liked the data. I mean, look, if you're not a digital business today I feel like you're out of business only 'cause.... I'm sure there's some exceptions, but you got to get on the digital bandwagon. I think pre-pandemic, a lot of times people really didn't know what it meant. We know now what it means. Okay, Keith, let's get into the news when we do these things. I love that you guys always have something new to share. What do you have? >> No, you got it. And you know, as we said, the world is hybrid and the world is multicloud. And so, customers are expecting these solutions. And so, we're continuing to really drive up the innovation and we're adding additional cloud services to GreenLake. We just recently went to General AVailability of our MLOps, Machine Learning Operations, and our containers for cloud services along with our virtual desktop which has become very big in a pandemic world where a lot more people are working from home. And then we have shipped our SAP HEC, customer edition, which allows SAP customers to run on their premise whether it's the data center or the colo. And then today we're introducing our new Bare Metal capabilities as well as containers on Bare Metal as a Service, for those folks that are running cloud native applications that don't require any sort of hypervisor. So we're really excited about that. And then second, I'd say similar to that HPC as a Service experience we talked about before, where we were bringing HPC down to a broader set of customers. We're expanding the entry point for our private cloud, which is virtual machines, containers, storage, compute type capabilities in workload optimized systems. So again, this is one of the key benefits that HPE brings is it combines all of the best of our hardware, software, third-party software, and our services, and financial services into a package. And we've workload optimized this for small, medium, large and extra-large. So we have a real sort of broader base for our customers to take advantage of and to really get that cloud experience through HPE GreenLake. And, you know, from a partner standpoint we also want to make sure that we continue to make this super easy. So we're adding self-service capabilities we're integrating into our distributors marketplaces through a core set of APIs to make sure that it plugs in for a very smooth customer experience. And this expands our reach to over 100,000 additional value-added resellers. And, you know, we saw just fantastic growth in the channel in Q1, over 118% year over year growth for GreenLake Cloud Services through the channel. And we're continuing to expand, extend and expand our partner ecosystem with additional key partnerships like our colos. The colocation centers are really key. So Equinix, CyrusOne and others that we're working with and I'll let George talk more about. >> Yeah, I wonder if you could pick up on that George. I mean, look, if I'm a partner and and I mean, I see an opportunity here.. Maybe, you know, I made a lot of money in the old days moving iron. But I got to move, I got to pivot my business. You know, COVID's actually, you know, accelerating a lot of those changes, but there's a lot of complexity out there and partners can be critical in helping customers make that journey. What do you see this meaning to partners, George? >> So I completely agree with Keith and through and with our partners we give our customers choice. Right, they don't have to worry about security or cost as they would with public cloud or the hyperscalers. We're driving special initiatives via Cloud28 which we run, which is the world's largest cloud aggregator. And also, in collaboration with our distributors in their marketplaces as Keith mentioned. In addition, customers can leverage our expertise and support of our service provider ecosystem, our SI's, our ISV's, to find the right mix of hybrid IT and decide where each application or workload should be hosted. 'Cause customers are now demanding robust ecosystems, cloud adjacency, and efficient low latency networks. And the modern workload demands, secure, compliant, highly available, and cost optimized environments. And Keith touched on colocation. We're partnering with colocation facilities to provide our customers with the ability to expand bandwidth, reduce latency, and get access to a robust ecosystem of adjacent providers. We touched on Equinix a bit as one of them, but we're partnering with them to enable customers to connect to multiple clouds with private on-demand interconnections from hundreds of data center locations around the globe. We continue to invest in the partner and customer experience, you know, making ourselves easier to do business with. We've now fully integrated partners in GreenLake Central, and could provide their customers end to end support and managing the entire hybrid IT estate. And lastly, we're providing partners with dedicated and exclusive enablement opportunities so customers can rely on both HPE and partner experts. And we have a competent team of specialists that can help them transform and differentiate themselves. >> Yeah, so, I'm hearing a theme of simplicity. You know, I talked earlier about this being customer-driven. To me what the customer wants is they want to come in, they want simple, like you mentioned, self-serve. I don't care if it's on-prem, in the cloud, across clouds, at the edge, abstract, all that complexity away from me. Make it simple to do, not only the technology to work, you figure out where the workload should run and let the metadata decide and that's a bold vision. And then, make it easy to do business. Let me buy as-a-Service if that's the way I want to consume. And partners are all about, you know, reducing friction and driving that. So, anyway guys, final thoughts, maybe Keith, you can close it out here and maybe George can call it timeout. >> Yeah, you summed it up really nice. You know, we're excited to continue to provide what we view as the largest and most flexible hybrid cloud for our customers' apps, data, workloads, and solutions. And really being that leading on-prem solution to meet our customer's needs. At the same time, we're going to continue to innovate and our ears are wide open, and we're listening to our customers on what their needs are, what their requirements are. So we're going to expand the use cases, expand the solution sets that we provide in these workload optimized offerings to a very very broad set of customers as they drive forward with that digital transformation and modernization efforts. >> Right, George, any final thoughts? >> Yeah, I would say, you know, with our partners we work as one team and continue to hone our skills and embrace our competence. We're looking to help them evolve their businesses and thrive, and we're here to help now more than ever. So, you know, please reach out to our team and our partners and we can show you where we've already been successful together. >> That's great, we're seeing the expanding GreenLake portfolio, partners key part of it. We're seeing new tools for them and then this ecosystem evolution and build out and expansion. Guys, thanks so much. >> Yeah, you bet, thank you. >> Thank you, appreciate it. >> You're welcome. (bright soft music) >> Okay, we're here with Jo Peterson the VP of Cloud & Security at Clarify360. Hello, Jo, welcome to theCUBE. >> Hello. >> Great to see you. >> Thanks for having me. >> You're welcome, all right, let's get right into it. How do you think about cloud where we are today in 2021? The definitions evolve, but where do you see it today and where do you see it going? >> Well, that's such an interesting question and is so relevant because the labels are disappearing. So over the last 10 years, we've sort of found ourselves defining whether an environment was public or whether it was private or whether it was hybrid. Here's the deal, cloud is infrastructure and infrastructure is cloud. So at the end of the day cloud in whatever form it's taking is a platform, and ultimately, this enablement tool for the business. Customers are consuming cloud in the best way that works for their businesses. So let's also point out that cloud is not a destination, it's this journey. And clients are finding themselves at different places on that road. And sometimes they need help getting to the next milestone. >> Right, and they're really looking for that consistent experience. Well, what are the big waves and trends that you're seeing around cloud out there in the marketplace? >> So I think that this hybrid reality is happening in most organizations. Their actual IT portfolios include a mix of on-premise and cloud infrastructure, and we're seeing this blurred line happening between the public cloud and the traditional data center. Customers want a bridge that easily connects one environment to the other environment, and they want end-to-end visibility. Customers are becoming more intentional and strategic about their cloud roadmaps. So some of them are intentionally and strategically selecting hybrid environments because they feel that it affords them more control, cost, balance, comfort level around their security. In a way, cloud itself is becoming borderless. The major tech providers are extending their platforms in an infrastructure agnostic manner and that's to work across hybrid environments, whether they be hosted in the data center, whether it includes multiple cloud providers. As cloud matures, workload environments fit is becoming more of a priority. So forward thinking where the organizations are matching workloads to the best environment. And it's sort of application rationalization on this case by case basis and it really makes sense. >> Yeah, it does makes sense. Okay, well, let's talk about HPE GreenLake. They just announced some new solutions. What do you think it means for customers? >> I think that HPE has stepped up. They've listened to not only their customers but their partners. Customers want consumable infrastructure, they've made that really clear. And HPE has expanded the cloud service portfolio for clients. They're offering more choices to not only enterprise customers but they're expanding that offering to attract this mid-market client base. And they provided additional tools for partners to make selling GreenLake easier. This is all helping to drive channel sales. >> Yeah, so better granularity, just so it increases the candidates, better optionality for customers. And this thing is evolving pretty quickly. We're seeing a number of customers that we talked to interested in this model, trying to understand it better and ultimately, I think they're going to really lean in hard. Jo, I wonder if you could maybe think about or share with us which companies are, I got to say, getting it right? And I'm really interested in the partner piece, because if you think about the partner business, it's really, it's changing a lot, right? It's gone from this notion of moving boxes and there was a lot of money to be made over the decades in doing that, but they have to now become value-add suppliers and really around cloud services. And in the early days of cloud, I think the channel was a little bit freaked out, saying, uh-oh, they're going to cut out the middleman. But what's actually happened is those smart agile partners are adding substantial value, they've got deep relationships with customers and they're serving as really trusted advisors and executors of cloud strategies. What do you see happening in the partner community? >> Well, I think it's been a learning curve and everything that you said was spot on. It's a two way street, right? In order for VARs to sell residual services, monthly recurring services, there has to have been some incentive to do that and HPE really got it right. Because they, again listened to that partner community, and they said, you know what? We've got to incentivize these guys to start selling this way. This is a partnership and we expect it to be a partnership. And the tech companies that are getting right are doing that same sort of thing, they're figuring out ways to make it palatable to that VAR, to help them along that journey. They're giving them tools, they're giving them self-serve tools, they're incentivizing them financially to make that shift. That's what's going to matter. >> Well, that's a key point you're making, I mean, the financial incentives, that's new and different. Paying, you know, incentivizing for as-a-Service models versus again, moving hardware and paying for, you know, installing iron. That's a shift in mindset, isn't it? >> It definitely is. And HPE, I think is getting it right because I didn't notice but I learned this, 70% of their annual sales are actually transacted through their channel. And they've seen this 116% increase in HPE GreenLake orders in Q1, from partners. So what they're doing is working. >> Yeah, I think you're right. And you know, the partner channel it becomes super critical. It's funny, Jo, I mean, again, in the early days of cloud, the channel was feeling like they were going to get disrupted. I don't know about you, but I mean, we've both been analysts for awhile and the more things get simple, the more they get complicated, right? I mean the consumerization of IT, the cloud, swipe your credit card, but actually applying that to your business is not easy. And so, I see that as great opportunities for the channel. Give you the last word. >> Absolutely, and what's going to matter is the tech companies that step up and realize we've got this chance, this opportunity to build that bridge and provide visibility, end-to-end visibility for clients. That's what going to matter. >> Yeah, I like how you're talking about that bridge, because that's what everybody wants. They want that bridge from on-prem to the public cloud, across clouds, going to to be moving out to the edge. And that is to your point, a journey that's going to evolve over the better part of this coming decade. Jo, great to see you. Thanks so much for coming on theCUBE today. >> Thanks for having me. (bright soft music) >> Okay, now we're going to into the GreenLake power panel to talk about the cloud landscape, hybrid cloud, and how the partner ecosystem and customers are thinking about cloud, hybrid cloud as a Service and of course, GreenLake. And with me are C.R. Howdyshell, President of Advizex. Ron Nemecek, who's the Business Alliance Manager at CBTS. Harry Zarek is President of Compugen. And Benjamin Klay is VP of Sales and Alliances at Arrow Electronics. Great to see you guys, thanks so much for coming on theCUBE. >> Thanks for having us. >> Good to be here. >> Okay, here's the deal. So I'm going to ask you guys each to introduce yourselves and your companies, add a little color to my brief intro, and then answer the following question. How do you and your customers think about hybrid cloud? And think about it in the context of where we are today and where we're going, not just the snapshot but where we are today and where we're going. C.R., why don't you start please? >> Sure, thanks a lot, Dave, appreciate it. And again, C.R. Howdyshell, President of Advizex. I've been with the company for 18 years, the last four years as president. So had the great opportunity here to lead a 45 year old company with a very strong brand and great culture. As it relates to Advizex and where we're headed to with hybrid cloud is it's a journey. So we're excited to be leading that journey for the company as well as HPE. We're very excited about where HPE is going with GreenLake. We believe it's a very strong solution when it comes to hybrid cloud. Have been an HPE partner since, well since 1980. So for 40 years, it's our longest standing OEM relationship. And we're really excited about where HPE is going with GreenLake. From a hybrid cloud perspective, we feel like we've been doing the hybrid cloud solutions, the past few years with everything that we've focused on from a VMware perspective. But now with where HPE is going, we think, probably changing the game. And it really comes down to giving customers that cloud experience with the on-prem solution with GreenLake. And we've had great response for customers and we think we're going to continue to see that kind of increased activity and reception. >> Great, thank you C.R., and yeah, I totally agree. It is a journey and we've seen it really come a long way in the last decade. Ron, I wonder if you could kickoff your little first intro there please. >> Sure Dave, thanks for having me today and it's a pleasure being here with all of you. My name is Ron Nemecek, I'm a Business Alliance manager at CBTS. In my role, I'm responsible for our HPE GreenLake relationship globally. I've enjoyed a 33 year career in the IT industry. I'm thankful for the opportunity to serve in multiple functional and senior leadership roles that have helped me gather a great deal of education and experience that could be used to aid our customers with their evolving needs, for business outcomes to best position them for sustainable and long-term success. I'm honored to be part of the CBTS and OnX Canada organization. CBTS stands for Consult Build Transform and Support. We have a 35 year relationship with HPE. We're a platinum and inner circle partner. We're headquartered in Cincinnati, Ohio. We service 3000 customers generating over a billion dollars in revenue and we have over 2000 associates across the globe. Our focus is partnering with our customers to deliver innovative solutions and business results through thought leadership. We drive this innovation via our team of the best and brightest technology professionals in the industry that have secured over 2,800 technical certifications, 260 specifically with HPE. And in our hybrid cloud business, we have clearly found that technology, new market demands for instant responses and experiences, evolving economic considerations with detailed financial evaluation, and of course the global pandemic, have challenged each of our customers across all industries to develop an optimal cloud strategy. We now play an enhanced strategic role for our customers as their technology advisor and their guide to the right mix of cloud experiences that will maximize their organizational success with predictable outcomes. Our conversations have really moved from product roadmaps and speeds and feeds to return on investment, return on capital, and financial statements, ratios, and metrics. We collaborate regularly with our customers at all levels and all departments to find an effective comprehensive cloud strategy for their workloads and applications ensuring proper alignment and cost with financial return. >> Great, thank you, Ron. Yeah, today it's all about the business value. Harry, please. >> Hi Dave, thanks for the opportunity and greetings from the Great White North. We're a Canadian-based company headquartered in Toronto with offices across the country. We've been in the tech industry for a very long time. We're what we would call a solution provider. How hard for my mother to understand what that means but what our goal is to help our customers realize the business value of their technology investments. Just to give you an example of what it is we try and do. We just finished a build out of a new networking endpoint and data center technology for a brand new hospital. It's now being mobilized for COVID high-risk patients. So talk about our all being in an essential industry, providing essential services across the whole spectrum of technology. Now, in terms of what's happening in the marketplace, our customers are confused. No question about it. They hear about cloud, I mean, cloud first, and everyone goes to the cloud, but the reality is there's lots of technology, lots of applications that actually still have to run on premises for a whole bunch of reasons. And what customers want is solid senior serious advice as to how they leverage what they already have in terms of their existing infrastructure, but modernize it, update it, so it looks and feels a lot like the cloud. But they have the security, they have the protection that they need to have for reasons that are dependent on their industry and business to allow them to run on-prem. And so, the GreenLake philosophy is perfect. That allows customers to actually have one foot in the cloud, one foot in their traditional data center but modernize it so it actually looks like one enterprise entity. And it's that kind of flexibility that gives us an opportunity collectively, ourselves, our partners, HPE, to really demonstrate that we understand how to optimize the use of technology across all of the business applications they need to run. >> You know Harry, it's interesting about what you said is, the cloud it is kind of chaotic my word, not yours. But there is a lot of confusion out there, I mean, what's cloud, right? Is it public cloud, is it private cloud, the hybrid cloud? Now, it's the edge and of course the answer is all of the above. Ben, what's your perspective on all this? >> From a cloud perspective, you know, I think as an industry, I think we we've all accepted that public cloud is not necessarily going to win the day and we're in fact, in a hybrid world. There's certainly been some commentary and press that was sort of validate that. Not that it necessarily needs any validation but I think is the linkages between on-prem and cloud-based services have increased. It's paved the way for customers more effectively, deploy hybrid solutions in in the model that they want or that they desire. You know, Harry was commenting on that a moment ago. As the trend continues, it becomes much easier for solution providers and service providers to drive their services initiatives, you know, in particular managed services. >> From an Arrow perspective is we think about how we can help scale in particular from a GreenLake perspective. We've got the ability to stand up some cloud capabilities through our ArrowSphere platform that can really help customers adopt GreenLake and to benefit from some alliances opportunities, as well. And I'll talk more about that as we go through. >> And Ben, I didn't mean to squeeze you on Arrow. I mean, Arrow has been around longer than computers. I mean, if you Google the history of Arrow it'll blow your mind, but give us a little quick commercial. >> Yeah, absolutely. So I've been with Arrow for about 20 years. I've got responsibility for Alliance organization in North America, We're a global value added distribution, business consulting and channel enablement company. And we bring scope, scale and and expertise as it relates to the IT industry. I love the fast pace that comes with the market that we're all in. And I love helping customers and suppliers both, be positioned for long-term success. And you know, the subject matter here today is just a great example of that. So I'm happy to be here and look forward to the discussion. >> All right, we got some good brain power in the room. Let's cut right to the chase. Ron, where's the pain? What are the main problems that CBTS I love what it stands for, Consult Build Transform and Support. What's the main pain point that customers are asking you to solve when it comes to their cloud strategies? >> Sure, Dave. Our customers' concerns and associated risks come from the market demands to deliver their products, services, and experiences instantaneously. And then the challenge is how do they meet those demands because they have aging infrastructure, processes, and fiscal constraints. Our customers really need us now more than ever to be excellent listeners so we can collaborate on an effective map with the strategic placement of workloads and applications in that spectrum of cloud experiences while managing their costs, and of course, mitigating risks to their business. This collaboration with our customers, often identify significant costs that have to be evaluated, justified or eliminated. We find significant development, migration, and egress charges in their current public cloud experience, coupled with significant over provisioning, maintenance, operational, and stranded asset costs in their on-premise infrastructure environment. When we look at all these costs holistically, through our customized workshops and assessments, we can identify the optimal cloud experience for the respective workloads and applications. Through our partnership with HPE and the availability of the HPE GreenLake solutions, our customers now have a choice to deliver SLA's, economics, and business outcomes for their workloads and applications that best reside on-premise in a private cloud and have that experience. This is a rock solid solution that eliminates, the development costs that they experience and the egress charges that are associated with the public cloud while utilizing HPE GreenLake to eliminate over provisioning costs and the maintenance costs on aging infrastructure hardware. Lastly, our customers only have to pay for actual infrastructure usage with no upfront capital expense. And now, that achieves true utilization to cost economics, you know, with HPE GreenLake solutions from CBTS. >> I love focus on the business case, 'cause it's measurable and it's sort of follow the money. That's where the opportunity is. Okay, C.R., so question for you. Thinking about Advizex customers, how are they, are they leaning into GreenLake? What are they telling you is the business impact when they experience GreenLake? >> Well, I think it goes back to what Ron was talking about. We had to solve the business challenges first and so far, the reception's been positive. When I say that is customers are open. Everybody wants to, the C-suite wants to hear about cloud and hybrid cloud fits. But what we hear and what we're seeing from our customers is we're seeing more adoption from customers that it may be their first foot in, if you will, but as important, we're able to share other customers with our potentially new clients that say, what's the first thing that happens with regard to GreenLake? Well, number one, it works. It works as advertised and as-a-Service, that's a big step. There are a lot of people out there dabbling today but when you can say we have a proven solution it's working in our environment today, that's key. I think the second thing is,, is flexibility. You know, when customers are looking for this hybrid solution, you got to be flexible for, again, I think Ron said (indistinct). You don't have a big capital outlay but also what customers want to be able to do is we want to build for growth but we don't want to pay for it. So we'll pay as we grow not as we have to use, as we used to do, it was upfront, the capital expenditure. Now we'll just pay as we grow, and that really facilitates in another great example as you'll hear from a customer, this afternoon. But you'll hear where one of the biggest benefits they just acquired a $570 million company and their integration is going to be very seamless because of their investment in GreenLake. They're looking at the flexibility to add to GreenLake as a big opportunity to integrate for acquisitions. And finally is really, we see, it really brings the cloud experience and as-a-Service to our customers. And with HPE GreenLake, it brings the best of breed. So it's not just what HPE has to offer. When you look at Hyperconverged, they have Nutanix, they have Cohesity. So, I really believe it brings best of breeds. So, to net it out and close it out with our customers, thus far, the customer experience has been exceptional. I mean, with GreenLake Central, as interface, customers have had a lot of success. We just had our first customer from about a year and a half ago just reopened, it was a highly competitive situation, but they just said, look, it's proven, it works, and it gives us that cloud experience so. Had a lot of great success thus far and looking forward to more. >> Thank you, so Harry, I want to pick up on something C.R. said and get your perspectives. So when I talk to the C-suite, they do all want to hear about, you know, cloud, they have a cloud agenda. And what they tell me is it's not just about their IT transformation. They want that but they also want to transform their business. So I wonder if you could talk, Harry, about Compugen's perspective on the potential business impact of GreenLake. And also, I'm interested in how you guys are thinking about workloads, how to manage work, you know, how to cost optimize in IT, but also, the business value that comes out of that capability. >> Yeah, so Dave, you know if you were to talk to CFO and I have the good fortune to talk to lots of CFOs, they want to pay the costs when they generate the revenue. They don't want to have all the costs upfront and then wait for the revenue to come through. A good example of where that's happening right now is you know, related to the pandemic, employees that used to work at the office have now moved to working from home. And now, they have to connect remotely to run the same application. So use this thing called VDI, virtual interfacing to allow them to connect to the applications that they need to run in the office. I don't want to get into too much detail but to be able to support that from an an at-home environment, they needed to buy a lot more computing capacity to handle this. Now, there's an expectation that hopefully six months from now, maybe sooner than that, people will start returning to the office. They may not need that capacity so they can turn down on the costs. And so, the idea of having the capacity available when you need it, but then turning it off when you don't need it, is really a benefit of the variable cost model. Another example that I would use is one in new development. If a customer is going to implement a new, let's say, line of business application. SAP is very very popular. You know, it actually, unfortunately, takes six months to two years to actually get that application set up, installed, validated, tested, then moves through production. You know, what used to happen before? They would buy all that capacity upfront, and it would basically sit there for two years, and then when they finally went to full production, then they were really value out of that investment. But they actually lost a couple of years of technology, literally sitting almost sidle. And so, from a CFO perspective, his ability to support the development of those applications as he scales it, perfect. GreenLake is the ideal solution that allows him to do that. >> You know, technology has saved businesses in this pandemic. There's no question about it. When Harry was just talking about with regard to VDI, you think about that, there's the dialing up and dialing down piece which is awesome from an IT perspective. And then the business impact there is the productivity of the end users. And most C-suite executives I've talked to said productivity actually went up during COVID with work from home, which is kind of astounding if you think about it. Ben, we said Arrow's been around for a long, long time. Certainly, before all of us were born and it's gone through many many industry transitions during our lifetimes. How does Arrow and how do your partners think about building cloud experiences and where does GreenLake fit in from your perspective? >> Great question. So from an Arrow perspective, when you think about cloud experience in of course us taking a view as a distribution partner, we want to be able to provide scale and efficiency to our network of partners. So we do that through our ArrowSphere platform. Just a bit of, you know, a bit of a commercial. I mean, you get single quote, single bill, auto provision, multi supplier, if you will, subscription management, utilization reporting from the platform itself. So if we pivot that directly to HPE, you're going to get a bit of a scoop here, Dave. And we're excited today to have GreenLake live in our platform available for our partner community to consume. In particular, the Swift solutions that HPE has announced so we're very excited to share that today. Maybe a little bit more on GreenLake. I think at this point in time, that it's differentiated in a sense that, if you think about some of the other offerings in the market today and further with having the the solutions themselves available in ArrowSphere. So, I would say, that we identify the uniqueness and quickly partner with HPE to work with our ArrowSphere platform. One other sort of unique thing is, when you think about platform itself, you've got to give a consistent experience. The different geographies around the world so, you know, we're available in North of 20 countries, there's thousands of resellers and transacting on the platform on a regular basis. And frankly, hundreds of thousands end customers. that are leveraging today. So that creates an opportunity for both Arrow, HPE and our partner community. So we're excited. >> You know, I just want to open it up. We don't have much time left, but thoughts on differentiation. Some people ask me, okay, what's really different about HPE and GreenLake? These others, you know, are doing things with as-a-Service. To me, I always say cultural, it starts from the top with Antonio, and it's like the company's all in. But I wonder from your perspectives, 'cause you guys are hands on. Are there other differentiable factors that you would point to? Let me just open that up to the group. >> Yeah, if I could make a comment. GreenLake is really just the latest invocation of the as-a-Service model. And what does that mean? What that actually means is you have a continuous ongoing relationship with the customer. It's not a sell and forget. Not that we ever forget about customers but there are highlights. Customer buys, it gets installed, and then for two or three years you may have an occasional engagement with them but it's not continuous. When you move to our GreenLake model, you're actually helping them manage that. You are in the core, in the heart of their business. No better place to be if you want to be sticky and you want to be relevant and you want to be always there for them. >> You know, I wonder if somebody else could add to it in your remarks. From your perspective as a partner, 'cause you know, hey, a lot of people made a lot of money selling boxes, but those days are pretty much gone. I mean, you have to transform into a services mindset, but other thoughts? >> I think to add to that Dave. I think Harry's right on. The way he positioned it it's exactly where he did own the customer. I think even another step back for us is, we're able to have the business conversation without leading with what you just said. You don't have to leave with a storage solution, you don't have to lead with compute. You know, you can really have step back, have a business conversation. And we've done that where you don't even bring up HPE GreenLake until you get to the point where the customer says, so you can give me an on-prem cloud solution that gives me scalability, flexibility, all the things you're talking about. How does that work? Then you bring up, it's all through this HPE GreenLake tool. And it really gives you the ability to have a business conversation. And you're solving the business problems versus trying to have a technology conversation. And to me, that's clear differentiation for HPE GreenLake. >> All right guys, C.R., Ron, Harry, Ben. Great discussion, thank you so much for coming on the program. Really appreciate it. >> Thanks for having us, Dave. >> Appreciate it Dave. >> All right, keep it right there for more great content at GreenLake Day, be right back. (bright soft music) (upbeat music) (upbeat electronic music)

>> Narrator: Live from Munich Germany, it's theCUBE, covering IBM, Fast Track Your Data. Brought to you by IBM. >> We're back. This is Dave Vellante with Jim Kobielus, and this is theCUBE, the leader in live tech coverage. We go out to the events. We extract the signal from the noise. We are here covering special presentation of IBM's Fast Track your Data, and we're in Munich Germany. It's been a day-long session. We started this morning with a panel discussion with five senior level data scientists that Jim and I hosted. Then we did CUBE interviews in the morning. We cut away to the main tent. Kate Silverton did a very choreographed scripted, but very well done, main keynote set of presentations. IBM made a couple of announcements today, and then we finished up theCUBE interviews. Jim and I are here to wrap. We're actually running on IBMgo.com. We're running live. Hilary Mason talking about what she's doing in data science, and also we got a session on GDPR. You got to log in to see those sessions. So go ahead to IBMgo.com, and you'll find those. Hit the schedule and go to the Hilary Mason and GDP our channels, and check that out, but we're going to wrap now. Jim two main announcements today. I hesitate to call them big announcements. I mean they were you know just kind of ... I think the word you used last night was perfunctory. You know I mean they're okay, but they're not game changing. So what did you mean? >> Well first of all, when you look at ... Though IBM is not calling this a signature event, it's essentially a signature event. They do these every June or so. You know in the past several years, the signature events have had like a one track theme, whether it be IBM announcing their investing deeply in Spark, or IBM announcing that they're focusing on investing in R as the core language for data science development. This year at this event in Munich, it's really a three track event, in terms of the broad themes, and I mean they're all important tracks, but none of them is like game-changing. Perhaps IBM doesn't intend them to be it seems like. One of which is obviously Europe. We're holding this in Munich. And a couple of things of importance to European customers, first and foremost GDPR. The deadline next year, in terms of compliance, is approaching. So sound the alarm as it were. And IBM has rolled out compliance or governance tools. Download and the go from the information catalog, governance catalog and so forth. Now announcing the consortium with Hortonworks to build governance on top of Apache Atlas, but also IBM announcing that they've opened up a DSX center in England and a machine-learning hub here in Germany, to help their European clients, in those countries especially, to get deeper down into data science and machine learning, in terms of developing those applicants. That's important for the audience, the regional audience here. The second track, which is also important, and I alluded to it. It's governance. In all of its manifestations you need a master catalog of all the assets for building and maintaining and controlling your data applications and your data science applications. The catalog, the consortium, the various offerings at IBM is announced and discussed in great detail. They've brought in customers and partners like Northern Trust, talk about the importance of governance, not just as a compliance mandate, but also the potential strategy for monetizing your data. That's important. Number three is what I call cloud native data applications and how the state of the art in developing data applications is moving towards containerized and orchestrated environments that involve things like Docker and Kubernetes. The IBM DB2 developer community edition. Been in the market for a few years. The latest version they announced today includes kubernetes support. Includes support for JSON. So it's geared towards new generation of cloud and data apps. What I'm getting at ... Those three core themes are Europe governance and cloud native data application development. Each of them is individually important, but none of them is game changer. And one last thing. Data science and machine learning, is one of the overarching envelope themes of this event. They've had Hilary Mason. A lot of discussion there. My sense I was a little bit disappointed because there wasn't any significant new announcements related to IBM evolving their machine learning portfolio into deep learning or artificial intelligence in an environment where their direct competitors like Microsoft and Google and Amazon are making a huge push in AI, in terms of their investments. There's a bit of a discussion, and Rob Thomas got to it this morning, about DSX. Working with power AI, the IBM platform, I would like to hear more going forward about IBM investments in these areas. So I thought it was an interesting bunch of announcements. I'll backtrack on perfunctory. I'll just say it was good that they had this for a lot of reasons, but like I said, none of these individual announcements is really changing the game. In fact like I said, I think I'm waiting for the fall, to see where IBM goes in terms of doing something that's actually differentiating and innovative. >> Well I think that the event itself is great. You've got a bunch of partners here, a bunch of customers. I mean it's active. IBM knows how to throw a party. They've always have. >> And the sessions are really individually awesome. I mean terms of what you learn. >> The content is very good. I would agree. The two announcements that were sort of you know DB2, sort of what I call community edition. Simpler, easier to download. Even Dave can download DB2. I really don't want to download DB2, but I could, and play with it I guess. You know I'm not database guy, but those of you out there that are, go check it out. And the other one was the sort of unified data governance. They tried to tie it in. I think they actually did a really good job of tying it into GDPR. We're going to hear over the next, you know 11 months, just a ton of GDPR readiness fear, uncertainty and doubt, from the vendor community, kind of like we heard with Y2K. We'll see what kind of impact GDPR has. I mean it looks like it's the real deal Jim. I mean it looks like you know this 4% of turnover penalty. The penalties are much more onerous than any other sort of you know, regulation that we've seen in the past, where you could just sort of fluff it off. Say yeah just pay the fine. I think you're going to see a lot of, well pay the lawyers to delay this thing and battle it. >> And one of our people in theCUBE that we interviewed, said it exactly right. It's like the GDPR is like the inverse of Y2K. In Y2K everybody was freaking out. It was actually nothing when it came down to it. Where nobody on the street is really buzzing. I mean the average person is not buzzing about GDPR, but it's hugely important. And like you said, I mean some serious penalties may be in the works for companies that are not complying, companies not just in Europe, but all around the world who do business with European customers. >> Right okay so now bring it back to sort of machine learning, deep learning. You basically said to Rob Thomas, I see machine learning here. I don't see a lot of the deep learning stuff quite yet. He said stay tuned. You know you were talking about TensorFlow and things like that. >> Yeah they supported that ... >> Explain. >> So Rob indicated that IBM very much, like with power AI and DSX, provides an open framework or toolkit for plugging in your, you the developers, preferred machine learning or deep learning toolkit of an open source nature. And there's a growing range of open source deep learning toolkits beyond you know TensorFlow, including Theano and MXNet and so forth, that IBM is supporting within the overall ESX framework, but also within the power AI framework. In other words they've got those capabilities. They're sort of burying that message under a bushel basket, at least in terms of this event. Also one of the things that ... I said this too Mena Scoyal. Watson data platform, which they launched last fall, very important product. Very important platform for collaboration among data science professionals, in terms of the machine learning development pipeline. I wish there was more about the Watson data platform here, about where they're taking it, what the customers are doing with it. Like I said a couple of times, I see Watson data platform as very much a DevOps tool for the new generation of developers that are building machine learning models directly into their applications. I'd like to see IBM, going forward turn Watson data platform into a true DevOps platform, in terms of continuous integration of machine learning and deep learning another statistical models. Continuous training, continuous deployment, iteration. I believe that's where they're going, or probably she will be going. I'd like to see more. I'm expecting more along those lines going forward. What I just described about DevOps for data science is a big theme that we're focusing on at Wikibon, in terms where the industry is going. >> Yeah, yeah. And I want to come back to that again, and get an update on what you're doing within your team, and talk about the research. Before we do that, I mean one of the things we talked about on theCUBE, in the early days of Hadoop is that the guys are going to make the money in this big data business of the practitioners. They're not going to see, you know these multi-hundred billion dollar valuations come out of the Hadoop world. And so far that prediction has held up well. It's the Airbnbs and the Ubers and the Spotifys and the Facebooks and the Googles, the practitioners who are applying big data, that are crushing it and making all the money. You see Amazon now buying Whole Foods. That in our view is a data play, but who's winning here, in either the vendor or the practitioner community? >> Who's winning are the startups with a hot new idea that's changing, that's disrupting some industry, or set of industries with machine learning, deep learning, big data, etc. For example everybody's, with bated breath, waiting for you know self-driving vehicles. And the ecosystem as it develops somebody's going to clean up. And one or more companies, companies we probably never heard of, leveraging everything we're describing here today, data science and containerized distributed applications that involve you know deep learning for you know image analysis and sensor analyst and so forth. Putting it all together in some new fabric that changes the way we live on this planet, but as you said the platforms themselves, whether they be Hadoop or Spark or TensorFlow, whatever, they're open source. You know and the fact is, by it's very nature, open source based solutions, in terms of profit margins on selling those, inexorably migrate to zero. So you're not going to make any money as a tool vendor, or a platform vendor. You got to make money ... If you're going to make money, you make money, for example from providing an ecosystem, within which innovation can happen. >> Okay we have a few minutes left. Let's talk about the research that you're working on. What's exciting you these days? >> Right, right. So I think a lot of people know I've been around the analyst space for a long long time. I've joined the SiliconANGLE Wikibon team just recently. I used to work for a very large solution provider, and what I do here for Wikibon is I focus on data science as the core of next generation application development. When I say next-generation application development, it's the development of AI, deep learning machine learning, and the deployment of those data-driven statistical assets into all manner of application. And you look at the hot stuff, like chatbots for example. Transforming the experience in e-commerce on mobile devices. Siri and Alexa and so forth. Hugely important. So what we're doing is we're focusing on AI and everything. We're focusing on containerization and building of AI micro-services and the ecosystem of the pipelines and the tools that allow you to do that. DevOps for data science, distributed training, federated training of statistical models, so forth. We are also very much focusing on the whole distributed containerized ecosystem, Docker, Kubernetes and so forth. Where that's going, in terms of changing the state of the art, in terms of application development. Focusing on the API economy. All of those things that you need to wrap around the payload of AI to deliver it into every ... >> So you're focused on that intersection between AI and the related topics and the developer. Who is winning in that developer community? Obviously Amazon's winning. You got Microsoft doing a good job there. Google, Apple, who else? I mean how's IBM doing for example? Maybe name some names. Who do you who impresses you in the developer community? But specifically let's start with IBM. How is IBM doing in that space? >> IBM's doing really well. IBM has been for quite a while, been very good about engaging with new generation of developers, using spark and R and Hadoop and so forth to build applications rapidly and deploy them rapidly into all manner of applications. So IBM has very much reached out to, in the last several years, the Millennials for whom all of this, these new tools, have been their core repertoire from the very start. And I think in many ways, like today like developer edition of the DB2 developer community edition is very much geared to that market. Saying you know to the cloud native application developer, take a second look at DB2. There's a lot in DB2 that you might bring into your next application development initiative, alongside your spark toolkit and so forth. So IBM has startup envy. They're a big old company. Been around more than a hundred years. And they're trying to, very much bootstrap and restart their brand in this new context, in the 21st century. I think they're making a good effort at doing it. In terms of community engagement, they have a really good community engagement program, all around the world, in terms of hackathons and developer days, you know meetups here and there. And they get lots of turnout and very loyal customers and IBM's got to broadest portfolio. >> So you still bleed a little bit of blue. So I got to squeeze it out of you now here. So let me push a little bit on what you're saying. So DB2 is the emphasis here, trying to position DB2 as appealing for developers, but why not some of the other you know acquisitions that they've made? I mean you don't hear that much about Cloudant, Dash TV, and things of that nature. You would think that that would be more appealing to some of the developer communities than DB2. Or am I mistaken? Is it IBM sort of going after the core, trying to evolve that core you know constituency? >> No they've done a lot of strategic acquisitions like Cloudant, and like they've acquired Agrath Databases and brought them into their platform. IBM has every type of database or file system that you might need for web or social or Internet of Things. And so with all of the development challenges, IBM has got a really high-quality, fit-the-purpose, best-of-breed platform, underlying data platform for it. They've got huge amounts of developers energized all around the world working on this platform. DB2, in the last several years they've taken all of their platforms, their legacy ... That's the wrong word. All their existing mature platforms, like DB2 and brought them into the IBM cloud. >> I think legacy is the right word. >> Yeah, yeah. >> These things have been around for 30 years. >> And they're not going away because they're field-proven and ... >> They are evolving. >> And customers have implemented them everywhere. And they're evolving. If you look at how IBM has evolved DB2 in the last several years into ... For example they responded to the challenge from SAP HANA. We brought BLU Acceleration technology in memory technology into DB2 to make it screamingly fast and so forth. IBM has done a really good job of turning around these product groups and the product architecture is making them cloud first. And then reaching out to a new generation of cloud application developers. Like I said today, things like DB2 developer community edition, it's just the next chapter in this ongoing saga of IBM turning itself around. Like I said, each of the individual announcements today is like okay that's interesting. I'm glad to see IBM showing progress. None of them is individually disruptive. I think the last week though, I think Hortonworks was disruptive in the sense that IBM recognized that BigInsights didn't really have a lot of traction in the Hadoop spaces, not as much as they would have wished. Hortonworks very much does, and IBM has cast its lot to work with HDP, but HDP and Hortonworks recognizes they haven't achieved any traction with data scientists, therefore DSX makes sense, as part of the Hortonworks portfolio. Likewise a big sequel makes perfect sense as the sequel front end to the HDP. I think the teaming of IBM and Hortonworks is propitious of further things that they'll be doing in the future, not just governance, but really putting together a broader cloud portfolio for the next generation of data scientists doing work in the cloud. >> Do you think Hortonworks is a legitimate acquisition target for IBM. >> Of course they are. >> Why would IBM ... You know educate us. Why would IBM want to acquire Hortonworks? What does that give IBM? Open source mojo, obviously. >> Yeah mojo. >> What else? >> Strong loyalty with the Hadoop market with developers. >> The developer angle would supercharge the developer angle, and maybe make it more relevant outside of some of those legacy systems. Is that it? >> Yeah, but also remember that Hortonworks came from Yahoo, the team that developed much of what became Hadoop. They've got an excellent team. Strategic team. So in many ways, you can look at Hortonworks as one part aqui-hire if they ever do that and one part really substantial and growing solution portfolio that in many ways is complementary to IBM. Hortonworks is really deep on the governance of Hadoop. IBM has gone there, but I think Hortonworks is even deeper, in terms of their their laser focus. >> Ecosystem expansion, and it actually really wouldn't be that expensive of an acquisition. I mean it's you know north of ... Maybe a billion dollars might get it done. >> Yeah. >> You know so would you pay a billion dollars for Hortonworks? >> Not out of my own pocket. >> No, I mean if you're IBM. You think that would deliver that kind of value? I mean you know how IBM thinks about about acquisitions. They're good at acquisitions. They look at the IRR. They have their formula. They blue-wash the companies and they generally do very well with acquisitions. Do you think Hortonworks would fit profile, that monetization profile? >> I wouldn't say that Hortonworks, in terms of monetization potential, would match say what IBM has achieved by acquiring the Netezza. >> Cognos. >> Or SPSS. I mean SPSS has been an extraordinarily successful ... >> Well the day IBM acquired SPSS they tripled the license fees. As a customer I know, ouch, it worked. It was incredibly successful. >> Well, yeah. Cognos was. Netezza was. And SPSS. Those three acquisitions in the last ten years have been extraordinarily pivotal and successful for IBM to build what they now have, which is really the most comprehensive portfolio of fit-to-purpose data platform. So in other words all those acquisitions prepared IBM to duke it out now with their primary competitors in this new field, which are Microsoft, who's newly resurgent, and Amazon Web Services. In other words, the two Seattle vendors, Seattle has come on strong, in a way that almost Seattle now in big data in the cloud is eclipsing Silicon Valley, in terms of where you know ... It's like the locus of innovation and really of customer adoption in the cloud space. >> Quite amazing. Well Google still hanging in there. >> Oh yeah. >> Alright, Jim. Really a pleasure working with you today. Thanks so much. Really appreciate it. >> Thanks for bringing me on your team. >> And Munich crew, you guys did a great job. Really well done. Chuck, Alex, Patrick wherever he is, and our great makeup lady. Thanks a lot. Everybody back home. We're out. This is Fast Track Your Data. Go to IBMgo.com for all the replays. Youtube.com/SiliconANGLE for all the shows. TheCUBE.net is where we tell you where theCUBE's going to be. Go to wikibon.com for all the research. Thanks for watching everybody. This is Dave Vellante with Jim Kobielus. We're out.

>> Announcer: Live from Munich, Germany; it's The Cube. Covering IBM; fast-track your data. Brought to you by IBM. (upbeat techno music) >> For you here at the show, generally; and specifically, what are you doing here today? >> There's really three things going on at the show, three high level things. One is we're talking about our new... How we're repositioning our hybrid data management portfolio, specifically some announcements around DB2 in a hybrid environment, and some highly transactional offerings around DB2. We're talking about our unified governance portfolio; so actually delivering a platform for unified governance that allows our clients to interact with governance and data management kind of products in a more streamlined way, and help them actually solve a problem instead of just offering products. The third is really around data science and machine learning. Specifically we're talking about our machine learning hub that we're launching here in Germany. Prior to this we had a machine learning hub in San Francisco, Toronto, one in Asia, and now we're launching one here in Europe. >> Seth, can you describe what this hub is all about? This is a data center where you're hosting machine learning services, or is it something else? >> Yeah, so this is where clients can come and learn how to do data science. They can bring their problems, bring their data to our facilities, learn how to solve a data science problem in a more team oriented way; interacting with data scientists, machine learning engineers, basically, data engineers, developers, to solve a problem for their business around data science. These previous hubs have been completely booked, so we wanted to launch them in other areas to try and expand the capacity of them. >> You're hosting a round table today, right, on the main tent? >> Yep. >> And you got a customer on, you guys going to be talking about sort of applying practices and financial and other areas. Maybe describe that a little bit. >> We have a customer on from ING, Heinrich, who's the chief architect for ING. ING, IBM, and Horton Works have a consortium, if you would, or a framework that we're doing around Apache Atlas and Ranger, as the kind of open-source operating system for our unified governance platform. So much as IBM has positioned Spark as a unified, kind of open-source operating system for analytics, for a unified governance platform... For a governance platform to be truly unified, you need to be able to integrate metadata. The biggest challenge about connecting your data environments, if you're an enterprise that was not internet born, or cloud born, is that you have proprietary metadata platforms that all want to be the master. When everyone wants to be the master, you can't really get anything done. So what we're doing around Apache Atlas is we are setting up Apache Atlas as kind of a virtual translator, if you would, or a dictionary between all the different proprietary metadata platforms so that you can get a single unified view of your data environment across hybrid clouds, on premise, in the cloud, and across different proprietary vendor platforms. Because it's open-sourced, there are these connectors that can go in and out of the proprietary platforms. >> So Seth, you seem like you're pretty tuned in to the portfolio within the analytics group. How are you spending your time as the Chief Data Officer? How do you balance it between customer visits, maybe talking about some of the products, and then you're sort of day job? >> I actually have three days jobs. My job's actually split into kind of three pieces. The first, my primary mission, is really around transforming IBM's internal business unit, internal business workings, to use data and analytics to run our business. So kind of internal business unit transformation. Part of that business unit transformation is also making sure that we're compliant with regulations like GDBR and other regulations. Another third is really around kind of rethinking our offerings from a CDO perspective. As a CDO, and as you, Dave, I've only been with IBM for seven months. As a former client recently, and as a CDO, what is it that I want to see from IBM's offerings? We kind of hit on it a little bit with the unified governance platform, where I think IBM makes fantastic products. But as a client, if a salesperson shows up to me, I don't want them selling me a product, 'cause if I want an MDM solution, I'll call you up and say, "Hey, I need an MDM solution. "Give me a quote." What I want them showing up is saying, "I have a solution that's going to solve "your governance problem across your portfolio." Or, "I'm going to solve your data science problem." Or, "I'm going to help you master your data, "and manage your data across "all these different environments." So really working with the offering management and the Dev teams to define what are these three or four, kind of business platforms that we want to settle on? We know three of them at least, right? We know that we have a hybrid data management. We have unified governance. We have data science and machine learning, and you could think of the Z franchise as a fourth platform. >> Seth, can you net out how governance relates to data science? 'Cause there is governance of the statistical models, machine learning, and so forth, version control. I mean, in an end to end machine learning pipeline, there's various versions of various artifacts they have to be managed in a structured way. Is your unified governance bundle, or portfolio, does it address those requirements? Or just the data governance? >> Yeah, so the unified governance platform really kind of focuses today on data governance and how good data governance can be an enabler of rapid data science. So if you have your data all pre-governed, it makes it much quicker to get access to data and understand what you can and can't do with data; especially being here in Europe, in the context of the EU GDPR. You need to make sure that your data scientists are doing things that are approved by the user, because basically your data, you have to give explicit consent to allow things to be done with it. But long term vision is that... essentially the output of models is data, right? And how you use and deploy those models also need to be governed. So the long term vision is that we will have a governance platform for all those things, as well. I think it makes more sense for those things to be governed in the data science platform, if you would. And we... >> We often hear separate from GDPR and all that, is something called algorithmic accountability; that more is being discussed in policy circles, in government circles around the world, as strongly related to everything you're describing. Being able to trace the lineage of any algorithmic decision back to the data, the metadata, and so forth, and the machine learning models that might have driven it. Is that where IBM's going with this portfolio? >> I think that's the natural extension of it. We're thinking really in the context of them as two different pieces, but if you solve them both and you connect them together, then you have that problem. But I think you're absolutely right. As we're leveraging machine learning and artificial intelligence, in general, we need to be able to understand how we got to a decision, and that includes the model, the data, how the data was gathered, how the data was used and processed. So it is that entire pipeline, 'cause it is a pipeline. You're not doing machine learning or AI in a vacuum. You're doing it in the context of the data, and you're doing it in the context about the individuals or the organizations that you're trying to influence with the output of those models. >> I call it Dev ops for data science. >> Seth, in the early Hadoop days, the real headwind was complexity. It still is, by the way. We know that. Companies like IBM are trying to reduce that complexity. Spark helps a little bit So the technology will evolve, we get that. It seems like one of the other big headwinds right now is that most companies don't have a great understanding of how they can take data and monetize it, turn it into value. Most companies, many anyway, make the mistake of, "Well, I don't really want to sell my data," or, "I'm not really a data supplier." And they're kind of thinking about it, maybe not in the right way. But we seem to be entering a next wave here, where people are beginning to understand I can cut costs, I can do predictive maintenance, I can maybe not sell the data, but I can enhance what I'm doing and increase my revenue, maybe my customer retention. They seem to be tuning, more so; largely, I think 'cause of the chief data officer roles, helping them think that through. I wonder if you would give us your point of view on that narrative. >> I think what you're describing is kind of the digital transformation journey. I think the end game, as enterprises go through a digital transformation, the end game is how do I sell services, outcomes, those types of things. How do I sell an outcome to my end user? That's really the end game of a digital transformation in my mind. But before you can get to that, before you transform your business's objectives, there's a couple of intermediary steps that are required for that. The first is what you're describing, is those kind of data transformations. Enterprises need to really get a handle on their data and become data driven, and start then transforming their current business model; so how do I accelerate my current business leveraging data and analytics? I kind of frame that, that's like the data science kind of transformation aspect of the digital journey. Then the next aspect of it is how do I transform my business and change my business objectives? Part of that first step is in fact, how do I optimize my supply chain? How do I optimize my workforce? How do I optimize my goals? How do I get to my current, you know, the things that Wall Street cares about for business; how do I accelerate those, make those faster, make those better, and really put my company out in front? 'Cause really in the grand scheme of things, there's two types of companies today; there's the company that's going to be the disruptor, and there's companies that's going to get disrupted. Most companies want to be the disruptors, and it's a process to do that. >> So the accounting industry doesn't have standards around valuing data as an asset, and many of us feel as though waiting for that is a mistake. You can't wait for that. You've got to figure out on your own. But again, it seems to be somewhat of a headwind because it puts data and data value in this fuzzy category. But there are clearly the data haves and the data have-nots. What are you seeing in that regard? >> I think the first... When I was in my former role, my former company went through an exercise of valuing our data and our decisions. I'm actually doing that same exercise at IBM right now. We're going through IBM, at least in the analytics business unit, the part I'm responsible for, and going to all the leaders and saying, "What decisions are you making?" "Help me understand the decisions that you're making." "Help me understand the data you need "to make those decisions." And that does two things. Number one, it does get to the point of, how can we value the decisions? 'Cause each one of those decisions has a specific value to the company. You can assign a dollar amount to it. But it also helps you change how people in the enterprise think. Because the first time you go through and ask these questions, they talk about the dashboards they want to help them make their preconceived decisions, validated by data. They have a preconceived notion of the decision they want to make. They want the data to back it up. So they want a dashboard to help them do that. So when you come in and start having this conversation, you kind of stop them and say, "Okay, what you're describing is a dashboard. "That's not a decision. "Let's talk about the decision that you want to make, "and let's understand the real value of that decision." So you're doing two things, you're building a portfolio of decisions that then becomes to your point, Jim, about Dev ops for data science. It's your backlog for your data scientists, in the long run. You then connect those decisions to data that's required to make those, and you can extrapolate the data for each decision to the component that each piece of data makes up to it. So you can group your data logically within an enterprise; customer, product, talent, location, things like that, and you can assign a value to those based on decisions they support. >> Jim: So... >> Dave: Go ahead, please. >> As a CDO, following on that, are you also, as part of that exercise, trying to assess the value of not just the data, but of data science as a capability? Or particular data science assets, like machine learning models? In the overall scheme of things, that kind of valuation can then drive IBM's decision to ramp up their internal data science initiatives, or redeploy it, or, give me a... >> That's exactly what happened. As you build this portfolio of decisions, each decision has a value. So I am now assigning a value to the data science models that my team will build. As CDOs, CDOs are a relatively new role in many organizations. When money gets tight, they say, "What's this guy doing?" (Dave laughing) Having a portfolio of decisions that's saying, "Here's real value I'm adding..." So, number one, "Here's the value I can add in the future," and as you check off those boxes, you can kind of go and say, "Here's value I've added. "Here's where I've changed how the company's operating. "Here's where I've generated X billions of dollars "of new revenue, or cost savings, or cost avoidance, "for the enterprise." >> When you went through these exercises at your previous company, and now at IBM, are you using standardized valuation methodologies? Did you kind of develop your own, or come up with a scoring system? How'd you do that? >> I think there's some things around, like net promoter score, where there's pretty good standards on how to assign value to increases in net promoter score, or decreases in net promoter score for certain aspects of your business. In other ways, you need to kind of decide as an enterprise, how do we value our assets? Do we use a three year, five year, ten year MPV? Do we use some other metric? You need to kind of frame it in the reference that your CFO is used to talking about so that it's in the context that the company is used to talking about. Most companies, it's net present value. >> Okay, and you're measuring that on an ongoing basis. >> Seth: Yep. >> And fine tuning as you go along. Seth, we're out of time. Thanks so much for coming back in The Cube. It was great to see you. >> Seth: Yeah, thanks for having me. >> You're welcome, good luck this afternoon. >> Seth: Alright. >> Keep it right there, buddy. We'll be back. Actually, let me run down the day here for you, just take a second to do that. We're going to end our Cube interviews for the morning, and then we're going to cut over to the main tent. So in about an hour, Rob Thomas is going to kick off the main tent here with a keynote, talking about where data goes next. Hilary Mason's going to be on. There's a session with Dez Blanchfield on data science as a team sport. Then the big session on changing regulations, GDPRs. Seth, you've got some customers that you're going to bring on and talk about these issues. And then, sort of balancing act, the balancing act of hybrid data. Then we're going to come back to The Cube and finish up our Cube interviews for the afternoon. There's also going to be two breakout sessions; one with Hilary Mason, and one on GDPR. You got to go to IBMgo.com and log in and register. It's all free to see those breakout sessions. Everything else is open. You don't even have to register or log in to see that. So keep it right here, everybody. Check out the main tent. Check out siliconangle.com, and of course IBMgo.com for all the action here. Fast track your data. We're live from Munich, Germany; and we'll see you a little later. (upbeat techno music)

>> Narrator: Live, from Munich, Germany, it's the CUBE. Covering IBM, Fast Track Your Data. Brought to you by IBM. >> Welcome to Munich everybody. This is a special presentation of the CUBE, Fast Track Your Data, brought to you by IBM. My name is Dave Vellante. And I'm here with my cohost, Jim Kobielus. Jim, good to see you. Really good to see you in Munich. >> Jim: I'm glad I made it. >> Thanks for being here. So last year Jim and I hosted a panel at New York City on the CUBE. And it was quite an experience. We had, I think it was nine or 10 data scientists and we felt like that was a lot of people to organize and talk about data science. Well today, we're going to do a repeat of that. With a little bit of twist on topics. And we've got five data scientists. We're here live, in Munich. And we're going to kick off the Fast Track Your Data event with this data science panel. So I'm going to now introduce some of the panelists, or all of the panelists. Then we'll get into the discussions. I'm going to start with Lillian Pierson. Lillian thanks very much for being on the panel. You are in data science. You focus on training executives, students, and you're really a coach but with a lot of data science expertise based in Thailand, so welcome. >> Thank you, thank you so much for having me. >> Dave: You're very welcome. And so, I want to start with sort of when you focus on training people, data science, where do you start? >> Well it depends on the course that I'm teaching. But I try and start at the beginning so for my Big Data course, I actually start back at the fundamental concepts and definitions they would even need to understand in order to understand the basics of what Big Data is, data engineering. So, terms like data governance. Going into the vocabulary that makes up the very introduction of the course, so that later on the students can really grasp the concepts I present to them. You know I'm teaching a deep learning course as well, so in that case I start at a lot more advanced concepts. So it just really depends on the level of the course. >> Great, and we're going to come back to this topic of women in tech. But you know, we looked at some CUBE data the other day. About 17% of the technology industry comprises women. And so we're a little bit over that on our data science panel, we're about 20% today. So we'll come back to that topic. But I don't know if there's anything you would add? >> I'm really passionate about women in tech and women who code, in particular. And I'm connected with a lot of female programmers through Instagram. And we're supporting each other. So I'd love to take any questions you have on what we're doing in that space. At least as far as what's happening across the Instagram platform. >> Great, we'll circle back to that. All right, let me introduce Chris Penn. Chris, Boston based, all right, SMI. Chris is a marketing expert. Really trying to help people understand how to get, turn data into value from a marketing perspective. It's a very important topic. Not only because we get people to buy stuff but also understanding some of the risks associated with things like GDPR, which is coming up. So Chris, tell us a little bit about your background and your practice. >> So I actually started in IT and worked at a start up. And that's where I made the transition to marketing. Because marketing has much better parties. But what's really interesting about the way data science is infiltrating marketing is the technology came in first. You know, everything went digital. And now we're at a point where there's so much data. And most marketers, they kind of got into marketing as sort of the arts and crafts field. And are realizing now, they need a very strong, mathematical, statistical background. So one of the things, Adam, the reason why we're here and IBM is helping out tremendously is, making a lot of the data more accessible to people who do not have a data science background and probably never will. >> Great, okay thank you. I'm going to introduce Ronald Van Loon. Ronald, your practice is really all about helping people extract value out of data, driving competitive advantage, business advantage, or organizational excellence. Tell us a little bit about yourself, your background, and your practice. >> Basically, I've three different backgrounds. On one hand, I'm a director at a data consultancy firm called Adversitement. Where we help companies to become data driven. Mainly large companies. I'm an advisory board member at Simply Learn, which is an e-learning platform, especially also for big data analytics. And on the other hand I'm a blogger and I host a series of webinars. >> Okay, great, now Dez, Dez Blanchfield, I met you on Twitter, you know, probably a couple of years ago. We first really started to collaborate last year. We've spend a fair amount of time together. You are a data scientist, but you're also a jack of all trades. You've got a technology background. You sit on a number of boards. You work very active with public policy. So tell us a little bit more about what you're doing these days, a little bit more about your background. >> Sure, I think my primary challenge these days is communication. Trying to join the dots between my technical background and deeply technical pedigree, to just plain English, every day language, and business speak. So bridging that technical world with what's happening in the boardroom. Toe to toe with the geeks to plain English to execs in boards. And just hand hold them and steward them through the journey of the challenges they're facing. Whether it's the enormous rapid of change and the pace of change, that's just almost exhaustive and causing them to sprint. But not just sprint in one race but in multiple lanes at the same time. As well as some of the really big things that are coming up, that we've seen like GDPR. So it's that communication challenge and just hand holding people through that journey and that mix of technical and commercial experience. >> Great, thank you, and finally Joe Caserta. Founder and president of Caserta Concepts. Joe you're a practitioner. You're in the front lines, helping organizations, similar to Ronald. Extracting value from data. Translate that into competitive advantage. Tell us a little bit about what you're doing these days in Caserta Concepts. >> Thanks Dave, thanks for having me. Yeah, so Caserta's been around. I've been doing this for 30 years now. And natural progressions have been just getting more from application development, to data warehousing, to big data analytics, to data science. Very, very organically, that's just because it's where businesses need the help the most, over the years. And right now, the big focus is governance. At least in my world. Trying to govern when you have a bunch of disparate data coming from a bunch of systems that you have no control over, right? Like social media, and third party data systems. Bringing it in and how to you organize it? How do you ingest it? How do you govern it? How do you keep it safe? And also help to define ownership of the data within an organization within an enterprise? That's also a very hot topic. Which ties back into GDPR. >> Great, okay, so we're going to be unpacking a lot of topics associated with the expertise that these individuals have. I'm going to bring in Jim Kobielus, to the conversation. Jim, the newest Wikibon analyst. And newest member of the SiliconANGLE Media Team. Jim, get us started off. >> Yeah, so we're at an event, at an IBM event where machine learning and data science are at the heart of it. There are really three core themes here. Machine learning and data science, on the one hand. Unified governance on the other. And hybrid data management. I want to circle back or focus on machine learning. Machine learning is the coin of the realm, right now in all things data. Machine learning is the heart of AI. Machine learning, everybody is going, hiring, data scientists to do machine learning. I want to get a sense from our panel, who are experts in this area, what are the chief innovations and trends right now on machine learning. Not deep learning, the core of machine learning. What's super hot? What's in terms of new techniques, new technologies, new ways of organizing teams to build and to train machine learning models? I'd like to open it up. Let's just start with Lillian. What are your thoughts about trends in machine learning? What's really hot? >> It's funny that you excluded deep learning from the response for this, because I think the hottest space in machine learning is deep learning. And deep learning is machine learning. I see a lot of collaborative platforms coming out, where people, data scientists are able to work together with other sorts of data professionals to reduce redundancies in workflows. And create more efficient data science systems. >> Is there much uptake of these crowd sourcing environments for training machine learning wells. Like CrowdFlower, or Amazon Mechanical Turk, or Mighty AI? Is that a huge trend in terms of the workflow of data science or machine learning, a lot of that? >> I don't see that crowdsourcing is like, okay maybe I've been out of the crowdsourcing space for a while. But I was working with Standby Task Force back in 2013. And we were doing a lot of crowdsourcing. And I haven't seen the industry has been increasing, but I could be wrong. I mean, because there's no, if you're building automation models, most of the, a lot of the work that's being crowdsourced could actually be automated if someone took the time to just build the scripts and build the models. And so I don't imagine that, that's going to be a trend that's increasing. >> Well, automation machine learning pipeline is fairly hot, in terms of I'm seeing more and more research. Google's doing a fair amount of automated machine learning. The panel, what do you think about automation, in terms of the core modeling tasks involved in machine learning. Is that coming along? Are data scientists in danger of automating themselves out of a job? >> I don't think there's a risk of data scientist's being put out of a job. Let's just put that on the thing. I do think we need to get a bit clearer about this meme of the mythical unicorn. But to your call point about machine learning, I think what you'll see, we saw the cloud become baked into products, just as a given. I think machine learning is already crossed this threshold. We just haven't necessarily noticed or caught up. And if we look at, we're at an IBM event, so let's just do a call out for them. The data science experience platform, for example. Machine learning's built into a whole range of things around algorithm and data classification. And there's an assisted, guided model for how you get to certain steps, where you don't actually have to understand how machine learning works. You don't have to understand how the algorithms work. It shows you the different options you've got and you can choose them. So you might choose regression. And it'll give you different options on how to do that. So I think we've already crossed this threshold of baking in machine learning and baking in the data science tools. And we've seen that with Cloud and other technologies where, you know, the Office 365 is not, you can't get a non Cloud Office 365 account, right? I think that's already happened in machine learning. What we're seeing though, is organizations even as large as the Googles still in catch up mode, in my view, on some of the shift that's taken place. So we've seen them write little games and apps where people do doodles and then it runs through the ML library and says, "Well that's a cow, or a unicorn, or a duck." And you get awards, and gold coins, and whatnot. But you know, as far as 12 years ago I was working on a project, where we had full size airplanes acting as drones. And we mapped with two and 3-D imagery. With 2-D high res imagery and LiDAR for 3-D point Clouds. We were finding poles and wires for utility companies, using ML before it even became a trend. And baking it right into the tools. And used to store on our web page and clicked and pointed on. >> To counter Lillian's point, it's not crowdsourcing but crowd sharing that's really powering a lot of the rapid leaps forward. If you look at, you know, DSX from IBM. Or you look at Node-RED, huge number of free workflows that someone has probably already done the thing that you are trying to do. Go out and find in the libraries, through Jupyter and R Notebooks, there's an ability-- >> Chris can you define before you go-- >> Chris: Sure. >> This is great, crowdsourcing versus crowd sharing. What's the distinction? >> Well, so crowdsourcing, kind of, where in the context of the question you ask is like I'm looking for stuff that other people, getting people to do stuff that, for me. It's like asking people to mine classifieds. Whereas crowd sharing, someone has done the thing already, it already exists. You're not purpose built, saying, "Jim, help me build this thing." It's like, "Oh Jim, you already "built this thing, cool. "So can I fork it and make my own from it?" >> Okay, I see what you mean, keep going. >> And then, again, going back to earlier. In terms of the advancements. Really deep learning, it probably is a good idea to just sort of define these things. Machine learning is how machines do things without being explicitly programmed to do them. Deep learning's like if you can imagine a stack of pancakes, right? Each pancake is a type of machine learning algorithm. And your data is the syrup. You pour the data on it. It goes from layer, to layer, to layer, to layer, and what you end up with at the end is breakfast. That's the easiest analogy for what deep learning is. Now imagine a stack of pancakes, 500 or 1,000 high, that's where deep learning's going now. >> Sure, multi layered machine learning models, essentially, that have the ability to do higher levels of abstraction. Like image analysis, Lillian? >> I had a comment to add about automation and data science. Because there are a lot of tools that are able to, or applications that are able to use data science algorithms and output results. But the reason that data scientists aren't in risk of losing their jobs, is because just because you can get the result, you also have to be able to interpret it. Which means you have to understand it. And that involves deep math and statistical understanding. Plus domain expertise. So, okay, great, you took out the coding element but that doesn't mean you can codify a person's ability to understand and apply that insight. >> Dave: Joe, you have something to add? >> I could just add that I see the trend. Really, the reason we're talking about it today is machine learning is not necessarily, it's not new, like Dez was saying. But what's different is the accessibility of it now. It's just so easily accessible. All of the tools that are coming out, for data, have machine learning built into it. So the machine learning algorithms, which used to be a black art, you know, years ago, now is just very easily accessible. That you can get, it's part of everyone's toolbox. And the other reason that we're talking about it more, is that data science is starting to become a core curriculum in higher education. Which is something that's new, right? That didn't exist 10 years ago? But over the past five years, I'd say, you know, it's becoming more and more easily accessible for education. So now, people understand it. And now we have it accessible in our tool sets. So now we can apply it. And I think that's, those two things coming together is really making it becoming part of the standard of doing analytics. And I guess the last part is, once we can train the machines to start doing the analytics, right? And get smarter as it ingests more data. And then we can actually take that and embed it in our applications. That's the part that you still need data scientists to create that. But once we can have standalone appliances that are intelligent, that's when we're going to start seeing, really, machine learning and artificial intelligence really start to take off even more. >> Dave: So I'd like to switch gears a little bit and bring Ronald on. >> Okay, yes. >> Here you go, there. >> Ronald, the bromide in this sort of big data world we live in is, the data is the new oil. You got to be a data driven company and many other cliches. But when you talk to organizations and you start to peel the onion. You find that most companies really don't have a good way to connect data with business impact and business value. What are you seeing with your clients and just generally in the community, with how companies are doing that? How should they do that? I mean, is that something that is a viable approach? You don't see accountants, for example, quantifying the value of data on a balance sheet. There's no standards for doing that. And so it's sort of this fuzzy concept. How are and how should organizations take advantage of data and turn it into value. >> So, I think in general, if you look how companies look at data. They have departments and within the departments they have tools specific for this department. And what you see is that there's no central, let's say, data collection. There's no central management of governance. There's no central management of quality. There's no central management of security. Each department is manages their data on their own. So if you didn't ask, on one hand, "Okay, how should they do it?" It's basically go back to the drawing table and say, "Okay, how should we do it?" We should collect centrally, the data. And we should take care for central governance. We should take care for central data quality. We should take care for centrally managing this data. And look from a company perspective and not from a department perspective what the value of data is. So, look at the perspective from your whole company. And this means that it has to be brought on one end to, whether it's from C level, where most of them still fail to understand what it really means. And what the impact can be for that company. >> It's a hard problem. Because data by its' very nature is now so decentralized. But Chris you have a-- >> The thing I want to add to that is, think about in terms of valuing data. Look at what it would cost you for data breach. Like what is the expensive of having your data compromised. If you don't have governance. If you don't have policy in place. Look at the major breaches of the last couple years. And how many billions of dollars those companies lost in market value, and trust, and all that stuff. That's one way you can value data very easily. "What will it cost us if we mess this up?" >> So a lot of CEOs will hear that and say, "Okay, I get it. "I have to spend to protect myself, "but I'd like to make a little money off of this data thing. "How do I do that?" >> Well, I like to think of it, you know, I think data's definitely an asset within an organization. And is becoming more and more of an asset as the years go by. But data is still a raw material. And that's the way I think about it. In order to actually get the value, just like if you're creating any product, you start with raw materials and then you refine it. And then it becomes a product. For data, data is a raw material. You need to refine it. And then the insight is the product. And that's really where the value is. And the insight is absolutely, you can monetize your insight. >> So data is, abundant insights are scarce. >> Well, you know, actually you could say that intermediate between insights and the data are the models themselves. The statistical, predictive, machine learning models. That are a crystallization of insights that have been gained by people called data scientists. What are your thoughts on that? Are statistical, predictive, machine learning models something, an asset, that companies, organizations, should manage governance of on a centralized basis or not? >> Well the models are essentially the refinery system, right? So as you're refining your data, you need to have process around how you exactly do that. Just like refining anything else. It needs to be controlled and it needs to be governed. And I think that data is no different from that. And I think that it's very undisciplined right now, in the market or in the industry. And I think maturing that discipline around data science, I think is something that's going to be a very high focus in this year and next. >> You were mentioning, "How do you make money from data?" Because there's all this risk associated with security breaches. But at the risk of sounding simplistic, you can generate revenue from system optimization, or from developing products and services. Using data to develop products and services that better meet the demands and requirements of your markets. So that you can sell more. So either you are using data to earn more money. Or you're using data to optimize your system so you have less cost. And that's a simple answer for how you're going to be making money from the data. But yes, there is always the counter to that, which is the security risks. >> Well, and my question really relates to, you know, when you think of talking to C level executives, they kind of think about running the business, growing the business, and transforming the business. And a lot of times they can't fund these transformations. And so I would agree, there's many, many opportunities to monetize data, cut costs, increase revenue. But organizations seem to struggle to either make a business case. And actually implement that transformation. >> Dave, I'd love to have a crack at that. I think this conversation epitomizes the type of things that are happening in board rooms and C suites already. So we've really quickly dived into the detail of data. And the detail of machine learning. And the detail of data science, without actually stopping and taking a breath and saying, "Well, we've "got lots of it, but what have we got? "Where is it? "What's the value of it? "Is there any value in it at all?" And, "How much time and money should we invest in it?" For example, we talk of being about a resource. I look at data as a utility. When I turn the tap on to get a drink of water, it's there as a utility. I counted it being there but I don't always sample the quality of the water and I probably should. It could have Giardia in it, right? But what's interesting is I trust the water at home, in Sydney. Because we have a fairly good experience with good quality water. If I were to go to some other nation. I probably wouldn't trust that water. And I think, when you think about it, what's happening in organizations. It's almost the same as what we're seeing here today. We're having a lot of fun, diving into the detail. But what we've forgotten to do is ask the question, "Well why is data even important? "What's the reasoning to the business? "Why are we in business? "What are we doing as an organization? "And where does data fit into that?" As opposed to becoming so fixated on data because it's a media hyped topic. I think once you can wind that back a bit and say, "Well, we have lot's of data, "but is it good data? "Is it quality data? "Where's it coming from? "Is it ours? "Are we allowed to have it? "What treatment are we allowed to give that data?" As you said, "Are we controlling it? "And where are we controlling it? "Who owns it?" There's so many questions to be asked. But the first question I like to ask people in plain English is, "Well is there any value "in data in the first place? "What decisions are you making that data can help drive? "What things are in your organizations, "KPIs and milestones you're trying to meet "that data might be a support?" So then instead of becoming fixated with data as a thing in itself, it becomes part of your DNA. Does that make sense? >> Think about what money means. The Economists' Rhyme, "Money is a measure for, "a systems for, a medium, a measure, and exchange." So it's a medium of exchange. A measure of value, a way to exchange something. And a way to store value. Data, good clean data, well governed, fits all four of those. So if you're trying to figure out, "How do we make money out of stuff." Figure out how money works. And then figure out how you map data to it. >> So if we approach and we start with a company, we always start with business case, which is quite clear. And defined use case, basically, start with a team on one hand, marketing people, sales people, operational people, and also the whole data science team. So start with this case. It's like, defining, basically a movie. If you want to create the movie, You know where you're going to. You know what you want to achieve to create the customer experience. And this is basically the same with a business case. Where you define, "This is the case. "And this is how we're going to derive value, "start with it and deliver value within a month." And after the month, you check, "Okay, where are we and how can we move forward? "And what's the value that we've brought?" >> Now I as well, start with business case. I've done thousands of business cases in my life, with organizations. And unless that organization was kind of a data broker, the business case rarely has a discreet component around data. Is that changing, in your experience? >> Yes, so we guide companies into be data driven. So initially, indeed, they don't like to use the data. They don't like to use the analysis. So that's why, how we help. And is it changing? Yes, they understand that they need to change. But changing people is not always easy. So, you see, it's hard if you're not involved and you're not guiding it, they fall back in doing the daily tasks. So it's changing, but it's a hard change. >> Well and that's where this common parlance comes in. And Lillian, you, sort of, this is what you do for a living, is helping people understand these things, as you've been sort of evangelizing that common parlance. But do you have anything to add? >> I wanted to add that for organizational implementations, another key component to success is to start small. Start in one small line of business. And then when you've mastered that area and made it successful, then try and deploy it in more areas of the business. And as far as initializing big data implementation, that's generally how to do it successfully. >> There's the whole issue of putting a value on data as a discreet asset. Then there's the issue, how do you put a value on a data lake? Because a data lake, is essentially an asset you build on spec. It's an exploratory archive, essentially, of all kinds of data that might yield some insights, but you have to have a team of data scientists doing exploration and modeling. But it's all on spec. How do you put a value on a data lake? And at what point does the data lake itself become a burden? Because you got to store that data and manage it. At what point do you drain that lake? At what point, do the costs of maintaining that lake outweigh the opportunity costs of not holding onto it? >> So each Hadoop note is approximately $20,000 per year cost for storage. So I think that there needs to be a test and a diagnostic, before even inputting, ingesting the data and storing it. "Is this actually going to be useful? "What value do we plan to create from this?" Because really, you can't store all the data. And it's a lot cheaper to store data in Hadoop then it was in traditional systems but it's definitely not free. So people need to be applying this test before even ingesting the data. Why do we need this? What business value? >> I think the question we need to also ask around this is, "Why are we building data lakes "in the first place? "So what's the function it's going to perform for you?" There's been a huge drive to this idea. "We need a data lake. "We need to put it all somewhere." But invariably they become data swamps. And we only half jokingly say that because I've seen 90 day projects turn from a great idea, to a really bad nightmare. And as Lillian said, it is cheaper in some ways to put it into a HDFS platform, in a technical sense. But when we look at all the fully burdened components, it's actually more expensive to find Hadoop specialists and Spark specialists to maintain that cluster. And invariably I'm finding that big data, quote unquote, is not actually so much lots of data, it's complex data. And as Lillian said, "You don't always "need to store it all." So I think if we go back to the question of, "What's the function of a data lake in the first place? "Why are we building one?" And then start to build some fully burdened cost components around that. We'll quickly find that we don't actually need a data lake, per se. We just need an interim data store. So we might take last years' data and tokenize it, and analyze it, and do some analytics on it, and just keep the meta data. So I think there is this rush, for a whole range of reasons, particularly vendor driven. To build data lakes because we think they're a necessity, when in reality they may just be an interim requirement and we don't need to keep them for a long term. >> I'm going to attempt to, the last few questions, put them all together. And I think, they all belong together because one of the reasons why there's such hesitation about progress within the data world is because there's just so much accumulated tech debt already. Where there's a new idea. We go out and we build it. And six months, three years, it really depends on how big the idea is, millions of dollars is spent. And then by the time things are built the idea is pretty much obsolete, no one really cares anymore. And I think what's exciting now is that the speed to value is just so much faster than it's ever been before. And I think that, you know, what makes that possible is this concept of, I don't think of a data lake as a thing. I think of a data lake as an ecosystem. And that ecosystem has evolved so much more, probably in the last three years than it has in the past 30 years. And it's exciting times, because now once we have this ecosystem in place, if we have a new idea, we can actually do it in minutes not years. And that's really the exciting part. And I think, you know, data lake versus a data swamp, comes back to just traditional data architecture. And if you architect your data lake right, you're going to have something that's substantial, that's you're going to be able to harness and grow. If you don't do it right. If you just throw data. If you buy Hadoop cluster or a Cloud platform and just throw your data out there and say, "We have a lake now." yeah, you're going to create a mess. And I think taking the time to really understand, you know, the new paradigm of data architecture and modern data engineering, and actually doing it in a very disciplined way. If you think about it, what we're doing is we're building laboratories. And if you have a shabby, poorly built laboratory, the best scientist in the world isn't going to be able to prove his theories. So if you have a well built laboratory and a clean room, then, you know a scientist can get what he needs done very, very, very efficiently. And that's the goal, I think, of data management today. >> I'd like to just quickly add that I totally agree with the challenge between on premise and Cloud mode. And I think one of the strong themes of today is going to be the hybrid data management challenge. And I think organizations, some organizations, have rushed to adopt Cloud. And thinking it's a really good place to dump the data and someone else has to manage the problem. And then they've ended up with a very expensive death by 1,000 cuts in some senses. And then others have been very reluctant as a result of not gotten access to rapid moving and disruptive technology. So I think there's a really big challenge to get a basic conversation going around what's the value using Cloud technology as in adopting it, versus what are the risks? And when's the right time to move? For example, should we Cloud Burst for workloads? Do we move whole data sets in there? You know, moving half a petabyte of data into a Cloud platform back is a non-trivial exercise. But moving a terabyte isn't actually that big a deal anymore. So, you know, should we keep stuff behind the firewalls? I'd be interested in seeing this week where 80% of the data, supposedly is. And just push out for Cloud tools, machine learning, data science tools, whatever they might be, cognitive analytics, et cetera. And keep the bulk of the data on premise. Or should we just move whole spools into the Cloud? There is no one size fits all. There's no silver bullet. Every organization has it's own quirks and own nuances they need to think through and make a decision themselves. >> Very often, Dez, organizations have zonal architectures so you'll have a data lake that consists of a no sequel platform that might be used for say, mobile applications. A Hadoop platform that might be used for unstructured data refinement, so forth. A streaming platform, so forth and so on. And then you'll have machine learning models that are built and optimized for those different platforms. So, you know, think of it in terms of then, your data lake, is a set of zones that-- >> It gets even more complex just playing on that theme, when you think about what Cisco started, called Folk Computing. I don't really like that term. But edge analytics, or computing at the edge. We've seen with the internet coming along where we couldn't deliver everything with a central data center. So we started creating this concept of content delivery networks, right? I think the same thing, I know the same thing has happened in data analysis and data processing. Where we've been pulling social media out of the Cloud, per se, and bringing it back to a central source. And doing analytics on it. But when you think of something like, say for example, when the Dreamliner 787 from Boeing came out, this airplane created 1/2 a terabyte of data per flight. Now let's just do some quick, back of the envelope math. There's 87,400 fights a day, just in the domestic airspace in the USA alone, per day. Now 87,400 by 1/2 a terabyte, that's 43 point five petabytes a day. You physically can't copy that from quote unquote in the Cloud, if you'll pardon the pun, back to the data center. So now we've got the challenge, a lot of our Enterprise data's behind a firewall, supposedly 80% of it. But what's out at the edge of the network. Where's the value in that data? So there are zonal challenges. Now what do I do with my Enterprise versus the open data, the mobile data, the machine data. >> Yeah, we've seen some recent data from IDC that says, "About 43% of the data "is going to stay at the edge." We think that, that's way understated, just given the examples. We think it's closer to 90% is going to stay at the edge. >> Just on the airplane topic, right? So Airbus wasn't going to be outdone. Boeing put 4,000 sensors or something in their 787 Dreamliner six years ago. Airbus just announced an 83, 81,000 with 10,000 sensors in it. Do the same math. Now the FAA in the US said that all aircraft and all carriers have to be, by early next year, I think it's like March or April next year, have to be at the same level of BIOS. Or the same capability of data collection and so forth. It's kind of like a mini GDPR for airlines. So with the 83, 81,000 with 10,000 sensors, that becomes two point five terabytes per flight. If you do the math, it's 220 petabytes of data just in one day's traffic, domestically in the US. Now, it's just so mind boggling that we're going to have to completely turn our thinking on its' head, on what do we do behind the firewall? What do we do in the Cloud versus what we might have to do in the airplane? I mean, think about edge analytics in the airplane processing data, as you said, Jim, streaming analytics in flight. >> Yeah that's a big topic within Wikibon, so, within the team. Me and David Floyer, and my other colleagues. They're talking about the whole notion of edge architecture. Not only will most of the data be persisted at the edge, most of the deep learning models like TensorFlow will be executed at the edge. To some degree, the training of those models will happen in the Cloud. But much of that will be pushed in a federated fashion to the edge, or at least I'm predicting. We're already seeing some industry moves in that direction, in terms of architectures. Google has a federated training, project or initiative. >> Chris: Look at TensorFlow Lite. >> Which is really fascinating for it's geared to IOT, I'm sorry, go ahead. >> Look at TensorFlow Lite. I mean in the announcement of having every Android device having ML capabilities, is Google's essential acknowledgment, "We can't do it all." So we need to essentially, sort of like a setting at home. Everyone's smartphone top TV box just to help with the processing. >> Now we're talking about this, this sort of leads to this IOT discussion but I want to underscore the operating model. As you were saying, "You can't just "lift and shift to the Cloud." You're not going to, CEOs aren't going to get the billion dollar hit by just doing that. So you got to change the operating model. And that leads to, this discussion of IOT. And an entirely new operating model. >> Well, there are companies that are like Sisense who have worked with Intel. And they've taken this concept. They've taken the business logic and not just putting it in the chip, but actually putting it in memory, in the chip. So as data's going through the chip it's not just actually being processed but it's actually being baked in memory. So level one, two, and three cache. Now this is a game changer. Because as Chris was saying, even if we were to get the data back to a central location, the compute load, I saw a real interesting thing from I think it was Google the other day, one of the guys was doing a talk. And he spoke about what it meant to add cognitive and voice processing into just the Android platform. And they used some number, like that had, double the amount of compute they had, just to add voice for free, to the Android platform. Now even for Google, that's a nontrivial exercise. So as Chris was saying, I think we have to again, flip it on its' head and say, "How much can we put "at the edge of the network?" Because think about these phones. I mean, even your fridge and microwave, right? We put a man on the moon with something that these days, we make for $89 at home, on the Raspberry Pie computer, right? And even that was 1,000 times more powerful. When we start looking at what's going into the chips, we've seen people build new, not even GPUs, but deep learning and stream analytics capable chips. Like Google, for example. That's going to make its' way into consumer products. So that, now the compute capacity in phones, is going to, I think transmogrify in some ways because there is some magic in there. To the point where, as Chris was saying, "We're going to have the smarts in our phone." And a lot of that workload is going to move closer to us. And only the metadata that we need to move is going to go centrally. >> Well here's the thing. The edge isn't the technology. The edge is actually the people. When you look at, for example, the MIT language Scratch. This is kids programming language. It's drag and drop. You know, kids can assemble really fun animations and make little movies. We're training them to build for IOT. Because if you look at a system like Node-RED, it's an IBM interface that is drag and drop. Your workflow is for IOT. And you can push that to a device. Scratch has a converter for doing those. So the edge is what those thousands and millions of kids who are learning how to code, learning how to think architecturally and algorithmically. What they're going to create that is beyond what any of us can possibly imagine. >> I'd like to add one other thing, as well. I think there's a topic we've got to start tabling. And that is what I refer to as the gravity of data. So when you think about how planets are formed, right? Particles of dust accrete. They form into planets. Planets develop gravity. And the reason we're not flying into space right now is that there's gravitational force. Even though it's one of the weakest forces, it keeps us on our feet. Oftentimes in organizations, I ask them to start thinking about, "Where is the center "of your universe with regard to the gravity of data." Because if you can follow the center of your universe and the gravity of your data, you can often, as Chris is saying, find where the business logic needs to be. And it could be that you got to think about a storage problem. You can think about a compute problem. You can think about a streaming analytics problem. But if you can find where the center of your universe and the center of your gravity for your data is, often you can get a really good insight into where you can start focusing on where the workloads are going to be where the smarts are going to be. Whether it's small, medium, or large. >> So this brings up the topic of data governance. One of the themes here at Fast Track Your Data is GDPR. What it means. It's one of the reasons, I think IBM selected Europe, generally, Munich specifically. So let's talk about GDPR. We had a really interesting discussion last night. So let's kind of recreate some of that. I'd like somebody in the panel to start with, what is GDPR? And why does it matter, Ronald? >> Yeah, maybe I can start. Maybe a little bit more in general unified governance. So if i talk to companies and I need to explain to them what's governance, I basically compare it with a crime scene. So in a crime scene if something happens, they start with securing all the evidence. So they start sealing the environment. And take care that all the evidence is collected. And on the other hand, you see that they need to protect this evidence. There are all kinds of policies. There are all kinds of procedures. There are all kinds of rules, that need to be followed. To take care that the whole evidence is secured well. And once you start, basically, investigating. So you have the crime scene investigators. You have the research lab. You have all different kind of people. They need to have consent before they can use all this evidence. And the whole reason why they're doing this is in order to collect the villain, the crook. To catch him and on the other hand, once he's there, to convict him. And we do this to have trust in the materials. Or trust in basically, the analytics. And on the other hand to, the public have trust in everything what's happened with the data. So if you look to a company, where data is basically the evidence, this is the value of your data. It's similar to like the evidence within a crime scene. But most companies don't treat it like this. So if we then look to GDPR, GDPR basically shifts the power and the ownership of the data from the company to the person that created it. Which is often, let's say the consumer. And there's a lot of paradox in this. Because all the companies say, "We need to have this customer data. "Because we need to improve the customer experience." So if you make it concrete and let's say it's 1st of June, so GDPR is active. And it's first of June 2018. And I go to iTunes, so I use iTunes. Let's go to iTunes said, "Okay, Apple please "give me access to my data." I want to see which kind of personal information you have stored for me. On the other end, I want to have the right to rectify all this data. I want to be able to change it and give them a different level of how they can use my data. So I ask this to iTunes. And then I say to them, okay, "I basically don't like you anymore. "I want to go to Spotify. "So please transfer all my personal data to Spotify." So that's possible once it's June 18. Then I go back to iTunes and say, "Okay, I don't like it anymore. "Please reduce my consent. "I withdraw my consent. "And I want you to remove all my "personal data for everything that you use." And I go to Spotify and I give them, let's say, consent for using my data. So this is a shift where you can, as a person be the owner of the data. And this has a lot of consequences, of course, for organizations, how to manage this. So it's quite simple for the consumer. They get the power, it's maturing the whole law system. But it's a big consequence of course for organizations. >> This is going to be a nightmare for marketers. But fill in some of the gaps there. >> Let's go back, so GDPR, the General Data Protection Regulation, was passed by the EU in 2016, in May of 2016. It is, as Ronald was saying, it's four basic things. The right to privacy. The right to be forgotten. Privacy built into systems by default. And the right to data transfer. >> Joe: It takes effect next year. >> It is already in effect. GDPR took effect in May of 2016. The enforcement penalties take place the 25th of May 2018. Now here's where, there's two things on the penalty side that are important for everyone to know. Now number one, GDPR is extra territorial. Which means that an EU citizen, anywhere on the planet has GDPR, goes with them. So say you're a pizza shop in Nebraska. And an EU citizen walks in, orders a pizza. Gives her the credit card and stuff like that. If you for some reason, store that data, GDPR now applies to you, Mr. Pizza shop, whether or not you do business in the EU. Because an EU citizen's data is with you. Two, the penalties are much stiffer then they ever have been. In the old days companies could simply write off penalties as saying, "That's the cost of doing business." With GDPR the penalties are up to 4% of your annual revenue or 20 million Euros, whichever is greater. And there may be criminal sanctions, charges, against key company executives. So there's a lot of questions about how this is going to be implemented. But one of the first impacts you'll see from a marketing perspective is all the advertising we do, targeting people by their age, by their personally identifiable information, by their demographics. Between now and May 25th 2018, a good chunk of that may have to go away because there's no way for you to say, "Well this person's an EU citizen, this person's not." People give false information all the time online. So how do you differentiate it? Every company, regardless of whether they're in the EU or not will have to adapt to it, or deal with the penalties. >> So Lillian, as a consumer this is designed to protect you. But you had a very negative perception of this regulation. >> I've looked over the GDPR and to me it actually looks like a socialist agenda. It looks like (panel laughs) no, it looks like a full assault on free enterprise and capitalism. And on its' face from a legal perspective, its' completely and wholly unenforceable. Because they're assigning jurisdictional rights to the citizen. But what are they going to do? They're going to go to Nebraska and they're going to call in the guy from the pizza shop? And call him into what court? The EU court? It's unenforceable from a legal perspective. And if you write a law that's unenforceable, you know, it's got to be enforceable in every element. It can't be just, "Oh, we're only "going to enforce it for Facebook and for Google. "But it's not enforceable for," it needs to be written so that it's a complete and actionable law. And it's not written in that way. And from a technological perspective it's not implementable. I think you said something like 652 EU regulators or political people voted for this and 10 voted against it. But what do they know about actually implementing it? Is it possible? There's all sorts of regulations out there that aren't possible to implement. I come from an environmental engineering background. And it's absolutely ridiculous because these agencies will pass laws that actually, it's not possible to implement those in practice. The cost would be too great. And it's not even needed. So I don't know, I just saw this and I thought, "You know, if the EU wants to," what they're essentially trying to do is regulate what the rest of the world does on the internet. And if they want to build their own internet like China has and police it the way that they want to. But Ronald here, made an analogy between data, and free enterprise, and a crime scene. Now to me, that's absolutely ridiculous. What does data and someone signing up for an email list have to do with a crime scene? And if EU wants to make it that way they can police their own internet. But they can't go across the world. They can't go to Singapore and tell Singapore, or go to the pizza shop in Nebraska and tell them how to run their business. >> You know, EU overreach in the post Brexit era, of what you're saying has a lot of validity. How far can the tentacles of the EU reach into other sovereign nations. >> What court are they going to call them into? >> Yeah. >> I'd like to weigh in on this. There are lots of unknowns, right? So I'd like us to focus on the things we do know. We've already dealt with similar situations before. In Australia, we introduced a goods and sales tax. Completely foreign concept. Everything you bought had 10% on it. No one knew how to deal with this. It was a completely new practice in accounting. There's a whole bunch of new software that had to be written. MYRB had to have new capability, but we coped. No one actually went to jail yet. It's decades later, for not complying with GST. So what it was, was a framework on how to shift from non sales tax related revenue collection. To sales tax related revenue collection. I agree that there are some egregious things built into this. I don't disagree with that at all. But I think if I put my slightly broader view of the world hat on, we have well and truly gone past the point in my mind, where data was respected, data was treated in a sensible way. I mean I get emails from companies I've never done business with. And when I follow it up, it's because I did business with a credit card company, that gave it to a service provider, that thought that I was going to, when I bought a holiday to come to Europe, that I might want travel insurance. Now some might say there's value in that. And other's say there's not, there's the debate. But let's just focus on what we're talking about. We're talking about a framework for governance of the treatment of data. If we remove all the emotive component, what we are talking about is a series of guidelines, backed by laws, that say, "We would like you to do this," in an ideal world. But I don't think anyone's going to go to jail, on day one. They may go to jail on day 180. If they continue to do nothing about it. So they're asking you to sort of sit up and pay attention. Do something about it. There's a whole bunch of relief around how you approach it. The big thing for me, is there's no get out of jail card, right? There is no get out of jail card for not complying. But there's plenty of support. I mean, we're going to have ambulance chasers everywhere. We're going to have class actions. We're going to have individual suits. The greatest thing to do right now is get into GDPR law. Because you seem to think data scientists are unicorn? >> What kind of life is that if there's ambulance chasers everywhere? You want to live like that? >> Well I think we've seen ad blocking. I use ad blocking as an example, right? A lot of organizations with advertising broke the internet by just throwing too much content on pages, to the point where they're just unusable. And so we had this response with ad blocking. I think in many ways, GDPR is a regional response to a situation where I don't think it's the exact right answer. But it's the next evolutional step. We'll see things evolve over time. >> It's funny you mentioned it because in the United States one of the things that has happened, is that with the change in political administrations, the regulations on what companies can do with your data have actually been laxened, to the point where, for example, your internet service provider can resell your browsing history, with or without your consent. Or your consent's probably buried in there, on page 47. And so, GDPR is kind of a response to saying, "You know what? "You guys over there across the Atlantic "are kind of doing some fairly "irresponsible things with what you allow companies to do." Now, to Lillian's point, no one's probably going to go after the pizza shop in Nebraska because they don't do business in the EU. They don't have an EU presence. And it's unlikely that an EU regulator's going to get on a plane from Brussels and fly to Topeka and say, or Omaha, sorry, "Come on Joe, let's get the pizza shop in order here." But for companies, particularly Cloud companies, that have offices and operations within the EU, they have to sit up and pay attention. So if you have any kind of EU operations, or any kind of fiscal presence in the EU, you need to get on board. >> But to Lillian's point it becomes a boondoggle for lawyers in the EU who want to go after deep pocketed companies like Facebook and Google. >> What's the value in that? It seems like regulators are just trying to create work for themselves. >> What about the things that say advertisers can do, not so much with the data that they have? With the data that they don't have. In other words, they have people called data scientists who build models that can do inferences on sparse data. And do amazing things in terms of personalization. What do you do about all those gray areas? Where you got machine learning models and so forth? >> But it applies-- >> It applies to personally identifiable information. But if you have a talented enough data scientist, you don't need the PII or even the inferred characteristics. If a certain type of behavior happens on your website, for example. And this path of 17 pages almost always leads to a conversion, it doesn't matter who you are or where you're coming from. If you're a good enough data scientist, you can build a model that will track that. >> Like you know, target, infer some young woman was pregnant. And they inferred correctly even though that was never divulged. I mean, there's all those gray areas that, how can you stop that slippery slope? >> Well I'm going to weigh in really quickly. A really interesting experiment for people to do. When people get very emotional about it I say to them, "Go to Google.com, "view source, put it in seven point Courier "font in Word and count how many pages it is." I guess you can't guess how many pages? It's 52 pages of seven point Courier font, HTML to render one logo, and a search field, and a click button. Now why do we need 52 pages of HTML source code and Java script just to take a search query. Think about what's being done in that. It's effectively a mini operating system, to figure out who you are, and what you're doing, and where you been. Now is that a good or bad thing? I don't know, I'm not going to make a judgment call. But what I'm saying is we need to stop and take a deep breath and say, "Does anybody need a 52 page, "home page to take a search query?" Because that's just the tip of the iceberg. >> To that point, I like the results that Google gives me. That's why I use Google and not Bing. Because I get better search results. So, yeah, I don't mind if you mine my personal data and give me, our Facebook ads, those are the only ads, I saw in your article that GDPR is going to take out targeted advertising. The only ads in the entire world, that I like are Facebook ads. Because I actually see products I'm interested in. And I'm happy to learn about that. I think, "Oh I want to research that. "I want to see this new line of products "and what are their competitors?" And I like the targeted advertising. I like the targeted search results because it's giving me more of the information that I'm actually interested in. >> And that's exactly what it's about. You can still decide, yourself, if you want to have this targeted advertising. If not, then you don't give consent. If you like it, you give consent. So if a company gives you value, you give consent back. So it's not that it's restricting everything. It's giving consent. And I think it's similar to what happened and the same type of response, what happened, we had the Mad Cow Disease here in Europe, where you had the whole food chain that needed to be tracked. And everybody said, "No, it's not required." But now it's implemented. Everybody in Europe does it. So it's the same, what probably going to happen over here as well. >> So what does GDPR mean for data scientists? >> I think GDPR is, I think it is needed. I think one of the things that may be slowing data science down is fear. People are afraid to share their data. Because they don't know what's going to be done with it. If there are some guidelines around it that should be enforced and I think, you know, I think it's been said but as long as a company could prove that it's doing due diligence to protect your data, I think no one is going to go to jail. I think when there's, you know, we reference a crime scene, if there's a heinous crime being committed, all right, then it's going to become obvious. And then you do go directly to jail. But I think having guidelines and even laws around privacy and protection of data is not necessarily a bad thing. You can do a lot of data, really meaningful data science, without understanding that it's Joe Caserta. All of the demographics about me. All of the characteristics about me as a human being, I think are still on the table. All that they're saying is that you can't go after Joe, himself, directly. And I think that's okay. You know, there's still a lot of things. We could still cure diseases without knowing that I'm Joe Caserta, right? As long as you know everything else about me. And I think that's really at the core, that's what we're trying to do. We're trying to protect the individual and the individual's data about themselves. But I think as far as how it affects data science, you know, a lot of our clients, they're afraid to implement things because they don't exactly understand what the guideline is. And they don't want to go to jail. So they wind up doing nothing. So now that we have something in writing that, at least, it's something that we can work towards, I think is a good thing. >> In many ways, organizations are suffering from the deer in the headlight problem. They don't understand it. And so they just end up frozen in the headlights. But I just want to go back one step if I could. We could get really excited about what it is and is not. But for me, the most critical thing there is to remember though, data breaches are happening. There are over 1,400 data breaches, on average, per day. And most of them are not trivial. And when we saw 1/2 a billion from Yahoo. And then one point one billion and then one point five billion. I mean, think about what that actually means. There were 47,500 Mongodbs breached in an 18 hour window, after an automated upgrade. And they were airlines, they were banks, they were police stations. They were hospitals. So when I think about frameworks like GDPR, I'm less worried about whether I'm going to see ads and be sold stuff. I'm more worried about, and I'll give you one example. My 12 year old son has an account at a platform called Edmodo. Now I'm not going to pick on that brand for any reason but it's a current issue. Something like, I think it was like 19 million children in the world had their username, password, email address, home address, and all this social interaction on this Facebook for kids platform called Edmodo, breached in one night. Now I got my hands on a copy. And everything about my son is there. Now I have a major issue with that. Because I can't do anything to undo that, nothing. The fact that I was able to get a copy, within hours on a dark website, for free. The fact that his first name, last name, email, mobile phone number, all these personal messages from friends. Nobody has the right to allow that to breach on my son. Or your children, or our children. For me, GDPR, is a framework for us to try and behave better about really big issues. Whether it's a socialist issue. Whether someone's got an issue with advertising. I'm actually not interested in that at all. What I'm interested in is companies need to behave much better about the treatment of data when it's the type of data that's being breached. And I get really emotional when it's my son, or someone else's child. Because I don't care if my bank account gets hacked. Because they hedge that. They underwrite and insure themselves and the money arrives back to my bank. But when it's my wife who donated blood and a blood donor website got breached and her details got lost. Even things like sexual preferences. That they ask questions on, is out there. My 12 year old son is out there. Nobody has the right to allow that to happen. For me, GDPR is the framework for us to focus on that. >> Dave: Lillian, is there a comment you have? >> Yeah, I think that, I think that security concerns are 100% and definitely a serious issue. Security needs to be addressed. And I think a lot of the stuff that's happening is due to, I think we need better security personnel. I think we need better people working in the security area where they're actually looking and securing. Because I don't think you can regulate I was just, I wanted to take the microphone back when you were talking about taking someone to jail. Okay, I have a background in law. And if you look at this, you guys are calling it a framework. But it's not a framework. What they're trying to do is take 4% of your business revenues per infraction. They want to say, "If a person signs up "on your email list and you didn't "like, necessarily give whatever "disclaimer that the EU said you need to give. "Per infraction, we're going to take "4% of your business revenue." That's a law, that they're trying to put into place. And you guys are talking about taking people to jail. What jail are you? EU is not a country. What jurisdiction do they have? Like, you're going to take pizza man Joe and put him in the EU jail? Is there an EU jail? Are you going to take them to a UN jail? I mean, it's just on its' face it doesn't hold up to legal tests. I don't understand how they could enforce this. >> I'd like to just answer the question on-- >> Security is a serious issue. I would be extremely upset if I were you. >> I personally know, people who work for companies who've had data breaches. And I respect them all. They're really smart people. They've got 25 plus years in security. And they are shocked that they've allowed a breach to take place. What they've invariably all agreed on is that a whole range of drivers have caused them to get to a bad practice. So then, for example, the donate blood website. The young person who was assist admin with all the right skills and all the right experience just made a basic mistake. They took a db dump of a mysql database before they upgraded their Wordpress website for the business. And they happened to leave it in a folder that was indexable by Google. And so somebody wrote a radio expression to search in Google to find sql backups. Now this person, I personally respect them. I think they're an amazing practitioner. They just made a mistake. So what does that bring us back to? It brings us back to the point that we need a safety net or a framework or whatever you want to call it. Where organizations have checks and balances no matter what they do. Whether it's an upgrade, a backup, a modification, you know. And they all think they do, but invariably we've seen from the hundreds of thousands of breaches, they don't. Now on the point of law, we could debate that all day. I mean the EU does have a remit. If I was caught speeding in Germany, as an Australian, I would be thrown into a German jail. If I got caught as an organization in France, breaching GDPR, I would be held accountable to the law in that region, by the organization pursuing me. So I think it's a bit of a misnomer saying I can't go to an EU jail. I don't disagree with you, totally, but I think it's regional. If I get a speeding fine and break the law of driving fast in EU, it's in the country, in the region, that I'm caught. And I think GDPR's going to be enforced in that same approach. >> All right folks, unfortunately the 60 minutes flew right by. And it does when you have great guests like yourselves. So thank you very much for joining this panel today. And we have an action packed day here. So we're going to cut over. The CUBE is going to have its' interview format starting in about 1/2 hour. And then we cut over to the main tent. Who's on the main tent? Dez, you're doing a main stage presentation today. Data Science is a Team Sport. Hillary Mason, has a breakout session. We also have a breakout session on GDPR and what it means for you. Are you ready for GDPR? Check out ibmgo.com. It's all free content, it's all open. You do have to sign in to see the Hillary Mason and the GDPR sessions. And we'll be back in about 1/2 hour with the CUBE. We'll be running replays all day on SiliconAngle.tv and also ibmgo.com. So thanks for watching everybody. Keep it right there, we'll be back in about 1/2 hour with the CUBE interviews. We're live from Munich, Germany, at Fast Track Your Data. This is Dave Vellante with Jim Kobielus, we'll see you shortly. (electronic music)

>> Announcer: Live from Munich, Germany, it's theCUBE. Covering IBM, Fast Track Your Data, brought to you by IBM. >> We're back in Munich, Germany this is Fast Track Your Data and this is theCUBE, the leader in live tech coverage, we go out to the events. We extract a signal from the noise my name is Dave Vellante and I'm here with my co-host Jim Kobielus. We just came off of the main stage. IBM had a very choreographed, really beautiful, Kate Silverton was there of BBC Fame talking to various folks within the IBM community. IBM executives, practitioners, and quite a main stage production Jim. IBM always knows how to do it right. Manish Goyal here, he's the Director of Product Management for the Watson Data Platform. Something we covered on theCUBE extensively, that announcement last year in New York City. Manish welcome to theCUBE. >> Thank you for having me. >> Dave: So this is, it really was your signature moment back in last fall at Strata in New York City. We covered that, big announcement, lot of customers there. You guys demonstrated sort of the next generation of platform that you guys are announcing. >> Manish: That's right. >> So take us, bring us up to date. How's it going, where are we at, and what are you guys doing here? >> So, again thank you for having me. >> Dave: You're welcome. >> Let me take a minute to just let all the viewers know what is alternate about form. So the Watson Data Platform is our cloud analytics platform, and it's really three things. It's a set of composable data services, for ingest, analyze, processed. It's a set of tailor-made experiences for the different personas. Whether you are a data engineer, a business analyst, data scientist, or the steward. And connecting all of these, both of these is a set of data fabric, which is really the secret sauce. And think of this as being the governance layer that ensures that everything that we're doing, that everything that is being done by any of these personas is working on trusted data, and that the insights that are being generated can be trusted by the risk folks, the business folks, as they put the analytics into production. >> Dave: So just to review for our audience, there are a number of components to the Watson Data Platform. >> That's right, yep. >> Dave: There's the governance components you mentioned, there's the visualization, there's analytics. Now, many people criticized Watson Data Platform, they said oh it's just IBM putting a bunch of despaired products together, some acquisitions and then wrapping some services around it. When we talked to you guys in October, you said no, no, that's not the case. But can you affirm that? >> That is exactly right, that is not the case. It's not just us putting stuff together and calling it a new name, and think oh that's the platform, just a set of despaired services. That is absolutely not, and that's why I was emphasizing this common data fabric, right. I've got a couple of, let me sort of dive a little bit deeper into it. >> Sure, great. >> Manish: So the biggest problem that customers and data users in general complain about is, extremely hard to find data, right. The tools that they're working with are all siloed. So even if, you know, you and I are working on, you know our analytics projects, very hard for me to share what I'm working on with you, the environment that I am running on with you, et cetera. And this... The third piece is, a real issue with is the data that I'm working with trusted? Like can I actually believe that this is the best data that I can use, so that when I put something into production when I create my machine learning models I put them into my production environment. The risk guys are going to be fine with it, I'm going to be fine with it, I see the results that I'm getting. And so, getting this data fabric which is addressing these issues. One, it's addressing it first and foremost with a data catalog, a governance layer. So that it's very clear, irrespective, whether you're a data engineer, business analyst, data scientist or the data steward, from the CDO's office, you're all working off the same version of the truth, right. >> Jim: Manish is that something a DevOps platform, is it like DevOps for data science or for machine learning development or is it... How would you describe... Does that make sense? The automated release pipeline that's-- >> Manish: In a way yes. >> With the governance baked in? >> Yes, in a way that's one way to describe it. So that's one aspect right? Making sure that you're working with the trusted data, making it very easy to find the data, so that's sort of the governance aspect. The second piece that sort of really makes this a platform is that you're working off the same notion of a workspace, we call it a project. So, you may start out as a data engineer being asked yourself, take all these different data sources that are coming in and create and publish a data set that can be consumed for dashboarding, for data analysis whatever. And you're working on that in a project, now if you have a data science team that needs to be working on the same thing, you can just invite them to the same project. So they're working on the same thing, similarly to a business analyst, et cetera. And all of these results, and when we talk about governance it's not about just data sets, it's all analytical products. So it is the model that you're creating are being put back into the catalog and governed. Data flows-- >> It's model governance. >> Jim: Model governance, it's model governance? >> Exactly. >> And aiding governance. >> Manish: So it's a huge problem that customers have. I was just talking to a large insurance company yesterday, and they're question was, "What are you doing to make sure that I don't have to spend an enormous amount of time that I have to with the risk group, before I can put a model into production." Because they want complete lineage all the way back, saying "Okay you created this model, you're going to put it into production, whether it's for allowing credit card insurance, whatever your product is that you're selling. How do you make sure that there's no bias in the model that is created, can you show me the data set on which you trained it? And then when you re-trained it can you show me that data set?" So in case they're audited, that there's complete way to go back from the production model all the way back to the data set that was created. And which goes even further back from all the different data sources. Where it was cleansed, et cetera, the ETL, where it was published, and then picked up by data science team. So all of these things, putting it together with this data fabric. Governance being a huge, huge portion of that that goes across everything that we're doing. Giving these tailor-made experiences for the different business personas, oh sorry, the data personas, and just making it extremely simple for generating insights that can be trusted. So that is what we are trying to do with the Watson Data Platform. As, since last fall when we announced it, we have had a huge update on our data science experience, you heard a lot about that in the presentation this morning. As well as, all of our other cloud data services and the governance put forth. >> Dave: And that data science experience is embedded fundamental to the platform. >> It is, it is. >> Dave: You know I want to ask you about that. Because I don't know if you remember Jim and Manish, a few years ago, several years ago, Pivotal announced this thing called Chorus and it went, it was a collaboration platform and it really went nowhere. Now part of the reason it went nowhere was because it was early days, but also there wasn't the analytics solution underneath it. But a lot of people questioned, "Well do we really need to collaborate across those personas?" Again maybe they were immature at the time. So convince me that there's a need for that and that this is actually getting used in the world. >> There was an example, probably you've always seen the venn diagram or for data scientist, right? With all the different skills that they need, they are a unicorn, and there are no unicorns. It's extremely hard for our customers, in fact just finding really good data scientist is extremely hard. It's a very limited supply of that talent. So that's one thing right. So you can't find enough of these folks to scale out the level of analytics that is needed, if you want to use data for a comparative advantage. So that's one aspect right, of talent being a huge issue. The second aspect of it is you really do need specialized skill in data engineer. You don't want your PhD data scientist spending 60% of their time finding cleansing data. You have folks who really do that well and you want to enable them to work closely with the data science team. And you really do need business analyst who are the key to sort of understanding the business problem that needs to be solved, because that's where you always want to start any analytics product. What is it that you're trying to improve, or reduce cost on, or whatever your problem is that you're addressing. And so you really need, it is a team sport. You can't just do it without. Now if it is a team sport, how are these folks going to collaborate, right? And that is why, in all of our interactions with our customers and their data science teams. They absolutely love the collaboration features that we have put in, and we have put in a lot of effort in data science experience and the same collaboration features are actually going to extend across the portfolio of these experiences on the data platform. >> And the whole notion of personas is so fundamental to Watson Data Platform. And I'm wondering, is IBM evolving the range and variety of personas for which you're providing these experiences? And what I mean by that is, examples, we see more and more data science application development projects focusing on for example, chat bots. That involves human conversation, you need a bit more, possibly a persona, a computational linguist. Or cognitive IoT, like Watson, you know IoT, that's sensors, that's hardware devices maybe hardware engineers, hardware engineering experiences. You see what I'm getting at is that data science centric projects are increasingly moving from the totally virtual world, to being very much embedding in the physical world and the world of human guided, machine learning guided conversation. What are your thoughts about evolving the personas mix? >> So application, application developers, or the persona I actually missed when I was talking about this before, it's absolutely central because almost anything that the data science team is doing is going to create, at the end of the day, sort of create models. But the hope is that it's going to put into production system. And that job typically is the role of an application developer. Now, Jim you mentioned sort of, there's a lot of emphasis these days on conversational chat bots. And again, at the end of the day with data science projects you are in many ways, trying to improve the experience that you're giving your customers. Or personalizing the experience that you're giving your customers. A celebrity experience that Rob talked about this morning. And there are other personas involved in that sense, so to get a chat bot right, I mean there is data that you can obviously harvest and use to create that flow, an intelligence in chat bot. But there are elements where you do need a subject matter expert to curate that. To make sure that it doesn't seem robotic, that it does feel genuine. And so there is a role for a subject matter expert, we sort of collaborate with a business analyst role, or persona. But yes, all of these roles play an important part in sort of putting together the entire package. It just feels seamless, and that's why I sort of come back to saying that it is a team sport and if you do not enable the teams to work closely together, and enhance their productivity, you can go after all the data that's being generated and all the opportunity that data is presenting. And the prize is to gain a competitive advantage. >> Dave: One of the things Manish, you demonstrated last fall was this sort of, it was sort of a recommendation engine and very personalized. And it was quite a nice demo and it wasn't a fake demo from what I understood, it was real data. Can you share with us in the time we have remaining, just some of your favorite examples of how people are applying the Watson Data Platform and affecting business? >> Manish: Sure yeah so, I'll tell you a couple of examples. So I was actually in London earlier this week, meeting with a customer and they are using DSX, our data science experience, with a couple of utility companies. One is a water company, water utility company. And the problem that they're trying to solve is, they're supplying water in a hilly area and they want to optimize the power that they use to power the pumps to pump out water. Because it can be very expensive if the pumps are running all the time, et cetera. And so they're using data science experience to optimize when and how, and how long the pumps need to run to enable that the customers are happy with the level of water supply that they're getting and the force that they're getting it with. While the utility company is optimizing the expense in actually powering these things. So that's just a recent example that comes to mind. There are others, there's a logistics, huge logistics in transportation company who's using data science experience to optimize how the refrigeration of the storage units that are going all across the globe for transporting sort of food and other articles like that. How they can optimize the temperature of the goods that they're transporting, again to make sure that there's absolutely the minimum amount of wastage that occurs in the transportation process. But at the same time optimizing the cost that they incur, because all of that sort of shows up in the end product that you and I buy from retailers. >> Dave: And is there instrumentation in the field involved in that? Is that kind of a semi-IoT example? >> Absolutely, right, so in this case, actually both of these cases, in one case there are smart meters that are throwing out data every 15 minutes. In the other example of the logistics one, it is data that is almost streaming coming in. So in one case you can use batch processing, even though it's coming in at a 15 minute intervals, to predict out what you want to do. In the other case it's streaming data, which you want to analyze as it streams. >> Excellent, alright well exciting times here for you and your group. >> Absolutely >> Dave: Congratulations on getting the product out and getting it adopted. >> Thank you. >> Glad to see that. And thanks for coming on theCUBE. >> Manish: Thank you. Thanks for having me. >> Alright! >> Dave: Keep it right there everybody. Jim and I will be back, we're live from Munich, Germany, unscripted, bringing theCUBE to you. Bringing Fast Track Your Data. We'll be right back. (techno music)

>> Announcer: Live from Munich, Germany; it's The Cube! Covering IBM Fast Track Your Data, brought to you by IBM. >> Welcome back to Munich, Germany everybody. This is The Cube, the leader in live tech coverage. We're covering Fast Track Your Data, IBM's signature moment here in Munich. Big themes around GDPR, data science, data science being a team sport. I'm Dave Vellante, I'm here with my co-host Jim Kobielus. Marc Altshuller is here, he's the general manager of IBM Business Analytics. Good to see you again Marc. >> Hey, always great to see you. Welcome, it's our first time together. >> Okay so we heard your key note, you were talking about the caveats of correlations, you were talking about rear view mirror analysis versus sort of looking forward, something that I've been sort of harping on for years. You know, I mean I remember the early days of "decision support" and the promises of 360 degree views of the customer, and predictive analytics, and I've always said it, "DSS really never lived up to that", y'know? "Will big data live up to that?" and we're kind of living that now, but what's your take on where we're at in this whole databean? >> I mean look, different customers are at different ends of the spectrum, but people are really getting value. They're becoming these data driven businesses. I like what Rob Thomas talked about on stage, right. Visiting companies a few years ago where they'd say "I'm not a technology company.". Now, how can you possibly say you're not a technology company, regardless of the industry. Your competitors will beat you if they are using data and you're not. >> Yeah, and everybody talks about digital transformation. And you hear that a lot at conferences, you guys haven't been pounding that theme, other than, y'know below the surface. And to us, digital means data, right? And if you're going to transform digitally, then it's all about the data, you mentioned data driven. What are you seeing, I mean most organizations in our view aren't "data driven" they're sort of reactive. Their CEO's maybe want to be data driven, maybe they're aboard conversations as to how to get there, but they're mostly focused on "Alright, how do we keep "the lights on, how do we meet our revenue targets, "how do we grow a little bit, and then whatever money "we have leftover we'll try to, y'know transform." What are you seeing? Is that changing? >> I would say, look I can give you an example right from my own space, the software space. For years we would have product managers, offering managers, maybe interviewing clients, on gut feel deciding what features to put at what priority within the next release. Now we have all these products instrumented behind the scenes with data, so we can literally see the friction points, the exit points, how frequently they come back, how long they're sessions are, we can even see them effectively graduating within the system where they continue to learn, and where they had shorter sessions, they're now going the longer sessions. That's really, really powerful for us in terms of trying to maximize our outcome from a software perspective. So that's where we kind of like, drink our own champagne. >> I got to ask you, so in around 2003, 2004 HBR had an article, front page y'know cover article of how "gut feel beats data and analytics", now this is 2003, 2004, software development as you know it's a lot of art involved, so my question is how are you doing? Is the data informing you in ways that are nonintuitive? And is it driving y'know, business outcomes for IBM? >> It is, look you see, I'll see like GM's of sports teams talking about maybe pushing back a little bit on the data. It's not all data driven, there's a little bit of gut, like is the guy going to, is he a checker in hockey or whatever that happens to be, and I would say, when you actually look at what's going on within baseball, and you look at the data, when you watch baseball growing up, the commentator might say something along the lines of "the pitcher has their stuff" right? "Does the pitcher have their stuff or not?". Now they literally know, the release point based on elevation, IOT within the state of the release point, the spin velocity of the ball, where they mathematically know "does the pitcher have their stuff?", are they hitting their locations? So all that stuff has all become data driven, and if you don't want to embrace it, you get beat, right? I mean even in baseball, I remember talking to one of these Moneyball type guys where I said like "Doesn't weather impact baseball?" And they're like "Yeah, we've looked at that, it absolutely impacts it." 'Cause you always hear of football and remember the old Peyton Manning thing? Don't play Peyton Manning in cold weather, don't bet on Peyton Manning in cold weather. So "I'm like isn't the same in baseball?", And he's like, absolutely it's the same in baseball, players preform different based on the climate. Do any mangers change their lineup based on that? Never. >> Speaking of HBR, I mean in the last few years there was also an article or two by Michael Shrage about the whole notion of real world experimentation and e-commerce, driven by data, y'know in line, to an operational process, like tuning the design iteratively of say, a shopping cart within your e-commerce environment, based on the stats on what work and what does not work. So, in many ways I mean AB testing, real world experimentation thrives on data science. Do you see AB testing becoming a standard business practice everywhere, or only in particular industries like you know, like the Wal-marts of the world? >> Yeah, look so, AB testing, multi-variant testing, they're pervasive, pretty much anyone who has a website ought to be doing this if they're not doing it already. Maybe some startups aren't quite into it. They prioritized in different spots, but mainstream fortune 500 companies are doing this, the tools have made it really easy. I would say, maybe the Achilles heel or the next frontier is, that is effectively saying, kind of creating one pattern of user, putting everyone in a single bucket, right? "Does this button perform better "when it's orange or when it's green? "Oh, it performs better orange." Really, does it perform well for every segmentation orange better than green or is it just a certain segmentation? So that next kind of frontier is going to be, how do we segment it, know a little bit more about you when you're coming in so that AB testing starts to build these kind of sub-profiles, sub-segmentation. >> Micro-segmentation, and of course, the end extreme of that dynamic is one-to-one personalization of experiences and engagements based on knowing 360 degrees about you and what makes you tick as well, so yeah. >> Altshuller: And add onto that context, right? You have your business, let's even keep it really simple, right, you've got your business life, you've got your social life, and your profile of what you're looking for when you're shopping your social life or something is very different than when you're shopping your business life. We have to personalize it to the idea where, I don't want to say schizophrenic but you do have multiple personalities from an online perspective, right? From a digital perspective it all depends in the moment, what is it that you're actually doing, right? And what are you, who are you acting for? >> Marc, I want to ask you, you're homies, your peeps are the business people. >> Yes. >> That's where you spend your time. I'm interested in the relationship between those business people and the data science teams. They're all, we all hear about how data science and unicorns are hard to find, difficult to get the skills, citizen data science is sort of a nirvana. But, how are you seeing businesses bring the domain expertise of the business and blending that with data science? >> So, they do it, I have some cautionary tales that I've experienced in terms of how they're doing it. They feel like, let's just assign the subject matter expert, they'll work with the data scientist, they'll give them context as they're doing their project, but unfortunately what I've seen time and time again, is that subject matter expert right out of the gate brings a tremendous amount of bias based on the types of analysis they've done in the past. >> Vellante: That's not how we do it here. >> Yeah, exactly, like "did you test this?". "Oh yeah, there's no correlation there, we've tried it." Well, just because there's no correlation, as I talked about onstage, doesn't mean it's not part of the pattern in terms of, like you don't want someone in there right off the bat dismissing things. So I always coach, when the business user subject matter experts become involved early, they have to be tremendously open-minded and not all of them can be. I like bringing them in later, because that data scientist, they are unbiased, like they see this data set, it doesn't mean anything to them, they're just numerically telling you what the data set says. Now the business user can then add some context, maybe they grabbed a field that really is an irrelevant field and they can give them that context afterwards. But we just don't want them shutting down, kind of roots, too early in the process. >> You know, we've been talking for a couple of years now within our community about this digital matrix, this digital fabric that's emerged and you're seeing these horizontal layers of technology, whether it's cloud or, you know, security, you all OAuth in with LinkedIn, Facebook, and Twitter. There's a data fabric that's emerging and you're seeing all these new business models, whether it's Uber or Airbnb or WAZE, et cetera, and then you see this blockbuster announcement last week, Amazon buying Whole Foods. And it's just fascinating to us and it's all about the data that a company like an Amazon can be a content company, could be a retail company, now it's becoming a grocer, you see Apple getting into financial services. So, you're seeing industries being able to traverse or companies being able traverse industries and it's all because of the data, so these conversations absolutely are going on in boardrooms. It's all about the digital transformation, the digital disruption, so how do you see, you know, your clients trying to take advantage of that or defend against that? >> Yeah look, I mean, you have to be proactive. You have to be willing to disrupt yourself in all these tech industries, it's just moving too quickly. I read a similar story, I think yesterday, around potentially Blockchain disrupting ridesharing programs, right? Why do you need the intermediary if you have this open ledger and these secure transactions you can do back and forth with this ecosystem. So there's another interesting disruption. Now do the ridesharing guys proactively get into that and promote it, or do they almost in slow motion, get replaced by that at some point. So yeah I think it's a come-on on all of us, like you don't remain a market lead, every market leader gets destructive at some point, the key is, do you disrupt yourself and you remain the market leader, or do you let someone else disrupt you. And if you get disrupted, how quickly can you recover. >> Well you know, you talked to banking executives and they're all talking Blockchain. Blockchain is the future, Bitcoin was designed to disintermediate the bank, so they're many, many banks are embracing it and so it comes back to the data. So my question I have, the discussion I'd like to have is how organizations are valuing data. You can't put data as a value on, y'know an asset on your balance sheet. The accounting industry standards don't exist. They probably won't for decades. So how are companies, y'know crocking data value, is it limiting their ability to move toward a data driven economy, is it a limiting factor that they don't have a good way to value their data, and understand how to monetize it. >> So I have heard of cases where companies have but data on their balance sheet, it's not mainstream at this point, but I mean you've seen it sometimes, and even some bankruptcy proceedings, their industry that's being in a bankruptcy protection where they say "Hey, but this data asset "is really where the value is." >> Vellante: And it's certainly implicit in valuations. >> Correct, I mean you see bios all the time based on the actual data sets, so yeah that data set, they definitely treasure it, and they realize that a lot of their answers are within that data set. And they also I think, understand that they're is a lot of peeling the onion that goes on when you're starting to work through that data, right? You have your initial thoughts, then you correct something based on what the data told you to do, and then the new data comes in based on what your new experience is, and then all of a sudden you have, you see what your next friction point is. You continue to knock down these things, so it is also very iterative working with that data asset. But yeah, these companies are seeing it's very value when they collect the data, but the other thing is the signal of what's driving your business may not be in your data, more and more often it may be in market data that's out there. So you think about social media data, you think about weather data and being able to go and grab that information. I remember watching the show Millions, where they talk about the hedge fund guys running satellites over like Wal-mart parking lots to try to predict the redux for the quarter, right? Like, you're collecting all this data but it's out there. >> Or maybe the value is not so much in the data itself, but in what it enables you to develop as a derivative asset, meaning a statistical predictive model or machine learning model that shows the patterns that you can then drive into, recommendation engines, and your target marketing y'know applications. So you see any clients valuate, doing their valuation of data on those derivative assets? >> Altshuller: Yeah. >> In lieu of... >> In these new business models I see within corporations that have been around for decades, it's actual data offers that they make to maybe their ecosystem, their channel. "Here's data we have, here's how you interpret it, "we'll continue to collect it, we'll continue to curate it, "we'll make it available." And this is really what's driving your business. So yeah, data assets become something that, companies are figuring out how to monetize their data assets. >> Of course those derived assets will decay if those models of, for example machine learning models are not trained with fresh, y'know data from the sources. >> And if we're not testing for new variable too, right? Like if the variable was never in the model, you still have to have this discovery process, that's always going on the see what new variables might be out there, what new data set, right. Like if a new IOT sensor in the baseball stadium becomes available, maybe that one I talked about with elevation of the pitcher, like until you have that you can't use it, but once you have it you have to figure out how to use it. >> Alright lets bring it back to your business, what can I buy from you, what do sell, what are your products? >> Yeah so after being in business analytics is Cognos analytics, Watson analytics, Watts analytics for social media, and planning analytics. Cognos is the "what", what's going on in my business. Watts analytics is the "why", planning analytics is "what do we think is going to happen?". We're starting to do more and more smarter, what do we think's going to happen based on these predictive models instead of just guessing what's going to happen. And then social media really gets into this idea of trying to find the signal, the sentiment. Not just around your own brand, it could be a competitor recall, and what now the intent is of that customer, are they going to now start buying other products, or are they going to stick with the recall company. >> Vellante: Okay so the starting point of your business having Cognos, one of the largest acquisitions ever in IBM's history, and of course it was all about CFO's and reporting and Sarbanes-Oxley was a huge boom to that business, but as I was saying before it, it never really got us to that predictive era. So you're layering those predictive pieces on top. >> That's what you saw on stage. >> Yes, that's right, what, so we saw on stage, and then are you selling to the same constituencies? Or how is constituency that you sell to changing? >> Yeah, no it's actually the same. Well Cognos BI, historically was selling to IT, and Cognos Analytics is selling to the business. But if we take that leap forward then we're now in the market, we have been for a few years now at Cognos Analytics. Yeah, that capability we showed onstage where we talked about not only what's going on, why it's going on, what will happen next, and what we ought to do about it. We're selling that capability for them, the business user, the dashboard becomes like a piece of glass to them. And that glass is able to call services that they don't have to be proficient in, they just want to be able to use them. It calls the weather service, it calls the optimization service, it calls the machine learning data sign service, and it actually gives them information that's forward looking and highly accurate, so they love it, 'cause it's cool they haven't had anything like that before. >> Vellante: Alright Marc Altshuller, thanks very much for coming back on The Cube, it's great to see you. >> Thank you. >> "You can't measure heart" as we say in boston, but you better start measuring. Alright keep right there everybody, Jim and I will right back after this short break. This is The Cube, we're live from Fast Track Your Data in Munich. We'll be right back. (upbeat jingle) (thoughtful music)

>> Narrator: Live from Munich, Germany it's theCUBE, covering IBM, fast track your data. brought to you by IBM. >> Welcome back to Munich, Germany everybody. This is theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise, and we're here at the IBM signature moment, Fast Track Your Data in Munich, where enterprise data governance is a huge theme. We're going to talk about that right now, I'm Dave Vellante with my co-host, Jim Kobielus. Sanjay Saxena is here, he's the senior Vice-President at Northern Trust. Sanjay, welcome to theCUBE, thanks for coming on. >> Thank you, thank you, thanks for having me. >> So, enterprise data governance is a huge theme here, we're going to get into that, but set up Northern Trust, your organization, and your role. >> So, I am the head for enterprise data governance for Northern Trust. It's an essential enterprise role across all the business units. I've been working with Northern Trust for the last three years to set up the program, and prior to this I worked with Bank of Montreal and other institutions doing similar things. >> So how is enterprise governance evolving? I mean, I go back to, sort of, 2006 for the federal rules of civil procedure when electronic, you know, records became admissible in courts, and that set off a whole chain reaction, and plugging the holes with email archiving, and it was just really scratching the surface. We've kind of evolved there, is governance a strategic imperative? Why is it a strategic imperative? And how is it evolving? >> Well, our program has significantly evolved over the past three years. Partly because of how the market conditions are, and what the regulators expect. We fundamentally started our program focused on regulations, risk and compliance, like most banks did. But now we are a very broad based program within the company. So not just a risk department or the finance department, but also the business units are asking for data governance and for quality. And we are in the asset management, asset servicing business. And a lot of our customers, we manage their data. So they are expecting this as stable stakes at this point in time. So we are realizing a lot of value of the data governance in the business units as well, in addition to the risk and compliance usage. >> So how has governance evolved? I mean I went back ten, eleven years which is like ancient history in these days. How has your, sort of, data governance strategy evolved, and where has it come from, and where are you now, and where are you going? >> So, about two to four years back there wasn't anything formal when it comes to governance, it was very specific to certain units of data, certain types of data. For example, most companies are very concerned about the pricing data. And that's where they would have governance. But it was never a broad based program. Nor was there an operating model around governance and organization, structure, teams of people, so over the past three or four years we've seen that evolution. So now I have a number of data stewards as part of my team, and within the business units, whose sole business is to do governance. We have formally establishing data governance principles and practices and policies. Years back, even three years back, you'd go to most organizations you wouldn't find any policies and practices of data governance. So those are two distinct ways that the governance has evolved in terms of the model. And also along with that has been an evolution of tools and technology and where IBM has heard this a lot. >> Generally the line of business people, you know, governance, compliance, even security, and it's changing but, generally if I hear those words as a business person, it's ugh, it's going to slow me down, it's going to cost me time, it's going to cost me money, bureaucracy overhead. How do you as a governance professional address that? Can you make governance a source of value? >> Right, so governance is a very abstract concept. Most people, most businesses, don't want they want to run away from anything close to governance, right? >> Dave: No accountability. >> No accountability, right? They want to be focused on their revenues, etc. So one way to make that, and what we've done is we've made it very tangible by showing them data quality in terms of metrics, in terms of dashboards, in terms of showing them cost of poor data quality, right? In terms of, for example, a simple example is, a customer names an address as being wrong, may not mean very much to a regulator, but it is really important from a business perspective for a relationship manager in our business. So what we've done is shown that to them and shown positive trending towards the mediation and tied it to the business outcomes. So I wouldn't say that we are there yet, it's a journey, but there's been a lot of evolution in the process, they are accepting my organization, they are accepting the roles, and they are accepting the work we're doing. And they want to be part of it. So that's how I see them evolve, I see this as a continuous evolvement even beyond that. And ultimately I see them using governance almost as a product. Right now it's, we provide a lot of data to our end consumers, to our asset management, to management companies, to fund administrators, and others, right? And data governance is an implicit component of that, right? We don't charge money for it, right? But in the world of the future I see that, depending on the tier of the customer, depending on the kind of data that we're supplying them, we can have different tiers of data quality and governance around that, and we could explicitly charge. So they're excited about that project, about that prospect, and they want to work with us on that. >> And you, do you have a chief data officer? >> Sanjay: Yes. >> Okay, so, is it a relatively new role? Or it's been around, I mean typically in your industry it's regulated and so you tend to have more propensity for CDOs, but has there been one for a while, or a couple years? >> Sanjay: It's been around for two years. >> Just two years? Okay. >> Sanjay: Yeah, two plus years, yes. >> Okay, so that chief data officer that emergent role, looks at things like data quality, looks at how to monetize data, tries to form relationships with the line of business, all those things. Companies generally are just starting to understand, all right, how do I, how does data effect my monetization? Not so much how do I sell the data, but how does data help my cut costs or increase revenue? >> Yeah, well, or, yeah, related to that very much is, for example, do you compute a metric such as customer lifetime value that you would sacrifice if you don't, if your business doesn't consolidate multiple inconsistent customer data sets down to one canonical data set that you can use then to, high quality, that you can use to drive targeting marketing, and better engagement. Do you report like a CLV, customer lifetime value, as part of your overall governance strategy or thought about doing that? >> We've thought about doing that, and those metrics are evolving in our organization, but even a little bit more basic metrics around is your customer contactable, right? Do you have the right information about them? Or, for the share of the wallet, is it actually a better example? Like we have different investment products, and we have different products that we sell to our wealthy individuals. What portion of those, what is the average number of products that they have from us? And to be able to monitor, and measure it, across a meter of time, is a really important thing for businesses to do. >> Okay, let's, I see your button here, your badge here, it says IBM analytics, global elite, I think there was a little reception last night by the lake, and you know, all the execs took you guys out and wined and dined you and, you know, that's good. We saw that action going on. But so, what is that mean, a global elite? So that means you're a top-tier customer, what's your relationship with IBM, and how has that evolved? >> Right, so yeah, so North Interest buys a lot of stuff from IBM, lot of technology, tools, consulting, so we are, we are one of the top tier customers, and that's why we are part of the global elite program. And our relationship has really really evolved over time, especially in the governance base I'm talking about, and IBM has been a significant partner for us in terms of the initial strategy around governance, which we implemented and we are still on track to get that fully implemented. Equally important is the tools and technologies that they brought into the space. So most of the vendors provide segregated tools for different portions of data governance. You'll find some people good in lineators, good in meta data, glossary, etc. But IBM has an end to end suite, and we've been able to integrate that, we've been able to make it a single solution, single integrated solution, and that's really benefited us. So that's really been the contribution of IBM. >> And, okay, so can you talk more about the business impact about that single integrated solution? >> So the business impact is that today, unlike ever in the past, we have data quality dashboards. And this is, we are measuring data quality across thousands of data attributes on a monthly basis. We are publishing trends around data quality. We have that, we are also, for people, developers, for business people who are interested in where the data is coming from, we have lineage, we have an enterprise glossary. So it's a one stop solution across all of those. The business people are able to look at that, whether it's risk, finance, or business units, they're able to look at that on a monthly basis. We're able to provide implications of quality, we provide trending, so it is really taking us towards making us a data driven organization. >> Have you been a user, at least a beta-user of the governance catalog that IBM has announced today? What are your thoughts about that? >> Yes, so the information governance catalog we've been using that for the last three years. We have, as I said, about several thousand data elements in the information governance catalog. And what that does is, it creates that single vocabulary within the bank, and you cannot even imagine how difficult that is. Because for two business units to agree on the meaning of a term, it requires a lot of discussions and deliberations. But having a one simple repository that has all of the meta-data is one aspect of it. The second thing is, which has got implications in terms of data security and protection, that we are able to tag the data as sensitive data. For example, for GDPR, so we are using the same tool to be able to tag sensitive data elements and, as I said, the whole lineage, where does it reside, where does the data flow into, all of those things are very very easy and have been implemented in the IGC. >> Sanjay, what would you say is your biggest challenge as an enterprise data governance professional? >> Team management is still the biggest challenge, it is. As I said, it's a journey. And getting to every individual in the enterprise, for example, to start using this glossary that I just talked about. Or getting people to systematically look at data quality across the board. The other piece is the funding around data initiatives, right? So everyone's used to large transformation programs, but when I come up with a list of, here are the top ten data quality issues that need to be fixed, everybody looks over everybody else's shoulder I guess, and says, who's going to pay for it, right? And is this really our problem, or is this the problem of somebody else, right? So we get into a lot of those discussions, but it's a journey, as I said. >> Well, so you need executive support. To get executive support you have to demonstrate how it drives business values. So that's where it's, there's some carrot and stick involved. Well the stick is, well, we got to comply. We've heard a lot about GDRP and how that's going to, you know, cause pain. Okay, so that's the stick. The carrot is the data monetization, and the data value piece, connecting data quality to data value is that, you know, enticement, is it not. >> That's absolutely right, and the more and more we can show monetization of data, or even the fact that, because of that data governance or quality, we were able to acquire a new customer. It doesn't all need to be tangible is what I'm saying. But the more and more we can show monetization, the better off we'll be in terms of selling the program. >> Excellent. Well, Sanjay thanks very much for coming to theCUBE and sharing your experience, we really appreciate it. >> Sanjay: Thank you, thank you very much. >> You're welcome. (techno music)

>> Announcer: Live from Munich, Germany, it's theCUBE. Covering IBM: Fast Track Your Data. Brought to you by IBM. >> Welcome, everybody, to Munich, Germany. This is Fast Track Your Data brought to you by IBM, and this is theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise. My name is Dave Vellante, and I'm here with my co-host Jim Kobielus. Rob Thomas is here, he's the General Manager of IBM Analytics, and longtime CUBE guest, good to see you again, Rob. >> Hey, great to see you. Thanks for being here. >> Dave: You're welcome, thanks for having us. So we're talking about, we missed each other last week at the Hortonworks DataWorks Summit, but you came on theCUBE, you guys had the big announcement there. You're sort of getting out, doing a Hadoop distribution, right? TheCUBE gave up our Hadoop distributions several years ago so. It's good that you joined us. But, um, that's tongue-in-cheek. Talk about what's going on with Hortonworks. You guys are now going to be partnering with them essentially to replace BigInsights, you're going to continue to service those customers. But there's more than that. What's that announcement all about? >> We're really excited about that announcement, that relationship, just to kind of recap for those that didn't see it last week. We are making a huge partnership with Hortonworks, where we're bringing data science and machine learning to the Hadoop community. So IBM will be adopting HDP as our distribution, and that's what we will drive into the market from a Hadoop perspective. Hortonworks is adopting IBM Data Science Experience and IBM machine learning to be a core part of their Hadoop platform. And I'd say this is a recognition. One is, companies should do what they do best. We think we're great at data science and machine learning. Hortonworks is the best at Hadoop. Combine those two things, it'll be great for clients. And, we also talked about extending that to things like Big SQL, where they're partnering with us on Big SQL, around modernizing data environments. And then third, which relates a little bit to what we're here in Munich talking about, is governance, where we're partnering closely with them around unified governance, Apache Atlas, advancing Atlas in the enterprise. And so, it's a lot of dimensions to the relationship, but I can tell you since I was on theCUBE a week ago with Rob Bearden, client response has been amazing. Rob and I have done a number of client visits together, and clients see the value of unlocking insights in their Hadoop data, and they love this, which is great. >> Now, I mean, the Hadoop distro, I mean early on you got into that business, just, you had to do it. You had to be relevant, you want to be part of the community, and a number of folks did that. But it's really sort of best left to a few guys who want to do that, and Apache open source is really, I think, the way to go there. Let's talk about Munich. You guys chose this venue. There's a lot of talk about GDPR, you've got some announcements around unified government, but why Munich? >> So, there's something interesting that I see happening in the market. So first of all, you look at the last five years. There's only 10 companies in the world that have outperformed the S&P 500, in each of those five years. And we started digging into who those companies are and what they do. They are all applying data science and machine learning at scale to drive their business. And so, something's happening in the market. That's what leaders are doing. And I look at what's happening in Europe, and I say, I don't see the European market being that aggressive yet around data science, machine learning, how you apply data for competitive advantage, so we wanted to come do this in Munich. And it's a bit of a wake-up call, almost, to say hey, this is what's happening. We want to encourage clients across Europe to think about how do they start to do something now. >> Yeah, of course, GDPR is also a hook. The European Union and you guys have made some talk about that, you've got some keynotes today, and some breakout sessions that are discussing that, but talk about the two announcements that you guys made. There's one on DB2, there's another one around unified governance, what do those mean for clients? >> Yeah, sure, so first of all on GDPR, it's interesting to me, it's kind of the inverse of Y2K, which is there's very little hype, but there's huge ramifications. And Y2K was kind of the opposite. So look, it's coming, May 2018, clients have to be GDPR-compliant. And there's a misconception in the market that that only impacts companies in Europe. It actually impacts any company that does any type of business in Europe. So, it impacts everybody. So we are announcing a platform for unified governance that makes sure clients are GDPR-compliant. We've integrated software technology across analytics, IBM security, some of the assets from the Promontory acquisition that IBM did last year, and we are delivering the only platform for unified governance. And that's what clients need to be GDPR-compliant. The second piece is data has to become a lot simpler. As you think about my comment, who's leading the market today? Data's hard, and so we're trying to make data dramatically simpler. And so for example, with DB2, what we're announcing is you can download and get started using DB2 in 15 minutes or less, and anybody can do it. Even you can do it, Dave, which is amazing. >> Dave: (laughs) >> For the first time ever, you can-- >> We'll test that, Rob. >> Let's go test that. I would love to see you do it, because I guarantee you can. Even my son can do it. I had my son do it this weekend before I came here, because I wanted to see how simple it was. So that announcement is really about bringing, or introducing a new era of simplicity to data and analytics. We call it Download And Go. We started with SPSS, we did that back in March. Now we're bringing Download And Go to DB2, and to our governance catalog. So the idea is make data really simple for enterprises. >> You had a community edition previous to this, correct? There was-- >> Rob: We did, but it wasn't this easy. >> Wasn't this simple, okay. >> Not anybody could do it, and I want to make it so anybody can do it. >> Is simplicity, the rate of simplicity, the only differentiator of the latest edition, or I believe you have Kubernetes support now with this new addition, can you describe what that involves? >> Yeah, sure, so there's two main things that are new functionally-wise, Jim, to your point. So one is, look, we're big supporters of Kubernetes. And as we are helping clients build out private clouds, the best answer for that in our mind is Kubernetes, and so when we released Data Science Experience for Private Cloud earlier this quarter, that was on Kubernetes, extending that now to other parts of the portfolio. The other thing we're doing with DB2 is we're extending JSON support for DB2. So think of it as, you're working in a relational environment, now just through SQL you can integrate with non-relational environments, JSON, documents, any type of no-SQL environment. So we're finally bringing to fruition this idea of a data fabric, which is I can access all my data from a single interface, and that's pretty powerful for clients. >> Yeah, more cloud data development. Rob, I wonder if you can, we can go back to the machine learning, one of the core focuses of this particular event and the announcements you're making. Back in the fall, IBM made an announcement of Watson machine learning, for IBM Cloud, and World of Watson. In February, you made an announcement of IBM machine learning for the z platform. What are the machine learning announcements at this particular event, and can you sort of connect the dots in terms of where you're going, in terms of what sort of innovations are you driving into your machine learning portfolio going forward? >> I have a fundamental belief that machine learning is best when it's brought to the data. So, we started with, like you said, Watson machine learning on IBM Cloud, and then we said well, what's the next big corpus of data in the world? That's an easy answer, it's the mainframe, that's where all the world's transactional data sits, so we did that. Last week with the Hortonworks announcement, we said we're bringing machine learning to Hadoop, so we've kind of covered all the landscape of where data is. Now, the next step is about how do we bring a community into this? And the way that you do that is we don't dictate a language, we don't dictate a framework. So if you want to work with IBM on machine learning, or in Data Science Experience, you choose your language. Python, great. Scala or Java, you pick whatever language you want. You pick whatever machine learning framework you want, we're not trying to dictate that because there's different preferences in the market, so what we're really talking about here this week in Munich is this idea of an open platform for data science and machine learning. And we think that is going to bring a lot of people to the table. >> And with open, one thing, with open platform in mind, one thing to me that is conspicuously missing from the announcement today, correct me if I'm wrong, is any indication that you're bringing support for the deep learning frameworks like TensorFlow into this overall machine learning environment. Am I wrong? I know you have Power AI. Is there a piece of Power AI in these announcements today? >> So, stay tuned on that. We are, it takes some time to do that right, and we are doing that. But we want to optimize so that you can do machine learning with GPU acceleration on Power AI, so stay tuned on that one. But we are supporting multiple frameworks, so if you want to use TensorFlow, that's great. If you want to use Caffe, that's great. If you want to use Theano, that's great. That is our approach here. We're going to allow you to decide what's the best framework for you. >> So as you look forward, maybe it's a question for you, Jim, but Rob I'd love you to chime in. What does that mean for businesses? I mean, is it just more automation, more capabilities as you evolve that timeline, without divulging any sort of secrets? What do you think, Jim? Or do you want me to ask-- >> What do I think, what do I think you're doing? >> No, you ask about deep learning, like, okay, that's, I don't see that, Rob says okay, stay tuned. What does it mean for a business, that, if like-- >> Yeah. >> If I'm planning my roadmap, what does that mean for me in terms of how I should think about the capabilities going forward? >> Yeah, well what it means for a business, first of all, is what they're going, they're using deep learning for, is doing things like video analytics, and speech analytics and more of the challenges involving convolution of neural networks to do pattern recognition on complex data objects for things like connected cars, and so forth. Those are the kind of things that can be done with deep learning. >> Okay. And so, Rob, you're talking about here in Europe how the uptick in some of the data orientation has been a little bit slower, so I presume from your standpoint you don't want to over-rotate, to some of these things. But what do you think, I mean, it sounds like there is difference between certainly Europe and those top 10 companies in the S&P, outperforming the S&P 500. What's the barrier, is it just an understanding of how to take advantage of data, is it cultural, what's your sense of this? >> So, to some extent, data science is easy, data culture is really hard. And so I do think that culture's a big piece of it. And the reason we're kind of starting with a focus on machine learning, simplistic view, machine learning is a general-purpose framework. And so it invites a lot of experimentation, a lot of engagement, we're trying to make it easier for people to on-board. As you get to things like deep learning as Jim's describing, that's where the market's going, there's no question. Those tend to be very domain-specific, vertical-type use cases and to some extent, what I see clients struggle with, they say well, I don't know what my use case is. So we're saying, look, okay, start with the basics. A general purpose framework, do some tests, do some iteration, do some experiments, and once you find out what's hunting and what's working, then you can go to a deep learning type of approach. And so I think you'll see an evolution towards that over time, it's not either-or. It's more of a question of sequencing. >> One of the things we've talked to you about on theCUBE in the past, you and others, is that IBM obviously is a big services business. This big data is complicated, but great for services, but one of the challenges that IBM and other companies have had is how do you take that service expertise, codify it to software and scale it at large volumes and make it adoptable? I thought the Watson data platform announcement last fall, I think at the time you called it Data Works, and then so the name evolved, was really a strong attempt to do that, to package a lot of expertise that you guys had developed over the years, maybe even some different software modules, but bring them together in a scalable software package. So is that the right interpretation, how's that going, what's the uptake been like? >> So, it's going incredibly well. What's interesting to me is what everybody remembers from that announcement is the Watson Data Platform, which is a decomposable framework for doing these types of use cases on the IBM cloud. But there was another piece of that announcement that is just as critical, which is we introduced something called the Data First method. And that is the recipe book to say to a client, so given where you are, how do you get to this future on the cloud? And that's the part that people, clients, struggle with, is how do I get from step to step? So with Data First, we said, well look. There's different approaches to this. You can start with governance, you can start with data science, you can start with data management, you can start with visualization, there's different entry points. You figure out the right one for you, and then we help clients through that. And we've made Data First method available to all of our business partners so they can go do that. We work closely with our own consulting business on that, GBS. But that to me is actually the thing from that event that has had, I'd say, the biggest impact on the market, is just helping clients map out an approach, a methodology, to getting on this journey. >> So that was a catalyst, so this is not a sequential process, you can start, you can enter, like you said, wherever you want, and then pick up the other pieces from majority model standpoint? Exactly, because everybody is at a different place in their own life cycle, and so we want to make that flexible. >> I have a question about the clients, the customers' use of Watson Data Platform in a DevOps context. So, are more of your customers looking to use Watson Data Platform to automate more of the stages of the machine learning development and the training and deployment pipeline, and do you see, IBM, do you see yourself taking the platform and evolving it into a more full-fledged automated data science release pipelining tool? Or am I misunderstanding that? >> Rob: No, I think that-- >> Your strategy. >> Rob: You got it right, I would just, I would expand a little bit. So, one is it's a very flexible way to manage data. When you look at the Watson Data Platform, we've got relational stores, we've got column stores, we've got in-memory stores, we've got the whole suite of open-source databases under the composed-IO umbrella, we've got cloud in. So we've delivered a very flexible data layer. Now, in terms of how you apply data science, we say, again, choose your model, choose your language, choose your framework, that's up to you, and we allow clients, many clients start by building models on their private cloud, then we say you can deploy those into the Watson Data Platform, so therefore then they're running on the data that you have as part of that data fabric. So, we're continuing to deliver a very fluid data layer which then you can apply data science, apply machine learning there, and there's a lot of data moving into the Watson Data Platform because clients see that flexibility. >> All right, Rob, we're out of time, but I want to kind of set up the day. We're doing CUBE interviews all morning here, and then we cut over to the main tent. You can get all of this on IBMgo.com, you'll see the schedule. Rob, you've got, you're kicking off a session. We've got Hilary Mason, we've got a breakout session on GDPR, maybe set up the main tent for us. >> Yeah, main tent's going to be exciting. We're going to debunk a lot of misconceptions about data and about what's happening. Marc Altshuller has got a great segment on what he calls the death of correlations, so we've got some pretty engaging stuff. Hilary's got a great piece that she was talking to me about this morning. It's going to be interesting. We think it's going to provoke some thought and ultimately provoke action, and that's the intent of this week. >> Excellent, well Rob, thanks again for coming to theCUBE. It's always a pleasure to see you. >> Rob: Thanks, guys, great to see you. >> You're welcome; all right, keep it right there, buddy, We'll be back with our next guest. This is theCUBE, we're live from Munich, Fast Track Your Data, right back. (upbeat electronic music)

>> Narrator: Live from Munich, Germany, it's theCube, covering IBM, Fast Track Your Data. Brought to you by IBM. >> Welcome to Fast Track Your Data, everybody, welcome to Munich, Germany, this is theCube, the leader in live tech coverage, I'm Dave Vellante with my co-host Jim Kobielus. Dinesh Nirmal is here, he's the vice president of IBM Analytics Development, of course, at IBM, and he's joined by Roland Voelskow, who is the Portfolio Executive at T-Systems, which is a division of Deutche Telekom. Gentlemen, welcome to theCube, Dinesh, good to see you again. >> Thank you. Roland, let me start with you. So your role inside T-Systems, talk about that a little bit. >> Yeah, so thank you for being here, at T-Systems we serve our customers with all kinds of informal hosting services, from infrastructure up to application services, and we have recently, I'd say, about five years ago started to standardize our offerings as a product portfolio and are now focusing on coming from the infrastructure and infrastructure as a service offerings. We are now putting a strong effort in the virtualization container, virtualization to be able to move complete application landscapes from different platforms from, to T-Systems or between T-Systems platforms. The goal is to make, to enable customers to talk with us about their application needs, their business process needs, and have everything which is related to the right place to run the application will be managed automatically by our intelligent platform, which will decide in a multi-platform environment if an application, particularly a business application runs on high available private cloud or a test dev environment, for example, could run on a public cloud, so the customer should not need to deal with this kind of technology questions anymore, so we want to cover the application needs and have the rest automated. >> Yeah, we're seeing a massive trend in our community for organizations like yours to try to eliminate wherever possible undifferentiated infrastructure management, and provisioning of hardware, and Lund management and those things that really don't add value to the business trying to support their digital transformations and raise it up a little bit, and that's clearly what you just described, right? >> Roland: Exactly. >> Okay, and one of those areas that companies want to invest, of course, is data, you guys here in Munich, you chose this for a reason, but Dinesh, give us the update in what's going on in your world and what you're doing here, in Fast Track Your Data. >> Right, so actually myself and Roland was talking about this yesterday. One of the challenges our clients, customers have is the hybrid data management. So how do you make sure your data, whether it's on-premise or on the cloud, you have a seamless way to interact with that data, manage the data, govern the data, and that's the biggest challenge. I mean, lot of customers want to move to the cloud, but the critical, transactional data sits still on-prem. So that's one area that we are focusing in Munich here, is, especially with GDPR coming in 2018, how do we help our customers manage the data and govern the data all through that life cycle of the data? >> Okay, well, how do you do that? I mean, it's a multi-cloud world, most customers have, they might have some Bluemix, they might have some Amazon, they have a lot of on-prem, they got mainframe, they got all kinds of new things happening, like containers, and microservices, some are in the cloud, some are on-prem, but generally speaking, what I just described is a series of stovepipes, they each have their different lifecycle and data lifecycle and management frameworks. Is it your vision to bring all of those together in a single management framework and maybe share with us where you are on that journey and where you're going. >> Exactly, that's exactly our effort right now to bring every application service which we provide to our customers into containerized version which we can move across our platforms or which we can also transform from the external platforms from competition platforms, and onboard them into T-Systems when we acquire new customers. Is also a reality that customers work with different platforms, so we want to be the integrator, and so we would like to expand our product portfolio as an application portfolio and bring new applications, new, attractive applications into our application catalog, which is the containerized application catalog, and so here comes the part, the cooperation with IBM, so we are already a partner with IBM DB2, and we are now happy to talk about expanding the partnership into hosting the analytics portfolio of IBM, so we bring the strength of both companies together the marked excess credibility, security, in terms of European data law for T-Systems, from T-Systems, and the very attractive analytics portfolio of IBM so we can bring the best pieces together and have a very attractive offering to the market. >> So Dinesh, how does IBM fulfill that vision? Is it a product, is it a set of services, is it a framework, series of products, maybe you could describe in some more depth. >> Yeah, it all has to start with the platform. So you have the underlying platform, and then you build what you talked about, that container services on top of it, to meet the need of our enterprise customers, and then the biggest challenge is that how do you govern the data through the lifecycle of that data, right? Because that data could be sitting on-prem, data could be sitting on cloud, on a private cloud, how do you make sure that you can take that data, who touched the data, where that tech data went, and not just the data, but the analytical asset, right, so if your model's built, when was it deployed, where was it deployed? Was it deployed in QA, was it deployed in development? All those things have to be governed, so you have one governance policy, one governance console that you can go as a CDO to make sure that you can see where the data is moving and where the data is managed. So that's the biggest challenge, and that's what we are trying to make sure that, to our enterprise customers, we solve that problem. >> So IBM has announced at this show a unified governance catalog. Is that an enabler for this-- >> Dinesh: Oh, yeah. >> capability you're describing here? >> Oh yeah, I mean, that is the key piece of all of this would be the unified governance, >> Jim: Right. >> which is, you have one place to go govern that data as the CDO. >> And you've mentioned, as has Roland, the containerization of applications, now, I know that DB2 Developer Community Edition, the latest version, announced at this show, has the ability to orchestrate containerized applications, through Kubernetes, can you describe how that particular tool might be useful in this context? And how you might play DB2 Developer Community Edition in an environment where you're using the catalog to manage all the layers of data or metadata or so forth associated with these applications. >> Right, so it goes back to Dave's question, How do you manage the new products that's coming, so our goal is to make every product a container. A containerized way to deliver, so that way you have a doc or registry where you can go see what the updates are, you can update it when you're ready, all those things, but once you containerize the product and put it out there, then you can obviously have the governing infrastructures that sits on top of it to make sure all those containerized products are being managed. So that's one step towards that, but to go back to your DB2 Community Edition, our goal here is how do we simplify our product for our customers? So if you're a developer, how can we make it easy enough for you to assemble your application in matter of minutes, so that's our goal, simplify, be seamless, and be able to scale, so those are the three things we focused on the DB2 Community Edition. >> So in terms of the simplicity aspect of the tool, can you describe a few features or capabilities of the developer edition, the community edition, that are simpler than in the previous version, because I believe you've had a community edition for DB2 for developers for at least a year or two. Describe the simplifications that are introduced in this latest version. >> So one, I will give you is the JSON support. >> Okay. >> So today you want to combine the unstructured data with structured data? >> Yeah. >> I mean, it's simple, what we have a demo coming up in our main tent, where asset dialup, where you can easily go, get a JSON document put it in there, combined with your structured data, unstructured data, and you are ready to go, so that's a great example, where we are making it really easy, simple. The other example is download and go, where you can easily download in less than five clicks, less than 10 minutes, the product is up and running. So those are a couple of the things that we are doing to make sure that it is much more simpler, seamless and scalable for our customers. >> And what is Project Event Store, share with us whatever you can about that. >> Dinesh: Right. >> You're giving a demo here, I think, >> Dinesh: Yeah, yeah. >> So what is it, and why is it important? >> Yeah, so we are going to do a demo at the main tent on Project Event Store. It's about combining the strength of IBM Innovation with the power of open source. So it's about how do we do fast ingest, inserts into a object store, for example, and be able to do analytics on it. So now you have the strength of not only bringing data at very high speed or volume, but now you can do analytics on it. So for example, just to give you a very high level number we can do more than one million inserts per second. More than one million. And our closest competition is at 30,000 inserts per second. So that's huge for us. >> So use cases at the edge, obviously, could take advantage of something like this. Is that sort of where it's targeted? >> Well, yeah, so let's say, I'll give you a couple of examples. Let's say you're a hospital chain, you want the patient data coming in real time, streaming the data coming in, you want to do analytics on it, that's one example, or let's say you are a department store, you want to see all the traffic that goes into your stores and you want to do analytics on how well your campaign did on the traffic that came in. Or let's say you're an airline, right? You have IOT data that's streaming or coming in, millions of inserts per second, how do you do analytics, so this is, I would say this is a great innovation that will help all kinds of industries. >> Dinesh, I've had streaming price for quite awhile and fairly mature ones like IBM Streams, but also the structured streaming capability of Spark, and you've got a strong Spark portfolio. Is there any connection between Product Event Store and these other established IBM offerings? >> No, so what we have done is, like I said, took the power of open source, so Spark becomes obviously the execution engine, we're going to use something called the Parquet format where the data can be stored, and then we obviously have our own proprietary ingest Mechanism that brings in. So some similarity, but this is a brand new work that we have done between IBM research and it has been in the works for the last 12 to 18 months, now we are ready to bring it into the market. >> So we're about out of time, but Roland, I want to end with you and give us the perspective on Europe and European customers, particular, Rob Thomas was saying to us that part of the reason why IBM came here is because they noticed that 10 of the top companies that were out-performing the S&P 500 were US companies. And they were data-driven. And IBM kind of wanted to shake up Europe a little bit and say, "Hey guys, time to get on board." What do you see here in Europe? Obviously there are companies like Spotify which are European-based that are very data-driven, but from your perspective, what are you seeing in Europe, in terms of adoption of these data-driven technologies and to use that buzzword. >> Yes, so I think we are in an early stage of adoption of these data-driven applications and analytics, and the European companies are certainly very careful, cautious about, and sensitive about their data security. So whenever there's news about another data leakage, everyone is becoming more cautious and so here comes the unique, one of the unique positions of T-Systems, which has history and credibility in the market for data protection and uninterrupted service for our customers, so that's, we have achieved a number of cooperations, especially also with the American companies, where we do a giant approach to the European markets. So as I said, we bring the strength of T-Systems to the table, as the very competitive application portfolio, analytics portfolio, in this case, from our partner IBM, and the best worlds together for our customers. >> All right, we have to leave it there. Thank you, Roland, very much for coming on. Dinesh, great to see you again. >> Dinesh: Thank you. >> All right, you're welcome. Keep it right there, buddy. Jim and I will be back with our next guests on theCube. We're live from Munich, Germany, at Fast Track Your Data. Be right back.

>> From Palo Alto, California it's theCUBE, covering VMware Women Transforming Technology 2019. Brought to you by VMware. >> Hi Lisa Martin on the ground with theCUBE at VMware Palo Alto California at the 4th annual Women Transforming Technology event wt². And pleased to welcome to theCUBE for the first time Rashim Mogha, the Head of Product at Automation Anywhere. Rashim it's great to have you on theCUBE. >> Thank you so much Lisa very excited to be here >> And good to see you again you and I were, moderating the together woman achieve event a few months ago that Dell sponsors back in I want to say November 2018 >> Yeah. >> where you one of the exciting things in that swag bag was one of your five books, Fast-Track Your Leadership Career. Tell me about the book what inspired it what can readers learn in that book. >> Absolutely so I come from a project management background and for me everything has to be in the form of a template and that's how it works, right? So when I was new to my leadership career, I would read all these leadership books but they would just focus on one area so you had to read like so many books and skim through all those books to extract what worked for you. Now for me it was important to kind of templatize that and when I templatized it, I actually started talking about it at various events, one of them was Women Transforming Technology last year and as I gave that after I finished that session and we started I started walking out, one of the attendees came to me and said, this was such great information do you have a book? and I said no I don't but I'll have one soon and then I met with my publisher whom I met through one of the speakers at WT2 and we started working on it and in September we had a book. >> September 2018 and then, probably surprisingly to you 11 hours later, this book was on the Amazon number-one bestseller list. >> Yes it was >> that must have been like whiplash what? >> It was a very emotional day it was a roller coaster so we had thought about my publishers had more belief than I did in terms of the book having the potential to be an Amazon bestseller. And number one bestseller to be precise and I was like okay let's give it a try. So I was supposed to go to Grace Hopper Conference last year at that time, and I decided to stay back because the book launch was planned on that day. So we launched we started telling everybody that the book is on Amazon, at about ten o'clock in the morning and by seven o'clock I got an got a text message from my publisher with the screenshot, saying it was number one. >> So yeah very exciting it it took me a few days to realize what it really meant to be an Amazon bestseller. >> I bet that feels amazing. So tell me a little bit before we dig into the book and what you're doing here at wt² today, tell me a little bit about your career path in technology so we can understand some of the recommendations that you're giving the current and subsequent generations about how to fast-track it. Where did you start was it I was a stem interested kid to college. >> Yeah so I was actually studying to be a doctor because I come from India so in India they're just three careers, you're either a doctor or an engineer or you're nobody right so and this was when I was growing up so I actually unfortunately fell sick and could not take my medical exam and missed it actually took the exam, missed it by a few points and and did not know what to do because all my life I had thought about becoming a doctor and it just so happened that there was a computer science program that was out there and my mom saw, saw in a scholarship opportunity over there and she said well just give it a try if you get the scholarship then we'll talk about it and then fortunately for me I got 75% scholarship in that. So I was like okay I'll give it a try so I botany majored and did computer science and that's where my journey started into into the technology field. And got an opportunity to be absorbed within that group the same company absorbed me as as a developer. And within six months I get an opportunity to write a book and that was amazing because I never thought that I could be a teacher or be in front of anybody because I am so impatient as a person right? So so then we started when I started writing the book I realized , this is a great way to empower people and you know and it's a it's a great way to use my technical skills but also my writing abilities. And then you know six months down the line, I got an opportunity to be a project manager I took that so in my life if you see if my career path I've kind of bounced around a little bit, taken risks early on in my career and I continue to take risks in my career because if you don't give it a try you would never know. >> Exactly. >> So and that's what I tell women today like when you come out of college or even if you are in somewhere in your mid-career. You know don't don't tie yourself to a particular job role, or to a particular area try out different things and if there's an opportunity that's given to you, grab it with both your hands and then figure out how you're going to do the job well. >> I like that I always think if you have a goal that doesn't give you butterflies, it's not worth having. >> Yeah >> So in in just giving our viewers a little bit of a snapshot what are some of the things that they can learn and take away from Fast-Track Your Leadership Career book. >> Yeah so first and foremost is understanding your superpower right? How are you different from other people what do you bring to the table that others do not. Because in today's day and age, almost everybody does a great job right? What sets you apart for the next role is what you should always know. Building your personal brand most often we introduce ourselves as what job title we have and the company that we work for. It's important to know and have your identity beyond the company. The third piece is understanding the difference between sponsors and mentors. And that is the place where I think women really need to invest some time because we normally seek mentors. We very rarely go out and look at people and say you know what this person is going to be my sponsor and she or he is actually going to be my cheerleader when I'm not there in the room and and recommend me for that next job. >> So that's the difference between a sponsor I like that a sponsor and a mentors. Mentor is giving you advice and guidance, a sponsor is actually out there championing, >> Absolutely >> why you should hire a Rashim bring her into your team, these are all the great things that she does. >> Absolutely and then then there are other topics that we cover we cover navigating work politics. Most of us tend to stay away from politics but actually how to get into that you know understanding that I would call it work force intelligence if you will and leveraging it to further your projects in a good way. And then also building your support system now typically when we women talk about support system, we think about just two aspects. Emotional support system and the logistic support system but but there is also financial support system and intellectual support system and that's what you need to start building, to be able to further your career. >> I got to get a copy of this book. You probably have some, I'm guessing (mumbles). So you have a couple of sessions here at WT wt², building voice experiences through Alexa skills but one that I want to dig into in the last few minutes that we have. Project you a DevOps approach to a leadership career. Tell me about that pan and that breakout. >> Yeah so if you if you really look at the concept of DevOps it's or CI/CD model its development and then pushing it into operations and then moving into development again and then operations. So when you actually start preparing for your leadership career, that's the way you go. You you rinse and repeat the cycle what works for you in this role, will not work for you in your next role. So how are you continuously preparing yourself and using that DevOps approach, to kind of move to the next level, is what we'll cover in that session. >> That's fantastic. So one thing I also want to mention is that so we talked about becoming a number one Amazon bestseller, the book Fast-Track Your Leadership Career, just about six months ago in fall of 2018. It also inspired you to found, an initiative called eWOW, empowered Women of the World. Tell me a little bit about eWOW and why this book book number five being so instantly successful was so inspirational for eWOW. >> Yeah so I come from a training and enablement background so for me it was and and you know when you when you look at my personal brand, it's all about enabling and empowering people. So I wanted to basically find avenues, to be able to empower other woman. And essentially you know at eWOW, we believe that every woman, has the capability or is a leader in her own, you know her own right. And all that she needs is an intellectual platform and a framework and that's where eWOW came into being. We started off with just podcast, doing weekly podcast picking up topics around leadership and technical topics, we have audience in about 20 countries right now and then as an extension to that, we also launched five Alexa skills and that's going to be the topic that I'm going to be speaking about later today and it was all about you know different ways of enabling and empowering people. >> I love that. Well Rashim it's been such a pleasure, to have you on theCUBE. We thank you for giving us some of your time and we look forward to talking with you again about, maybe book number six? >> Well you never know. Last time I walked out of this conference, I had a book in ring so you never know what's up. >> You never know. But thank you so much. Your story is very inspiring and and i can't wait to, get my hands on a copy of that book. >> Thank you so much. >> My pleasure, Lisa Martin with theCUBE on the ground at wt² from VMware. Thanks for watching. (upbeat music)

>> Narrator: Live from Orlando, Florida. It's theCUBE... covering Accelerate '19. (electronic music) Brought to you by Fortinet. >> Welcome back to theCUBE. We are at Fortinet Accelerate 2019 in Orlando, Florida. I'm Lisa Martin with Peter Burris. We've been here all day talking with Fortinet executives, with partners, really understanding the evolution of cybersecurity and how they are helping customers to combat those challenges to be successful. We're pleased to welcome back to theCUBE alumni John Bove, the VP of North America's channel for Fortinet. John welcome back to the program. >> Thanks for having me, great to see you both again. >> Likewise, so, so much going on today, some news coming out. The keynote this morning started with a lot of electricity around Fortinet's industry leadership, product leadership, there was a lot of growth numbers shared >> John: Yup >> There's also a lot of people here about close to four thousand. >> John: Close to four thousand people, yup. >> And you saying that a good percentage of that is partners, forty countries represented. What are some of the things from your perspective, that you've observed today, in terms of the reaction from the channel to all of this news coming out. >> Yeah so first off, the heritage of this event really was a partner conference going back to its infancy and you know as Fortinet continues to grow and our customer profile continues to you know, move up market, we've now invited customers. So it's really great the synergy that we have. We've got a number of partners with their customers coming to meetings and meeting with executives, and so it's just really fantastic. You know relative to the announcements about the partner program, we've seen really positive feedback. I think the program was introduced about a decade ago and it really was time for a refresh, and so, what we've done is, we want to bring a program to our partner community that, allows them to engage with us in how they see fit, and then we want to build the go to market that's a little bit more in tune with the market that exists here, as we're moving into the year 2020 and beyond. So we're really assimilating a reseller, MMSP and Cloud as types of partner go to markets, and organizing that all underneath the Fortinet partner program umbrella. We'll also be introducing a consultancy track because we want to insure that the assets within the network security expert program are available to those consultants that are working with customers on their journey to the Cloud, for instance, or through this digital transformation. And then finally we're introducing what we're calling a competency focus. So as Fortinet continues to grow as a company there's a number of competencies that we feel if we enable partners appropriately they're going to be able to benefit from. They're going to build a stronger business around the Fortinet Security Fabric. So, we're going to focus on SD-WAN, we're going to focus on Fabric, we're going to focus on Data Center, operational technologies and then S.A.C., because we do think, you know, S.A.C. operations, is an area, that cybersecurity and the number of tool sets are introduced, it's an area that we need to grow into as a company as well. >> Lots going on. >> Lot's going on, yes. >> So as you consider some of the challenges that your partners face, we talked a little bit about this with Patrice, partners, throughout the industry are hurting as they try to transition from a more traditional hardware to whatever's going to be the steady state, >> John: That's right >> with the Cloud and the Edge having such an impact. Education is crucial. You not just get your customers educated about how cybersecurity works, but your partners need to be increasingly educated so they can find those opportunities, niches, stay in business, help you engage, how's that playing out? >> My number one initiative as the channel leader is to drive partner competency and preference. And so, going back to competency, if we can build partner competencies, they're going to build a healthier, more margin rich business around the Security Fabric, which then, selfishly, is going to lead them to delivering more preference around Fortinet. But there's no doubt, it's a changing dynamics. Business models are changing on the fly. We're seeing evolution of VAR to MSP, and MSP to MSSP, and we are laser focused on capitalizing that. Our FortiSIEM technology for instance is, I really view as a Beachhead technology for us to go capitalize that MSP market in the mid-market. I think that the evolution of consumption to more of a consumption model away from a transactional acquisition, also lends itself to new and innovative programs that need to be delivered. In fact with our North American distributors, in the past six months, we've introduced hardware as a service, to reduce, you know, to position things as an operational expense, which may be more in tune with how customers are purchasing today, and we've introduced FortiSIEM for MSSP. The evolution of VAR to a service provider can be very capital intensive, and so one of the things that we've done with our hardware as a service and FortiSIEM for MSSP, we've really tried to reduce the cost of the entry point, and drive more day one margin opportunity for those partners. >> Let me build on that if I may Lisa, so Ken and Mike have done a pretty phenomenal job of steering Fortinet into the future and anticipating some of the big changes that have occurred. You guys have therefore pretty decent visibility into how things are going to play out, and are now large enough that your actually participating in making the future that >> Right >> Everybody else is thinking about. When you introduce a product, I mean, it takes a period of time for your partners to get educated, to up-skill, to really set themselves up to succeed in this dynamic world. Are you introducing educational regimens, competency tests, providing advice and council about the new competencies they're going to need, in anticipation to some of these, some of the roadmap of the, to the future that you see? >> Yeah, so two things I'll touch on there is you know, the NSC program has been wildly successful program for ... >> Peter: No what does NSE stand for? >> Network Security Expert so it's a training course where for a partner and you've got new team members coming on board, the NSE113 really enables them of how to position, you know, Fortinet, and what the challenges are in a network in a cybersecurity environment today. With the elements four through eight being more technical. We've seen over 200,000 certifications being adopted globally, so, I think, part of the visionary capabilities that Michael and Ken have, is they've incorporated the education piece of it, and so carrying that along, and so as we do introduce new products, it's built into the NSE modules. I'll point to one of the most successful things we did in 2018 was called Fast Tracks, and so we've basically taken the NSE content and put it into consumable two hour, hands on, technical labs for our partners and customers. We had a goal in 2018 to hit about a thousand people going through the Fast Track program, we hit over eight thousand people. So, we know that there is a thirst for knowledge out there and the company's done a really good job, through the NSE program, the Network Security Expert Program, through out Network Security Academy Program, and through our Fast Tracks to drive that necessary enablement. >> Peter: That's very exciting. >> Yeah I know absolutely, I mean, it's a fantastic time to be at Fortinet, its a fantastic time to be a Fortinet partner, and I think with the announcements that we made today, we're really trying to set our partners up for success, and help them build a all encompassing business around the Security Fabric. It's a very noisy industry out there. There's a lot of point based solutions that, that lack the integration and really you need an integrated set of solutions in this, you know, expanding digital footprint that customers are faced with. >> So when we talk about education and I'm glad that you guys brought that up, that was a big topic, it was a pillar that Ken talked about, that Patrice talked about as well, it was one of the core pillars that was talked about at the World Economic Forum that was just a couple of months ago. So as we talk about education and educating your partners, I'd like to kind of flip that and ask how are your partners educating you on, these are the trends and concerns and the issues that we're seeing in the market today, to help influence the direction of Fortinet's technology? >> Yup, you know it's funny that you say that, I've been in partner meetings all day today, and it's great I get to spend, I don't think I've ever been this popular and definitely not in high school or college, but in spending time with partners and understanding their challenges it's good to see that our focus on the competency and preference and providing consumption modeling, fits to exactly the challenges that they're faced with, because VARS will tell you that the transition from being a reseller to an MSP can be very, very expensive. And so, with FortiSIEM for MSSP and the as of service offerings, we're reducing that. And so, there are , they're resonating to that. But the other thing is, for the mid-market customer, the Security Fabric alleviates the need for the Cyber skills gap, right? We can't hire fast enough, and so, by depending upon the broad integrated and automated posture that this Fortinet Security Fabric allows, it really allows partners and customers to overcome some of the challenges, just from a head count standpoint. And I think that the NSE program also does a very good job of filling that gap as well. >> So the partner used to mean, these are the, for that group of customers, who our direct sales organization can't make money on, we will give them to partners, or the very, very large, for a very, very large company that's owned by Accenture or owned by Dimension Data, or something like that, >> Yup >> We'll work with them and deliver it. And that kind of middle was kind of lost. But even today, that Loewen, that idea of segmenting purely on the basis of how big they are, is problematic because there's a lot of small companies happening because of this digital transformation they're going to very rapidly grow into some very, very big footprints. >> Absolutely >> So how is that line between what Fortinet does, what the partner does, what the customer does, to achieve these outcomes, starting to shift? >> We're going to be introducing an ecosystem based approach. It's called Partner to Partner Connect, and it is to actually do that very thing. For those partners that may be in the mid-market, that need those expertise, we're going to allow partners to create almost a marketplace of service offerings so they can fill their gaps and they can build meaningful practices, leveraging what Fortinet is doing, but also leveraging somewhat some of our other partners are doing. We're seeing this immediately done with our distribution partners, in North America, and we're going to be introducing the Partner to Partner Connect later this year, and accessible through our Partner Portal. >> And those competencies that are associated with the NSE and the education, then become part of those Partner to Partner brands >> John: Absolutely >> Which makes it easy for those partners to be more trustworthy of whatever accommodations they put together to serve customers. >> Yup, I'll give you an example. So, we're also going to be announcing tomorrow afternoon in our North America breakout session, a Cloud Channel Initiative, and so our goal with this Cloud Channel Initiative, is to allow partners to build meaningful security and networking businesses in the public Cloud. We're going to utilize blueprints for reference architectures, we're going to align with education and certification, and then we're going to guide them through enablement to go to market. That's one of the things also we released this week was the NSE7 for public and private Cloud. So again, as we introduce new technologies and we introduce new opportunities, we're also aligning that to education as well, so the partners can be self service, because the better job a partner does is developing that competency , then the more services rich they're going to be able to deliver to the end customer themselves. >> What are some of your expectations in terms of FY19, I know this is a 20% year on your growth that Fortinet as a company achieved last year, I imagine a good amount of that was driven and influenced by the channel, but as this momentum continues to grow, as we saw this morning, and we've heard throughout this show today, what are some of your expectations about growing the number of partners in the programs that you talked about, like by the end of this year? >> Yes, we recognize, you know, first of all we appreciate our partners so much, and we want to ensure that we are enabling their business we're absolute in active recruitment mode. You know, we're currently going through recruitment and reactivation campaigns with partners that we want or maybe have done business with us before. We see we're coming off of a quarter in which we set a record for the most deal registrations and so that's really the metric in which we look for partner impact. They bring us an opportunity, we give them additional margin and we protect them. So, Q1, fiscal Q1 for us, was our largest deal registration quarter we've ever had. And in 2018 we saw a 52% increase in closed opportunities through our deal registration program. So the impact of the North American Channel is absolutely being felt and we're really excited about the new partner program and what it's going to allow us to do as we expand more into the MSP market, more into the Cloud market, and then hopefully go enable that whole consultancy layer that's out there as well, to help customers on their journey. >> So in terms of your session tomorrow, 'Transforming Your Profitability with Fortinet's Tailor Made Programs,' you mentioned some of the new announcements, what are like the top three take aways that attendees from that session are going to walk away with? >> Well it's going to be, we want to drive partner initiated revenue, we want to do that through competency development, through Widespace account penetration, and through meaningful investments that allow our partners to scale their business. >> Lisa: Lot of momentum, John thank you so much for visiting with Peter and me on theCUBE this afternoon, we can't wait to hear what great news you have next year. >> I look forward to it, thank you both. >> Excellent, our pleasure. For Peter Burris, I'm Lisa Martin, you're watching theCUBE. (electronic music)