[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)

(upbeat 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 have a CUBE alum joining us Graeme Thompson. SVP and CIO of Informatica. Thank you so much for coming, for returning to theCUBE. >> Pleasure to be here. >> So one of the themes we talk a lot about on theCUBE, It's the 10th anniversary of theCUBE, is the changing role of the CIO and you are a CIO so you are well positioned to answer this question. In addition to the changing role there's also the perception of what it is versus the reality. Can you talk a little about how you see the role having evolved both at Informatica as well as your other peers at other companies as well as sort of what the industry is expecting or maybe those not in the industry thinks you do versus actually what you do? >> Yeah that's a long frustration thank you >> I'm sorry >> You're keeping me on my toes here Yeah so a lot of things, the outcomes are the same but with different methods so vendor management has always been important, cost management has always been important but as it moves from being predominantly on prem to be primarily in the cloud The dynamics of how these deals are put together changes so you need a different kind of approach to how you manage the portfolio of cloud applications. Security if different in the cloud it's still important it always has been always will be. But it's different in the cloud you have to look much more as vendor risk management make sure that you're comfortable with the risk posture of the vendors you are sourcing your applications from. So those things I would put in the category of You're trying to accomplish the same thing you're just doing it differently because your application work load is more likely to be in the cloud. Things that are different though, completely different are expectations. So everyone can see the power of data and the power of having speed and agility in the cloud but they want it immediately and they don't want to do the hard work to get there so I find that the CIO sometimes has to be the educator or the evangelist for change to explain that if you want all this data to generate all these miraculous new outcomes you have to focus on the process and then you have to enable that process within an application that's going to meet your needs today and tomorrow. You have to think end to end which means you have to integrate applications like Marketo and Salesforce. Then you need to find a way to get it all in your data lake That is completely different it's a completely different sport From what we were playing as CIOs 5 years ago and it's definitely the biggest area of change I've seen both internally and talking to PIOs >> Graeme, we've talked in the past, goo to see you again. You are a CIO your work for Informatica so you're the CIO of Informatica so you don't need to be sold on the value of data You're in the data business. You have a data company that thinks hard and has been building products for years in private, in retooling, you see the wave, we've talked about it you've been on the same wave, a great wave, for 4 years everyone else is now on it. SO as a CIO who works for a company that's you know you're not going to get in trouble for doing a data driven project what are some of the things that you've got going on because you do have relationships with all the different cloud providers you do have a great on premises large install base and now you guys as a company what are some of the projects you're doing that would a nice guiding light to folks watching who were really kicked in the tires on digital transmission, not just like talking about it but like okay architecure, roadmap, really thinking through all the hairy problems of what's coming down the pipe for them. What are you working on? >> Yeah so I think our marketing team has done a really good job framing things in the four journeys. So talk about it within that context. So the first one is next gen analytics. So a lot of companies go into this thinking Right all I have to do is find out where the data lives, ingest the data into my data lake or data warehouse, put Tableau on top of it and job done. Not the case, right? So as soon as you start shading data across more than one function, marketing are really good at knowing their data. They know how its generated. They know how it can be used. As soon as you let someone else loose on marketing's data, it's use at your own risk. Right so that introduces the need for governance. If you're going to use data in one organization that was generated in another one, you have to agree on the definition of terms. You have to agree on calculations so that you don't get the finance team and the sales team debating what the renewal rate is. So the next gen analytics journey for us has been an interesting one. We started with an on-prem data warehouse that's now on AZUR. The tipping point for us was when most of the data is generated in the cloud, why move it back on-prem just to do analytics on it? So we made a decision to build that on the cloud with AZUR. >> So leave it on the cloud, it's there. >> Yeah >> and then have the on-premise piece >> Go onto the cloud. >> It's where the MDM it's where the pieces kind of come together? >> Yep >> All right so... >> So that's the analytics journey. >> So I'll give you another curve bal here. So as you come in here, you say okay great the next step is well you know I need to actually make my AI work. Your clear, you know "the clarity starts here" it's a nice slogan. Nice play on words there but AI is ultimately where everyone wants to get to. >> Yeah >> AI is fed by data, machine learning, other things, really kind of feeding the outcome for AI. But without good data, and/or data can can help the AI get smarter. This kind of brings up the conversation of more data or diverse data - different data sets. So accessing data sets actually is a new dynamic that people are getting into and proving it adds value to AI. >> Yeah >> How do you see that playing out because this is really kind of brings up the real complex question which is that as you mentioned earlier; terms, rights, marketplaces, sharing data, uh you know, all these new things? What's your view on this notion of having more data sets feeding intelligent AI? >> So part of the increase in enthusiasm about AI and ML is really the convergence of.. the technology's actually ready to help, its not a science project off to the side anymore. And the need for it has never been greater. There's no way a human can keep up with all the data that's being generated even at a company like ours. So if you want to find out where the data is created, where it's used, who has access to it, then your going to have to apply some AI to it otherwise there's no shot. You'd need an ever increasing team of humans who would fail to do the job adequately. >> So you see data sets merging... not merging but like being merchandised, if you will, for lack of a better word? >> Yeah well you have to manage the linage of it. >> All right. So you have to know where it's created, where it's used, you know, who has access to it? Is that access appropriate? Uh... all those thing have to be taken into account. Especially when you look at all the compliance and privacy things that we're all faced with now that 18 months ago we weren't all that concerned about. >> And that really goes back to what you said earlier in our conversation in that the role of the CIO is so much as an educator and an evangelist. So can you talk a little bit about what you've learned in terms of making that message really sink in with employees in terms of understand where the data lives, who has access to it, all the obstacles that you just talked about? >> Yeah so part of it is those managing the IT team and then those managing the relationships with your business constituents. So let's take the IT team first. Really good IT people, like really good engineers, will work on the most interesting problem available. It's our job as a CIO to make sure that the most profitable problem is also the most interesting one. Fight number one is getting people working on the right things cause IT people with work incredibly hard . You just need to make sure they're working incredibly hard on the right stuff with a focus on the right outcome at the end of it. So that's the IT part. Then working with the business stakeholders, its really setting expectations. Cause quite rightly, they want everything as soon as they can describe it, it should be available. There's often a lot of technical dept that we have as organizations, you know? We had a more than 10 year old deployment of sales force, you got to believe there was a ton of technical debt in there because it was built to perfection for our old business. It wasn't built for our new business. So you have to work with the buiness stakeholders. Bring them along with you on what to do first, what to do next, what the dependencies are, ' and focus on setting exceptions that its not going to be done overnight. >> So about governance. Obviously governance has been around for awhile, we've talked about it before. But now more than ever your seeing in the news first anniversary of GDPR, I predicted that would be... I won't say it... I said like, months before... bad words.. BS basically. But it's reality. More privacy stuff your seeing more and more, um, regions in cloud dealing with certain restrictions. So when it hear regulation, I hear constrained data. That goes in my mind, I hear oh my god. Regulation and innovation are always sometimes at odds. So it's a balancing act. What are you guys doing to address that? What's the solution today and how do you see that playing out because SAS is about data and agility and that's why SAS has been so popular and that's what digital transformation is going to get to is these SAS-like benefits. Agile, risk-taking, high reward. Low-risk, high reward kind of things. How do you get the balance between, you know, regulation, compliance, risk, and innovation? >> Yeah, so I can talk about how we look at it internally and then a little bit about how our customers look at it. So, for us you can look at it like a tax. As a tax on innovation. Or, if you look at a little bit more optimistically, who wouldn't want to honor the customer's right to be forgotten? Who wouldn't want to consult their customer on where you use their data? So you can also look at it as way that by implementing the GDPR or the California Privacy Standard or whatever it is, it makes your company better. It allows you to be the company that you would like to aspire to be. So you don't have to just look at it as a tax. Now I'm going to look at our customers. They fall into 2 categories: those that have to do it because they're in a regulated industry like financial services or healthcare, and then there's those that do it because they know it will help them serve their customers better. And you see a lot of governance and compliance projects starting from a place of defensiveness. They have to do it because they have to comply with new regulations that apply to them and often its companies that are really trying to make the best use of their data but they want to do it in a really responsible way. Um - if done properly and responsibly, it can be something that's good for everyone, I believe. >> I just have one final question about the skills gap. And this is something we've been really talking a lot about here. What are you doing to address it? And is the problem really as bad as the headlines are making it out to be? >> Yeah so there's the macro problem of aging workforce and where are the new people coming from? There's that one - it's been with us for awhile and applies across all functions. Then there's specific skills areas in IT that are always a shortage. Security is one - it's really, really difficult to find really good IT security.. information security people. Often these groups can be ivory tower-ish so its hard to find people who are really practitioners. It's hard to select them and it's hard to retain them because they always want to build and then move on and build something new. So security is one. Obviously data and analytics is a huge one. Finding people that can, that know a little bit more than what an oric in our warehouse does is a challenge and then once you get those people, you have to make sure they are working on things that they find are worthy of their time so that they are motivated to work as hard as you need them to work. And other areas like managing cloud vendors is I think a skill set that will start to grow up. Um - these cloud contracts get really expensive as you scale and there's no friction at the point of consumption. You know, we've got engineers that aren't allowed to order a stapler from Amazon without approval. But they can sign the company up for tens of thousands of dollars worth of compute cost obligations. You need governance and skills to manage uh - that. If you ask an engineer do you want slow or fast and big or small, they're going to pick fast and large, right? >> Just a dumb follow up on that skills gap question. For the folks who are graduating collage, high school, elementary school.. ..education is obviously kind of a little bit linear but you know people have argued that there's no one playbook for the kinds of courses you would take to get into the data kind of world there. Is there any pattern your seeing where the folks who are really excelling in this new environment have certain skills and classes? So if someone is going into collage maybe honing a class on you know on a particular class or dicipline? Have you seen some things that work? >> No >> No? >> What I have seen that works is finding people who have a track record of solving important business problems and using that to select the people that you hire. Cause the.. having a sound education in technology is one thing. You got to understand the business domain and the problem that you are trying to solve. That's where the value comes from. The business stakeholders value someone that can understand the problem they are trying to solve or the opportunity that they are trying to take advantage of. So finding those people that have a track record of solving meaningful problems, uh to me, has been a way to find the right folks in that area. >> Multitalent is then.. it's early, too, I mean, Berkley just had their first graduating class of you know, Data Sciences, kind of gives you an idea of how early this is. >> Yeah and it takes 2 to 4 years to have a University course accredited. By the time you've done that it's out of date. >> It's out of date. >> So that has to change. >> My final question for you, Graeme, is what's the um... For the folks that aren't here at Informatica World 2019 whats the summary in your view? The theme of the show? What's the key highlights that people should walk away with this year for the focus of Informatica World 2019? >> So it's not a new theme, it's more of a expansion on the' theme from the last couple of years. So the importance of the platform is key. You can go off as an IT professional and source one product to solve one problem and before you're done I guarantee you'll have found an adjacent problem and you're going to wish you'd chosen a platform instead of an individual product. So if you listen to Anneals Keynote this morning and Ahmet got into more detail, its really about the platform and the power of Claire and the AI part as part of that overall platform - that's really the theme - but its not new. It's not something we just came up with last week it's been our strategy for at least 24 months so we just continue to build on it. >> Bad data or no data, there's no AI, or bad data is bad AI and no data is no AI? That's essentially the reality as AI becomes mainstream. >> Yeah. >> All right, thank you. >> Great. Well, thank you so much for coming on the show, Graeme >> Pleasure. >> You're watching theCUBE's live coverage of Informatica World 2019 I'm Rebecca Knight and John Furrier. Thanks for staying 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 2019. I'm your host Rebecca Knight, along with my co-host John Furrier. We're joined by Ali Ghodsi, he is the CEO of Databricks, thank you so much for coming on, for returning to theCUBE. You're a CUBE veteran. >> Yes, thank you for having me. >> So I want to pick up on something that you said up on the main stage, and that is that every enterprise on the planet wants to add AI capabilities, but the hardest part of AI is not AI, it's the data. >> Yeah. >> Can you riff on that a little bit for our viewers? Elaborate? >> Yeah, actually, the interesting part is that, if you look at the company that succeeded with AI, the actual AI algorithms they're using, are actually algorithms from the 70s, you know, they're actually developed in the 70s, that's 50 years ago. So then how come they're succeeding now? When actually the same algorithms weren't working in the 70s, so people gave up on them. Like, these things called neural nets, right? Now they're en vogue and they're, you know, super successful. The reason is you have to apply orders of magnitude more data. If you feed those algorithms that we thought were broken orders of magnitude more data, you actually get great results, but that's actually hard. You know, dealing with petabyte scale data and cleaning it, making sure that it's actually the right data for the task at hand is not easy. So that's the part that people are struggling with. >> I saw you up on stage, I'm like ah, Ali's here, Databricks is here, that's awesome. Psyched that you stopped by theCUBE. Been a while. I wanted to get a quick update, 'cause you guys have been on a tear, doing some great work at Cal, we were just told before we came on camera. But what are you doing here? What's the, is there any announcements or news with Informatica? What's the story? >> Yeah, it's, we're doing partnership around Delta Lake, which is our next generation engine that we built, so we're super excited about that. It integrates with all of the Informatica platform. So their ingestion tools, their transformation tools, and the catalog that they also have. So we think together, this can actually really help enterprises make that transition into the AI era. >> So you know, we've been followers, our 10th year, so remember when we were in the cloud era office of Mike Olsen and Amr Awadallah when we first started and now, Hadoop movement started, and then the cloud came along. Right when you guys started your company, the cloud growth took off. You guys were instrumental in changing the equation in dealing with data, data lakes, whatever they're calling it back then. So now, data, holistically, is a systems architecture. On premise it's a huge challenge, cloud native, well no real challenge, people love that. Data feeds AI, lot of risk taking, lot of reward. We're seeing the SaaS business explode, Zoom communications. The list goes on and on. Do you know, enterprise that's trying to be SAS is hard. So you can't just take data from an enterprise and make it SaaS-ified. You really got to think differently. What are you guys doing? How have you guys evolved and vectored into that challenge, because this is where your core value proposition initially started change. Take us through that Databricks story and how you're solving that problem today. >> Yeah, it's a great question. Really what happened is that people started collecting a lot of our data about a decade ago. And the promise was, you can do great things with this. There are all these aspirational use cases around machine learning, real time, it's going to be amazing. Right? So people started collecting it. They started storing one petabytes, two petabytes, and they kept going back to their boss and saying this project is real successful I now have five petabytes in it. But at some point the business said, okay that's great but what can you do with it? What business problems are you actually addressing? What are you solving? And so, in the last couple years there's been a push towards let's prove the value of these data lakes. And actually, many of these projects are falling short. Many are failing. And the reason is, people have just been dumping this data into data lakes without thinking about, the structure, the quality, how it's going to be used. The use cases have been an afterthought. So the number one thing in the top of mind for everyone right now is how do we make these data lakes that we have successful so we can prove some business value to our management? Towards this, this is the main problem that we're focusing on. Towards this, we built something called Delta Lake. It's something you situate on top of your data lake. And what it does is it increases the quality, the reliability, the performance, and the scale of your data lake. >> (John) So it's like a filter. >> Yeah. >> The cream rises to the top. >> (Ari) Exactly. >> Let's the sludge, the data swamp stay below the clean water, if you will. >> Exactly actually you nailed it. So basically, we look at the data as it comes in, filter as you said, and then look at, if there's any quality issues we then put it back in the data lake. It's fine, it can stay there. We'll figure out how to get value out of it later. But if it makes it into the Delta Lake, it will have high quality. Right? So that's great. And since we're anyway already looking at all the data as it's coming in, we might as well also store a lot of inducees and a lot of things that let us performance optimize it later on. So that, later, when people are actually trying to use that data they get really high performance, they get really good quality. And we also added asset transactions to it so that now you're also getting all those transactional use cases working on your existing data lake. >> I saw, at my daughter's graduation in Cal Berkley this weekend and yesterday, people around with Databricks backpacks. Very popular in academic. You guys got the young generation coming in. What's the update on the company? How many employees? What's the traction? Give us a quick business update. >> Yeah we're about 800 employees now. About 100 people in Europe, I would say, and maybe 40-50 people in Asiapac. We're expanding the ME and the Asia business. >> (John) Growth mode. >> Yeah, growth mode. So it's expanding as fast as possible. I mean, I actually, as a CEO, I try to always, slow the hiring down to make sure that we keep the quality bars. So that's actually top of mind for me. But yeah we're-- >> (John) You did Delta Lake on that one. >> Yeah (laughing) >> Exactly. Yeah and we're super excited about working with these universities. We get a lot of graduate students from top universities-- >> And Cal had the first ever class in college of data analytics, what was that? Data analytics are the first inagaural class graduated. Shows how early it is. >> Yeah, yeah, yeah. And actually used Databricks, the community edition, for a class of over a thousand students at Cal used the platform. So they're going to be trained in data science as they come out. >> So I want to ask about that because as you said you're trying to slow down the hiring to make sure that you are maintaining a high bar for your new hires. But yet, I'm sure there's a huge demand because you are in growth mode. So what are you doing? You said you're working with universities to make sure that the next generation is trained up and is capable of performing at Databricks. So tell us more about those efforts. >> Yeah I mean, so, obviously university recruiting is big for us. Cal, I think Databricks has the longest line of all the companies that come there on the career fair day. So, we work very closely with these universities. I think, next generation, as they come out, this generation that's coming out today actually is data science trained. So it's a big difference. There is a huge skills gap out there. Every big enterprise you talk tells you my biggest problem is actually, I don't have skilled people. Can you help me hire people? I say, hey we're not in the recruiting business. But, the good news is, if you look at the universities, they're all training thousands and thousands of data scientists every year now. I can tell you just at Cal, because, I happpen to be on the faculty there, is, almost every applicant now, to grad school, wants to do something AI related. Which has actually led to, if you look at all the programs in universities today, people used to do networking, professors used to do networking, say we do intelligent networks. People who do databases say, we do intelligent databases. People who do systems research say, hey we do intelligent systems, right? So what that means is, in a couple years you'll have lots of students coming out and these companies, that are now struggling hiring, then will be able to hire this talent and will actually succeed better with these AI projects. >> As they say in Berkley, nothing like a good revolution once in a while. AI is kind of changing everyone over. I got to ask you for the young kids out there, and parents who have kids either in elementary school or high school, everyone is trying to figure out, and there's no yet clear playbook, we're starting to see first generation training, but is there a skill set, because there's a range in surface area, you got hardcore coding to ethics, and everything in between from visualization, multiple dimensions of opportunities. What skills do you that people could hone or tweak that may not be on a curriculum that they could get, or pieces of different curriculums in school that would be a good foundation for folks learning and wanting to jump in to data and data value, whether it's coding to ethics? >> Yeah, just looking at my own background and seeing how, what I got to learn in school, the thing that was lacking, compared to what's needed today, is statistics. Understanding of statistics, statistical knowledge, That I think, it's going to be pervasive. So I think, 10, 15 years from now, no matter which field you're in, actually whatever job you have, you have to have some basic level of statistical understanding 'cause the systems you're working with will be, they'll be spitting out statistics and numbers and you need to understand what is false positives, what is this, what is the sample, what is that? What do these things mean? So that's one thing that's definitely missing and actually it's coming, that's one. The second is computing will continue being important. So, in the intersection of those two is, I think a lot of those jobs. >> In all fields, we were talking about earlier, biology, everything's intersecting, biochemistry to whatever right? >> (Ali) Yeah. >> I got to ask you about, well I'm a little old school, I'm 53 years old but I remember when I broke into the business coding, I used to walk into departments, they were called DP, data processing. So we're getting into the data processing world now, you've got statistics, you've got pipeline, these are data concepts. So I got to ask you as companies that are in the enterprise may be slower to move to the cutting edge like you guys are, they got to figure out where to store the data. So can you share your opinion or view on how customers are thinking and how they maybe should be architecting data on premise, in the cloud. Certainly cloud's great, if you're getting cloud native for pure SAS, and born in the cloud like a start-up. But if you're a large enterprise, and you want to be SAS-like, to have all that benefit, take the risk with the reward of being agile, you got to have data because if you don't the data into the machine learning or AI, you're not going to have good AI. So you need to get that data feeding in fast. And if it's constrained with regulation compliance you're screwed. So what's your view on this? Where should it be stored? What's your opinion? >> Yeah, we've had the same opinion for five, six years, right? Which is the data belongs in the cloud. Don't try to do this yourself. Don't try to do this on prem. Don't store it in, at Duke, it's not built for this. Store it in the cloud. In the cloud, first of all, you get a lot of security benefits that the cloud vendors are already working on. So that's one good thing about it. Second, you get it, it's realiable. You get the 10, 11 lines of availability, so that's great, you get that. Start collecting data there. Another reason you want to do it in the cloud is that a lot of the data sets that you need to actually get good quality results, are available in the cloud. Often times what happens with AI is, you build a predictive model, but actually, it's terrible. It didn't work well. So you go back, and then the main trick, the first tricks you use to increase the quality is actually augmenting that data with other data sets. You might purchase those data sets from other vendors. You don't want to be shipping hard drives around or, you know, getting that into your data center. Those will be available in the cloud, so you can augment that data. So we're big fans of storing your data in data lakes, in the cloud. We obviously believe that you need to make that data high quality and reliable. With that we believe the Delta Lake platform, open-source project that we created is a great vehicle for that. But I think moving to the cloud is the number one thing. >> (John) And hybrid works with that if you need to have something on premise? >> In my opinion the two worlds are so different, that it's hard. You hear a lot of vendors that say we're the hybrid solution that works on both and so on. But the two models are so different, fundamentally, that it's hard to actually make them work well. I have not yet seen a customer yet or enterprise. You see a lot of offerings, where people say hybrid is the way. Of course, a lot of on prem vendors are now saying, hey, we're the hybrid solution. I haven't actually seen that be successful to be frank. Maybe someone will crack that nut but-- >> I think it's an operational question to see who can make it work. Ali, congratulations on all your success. Great to see you. >> Yeah it's been great having you on the show. >> Thank you so much for having me. >> You are watching theCUBE, Informatica 2019. I'm Rebecca Knight, for John Furrier, stay tuned.