>> live from Las Vegas. It's the queue covering Inform Attica! World 2019. Brought to you by in from Attica. >> Welcome back, everyone to the cubes. Coverage of Infra Matic. A world here in Las Vegas. I'm your host, Rebecca Knight. I'm doing by two guests. For the segment we have Rick Tam Daniels. He is the VP. Strategic ecosystems and technology than from Attica. Welcome, Rick and Yoav. Enough! He is the VP product for Giga Space. Welcome >> to be here. >> So this is a fun segment. You are the winner of the infirm Attica World 2,019 Solution Expo Cloud and Innovation. I want to get to you in a second and hear all about Giga Space. But I want to start with you. Rick, talk a little bit about this award and about the genesis of it. Where did the idea come from? >> Yes, So one of the things we really wanted to do it in from Attica World this year is create address Some of the most important topics that the customers want to hear about. It's a cloud and I two of the hottest tops the industry every wants to know about it and We wanted to take a lot of our emerging partners there doing some very innovative things than from Attica technology and put them front center. So if you look at the Expo Hall floor right in the middle, we have this almost like an art gallery of all this cool innovation have going on around the inn from Attica. Technology on the idea was that we had attendees come in and actually review the solutions. They had to be really full demos for working demos. Andi could vote on the app. They could say what their favorites were, and the end result is happy announced. Giga Spaces are big winner. >> And so yeah, attendees would vote on the app and so get so big a space. Tell us about it. You're based in Israel. >> Yeah, so aren't is based in Israel or H Q is in New York. Basically, the biggest bass was we've been in the market for more than a decade, deployed like in the largest enterprise in the world. You like banks like Bank of America, like international. I ot like another electric largest airline, largest railway companies, and basically we provide the speed for the application and big data infer structures so they deploy, like real time use cases like fraud detection, economic pricing, predictive maintenance, all those different types of services that required the speed on the big data side. >> You're all about speed, >> all about spirit. If you need the speed, we're the provided for you. >> Well, that's that's very exciting. So talk a little bit about the conversations that you were having with some of the attendees. What kinds of questions were you getting? >> So I think a lot of customers, during customers of ours and informative are talking about the move from kind of historical analysis to more proactive, event driven analytics when you want to be able to instead of interact with the data you want today, one so and now you want to baby toe Dr Analytical on the moment as soon as it happened to provide it that burrito Theron your online processes and instead of kind of offline processes. So, for example, fraud detection, which is the most, is the example. You want to be able to 100 further analysis on on the payment of a soon as it happens and Emilie second level and not like a few seconds after the transaction was over. So it's again. We're talking about the speed. They're very to handle high or amount of data with related sub second response time. >> And how are you using in from Attica? >> Cool So well, We've been working lately with Informatica very tightly with both their product team, and there are in the team because Israel, India, the US, on integrating with some of their different products were basically we've built kind of what Gardner calls the digital integration hub. It's like the next Jan big data architecture, which provides you both. Informatica side that allows to ingest any type of data could be taxed logs, transaction payments, anything you have together with their medal, the meta data management and on top of it, using Giga spaces for the real Time analytics and the high performance in speed. >> So, Rick, I know that this was attendee chosen, so there's no rigging here, but I'd love to hear what your thoughts are in Giga Space in terms of the innovations that they're doing in these in these very important problems, like fraud detection and predictive maintenance, these air these air big problems. That company's heir really wrestling with. >> And I think what's exciting about the solution they had. It was a great business case, right? I think that really resonated. Attendees looking at Everyone can identify with Fraud Analytics. Everyone's unfortunately, probably on a victim of it, so they could see how it works. I think it also focuses on the aspect of a iva. How do operationalized a I? So is the whole model building piece of it, And Infra Matic has a strong player there as well. But now you say, Well, let's actually have the model we need to execute quickly. How do we do that? You know what the biggest spaces technology, but also combine it with the right historical context, right to make the right decisions. So they're really does hit on. How do you actually take a I and make it a real thing? >> And the other important part is the business case in what you were just saying in terms of if a if a customer is the victim of fraud here, she blames the institution, not the hacker on. And if there's a problem with with an airline maintenance problem, you blame the airline. Of course not the faulty problems that it was having. So so I think that that also really shows what what's in the future. What are you seeing? Kind of Mohr innovations that you want to add to the biggest space platform. So >> I think we're working to their lot about like Rick was mentioning about operationalize ing A. I so a lot of challenge today off moving from the research development training part of Day I or the machine anymore to move to production. Let's say you're a payment provider you have the more than you can detect fraud, but your ability for you to run it on millions of transactions a second in a sub lets a few millisecond level. That's the biggest challenge. And if you do it in there a few seconds after the transaction was over, then the you know the last of the fraud or the wire was already happened. So again, the operation was part of taking your more than formula that sound flat from putting in production with the scale of the ingestion rate low latest c you know, scaling on pick events like Black Friday or Cyber Monday. That's the biggest challenges on the production systems. >> Now the speed is of the essence. Rick, this has been a successful experiment trying this. What are you hearing from attendees? Did they like it where they sort of How do we Dad? Does this work? What is this about? >> I think they're really enjoyed it. Every time I look, I went over to the zone. It was full of people having deep conversations, really getting into the technology and understanding. Because as I mentioned these air topics that I think everyone came here to the show to really learn more about How are they going to get where they're going There, Cloud journey where they're going to go in there, eh? I journey. It's a great feedback from attendees. Lot of active participation. So I'm going >> to do it. We're going to see it in >> your batter. It's gonna be great. >> So now that you're the winner, you're going to be up there on the main stage, getting some recognition. That's exciting. What? What are you going to take back? Teo, I know you're based in both Israel and New York. What? What? What does this mean for your company? >> So I think the next step is taking it to the business side. Right? We want to make sure that the joint offering and the joy in partnership moves to the next stage taking it to the next customer. We have some joint customer. We have some new prospect. Were a lot of late from the show here, sitting next to me, sitting side by side with the other partners of Info Matic. I like data breaks and slow flaked and clothes are so we have a lot of joint offering and solving real time like business and off the largest, most challenging enterprise we have, like, you know, largest banks, largest airlines, largest like railways companies. So I think the next step is moving, taking it from the exhibition to the field. >> Great. Well, this is terrific. Congratulations. Once again. Really exciting. Really happy for you. Thanks so much for coming on the show. Thank you. You have been watching the cubes live coverage of in from Attica, World 2019. I'm Rebecca night. Stay tuned

>> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone, to theCUBE's live coverage of Informatica World 2019. I'm your host Rebecca Knight, along with my co-host John Furrier. We are joined by Tracey Newell, she is the President Global Field Operations at Informatica. Thank you so much for coming on theCUBE, for coming back on theCUBE. >> Coming back on theCUBE, it's great to be here. >> So the last time you were on, you had just taken over as the president of Global Field Operations. Give our viewers a catch up on exactly what you've been doing over these past two years, and what the journey's been like. >> Yeah, no that's great, thanks so much. As a reminder the last time we were together, I had just joined the company. I was literally two weeks in, and yet I actually did join Informatica three years ago. So I joined on the board of directors, and I was on the board for two years, and the company was doing so extremely well that after a couple of years we all agreed that I would step off the board and join the management team. >> I got to get in on this! >> I know, exactly. I've got to get off the sidelines and get into the game. >> Both sides of the table, literally. >> Exactly. >> So that's really interesting that you were on the board watching this growth and seeing, obviously participating in it, too, as a board member, but then you said, "I want to be here, I want to be doing this." What was it about the opportunity that so excited you that you felt that way? >> Well, it's funny, because when I did join the management team I spent two months on a listening tour, and the first question from all the employees and our partners was, "Why'd you do that?" Usually it goes the other way around, you go from the management team to the board. And the answer was really simple in that my hypothesis in joining the board was that digital transformation is an enterprise board of director's decision, that governments and large organizations are trying to figure this out with the CEO, the board, the management team, because it's critical, and yet it's also really hard. It's complicated, the data is everywhere. And so when you have something that's important and really complicated, you need a thought leader. And so my belief was that Informatica should be that thought leader. And two years in we were doing so phenomenally well with the platform play that we had been driving from an R&D standpoint, it just seemed like such an amazing opportunity to literally get off the sidelines and get into the game. And it's just been fabulous. >> And you have experience, obviously, doing field organizations so you've been there, done that. Also you have some public sector experience, so also being on the board was a time when Informatica went private. And that was a good call because they don't have to deal with the shot clock of the public markets and doing all those mandatory filings, and a lot of energy, management energy goes into being public company. >> That's right. >> At the time where they could get the product development and reposition some of the assets, and the thing that was interesting with you guys, they had customers already. So they didn't have to go out and get new customers to test new theses. >> That's right. >> They had existing customers. >> Oh no, we serve the biggest companies and governments on the planet. Globally, a very large percentage of the global 2000, is kind of our sweet spot. And yet thousands and thousands of customers in the mid market. And so to your point, John, exactly we had built out this platform that included all things on-premise, we're almost synonymous, PowerCenter and ETL, that's kind of been our sweet spot. And MDM data quality, but adding in all of the focus on big data, all the area of IPAAS, all the work that everybody's doing with AWS, with Azure, with Salesforce.com, with Google Cloud, and suddenly we've got this platform play, backed by AI and machine learning, and it's a huge differentiator. >> So you've seen a lot of experience, again you worked in the industry for a long time, you know what the field playbook is, VCs say the enterprise playbook. It's changing, though, you're seeing some shifts and Bruce Chizen was talking to me yesterday about this, there's a shift back to technology advantage and openness. It used to be technology advantage, protect it, that's your competitive advantage, hold it, lock in, but it's changing from that to technology, but open. This is the new equation, what's your take on that? >> Our strategy's been really simple, that we want to be best of breed in everything that we do. And Gartner seems to agree with us. In all five categories we play in we are up and to the right. And yet we want you to get a benefit that if you do decide to buy one product, and then add a second, or a third, or a fourth family, you're going to get the benefit of all that being backed by a platform play, and by AI and machine learning. And so this concept of we'll work with everybody, a customer called us Switzerland of Data, and that's certainly true, we partner with everybody. Where you do see synergies to leverage your entire data platform, you're going to get a real advantage that no one else will have. >> You've got a lot of customers, this is a very intimate conference here at Informatica, this is our fourth year covering it, it's been great to watch the journey, but also the evolution and the tailwinds you guys have. What are some of the customer conversations you're having? You're in all the top meetings here, I know you guys are busy running around, I see you doing meetings and the whole team's here. What are some of the top-level priorities and challenges and opportunities that your customers have? >> We literally have thousands of people at the conference here as you know, and it's just been phenomenal. So I've been in back-to-back meetings, meeting with some of the largest companies in retail that are trying to figure out, "How do I serve my customer base online?" "And yet when they walk into one of my stores, "I want to know that. "My salesperson needs to know exactly what that person's "been shopping for, and looking on the Internet for, "if they're on my site, "or perhaps what they've been tweeting about." So they want to know everything about their customer that there is to know. The banks want to know who their high wealth clients are. And hey want to make sure that if they call in on a checking account and have a bad customer service experience, they want to know that. If it's a hospitality company, they want to understand what's going on every time you check into a hotel. If you looked for a quote and you don't actually follow through, they want to understand that. And so there's this theme of understanding everything that there is to know about a customer. And yet at the same time, a huge requirement for governance, in the California Privacy Act, the CCPA and GDPR are changing everything. I had a large bank once say, and this was years ago, "How can I forget you?" Which is what GDPR says I have the right, you have the right to be forgotten in Europe. How can I forget you if I don't know who you are? Again that's because data's everywhere, and again we're enabling that, so it's a pretty exciting time. It literally is about companies transforming themselves. >> I remember the industry when search engines came out, when the web came out, you had Google and those greenfield opportunities, they were excellent, you type in a keyword and you get results. When people tried to do enterprise search, it was like all these different databases, so you had constraints and you had legacy. Similar today, right? So how has that changed? What's different about it now? And again you had compliance and regulation coming over the top. How does an enterprise unlock those constraints? >> It's funny, you say unlock the power of data is one of our catchphrases. I'm meeting with CIOs around the planet who sound like they're CMOs, because they're using these phrases. They're saying things like, "I need to disrupt myself before someone disrupts me." Or there was one, it was a large oil and energy, it was a CIO at this massive company said, "Data's the new goldmine, and I need a shovel." So they're using these phrases, and to your point, how do you do that? Again, we do think it is about getting the right platform that plays both on-premise and ties in everything the customers are doing in cloud. So we see partnerships as being critical here. But at the same time, one of our fastest growing solutions has been our enterprise data catalog, which is operating at the metadata level. My peer in products Amit Walia likes to say, "How come you can ask the Internet anything at all?" You're so used to it, when your kids ask you a question, you just get online, I don't know, and get the answer. But you can't do that in your own enterprise. And suddenly, because of what we're doing at the metadata level working with all of the different companies around the globe through open APIs, you can now do that inside your enterprise, and that is really unlocking the capabilities for companies to run their businesses. >> You're giving us so much great insight into the kinds of conversations you're having about this deep desire to know the customer and understand his wants and needs at every moment. And yet the technology is so often the easy part, and the hard part of the implementation are the people and the processes. Can you talk a little bit about the stumbling blocks and the challenges that you're seeing with customers as they are embarking on their digital transformations? >> That's a great question. Because one of the things that I caution our clients about is companies get so focused on, I've got to pick the right technology. And we agree with that, again, that's why we focus so much, we've got to be best in breed in every decision. We're not going to lock you into something that doesn't make sense. And yet half of the battle, if you would, in these projects, it's not about the technology, it's a people/process issue. So think about to have a comprehensive view of your data, if you're a large CPG company or a large bank, you might have 10 CIOs, 50 CIOs. We have customers that have 10 ERP systems, we have folks that talk about 50 ERP systems. These are very cross functional, complex projects, and so our focus is on customer success and customer for life. I have more people in customer success than I do in sales by design. Literally thousands of people around the world, this is all that we do, that are focused on business outcomes. And so we really give an extra guarantee, if you would, to our customers to make sure they know that we're in this to make sure that they're successful, and when we start running into challenges, we're going to raise those high so that both organizations can make sure that we get to that promise that everybody is committed to. >> Talk about the ecosystem, because you continue to get success with the catalog, which is looking good. Great that, by the way, we covered that on theCUBE, I remember those conversations like it was yesterday. That really enables a lot, so you're seeing some buzz here around obviously the big clouds, the Google announcement, Amazon, and Microsoft are all here, on-premise, you've got that covered. But the ecosystem partners have a huge economic opportunity, because with the value proposition that you guys are putting forth that's rolling out with a huge customer base, the value-to-economic shift has changed, so that the economics are changing for the better for the customer and the value's increasing. That's kind of an Amazon-like effect if you think about that flywheel. That's attracting a lot of people in to your ecosystem because there's a money making opportunity. >> That's right. >> Talk about that dynamic. >> It's been humbling. I'm really pleased with Informatica World and how things are shaping up because we've had some amazing speakers here as you mentioned, from Amazon, Thomas Crane here from Google Cloud, AWS sending their CMO. It's just been a phenomenal event, yet if you go to the show for literally dozens and dozens and dozens of other providers that are critical to our customers that we want to partner with. When we say partner, we actually do deep R&D together so that there's a true value proposition where the customer gets more and a better-together solution when they choose Informatica and their critical partners. There's another category of partners that I think you're hinting at which is the large GSIs. >> The global system integrators, yeah. >> The global systems integrators. >> Accenture, Deloitte. >> Accenture, Deloitte, Cognizant have been phenomenal partners to us. And so again, when you talk about this being a board level discussion, which literally I've met with so many CIOs who say, "I just presented to my board last week, "let me tell you about this journey that we're on." Of course the large global system integrators are in the middle of that and we are very clear, we don't want to compete with those folks that are so good at both the vision and also really good in arms and legs and execution to help drive massive workflow change for our clients. So we work together brilliantly with those folks. >> And these are meaty projects, too, so it's not like they're used to, back in the old days when these projects were massive, rolling out these big ERP systems, the CRMs, back when people were instrumenting their operation of businesses. Similar now with data, these are massive, lucrative, profitable opportunities. >> These are really strategic for the client, the global system integrator, and for us for all of the same reasons. This drives massive change in a good way for our clients to keep ahead of whoever's nipping at their heels, but certainly it's a tremendous services opportunity for the large integrators, there's no question. >> Being humble. >> One of the things that's really coming through here is Informatica's commitment to solving the skills gap, especially with the Next 25 program, and this is something your company's being really thoughtful about. I'm interested from your perspective, particularly as somebody who's been in the technology industry and was on the board for a while, how do you see the skills gap and what the technology industry is doing as a whole to combat it? And then your advice from your vantage point in terms of what you think are the next things that kids should be studying in schools? >> This reminds me, and Furrier, you're talking about the old days, so I'm going to date myself, it reminds me a lot of when the Internet first started to occur. This is a very similar type change. People have been, companies have been trying to make these changes and they're starting to realize that it does start, they've got to have a good grasp of the data in order to run all of these strategic initiatives that they've got. And so it's tremendous opportunity, to your point, for young people. So how do we think about that? Certainly we do our fair share of hiring interns trying to get them early in life, when they're sophomores, juniors coming into senior year and then hiring those folks. So we see an opportunity for our own company to bring in those young people, if you would. And then the GSIs, the global systems integrators, we partner quite a bit with them, because we see them as massive scalers, they have-- >> How about people specialize in majors, any areas of interest that someone might want to specialize in to be a great contributor in the data world? Obviously stats and math are clear on machine learning and that side. But there's affects, there's societal, business outcome challenges that have not yet been figured out. What areas do you see that someone can go after, have a career around? >> So it literally is a business and a technical problem that we're solving, and so there's going to be career opportunities for everyone that's in school. Whether it be on the business side, whether it's business management, marketing, sales, because again think about when you talk about change of management, it is a CMO trying to rethink how do they reach their clients. It is a sales leader thinking, "How do I get better analytics as to what's working "and what's not working?" And then of course it crosses over into computer science and engineering, as well, where you're actually developing these products, and developing these AI applications that are just beginning to take off. But it's in the early days, so for young folks coming out of schools this is a tremendous opportunity. >> Well, next you'll have to find what's up with the field, and your customers, and then next year, next event. >> Yeah, I can't wait, it's great. I've really enjoyed spending time with you all, and we look forward to seeing you soon. >> Indeed, well thank you so much for coming on theCUBE, Tracey. >> Okay, thank you. >> Thank you. I'm Rebecca Knight, for John Furrier, you've been watching theCUBE's live coverage of Informatica World, stay tuned. (upbeat music)

(upbeat music) >> Live from Las Vegas, it's theCUBE covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We're joined by Steven Guggenheimer, he is the corporate vice president of AI and ISV engagement at Microsoft. Thank you so much for coming on theCUBE. >> Sure, thanks for having me. >> So one of the things that we're hearing so much at this conference is, "data needs AI but AI needs data." I'm wondering from your perspective, AI engagement, where do you come down on this? What are you hearing? what are your thoughts on that big theme? >> Um, well, data is the -- some people say the oil for AI, pick your terminology, but there is no AI without data. The reason that AI is such a hot topic right now is the combination of sort of compute storage and networking at scale, which means the access for developers and data scientists to work with large sets of data and then the actual data. If you don't have data you can't build models, if you can't build models, that's what is the definition of AI. So you need data. I always-- all the coaching I do is about sort of, BI before AI. If you can't actually get insight out of your data, let's not try to add intelligence. If you can't get insight out of your data, it means your data is not in a good-- your data state is not in order. So data first. >> A lot of architectural work is being done on data. I see a horizontally scalable cloud, gives a nice access to a lot of different you know, observational data sets. >> Yeah >> It used to be give the guy the silo, got the data, go get more data, slower. Now, data feeds the developer process because SaaS business models have been proven that data and SaaS work well together. So how do we get more-- what's the sequence of architecture to usability of data so that not only can you just have analytical systems, but where developers can start building their SaaS apps with data? >> Yeah, I mean we have this notion where we often talk about sort of, blades or feedback loops. There's sort of four or five things most companies do. You work with customers, you have employees, you have a supply chain or some type of partner chain, You run your finance and operations. So the question becomes, in each of those processes, there's data. Human-generated forms over data or pick your loop and now you getting tons and tons of data. The trick now is to make it reusable. Mostly what we've done for years, form over data, take the data, form over data. And what we do is we get all these different databases. We try and create some layer that brings it all together. We build cubes out of it to view and then we get this hopeless spaghetti. So the trick right now, we're working on something called Common Data Model, which others are well, or Common Data Service. Let's get the entities lined up from the very beginning. We've worked with Adobe and SAP on the Open Data Initiative. Let's start at the core, let's make the data layer reusable, We're you know, databases have become data warehouses have become data lakes. We're heading towards a data tidal wave, and if we don't get the data estate in order to run the line of business applications, to feed all of the things we do to use the ML and AI on top of it, we're going to drown in data and not get what we want out of it. So, architecturally I think about the Common Data Model and the Common Data Service both generically by industry, we build accelerators for that, getting the big organizations like the three I mentioned aligned around that, making it such that any, you know, organization can build from that and then building on top of that. For big companies you have to decide, what do I keep and what do I throw out? You know, what do I just give up on and start from fresh? What do I actually clean? Where do I use tools from Informatica or others to help me clean it, secure it? But you've got to put all that thought in. >> You know we were chatting before we came on camera about the internet days and the storied history that you had at Microsoft. And during the internet, search was the big application. And search on the internet actually worked really well because they didn't have a legacy. And the people that tried to crack the code on search inside an enterprise, much harder problem (Giggles). Because of the database things you mentioned. How does today's enterprise get the benefit of SaaS as if they were cloud-native SaaS with the data? So you know, the challenge we're hearing here is having a Common Data Model is all great, but I just want to be a SaaS player, I want to use my data to feed into my business value. How does a company move out of those legacy constraints? What do you see as-- >> Well there's different paths that different companies will take. I mean, the good news is that if you get your data in order to do what you said, then whether you build, buy or partner for the SaaS services, you can use that data underneath and you should be feeding it back in and making it such that it's sort of reusable and the pipeline is consistent. The truth is on all this, it's just going to end up infused anyway. When you used the internet, which is a funny analogy 'cause I remind people, you know, when the internet came out we had internet products, we had internet events, we had internet shows. We don't have any of that anymore. It's just woven into everything we do. AI is going to be the same. You have all this hype right now, you have AI shows, you have, you know, AI groups. The truth is, in 10, 15 years, AI it's just going to be woven into everything. The data is going to be set up for that. >> So what's the misconception on AI? 'Cause, first of all, I love the fact that AI is hyped up because my kids love it. Machine learning they learn because they hear about AI and they hear all this coolness. So machine learning goes hand-in-hand with AI, you feed machine learning, machine learning feeds the AI application. But a lot of people have aspirations around AI. Some of them are ungettable and so that's probably a misalignment around the hype. What's your feeling of where the reality is and what's the misconceptions, how should people approach AI? Any thoughts there. >> I think a lot about the AI journey, the first year we were having these AI conversations, we talked about AI for everybody, just go play. Now the conversation is, I call it pragmatic AI. Look, lets talk about, you know, how you want to think about AI, it's going to end up everywhere, so the question becomes, what's your differentiation as a company, and how is AI going to support it? Like any other new technology, in the beginning, people just want to play. Just because you can -- let's just say just you can build a virtual agent, doesn't mean every company should. So the question becomes, first off, BI before AI, get your data state in order. Second, in a build buy partner model, what's your differentiation as a company? Whether you want to use either your unique data or your unique skill sets to use AI against that differentiation to help you grow. Otherwise, like, expect somebody else to have infused AI into the products you buy, the SaaS services, you know, use that, then build whatever you want and then there's, you know, if you think you're going to build a new business based on your unique data or your unique AI capabilities, great, let's have that conversation, we need that too but rarely does that become the state. so, most of the conversations move from, you know, the hype to okay, let's get pragmatic which is why I always come back to data first 'cause if you not doing that, you're not setting up for the long run. Let's build for the long run, then let's just have a business conversation like, how do you differentiate yourself as a business? Okay, how is this tool going to help you? >> I want to ask about, uh about innovation, and particularly because Microsoft is a company that's now entering its middle age (giggling) and-- >> What does that say about me, oh no >> As one of famously innovative company, but how do you stay on the cutting edge? I mean, I'm wondering internally how you think about AI for Microsoft's business purposes. What are the conversations around AI? >> One of such is, core conversations around this notion of tech intensity you know, from where we focus on how we think about things we think about tech intensity against different areas, AI being one of those. Look, AI is really this interesting thing. I would say we're plumbers by trade, we build software plumbing for others. So, we do three things right, with AI. Basically, there's a layer growing on top of the core development stack, compute, storage, networking for AI. So we're building a layer, cognitive services, bot services, machine learning, set of tools for developers to infuse AI into things that they've built, so that's thing number one. Thing number two, is we infuse AI into our own products, into Windows, into Office, into Azure, into dynamics. You don't see it, we don't talk about it, we don't say Microsoft Windows Inking brought to you by Azure AI. It just works, but our inking works, our face login works, oh, you know, I can -- it's helping me write a better resume in LinkedIn, that's all AI behind the scenes. Now, the third thing you think about then is, "how do you actually use AI to run the business better"? So, how do you think about, AI assisting professionals, how do we think about the, how we do mocking better, How we forecasting sales, so AI is about plumbing, let's build a platform for others, let's use it ourselves on our own products, and then let's think about how you actually use it to run the company better. And that's how we think about it-- >> That's pragmatic >> Very pragmatic AI is kind of -- >> Yeah, that's how I think about it and we, you know, it's interesting 'cause back to the tech intensity point, we get together on an AI conversation, we searching with the senior leadership team about once every other week, and we're round robin between a research topic, the platform and one of the solutions. So it's, you're always getting constant feedback about is the platform doing what we need to build solutions? Is the research feeding the platform? So, you're getting this really nice feedback loop right now and that tech intensity. >> Quality data always has been a big part of the data modeling in the past, Cloud now allows for data marketplaces I've seen sharing of data as a dynamic, almost like sharing libraries of your developer back in the day, so data is now being merchandised in a new way. This is a trend, what's your thought on it? Because if this continues, you're going to have more data inputs, does that-- >> Err, there are places where data is aggregated and potentially can be re-used. We can -- Bing is an example, Google would be an example um, I know people who aggregate data for different industries, etcetera. It's not an easy business, the rules and rights around data, the GPR compliance, the rest of it. I think there's a deer there but you really have to be in the business for-- the trick you run into is, if you're going to be an aggregator, and then a reseller of data, where's that data coming from? What are the rights, what's the security? And then, are the people who are providing that data comfortable with their competitors getting the data? 'cause if you're really going to be a data provider marketplace, first person who's going to want on is the competitor, so, I think it's an interesting conversation, I think it's kind of growing and there's some real good work there, I don't think it's as-- >> not viable yet >> Easily to do it at scale, for as many people who think they have the data asset as believed they do. But that's Steve's view, that's not a Microsoft's statement. (laughing) >> good disclaimer >> Steve's view, so I want to hear Steve's view on the skills gap, this is a huge problem in the technology industry, as so few people to fill roles. How's Microsoft dealing-- what's your view-- >> my view is I'm glad I work at Microsoft, 'cause we spend a lot of energy on that, um, I wish there were a single solution, but we have Minecraft for education, starting with kids, how do you help, you know, Minecraft is this great tool that teachers use help kids get started, so that's a tool set we work on something called tills, which is uh, basically, our developers teach school kids remotely, junior, high school level, you know, coding. Um, we have made investments against this, we have online training, you know, we work with universities. I don't know the perfect answer, um, but I do know we invest and we work with Hadi Partovi and his group on code.org, I mean any place that there is work going on, we work with the military for people coming out of the military service. So we're heavily invested. I'm hopeful that the ease of use of some of the tools and just from a job area, it drives people but I don't know the perfect answer. Steve's view is I don't know the answer, I do know we try every trick in the book-- >> Multipronged attack >> I'm a parent of two kids, like I have my daughter, you know, working on more on the tech side and you know, it's hard to keep kids on a track for that-- >> There's no degree yet, but we had a first degree this year, graduated from the school but there's kind of like a skills portfolio of different things depending on the make-up I mean, domain expertise is critical, if you don't know what you're tryna do, that's -- >> I think we got a mix, because what you're starting to see is, the tools for subject matter experts, are getting better, we have something called the power platfrom, which allows people who aren't necessarily coders by trade, but want to be able to build, you know, sort of apps or services to be able to do that more easily and mix their subject matter expertise. And you see many more people come out of any program, take biology, with sort of computer knowledge to a decent level. AI and ML research, different area, hard skills gap right there >> Steve, great insights, thanks for spending some time with us, great insights on the skills gap and just overall >> thanks for coming on theCUBE >> We didn't talk about rugby, but okay, fine. Thanks, next time >> next time >> You're one of those ballsmen >> we'd track you down >> The ballsmen can throw >> Exactly, shout out to them >> There we go, >> thank you >> Ah, you are watching theCUBE we'd come right back with more from Informatica World I'm Rebecca Knight for John Furrier, stay tuned (upbeat music)

>> Live from Las Vegas, it's theCUBE covering Informatica World 2019 brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World. I am your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Amit Walia, President - Product and Marketing here at Informatica. Thank you for coming back on theCUBE. So we're here at Informatica World, there's a lot of buzz, a lot of energy, obviously CLAIRE is a big story, your company got great press yesterday from The Wall Street Journal teaming up with Google to tame the data. One of the themes we keep hearing is that data needs AI, but AI needs data. Elaborate on that a little bit. >> That's a great point, in fact I would extend that and say I believe, and I will talk about that today in the closing keynote, is the language that AI needs or speaks is data. Because to be honest, without data, there's no great AI. And I think something that we've known all this while, but now that AI is really becoming pervasive and has skill, you really, really need to give it relevant, good, contextual data for a Siri or a Cortana or Alexa to make some contextual decisions, right? And we see that happening a lot in the world of enterprise now. Finally enterprises are arriving at the point where they want to use AI for P-to-P use cases, not just consumer use cases that you and me are used to. And then, to your other question, AI is a part of everything that we do in data. Because, to be honest, it really helps improve productivity, automate mundane tasks. And I think we were talking before this, there is a massive skills gap. And I think you look around, the economy's kind of fully saturated with jobs, and there's still so much more work to be done with more data, different data, so AI's helping making some of those mundane activities become a lot more easier or autonomous, if I may. >> What's the use cases for CLAIRE in AI around as it grows? Because, you know, the data world, you guys have been doing it for 25 years at Informatica, private for 4 so, innovating on the products side, but it used to be, here's the data department, they handle it. The data warehousing in the fenced out area in the company, now it's strategically part of everything, right? So you guys have the MDM, you've got the Catalog, you've got all kinds of solutions. How is that role changing within your customer base? And what are some of those use cases? Because now they have to think end-to-end, you've got Cloud and On-premise, these are challenges and opportunities. But the role of data and the data teams is expanding rapidly. >> In a significant way. A significant way. I think I kind of was joking with our practitioners yesterday that they were all becoming, they were going from heroes to superheroes, if you are enjoying the Avengers movies, and that analogy. But genuinely, because if you think about it, right, I think what we are seeing in this world, we call it the data three data where the data is becoming a platform of a sort. It is getting decoupled from the data bases, from the applications, from the infrastructure, because to truly be able to leverage AI, and build applications on top, you cannot let it be siloed and be hostage to its individual infrastructure components. So we're seeing that fundamental change happening where data as a platform is coming along, and in that context the catalog becomes a very, very pivotal start, because you want to get a full view of everything. And look, you're not going to be able to move all your data in one place, it's impossible. But understanding that through metadata is where enterprises are going, and then from there, John, as Rebecca's talked about, you can have a customer experience journey with MDM. You can have a analytics journey in the Cloud with an AWS or (inaudible) or a JCP. Or you can have a complete governance and security and privacy journey understanding anomalous activity. >> So before I go any further I just want to ask you about this one point because you guys made a big bet with the Catalog >> Okay, and it's looking good. A lot of good bets. You know, AI, Catalog, Cloud, early on the Cloud, but one of the things I hear a lot is that data's at the blood stream, you want the blood flowing around the system, the body. People looking at data like an operating system kind of architecture where you got to have the data free flowing. So the Catalog seems to be a big bet there. How is that helping the AI peeps because if you can have the data flowing -- >> Yep. No I think -- >> You're going to have feeding the machine learning >> Absolutely. >> The machine learning feeds the application of AI, you got to have the data, the data's not flowing, you can't just inject it at certain times. >> The way we think about it is, you're exactly right. I would just, in fact it's so ah, interesting, the analogy I use is that data is everywhere. It's like the blood flowing through your body, right? You're not going to get all the data in one place to do any kind of analytics, right? You're going to let it be there. So we say metadata is the new OS. Bring the metadata, which is data about the data in one place. And from there let AI run on it. And what we think about AI is that, think about this. LinkedIn is a beautiful place where they leveraged the machine learning algorithm to create and social graph about you and me. So if I'm connected with John, I know now that I can be connected with you. The same thing can happen to the data layer. So when I'm doing analytics, and I'm basically searching for some report, I don't know, through that same machine learning algorithm at the catalog level, now we can tell you, you know what? This is another table. This is another report. This is another user. And so on. And we can give you back ratings within that environment for you to do what I call analytics on your fingertips at enterprise scale. So that's an extremely powerful use case of taking analytics which is the most commonly done activity in an enterprise and make it accurate at an enterprise scale. >> Well the LinkedIn example, you know, of course I have a different opinion on that. They're a siloed platform. They don't have any API's, it's only within LinkedIn. But it begs the question, since you're both that kind of consumer, look at a company like Slack, going public, very successful, their numbers are off the charts in terms of adoption, usage, a simple utility in IRC message chat room that has a great UI on it. But their success came when they integrated. >> Sure. >> Integration was a big part of their success. They wanted to have API's and let customers use the software, SAS software, with a lot of data. So they were really open. >> Yes. >> How were you guys from a business standpoint taking that concept of SAS openness connecting with other apps because I might have, bring my own app to the table as data, and integrate that piece into Informatica. How does that work? >> Very similarly. So the way we've done it is that our whole platform is fully API based. So we have opened up the API's, any application can hook on to that. So we believe that we are the Switzerland of data. So you may have any underlying infrastructure stack. On-prem, in the Cloud, multi-Cloud, whatever it is. Different applications, different Cloud applications, right? So our goal is that at the layer which is the metadata layer on which CLAIRE runs, we've opened up the API's, we've hooked to everything, and so we can consume the metadata, and there we truly provide a true data platform to our organization. So if you are running a Server Snap, a Salesforce.com, Adobe, Google, AWS, you can still bring all that stuff together and make contextual business decisions. >> One of the things you had talked about on the main stage is how the Millennials that you're hiring have higher expectations in their personal lives from the technology that they're using, and that's really pushing you to deliver different kinds of products and services that have the same level of innovation and high touch. Can you talk a little bit about that and how, and how this new generation of the workforce, and there's obviously Gen Y coming right behind it, is really pushing innovation in your company. >> Well you know, I have a fourteen-year-old, so I get a taste of that every day at home. (laughing) So you know, what they want to experience, so I, you know, I use this word, experiences are changing. And by the way they are pushing the boundary for us too. We grew up in the infrastructure software world which you know, twenty-five years ago was all, you can go down to the command line interface. Not any more. You really really have to make it simple. I think users today don't want to waste their time what I call doing mundane activities. They want to get to value fast. That's pushing the boundary for us. In fact that's where we're leveraging AI in our products to make sure we can remove the mundane clutter activities for them, for them to do value added activities. For example, I want to discover data to do some analysis. I don't want to go around discovering. Discover it for me. So that's where CLAIRE comes in and the catalog, right? Discover it for me. You know what? I don't want to figure out whether this data is accurate or not accurate. Tell me. So we are taking that philosophy, really really pushing the boundary for us, but in a good way. Because definitely those users want what I call very simplified and value added experiences. >> And that's really what SAS and consumer applications have shown us, and that's proven to be hard in the enterprise. So I got to ask you as you take this data concept to the infrastructure, a lot of enterprises are re-architecting, you hear words like multi-Cloud, hybrid Cloud, public Cloud, and you start to see a holistic new kind of persona, a Cloud architect. >> Yes. >> They're re-architecting their infrastructure to be SAS-like, to take advantage of data. >> Correct. >> That's kind of known out there, it's been reported on, we've been reporting on it. So the question is, that isn't alignment, that's not just the data people, it's data meets infrastructure. >> Absolutely. >> What's your advice to the companies out there that are doing this, because you guys have Cloud, Google, Amazon, Azure, Cloud, On-premise. You can work anywhere. What's you're advice? >> Yeah, no, I think it's a very good, it's a very topical question. Because I do think that the infra, the old days of separating different layers of the stack are are gone. Especially the old infrastructure all the way to platform as a server stack has to be very well though out together. To your point, customers running a hybrid multi-cloud world, right? So think about it, if you're in the world of improving customer experiences, I may have my marketing cloud running somewhere, I may have my sales cloud running somewhere, and a service cloud running somewhere. But to give a great experience I have to bring it all together. So you have to think about the infrastructure and the data together for enterprises to give a better experience to their customers. And I see innovative customers of companies truly think through that one and succeed. And the ones that are still lagging behind are still looking at that in silos. And then be able to have the data layer for hyper scale. Well these are all hyper scale platforms. You cannot run a little experiment over here. So we've invested in that whole concept of hyperscale, multi-cloud, hybrid cloud, and make sure it touches everything through API's. >> So we've been covering you guys for four years here at Informatica World. It's great to see the journey, nothing's really changed on the messaging and the strategy, you say you're going to do something and you keep doing it, and some little course corrections here, and acquisitions here and there to kind of accelerate it. But when we talk to your customers we hear a couple of different things. We hear platform, Informatica, when describing Informatica. You guys win the whole data thing, you're there, it's the business you're in. In the data business. But I'm hearing new words, platform. Scale. These are kind of new signals we're hearing from your customer base and some of the people here at the show. Talk about that impact, how you guys are investing in the platform, what it means for customers, and what does scale mean for your business and customers? >> No, we've heard that from our customers too. Customers said look, they all recognize that they have to invest in data as a platform. But you know, it's not like an original platform so they want it because we serve the broader state of management needs, so they want us to be like a platform. So we've invested that, couple of years ago we went completely ground-up, re-built everything, micro-services based. All API driven. Containerized. Modular. So the idea is that nobody is buying a monolithic platform. Nobody buying a platform, it just builds by itself. And they can compartmentize it, I want this now, I want that later, so like a Lego block it builds. And, you know what, through an API it also hooks into any of the existing infrastructure they have, or anything new that they want to bring in. So that really pushed the boundary for us. We invested in that. By the way, that platform today, in the Cloud, which you call IICS, runs eight trillion transactions a month. Eight trillion transactions a month. And by the way, last Informatica World, it was running two-and-a-half trillion transactions. So in one year it's gone from two-and-a-half to eight. So we are seeing that really hyper scale. >> And you, and I'm going to ask you if you believe, just, and you can answer yes or no or maybe, or answer on your own, do you believe that data is critical for SAS success? >> Oh absolutely. No doubt about it. I have not met a single customer who ever said anything different. In fact, the thing that I see is like, it's becoming more and more and more a sea-level conversation. That hey, what are we going to do with our data? How do we bring that data together to make decisions? How do we leverage AI and data together? It's truly in our sea-level discussion, whereas it was never a sea-level discussion years ago, it was more about what application am I going to use? What infrastructure am I going to use? Now they're all about, how do I manage this data? >> I wanted to talk about ethics (laughs) and this is, because recently had published a paper about Tech for Good, and it's about this idea of using AI and machine learning to help society achieve better outcomes, and then also to help us measure it's impact on our welfare beyond GDP. Because think about the value that technology brings to our lives. What's your take on this? I mean how much value do you think AI brings to the enterprise in terms of this Tech for Good idea? >> No, so, by the way one of Informatica's values is "Do good". And we are firm believers in that look, there is an economic value to everything in life. But then we all have something to give back to the society. There is something to create value out there which is outside the realm of just pure economics which is the point you were asking. And we are firm believers in that. I do think that by the way, there is a very high bar for all of us in the industry to make sure that not only, it's not just about ethics of AI also at the same time, because we cannot abuse the data. We're collecting a lot of information. You and me as consumers are giving a lot of information and I talked about that yesterday as well, that we believe that the ethics of AI are going to play a fundamental and differentiating role going forward. I think the Millennials we're talking about, they are very aware of that one. They are very purposeful. So they'll look back and say, who actually has a values system to take this technology innovation and do something better with it, not just creating money out of it. And I think I totally agree, and by the way in the very early stages, industry has to still learn that, and internalize that, then do something about it. >> Well Amit, yeah I think you're right on, early days, and I can give you an anecdotal example is that this year, University of California, Berkeley, graduated its first inaugural class of data science analytics. First! First ever class for them. They're a pioneer, they're usually having protests and doing things with revolutionary things. That shows it's so early. So the question I got to ask you is, you've got your fourteen-year-old, you know I have kids, we follow each other on Facebook. I'm always asked the question and I want to get this exposed. People are really discovering new ways to learn. Not just in school, you got YouTube videos, you've got CUBE videos, you got all kinds of great things out there. But really people are trying to figure out where to double down on, what dials to turn, what classes to take, what disciplines are going to help me. It used to be oh, go into computer science, you'll get a great job. And certainly that's still true. But there's now new opportunities for people, data's now grown from you know, programming deeply to ethics. And you don't need to have a CS degree to get in and be successful to fill the job openings or contribute to society. So what are those areas that you see that people who are watching might say hey you know what? I'm good at that, I'm good at art, I'm good at society or philosophy or I'm really good at math or, what skills do you, should people think about if they want to be successful in data? >> You know, I think it's a very foundational question. I think you're right, I think programming has become a lot easier. So I think if I'd stepped back to the days we graduated, right? It's become a lot easier so I don't think that necessarily learning programming is a differentiating, I do think that back where you were going, people who'd generally think about what to do with that. I think there is analytical skills that we all need, but I think the soft skills I believe in the society, we are kind of leaving behind, right? A little bit of the psychology of how users want to use something. Design thinking. By the way I still think that design thinking is not yet completely out there. Um, the ability to marry what I call the left brain to the right brain, I mean, I think that's key. And I do think that we cannot run away completely to the right brain, as much as I am an analytical person myself. I think marrying the left and the right, I do believe, like I, as I said I have a fourteen-year-old. My advice to all those who say, he wants to do Computer Science, is to take enough psychology or design classes to kind of have that balance. So my encouragement would be have the balance. We cannot all just be hyper-analytical. We have to kind of have the balance to see ... >> I think just be smart, balance, I mean again, I have not found one, well I guess the answers are stats and math, have the check, that's easy to say, but ... >> The emotional skills. But you need more of those, I think a little bit more of those left-brain skills also to complement them. >> Well and also for the experience, study art, music, what delights people. What inspires the passion? >> I agree with that. >> Yeah. Absolutely. Amit, always a pleasure to see you. Thank you so much. >> Thank you very much. Always a pleasure to be here. >> Great conversation. Good insight. >> I'm Rebecca Knight for John Furrier, stay tuned at theCUBE's live coverage at Informatica World. (Upbeat music)

>> Live from Las Vegas, it's theCUBE, covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone, to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight, along with my cohost, John Furrier. We have two guests for this segment. We have Charlie Emer. He is the senior director data management and governance strategy at Honeywell. Thanks for joining us. >> Thank you. >> And Randy Mickey, senior vice president global professional services at Informatica. Thanks for coming on theCUBE. >> Thank you. >> Charlie, I want to start with you. Honeywell is a household name, but tell us a little bit about the business now and about your role at Honeywell. >> Think about it this way. When I joined Honeywell, even before I knew Honeywell, all I thought was thermostats. That's what people would think about Honeywell. >> That's what I thought. >> But Honeywell's much bigger than that. Look, if you go back to the Industrial Revolution, back in, I think, '20s, we talked about new things. Honeywell was involved from the beginning making things. But we think this year and moving forward in this age, Honeywell is looking at it as the new Industrial Revolution. What is that? Because Honeywell makes things. We make aircraft engines, we make aircraft parts. We make everything, household goods, sensors, all types of sensors. We make things. So when we say the new Industrial Revolution is about the Internet of Things, who best to participate because we make those things. So what we are doing now is what we call IIOT, Industrial Internet of Things. Now, that is what Honeywell is about, and that's the direction we are heading, connecting those things that we make and making them more advancing, sort of making life easier for people, including people's quality of life by making those things that we make more usable for them and durable. >> Now, you're a broad platform customer of Informatica. I'd love to hear a little bit from both of you about the relationship and how it's evolved over the years. >> Look, we look at Informatica as supporting our fundamentals, our data fundamentals. For us to be successful in what we do, we need to have good quality data, well governed, well managed, and secure. Not only that, and also accessible. And we using Informatica almost end to end. We are using Informatica for our data movement ETL platform. We're using Informatica for our data quality. We're using Informatica for our master data management. And we have Informatica beginning now to explore and to use Informatica big data management capabilities. And more to that, we also utilize Informatica professional services to help us realize those values from the platforms that we are deploying. IIoT, Industrial IoT has really been a hot trend. Industrial implies factories building big things, planes, wind farms, we've heard that before. But what's interesting is these are pre-existing physical things, these plants and all this manufacturing. When you add digital connectivity to it and power, it's going to change what they were used to be doing to new things. So how do you see Industrial IoT changing or creating a builder culture of new things? Because this connect first, got to have power and connectivity. 5G's coming around, Wi-Fi 6 is around the corner. This is going to light up all these devices that might have had battery power or older databases. What's the modernization of these industrial environments going to look like in your view? First of all, let me give you an example of the value that is coming with this connectivity. Think of it, if you are an aircraft engineer. Back in the day, a plane landed in Las Vegas. You went and inspected it, physically, and checked in your manual when to replace a part. But now Honeywell is telling you, we're connecting directly to the mechanic who is going to inspect the plane, and there will be sort of in their palms they can see and say wait a minute. This part, one more flight and I should replace this part. Now, we are advising you now, doing some predictive analytics, and telling you when this part could even fail. We're telling you when to replace it. So we're saying okay, the plane is going to fly from here to California. Prepare the mechanics in California when it lands with the part so they can replace it. That's already safety 101. So guaranteeing safety, sort of improving the equity or the viability of the products that we produce. When we're moving away from continue to build things because people still need those things built, safety products, but we're just making them more. We've heard supply chain's a real low-hanging fruit on this, managing the efficiency so there's no waste. Having someone ready at the plane is efficient. That's kind of low-hanging fruit. Any ideas on some of the creativity of new applications that's going to come from the data? Because now you start getting historical data from the connections, that's where I think the thing can get interesting here. Maybe new jobs, new types of planes, new passenger types. >> We are not only using the data to improve on the products and help us improve customer needs, design new products, create new products, but we also monitorizing that data, allowing our partners to also get some insights from that data to develop their own products. So creating sort of an environment where there is a partnership between those who use our products. And guess what, most of the people who use our products, our products actually input into their products. So we are a lot more business-to-business company than a B2C. So I see a lot of value in us being able to share that intelligence, that insight, in our data at a level of scientific discovery for our partners. >> Randy, I want to bring you into the conversation a little bit here (laughs). >> Thanks. >> So you lead Informatica's professional services. I'm interested to hear your work with Honeywell, and then how it translates to the other companies that you engage with. Honeywell is such a unique company, 130 years of innovation, inventor of so many important things that we use in our everyday lives. That's not your average company, but talk a little bit about their journey and how it translates to other clients. >> Sure, well, you could tell, listening to Charlie, how strategic data is, as well as our relationship. And it's not just about evolution from their perspective, but also you mentioned the historicals and taking advantage of where you've been and where you need to go. So Charlie's made it very clear that we need to be more than just a partner with products. We need to be a partner with outcomes for their business. So hence, a professional services relationship with Honeywell and Charlie and the organization started off more straightforward. You mentioned ETL, and we started off 2000, I believe, so 19 years ago. So it's been a journey already, and a lot more to go. But over the years you can kind of tell, using data in different ways within the organization, delivering business outcomes has been at the forefront, and we're viewed strategically, not just with the products, but professional services as well, to make sure that we can continue to be there, both in an advisory capacity, but also in driving the right outcomes. And something that Charlie even said this morning was that we were kind of in the fabric. We have a couple of team members that are just like Honeywell team members. We're in the fabric of the organization. I think that's really critically important for us to really derive the outcomes that Charlie and the business need. >> And data is so critical to their business. You have to be, not only from professional services, but as a platform. Yes. This is kind of where the value comes from. Now, I can't help but just conjure up images of space because I watch my kids that watch, space is now hot. People love space. You see SpaceX landing their rocket boosters to the finest precision. You got Blue Origin out there with Amazon. And they are Honeywell sensors either. Honeywell's in every manned NASA mission. You have a renaissance of activity going on in a modern way. This is exciting, this is critical. Without data, you can't do it. >> Absolutely, I mean, also sometimes we take a break. I'm a fundamentalist. I tell everybody that excitement is great, but let's take a break. Let's make sure the fundamentals are in place. And we actually know what is it, what are those critical data that we need to be tracking and managing? Because you don't just have to manage a whole world of data. There's so much of it, and believe me, there's not all value in everything. You have to be critical about it and strategic about it. What are the critical data that we need to manage, govern, and actually, because it's expensive to manage the critical data. So we look at a value tree as well, and say, okay, if we, as Honeywell, want to be able to be also an efficient business enabler, we have to be efficient inside. So there's looking out, and there's also looking inside to make sure that we are in the right place, we are understanding our data, our people understand data. Talking about our relationship with IPS, Informatica Professional Services, one of the things that we're looking at is getting the right people, the engineers, the people to actually realize that okay, we have the platform, we've heard of Clare, We heard of all those stuff. But where are the people to actually go and do the real stuff, like actually programming, writing the code, connecting things and making it work? It's not easy because the technology's going faster than the capabilities in terms of people, skills. So the partnership we're building with Informatica professional services, and we're beginning to nurture, inside that, we want to be in a position were Honeywell doesn't have to worry so much about the churn in terms of getting people and retraining and retraining and retraining. We want to have a reliable partner who is also moving with the certain development and the progress around the products that we bought so we can have that success. So the partnership with IPS is for the-- >> The skill gaps we've been talking about, I know she's going to ask next, but I'll just jump in because I know there's two threads here. One is there's a new generation coming into the workforce, okay, and they're all data-full. They've been experiencing the digital lifestyle, the engineering programs. To data, it's all changing. What are some of the new expertise that really stand out when evaluating candidates, both from the Informatica side and also Honeywell? What's the ideal candidate look like, because there's no real four-year degree anymore? Well, Berkeley just had their first class of data analytics. That's new two-generation. But what are some of those skills? There's no degree out there. You can't really get a degree in data yet. >> Do you want to talk about that? >> Sure, I can just kick off with what we're looking at and how we're evolving. First of all, the new graduates are extremely innovative and exciting to bring on. We've been in business for 26 years, so we have a lot of folks that have done some great work. Our retention is through the roof, so it's fun to meld the folks that have been doing things for over 10, 15 years, to see what the folks have new ideas about how to leverage data. The thing I can underscore is it's business and technology, and I think the new grads get that really, really well in terms of data. To them, data's not something that's stored somewhere in the cloud or in a box. It's something that's practically applied for business outcomes, and I think they get that right out of school, and I think they're getting that message loud and clear. Lot of hybrid programs. We do hire direct from college, but we also hire experienced hires. And we look for people that have had degrees that are balanced. So the traditional just CS-only degrees, still very relevant, but we're seeing a lot of people do hybrids because they know they want to understand supply chain along with CS and data. And there are programs around just data, how organizations can really capitalize on that. >> And also we're hearing, too, that having domain expertise is actually just as important as having the coding skills because you got to know what an outcome looks like before you collect the data. You got to know what checkmate is if you're going to play chess. That's the old expression, right? >> I think people with the domain, both the hybrid experience or expertise, are more valuable to the company because maybe from the product perspective, from building products, you could be just a scientist, code the code. But when you come to Honeywell, for example, we want you to be able to understand, think about materials. Want you to be able to understand what are the products, what are the materials that we use. What are the inputs that we have to put into these products? Now a simple thing like a data scientist deciding what the right correct value of what an attribute should be, that's not something that because you know code you can determine. You have to understand the domain, the domain you're dealing with. You have to understand the context. So that comes, the question of context management, understanding the context and bringing it together. That is a big challenge, and I can tell you that's a big gap there. >> Big gap indeed, and understand the business and the data too. >> Yes. >> Charles, Randy, thank you both so much for coming on theCUBE. It's been a great conversation. >> Thank you. >> Thank you. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (funky techno music)

(upbeat music) >> Live, from Las Vegas it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host Rebecca Knight along with my co-host John Furrier. We are joined by Adam Mariano, he is the Vice-President Health Informatics at HighPoint Solutions. Thanks for coming on theCUBE! >> Thank you for having me. >> So tell our viewers a little bit about HighPoint Solutions, what the company does and what you do there. >> Sure, HighPoint is a consulting firm in the Healthcare and Life Sciences spaces. If it's data and it moves we probably can assist with it. We do a lot of data management, we implement the full Infomatica stack. We've been an Infomatica partner for about 13 years, we were their North American partner of the year last year. We're part of a much larger organization, IQVIA, which is a merger of IMS quintiles, large data asset holder, big clinical research organization. So we're very much steeped in the healthcare data space. >> And what do you do there as Vice President of Health and Formatics? >> I'm in an interesting role. Last year I was on the road 51 weeks. So I was at over a hundred facilities, I go out and help our customers or prospective customers or just people we've met in the space, get strategic about how they're going to leverage data as a corporate asset, figure out how they're going to use it for clinical insight, how they're going to use it for operational support in payer spaces. And really think about how they're going to execute on their next strategy for big data, cloud strategy, digital re-imaginment of the health care space and the like. >> So we know that healthcare is one of the industries that has always had so much data, similar to financial services. How are the organizations that you're working with, how are they beginning to wrap their brains around this explosion of data? >> Well it's been an interesting two years, the last augur two years there isn't a single conversation that hasn't started with governance. And so it's been an interesting space for us. We're a big MDM proponent, we're a big quality proponent, and you're seeing folks come back to basics again, which is I need data quality, I need data management from a metadata perspective, I need to really get engaged from a master data management perspective, and they're really looking for integrated metadata and governance process. Healthcare's been late to the game for about five or six years behind other industries. I think now that everybody's sort of gone through meaningful use and digital transformation on some level, we're now arcing towards consumerism. Which really requires a big deep-dive in the data. >> Adam, data governance has been discussed at length in the industry, certainly recently everyone knows GDPR's one year anniversary, et cetera, et cetera. But the role of data is really critical applications for SAS and new kinds of use cases, and the term Data Provisioning as a service has been kicked around. So I'd love to get your take on what that means, what is the definition, what does it mean? Data Provisioning as a service. >> The industry's changed. We've sort of gone through that boomerang, alright, we started deep in the sort of client server, standard warehouse space. Everything was already BMS. We then, everybody moved to appliances, then everybody came back and decided Hadoop, which is now 15 year old technology, was the way to go. Now everybody's drifting to Cloud, and you're trying to figure out how am I going to provision data to all these self-service users who are now in the sort of bring your own tools space. I'd like to use Tablo, I'd like to use Click. I like SAS. People want to write code to build their own data science. How can you provision to all those people, and do so through a standard fashion with the same metadata with the same process? and there isn't a way to do that without some automation at this point. It's really just something you can't scale, without having an integrated data flow. >> And what's the benefits of data provisioning as a service? What's the impact of that, what does it enable? >> So the biggest impact is time to market. So if you think about warehousing projects, historically a six month, year-long project, I can now bring data to people in three weeks. In two days, in a couple of hours. So thinking about how I do ingestion, if you think about the Informatica stack, something like EDC using enterprise data catalog to automatically ingest data, pushing that out into IDQ for quality. Proving that along to AXON for data governance and process and then looking at enterprise data lake for actual self-service provisioning. Allowing users to go in and look at their own data assets like a store, pick things off the shelf, combine them, and then publish them to their favorite tools. That premise is going to have to show up everywhere. It's going to have to show up on AWS, and on Amazon, and on Azure. It's going to have to show up on Google, it's going to have to show up regardless of what tool you're using. And if you're going to scale data science in a real meaningful way without having to stack a bunch of people doing data munging, this is the way it's going to have to go. >> Now you are a former nurse, and you now-- >> I'm still a nurse, technically. >> You're still a nurse! >> Once a nurse, always a nurse. Don't upset the nurses. >> I've got an ear thing going on, can you help me out here? (laughter) >> So you have this really unique vantage point, in the sense that you are helping these organizations do a better job with their data, and you also have a deep understanding of what it's like to be the medical personnel on the other side, who has to really implement these changes, and these changes will really change how they get their jobs done. How would you say, how does that change the way you think about what you do? And then also what would you say are the biggest differences for the nurses that are on the floor today, in the hospital serving patients? >> I think, in America we think about healthcare we often talked about Doctors, we only talk about nurses in nursing shortages. Nurses deliver all the care. Physicians see at this point, the way that medicine is running, physicians see patients an average two to four minutes. You really think about what that translates to if you're not doing a surgery on somebody, it's enough time to talk to them about their problem, look at their chart and leave. And so nursing care is the point of care, we have a lot of opportunity to create deflection and how care is delivered. I can change quality outcomes, I can change safety problems, I can change length of stay, by impacting how long people keep IVs in after they're no longer being used. And so understanding the way nursing care is delivered, and the lack of transparency that exists with EMR systems, and analytics, there's an opportunity for us to really create an open space for nursing quality. So we're talking a lot now to chief nursing officers, who are never a target of analytics discussion. They don't necessarily have the budget to do a lot of these things, but they're the people who have the biggest point of control and change in the way care is delivered in a hospital system. >> Care is also driven by notifications and data. >> Absolutely. >> So you can't go in a hospital without hearing all kinds of beeps and things. In AI and all the things we've been hearing there's now so many signals, the question is what they pay attention to? >> Exactly. >> This becomes a really interesting thing, because you can get notifications, if everything's instrumented, this is where kind of machine learning, and understanding workflows, outcomes play a big part. This is the theme of the show. It's not just the data and coding, it's what are you looking for? What's the problem statement or what's the outcome or scenario where you want the right notification, at the right time or a resource, is the operating room open? Maybe get someone in. These kinds of new dynamics are enabled by data, what's your take on all this? >> I think you've got some interesting things going on, there's a lot of signal to noise ratio in healthcare. Everybody is trying to build an algorithm for something. Whether that's who's going to overstay their visit, who's going to be readmitted, what's the risk for somebody developing sepsis? Who's likely to follow up on a pharmacy refill for their medication? We're getting into the space where you're going to have to start to accept correlation as opposed to causation, right? We don't have time to wait around for a six month study, or a three year study where you employ 15,000 patients. I've got three years of history, I've got a current census for the last year. I want to figure out, when do I have the biggest risk for falls in a hospital unit? Low staffing, early in their career physicians and nurses? High use of psychotropic meds? There are things that, if you've been in the space, you can pretty much figure out which should go into the algorithm. And then being pragmatic about what data hospitals can actually bring in to use as part of that process. >> So what you're getting at is really domain expertise is just as valuable as coding and wrangling data, and engineering data. >> In healthcare if you don't have SMEs you're not going to get anything practical done. And so we take a lot of these solutions, as one of the interesting touch points of our organization, I think it's where we shine, is bringing that subject matter expertise into a space where pure technology is not going to get it done. It's great if you know how to do MDM. But if you don't know how to do MDM in healthcare, you're going to miss all the critical use cases. So it really - being able to engage that user base, and the SMEs and bring people like nurses to the forefront of the conversation around analytics and how data will be used to your point, which signals to pay attention to. It's critical. >> Supply chains, another big one. >> Yeah. >> Impact there? >> Well it's the new domain in MDM. It's the one that was ignored for a long time. I think people had a hard time seeing the value. It's funny I spoke at 10 o'clock today, about supply chain, that was the session that I had with Nathan Rayne from BJC. We've been helping them embark on their supply chain journey. And from all the studies you look at it's one of the easiest places to find ROI with MBM. There's an unbelievable amount of ways- >> Low hanging fruit. >> $24.5 billion in waste a year in supply chain. It's just astronomical. And it's really easy things, it's about just in time supplies, am I overstocking, am I losing critical supplies for tissue samples, that cost sometimes a $100,000, because a room has been delayed. And therefore that tissue sits out, it ends up expiring, it has to be thrown away. I'll bring up Nathan's name again, but he speaks to a use case that we talked about, which is they needed a supply at a hospital within the system, 30 miles away another hospital had that supply. The supply costs $40,000. You can only buy them in packs of six. The hospital that needed the supply was unaware that one existed in the system, they ordered a new pack of six. So you have a $240,000 price that you could have resolved with a $100 Uber ride, right? And so the reality is that supply could have been shipped, could have been used, but because that wasn't automated and because there was no awareness you couldn't leverage that. Those use cases abound. You can get into the length of stay, you can get into quality of safety, there's a lot of great places to create wins with supply chain in the MDM space. >> One of the conversations we're having a lot in theCUBE, and we're having here at Informatica World, it centers around the skills gap. And you have a interesting perspective on this, because you are also a civil rights attorney who is helping underserved people with their H1B visas. Can you talk a little bit about the visa situation, and what you're seeing particularly as it relates to the skills gap? >> We're in an odd time. We'll leave it at that. I won't make a lot of commentary. >> Yes. >> I'm a civil rights and immigration attorney, and on the immigration side I do a lot of pro bono work with primarily communities of color, but communities at risk looking to help adjust their immigration status. And what you've had is a lot of fear. And so you have, well you might have an H1B holder here, you may have somebody who's on a provisional visa, or family members, and because those family members can no longer come over, people are going home. And you're getting people who are now returning. So we're seeing a negative immigration of places like Mexico, you're seeing a lot of people take their money, and their learnings and go back to India and start companies there and work remotely. So we're seeing a big up-tick in people who are looking for staffing again. I think the last quarter or so has been a pretty big ramp-up. And I think there's going to continue to be this hole, we're going to have to find new sources of talent if we can't bring people in to do the jobs. We're still also, I think it just speaks to our STEM education the fact that we're not teaching kids. I have a 28 year old daughter who loves technology, but I can tell you, her education when she was a kid, was lacking in this technology space. I think it's really an opportunity for us to think about how do we train young people to be in the new data economy. There's certainly an opportunity there today. >> And what about the, I mean you said you were talking about your daughter's education. What would you have directed her toward? What kinds of, when you look ahead to the jobs of the future, particularly having had various careers yourself, what would you say the kids today should be studying? >> That's two questions. So my daughter, I told her do what makes you happy. But I also made her learn Sequel. >> Be happy, but learn Sequel. >> But learn sequel. >> Okay! >> And for kids today I would say look, if you have an affinity and you think you enjoy the computer space, so you think about coding, you like HTML, you like social media. There are a plethora of jobs in that space and none of them require you to be an architect. You can be a BA, you can be a quality assurance person, you can be a PM. You can do analysis work. You can do data design, you can do interface design, there's a lot of space in there. I think we often reject kids who don't go to college, or don't have that opportunity. I think there's an opportunity for us to reach down into urban centers and really think about how we make alternate pathways for kids to get into the space. I think all the academies out there, you're seeing rise, Udemy, and a of of these other places that are offering academy based programs that are three, six months long and they're placing all of their students into jobs. So I don't think that the arc that we've always chased which is you've got to come from a brand named school to get into the space, I don't think it's that important. I think what's important is can I get you the clinical skill, so that you've understood how to move data around, how to process it, how to do testing, how to do design, and then I can bring you into the space and bring you in as an entry level employee. That premise I think is not part of the American dream but it should be. >> Absolutely, looking for talent in these unexpected places. >> College is not the only in point. We're back to having I think vocational schools for the new data economy, which don't exist yet. That's an opportunity for sure. >> And you said earlier, domain expertise, in healthcare as an example, points to what we've been hearing here at the conference, is that with data understanding outcomes and value of the data actually is just as important, as standing up, wrangling data, because if you don't have the data-- >> You make a great point. The other thing I tell young people in my practice, young people I interact with, people who are new to the space is, okay I hear you want to be a data scientist. Learn the business. So if you don't know healthcare get a healthcare education. Come be on this project as a BA. I know you don't want to be a BA, that's fine. Get over it. But come be here and learn the business, learn the dialogue, learn the economy of the business, learn who the players are, learn how data moves through the space, learn what is the actual business about. What does delivering care actually look like? If you're on the payer side, what does claims processing look like from an end to end perspective? Once you understand that I can put you in any role. >> And you know digital four's new non-linear ways to learn, we've got video, I see young kids on YouTube, you can learn anything now. >> Absolutely. >> And scale up your learning at a pace and if you get stuck you can just keep getting through it no-- >> And there are free courses everywhere at this point. Google has a lot of free courses, Amazon will let you train for free on their platform. It's really an opportunity-- >> I think you're right about vocational specialism is actually a positive trend. You know look at the college University scandals these days, is it really worth it? (laughter) >> I got my nursing license through a vocational school originally. But the nursing school, they didn't have any technology at that point. >> But you're a great use case. (laughter) Excellent Adam, thank you so much for coming on theCUBE it's been a pleasure talking to you. >> Thank you. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (techno music)

(upbeat music) >> Live from Las Vegas, it's theCUBE! Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back to theCUBE, everyone. We are in the middle of two days of coverage of Informatica World here in Las Vegas. I'm your host, Rebecca Knight, along with my cohost, John Furrier. We are joined by Ansa Sekharan, he is the Executive Vice President and Chief Customer Officer at Informatica. Thanks so much for coming on the Cube, Ansa. >> My pleasure to be back on theCUBE. >> Great to see you. >> Thank you. >> So, let's talk about your role as the Chief Customer Officer. Last year you announced this change from a customer service model to a customer success model. How has that been? How have you implemented it and how's it going? >> Now, we have a great opportunity ahead of us. You see a number of enterprises embarking on a data transformation journey. As we offer the best products, it was quite apparent we had to take the services to the next level. We had to take the services and connect them to customers' business values. So we are blurring the lines between the various services functions: support, professional services, university, customer success, we want to abstract them, along with their products, we want to offer the best value to the customers. It's very simple. We sign up a new customer. The first thing we want to do is to work with the customer and define the success plan. What does success mean to them? Success, in two words, business outcomes. It's not about go-lives. Are the business users adopting and realizing value? That's where Informatica is very different from other enterprises, and I think that's going to further fuel our growth in the future. >> Ansa, you've been in the industry a very long time, Informatica many many years, how many years? >> 23 and counting. >> So, I'd consider you a historian of Informatica. (speaks indistinctly) I never saw myself as a historian. You've seen the transformations. Talk about what's going on now because, and certainly going private affords a lot of good things, in the public eye anymore in terms of shot clock earnings, being on that treadmill. You guys really did a lot of digging in to innovate. Now four years later, you start to see the fruit coming off that tree in the form of good catalog decision with the catalog, cloud early, AI early, the horizontal scalability of the infrastructure now and one operating model. Interesting kind of tailwinds for you guys. What's going on? How do you talk to customers who have kind of living in a cave, I won't want to say living in a cave, but they've been not as on the front end as you guys have been. >> I think when you use the word innovation it's just not about products. As a company we have been innovating. Along with the products, we have been innovating on all fronts, being at the services. We have, used to have, a major release every four years on services. We have shortened the cycle to two years. As a company we are now offering all our products on the cloud. What does it mean? What does it mean in customer support? We are having to redefine the entire delivery model end to end. You heard in the conference eight trillion transactions we process in a month. That was grown 3X just in a year. We have so much data. It's all about what is the information we can glean from these transactions. We have over a billion interactions with the customers every year. How can we put these transactions and interactions, package it in the form of we have the best telemetry products? We are leveraging this data to better sell the customers so that we can drive them, accelerate the business outcomes. When I started off we were a one product portfolio company. We had power center. Now we are the leader in six categories, and our user base is now, not only IT business, it's a great opportunity for us. >> The other thing that's a perfect storm, at least for innovation that's also happening, is the absolute validation that SAS business models have agility benefits, meaning you can take risk using data, understanding data, to get big rewards if scaled properly with cloud, so the role of data in pure SAS has been proven. Enterprises are recognizing that. Not that easy but still that's the path that people are now seeing clear visibility to. You guys are going after that. What's your take on that? >> I think when it comes to SAS, I think customers realize they should be focusing more on their business processes, and push the technology aside to the vendor. Try to partner with the vendor on how they can leverage on the technology side. That's where Informatica has put in a number of programs around that. Imagine a scenario, I'll give you a quick scenario. There's always this risk of putting this data on the cloud. What if you were to say, and there's upgrades every quarter, we push a lot of features and there's always the worry is something going to break. We are going to come out of the program, it's going to guarantee that we're going to foolproof the upgrades. Your stuff will work better, faster with every upgrade. That's the kind of, what customers expect. >> Guarantee that it won't break, basically? >> That's the kind of programs we're going to offer to our customers. We're going to have them for a day at scale, MDM is coming on the cloud you saw the demos we showed yesterday. I think we are redefining our model and going to push the envelope further on. >> Are customers asking for that assurance or is it more of you guys going to make that a table stakes because it's an opportunity for you? >> Both. >> Okay. >> Within the company our philosophy is very simple. I'll say an equation, CS equal to IS, customer success is equal to Informatica success. In my humble opinion, we both need each other. >> Just like data and AI. A symbiotic relationship. So I want to get back to what you were saying in terms of how you are defining this kind of customer success. We're working together with customers to define the business outcome and then working to see, okay, how do we get there? You have a lot of great customers, many in the Fortune 500, 100. Tell us a little bit about what you've seen over the past year in terms of, maybe without naming names or name names if you want to, but in terms of how these companies have seen a difference since you've changed this model. >> We sell a platform. I think we're the only vendor which offers a platform for data management. There are a number of vendors with poor installations. Informatica is the only vendor which offers late inclusion data platforms. Customers buy into the vision because data is, everyone is looking to leverage the power of data. As they buy this platform, they work with us to see how should they approach. This blueprint needs to evolve. We need to define the building blocks. Should they start with the catalog, should they validate what they're assets are? Where are we trying to push the service's frontiers that's not around technology? How can we help on the business processes side, as well? It's a big journey we are going to undertake and I think that's going to pay off big. I can quote a number of examples. I was sitting in a meeting this morning with a large bank and meeting up with the Chief Data Officer, and she kind of laid out her data strategy and we discussed how Informatica is going to be player owned. They are depending on us, and now we are going to keep our commitment, we are going to deliver on that promise we have made to them. >> How many customers do you guys see really thinking about data location storage where on premise versus cloud or are they more thinking differently around knowing that they're probably going to store it everywhere or somewhere? Can you share any insight into what the trends are there with your customers? >> Informatica's uniquely position is, there's future workloads which go to the cloud. It's hard to change systems that working, there's always going to be data in the premises. That shift, if something is working, customers don't quickly shut it down. So we see future workloads going to the cloud, traditional workloads, even we have a number of large clients still on mainframes. We offer the best products on mainframes as well as, it does not get much press, but-- >> This is the end to ending benefits that you guys are-- >> Correct. We go all the way, we cover the entire gambit of the data spectrum. >> What's the key enabler to make that happen? Is it the catalog, what's the big-- >> Catalog was the big, I think, last year that was the turning point with the catalog coming in, and now through professional services we offer a lot of workshops at no cost to our customer on how they should put their strategy, as well. >> One of the things that I'm hearing from you is the importance of really understanding the business in addition to the technology. I'm interested to hear how you hire. Obviously we hear so much about the importance of technical talent, and the problem of the skills gap in Silicon Valley and beyond, but you obviously are looking for candidates who also really get the business. So, what are the kinds of things that you're looking for and what kind of problems do you see in terms of the candidates that you're getting for your open roles? >> Customer support could be a hard job. We really want to, we look for people who want to make a difference. And if you have that attitude you get plenty of opportunities to make a difference. Now, with so much talk about AI, service automation, Chadbot, robotics, you know at the end of the day employees are still the core of the apple tree. I think the current trainers don't forget the people. The technology is not going to replace the people overnight, so I think we have a fabulous team at Informatica of customer support professionals. Our average retention rate is the mid 90s. So, we hire the best people, and they stay with us because this is a great platform. They move around products, but as long as we can give them that spectrum to grow, over time as they sell customers they build that tribal knowledge, and they can sell them better. And so we look for, I mean, there's a lot of data scientists coming in. We look, we always hire from colleges, groom them. I started off that way, and still with the company 23 years. I want to give that chance for the rest of team, as well. >> So how many other folks in the company have been there that long? That's a long time. You've been there a very, very long time. >> You'd be surprised at the number of people who have been long-timers at Informatica. It's a great company. >> How do you maintain the startup mentality? You were there when it was three years old, and now it's... >> I think personally what drives me is the fear of failure. Having set the bar high, you have to push, and if you want to keep at the pace you need to have the startup mentality. We have a number of projects in flight, and some, you have to have that mindset, and now we are a distributor team. We have to keep that spirit going throughout. And like I said, coming back to my equation, customer success equals Informatica success. That's what we believe as the company. >> You said CS is IS, customer success is. I mean, right? >> There you go. You made it sound even better. >> So just getting back to that, one of the biggest problems in the technology industry is the skills gap. Are you finding enough people to fill the roles you have? >> We do not have a problem hiring. The ramp up time, we have a good enablement program, which is good. Take the space of big data. The whole industry landscape changes every six months, so it's that mindset you need to have. Even I have that mindset today. I come in thinking I'm going to learn something new. Learning never stops. So you've just got to keep learning everyday. And I'm not setting expectations, we're going to groom them. I want people who learn on their own. They have to, they have to keep pace with the current technology. >> Any skills in school, kids in school that might, or parents watching with their kids, in high school or elementary school, what disciplines can they turn up, turn down, you think would make them successful in the future of how the data is going to impact society? There's a lot of new jobs coming out that don't have degrees for. Cal Berkeley just graduated their first inaugural class in data analytics. It's just a tell sign of how early it is, so still, you go back to sixth grade, you go back at the high school. Kids are looking to, they're gamers. They're into tech. They want to dial up some-- >> When I went to high school in 1984 I was the first batch of computer science, and we learned basic programming, things have really changed. My girls don't want to do computers, but it is something which we have to evolve constantly right, but-- >> Any classes right now that jump out at you that think, that's important? >> Data science is hard now, you know? >> A hard one. >> Yeah, it's hard. And with all the emphasis, we have a number of initiatives within support that will leverage AI, ML, as well. And I talked about it in the last year's program, but there could be some skills gap in some pockets, always you fill that that's going to be out of their pocket. You just got to be constantly pushing at it. >> Ansa, thank you so much for coming on theCUBE. >> It's a pleasure being on here, thank you. >> Thank you. >> Thank you, great job. >> I'm Rebecca Knight, for John Furrier, you are watching theCUBE's live coverage of Informatica World. Stay tuned. (upbeat music)

(upbeat music) >> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World. I'm your host, Rebecca Knight. We have two guests for this segment. We have Ronen Schwartz. He is the senior vice-president and general manager, Big Data Cloud and Data Integration at Informatica. Welcome Ronen, Welcome back, Ronen. >> Yes, pleasure to be here. Welcome to Informatica World. >> Thank you. And we have Daniel Jewett, VP Product Management at Tableau. Thank you so much for coming on theCUBE. >> Thank you for the welcome, Rebecca. Happy to be here. >> Yes So there's some big news that's going to be announced later today. Tell us about the partnership with Tableau and Informatica. I Want to start with you, Ronen. >> Yes, So Tableau been an amazing innovator in the area of data visualization, analytics. I think more than all they've actually opened the ability for more people to use data. And Informatica have been very excited to partner with Tableau on this journey of how do we empower more users, more company, to become data driven. So I think very exciting partnership. A lot of innovation. A lot of great capabilities. >> So we hear so much about the explosion of data and how much its use is being just across the enterprise. More and more functions are using data to make their decisions. How does this impact the strategic importance of data? >> Yeah, absolutely. Well, the relationship with Informatica for us has become important over the years as that data has exploded. Right, it used to start off, you had a spreadsheet of some numbers and you wanted to try and understand what was in there and Tableau helped you with that. But then as data lake started coming on the scene and not just a single data lake but multiple feeds of data and streaming data and data's here, and data all over in Europe, and data's wherever it happens to be, that becomes a real challenge for the individuals who have some questions about data. So Tableau's only as good as the data that we can get our hands on. So to have a great partner like Informatica, who can marshal and rationalize where all that data is is a valuable partnership for us to have. >> And it's really about data governance but then also about democratization of data and analytics. Want to talk about that a little bit, Ronen? >> Yes, so I think democratization of data actually depends on your ability to have built-in governance. So that the users are using the right data at the right time. And the organization actually understands what is available where. I think this is actually one of the sweet spots for the partnership. >> Right. >> Actually, the ability of Tableau with a very easy interface to allow everybody to really work with data and the ability of Informatica to enable everybody to get the data in a governed way when you can actually control the quality and the availability of the data is actually our sweet spot as partners. >> There's some real tension there between the democratization and the governance side, right? So from a business user's perspective, democratization means, I want to use that data and I want to start working with it. From a business user's perspective governance, typically means no. IT says you can't use that data or you can't have it or it's too complicated for you. So to be able to break that down and say no. Data catalog and some of the tools from Informatica make the data available in an accessible and friendly manner and understandable manner, is what enables the democratization to happen. So it's kind of turning that "no" into a "yes, let me help you", which is a big difference. >> And how is that relationship between IT and the business side? How would you say that it has evolved in recent years as there is more of a push and pull between these two functions. >> Yes, it's definitely evolved over the years. So as Ronen said, we have been working for a long time. I think we officially became partners back in 2011. There was probably some tension out of a lot of accounts between the IT camp and the business camp and we were always the flag bearers of the business users As we've seen over the years, business users get frustrated by untrusted data and not being able to find data. So as the IT organizations have helped bridge that gap I would like to think we're helping put that olive branch in between the two. The two camps have companies with the products working together. >> I think, imagine that instead of IT actually being on the way of people using data. IT is really giving the power to find the right data to the business users. And this is actually, instead of, like, the user really, working really really hard to get the data, now it's in their fingertip. They can find it. And when they find it, they can use it all the way from the source into Tableau in a very very easy way. >> And trust it. >> And trust it. >> The value add >> The veracity, exactly. >> I can find a lot of data easily but most of it is not trustworthy and I don't know if I want to do my analysis on untrustworthy data. So to be able to trust that data that I've come across is really important. >> We're talking a lot about AI and machine learning here. How do those two concepts, ideas, approaches, methodologies play into Tableau's vision? >> For Tableau, we've always been the company that wants the human as part of the process, right? We think people are curious and we want them to explore that data and work with that. So, at first glance you might think AI and machine learning doesn't fit in with that but we think there's really a powerful way for it to do it. Instead of a machine learning solution handing you the answer, we want the machine solution to say, we think there's something interesting here that you should go explore more. So that's the angle that we're putting our investment in. >> So putting the human into these tech >> Human still needs to be >> Human centered >> in the loop >> machine learning. >> and the machine can help coach you along the right way to make those inferences around the data. >> Final question. We're talking a lot about the skills gap. It is a pressing problem in the technology industry. Ronen, I'm going to start with you. How much does this keep you up at night? And what are you doing to ensure that you have the right technical and business talent to fill the open roles you have on your team? I think, I don't know if, I probably answer it in a relatively unique way. I think one of our job as a vendor is actually to empower more users to do more complex tasks, actually without the necessity to build a huge skill set. And I think today, especially in this event, a lot of the clear AI technologies really coming to give user that are less skill a lot of power. And this is actually a critical thing in order to address the new needs, right? So the needs will continue to grow. The demand is going to continue to grow. We believe that a big part of answering the demand versus supply is by empowering new users to participate in an effective way within the integration, data management analytics space. So we're making a major major effort there. But we're also adding a lot of guided, a lot of advice, a lot of optimization that is done for the users automatically so the users are more effective. I still think that the need for talent is only going to grow. It's not just a growth in the data. It's the growth in the demand for data and the growth in the demand for good data. So I think a lot of enablement, a lot of investment in people, and the technology to actually empower more users. >> Daniel? >> Yeah so for us part of the onus is on us to make the software easy enough to use and understandable for the audiences that are coming across it. So there's really no reason why everybody can't be an analyst. They might be afraid of that title but you're all working with data. You're looking at your phone, You're looking at your steps, You're looking at everything. Data. It's as simple as that. But data comes across your landscape in a lot of ways. So it's up to us to make the analytic flow as easy as we can and understandable as we can. But it's also up to us to help grow the skills. You can only make it so easy 'cause sometimes doing analytic task and working with data is just hard. There are complicated things. So what can we do to uplift the skills? We do a lot with Tableau for teaching and trying to nurture education programs all the way from K to 12, and up in universities to try and seed the universities' and elementary school instructors to start introducing the concepts of working with data at early ages. And then in college, there's whole classes that people use Tableau in to help understand the analytic process. So it's a little step and it's a forward looking step. The payoff won't be for many years until those people get into the workforce. >> We're starting them young. (laughing) >> But you have to. >> Mommas, teach your babies data science. >> Absolutely. (laughing) >> Daniel, Ronen, Thank you both so much for coming on theCUBE. It's been a great conversation. >> Excellent, >> Thank you. >> thank you, Rebecca. >> I'm Rebecca Knight, we will have much more of theCUBE's live coverage of Informatica World 2019. Stay tuned. (upbeat music)

(upbeat music) >> Live from Las Vegas, it's theCUBE! Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World here in Las Vegas. I am your host, Rebecca Knight. We are joined by John Lieto. He is the Director, Data Management at Wolters Kluwer. Thank you so much for coming on the show. >> Very welcome. >> So, Wolters Kluwer is a global provider of professional information, software solutions, tax information. Tell our viewers a little bit more about the company and about your role at the company. >> Yeah, so Wolters Kluwer, I would say probably 20 years ago, was a typical holding company. Has a very long history of publishing in Europe. It's over 185 years old in Europe. But, went on a journey to acquire businesses that were in the services business with a focus on legal, but there are also big concentrations in health divisions, tax and accounting, really a professional company. Very, very, very big in print. What happened over the last 10, 15 years though, it's completely flipped over to digital. In fact, it's been one of the more successful transformations. So now we're mostly in the digital space and electronic space. So where I come in, and my business unit comes in, CT Corporation is a 126-year-old company. Number one player in registered agent services. Legal information, helping companies like Informatica stay in compliance. United States is 50 states with 50 sets of rules, plus international. So typically, companies of any size get a provider. Sometimes their law firms will do it, but a lot of times, it's going to be CT Corporations, things like that. My role in the company, I've been there 19 years, I've had a mix of roles, mostly in the business but a little technical. I'm the Director of Data Management, I am basically in charge of managing governance and data quality for the business. It is focused on the customer right now and all things related to customer, but we're expanding into other domains like vendors, products, suppliers and supporting of pretty large digital transformation. >> So I'm sure in your role you have a lot of practical insights for MDM practitioners but before we go there, I want to hear from you about the customer mindset, I mean, this is a moment for data governance and security... >> Sure >> and privacy, a real inflection point, and like Wolters Kluwer, so many companies undergoing their own digital transformations. How would you describe the customer mindset about all of this? How are customers wrapping their brains around it? >> So for us, we're not in a very regulated business. We touch customers that are heavily regulated, but we're not, we're a service company, right? Most of the stuff, the data we deal with is public knowledge, right? A company's data is public knowledge, you can go in any state website and find out when Informatica was formed, who the board of directors are, so it's all public. But customers are extremely sensitive about where their data is, and what we're doing with it, so we were on top of that, especially for our foreign customers. Internally the CT and Wolters Kluwer we have to be very, very, very customer-focused 'cause it's a very direct service, right? So it's all about the customer. How we got to this point of using Informatica MDM, Massive Data Management, is trying to get close to the customer, trying to understand the customer. Our customers go from J P Morgan to these big, big, big companies that have investments in companies that you wouldn't even know they're related to that customer. So they rely on us to help them stay compliant. How do I deal with these diverse businesses that are under my portfolio, and how do I keep them compliant in the States? So we have all this data and we help our customers understand it, and know what to do next, almost anticipate where they're going to fall out of compliance in the State. >> So what is your advice for the people who are really starting, for the executives starting at square one, trying to think about a master data management solution? >> Yeah, great question. And it's really where the heart of my devotion has been the last year. I would say the most important thing is start with a business case. Understand where your business is going. Make it about what outcomes are you looking for. Really thoroughly understand that. Also take the systems or the subjects that are important to you, your company, and profile it. Understand that data. You can come to an MDM project, a master data management project, with so much knowledge first, don't just say, well everybody is doing master data management, we should do it too. I mean, it might be true, but you're really not going to get the outcomes. And then focus your project to hit those business goals, 'cause MDM is a process and a tool, it's not an answer. You need to use that tool to get to where you are, so for us the number one thing was reduce duplication, okay, MDM tools do that, so we're trying to get to the golden record, okay. Data quality, I don't have the good phone numbers I have bad email addresses, oh, mass data management does that too. So, again, it's going for the outcomes you're driving for, and MDM happens to be a good tool for that. >> So it's really about defining the objectives before you even jump in. >> Absolutely. >> Do you recommend experiments? What's the approach you... >> Wonderful question. In data we call it profiling, right? And you want to go in small wins, because one of the things that will happen to anyone in this space is the business is really not sure about this investment. These days, data is becoming so huge that's becoming a lot easier for guys like me to win a business case, but two years ago it was pretty hard. I'm sorry I just lost my train of thought. >> But that's an interesting point, just talking about the overcoming the skepticism within these companies to latch on to this idea, and as you were saying, the announcing the small wins, really getting everyone on board. >> Thank you. What we did is, we had profiled, found a problem, oh, we have definitive cost duplication, we've got email addresses that are completely bogus. Let's just to take those two. And we did small little pilots. We'd use tools we had, completely manual ad-hoc, let's fix 200 records, let's take a really important customer that we're trying to onboard, or expand, and let's fix that data, and then show the outcomes. Go for the quick wins. Communicate, communicate, communicate. Once we did that, and we did a series of, I want to say, 30 or 40 of these. That built our requirement set. We built the requirement set by doing. It was so easy that way to show victories, but too, to really get the requirements to a point where we could build the system. We happened to fall on that method, from prior learnings of not doing well on projects that had nothing to do with MDM. So for this one, I think the other piece of advice that I would give folks, is we built a data management team of business analysts that know our business and data. It is really critical that you keep this function out of IT. IT is your supporter and your partner. This does not go to IT. So we know our data. I have a guy on my team that's 45 years in the company, a woman who's 28 years in the company, just for example. So we can do a lot without a tool, and what's happening is now we are live for going on eight months now, and we're staying on top, making sure the tool's delivering what it's supposed to deliver, based on our deep knowledge. >> And I think that what you're talking about really, is introducing this technology and this new way of thinking, and it's really all about change management. >> It truly is. >> One of the things that we're talking a lot here in theCUBE about is the skills gap, and this is a problem throughout the technology industry. How big a problem is it for you at Wolters Kluwer? And what are you doing to make sure that you have the right technical talent on your team, and as we're saying, not just the technical talent but also the understanding of the business? >> One thing to understand is Wolters Kluwer is a fairly big company, and we as a company are just starting this journey. I have a small data management team in one business unit at Wolters Kluwer. There's another business unit within our health division that has data management, and that's all that I know of that is a formal data management. That's pretty small, so it's just beginning. What we're doing, we're trying to communicate, communicate, communicate. I am having some success because in our next huge journey, which is a digital transformation, a six-year project, data now is center. I've been asked to actually be the business sponsor for the data track, which, two years ago, that would not have happened. So I take that as a win, but you make a fair point, skills and understanding, both at the business and technical level is always a challenge, and it's justifying bringing in that skill set. No we can just outsource that, or we'll just use a consultant. I'm right now fighting a battle to bring in a data architect, full-time, they don't understand that... >> Just that role. >> You have to architect things. We've now done that, so what you have, because I' doing the data governance piece right now, and what I'm finding is, it's not the Wild West, but you can't always know what the parts of the organization is doing, and a lack of an architect is not keeping all the plumbing all centralized. So, a I build this data governance, I'm going to centralize data definitions and data glossary, data catalog, but I'm going to be looking around and going, okay, how do I actually have the technology piece architected correctly and that's the piece I'm really trying to pump, so hopefully when we build this data layer we're building my goal is to prove to the business that you need to fill this role. It's not me, it's going to be someone who really is deep, deep, deep in architecture. >> Hire a contractor, get that small win. >> That's what we're doing. (laughing) >> And then, the proof. I learned that from you, John. >> I'm actually in the process of just doing that. >> Excellent! >> One of those vendors is here. >> Well, we'll look forward to talking to you next year and hearing an update. >> Yeah, there you go. >> John Lieto, thank you so much for coming on theCUBE. >> You're very welcome, thank you. >> I'm Rebecca Knight, we will have more of theCUBE's live coverage of Informatica World. Stay tuned! (upbeat musing)

>> live from Las Vegas. It's the queue covering Inform Attica, World 2019. Brought to you by in from Attica. >> Welcome back, everyone to the cubes. Live coverage of infra Matic A world. I am your host, Rebecca Night, along with my co host, John Furrier. We are joined by sir Rushman, and he is the senior vice president and general manager. Master Data Management here it in from Attica. Thank you so much for coming on the show. >> Thank you. It's great to be back. >> Great to welcome a Cube alum. So a major theme of this conference is customer 3 60 It's about customers need for trusted accurate data as they embark on their own digital transformation initiatives. Can you just talk a little bit about what you're hearing, what you're hearing from customers, what their priorities are? >> Yeah, absolutely. You know, with MGM, the promise of MGM has always been creating a trusted, authoritative version ofthe any business critical entity on DH who are the most important business critical entities for any organization customers. So almost 80 to 90% off. You know, if our customers are talking about re inventing a new customer experience because some >> of the >> things that they've been telling us is that we've all learned, you know, in the past that bad customer experience means that, you know, we've all had those experiences. We goto hotel, we use a particular airline, we have bad experience and we say, Promise ourselves we'll never go back there again. So organizations have always for years now understood that there is a cost to not delivering a good enough customer experience. The big change that I'm hearing, at least over the last you know, you're also now and especially at this event, is that organizations have now been able to quantify what great customer experience can mean in terms ofthe a premium that they can charge for that products or services. Now that is a big shift. When you start thinking about saying if I'd deliver a better customer experience, I'm actually be able to charge 10 cents more for a cup of coffee. I can charge, you know, 20% more for an airline ticket that now has a direct impact on the top line >> and data drives. This obviously data's a key part of it. What's changed this last year, I mean a lot happened. We see on the regular tourist my one year anniversary of GDP are a lot of pressure around regulation. We see everyone sees Facebook and goes, Oh my God, maybe I don't want to follow that trap. Woman Enterprise pressure to develop sass like applications with data because we know what cloud native and born the Cloud looks like. We've seen companies come out of the woodwork from his fresh start and used data as part of the input with a IE application for great software. So now the enterprise I want to do that exactly. It's hard, >> it's hard. And I think you know, they're in a lot of organizations minds, you know, collective minds. This is cushion pulled because in order to deliver that best possible customer experience, they realize they need to gather more data about us, right? Every in every touch, point, every interaction. If you can gain that complete 3 60 view, it just means that you'd be able to deliver better possible experience. But now you're gathering more data about customers into your example about Facebook. Now means that we in our custodians off what was you know, an explosion of data than what we used to have before. And if you're moving those to the cloud, how do I make sure that I don't end up, you know, in the front page of The Wall Street Journal? You know, like some of the other organizations have. So there is great, you know, volumes of data being collected. But how do I manage it? Secure it government effectively so that we don't have those? >> Don't ask a question. I have been talking a lot about fake news and Facebook lately because, you know, we're digital Cuba's official distribution. 10 years been doing it, putting out good payload with content. Great gets like yourself. But this really kind of too things. That's where I want to get your reaction to. There's the content payload. And then there's the infrastructure dynamics of network effect. So Facebook is an example where there was no regulation, I'll say they were incentive to actually get more data from the users, but she got content or data and then you got infrastructure kind of like dynamics. You guys are looking at an end to end. You got on premises to cloud that's it structure, and that's going to be powering the aye Aye, And the SAS data becomes the payload, right? So what? You're a zoo, a product management executive and someone thinking about the customer and talking to customers. How do you view that? What's the customers formula for success to take advantage of the best use of the content or data and digital while maximizing the opportunities around these new kinds of infrastructure scale and technology? >> Yeah, I think you know, they've come to the realization that data is not entirely sitting on premise animal, you know, in the in the in the old World, to get customer data, you go 23 applications of CR m nd R B and some kind of, you know, a couple of homegrown applications in on premise now for the same functionality. But that's wise of customer customer experience applications that whatever you call it, there's an app for it. And it happened to reside in the clouds. So now you have about 1,100 on average cloud applications that store components. So where do you where do you start bringing all of that content together? A lot of organizations have realized that, you know, do it in the cloud for two reasons because that's where the bulk of this data is being generated. That's where the bulk of this data is being consumed. But the other aspect of it is we're not no longer talking about hundreds of millions of records, but I just thought bringing in transaction data interaction later don't know billions of records, And where else can you scale with that? Much is other than the club s O. But at the same time, that is, there is a hybrid that is extremely important because those applications are sitting on premise are not going away. You know, they still serve up a lot of valuable customer data and continue to be frontline operation systems for a lot of the user. So a truly hybrid approach is being developed. I think that thought process is coming around where some domains live in the clouds. Some domains live on premise, but it's seamless experience across book. >> That's great insight I wanted Then follow up and ask you Okay, how did in from Attica fitted that because you guys want to provide that kind of horrors? Office scaleable data layer, depending on where the customer's needs are at any given time you got a pea Eye's out. There's things that Where do you guys How do you make that a reality? That statement you just made? >> Yeah. And the reality is eyes already being, you know, being lived today with a few of the few of our customers on it is that data layer that says, you know, we can, you know, bring data run work loads that are behind the firewall. We can do the same work, load in the cloud if that's where you want to scale the new workloads, but at the same time have a data layer that looks like one seamless bridge between the cloud and on premise. And that a number of different experiences that can, you know, help that we've invested in cloud, you know, designing and monitoring capabilities that allow view for a completely cloud like experience. But all of the data still decides on premise. It's still being managed and behind your firewalls, which is where a lot of the organizations are going as well, especially more conservative, more regulated organisations. >> One of things. I want to get your reaction to a swell, great great commentary, By the way, Great Insight is some success examples that might not be directly the inn from Attica, but kind of point to some of the patterns. Let's take slack, for instance, Great software. It's basically an IRC measures chat room with on the Web with great user experience. But the adoption really kicked in when they built integration points into other systems. So this seems to be a fundamental piece of informatics. Opportunity is, you kind of do this layer, but also integrating it. Because although you might have monitoring, I might want to use a better monitoring system. So So you're now thinking about immigration. How do you respond to that? What are you guys doing? Respected. Integration? What's What's the product touchpoints can He shared a commentary >> on Yeah, So you know, the openness off our entire data architecture and all of the solutions is something that we you know, I think they use the word Switzerland quite often. But what it also means is that you know, you are able to plug in a best of breed execution engine for a particular workload on a particular platform if you so desire. If you want to plug in a you know I am a model that happened to be developed on a specific let's say, an azure or a W You'd be ableto bring that in because the architecture's open completely FBI driven as a zoo mentioned. So we're able tto. Our customers have the flexibility to plug in, and we try to make that a little easier for them also, you know, as you might have seen some of the demos yesterday, we are providing recommendations and saying, You know, for this particular segment of your work, Lord, here are the choices that we recommend to you. And that's where Claire Gia, you know, comes in because it's very hard for users to keep up with all of the different possibilities. You know, our options that they might be having in that particular day, the landscape, and we can provide those recommendations to them. >> I want to ask about something you were saying earlier, and this is the company's heir using data to realize that they can charge a premium for a better customer experience. And that really requires a change in mindset from a gut driven decision making to a data driven decision making method and approach. How how are you seeing this? This mindset shift is it? Our company is still having a hard time sort of giving up my guts, telling me to do this in particular, with relationship to the new thie acquisition you made in February of all site. >> Yes. You know, I think the good news is, you know, across the board line of business leaders, CEOs, even boards are now recognizing custom experience. Customer engagement happened to be top of mind, but there's also equally react. You know, a recognition that data is what is going to help, you know, make this a reality. But so that was one of the reasons why you went out and, you know, do this acquisitions also, because if you think about it, customer data is no longer just a handful of slowly changing attributes like a name and address and telephone number or social media handles that, you know, you could be used to contact us. But it's really about now. Thousands of interactions we might have on the websites Click stream data Web chat, you know, even calls into call centers. All of this and even what we're tweeting about a product or service online is all the interactions and touch points that need to be pulled in and the dogs have to be connected in order. Bill that customer profile. So we have to do the scale, and that's something that Alcide, you know, has been doing very well. But it's now become more about just connecting the dots. So we can say, Here is this customer and this is the all the different Touchpoints customers had all the different products of purchase from us over the last few months. Few years. But now can we derive some inside some intelligence? So if I'm connecting four pieces of information cannot in for a life event, can I detect that an insurance customers ready to retire? Can I detect that this family is actually shopping for a vacation to Hawaii? That's the first level off Dr Intelligence Insight that we can now offer with. Also, the next level is also about saying >> cannot be >> understanding. You know, some of these, you know, intent. Can we also understand how happy is this customer, you know, have been mentioning competitive product, which can allow us to infer that person probably going to go off and buy a competitors product. If this problem they're having with this device or product is not resolved, so turn scoring, sentiment scoring. And now the third level on top of that which I think is really the game changer, is now. Can we in for what the next best action or interaction should be based upon all these things? Can we even do things such as, as I left here, not too happy customer with a particular maybe laptop that I, you know, perches I called the call center can before as a call is coming through, can we in for what I'm calling about based upon all of the interactions have had over the recent past and direct that call to 11 to 11 3 Technician who specialized in the laptop model >> that I have >> in orderto make me continue to be a customer for life. >> One of the biggest challenge is happening in the in the technology industry is the skills gap. I want to hear your thoughts on it and also how they help my how concerned are you about finding qualified candidates for your roles? >> So, you know, I think being a globally, you know, global organization with R and D centers distributed around the world. I think one of the luxuries we have is we're able to look across not just, you know, way from Silicon Valley, you know? And you know, there is a definitely a huge competition for skills over there. I think one of the things that we've been able to do is locations like Toronto we were just talking about. That's where Alcide is based. Extremely cool technology that's come out, that that's, you know, really transforming organisations and their approach. The customers stood guard, doubling bangle or Chennai Hyderabad. So you know, we are tapping into centers that have lots of skilled, you know, folks on DH calling hedging our you know, our approach and looking at this globally. Yes, there's definitely going to be even more of a demand as a lot of technology changes go for these skills. But I think, you know, by spreading you know that skills and having complete developed R and D centers in each of those locations helps us mitigate the farm. >> What about kids in school, elementary school, high school, college or even people retraining? Is there a certain discipline? Stats, philosophy, ethics will you see data opportunities for folks that may or may not have been obvious or even in place. I mean, Berkeley just had their first graduating class of data science this year. I mean, that's that's so early. People wanna hone in. What's what do you see? Its success for people attaining certain certain skills. What do you recommend? >> So I think that is definitely a combination ofthe technical skills, whether it is the new a n M L applications. But I think that is also, you know, in the past, we would have said, Let's go on higher than someone who has done computer science You know, on is very deep in that topic. But look at the problems we're trying to solve with data on the application of the animal. They're all in service of a business outcome, some kind of a business on DH more, we find people who are able to bridge the gap between strong application off the newer technologies on a animal and also an understanding off the broader world. And the business, I think, is really the combination of skills is really what's going to be required to succeed. >> Excellent, great note to end on. Thank you so much, sir. Arrest for coming on the show. >> Thank you. Thanks. >> I'm Rebecca Knight for John Furrier. You are watching the Cube.

>> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019, brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World here in Las Vegas. I'm your host, Rebecca Knight along with my co-host John Furrier, we are joined by Jitesh Ghai, he is the Senior Vice President and General Manager Data Quality, Security and Governance at Informatica. Thank you so much for coming or returning to the show Jitesh. >> My pleasure, happy to be here. >> So, this is a real moment for data governance, we have the anniversary of GDPR and the California Privacy Act it's a topic at Dabos, there is growing concern among the public and lawmakers over security and privacy, give us the lay of the land from your perspective. >> Right, you know it is a moment for data governance, what's exciting in the space is governance was born out of risk and compliance and managing for risk and compliance, but really what it was mandating was healthy data management practices, how do we give the regulators comfort that our data is of high quality, that we know the lineage of where data is coming from that we know how the business relies on the data what is critical data? And while it was born to give the regulators comfort, what organizations very quickly realized is well when you democratize data, you need to give everybody that comfort, you need to give your data scientists, your data analysts, that same level of contextual understanding of their data right, where did it come from? What's the quality of it? How does the business use it, rely on it? And so that has been a tremendous opportunity for us, we've supported organizations, financial services from a BCBS 239 CCAR, counterparty credit risk, but what's happened is from a data democratization, data scale perspective, self-service analytics perspective, is what moved from terabytes to petabytes. We've moved from data warehouses, to data lakes and you can't democratize data unless there's a governed framework. I don't know, it sounds kind of like wait, democratizing data is supposed to be free data everywhere, but without some governed framework, it's a bit of a mess, and so what we're enabling organizations is the effective consumption and understanding of where their data is, discovering it, so that the right people can consume the data that they care about, the right data scientists can build the right models, the right analysts can build the right reports and the executives get the right confidence on what reports they're getting, what KPI's they're getting. >> One of the things that we talked last year, you had a couple customers on, you had told a great story, you guys had had the benefit as a long-standing company, 25 years in the private for large-customer base, but the markets changed, you mentioned governance I mean we're in the one year-anniversary of GDPR. >> Right. >> And I think everyone's kind of like OK what happened last year? More privacy laws are coming and one of the themes this year is clarity with data, but also in the industry you know access to data, making data addressable, because AI needs data sets, cloud has proven that, SAS business models, using data winning formula, that's clear if you're born in the cloud. Enterprises now want that same kind of SAS-like execution on the applications side, whether it's SAS or using AI for instance, >> Right. >> So when you have more regulation, inherent nature is to oh like more complexity, how are customers dealing with the complexity of this, because they want to free it up, but at the same time they want to make sure that they can respect the laws for individuals, but also governments aren't that smart either so you know, the balance there, what's the strategy? >> And therein lies the challenges with privacy specifically, it's not just about quality counterparty credit risk in like five or seven systems in a data warehouse, it's all the data in your enterprise, it's the data in production, there's the data in your DevOps environment, it's all your data literally, structured all the way to unstructured data like Word, PDFs, Powerpoints. And you need a governing framework around it, you need to enable organizations to be able to discover where is there sensitive information, how is there sensitive information proliferating through the organization? Is it protected? Is it not protected? And what's particularly, you know, we're all consumers, I'm pretty confident some or all of our data has been breached at some point, enabling organizations, what these privacy regulations are doing is they are giving us, as individuals, rights to go to the organizations we transact with and ask them, what are you doing with out data? Forget my data or at least tell me how you're processing it and get my consent for the data. >> Yeah, I mean policy and business models are certainly driving that and with regulation, I see that, but the question is that when you move the impact to the enterprise, you got storage drives. You store it on drives as a storage administrator you've got software abstractions with data, like you guys do. So, it's complicated, so the question is, for you, is what are customers doing now? What's the answer to all this? >> The answer really comes down to you need to scale to the scope of the problem, it's a thousand x-increase, you're going from terabytes to petabytes right? And so, you need an AI, an ML, an intelligent solution that can discover all of this information, but it can map it to John Furrier, this is where John Furrier's information is, it's in the human capital management system, the CRM system, organizations know, may start knowing whether sensitive data is, but hey don't know who it belongs to, so when you go to invoke your right to be forgotten or portability, today, what we're enabling organizations with is hey, we'll help you discover the sensitive information, but we'll also tell you who it belongs to, so that when John shows up or Rebecca, you show up, you just have to punch in their name and we'll tell you all the systems, that it's in. That is something that requires teams of database administrators, lawyers, system administrators that needs to be automated, to truly realize the potential of these privacy regulations, while enabling organizations to continue to innovate and disrupt with data. >> What's your take on whether or not consumers truly understand the scope of these privacy regulations, I mean talking about GDPR and you get the pop-ups that say do you consent and you just say yes, I just need to get to this site and so you blithely, just press yes, yes, yes so you are technically giving your consent, but do you, I mean what's your take, do consumers truly understand what they're doing here? >> You know, I think historically, we've all said yes, yes, yes, over the last, I would say two years with growing regulations and significant breaches, there is a change in customer expectations, you know, there's a stat out there in the event of a data breach, two-thirds of consumers of a particular organization blame the organization for the breach, not the hackers, right, so it's a mindshift in all of us, where you're the custodian of my data I'm counting on you, whatever organization I'm transacting with ,to ensure and preserve my privacy, ensure my data's protected. So, that's a big shift that's happened, so whether you're doing it for regulatory reasons, CCPA North America, there's several other state-wide regulations coming out or GDPR, the consumer expectation, forget regulations, it's brand preservation, it's customer trust, it's customer experience, that organizations are really having to solve for from a privacy standpoint. >> Tell what the news around yesterday around the shift of the trust pieces, because that's a huge deal. Because trust is shifting, expectations are shifting, so when you have shifting expectations, with users and buyers, customers, the experience has to shift. So, take us through what's the new things? >> Well, the new things are, you know, you look at we're enabling organizations to be data-driven, we're enabling organizations to transform, build new products, new services, be more efficient and for that, you need to enable them to get access to data. The counter, the tension on the other end is how do we get them broad-based access while ensuring privacy, right, and that's the balance. How do we enable them to be customer-centric and optimal in engaging with their customers while preserving the privacy of their customers and that really comes down to having a detailed understanding of what your critical data is, where is it in the organization and how an organization is using that data. Enabling an organization to know that they're processing data with the appropriate consent. >> What's interesting to me, when I was with press yesterday, is also the addition of how the cloud players are coming onboard, because you know, one constituent that's not mentioned in that statement is that you guys are kind of keeping an eye on, that are impacted by this, is developers, because you know developers like infrastructures coded with DevOps. Don't want to be provisioning networks and storage, they just write to the API's. Data is kind of going through that similar experience where, if I'm a developer doing an IOT app, I'm just going to use the cloud. I put the data there, I don't need to have a mismatch of mechanisms to deal with some governance compliance rules. >> Correct and that's why it needs to be built-in by design. And you know there's this connotation that- >> Explain that, what does built-in by design mean. >> Well you need to have privacy built-into how you as a business operate, how you as a DevOps team or development team, build products, if that's built-in to how you operate, you enable the innovation without falling into the pitfalls of oh you know what we broke some privacy regulations there we breached our customers trust there, we used data or engaged with them in manner that they weren't comfortable with. >> So, don't retro-fit after the fact? Think holistically on the front-end of the transformation in architecture. >> It's an enabler, in that if you do it right to begin with, you can continue to innovate and engage effectively, versus bolting it on as an afterthought and retro-fitting. >> It really seems like it is this evolution in thinking from this risk and compliance, overdoing this to check all the boxes, versus here are our constraints, but our constraints are actually liberating, is what you're saying. >> Right, but you can't democratize data, without giving the consumers of that data an understanding of the quality of that data, the trustworthiness of that data, the relevance of the data to the business, you give them that and now you're enabling your analytics, your data scientists, your analytics organizations to innovate with that data with confidence and if you do it within a framework of privacy, you're ensuring that you're preserving customer trust while you're automating and building intelligent and engaging customer experiences. >> What I love about the data business right now, is it's exciting because it's real specific examples of impact, security, you know, national security, to hackers, to just general security, privacy of the laws, But, I've seen the development angles interesting too, so when you got these two things moving, customers can ignore this, it's not like back-up and recovery where same kind of ethos is there, you don't want to think about it after the fact, you want to build it in, you know, there's certainly reasons why you do that, in case there's a disaster, but data is highly impactful all the time. This is a challenge, you guys can pull this off. >> Well you know, it's a, with privacy, it's no longer about a few systems, it's all your data and so the scope is the challenge and the scale applies for privacy, the scale applies for making data available enterprise wide and that's where you need and you know we spoke about AI needs data, well data also needs AI. And that's where we're leveraging AI and ML. Building out intelligence, to help organizations solve that problem and not do it manually. >> You know, I've said it on theCUBE, you've probably heard it many times, I say it all the time, scale is the new competitive advantage. Value is the new lock-in. No proprietary software anymore, but technology is needed. I want to ask you, you've been talking about this with some of your customers last year around data is that you need more scale, because AI needs more access to data, because the more visibility into data, the smarter, machine learning and AI applications can become. So Scale is real. What is the, what are you, you guys have some scalosity in your customers, you got the end-to-end, got the catalog and everything is kind of looking good, but you have competition How would you compare to the competition, when people say hey Jitesh, a start-up just popped out or XYZ company's got the solution, why should I go with them or you? What's the difference, what's the competitive angle? >> You know, the way we're thinking the problem is founded on governance is an enabler it's not about locking things down for risk and compliance, because you know, the regulators want to know that this particular warehouse is highly tightly controlled, it's about getting the data out there, it's about enabling end-users to have a contextual understanding when you're doing that for all of your data, within around, that's a thousand X-increase in the data, it's a thousand X-increase in your constituents, you're not supporting, the risk and compliance portions of the organization, you're supporting marketing, you're supporting sales, you're supporting business operations, supply chain, customer-onboarding and so with the problem of scale, practices of the past, which were typically manual laborious, but hey at the risk of non-compliance, we just had to deal with them, don't practically in any way scale, to the requirements of the future which is a thousand X-increase in consumers and that's where intelligence and AI and ML come in. >> The question I have for you is, where should customers store their data? Is there an answer to that on premises or in the cloud? What are they doing? >> The answer is yes, (Knight laughs) the customer should store their data, what we see, the world is going to be hybrid, mainframes are still here, on-premise will still be here many years from now. >> So you're taking the middle of the road here, so >> There's Switzerland. >> You're saying whatever they want on-premise or cloud, is there a preference you see with customers? >> Well, you know it depends on the applications , depends on regulations, historically regulations especially in financial services, have mandated a more on-premise stance, but those regulations, are also evolving and so we see, the global investment banks all of a sudden, we're having all sorts of conversations about enabling them to move select portions of their data estate to the cloud, enabling them to be more agile, so the answer is yes and it will be for a very long time to come. >> Final question, one of the most pressing problems in the technology industry is the skills gap. I want to hear your thoughts on it, how as a Senior Executive at Informatica, how worried are you about finding qualified candidates for your open-roles? >> You know, it is a challenge, good news is, we're a global organization, my teams are globally-distributed. I have teams in Europe, North America and Asia and the good part about that is if you can't find it in the valley, you can certainly find the talent elsewhere, and so while, it is a challenge, we're able to find talented engineers, software developers, data scientists, to help us innovate and build the intelligence capabilities to solve the productivity challenges, the scale challenges of data consumption. >> Jitesh, talk about the skills required for people coming out of school, take your Informatica hat off, put your expertise hat on, data guru hat, knowing that data is going to continue to grow, continue to have more impact across the board, from coding to society affix, whatever, what are some of the key skills in training, classes or courses or areas of expertise that people an dial-up or dig into that might be beneficial to them that may or may not be on the radar curriculum or, say is, part of school curriculum, >> you know we engage with universities in North America, in Europe, in Asia, we have a large development center in India and we're constantly, engaging with them. We're on various boards at various universities, advisory standpoint, big data standpoint and what we're seeing is as we engage with these organizations, we're able to feed back on where the market is going, what the requirements are, the nature of data science, the enabling technologies such as platforms like Spark, languages like Python and so we're working with these schools to share our perspectives, they in turn, are incorporating this into their curriculums and how they train future data scientists. >> When you see a young gun out there that's kicking butt and taking names and data, what are some of the backgrounds? Is it math, is it philosophy, is there a certain kind of pattern that you've seen as the makeup of just the killer data person? >> You know, it's interesting, you mention philosophy, I'm a big, I've hired many philosophy majors that have been some of the best architects, having said that, from a data science perspective, it's all about stats, it's all about math and while that's an important skillset to have, we're also focused on making their lives easier, they're spending 70% of their time, doing data engineering versus data science and so while they are being educated from a stats, from a data science foundation, when they come into the industry, they end up spend 70% of their time doing data engineering, that's where we're helping them as well. >> So study your Socrates and study your stats. >> I like that. (Knight and Furrier laugh) >> Jitesh, thank you so much for coming on theCUBE. >> My pleasure, happy to be here, thank you. >> I'm Rebecca Knight for John Furrier, you are watching theCUBE.

>> Live from Las Vegas. It's theCUBE. Covering Informatica World 2019, brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World. I am your host, Rebecca Knight, along with co-host, John Furrier. We have two guests for this segment. We have Abhiman Matlapudi. He is the Product Master at Deloitte. Welcome. >> Thanks for having us. >> And we have Kubalahm Rajeev Krishnan, Specialist Leader at Deloitte. Thank you both so much for coming on theCUBE. >> Thanks Rebecca, John. It's always good to be back on theCUBE. >> Love the new logos here, what's the pins? What's the new take on those? >> It looks like a honeycomb! >> Yeah, so interesting that you ask, so this is our joined Deloitte- Informatica label pin. You can see the Deloitte green colors, >> Nice! They're beautiful. >> And the Informatica colors. This shows the collaboration, the great collaboration that we've had over, you know, the past few years and plans, for the future as well. Well that's what we're here to talk about. So why don't you start the conversation by telling us a little bit about the history of the collaboration, and what you're planning ahead for the future. Yeah. So, you know, if we go like you know, ten years back the collaboration between Deloitte and Informatica has not always been that, that strong and specifically because Deloitte is a huge place to navigate, and you know, in order to have those meaningful collaborations. But over the past few years, we've... built solid relationships with Informatica and vise versa. I think we seek great value. The clear leaders in the Data Management Space. It's easy for us to kind of advise clients in terms of different facets of data management. You know, because no other company actually pulls together you know, the whole ecosystem this well. >> Well you're being polite. In reality, you know where it's weak and where it's real. I mean, the reality is there's a lot of fun out there, a lot of noise, and so, I got to ask you, cause this is the real question, because there's no one environment that's the same. Customers want to get to the truth faster, like, where's the deal? What's the real deal with data? What's gettable? What's attainable? What's aspirational? Because you could say "Hey, well I make data, data-driven organization, Sass apps everywhere." >> Yeah. Yeah absolutely. I mean every, every company wants to be more agile. Business agility is what's driving companies to kind of move all of their business apps to the Cloud. The uh, problem with that is that, is that people don't realize that you also need to have your data management governance house in order, right, so according to a recent Gartner study, they say by next year, 75% of companies who have moved their business apps to the Cloud, is going to, you know, unless they have their data management and data assets under control, they have some kind of information governance, that has, you know, context, or purview over all of these business apps, 50% of their data assets are going to erode in value. So, absolutely the need of the hour. So we've seen that great demand from our clients as well, and that's what we've been advising them as well. >> What's a modern MDM approach? Because this is really the heart of the conversation, we're here at Informatica World. What's- What does it look like? What is it? >> So I mean, there are different facets or functionalities within MDM that actually make up what is the holistic modern MDM, right. In the past, we've seen companies doing MDM to get to that 360-degree view. Somewhere along the line, the ball gets dropped. That 360 view doesn't get combined with your data warehouse and all of the transaction information, right, and, you know, your business uses don't get the value that they were looking for while they invested in that MDM platform. So in today's world, MDM needs to provide front office users with the agility that they need. It's not about someone at the back office doing some data stewardship. It's all about empowering the front office users as well. There's an aspect of AIML from a data stewardship perspective. I mean everyone wants cost take out, right, I mean there's fewer resources and more data coming in. So how how do you manage all of the data? Absolutely you need to have AIML. So Informatica's CLAIRE product helps with suggestions and recommendations for algorithms, matching those algorithms. Deloitte has our own MDM elevate solution that embeds AIML for data stewardship. So it learns from human data inputs, and you know, cuts through the mass of data records that have to be managed. >> You know Rajeev, it was interesting, last year we were talking, the big conversation was moving data around is really hard. Now there's solutions for that. Move the data integrity on premise, on Cloud. Give us an update on what's going on there, because there seems to be a lot of movement, positive movement, around that. In terms of, you know, quality, end to end. We heard Google up here earlier saying "Look, we can go into end to end all you want". This has been a big thing. How are you guys handling this? >> Yeah absolutely, so in today's key note you heard Anil Chakravarthy and Thomas Green up on the stage and Anil announced MDM on GCP, so that's an offering that Deloitte is hosting and managing. So it's going to be an absolutely white-glove service that gives you everything from advice to implement to operate, all hosted on GCP. So it's a three-way ecosystem offering between Deloitte, Informatica, and GCP. >> Well just something about GCP, just as a side note before you get there, is that they are really clever. They're using Sequel as a way to abstract all the under the hood kind of configuration stuff. Smart move, because there's a ton of Sequel people out there! >> Exactly. >> I mean, it's not structured query language for structured data. It's lingua franca for data. They've been changing the game on that. >> Exactly, it should be part of their Cloud journey. So organizations, when they start thinking about Cloud, first of all, what they need to do is they have to understand where all the data assets are and they read the data feeds coming in, where are the data lakes, and once they understand where their datas are, it's not always wise, or necessary to move all their data to the Cloud. So, Deloitte's approach or recommendation is to have a hybrid approach. So that they can keep some of their legacy datas, data assets, in the on premise and some in the Cloud applications. So, Informatica, MDM, and GCP, powered by Deloitte, so it acts as an MDM nimble hub. In respect of where your data assets are, it can give you the quick access to the data and it can enrich the data, it can do the master data, and also it can protect your data. And it's all done by Informatica. >> Describe what a nimble hub is real quick. What does a nimble hub mean? What does that mean? >> So it means that, in respect of wherever your data is coming in and going out, so it gives you a very light feeling that the client wouldn't know. All we- Informatica, MDM, on GCP powered by Deloitte, what we are saying is we are asking clients to just give the data. And everything, as Rajeev said, it's a white-glove approach. It's that from engagement, to the operation, they will just feel a seamless support from Deloitte. >> Yeah, and just to address the nimbleness factor right, so we see clients that suddenly need to get into new market, or they want to say, introduce a new product, so they need the nimbleness from a business perspective. Which means that, well suddenly you've got to like scale up and down your data workloads as well, right? And that's not just transactional data, but master data as well. And that's where the Cloud approach, you know, gives them a positive advantage. >> I want to get back to something Abhiman said about how it's not always wise or necessary to move to the Cloud. And this is a debate about where do you keep stuff. Should it be on on prem, and you said that Deloitte recommends a hybrid approach and I'm sure that's a data-driven recommendation. I'm wondering what evidence you have and what- why that recommendation? >> So, especially when it depends on the applications you're putting on for MDM, and the sources and data is what you are trying to get, for the Informatica MDM to work. So, it's not- some of your social systems are already tied up with so many other applications within your on premise, and they don't want to give every other data. And some might have concerns of sending this data to the Cloud. So that's when you want to keep those old world legacy systems, who doesn't want to get upgrades, to your on premise, and who are all Cloud-savy and they can all starting new. So they can think of what, and which, need a lot of compute power, and storage. And so those are the systems we want to recommend to the Cloud. So that's why we say, think where you want to move your data bases. >> And some of it is also driven by regulation, right, like GDPR, and where, you know, which providers offer in what countries. And there's also companies that want to say "Oh well my product strategy and my pricing around products, I don't want to give that away to someone." Especially in the high tech field, right. Your provider is going to be a confidere. >> Rajeev, one of the things I'm seeing here in this show, is clearly that the importance of the Cloud should not be understated. You see, and you guys, you mentioned you get the servers at Google. This is changing not just the customers opportunity, but your ability to service them. You got a white-glove service, I'm sure there's a ton more head room. Where do you guys see the Cloud going next? Obviously it's not going away, and the on premise isn't going away. But certainly, the importance of the Cloud should not be understated. That's what I'm hearing clearly. You see Amazon, Azure, Google, all big names with Informatica. But with respect to you guys, as you guys go out and do your services. This is good for business. For you guys, helping customers. >> Yeah absolutely, I think there's value for us, there's value for our clients. You know, it's not just the apps that are kind of going to the Cloud, right? I mean you see all data platforms that are going to the Cloud. For example, Cloudera. They just launched CDP. Being GA by July- August. You know, Snowflake's on the Cloud doing great, getting good traction in the market. So eventually what were seeing is, whether it's business applications or data platforms, they're all moving to the Cloud. Now the key things to look out for in the future is, how do we help our clients navigate a multi Cloud environment, for example, because sooner or later, they wouldn't want to have all of their eggs invested in one basket, right? So, how do we help navigate that? How do we make that seamless to the business user? Those are the challenges that we're thinking about. >> What's interesting about Databricks and Snowflake, you mentioned them, is that it really is a tell sign that start-ups can break through and crack the enterprise with Cloud and the ecosystem. And you're starting to see companies that have a Sass-like mindset with technology. Coming into an enterprise marketed with these ecosystems, it's a tough crowd believe me, you know the enterprise. It's not easy to break into the enterprise, so for Databricks and Snowflake, that's a huge tell sign. What's your reaction to that because it's great for Informatica because it's validation for them, but also the start-ups are now growing very fast. I mean, I wouldn't call Snowflake 3 billion dollar start-up their unicorn but, times three. But it's a tell sign. It's just something new we haven't seen. We've seen Cloudera break in. They kind of ramped their way in there with a lot of raise and they had a big field sales force. But Data Bear and Snowflake, they don't have a huge set in the sales force. >> Yeah, I think it's all about clients and understanding, what is the true value that someone provides. Is it someone that we can rely on to keep our data safe? Do they have the capacity to scale? If you can crack those things, then you'll be in the market. >> Who are you attracting to the MDM on Google Cloud? What's the early data look like? You don't have to name names, but whats some of the huge cases that get the white glove service from Deloitte on the Google Cloud? Tell us about that. Give us more data on that. >> So we've just announced that, here at Informatica World, we've got about three to four mid to large enterprises. One large enterprise and about three mid-size companies that are interested in it. So we've been in talks with them in terms of- and that how we want to do it. We don't want to open the flood gates. We'd like to make sure it's all stable, you know, clients are happy and there's word of mouth around. >> I'm sure the end to end management piece of it, that's probably attractive. The end to end... >> Exactly. I mean, Deloitte's clearly the leader in the data analytics space, according to Gartner Reports. Informatica is the leader in their space. GCP has great growth plans, so the three of them coming together is going to be a winner. >> One of the most pressing challenges facing the technology industry is the skills gap and the difficulty in finding talent. Surveys show that I.T. managers can't find qualified candidates for open Cloud roles. What are Deloitte's thought on this and also, what are you doing as a company to address it? >> I mean, this is absolutely a good problem to have, for us. Right, which means that there is a demand. But unless we beat that demand, it's a problem. So we've been taking some creative ways, in terms of addressing that. An example would be our analytics foundry offering, where we provide a pod of people that go from data engineers you know, with Python and Sparks skills, to, you know, Java associates, to front end developers. So a whole stack of developers, a full stack, we provide that full pod so that they can go and address a particular business analytics problem or some kind of visualization issues, in terms of what they want to get from the data. So, we teach Leverate that pod, across multiple clients, I think that's been helping us. >> If you could get an automated, full time employee, that would be great. >> Yeah, and this digital FD concept is something that we'd be looking at, as well. >> I would like to add on that, as well. So, earlier- with the data disruption, Informatica's so busy and Informatica's so busy that Deloitte is so busy. Now, earlier we used plain Informatica folks and then, later on because of the Cloud disruption, so we are training them on the Cloud concepts. Now what the organizations have to think, or the universities to think is that having the curriculum, the Cloud concepts in their universities and their curriculum so that they get all their Cloud skills and after, once they have their Cloud skills, we can train them on the Informatica skills. And Informatica has full training on that. >> I think it's a great opportunity for you guys. We were talking with Sally Jenkins to the team earlier, and the CEO. I was saying that it reminds me of early days of VMware, with virtualization you saw the shift. Certainly the economics. You replaced servers, do a virtual change to the economics. With the data, although not directly, it's a similar concept where there's new operational opportunities, whether it's using leverage in Google Cloud for say, high-end, modern data warehousing to whatever. The community is going to respond. That's going to be a great ecosystem money making opportunity. The ability to add new services, give you guys more capabilities with customers to really move the needle on creating value. >> Yeah, and it's interesting you mention VMware because I actually helped, as VMware stood up there, VMCA, AW's and NSA's offerings on the Cloud. We actually helped them get ready for that GA and their data strategy, in terms of support, both for data and analytics friendliness. So we see a lot of such tech companies who are moving to a flexible consumption service. I mean, the challenges are different and we've got a whole practice around that flex consumption. >> I'm sure Informatica would love the VMware valuation. Maybe not worry for Dell technology. >> We all would love that. >> Rajeem, Abhiman, thank you so much for joining us on theCube today. >> Thank you very much. Good talking to you. >> I'm Rebecca Knight for John Furrier. We will have more from Informatica World tomorrow.

>> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone to theCUBE's live coverage of Informatica World 2019 I'm your host, Rebecca Knight, along with my cohost, John Furrier. We are joined by Sudhir Hasbe. He is the director of product management at Google Cloud. Thank you so much for coming on theCUBE. >> Thank you for inviting me. (laughing) >> So, this morning we saw Thomas Kurian up on the main stage to announce the expanded partnership. Big story in Wall Street Journal. Google Cloud and Informatica Team Up to Tame Data. Tell us more about this partnership. >> So if you take a look at the whole journey of data within organizations, lot of data is still siloed in different systems within different environments. Could be a hybrid on-prem. It could be multi-cloud and all. And customers need this whole end-to-end experience where you can go ahead and take that data, move it to Cloud, do data cleansing on it, do data preparation. You want to be able to go ahead and govern the data, know what data you have, like a catalog. Informatica provides all of those capabilities. And if you look at Google Cloud, we have some highly differentiated services like Google BigQuery, which customers love across the globe, to go ahead and use for analytics. We can do large scale analytics. We have customers from few terabytes to 100-plus petabytes, and storing that amount of data in BigQuery, analyzing, getting value out of it. And from there, all the A.I. capabilities that we have built on top of it. This whole journey of taking data from wherever it is, moving it, cleansing it, and then actually getting value out of it with Big Query, as with our A.I. capabilities. That whole end-to-end experience is what customers need. And with this partnership, I think we are bringing all the key components our customers need together for a perfect fit. >> Sadhir, first of all, great to see you. Since Google Next, we just had a great event by the way this year, congratulations. >> Thanks. >> A lot of great momentum in the enterprise. Explain for a minute. What is the relationship, what is the partnership? Just take a quick minute to describe what it is with Informatica that you're doing. >> Yeah, that's great. I think if you take a look at it, you can bring two key areas together in this partnership. There's data management. How do you get data into Cloud, how do you govern it, manage it, understand it. And then there is analyze the data and AI. So the main thing that we're bring together is these two capabilities. What do I mean by that? The two key components that will be available for our customers is the Intelligent Cloud services from Informatica, which will be available on GCP, will run on GCP. This will basically make sure that the whole end-to-end capability for that platform, like data pipelines and data cleansing and preparation, everything is now available natively on GCP. That's one thing. What that will also do is, Informatica team has actually optimized the execution as part of this migration. What that means is, now you'll be able to use products like Data Cloud, Dataproc. You'll be able to use some of the AI capabilities in BigQuery to actually go do the data cleansing and preparation and process-- >> So when you say "execute", you mean "running." >> Yeah, just running software. >> Not executing, go to market, but executing software. >> Executing software. If you have a data pipeline, you can literally layer this Dataproc underneath to go ahead and run some of the key processes. >> And so the value to the customer is seamless-- >> Seamless integration. >> Okay, so as you guys get more enterprise savvy, and it's clear you guys are doing good work, and obviously Thomas has got the chops there. We've covered that on theCUBE many times. As you go forward, this Cloud formula seems to be taking shape. Amazon, Azure, Google, coming in, providing onboarding to Cloud and vice-versa, so those relationships. The customers are scratching their heads, going, "Okay, where do I fit in that?" So, when you talk to customers, how do you explain that? Because, unlike the old days in computer science and the computer industry, there was known practices. You built a data center, you provisioned some servers, you did some things. It was the general-purpose formula. But every company is different. Their journey's different. Their software legacy make-up's different. Could be born in the cloud with on-prem compliance needs. So, how do customers figure this out? What's the playbook? >> I think the big thing is this: There's a trend in the industry, across the board, to go ahead and be more data-driven, build a culture that is data-driven culture. And as customers are looking at it, what they are seeing is, "Hey, traditionally I was doing a lot of stuff. "Managing infrastructure. Let me go build a data center. "Let me buy machines." That is not adding that much value. It is because. "I need to go do that." That's why they did that. But the real value is, if I can get the data, I can go analyze it, I can get better decisions from it. If I can use machine learning to differentiate my services, that's where the value is. So, most customers are looking at it and saying, "Hey, I know what I need to do in the industry now, "is basically go ahead and focus more on insights "and less on infrastructure." But as doing this, the most important thing is, data is still, as you mentioned, siloed. It's different applications, different data centers, still sitting in different places. So, I think what is happening with what we announced today is making it easy to get that data into Google Cloud and then leveraging that to go ahead and get insights. That's where the focus is for us. And as you get more of these capabilities in the cloud as native services, from Infomatica and Google, customers can now focus more on how to derive value from the data. Putting the data into Cloud, cleansing it, and data preparation, and all of that, that becomes easier. >> Okay, so that brings the solution question to the table. With the solutions that you see with Infomatica, because again, they have a broad space, a horizontal, on-prem and cloud, and they have a huge customer base with enterprise, 25 years, and big data is their thing. What us case is their low-hanging fruit right now? Where are people putting their toe in the water? Where are they jumping full in? Where do you see that spectrum of solutions? >> Great question. There are two or three key scenarios that I see across the board with talking to a lot of customers. Even today, I spoke to a lot of customers at this show. And the first main thing I hear is this whole thing, modedernization of the data warehousing and analytics infrastructure. Lot of data is still siloed and stuck into these different data systems that are there within organizations. And, if you want to go ahead and leverage that data to build on top of the data, democratize it with everybody within the organization, or to leverage AI and machine learning on top of it, you need to unwind what you've done and just take that data and put into Cloud and all. I think modernization of data warehouses and analytics infrastructure is one key play across the IT systems and IT operations. >> Before you go on to the next one, I just want to drill down on that. Because one of the things we're hearing, obviously here and all of the places, is that if you constrain the data, machine learning and AI application ultimately fails. >> Yes. >> So, legacy silos. You mentioned that. But also regulatory things. I got to have privacy now, forget my customer, GDPR first-year anniversary, new regulatory things around, all kinds of data, nevermind outside the United States. But the cloud is appealing, of just throwing it in there as one thing. It's an agility lag issue. Because lagging is not good for AI. You want real-time data. You need to have it fast. How does a customer do that? Is it best to store it in the cloud first, on-premise, with mechanisms? What's your take on this? >> I think it's different in different scenarios. I talk a lot of customers on this. Not all data is restricted from going anywhere. I think there are some data sets you want to have good governance in place. For example, if you have PII data, if you have important customer information, you want to make sure that you take the right steps to govern it. You want to anonymize it. You want to make sure that the right amount of data, per the policies within the organization, only gets into the right systems. And I think this is where, also, the partnership is helpful, because with Infomatica, the tooling that they're provided, or as you mentioned over 25 years, allows customers to understand what these data sets are, what value they're providing. And so, you can do anonymization of data before it lands into Cloud and all of that. So I think one thing is the tooling around that, which is critical. And the second thing is, if you can identify data sets that are real-time, and they don't have business-critical or PII-critical data, that you're fine as a business process to be there, then you can derive a lot of data in real time from all the data sets. >> Tell me about Google's big capabilities, because you guys have a lot of internal power platform features. BigQuery is one of them. Is BigQuery the secret weapon? Is that the big power source for managing the data? >> I would just say: Our customers love BigQuery, primarily because of the capability it provides. There are different capabilities. Let me just list a few. One is: We can do analytics at scale. So as organizations grow, even if data sets are small within organization, what I have seen is, over a period of time, when you derive a lot of value from data, you will start collecting more data within organization. And so, you have to think about scale, whether you are starting with one terabyte or one petabyte or 100 petabytes, it doesn't matter. Analyzing data at scale is what we're really good at, at different types of scale. Second is: democratizing data. We have done a good job of making data available through different tooling, existing tooling that customers have invested in and our tooling, to make it available to everybody. AirAsia is a good example. They have been able to go ahead and give right insights to everybody within the organization, which has helped them go save 5 to 10% in their operational costs. So that's one great example of democratizing access to insights. The third big thing is machine learning and AI. We all know there are just lack of resources to do, at once, analytics with AI and machine learning in the industry. So our goal has been democratize it. Make it easy within an organization. So investments that we have done with BigQuery ML, where you can do machine learning with just simple SQL statements or AutoML tables, which basically allows you to just, within the UI, map and say, "That's table in BigQuery, here's a column that I want to predict, and just automatically figure out what model you want to create, and then we can use neural networks to go do that. I think that kind of investments is what customers love about it from the platform side. >> What about the partnership from a particular functional part of the company, marketing? There's the old adage: 50% of my marketing budget is wasted. I just don't know which one. This one could really change that. >> Exactly right. >> So talk a little bit about the impact of it on marketing. >> I think the main thing is, if you think about the biggest challenge that CMOs have within organizations is how do you better marketing analytics and optimize the spend? So, one of the thing that we're doing with the partnership is not just breaking the silos, getting the data in BigQuery, all of that side and data governance. But another thing is with master data management capability that Infomatica brings to table. Now you can have all of your data in BigQuery. You leverage the Customer 360 that MDM provides and now CMOs can actually say, "Hey, I have a complete view of my customer. "I can do better segmentation. I can do better targeting. "I can give them better service." So that is actually going to derive lot of value with our customers. >> I want to just touch on that once, see if I can get this right. What you just said, I think might be the question I was just about to ask, which is: What is unique about Google's analytical portfolio with Infomatica specifically? Because there's other cloud deals they have. They have Azure and AWS. What's unique about you guys and Infomatica? Was it that piece? >> Yeah, I think there are a few things. One is the whole end-to-end experience of basically getting the data, breaking the silos, doing data governance, this tight integration between our product portfolio, where now you can get a great experience within the native GCP environment. That's one. And then on the other side, Cloud for Marketing is a big, big initiative for us. We work with hundreds of thousand of customers across the globe on their marketing spend and optimizing their marketing. And this is one of the areas where we can work together to go ahead and help those CMOs to get more value from their marketing investments. >> One of the conversations we're having here on theCUBE, and really that we're having in the technology industry, is about the skills gap. I want to hear what you're doing at Google to tackle this problem. >> I think one of the big things that we're doing is just trying to-- I have this team internally. In planning, I use "radical simplicity." And radical simplicity is: How do we take things that we are doing today and make it extremely simple for the next generation of innovation that we're doing? All the investments and BigQuery ML, you SQL for mostly everything. One of the other things that we announced at Next was SQL for data flow, SQL pipelines. What that means is, instead of writing Beam or Java code to build data flow pipelines, now you can write SQL commands to go ahead and create a whole pipeline. Similarly, machine learning with SQL. This whole aspect of simplifying capabilities so that you can use SQL and then AutoML, that's one part of it. And the second, of course, we are working with different partners to go ahead and have a lot of training that is available online, where customers don't have to go take classes, like traditional classes, but just go online. All the assets are available, examples are available. One of the big things in BigQuery we have is we have 70-plus public data sets, where you can go, with BigQuery sandbox, without credit card, you can start using it. You can start trying it out. You can use 70-plus data sets that already available and start learning the product. So I think that should help drive more-- >> Google's a real cultural tech company, so you guys obviously based that from Stanford. Very academic field, so you do hire a lot of smart people. But there's a lot of people graduating middle school, high school, college. Berkeley just graduated their first, inaugural class in data science and analytics. What's the skills, specifically, that young kids or people who are either retraining should either reboot, hone, or dial up? Is there any things that you see from people that are successful inside Google? I mean, sometimes you don't have to have that traditional math background or computer science, although math does help; it's key. But what is your observation? What's your personal view on this? >> I think the biggest thing I've noticed is the passion for data. I fundamentally believe that, in the next three to five years, most organizations will be driven with data and insights. Machine learning and AI is going to become more and more important. So this understanding and having the passion for understanding data, answering questions based on data is the first thing that you need to have. And then you can learn the technologies and everything else. They will become simpler and easier to use. But the key thing is this passion for data and having this data-driven decision-making is the biggest thing, so my recommendation to everybody who is going to college today and learning is: Go learn more about how to make better decisions with data. Learn more about tooling around data. Focus on data, and then-- >> It's like an athlete. If you're not at the gym shooting hoops, if you don't love it, if you're not living it, you're probably not going to be any-- (laughing) It's kind of like that. >> Sudhir, thank you so much for coming on theCUBE. It's a pleasure talking to you. >> Thank you. Thanks a lot for having me. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (techno music)

[Narrator] Live from Las Vegas! It's theCUBE covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone to theCUBE's live coverage of Informatica World, here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We're joined by Sally Jenkins. She is the executive vice president and CMO here at Informatica. Thank you so much for coming on theCUBE, Sally. >> Oh you're welcome, thank you for having me. Its nice to see you all again. >> So congrats on a great show, we're going to get to the stats of the show, but the framework of Informatica World is built around these four customer journeys. Next Gen analytics, Cloud Hybrid, 360 engagement, Data Governance and Privacy. Can you tell our viewers a little bit about how this framework reflects what you're hearing from customers and their priorities >> Yes absolutely, Rebecca and yes, you got the right and in the right order, thank you. So, we started this journey with our customers and trying to understand how do they want to be spoken to. What business problems are they solving? And how do they categorize them, if you will. And so, we've been validating these are the right journeys with our customers over the past few years. So everything that you see here at Informatica World is centered around those journeys. The breakouts, our keynotes, all the signage here in our solutions expo. So, its all in validation of how our customers think, and those business problems they're solving. >> So the show, 2600 attendees from 44 countries, 1200 sessions. What's new, what's new and exciting. >> Oh, gosh, there's so many things that are new this year. And one other stat you forgot, 92 customers presenting in our Breakouts. So our customers love to hear from other customers. As to what journeys they're on, what problems their solving. Those are record numbers for us. Record number of partners sponsoring. We've got AWS, we've got Google, we've got Microsoft, we've got the up and comers, that we're calling in the Cloud and AI Innovation zone. So people like DataBricks and Snowflake. We wanted to highlight these up and comer partners, what we call our ecosystem partners. Along with the big guys. You know, we're the Switzerland of data. We play with everybody. We play nicely with everybody. A lot of new things there. A few other things that are new, direct feedback from our customers last year. They said we want you to tell us which breakouts we should go to. Or what work shops should we attend. So we rolled out two things this year. One's called the Intelligent Scheduler. That's where we ask customers what journey are they on. What do they want to learn about. And then we make a smart recommendation to them about what their agenda should look like while they're here. >> You're using the data. >> Yes, AI, we're involving AI, and making the recommendations out to our customers. In addition, our customers said we want to connect with other customers that are like us, on their journeys, so we can learn from them. So we launched we called the Intelligent Connect and again this is part of our app. Which, our app's not new, but what we've done with our app this year is new. We've added gamification, in fact as part of the AI and Cloud Innovation zone, we are asking our customers and all of our attendees to vote on who they think is the one with the best innovation. They're using our app to use voting. They can win things, so there's lots of gaming. There's social that's involved in that, so the app's new. We're taking adavantage of day four. We usually end around lunchtime on day four, this year we're going all in, all day workshops, so that our practitioners can actually roll up their sleeves and get started working with our software. And our ecosystem partners are also leading a lot of those workshops. So a lot that's new this year. And as I mentioned, the Cloud and AI Innovation zone, that's new it's like a booth within a booth here on the solutions expo floor. So this is the year of new, for sure. >> You know one of the things that's been impressive, I was talking with Anil and also Bruce Chizen, who is a board member, The bets you guys have made is impressive. You look back, and this our tenth year in theCUBE, so we go to a lot of events, 100s events in a year, over 100 events over 10 years. We've seen this story with you guys, this is now our fourth year doing theCUBE here. And the story has not changed, its been early moves, big bets. Cloud, early. Going private to see this next big wave. AI, early before everyone else. This is really kind of showing, and I think the ecosystem part is on stage with Databricks, with Snowflake. Really kind of point to a new cast of characters in the ecosystem. >> That's right. >> You're seeing not just the classic enterprise, 'cause you guys have great big, large enterprises that you do business with. That want to be SAS like, they want the agility, they want all those great things but now you have Cloud. The markets seems to have changed. This is an ecosystem opportunity. >> That's right. >> Can you share what's new? Because you see Amazon, Google and Azure, at the cloud, you got On-Premise, you now Edge and IoT, everything's happening with data. Hard, complex, what's new, what's the ecosystem benefit? Can you just share some color commentary around how you guys view that as a company. >> Yeah, thanks, John, and that's a good question. I'm glad you're pointing out that our whole go to market motion is evolving. It's not changing it's evolving because we want to work with our customers in whatever environment they want to work in. So if they're working in a cloud environment, we want to make sure we're there with our cloud ecosystem partners. And it doesn't matter who, cause like I said, we work with everybody, we work nicely with everybody. So we are tying in our cloud ecosystem partners as it makes sense based on what our customer needs are. As well as our GSI partners. So we've got Accentra's here. They brought 35 people to Informatica World this year. We play nicely with Accentra, Deloitte, Cognizant, Capgemini so we really are wanting to make sure that we're doing what makes sense with our customer and working with those partners that our customers want to work with. >> Well I think one of the observations we've made on theCUBE and we said in our opening editorial segment this morning, and we're asking the question about the skill gaps, which we'll get into with you in second, but these big partners from the Global System Integraters to even indirect channel partners, whether they're software developers and or channel partners. They all are now enabled and are mandated to create value. >> Yes, that's right. >> And if they can't get to the value, those projects aren't going to get funded and they're not going to get renewed And so we've seen with the Hadoop cycle of just standing up infrastructure for infrastructure sake isn't going to fly. You got to get to the value. And data, the business that you're in, is the heart of it. >> Well, data's at the heart of it. That's why we're sitting at a really nice sweet spot, because data will always be relevant. And the theme of the conference here is data needs AI and AI needs data. So we're always going to be around. But like I said, I feel like we're sitting right in the middle of it. And we're helping our customers solve really complex problems. And again, like I said if we need to pull in a GSI partner for implementation, we'll do that we've got close to 400,000 people around the world, trained on how to use Informatica solutions. So we're poised and we are ready to go. >> We were talking before we came on camera. We were sitting there catching up, Sally. And I always make these weird metaphors and references, but I think you guys are in an enabling business. It reminds me of VMware, when virtualization came in. Because what that did was, it changed the game on what servers were from a physical footprint, but also changed the economics and change the development landscape. This seems to be the same kind of pattern we're seeing in data where you guys are providing an operational model with technical capabilities. Ecosystem lift, different economics. So kind of similar, and VMware had a good run. >> We'll take that analogy, John, thank you. >> What's your reaction? Do you see it that way? >> Yeah I do, and it all comes back to the journeys that we talk about right. Because our customers, they're never on just one journey. Most of them are on multiple journeys, that they are deploying at the same time. And so as they uncover insights around one journey, it could lead them to the next. So it really comes back to that and data is at the center of all that. >> I want to ask about the skills gap. And this is a problem that the technology industry is facing on a lot of different levels I want to hear about Informatica's thoughts on this. And what you're doing to tackle this problem. And also what kinds of initiatives you're starting around this. >> Well, I'm glad you asked because it's actually top of mind for us. So Informatica is taking a stance in managing the future, so that we can get rid of the skills gap in the future. And last year we launched a program we call the Next 25. That's where we are investing in middle school aged students for the next seven years. Its starts in 6th grade and takes them all the way through high school. They are part of a STEM program, in fact we partnered with Akash middle school here in Las Vegas. Cause we wanted to give back to the local communities since we spend so much time here. And so these kids who are part of the STEM program take part in what we call the Next 25. Where we help them understand beyond academics what they need to learn about in order to be ready for college. Whether that's social skills, or teamwork, or just how do we help them build the self confidence, so it goes beyond the academics. But one of the things that we're talking about tomorrow, is what's next as part of STEM. Cause we all know they're very good at STEM. And so we've engaged with one of the professors at UNLV to talk about what does she see as a gap when she sees middle school students and high school students coming to college and so that's where she recognizes that coding is so important. So we've got a big announcement that we're making tomorrow for the Next 25 kids around coding. >> Its interesting, cause we could talk about this all day, cause my daughter just graduated from Cal, so its fresh in my mind, but I was pointed out at the graduation ceremony on Saturday that the first ever class at University of California Berkley, graduated a data science, they graduated their inaugural class. That goes to show you how early it is. The other thing we're hearing also on these interviews as well as others, that the aperture or the surface area for opportunities isn't just technical. >> Right >> You could be pre med and study machine learning and computer science. There's so much more to it. What do you see just anecdotally or from a personal standpoint and professional, key skills that you think people should hone in on? What dials should they turn? More math, more coding, more cognitive, more social emotional, What do you see as skills they can tailor up for their-- >> Well so let's just start with the data scientist. We know LinkedIn has identified that there are 150,000 job openings just for data scientist in the US alone. So what's more interesting than that, is four times that are available for data engineers. And for the first time ever, data engineers' starting salaries are paying more than starting salaries on Wall Street. So, there's a huge opportunity, just in the data engineering area and the data scientist area. Now you can take that any which way you want. I'm in marketing and we use data all day long to make decisions. You don't have to be, you don't have to go down the engineering path. But you definitely have to have a good understanding of data and how data drives your next decisions, no matter what field you're in. >> And its also those others skills that you were talking about, particularly with those middle school kids, it is the collaboration and the team work and all of those too. >> It does, again, it goes beyond academics. These kids are brilliant. Most of them are 7th or 8th grade. But nothing holds them back, and that's exactly what we're trying to inspire within. So we have them solving big global problems. And you'll hear as they talk about how they're approaching this. They work in teams of five. And they realize to solve huge problems they need to start small and local. So some of these big global problems they're working on, like eradicating poverty, they're starting at the local shelters here in Las Vegas to see how they can start small and make a difference. And this is all on their own, I have folks on my team who are junior genius counselors with them, but that is really to foster some of the conversations. All the new ideas are coming directly from the kids. >> My final question is obviously for the folks who couldn't make it here, watching, know you guys, what's the theme of the show because the news right out of the gate is obviously the big cloud players. That's the key. And the new breed of partners, Snowflake, Databricks as an example. Hallway conversations that I'm hearing, can kind of be geeky and customer focused around "where do I store my data?" so you're seeing a range of conversations. What is the theme this year? What's different this year, or what more the same? Where are you doubling down? What's going on here for the show? What's the main content? >> Well so this is our 20th Informatica World if you can believe that. We've been around for 26 years, but this is our 20th Informatica World. And several years ago we started with the disruptive power of data. Then last year we talked about how we help our customers disrupt intelligently. And this year the theme is around ClAIrity Unleashed. You can tell the theme has been that we've been talking about for the past three years is all underpinned with AI. So it is all about how AI needs data and data needs AI. And how we help bring clarity to our customer's problems through data. >> And a play on words, ClAIr, your AI to clarity. >> Exactly, AI is at the center of our Intelligent data platform. So it is a play on AI but that is where ClAIrity Unleashed comes from. >> Terrific, thank you so much for coming on theCube, Sally. Its great having you. >> Great, thanks Rebecca. Thanks, John. >> Thank you. >> Nice to see you all. >> I'm Rebecca Knight for John Furrier. We will have more from Informatica World, stay tuned. (upbeat pop outro)

>> Live from Las Vegas. It's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight. We are joined by Sanjeev Vhora. He is the group technology officer and global data business lead at Accenture. Thank you so much for coming on theCUBE. >> Thanks. Thanks for having me here. >> We're hearing so much about AI lead data intelligence, and the other buzz word of course, that we hear so much of, is digital transformation. I'd love to hear your thoughts about data first approach to digital transformation. First of all, what does that mean? >> I think what we are seeing is that, if you... I think we do see that we are getting into a post digital era. Which means that in the last seven years, most bigger companies and businesses have invested in building a better customer engagement. What they did was they created properties, like portals, mobile applications, you name it, to just get better sense and touch their customers better than they were touching earlier. That was a whole investment that went in the last six, seven years. What they feel is that what's next. You do that, but does it really translate into revenue growth? Is it really translating into the experience in a sustained basis? Not one time, but on sustained basis. Every time when you touch a customer, they feel the same passion towards you. They feel that they are still engaged with you, and they want to come again to you for whatever your offering, your services or your goods. They felt that that's not actually happening. The reason why it's not happening is because the underlying data is not complete or comprehensive enough, or not accurate enough, for giving that experience. That realization is seeping up right now. They are asking for ensuring instead of looking at a use-case base approach of solving one problem for one business or one geography, is there a way to do it enterprise-wide? That a (mumbles). Point which is coming out is that they looked at that technology process that's old tradition model of looking at new businesses. Technology people processes and those three. But now they're looking to fourth element, which is foundation-call data. That's what we are calling data-first approach. You have to look at data as well, while looking at reforming your business services, and offers to the client. >> I want to touch on something you said earlier, and that is to make the customer feel passionate about interacting with you. I mean that's such a loaded, and almost romantic word to describe a customer interacting with a company. Why is it that companies are trying invoke passion, and insight passion, inspire passion? >> I think it's a way to differentiate yourself from the competition, so I think that's what in my view the businesses are doing right. Let me give an example to you to make it real, it may address your first question as well to some extent. We are working with a cruise company, one of the largest cruise companies North America based. They obviously are trying to make sure the experience of the customer is much better than had earlier. Which can resinate to a much higher revenue for them obviously, and inquisition of more customers. The friends of friends, friends of customers if you may. They had done a great job creating that digital property, and the transformation of the program. But they also realize that they are now, they realize that they don't really have a sense of who's the customer? Now that's a good question, after all this investment you still don't know who's the customer. That's where they came and talked about can I get a single view of my customer? The reason why they don't have a single view of customer is because they actually don't own all their individual customers. They only own their own individual customers, but they also work with their partners. As you can see Experian and others actually own that same customer. So they are not able to have a sense of that customer, their habits, and their behavior in one single place. They can really provide their accommodations, saying... well guest, if you're going to Italy we can probably help you this summer. >> So yes, exactly that's what I want to know; Is what, if you do have a sense of who your customer is, and that is everything from their basic demographic information, to what they do on Sunday afternoon with their families. What kinds of things then can the cruise company do to make that customer more passionate toward the cruise. >> They can do a lot, but I can tell you another example of another cruise company. Was looking at customer files and they did a fantastic job, and I'm assuming that you may have also experienced yourself. This customer they had covered the single view of customer obviously, but what they did was use a lot of IoT or sensors in their ships. They actually transformed the entire ship. Like the entire ship has been transformed to understand the customer movement, and give that flawless and seamless (mumbles) to customers. Which can help them have a pretty great on their vessels if you may. That's what they, from the day that you order the tickets for this service... From that time onward they actually send you a (mumbles). That tracks you as a person moving into the ship, and they can offer much more seamless services, and also reduces a friction of the operations staff. The staff is not in a hurry and hassle. They're actually able to understand who's actually the customer, what they want, and they are able to provide that service. So that's how they're using that feature of knowing the customer, to better serve them; being a better engagement with them. Plus also eases the operational friction in their own staff. >> So the customer wins because they feel the company gets them, and knows them, and understands them; and then the company wins because they're able to make more money off that customer, because they already have predicted what that customer wants and needs at every moment. >> And they can do more with less. They can do more with less staff, less resources. >> So one of that we are also talking a lot about here on theCUBE it's the tenth anniversary of theCUBE. So we've had a lot of these conversations, is how data is becoming a C-suite discussion, and there's this growing need to appoint a chief data officer to drive data strategy. What do you see as the evolving role of the CDO, at your company; and then also at the companies that you work with? >> We see this is a very significant step in the future. There are a lot of predictions from (mumbles) An analyst saying that there will be more and more roles, like three-fourth of the companies would have a CDO (mumbles). But I think our point is likely, you know, to augment that point I think what we believe is that, we do believe in respective of who actually owns (mumbles) That a chief data officer or a CI, or a CO. They definitely need a person at the C-suite, not below C-suite. To have that discussion at the table, and show that their data strategy is attached to their business strategy, and that's not true in many cases right now. So the data is (mumbles) which is two levels down in (mumbles), and that's why it's not getting that attention as a corporate asset as a (mumbles) asset from where you can actually extract value that you're looking at right. That's what we see; so we see a very broadened role, we see who so is in that role, we think there are a few qualities that person needs to have. The first one the person has to have a seat at the table. The second, is that person should be able to understand business quite well. (mumbles) He or she should have an insider business innovation, and if the person is tech savy it's good to have, but it's not must to have. We do believe that person should be able to prepare a strategy, and the governance of data across his or her peers. So they know that what value they are able to get from that data, and how they can share it across their functions. That's where the value comes in. Plus, beyond that the last point would be making sure whatever they do, they do responsibly. Do they actually make things work; whether it's using A.I., whether it's using any machine learning or anything else they have. They make sure that it's responsible data, and make it secure for themselves, for their enterprise, and for their customer. >> Well that is certainly a theme that we're hearing a lot about at Infomatica world. Tell me about the relationship between Accenture and Informatica. >> It's quite good, it's been good for years. We have been working together for years. The last two years, or two in a half years I think it has really taken a different shape within the new companies, and that's largely because we have really gone into a strategic discussion with the companies, and seeing what is the future. I think one thing that they are doing very well with their leadership. Anil himself is CEO; and Amit, and Tracy, and everybody else. And with our leadership is that we do believe that we are on the surface of un-tapping the value, one. Second thing is I don't think that used cases will draw the benefit which large organizations are looking at. It has to be something done at enterprise level. So think about like I think there another talk in the morning about enterprise data catalog. Amit was talking about, You need that. You need that to not do one used case for one particular business, for one particular country, or one particular customer segment. We need to do that for entire businesses across the enterprise. That can only happen if you have a sense of data, and you know how to do it effectively at scale. That's what I think that people are looking. Companies are going to be looking at the solution base, and I think it's the right timing for having the discussion. >> And there are going to be learnings that you can derive from financial services, and apply to retail, and healthcare, and all sorts of (mumble). Is that what you're finding here at Informatica World? Are you having those in conversations to learn the best practices? >> Oh yeah, I think we have our customers here; Accenture, as we have our customers here. we're presenting in different session. We had (mumbles) present today morning at eleven a.m. about how master data management can actually help you drive a better strategy on transforming your operations system like ESPE. That was never talked earlier, two years back nobody talked about saying how can MDM help you have a better transformation of your ESPE systems. Well that's where we are going. We are saying that, okay you have a trandiction systems, but you also need a system of right governance. Because all of your data, customer data or other data maybe sitting in ESPE or maybe sitting in sales force. How would you connect the dots? You need something to connect that dot so you have a single source of truth, and make sure that you know your customer, or vendor, or location, or everything else in the right fashion. >> Know your customer. So another thing I want to ask you about is the skills gap. I know that workforce of the future is something that you've worked passionately on. Passion keeps coming up in our conversation. (laughs amusingly) At Accenture. Tell us your story first in terms how you came to terms with this skill gap, and what you did at Accenture to remedy it. >> So this is four years back, and we were looking at our tech strategy, and our strategy to (mumbles) our business going forward, or where do we invest? And we are a people centric company so we are 470,000 people, that's a lot of people. In my role, one of the thing in my role is to make sure that I look at all the investment we do on our people. As CDO of our technology business, I need to make sure that we are investing in the right places. So this came to me saying that okay, will we be relevant as 470,000 people ten years from now? That's the question right? Because of A.I., because machine in our name, because people plus machine. What happens to our work force? So that's what I was trying to solve. Instead he's saying, what do we do next, and that was the whole point about workforce of the future. We will work more closely with the machines, and how will that happen. So what skills we will need as humans to work with machines, and everything else. What's going to happen in terms of automation going forward. And plus new talent which is required for the future. So we worked hard on this we built a strategy on what we need, then we did a very simple thing, we actually went to a high speed excursion, and agile sprints. We get it the few of principles actually. I can say a couple of them to use to resinate. One is the principle saying there's only (mumbles) available in the market. So don't spend creating stuff, but spend learning stuff. The second thing the chains of (mumbles) are a vision of our people vision, employee vision. It used to be saying, That you need to preform and grow. Something like that, if you preform high in our company, you'll grow faster. We changed the saying to learn and grow. So we said learning is more fundamental because performance will become automatic when you learn more. What we did was we changed. We worked really hard on the cultural aspect. And one of the things (mumbles) used to always say in the past ten years back, you used to learn a day in a month. Well that may not be enough today. Just because (mumbles) and the change of technology is much faster. It's 10x speed. So you can learn at 10x level, that doesn't mean you need to be learning at deep level for ten things, that's going to be hard for humans to do that. But you can use some help. That's what we do a 2 pronged approach. One is what we call a (mumble) training. Which means we make you more aware of everything that's happening in the world, and we give you a chance to support people-- >> I mean how do you do that, I mean that's a tall order. >> So what I did was we went to the market, and we looked at a lot of platforms. Okay you need technology to do everything. You get it right. You will be sitting here talking (mumbles). Using right technologies, right? Maybe show that our what we're talking is for (mumbles) people to watch us right. But the same thing there when we were looking at all the platforms. I looked at all things and I felt everything was great. (mumbles) It was not something which is exponential So I had to build a platform off it all, so I spent 6 months writing a whole platform. It was a really smart team, and all the logic I used was build a platform which treats or ploy a human in the center of your (mumbles) design. So we made a very personalized platform, where it helps a person to get there, and attracts you to come back. So it's very user friendly, or a very exponential platform. We call it Accenture Future Talent Platform. We deployed it across our entire businesses, we have 70+ number of people who are already being certified to their platform. They feel goof that they've gone to the next stage of their career. And now we are actually using the same platform for our clients. So we are giving them platforms so clients can use that effectively. >> From what I am hearing from you, it's about having technology skills, know how, and expertise. But also having this mindset of learning, and a hungry for learning, and wanting to know more. How do you make sure that, that culture is cultivated in the right way? >> We did some of the campaigns, so a very simple principle that we use is that like you do a marketing campaign to attract a customer. Whether he is selling a (mumbles), or selling a cruise experience, or vacation, or whatever. Use a similar principles for our own employers, and use it as learning campaigns. So marketing campaigns are learning campaigns. So one of the campaigns that we ran was, How important was it for you to be learning fit? So just like we always measure ourselves on health everyday, instead you measure yourself in learning. So our app was actually given to everybody, so you can see whether you are learning enough or not. We're in the culture of seeing how I'm doing against my own goals, but how am I doing against Rebecca's goal. >> Gameafying it, making it a little more fun. Making it a little competition. >> We also did (mumbles) as well, Because we felt that people look at their own models and say, well this person is very sexist, why would I want to be that person. That's a normal human. That's what people see so we made sure that our leaders do what they are saying. And they can buckle it down, they should start learning faster itself, from top management perspective. So people see them learning, they would say, I want to be like him. So that means I need to have the same behavior as this person. >> No, those are critical people in companies. Well, Sanjeev thank you so much for coming on theCUBE. It's been a pleasure having you. >> Same here, it was nice talking to you. >> I'm Rebecca Knight. You are watching theCUBE Informatica World 2019. (funky techno music)