>> Narrator: Live from Las Vegas it's theCUBE. Covering UiPath Forward Americas 2019. Brought to you by UiPath. >> Welcome back everyone to theCUBE's live coverage of UiPath Forward here at the Bellagio in Las Vegas. I'm your host Rebecca Knight along with my co-host Dave Vellante. We are joined by Craig Le Clair, he is the vice president of Forrester and also the author of the book "Invisible Robots in the Quiet of the Night: How AI and Automation will Restructure the Workforce". Thank you so much for coming on theCUBE. >> Craig: Thank you! Thanks for having me. >> And congratulations, it's already made #11 on Amazon's AI and automation bestseller list. >> Wow, it's not quite best seller but OK, that's great, thank you, it's doing well. >> So if anyone calls your book a bestseller you just take 'em on that. >> (Craig) I'll just take it. >> So it is a, it's a bleak story right now, I mean there's a lot, there's so many changes going on in the workforce and there's so much anxiety on the part of workers that they're going to lose their job that all these technologies are going to take away their their livelihood, so how are companies managing this? Are they managing it well, would you say, or is the anxiety misplaced? Give us an overview. >> Yeah, so I don't think companies are really aware of the broader implications of the automation and AI that's developing. They tend to focus on the things that companies focus on. They focus on more efficiency and productivity and so forth, and underlying that is this digital anxiety that we call it, and the fact that a lot of the jobs that we, particularly the middle class have, the working class have, are the targets of the invisible robots, and that's really the point of the invisible robot book is that there's a lot of media attention on the hardware aspects of robotics, in fact the Super Bowl last year had 10 commercials with hardware robots. But if you look at this conference you look at the number of people here. What are these people doing? They're going back to their companies and saying "You know, this UiPath, and there are other providers "in the market, we can build software robotics, "we can build bots to do some of these tasks "that a lot of these humans are doing." And while there is elevation of the human capability in spirit for many of them, there's also a comfort level in employees that do things that they have control over, have incited. And when you extract those you are left with a series of more exciting moments, perhaps, but it's not going to make you more relaxed as an employee. And then you look at the overall job numbers, and our estimates are very conservative compared to some of the other reports, that are 45, 50% of workers over 10 years being displaced. We think it's 16%, but still, when you look at just the US numbers, that's of 160 workers today, 160 million workers, that's a lot of people. >> Rebecca: It is indeed. >> So, displaced and then sort of re-targeted or? >> A percentage, >> Vaporized. >> No, no, well the 16% is the automation, is the net loss of jobs. Now in that, automation's expensive, so there are a tremendous number of new jobs that are created by the work that's been going on here. So we have a formula to calculate that for these 12 different work personas, and the work personas have different relationships to AI and automation, so you would be crossed so many knowledge workers and be very well protected for a long time. >> Rebecca: All right, there we go. >> So you're good, but... for coordinators, people that have clip boards in their hands, for those who work in cubicles, they're going to have a lot of people leaving those cubicles that aren't going to be able to migrate to other personas. And so we have a changed management issue, we need to start driving more education from the workplace through certification, and that's a really critical thing I'll talk about tomorrow, that the refresh of technology with automation is 18 months to 24 months, you can't depend on traditional education to keep up, so we need a different way to look at training and education and for many it's going to be a much better life, but there's going to be many that it will not be. >> What was the time frame for your net 16% loss? >> 10 years. >> 10 years, okay, to me a lower net loss number makes sense, and in fact if you can elongate your timeline it probably shows a net job creation, you can make that argument anyway I don't know if you. >> Craig: It's being made. >> Dave: You don't buy it though? >> I don't, the world economic foundation and others are having huge net new numbers for jobs based on AI. Some of the large integration companies that want to build AI platforms for you are talking about trillions of dollars that would be added value to the world economy, I just don't buy it, and you know the reason I wrote this book was because what's going on here is very quietly preparing to displace a lot of efforts starting with relatively small tasks, it's called task automation but then expanding to more and more work and eventually adding a level of intelligence to the task automation going on here, that's going to take a lot of jobs. And for most of those 20 million cubicle workers, they have high school educations. You know, the bigger problem is this level of anxiety, you know, you go into almost any bookstore and there's a whole section For Dummy books, and it's not, is it because we have this sort of cognitive recession or because there's a, it's because the world's getting faster and more complicated. And unless you have the digital skills to adapt to that, the digital skills gap is growing. And we need to have as much focus that you see here and energy on building automation. We need to have an equal amount of focus on the societal problems. >> Yeah, it really comes down to education, too. I mean if I were able to snap my fingers and transform the educational system, there might be a different outcome but that's very unlikely to happen. Craig, one of the things we talked about last year was you had made the statement that some of these moonshot digital transformations aren't happening for a variety of reasons but our PA is kind of a practical way to achieve automation. >> Still very true. >> Have you seen sort of a greater awareness in your client base that, "You know, hey, maybe we should dial down "some of these moonshots and just try to "pick some clear winners." >> Yeah, we have a number of prediction reports coming out from Forrester and they're all saying basically that. I'm doing reports on what I'm calling the intelligent process automation market and that's really our PA plus AI, but not all aspects of AI. You know, it's AI that you can see in ROI around, you know it's AI that deals with unstructured documents and content and email. It's not the moonshot, more transformative AI that we have been very focused on for a number of years. Now all of that's very very important. You're not going to transform your business by doing task automation even if it's a little more intelligent and handles some decision management, you still need to think about "How do I instantiate "my business algorithmically," with AI that's going to make predictions and move decision management and change the customer experience. All that's still true, as true as it was in 2014/2015, we're just seeing a more realistic pull back in terms of the invested profile. >> Well, and so we've been talking about that all day, it is taking automating processes that have been around for a long time, and you, I think identified this as one of the potential blockers before, if you get old processes that are legacy and I think you, you gave the story of "Hey, I flew out here "on American Airlines in the old SABRE system." How old are those processes, you know? We've, you know the old term "paving the cow path." So the question is, given all the hype around RPA, the valuations, et cetera, what role do you see RPA having in those sort of transformative use cases? >> Well here's the interesting thing that was, I think, somewhat accidental by the, you know what really changed from having simple desktop automation? Well you needed some place to house and essentially manage that automation, so the RPA platforms had to build a central management capability. UiPath calls that the orchestrator, others call it the control tower, but when you think of all the categories of AI none of them have a orchestration capability, so the ability to use events to link in machine intelligence and dispatch digital workers or task automation to coordinate various AI building blocks as we call them and apply it to a use case, that orchestration ability is pretty unique to the RPA platforms. So the sort of secret value of RPA is not in everything that's being talked about here but eventually is going to be as a coordinating mechanism for bringing together machine learning that'll begin in the cloud, conversational intelligence that might be in Google. Having the RPA bots work in conjunction with those. >> But if I recall, I mean that's something that you pointed out last year as well that RPA today struggles with unstructured data that... >> Well it can't do it. >> You're right, we've talked about it NLP versus RPA, RPA, given structured data, I can go after it. >> That's the RPA plus AI bit, though. I mean, you take text analytics layer, and you combine it with RPA bots and now you have the unstructured capability plus the structured capability that RPA does so well. And, with the combination of the two, you can reach. I think what the industry needs to do or the buyers of RPA need to take the pressure off this immediacy of the ROI. In a sense, that's what's driven the value. I can deploy something, I can get value in a few months but, to really make it effective and transformative you need to combine it with these AI components, that's going to take a little longer, so this sort of impatience that you see in a lot of companies, they should really step back and take a look at the more end to end capabilities and take a little hit on the ROI immediately so that you can do that. >> No, I mean I can definitely see a step function, okay, great, we've absorbed that value, we get the quick ROI, but there's, to your point there's got to be some patient capital to allow you to truly transform in order for RPA, I don't want to put words in your mouth, to live up to the hype. >> Absolutely, I totally agree. And I am still very, very high on the market, I think it's going to do extremely well. >> Well, if you look at the spending data, it's quite interesting. I mean RPA as a category is off the charts. You know, UiPath, from the, your last wave kind of took the lead but, Automation Anywhere, Blue Prism spending, even in traditional incumbents, maybe not even RPA, maybe more "process automation" like Pegasystems. Their spending data suggests that this is the rising tide lifting all boats so, my question to you is, how do you see this all shaking out? I mean, huge evaluations, the bankers are swarming around. You saw them in the media yesterday. You know, at some point there's got to be a winner takes most. The number two guy will do pretty well and then everybody else kind of consolidates. What's your outlook? >> Well, there are a lot of emerging players coming into the market and, part of my life is having to fend them off and talk to them, and the RPA wave is coming out in a week. It's going to have four new players in it. Companies like SAP. >> Well, they acquired a company right? >> They acquired and they built internally, and have some interesting approaches to the market. So you are going to see the big players come into the market. Others I won't mention that'll be in the market in a month It's getting a lot of attention. But also I think that there are domains, business domains that, the different platforms can start to specialize in. The majors, the UiPaths to the world, will be horizontal and remain that way. And depend on partners to tailor it for a particular application area. But you're going to see RPA companies come into the testing market, software testing market. You're going to see them come into the contact centers to deal with attended mode in more sophisticated ways perhaps than those that don't have that background. You're going to see tailored robots that are going to be in these robot communities that are springing up. That'll give a lot of juice to others to come into the market. >> And like you say you're going to see, we've talked about this as well Rebecca, the best of breed versus the suite, right? Whether its SAP, Inforce talking about it, I'm sure Oracle will throw its hat in the ring I mean, why not, right? Hey, we have that too. >> Well, if you're those companies that the RPA bots are feasting on, they're slowing the upgrades to your core platforms, in some ways making them less relevant, because their argument has been, let's integrate, you get self integration when you buy SAP, when you buy Oracle, when you buy these big platforms. Well, the bots actually make that argument less powerful because you can use the bots to give you that integration, as a layer, and so they're going to have to come up with some different stories I think if they're going to continue to move forward on their platforms, move them to the cloud and so forth. >> So, finally, your best advice for workers in this new landscape and how it is going to alter their working lives. And also, your best advice for companies and managers who are, as you said, maybe not quite, they're grappling with this issue but maybe not and they're not being disingenuous to workers about who's going to lose their jobs, but this idea of as they're coming to terms with understanding quite all of the implications of this new world. >> Yeah, I know, I'm presenting data tomorrow that shows that organizations, employees, and leaders are not ready and I have data to show that. They're not understanding it. My best advice, I love the concept of, it's not a Forrester concept, it's called constructive ambition. This is the ability in an employee to want to go a little bit out of the box, and learn, and to challenge themselves, and move into more digital to close that digital skills gap. And, we have to get better at, companies need to get better at identifying constructive ambition in people they're hiring, and also, ways to draw it out. And to walk these employees up the mountain in a way that's good for their career and good for the company. I can tell you, I'll tell a few stories on the main stage last night, I interviewed Walmart employees and machinists that could no longer deal with their machine because they had to put codes into it so they had to set it up with programming steps and the digital anxiety was such that they quit the job. So a clear lack of constructive ambition. On the other hand, a Walmart employee graduated from one of their 200 academies and was able to take on more and more responsibility. Somebody with no high school degree at all. She said, "I've never graduated "from anything in my life. "My kids have never seen me "succeed at anything, and I got this certification "from Walmart that said that I was doing this level "of standard work and that felt really, really good." So, you know, we, companies can take a different view towards this but they have to have some model of future of work of what it's going to look like so they can take a more strategic view. >> Well Craig, thank you so much for coming on theCUBE. It was a really great talk. Another plug for the book, "Invisible Robots in the Quiet of the Night" you can buy it on Amazon. >> Craig: Thank you. >> I'm Rebecca Knight for Dave Vellante, stay tuned for more of theCUBE's live coverage of UiPath Forward. (techno music)

from Boston Massachusetts it's the cube covering Activia 2019 data-driven to you by Activia welcome to Boston everybody this is Dave Volante and I'm here with stupid man finally still in our hometown you're watching the cube the leader in live tech coverage we're covering actifi Oh data-driven hashtag data-driven 19 activity it was a company that is focus started focused on copy data management they sort of popularized the term the I the concept the idea of data virtualization there's big data digital transformation all the buzz it's kind of been a tailwind for the company and we followed them quite closely over the years poly st. Claire is here she's the CEO of the state of Massachusetts that's chief of ditch and chief data officer Holly thanks for coming on the Q thanks for having me so it's kind of rare that somebody shares the title of chief digital officer of chief data officer I think it's rare right now I think that would change you think it will change I think those two roles will come together I just think data fuels our digital world and it both creates the content and also monitors how we're doing and it's just inevitably I think either they're gonna be joined at the hip or it's gonna be the same person that's interesting I always thought the chief data officer sort of emerged from this wonky back-office role data quality of this careful the word walking okay well yeah let's talk about that but the chief digital officer is kind of the mover the shaker has a little marketing genius but but okay so you see those two roles coming together that maybe makes sense because why because there's there some tension in a lot of organizations between those two roles well I think the challenge with the way that sometimes people think about data is they think about it's only a technical process data is actually very creative and you also have to tell a story in order to be good with it it's the same thing as marketing but it's just a little bit of a different hue a different type of audience a different type of pace there's a technical component to the data work but I'm looking at my organization that I'm surrounded by additional technical folks CTO CSO privacy officer CIO so we have a lot of supports that might take away some of those roles are scrunched in under the data officer or the digital so I used to turn wonky before it kind of triggered you a little bit but but you're a modeler you're a data scientist your development programmer right no but I know enough to I know enough to read code and get in trouble okay so you can direct coders and you have data scientists working for you yeah right so you've got that entire organization underneath you and your your mission is blank fill in the blank so our mission is to use the best information technology to ensure that every users experience with the Commonwealth is fast easy and wicked awesome awesome Holly our team just got back from a very large public sector event down in DC and digging into you know how our agency is doing with you know cloud force initiatives how are they doing the city environments you were state of Massachusetts and you know rolled out that that first chief data if you keep dipped officer gets a little bit of insight inside how Massachusetts doing with these latest waves of innovation uh well you know we have our legacy systems and as our opportunities come up to improve those systems our reinvest in them we are taking a step forward to cloud we're not so dogmatic that it's cloud only but it's definitely cloud when it's appropriate I do think we'll always have some on-prem services but really when it's possible whether it's a staff service off-the-shelf or it's a cloud environment to make sense than we are moving to that in your keynote this morning you you talked about something called data minimalism yeah and wonder if you could explain that for audience because for the longest time it's been well you want to hoard all the data you want to get all the data and you know what do you do with it how do you manage you right right I mean data's only as good as your ability to use it and I often find that we're ingesting all this data and we don't really know what to do with it or really rather our business leaders and decision-makers can't quite figure out how to connect that to the mission or to act properly interrogate the data to get the information they want and so this idea is an idea that's sort of coming a little bit out of Europe and or some of the other trends we see around some cyber security and hacking worlds and the idea is this actually came from fjords Digital Trends for 2019 is data minimalism the idea is that you strongly connect your business objectives to the data collection program that you have you don't just collect data until you're sure that it supports your objectives so you know one of the things that I also talked about in the keynote was not just data minimalism but doing a try test iterate approach we often collect data hoping to see that we can create a change I think we need to prove that we can create the change before we do a widespread scalable data collection program because often we collect data and you still can't see what you're doing has an effect within the data the signals too strong or too too weak or you're asking the wrong question of the data or it's the wrong plectra collection of the technique and that's largely driven from a sort of privacy a privacy privacy the reality of how costly sometimes the kennedys but you know storage of data is cheap but the actual reality of moving it and saving it and knowing where it is and accessing it later that takes time and energy of your of your actual people so I think it's just important for us to think carefully about a resource in government we have a little less resources sometimes in the private sector so we're very strategic on what we do and so I think we need to really think about the data we use if the pendulum swings remember back to the days of you know 2006 the Federal Rules of Civil Procedure said okay you got to keep electronic records for whatever seven years of depending on industry and people said okay let's get rid of it as soon as we can data was viewed as a liability and then of course all the big data height we've talked about a little bit in your in your speech everybody said I could collect everything throw it into a data Lake and we all know those became data swamps so do you feel like the pendulum is swinging and there's maybe a little balance are we reaching an equilibrium is it going to be a you know hard shift back to data as a liability what are your thoughts well I think isn't with any trend there's always a little bit of a pendulum swing as we're learning it's with it with the equilibrium is equilibrium is I think that's a great word I think the piece that I neglected to mention is the relationship to the consumer trust you know for us in government we have to have the trust of our constituents we do have a higher bar than public sector in terms of handling data in a way that's respectful of individuals privacy and their security of their data and so I think to the extent that we are able to lend transparency and show the utility and the data we're using and that will gain the trust of our users or customers but if we continue to do things behind the scenes and not be overt about it I think then that can cause more problems I think we face is organizations to ask ourselves is having more data worth the sort of vulnerability introduces and the possible liability of trust of our of our customers when you betray to test over your customers it's really hard to replace that and so you know to a certain extent I think we should be more deliberate about our data and earn the trust of our customers okay how how does Massachusetts look at the boundary of data between the public sector and the private sector I've talked to you know some states where you know we're helping business off parking by giving you know new mobile apps access to that information you talked a little bit about health care you know I've done interviews with the massive macleod initiative here locally how do you look at that balance of sharing I think it is a real balance you know I don't think we do very much of it yet and we certainly don't share data that were not allowed to by law and we have very strict laws here in Massachusetts the stricter at the ten most states and so I think it's very strategic when we do share data we are looking for opportunities when we can when I talk about demand driven data I look forward to opening the conversation a little bit to ask people what data are they looking for to ask businesses and different institutions we have throughout the Commonwealth what data would help you do your job better and grow our economy and our jobs and I think that's a conversation we need to have over time to figure out what the right balances someday it'll be easier for us to share than others and some will never be able to share the first data scientist I've ever met is somebody I interviewed the amazing Hilary Mason and she said something that I want to circle back to something you said in your talk if she said the hardest part of my job or one of the hardest parts is people come to me with data and and it's the most valuable thing I can do is show them which questions to ask and you have talked about well what's a lot of times you don't know what questions to ask until you look at the data or vice versa what comes first the chicken or the egg what's your experience pin well I do think we need to be driven by the business objectives and goals it doesn't mean there's not an iterative process in there somewhere but you know data wonks we can we can just throw data all day long and still might not give you the answer there forward but I think it's really important for us to be driven by the business and I think executives don't know how to ask the questions of the data they don't know how to interrogate it or honestly more realistically we don't have a date of actually answers the question they want to know so we often have to use proxies for that information but I do think if there's an iterative after you get to a starting point so I do think knowing what the business question is first I know you gotta go but I want to ask your last question bring it back to the state where both Massachusetts residents and your services it sounds like you're picking off some some good wins with a through the fast ROI I mean you mentioned you know driver's license renewals etc how about procurement has procurement been a challenge from the state standpoint you are you looking at sort of the digital process and how to streamline procurement that is a conversation that the secretary what is currently in and I think it's a good one I don't think we have any any solutions yet but I think we have a lot of the issues that were struggling with but we're not alone all public sectors struggling with this type of procurement question so we're working on it all right last question there's quick thoughts on you know what you've seen here I know you're in and out but data-driven yeah it's a great theme it's a really exciting agenda there's people for all these different organizations and approaches to data-driven you know from movie executives and casting to era it's just really exciting to see the program it's Nate Claire thanks so much I'm coming on the queue thank you great to meet you okay keep it right there everybody we'll be back with our next guest right after this short break well the cube is here at data-driven day one special coverage we'll be right back

>> From Times Square, in the heart of New York City, it's theCUBE. Covering Imagine 2018. Brought to you by, Automation Anywhere. >> Welcome back everybody, Jeff Frick here with theCUBE. We're in Manhattan, New York City, at Automation Anywhere's Imagine Conference 2018. About 1,100 professionals really talking about the future of work bots, and really how automation is gonna help people do the mundane a little bit easier, and hopefully free us all up to do stuff that's a little bit more important, a little higher value. We're excited to have our next guest, he's Craig Le Clair, the VP and Principal Analyst from Forrester, and he's been covering this space for a long time. Craig, great to see ya. >> Yeah, nice to see you, thanks for having me on. >> So, first off, just kind of general impressions of the event? Have you been to this before? It's our first time. >> Yes, I did a talk here last year, so it was a little bit smaller then. There's obviously more people here today, but it's pretty much, I think it was in Brooklyn last year. >> It was in Brooklyn, okay. >> So, this is an upgrade. >> So, RP Robotic Process Automation, more affectionately, probably termed as bots. >> Yeah. >> They're growing, we're seeing more and more time and our own interactions with companies, kind of on the customer service side. How are they changing the face of work? How are they evolving as really a way for companies to get more leverage? >> Yeah, so I'll make one clarification of your sentence, and that's, you know, bots do things on behalf of people. What we're talking to in a call center environment is a chat bot. So, they have the ability to communicate or really, I would say, attempt to communicate with people. They're not doing a very good job of it in my view. But, bots work more in the background, and they'll do things for you, right? So, you know, they're having a tremendous effect. I mean, one of the statistics I was looking at the other day, per one billion dollars of revenue, the average company had about 150 employees in finance and accounting ten years ago. Now, instead of having 120 or 130, it's already down to 70 or 80, and that's because the bots that we're talking about here can mimic that human activity for posting to a general ledger, for switching between applications, and really, move those folks on to different occupations, shall we say. >> Right, right. >> Yeah. >> Well it's funny, Jeff Immelt just gave his little keynote address, and he said, "This is the easiest money you'll find in digital transformation is implementing these types of technology." >> Yeah, it's a good point, and it was a great talk, by the way, by Jeff. But, you know, companies have been under a lot of pressure to digitally transform. >> Right. >> You know, due to really the mobile, you know, mobile peaked around 2012, and that pushed everyone into this gap that companies couldn't really deal with the consumer technology that was out there, right? So then you had the Ubers of the world and digital transformation. So, there's been a tremendous focus on digital transformation, but very little progress. >> Right. >> When we do surveys, only 11% are showing any progress at all. So, along comes this technology, Robotic Process Automation that allows you to build bots without changing any of the back end systems. There's no data integration. You know, there's no APIs involved. There's no big transformation consultants flying in. There's not even a Requirements Document because you're gonna start with recording the actual human activity at a work station. >> Right. >> So, it's been an elixir, you know, frankly for CIOs to go into their boss and say, "You know what, we're doing great, you know, I've just made this invoice process exist in a lot better way." You know, we're on our path to digital transformation. >> And it's really a different strategy, because, like you said, it's not kind of rip and replace the old infrastructure, you're not rewriting a lot of applications, you're really overlaying it, right? >> Which is one of the potential downfalls is that, you know, sometimes you need to move to that new cloud platform. You don't want, to some extent, the technology institutionalizes what could be a very bad process, one that needs to be modernized, one that needs to be blown up. You know, we're still using the airline reservation systems from 1950s, and layers, and layers, and layers and layers built upon them. At some point, you're gonna have to design a new experience with new technology, so there's some dangers with the seduction of building bots against core systems. >> Right, so the other thing that's happening is the ongoing, I love Moore's Law, it's much more about an attitude then the physics of a microprocessor, but you know, compute, and store, and networking, 5Gs just around the corner, cloud-based systems now really make that available in a much different way, and as you said, mobile experience delivers it to us. So as those continue to march on and asymptomatically approach zero and infinite scale, we're not there yet, but we're everyday getting a little bit closer. Now we're seeing AI, we're seeing machine-learning, >> Yes. >> We're seeing a new kind of class of horsepower, if you will, that just wasn't available before at the scale it's at today. So, now you throw that into the mix, these guys have been around 14 years, how does AI start to really impact things? >> It's a fascinating subject and question. I mean, we're, at Forrester, talking about the forces of automation. And, by the way, RPA is just a subset of a whole set of technologies: AI, you mentioned, and AI is a subset of automation, and there's Deep Learning, is a subset of AI and you go on and on, there are 30, 40 different automation technologies. And these will have tremendous force, both on jobs in the future, and on shifting control really to machines. So, right now, you can look at this little bubble we had of consumer technology and mobile, shifting a lot of power to the consumer, and that's been great for our convenience, but now with algorithms being developed that are gonna make more and more decisions, you could argue that the power is going to shift back to those who own the machines, and those who own the algorithms. So, there's a power shift, a control shift that we're really concerned about. There's a convergence of the physical and digital world, which is IOT and so forth, and that's going to drive new scale in companies, which are gonna further dehumanize some of our life, right? So that affects, it squeezes humans out of the process. Blockchain gets rid of intermediaries that are there to really transfer ideas and money and so forth. So, all of these forces of automation, which we think is gonna be the next big conversation in the industry, are gonna have tremendous effect societally and in business. >> Right. Well, there's certainly, you know, there's the case where you just you can't necessarily rescale a whole class of an occupation, right? The one that we're all watching for, obviously, is truck drivers, right? Employs a ton of people, autonomous vehicles are right around the corner. >> Right. >> On the other hand, there's going to be new jobs that we don't even know what they're gonna be yet, to quote all the graduating seniors, it's graduation season, most of them are going to work in jobs that don't even exist 10 years from now. >> Correct, correct, very true. >> And the other thing is every company we talk to has got tons of open reqs, and they can't get enough people to fulfill what they need, and then Mihir, I think touched on an interesting point in the keynote, where, ya know, now we're starting to see literal population growth slow down in developed countries, >> Yes. >> Like in Japan is at the leading edge, and you mentioned Europe, and I'm not sure where the US is, so it's kind of this interesting dichotomy: On one side, machines are going to take more and more of our jobs, or more and more portions of our job. On the other hand, we don't have people to do those jobs necessarily anyway, not necessarily today, but down the road, and you know, will we get to more of this nirvana-state where people are being used to do higher-value types of activities, and we can push off some of this, the crap and mundane that still, unfortunately, takes such a huge portion of our day to day world? >> Yeah, yeah. So, one thought that some of us believe at Forrester, I being one of them, is that we're at a, kind of, neutral right point now where a lot of the AI, which is really the most disruptive element we're talking about here, our PA is no autonomous learning capability, there's no AI component to our PA. But, when AI kicks in, and we've seen evidence of it as we always do first in the consumer world where it's a light version of AI in Netflix. There's no unlimited spreadsheets sitting there figuring out which one to watch, right? They're taking in data about your behavior, putting you in clusters, mapping them to correlating them, and so forth. We think that business hasn't really gotten going with AI yet, so in other words, this period that you just described, where there seems to be 200,000 people hired every month in the ADP reports, you know, and there's actually 50,000 truck driver jobs open right now. And you see help-wanted signs everywhere. >> Right, right. >> We think that's really just because business hasn't really figured out what to do with technology yet. If you project three or four years, our projections are that there will be a significant number of, particular in the cubicles that our PA attacks, a significant number of dislocation of current employment. And that's going to create this job transformation, we think, is going to be more the issue then replacement. And if you go back in history, automations have always led to transformation. >> Right. >> And I won't go through the examples because we don't have time, but there are many. And we think that's going to be the case here in that automation dividends, we call them, are going to be, are being way underestimated, that they're going to be new opportunities, and so forth. The skills mis-match is the issue that, you know, you have what RPA attacks are the 60 million that are in cubicles today in the US. And the average education there is high school. So, they're not gonna be thrown out of the cubicles and become data scientists overnight, right? So, there's going to be a massive growth in the gig economy, and there's an informal and a formal segment of that, that's going to result in people having to patch together their lives in ways they they hadn't had before, so there's gonna be some pain there. But there are also going to be some strong dividends that will result from this level of productivity that we're gonna see, again, in a few years, cause I think we're at a neutral point right now. >> Well, Amara's Law doesn't get enough credit, right? We overestimate in the short-term, and then underestimate the long-term needs affect. >> Absolutely. >> And one of the big things on AI is really moving from this, in real time, right? And all these fast databases and fast analytics, is we move from a world where we are looking in the rear view mirror and making decisions on what happened in the past to you know, getting more predictive, and then even more prescriptive. >> Yes. >> So, you know, the value unlock there is very very real, I'm never fascinated to be amazed by how much inefficiency there still is every time we go to these conferences. (Craig laughs) You know we thought we solved it all at SAP and ERP, that was clearly-- >> Clearly not the case. Funny work to do. >> But, it's even interesting, even from last year, you mentioned that there the significant delta just from year to year is pretty amazing. >> Yes, I've been amazed at the level of innovation in the core digital worker platforms, the RPA platforms, in the last year has been pretty amazing work. What we were talking about a year ago when I spoke at this conference, and what we're talking about now, the areas are different. You know, we're not talking about basic control of the applications of the desktop. We're talking about integration with text analytics. We're talking about comp combining process mining information with desktop analytics to create new visions of the process. You know, we weren't talking about any of that a year ago. We're talking about bot stores. They're out there, and downloadable robots. Again, not talking about last year at all. So, just a lot of good progress, good solid progress, and I'm very happy to be a part of it. >> And really this kind of the front end scene of so much of the development is manifested on the front end, where we used to always talk about citizen developers back in the day. You know, Fred Luddy, who was just highlighted Service Now, most innovative company. That was his, you know, vision of Citizen Developer. And then we've talked about citizen integrators, which is really an interesting concept, and now we're talking about really citizens, or analysts, having the ability via these tools to do integrations and to deliver new kind of work flows that really weren't possible before unless you were a hardcore programmer. >> Yeah, although I think that conversation is a little bit premature in this space, right? I think that most of the bot development requires programming skills today, and they're going to get more complicated in that most of the bot activities today are doing, you know, three decisions or less. Or they're looking at four or five apps that are involved, or they're doing a series of four or five hundred clicks that they're emulating. And the progression is to get the digital workers to get smarter and incorporating various AI components, so you're going to have to build, be able to deal statistically with algorithm developments, and data, and learning, and all of that. So, it's not.... The core of this, the part of it that's going to be more disruptive to business is going to be done by pretty skilled developers, and programmers, and data scientists, and statistical, you know, folks that are going through. But, having said that, you're going to have a digital workforce that's got to be managed, and you know, has to be viewed as an employee at some level to get the proper governance. So you have to know when that digital worker was born, when they were hired, who do they report to, when were they terminated, and what their performance review is. You gotta be doing performance reviews on the digital workers with the kind of dashboard analytics that we have. And that's the only way to really govern, because the distinction in this category is that you're giving these bots human credentials, and you're letting them access the most trusted application boundaries, areas, in a company. So, you better treat them like employees if you want proper governance. >> Which becomes tricky as Mihir said when you go from one bot to ten bots to ten thousand. Then the management of this becomes not insignificant. >> Right. >> So Craig, I want to give you the last word. You said, you know, big changes since last year. If we sit down a year from now, 2019, _ Oh. >> Lord knows where we'll be. What are we gonna talk about? What do you see as kind of the next, you know, 12-month progression? >> You know, I hope we don't go to Jersey after Brooklyn, New York, and-- >> Keep moving. >> I see Jersey over there, but it's where it belongs, you know, across the river. I'm from Jersey, so I can say that. You know, I think next year we're gonna see more integration of AI modules into the digital worker. I think with a lot of these explosive markets, like RPA is, there's always a bit of cooling off period, and I think you're going to see some tapering off of the growth of some of the platform companies, AA, but also their peers and compatriots. That's natural. I think that the area has been a little bit, you know, analysis and tech-industry loves change. If there's no change, there's nothing for us to write about. So, we usually over-project. Now, in this case, the 2.8 billion-dollar market project five years out that I did is being exceeded, which is rare. But I expect some tapering off in a year where there's not a ceiling hit, but that, you know, you end up with going through these more simple applications that can be robotized easily. And now you're looking at slightly more complicated scenarios that take a little more, you know, AI and analytics embedded-ness, and require a little more care, they have a little more opaque, and a little more thought, and that'll slow things down a bit. But, I still think we're on our way to a supermarket and a lot of productivity here. >> So just a little less low-hanging fruit, and you gotta step up the game a little bit. >> I guess you could, you said it much simpler then I did. >> I'm a simple guy, Craig. >> But that's why you're the expert on this panelist. >> Alright, Craig, well thanks for sharing your insight, >> Alright. >> Really appreciate it, and do look forward to talking to you next year, and we'll see if that comes true. >> Alright, appreciate it, take care now. >> He's Craig Le Clair and I'm Jeff Frick. You're watching theCUBE from Automation Anywhere Imagine 2018.

>> Narrator: From around the globe, it's theCUBE with digital coverage of Actifio Data Driven 2020, brought to you by Actifio. >> Hi everybody this is Dave Vellante, full of preview of Actifio Data Driven, and with me is Brian Reagan who is a long time cube alumni, good friend. Brian, awesome to see you thanks for coming on and help us set up Data Driven >> Dave it's always a pleasure to be here, thanks for having me. >> So this is one of our favorite events of the season, not only because it's historically been in Boston, but it's a really good intimate event, lot of customer content. Unfortunately this year, of course everything has gone virtual but tell us about that, what do you guys got planned for Data Driven this year? >> Well again we're delighted to be able to put the show on, in spite of all of the challenges of travel and face to face. As you know from years past, Data Driven has always been sort of by the customers for the customers, very much an event that is driven around understanding how customers are using data strategically, and how Actifio is helping them do that to power their businesses. This year is no different, I think what we've done is we've taken the best of the physical events, which is really facilitating fireside chats and panels of people using our technology to move the business forward with data, but also added a lot of things that frankly are impossible to do when you're strained by a physical event, which is be able to run a series of on demand technical sessions. Our technical tracks are always standing room only, so now we can offer more content, more discreet package content that can be consumed the day of the event and on for a year plus after the event. So we're excited to really sort of mix the best of both worlds virtual and the forums that have worked so well for us in the physical events. >> Well it's like I said I mean, lots of these events are sort of vendor fests, but what you do with Data Driven is you bring in the customer's voice. And I remember last year in theCUBE, we had Holly st. Clair who was with the state of Massachusetts, she was awesome. We had a guest from DraftKings, which was really, really tremendous. Of course, you see what's happening with those guys now just exploding. >> Exactly. >> But we also had a lot of fun, when of course Ash comes on, and all the Actifio folk, but we had Frank Gens on, the first and only time we've ever had him on theCUBE, he's now retired from IDC, I guess semiretired. We had Duplessie on, which was a lot of fun. So it's just a good vibe. >> Yeah, we made a conscious decision to your point not to avoid the traditional vendor fest, and bludgeoning people with PowerPoint throughout the day, and really wanted to make it spin it around, and have the customers tell their stories in their own words, and really talk about the themes that are both common, in terms of challenges, ways that they've addressed those challenges, but also dig into the real implications of when they do solve these challenges, what are the unintended consequences? It's sort of like the... In a lot of ways I think about the journey that customers went through with VMware and with the ability to spin up VMs effortlessly, was a fantastic first step, and then all of a sudden they realized they had all of these spun up Vms that were consuming resources that they didn't necessarily had thought about at the very beginning. I think that our customers as they progress through their journey with Actifio, once they realize the power of being able to access data and deliver data, no matter how big it is, in any form factor in any cloud, there's incredible power there, but there also comes with that a real need to make sure that the governance and controls and management systems are in place to properly deliver that. Particularly today when everything is distributed, everything is essentially at arms length, so that's part of the fun of these events is really being able to hear all of the ways these unique customers are, adding value, delivering value, gaining value, from the platform. >> What's it's interesting you mentioned VMs, it was like life changing when you saw your first VM get spun up and you're like, wow, this is unbelievable, and then it was so easy to spin up. and then you just save VM creep and copy creep. >> Right. >> And you're seeing some similar things now with cloud I mean example is the cloud data warehouses is so easy to spin those things up now. The CFOs are looking at the bill going Whoa, what are we doing here? >> (laughs) >> You're going to see the same thing >> Exactly. >> with containers as you begin to persist containers, you're going to have the same problem. So you guys created the category, it's always a marketing executives dreams to be able to create a category. You guys created the Copy Data Management category, and of course, you've extended that. But that was really good, it was something that you guys set forth and then all the analysts picked up on it, people now use that as a term and it kind of resonates with everybody. >> Right, right. It was bittersweet but also very satisfying to start to see other vendors come out with their own Copy Data Management offerings, and so yes the validating that in fact this is a real problem in the enterprise continues to be a real problem in the enterprise, and by using technologies that Actifio really pioneered and patented quite a bit of foundational technologies around, we're able to help customers address those copy data challenges, those spiraling costs of managing all of these duplicate, physical instances of data. And to your point, to some degree when you're on-prem in a data center and you've already bought your storage array. Okay, I'm consuming 20% more of the Ray or 100% more of the array than I really need to be, but I've already paid for the array. When it comes to cloud, those bills are adding up hourly, daily, weekly, monthly, and those are real costs, and so in many ways cloud is actually highlighting the power and frankly the problem of copy data, far more than the on-prem phenomenon ever did. >> Yeah I was on the phone with a former CIO, COO now of a healthcare organization, and he was saying to me there's a dark side of CapEx to OPEX, which is now that he's a COO he's like really concerned about the income statement and the variability of those costs, and so to your point I mean it's a big issue, the convenience seems to be outweighing some of that concern but nonetheless lack of predictability is a real concern there. >> Absolutely, absolutely. And I think we see that... You mentioned data lakes, and whether you call it a data lake or you just call it a massive data instance, one of the speakers of Data Driven this year is a customer of our Century Data Systems down in Florida. And they have 120 terabyte database that actually they're using, and this is an incredible story that we're excited to have them share with the world during Data Driven. They're using it to help the federal government get better data faster on COVID treatments and the efficacy of those treatments, and so to even consider being able to rapidly access and manage 120 terabyte instance. It breaks the laws of physics frankly. But again with Copy Data Management, we have the ability to help them really extend and really enhance their business and ultimately enhance the data flows that are hopefully going to accelerate the access to a vaccine for us in North American and worldwide, quite frankly. >> That's awesome, that's awesome. Now let's talk a little bit more about Data Driven what we can expect. Of course, the last couple of years you've been the host of Data Driven. They pulled a Ricky gervais' on you >> (Laughs loudly) like get the golden gloves, he's no longer being invited to host, but I think probably for different reasons, but what are some the major themes that we can expect this year? >> Yeah, we were disappointed that we couldn't get Tina Fey and Amy Poehler. >> (laughs quietly) I think we decided that in a virtual construct, the host duties were pretty amenable. So among the many things I talked about Sentry Data Systems and we have many customers who are going to be joining us and telling their stories. And again from accelerating data analytics to accelerating DevOps initiatives, to accelerating a move to the cloud, we're going to hear all of those different use cases described. One of the things that is different this year and we're really excited. Gene Kim sort of the author and noted DevOps guru, author of The Phoenix Project and The Unicorn Project, he's going to be joining us. We had previously intended to do a road show with Gene this year and obviously those plans got changed a bit. So really excited to have him join us, talk about his point of view around DevOps. Certainly it's a hugely important use case for us, really important for many of our customers, and actually registrant's between now and the event, which is September 15th and 16th, we'll get an eCopy an e-book copy of his Unicorn Project book. So we're eager to have people register and if they haven't already read him then I think they're going to be really pleasantly surprised to see how accessible his materials are, and yet how meaningful and how powerful they can be in terms of articulating the journeys that many of these businesses are going through. >> Yeah, I'm glad you brought that up. I'm stucked I have not read that material, but I've heard a lot about it, and when I signed up I saw that, said great I'm going to get the free book. So I'm going to check that out, >> Yeah It's obviously a very, very hot topic. Well Brian, I really appreciate you coming on, and setting up the event. What are the details? So where do I go to sign up? When is the event? What's the format? Give us the lowdown. >> It is September 15th and 16th, actifio.com will guide you through the registration process. You'll be able to create the event based on the content that you're eager to participate in. And again not only on the 15th and 16th, but then into the future, you'll be able to go back and re access or access content that you didn't have the time to do during the event window. So we're really excited to be able to offer that as an important part of the event. >> Fantastic and of course theCUBE will be there doing its normal wall to wall coverage. Of course, this time virtual, and you'll see us on social media with all the clips and all the work on Silicon Angle. So Brian great to see you and we will see you online in September. >> Thanks, Dave. >> All right, and thank you. Go to actifio.com, sign up register for Data Driven, this is Dave Vellante for theCUBE, we'll see you next time. (upbeat music)

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Hi. We're right outside of the Boston Haba. You're watching >> the cube on stew Minimum in. And this is active Geo data driven. 2019 due date. Two days digging into, You >> know, the role of data inside Cos on, you know, in an ever changing world, happy to welcome to the program of first time guests are China Oven countrymen who's a research manager at I. D. C. Coming to us from across the pond in London. Thanks so much for joining us. Pleasure. So tell us a little bit. I d c. We know. Well, you know, the market landscapes, you know, watching what's happening. Thie said it 77 Zita bites that was put up in the keynote. Came came from I D. C. Tells you you're focused. >> Yeah, so I'm part of the data protection and storage research team, But I have, ah, European focus. I covered the Western European markets where data protection is almost off a neurotic interest to us. So a lot of our investment is actually made on the context of data protection. And how do I become data driven without compromising on security and sovereignty and data locality. So that's something that I look at. I'm also part of our broader multi cloud infrastructure team on also develops practice. I'm looking at all these modern new trends from data perspective as well. So it's kind of nice being >> keeping you busy, huh? Yeah. So about a year ago, every show that I went to there would be a big clock up on the Kino stage counting down until gpr went way actually said on the Q. Many times it's like we'll know when GPR starts with lawsuits. Sister and I feel like it was a couple of days, if not a couple of weeks before some of the big tech firms got sued for this. So here we are 2019. It's been, you know, been a while now since since since this launch. How important is GDP are you know what? How is that impacting customers and kind of ripple effect? Because, you know, here in the States, we're seeing some laws in California and beyond that are following that. But they pushed back from the Oh, hey, we're just gonna have all the data in the world and we'll store it somewhere sure will protect it and keep it secure. But but But >> yeah, yeah, so it's suggestive. Here is a game changer and it's interesting you said this big clock ticking and everybody has been talking about it. So when the European Commission >> announced repairs >> coming, organizations had about two years to actually prepare for it. But there were a lot of naysayers, and they thought, This is not gonna happen. The regulators don't have enough resources to actually go after all of these data breaches, and it's just too complicated. Not everyone's going complaints just not gonna happen. But then they realised that the regulators we're sticking to it on towards the end. Towards the last six months in the race to GDP, and there was this helter skelter running. Their organizations were trying to just do some Die Ryan patch of exercise to have that minimum viable compliance. So there they wanted to make sure that they don't go out of business. They don't have any major data breaches when Jean Pierre comes a difference that that was the story of 2018 although they have so much time to react they didn't on towards the end. They started doing a lot of these patch up work to make sure they had that minimum by the compliance. But over time, what we're seeing is that a lot off a stewed organizations are actually using GDP are as to create that competitive differentiations. If you look at companies like Barclays, they have been so much on top of that game on DH. They include that in their marketing strategies and the corporate social responsibility to say that, Hey, you know our business is important to us, but your privacy and your data is much more valuable to us, and that kind of instantly helps them build that trust. So they have big GDP, our compliance into their operations so much and so well that they can actually sell those kind of GPR consultancy services because they're so good at it. And that's what we are seeing is happening 2019 on DH. Probably the next 12 to 18 months will be about scaling on operational izing GDP are moving from that minimum viable compliance. >> Its interest weighed a conversation with Holly St Clair, whose state of Massachusetts and in our keynote this morning she talked about that data minimalist. I only want as much data as I know what I'm going to do. How I'm goingto leverage it, you know, kind of that pendulum swing back from the I'm goingto poured all the data and think about it later. It is that Did you see that is a trend with, you know, is that just governments is that, you know, you seeing that throughout industries and your >> interesting. So there was seven gpr came into existence. There were a lot of these workshops that were happening for on for organizations and how to become GDP. And there was this Danish public sector organization where one of the employees went to do that workshop was all charged up, and he came back to his employer and said, Hey, can you forget me on it Took that organization about 14 employees and three months to forget one person. So that's the amount of data they were holding in. And they were not dilating on all the processes were manual which took them so long to actually forget one person on. So if you don't cleanse a pure data act now meeting with all these right to be forgotten, Andi, all these specific clauses within GPR is going to be too difficult. And it's going to just eat up your business >> tryingto connecting the dots here. One of the one of the big stumbling blocks is if you look at data protection. If I've got backup, if I've got archive, I mean, if I've taken a snapshot of something and stuck that under a mountain in a giant tape and they say forget about me Oh, my gosh, Do I have to go retrieve that? I need to manage that? The cost could be quite onerous. Help! Help us connect the dots as to what that means to actually, you know, what are the ramifications of this regulation? >> Yeah, So I think so. Judy PR is a beast. It's a dragon off regulations. It's important to dice it to understand what the initial requirements are on one was the first step is to get visibility and classified the data as to what is personal data. You don't want to apply policies to all the data because I might be some garbage in there, so you need to get visibility on A says and classified data on what is personal data. Once you know what data is personal, what do you want to retain? That's when you start applying policies too. Ensure that they are safe and they're anonymous. Pseudonym ized. If you want to do analytics at a later stage on DH, then you think about how you meet. Individual close is so see there's a jeep airframe, but you start by classifying data. Then you apply specific policies to ensure you protect on back up the personal data on. Then you go about meeting the specific requirements. >> What else can you tell us about kind of European markets? You know, I I know when I look at the the cloud space, governance is something very specific to, and I need to make sure my data doesn't leave the borders and like what other trends in you know issues when you hear >> it from Jenny Peered forced a lot ofthe existential threat to a lot of companies. Like, say, hyper scale. Er's SAS men does so they were the first ones to actually become completely compliant to understand their regulations, have European data data hubs, and to have those data centres like I think At that time, Microsoft had this good good collaboration with T systems to have a local data center not controlled by Microsoft, but by somebody who is just a German organizations. You cannot have data locality more than that, right? So they were trying different innovative ways to build confidence among enterprises to make sure that cloud adoption continues on what was interesting. That came out from a research was that way thought, Gee, DPR means people's confidence and cloud is going to plunge. People's confidence in public cloud is going to pledge. That didn't happen. 42% of organizations were still going ahead with their cloud strategies as is, but it's just that they were going to be a lot more cautious. And they want to make sure that the applications and data that they were putting in the cloud was something that they had complete visibility in tow on that didn't have too much of personal data and even if it had, they had complete control over. So they had a different strategy off approaching public cloud, but it didn't slow them down. But over time they realised that to get that control ofthe idea and to get that control of data. They need to have that multiple multi cloud strategy because Cloud had to become a two way street. They need to have an exit strategy. A swell. So they tried to make sure that they adopted multiple cloud technologies and have the data interoperability. Ahs Well, because data management was one of their key key. Top of my prayer. >> Okay, last question I had for you. We're here at the active you event. What? What do you hear from your customers about Octavio? Any research that you have relevant, what >> they're doing, it's going interesting. So copy data management. That's how active you started, right? They created a market for themselves in this competition, a management and be classified copy data management within replication Market on replication is quite a slow market, but this copy data management is big issue, and it's one of the fastest growing market. So So So they started off from a good base, but they created a market for themselves and people started noticing them, and now they have kind of grown further and grown beyond and tried to cover the entire data management space. Andi, I think what's interesting and what's going to be interesting is how they keep up the momentum in building that infrastructure, ecosystem and platform ecosystem. Because companies are moving from protecting data centers to protecting centers of data on if they can help organizations protect multiple centers of data through a unified pane of glass, I have a platform approach to data management. Then they can help organizations become data drivers, which gives them the competitive advantage. So if they can keep up that momentum there going great guns, >> Thank you so much for joining us in Cheshire, sharing the data that you have in the customer viewpoints from Europe. So we'll be back with more coverage here from Active EO data driven 2019 in Boston. Mess fuses on stew Minimum. Thanks for watching the Q. Thank you.

>> Narrator: Live, from Miami Beach, Florida it's theCUBE covering UiPathForward Americas. Brought to you by UiPath. >> Welcome back to Miami everybody. You're watching theCUBE, the leader in live tech coverage. I'm Dave Vellante with my cohost Stu Miniman. We got all the action going on behind us. We are seeing the ascendancy of Robotic Process Automation, software robots. one of the leader's in that industry, one of the innovators, Daniel Dines is here, he's the founder and CEO of UiPath. Hot off the keynote, Daniel, thanks for coming on theCUBE. >> Daniel: Thank you for inviting me. >> Dave: You're very welcome, so, the great setup here, the Fontainebleau in Miami's an awesome venue for a conference this size; about 1500 people. In your keynote, you talked about your vision and we want to get into that but, go back to why you started UiPath. >> Daniel: I started UiPath to have joy at work, to do what I like, and to build something big. >> Dave: And you're a Developer, right? I mean you code-- >> Daniel: I am a Software Engineer. >> Dave: I mean, I can tell by the way you're dressed. (laughter) Developer CEO. >> Daniel: Yeah. >> Dave: Yeah, okay, so but you have a vision. You talked about a robot for every person. You mentioned Bill Gates, the PC for every person. I said a chicken for every pot, Harry Truman. What is that vision? Tell us about it. >> Daniel: Well, in our old day they work, we do a lot of menial stuff, repetitive, boring stuff. It's-- that is not human-- it's not human-like. Why not having this robot that we can talk to, we can command and just do the boring stuff for us? I think it's no-brainer. >> Dave: Right. >> Daniel: We just didn't think it's possible. We showed with our technology this is possible, actually. This is an angle of automation that people didn't think it was possible before. >> Dave: Well, so I neglected to congratulate you on your early success, I mean, you said one of your tenants is you're humble. So you got a lot of work to do, we understand that. But you've raised over $400 million to date, you just had a giant raise, we had Carl Eschenbach on in our Palo Alto studios. He was-- he was one of the guys in the round. So that's confirmation that this is a big market, we've pegged it at around a billion dollars today, 10x growth by 2023, so very impressive growth potential. What's driving that growth? >> Daniel: It's all from the customers. When they see it working, it's a "wow," it's different, they won't go back to the same way of delivering work. It's changing how people really work. You see people becoming joyful when we show them the robot, and they say, "I don't need to do this stuff anymore? Wow." Imagine people doing the same reports every day, going through hundreds of page and clicking the same-- this is, this is nirvana. >> Dave: And we saw customers, UnitedHealth was on stage today, Mr. Yamamoto has a thousand robots, Wells Fargo's up there, you had some partners. So you're doing that hard integration work as well. Stu, you noted that the global presence of this company was impressing you. You're thoughts on that. >> Stu: Yeah, absolutely, I mean first of all, company started in Romania, we had-- you know you don't see too many American keynotes where there's a video up there in a foreign language. It's Japanese with English subtitles, you've got customers already starting with a global footprint. What's it like being a founder in a start-up from Europe playing in a global marketplace? >> Daniel: Well, actually it help us to become-- we've been born global. We are one of the first start-ups born global from day one. We've been this company, with Japanese talent, Indian talent, Romanian talent, American talent. And being from this remote part of Europe help us... think big, because really are-- we cannot build this start-up only with Romanians. That's clear, we don't have the pool of talent. So why not just go in global, get the best talent we can and spread global? And we are one of the few companies in the world that has their revenue split equally across the three big continents. >> Stu: Yeah, Daniel, the other thing that struck me-- you're growing the company very fast. We talked about the money, but you said you're going to have over 4,000 employees by 2019. You know, I play a lot in the open source world, it's often small-team, you've got to go marketplace, how come you need so many employees for a software company? Maybe explain a little bit that relationship with a customer, how much you, you're technical people, what they need to do to interact and help them to grow these; is it verticals, you know, what's that dynamic? >> Daniel: Well, first of all, we hire more than 1,000 people in last year alone. We started from 200 and now we are 1,400. We need all these people because this technology is at the intersection of software and services. We need to help our customers scale, and we need to inject a lot of customer success people making our customer successful. My, my way of building a company is customer first. We want to offer this boutique type of approach to our customers, and they are happy. And they-- and we build this trust relationship. This is why we need so many-- We have 2,000 customers. Next year, we have 5,000 customers. We need our people to help them grow. >> Dave: We're going to have Craig Le Clair on a little later. He's the Vice President of Forrester Research. They've done a deep dive in this marketplace in the last couple years now. UiPath has jumped from number three to number one in the Forrester wave, and when you look at that report, really, the feature and function analysis shows you guys lead in a number of places. In listening to your keynote, I discerned several things that I wonder if you could explain for our audience. It sounds like computer vision is a key linchpin to your architecture, and there seems to be an orchestrator and then maybe a studio to enable simple low code, or even no code automations to be developed. Can you describe, so a layperson-- your architecture, and why you've been able to jump into the lead. >> Daniel: Well, we've done everything wrong as a start-up. We spent like seven years building a computer vision technology that-- it was of little use, back then. We did it just because we liked it. And now, this is our powerful weapon, because, what's important for this robot is to be accurate, and to be able to work in any situations. Why our technology works better, is that we do way better the extra mile of automation. 80% of the job anyone can do, even with free software. But the last 20% is where the real issues is. And with the last 20% there is no automation. And we are doing way faster. So all our signal sources-- the fact that we've done something against Lean, against every principal in start-up, we had the lecture in building so many years technology, without even envisioning the use. But when we found the market, and it was a great product market, then we scale the company. >> Dave: There are a couple key statistics that I want to bring up and get your thoughts on. We know that there are now more jobs than there are people to fill those jobs. We also know that the productivity hasn't been increasing, so your vision is to really close that gap through RPA and automation. So your narrative is really that you're not replacing humans, you're augmenting humans, but at the same time, there's got to be some training involved. You guys are making a huge commitment in training. You're going to train a million people, that's the goal, within three years. We have Tom Clancy on next. We're going to ask him how he's going to do that. But talk about that skills gap and how you're embracing re-training. >> Daniel: Well, we realize that at some point that change management, it's kind of the key-- it's the cornerstone of delivering this technology. Because there is inertia, there is fear, and-- if we bring, at the same time, automation and training, it solves this-- that solve this issue. And we have to think big; this is why: one million is a big goal, but we will achieve it because we-- I love my way to think big. I was thinking small for so many years, and thinking big it's like, it's like liberty. You sat down and realize, "Yes, you can." >> Stu: Daniel, we talk a lot about digital transformation. The automation often doesn't get talked, but in big companies; Microsoft, Oracle, SAP, seems a natural fit, I saw some of them are your partners, you came from Microsoft, maybe talk about that dynamic about how some of the, you know, big players that, you know, have the business process applications, how your solution fits with them, you know, are they going to be paying attention to this space? >> Daniel: Well, digital transformation, it's a big initiative for everybody. And RPA, it's actually right now, recognizes the first step in digital transformation. And obviously that if was RPA, AI, big business applications, it's not one single angle, but we covered the last mile of automation. We've covered the impossible, before, before this. And our automation first view of the world is beyond digital transformation because companies will exist after they build for digital transformation. But automation first is a, is a mindset. It's rethinking your operations by applying automation first. >> Dave: You have an open mindset, which is interesting. You even said on stage that, "Look, our competitors are beginning to mimic "some of our features and functions and our approach." And you said, "That's okay." I was surprised by that, especially given your Microsoft background, which was like, grind competitors into the ground. What's changed? Why the open mindset and why do you believe that's the right approach? >> Daniel: Look at Microsoft, Microsoft has changed. This is the-- it's much better, it's-- you feel better as a human. When you can offer something, "This is up, take it, give me feedback." We've been able to build way faster than them, having our open and free community. Open the software-- It gives you more joy as a developer seeing thousands of people than just guarding my little secret just for fear someone will copy it. It's way better. >> Dave: Now, you said on stage that a lot of people laughed at you when you were starting this company, you dream big. Somebody once said, Stu, that, "If you believe you can do it, "or you don't believe you can do it, you're right." "So you got to believe," was one of the things that you said. >> Daniel: That's the first thing. >> Dave: Yeah, so share with the young people out here who are dreaming big, everybody in their early 20's, they're dreaming big. Tell us about your story, your dreams, people who laughed at you, what were they laughing about and how did you power through that? Where did you get your conviction? >> Daniel: Well, first of all, they don't dream big enough. It's very difficult to big dream enough because you have your, you know-- it's the common sense that comes into the picture and it's the fear of other people laughing at you. And we haven't dreamt big enough. For 10-- for the first 10 years, we just wanted to make a good technology, the best technology that we can but that's not big enough. Big enough is change the world, big enough is bring something that makes people life better. This is big enough. If they think making people lives better, that's big enough. Nothing else is big enough. >> Dave: Well I love the fact, Daniel, that your mission-driven; that's clear. You're having some fun. You know this-- these apps are really a lot of fun. Do you still code? >> Daniel: No but I do a lot of software design and review. >> Dave: Okay, so you help, so the coders, they-- how do-- what's that dynamic like? You have-- obviously experienced developer. Do you sort of, tell them which path to go down or which path not to go down? Do you challenge them? What's your style, as a leader? >> Daniel: I challenge them to do things faster, always. They-- I ask them, let's do this feature and they say, "Two month." "No, two days." Why not? And then we go and break that one and it's a lot of conversation but usually we will deliver. Fast-- fast is also a way of being. Fastest company wins, and fast is a-- it's not easy to change the mind. Because you want-- maybe you want to be very organized, very sophisticated. If you are fast, you have to be ready to make mistakes, reverse your decision going, but you will go fast in the end. >> Dave: So that is kind of Steve Jobs-like, set a really challenging goal, and people somehow will figure it out, but culturally, you seem friendlier, nicer. It's not grinding people anymore, it's inspiring them. Is that a fair assessment? >> Daniel: My goal is to have the happiest team employees everywhere. Hap-- I like to be happy. I started this company for the joy of doing what I like, why not, this is, this is what I want for everyone. And we are-- we recently scored in comparably as one of the best company in terms of people happiness. >> Dave: Well congratulations, thanks so much for coming on theCUBE. >> Daniel: Thank you very much for inviting me. >> Dave: Really a pleasure having you. Alright, Stu and I will be back with our next guest. Right after this short break, we're live from UiPath... in Miami, you're watching theCUBE. Stay right there. (electronic music)

>> Announcer: Live from Las Vegas, it's the Cube. Covering Informatica World, 2018. Brought to you by Informatica. >> Okay welcome back everyone, this is the Cube live here in Las Vegas for Informatica World 2018 exclusive coverage of the Cube. I'm John Furrier co-host of the Cube with Jim Kobielus, my co-host this segment and with that we'll keep on continue with the Cube. Our next guest is Vira Shanti who is the chief data officer at Lippo Digital Group, welcome to the Cube. >> Thank you so much, very excited to be here. >> Thank you for coming on, but people don't know before we came on camera, you and Jim were talking in the native tongue. Thanks for coming on. I know your chief data officer, we've got a lot of questions we love these conversations because we love data, but take a minute to explain what you guys are doing, what the company is, what the size is and the data challenges. >> Okay, maybe let me introduce myself first, so my name is Vira, my role is the chief data officer. Responsibility, that actually is cover for the big data transformation for the Lippo group data. Lippo group is actually part of the one of the largest in Indonesia, we serve a middle class for the consumer services, so we are connecting I think more than 120 million of the customers. What's Lippo as a group doing is actually we do many things. We are the largest of the hospital in Indonesia or just super market, we do department stores, coffee shop, cinema, data centers. We on bang as well, news, cable TV, what else? >> You have a lot of digital assets. >> What you do is you drive to any state in Indonesia and you see Lippo everywhere. >> Yeah, education as well, from the kindergarten to the university, that's why it's a lot of diversity of the business, that owned by Lippo. But recently we're endorsing a lot in the digital transformation, so we're releasing a new mobile app, it is called OVO, O, V, O. Actually it's like centralized loyalty E money to providing the priority bills to all the Lippo group customers, so they're not going to maintain their own membership loyalty program, it's going to just like the OVO, so it's not only being accepted by Lippo ecosystem, but also to the external ecosystem as well. We start to engage with the machine partner, we just today sorted like reaching out 30000 machine outlets. >> Let's get Jim's perspective, I want you to connect the dots for me, because the size and scope of data, you talk about deep learning a lot. And let's connect the dots, cuz we've heard a lot of customers here talking about being having data all over the place. How does deep learning, why do you catalog everything? If you've always diverse assets, I'm sure there are different silos. Is there a connection, how are you handling? >> Okay, differently it's not easy job to do, implementing big data for this kind of a lot of diversity of the business, because how to bring all of this data coming from the different source, coming from the different ecosystem to the single analytical platform is quite challenging. The thing is, we also need to learn first about the business, what kind of the business, how they operate, how they run the hospital, how they run the supermarket, how they run the cinema, how they run the coffee shop. By understanding this thing, my team is responsible to transform, not start from the calling the data, cleansing the data, transform the data, then generate the insight. It has to be an action inside. Then we also not only doing the BI things, but also how from their data we can developing the analytical product on top of the technology big data, that we own today. What we deliver is actually beyond the BI. Of course we do a lot of thing, for example, we really focusing in doing the customers 360 degree profile, because that's the only reason how we really can understand out customers. Today, we have more than 100s of customer attribute teaching for individual customers. I can understand what's your profile for the purchasing behaviors, what kind of the product, that you like. Let's say for the data coming from the supermarket, I know what's your brands, your favorite, whether you're spending is declining. How you spend your point, part of the loyalty program. Then many things, so by understanding very deep these, that we can engage with customers in the better way in providing the new customer experience, because we not only let's say providing them with the right deals, but also when would be the right time, we should connect to them providing something, that they might need. This is the way how from the data we try to connect with our customers. >> Yeah, provided more organic experience across the entire portfolio of Lippo brands throughout the ecosystem. It doesn't feel to the customer and so it isn't simply a federation of brands, it's one unified brand in some degree from the customer's point of view delivering value, that each of the individual components of the Lippo portfolio may not be able to provide. >> Yes, yes, so many things actually we can do on top of that 360 degree of the customers. Our big data outcome in the form of the API. Why it has to be in the API, because when we interact with the customer, there could be unlimited customer touch point to call this API. It could be like the mobile apps after smart customer touch point or could be the dashboard, that we develop for our Lippo internal business. Could be anything or even we can also connect to the other industry from the different business, then how we can connect each other using that big data API, so that's why-- >> Is it an ecosystem, isn't that one API, or it's one API, when unified API for accessing all the back end data and services? >> For something like this, there are to type of the API, that we develop, number one is the API, that belong to the customer 360 degree. Every entry would then attach to your profile and say we can convert it to the API. Let's say smart apps, as part of customer touch point, for example like OVO, we would like to engage with our customers, meaning, that the apps can just designing their online business orchestration, then calling a specific API by understanding let's say from the point of view of loyalty or product preference, that you like, so that then what kind of offers, that we need to push to the customer touch point general using the OVO apps. Or even let's say other supermarket have their on apps, so the apps can also following our API based on their data to understand what kind of the brand or the preference probably they like. Let's run in their apps, when the customer connects, it's going to be something, that really personalized. That's why it's in order to manage the future, actually it's very important for us to deliver this big data outcome in the form of the API. >> It scales too, not a lot of custom work, you don't have to worry about connecting people and making sure it works, expose an API and say, there it is and then. >> Different countries, in terms of privacy in the use of personally identifiable information, different countries and regions have their own different policies and regulations, clearly the European union is fairly strict, the European union with GDPR coming along, the US has its own privacy mandates, in Indonesia, are there equivalent privacy regulations or laws, that we require for example. You ask the customers to consent to particular uses of their data, that you're managing with your big data system, that sits behind OVO. Is that something in your overall program, that you reflect? >> Yes, there are some regulation in Indonesia governed by the government, they'll call having their own regulation, but we let's say part of the thing, that, yes, there is a specific regulation. But regulation for the retail is not really that clear yet for now, but we put ourself in the higher restricted regulation, that we put in place as part of our data protection, part of our data governance compliance as well. If until we do this demonetization or consolidating this data, there is no data, that's being shared outside the entity of the organization. Because let's say, when we do that demonetization everything's done by system to system, when it's called the API, so there is no hands off for other customer in individual data. Let's say if our partner FMCG digital agency or even advertiser, future wise they would like to call our API, what they can see, but that target lead of the customers, that they would like to connect is actually not individual of the data. It's going to be in the aggregated format. Even though many segmentation, that we can deliver is not going to expose every individual customer. >> You have a lot of use cases, that you can handle, because of the control governance piece. How about, by the way, that's fantastic and I know how hard it must be the challenge, but you have it setup nicely. Now that the setup with Informatica and the work you're doing, how are you interfacing with developers, cuz now you have the API. Is it just API based, are you looking at containers, kubernetes, clout technologies? Are you guys looking at that down the road or is that part of the, or is it just expose the API to the developers? >> For today, that actually who's going to consume our API actually? Definitely it's going to be the ecosystem of the Lippo internals, how the customer touch point can leverage the API. Then for the external, for example, like FMCG, the digital agency, when they call our API, usually it's like they can subscribe, there could be some kind of the business model divine there, but once again, like I mentioned to you, let's say it's not going to reveal any individual customer information, but the thing is, how we deliver this API things? We develop our own API system, we develop our API gateway, in simple thing, that actually how to put the permission or grant the access of any kind of digital channel, when they consumer our API and what kind of subscription meta? What we did for the big data actually is not really into, we investing a lot of technology in place for us to use. The thing, that makes my team so exciting about this transformation, because we like to create something, that's we create our own API gateway. We create some analytic product on top of the technology, that we have today. >> When they subscribe to the API, you're setting policy for the data, that they can get and you're done. >> Something like that. >> You automated that. Cool, well we see a lot of AI, any machine learning in your future, you, guys, doing any automation, how are you guys thinking about some of the tools we've been seeing here at the show around automation and AI, Clair, you tapping into any of the goodness? >> Yes, if everybody like to talk what AI right? >> John: You got API, you're good, you don't need anything. >> Many organization, when they're really implementing big data, sometimes they start jumping, I need to start doing the AI things. But from our point of view, yes, AI is very important, definitely we will go there, but for now, what's important for us is how we really can bring the data to single analytical platform, developing that 360 degree customer profile, because we really need to understand our customer better. Then thinking about how we can connect with them, how we can bring the new experience and especially at the right time. >> Actually let me break down AI, cuz I cover AI for Wiki bond, it's such an enormous topic, I break it down in specific things, like for example, speech recognition for voice activated access to digital assistance, that might be embedded in a mobile phones. Indonesia is a huge diverse country, it's an acapela, you have many groups living under the unitary national structure, but they speak different languages, they have different dialects, do you use or are you considering speech recognition? How you would tailor speech recognition in a country, that is so diverse as Indonesia. Is that something an application of AI you're considering using in terms of your user interface? >> Okay, for now we not really into there yet, because you are definitely correct. Developing that kind of library for Indonesia, because different dialect, different accent, it's tough, so the AI things, that we're looking for is actually going to be product recommendation engine. Because you know, let's say, that a lot of things on top of this customer 360 degree, that we can do, right? Because meaning it's going to open unlimited opportunity how I can engage to the customers, what kind of the right offer. Because there's a lot of brand owners, like FMCG, that they would like to connect, also getting in touch, reach out our customers. By developing this kind of product recommendation engine, let's say using the typical machine learning, so we can understand when we introduce this thing, customer like it, introduce that thing, they don't like it. >> Let me ask the next logical question there, it's such a big diverse country, do you, in modeling the customer profile, are you able to encode cultural sensitivities, once again, a very diverse country, there's probably things you could recommend in terms of products to some peoples, that other people might find offensive or insensitive, is that something, that in terms of modeling the customer, you take into consideration? It doesn't just apply to Indonesia, it applies here too or anywhere else, where you have many people. >> Of course can to do that the modeling, but we're doing right now, let's say once again, speaking about the personalized offer, from that point of view, what we see is to create the definition based on customer spending power first, buying power, we need to understand, that this customer's actually in which level of the buying power. By understanding this kind of buying power level, then we really can understand, that should we introduce this kind of the offers or not. Because this is too expensive or not. Because customer spending level can be also different. Let's say when our customers spend in our supermarket, maybe it's going to medium spending level, but let's say when they spend their money to purchase the coffee, maybe it's regular basis, so it's more spending. Could be different spending, so we also need to learn this kind of thing, because sometimes the low spending or medium spending or high spending, sometimes it's not something, that we put in the effort level for everything, sometimes it could be different. This is the thing, that also very exciting for us to understand this kind of spending, buying power. >> Great to have you on the Cube, thanks for coming, so I got to ask you one final question. I heard you were in an honorary Informatica innovation award honoree, congratulations. >> Thank you. >> What advice would you have for your peers, that might want to aspire to get the award next year? >> The thing is, our big data journey just start last year. Really start from the zero, so when yesterday we get an award for the analytics, so actually what we really focus on to do something, that actually is very simple. Some organization, when they're implementing big data sometimes they would like to do everything in the phase one. What we're planning to do is number one, how to bring the data very fast, then understand what kind of value of the data, that we can bring to the organization. Our favorite one is developing the customer 360 degree profile, because once you really understand your customer from any point of view, it's going to open unlimited opportunities how you can engage with your customers, it also open another opportunity how you can bring another ecosystem to our business to engage with our customers, that one point of view is already opening a lot of thing, huge. Either that thinking what would be the next step. Of course, that API is going to simplify your business in the future scale so on. That's becoming our main focus to allow us to deliver a lot of quick low hanging effort at the same time. I think that's a thing, that makes us really can, within a short period of time, can deliver a lot of things. >> The chief data officer at Lippo digital group, thanks for sharing your story, it's the Cube, we're here live in Las Vegas. They're going to be bonding here talking about all the greatness going on there. This is the Cube here in Las Vegas, stay with us for continuing day two coverage of Informatica world 2018, we'll be right back.

>> Announcer: Live from San Francisco, it's The Cube covering Informatica World 2017. Brought to you by Informatica. >> Okay, welcome back everyone. We are here live in San Francisco for The Cube's exclusive coverage of Informatica World 2017. I'm John Furrier, this is Siliconangle's flagship program, we go out to the events and (he mumbles). My next guest is Jitesh Ghai who's the Vice President General Manager of data quality and governance for Informatica. Welcome to The Cube, thanks for joining us today. >> Happy to be here, John. Pleasure. >> So, two things right out of the gate. One, data quality and governance, two of the hottest topics in the industry, never mind within Informatica. You guys are announcing a lot of stuff, customers are pretty happy, you got a solid customer base. >> That's right. >> Product's been blooming, you got a big brand behind you now. This is important. There's laws now in place coming online in 2018, I think it's the GDPR. >> That's right. >> And there's a variety of other things, but more importantly customers got to get hold of their data. >> That's right. >> What's your take and what are you announcing here at the show? >> Well, you know, from a data governance and compliance and overall quality standpoint, data governance started off as a stick, a threat of regulatory pressure, but really the heart of what it is is effective access to and consumption of data, trusted data. And through that exercise of the threat of a stick, healthy practices have been implemented and that's resulted in an appreciation for data governance as a carrot, as an opportunity to innovate, innovate with your data to develop new business models. The challenge is as this maturation in the practice of data governance has happened there's been a realization that there's a lot of manual work, there's a lot of collaboration that's required across a cross-functional matrixed organization of stakeholders. And there's the concept of ... >> There's some dogma too, let's just face it, within organizations. I got all this data, I did it this way before. >> Right. >> And now, whoa, the pressure's on to make data work, right, I mean that's the big thing. >> That's exactly right. So, you collaborate, you align, and you agree on what data matters and how you govern it. But then you ultimately have to stop documenting your policies but actually make it real, implement it, and that's where the underlying data management stack comes into place. That could be making it real for regulatory, financial regulations, like BCBS 239 and CCAR, where data quality is essential. It could be making it real for security related regulations where protection is essential, like GDPR, the data protection regulation in the EU. And that's where, Informatica is launching a holistic enterprise data governance offering that enables you to not just document it, or as one CDO said to me, "You know, at some point you've got to stop talking about it, "you actually have to do it." To connecting the conceptual, the policies, with the underlying physical systems, which is where intelligent automation with the underlying data management portfolio, the industry-leading data management portfolio that we have, really delivers significant productivity benefits, it's really redefining the practice of data governance. >> Yeah, most people think of data as being one of those things, it's been kind of like, whether it's healthcare, HIPAA old models, it's always been an excuse to say no. "Whoa, we don't do it that way." Or, "Hey." It's kind of become a no-op kind of thing where, "No, we don't want to do any more than data." But you guys introduced CLAIR which is the acronym for the clairvoyant or AI, it's kind of a clever way to brand. >> That's right. >> That's going to bring in machine learning augmented intelligence and cool things. That only, to me, feels like you're speeding things up. >> That's exactly right. >> When in reality governance is more of a slowdown, so how do you blend the innovation strategy of making data freely available ... >> Right. >> ..and yet managing the control layer of governance, because governance wants to go slow, CLAIR wants to go fast, you know. Help me explain that. >> Well, in short, sometimes you have to go slow to go fast. And that's the heart of what our automated intelligence that CLAIR provides in the practice of data governance, is to ensure that people are getting access to, efficient access to trusted data and consuming it in the right context. And that's where you can set, you can define a set of policies, but ultimately you need those policies to connect to the right data assets within the enterprise. And to do that you need to be able to scan an entire enterprise's data sets to understand where all the data is and understand what that data is. >> Talk about the silver bullet that everyone just wants to buy, the answer to the test, which is ungettable, by the way, I believe, we just had Allegis on, one of your customers, and their differentiation to their competition is that they're using data as an asset but they're not going all algorithmic. There's the human data relationship. >> Absolutely. >> So there's really no silver bullet in data. You could use algorithms like machine learning to speed things up and work on things that are repeatal tasks. >> Right. >> Talk about that dynamic because governance can be accelerated with machine learning, I would imagine, right? >> Absolutely, absolutely. Governance is a practice of ensuring an understanding across people, processes and systems. And to do that you need to collaborate and define who are the people, what are your processes, and what are the systems that are most critical to you. Once you've defined that it's, well, how do we connect that to the underlying data assets that matter, and that's where machine learning really helps. Machine learning tells you that if you define customer id as a critical data element, through machine learning, through CLAIR, we are able to surface up everywhere in your organization where customer id resides. It could be cmd id, it could be customer_id, could be customer space id, cust id. Those are all the inferences we can make, the relationships we can make, and surface all of that up so that people have a clear understanding of where all these data assets reside. >> Jitesh, let's take a step back. I want to get your thoughts on this, I really want you to take a minute to explain something for the folks watching. So, there's a couple of different use cases, at least I've observed in a row and the wikibon team has certainly observed. Some people have an older definition of governance. >> Right. >> What's the current definition from your standpoint? What should people know about governance today that's different than just last year or even a few years ago, what's the new picture, what's the new narrative for governance and the impact to business? >> You know, it's a great question. I held a CDO summit in February, we had about 20 Chief Data Officers in New York and I just held an informal survey. "Who implements data governance programs "for regulatory reasons?" Everybody put their hand up. >> Yeah. >> And then I followed that up with, "Who implements data governance programs "to positively affect the top line?" and everybody put their hand up. That's the big transition that's happened in the industry is a realization that data governance is not just about compliance, it's also about effective policies to better understand your data, work with your data, and innovate with your data. Develop new business models, support your business in developing those new business models so that you can positively affect the top line. >> Another question we get up on The Cube all the time, and we also observe, and we've heard this here from other folks at Informatica and your customers have said, getting to know what you actually have is the first step. >> Right. >> Which sounds counter-intuitive but the reality is that a lot of folks realize there's an asset opportunity, they raise their, hey, top line revenue. I mean, who's not going to raise their hand on that one, right, you get fired. I mean, the reality is this train's coming down the tracks pretty fast, data as an input into value creation. >> That's exactly right. >> So now the first step is oh boy, just signed up for that, raise my hand, now what the hell do I have? >> Right. >> How do you react to that? What's your perspective on that? >> That's where you need to be able to, google indexed the internet to make it more consumable. Actually, a few search engines indexed the internet. Google came up with sophistication through its page-ranking algorithm. Similarly, we are cataloging the enterprise and through CLAIR we're making it so that the right relevant information is surfaced to the right practitioner. >> And that's the key. >> That is the key. >> Accelerating the access method, so increase the surface area of data, have the control catalog for the enterprise. >> That's right. >> Which is like your google search analogy. A little harder than searching the internet, but even google's not doing a great job these days, in my opinion, I should say that. But there's so many new data points coming in. >> That's right. >> So now the followup question is, okay, it's really hard when you start having IOT come in. >> That's right. >> Or gesture data or any kind of data coming in. How do you guys deal with that? How does that rock your world, as they say? >> And that's where effective consumption of data permeates across big data, cloud, as well as streaming data. We have implemented, in service to governance, we've implemented in-stream data quality rules to filter out the noise from the signal in sensor data coming in from aircraft subsystems, as an example. That's a means of, well, first you need to understand what are the events that matter, and that's a policy definition exercise which is a governance exercise. And then there's the implementation of filtering events in realtime so that you're only getting the signal and avoiding the noise, that's another IOT example. >> What's your big, take your Informatica hat off, put your kind of industry citizen hat on. >> Mm-hm. >> What's your view of the marketplace right now? What's the big wave that people are riding? Obviously, data, you could say data, don't say data 'cause we know that already. >> Sure. >> What should people, what do you observe out there in the marketplace that's different, that's changing very rapidly? Obviously we see Amazon stock going up like a hockey stick, obviously cloud is there. What are you getting excited about these days? >> You know, what I'm excited about is bringing broad-based access of data to the right users in the right context, and why that's exciting is because there's an appreciation that it's not the analytics that are important, it's the data that fuels those analytics that's important. 'Cause if you're not delivering trusted, accurate data it's effectively a garbage in, garbage out analytics problem. >> Hence the argument, data or algorithms, which one's more important? >> Right. >> I mean data is more important than algorithms 'cause algorithms need data. >> That's exactly right and that's even more true when you get into non-deterministic algorithms and when you get into machine learning. Your machine learning algorithm is only as good as the data you train it with. >> I mean look, machine learning is not a new thing. Unsupervised machine learning's getting better. >> Right. >> But that's really where the compute comes in, and the more data you have the more modeling you can do. These are new areas that are kind of coming online, so the question is, to you, what new exciting areas are energizing some of these old paradigms? We hear neural nets, I mean, google's just announced neural nets that teach neural nets to make machine learning easier for humans. >> Right. >> Okay. I mean, it has a little bit of computer science baseball but you're seeing machine learning now hitting mainstream. >> Right. >> What's the driver for all this? >> The driver for all this comes down to productivity and automation. It's productivity and automation in autonomous vehicles, it's productivity and automation that's now coming into smart homes, it's productivity and automation that is being introduced through data-driven transformation in the enterprise as well, right, that's the driver. >> It's so funny, one of my undergraduate computer science degrees was databases. And in the '80s it wasn't like you went out to the tub, "Hey, I'm a databaser." (He mimics uncertain mumbling) And now it's like the hottest thing, being a data guy. >> Right. >> And what's also interesting is a lot of the computer science programs have been energized by this whole software defined with cloud data because now they have unlimited, potentially, compute power. >> Right. >> What's your view on the young generation coming in as you look to hire and you look to interview people? What are some of the disciplines that are coming out of the universities and the masters programs that are different than it was even five years ago? What are some trends you're seeing in the young kids coming in, what are they gravitating towards? >> Well, you know, there's always an appreciation of, a greater appreciation for, you know, the phrase I love is, "In god we trust, all others must have data." There's an increasing growing culture around being data-driven. But from a background of young people, it's from a variety of backgrounds, of course computer science but philosophy majors, arts majors in general, all in service to the larger cause of making information more accessible, democratizing data, making it more consumable. >> I think AI, I agree, by the way, I would just add, I think AI, although it is hyped and I don't really want to burst that bubble because it's really promoting software. >> Right. >> I mean, AI's giving people a mental model of, "Oh my god, some pretty amazing things are happening." >> Sure. >> I mean, autonomous vehicles is what most people point to and say, "Hey, wow, that's pretty cool." A Tesla's much different than a classic car. I mean, you test-drive a TESLA you go, "Why am I buying BMW, Audi, Mercedes?" >> Right, exactly. >> It's a no brainer. >> Right. >> Except it's like (he mumbles), you got to get it installed. But, again, that's going to change pretty quickly. >> At this point it's becoming a table sticks exercise. If you're not innovating, if you're not applying intelligence and AI, you're not doing it right. >> Right, final question. What's your advice to your customers who are in the trenches, they raise their hand, they're committed to the mandate, they're going down the digital business transformation route, they recognize that data's the center of the value proposition, and they have to rethink and reimagine their businesses. >> Right. >> What advice do you give them in respect to how to think architecturally about data? >> Well, you know, it all starts with your data-driven transformations are only as good as the data that you're driving your transformations with. So, ensure that that's trusted data. Ensure that that's data you agree as an organization upon, not as a functional group, right. The definition of a customer in support is different from the definition of a customer in sales versus marketing. It's incredibly important to have a shared understanding, an alignment on what you are defining and what you're reporting against, because that's how you're running your business. >> So, the old schema concept, the old database world, know your types. >> Right. >> But then you got the unstructured data coming in as well, that's a tsunami IOT coming in. >> Sure, sure. >> That's going to be undefined, right? >> And the goal and the power of AI is to infer and extract metadata and meaning from this whole landscape of semi-structured and unstructured data. >> So you're of the opinion, I'm sure you're biased with being Informatica, but I'm just saying, I'm sure you're in favor of collect everything and connect the dots as you see fit. >> Well ... >> Or is that ...? >> It's a nuance, you can't collect everything but you can collect the metadata of everything. >> Metadata's important. >> Data that describes the data is what makes this achievable and doable, practically implementable. >> Jitesh Ghai here sharing the metadata, we're getting all the metadata from the industry, sharing it with you here on The Cube. I'm John Furrier here live at Informatica World 2017, exclusive Cube coverage, this is our third year. Go to siliconangle.com, check us out there, and also wikibon.com for our great research. Youtube.com/siliconangle for all the videos. More live coverage here at Informatica World in San Francisco after this short break, stay with us.

>> Narrator: Live from San Francisco it's The Cube covering Informatica World 2017, brought to you by Informatica. >> Okay, welcome back everyone we're here live in San Francisco for the Cube's exclusive coverage of Informatica World 2017. I'm John Furrier of SiliconAngle Media. My cohost, Peter Burris, head of research at SiliconAngle Media as well as the general manager of Wikibon.com, Wikibon research, check it out. Some great research there on IoT, big data, and certainly cloud computing. Our next guest is Graeme Thompson, Executive Vice President and Chief Information Officer for Informatica, great to see you, welcome back to the Cube. >> Nice to see you, John. >> Conference here, lot of customers, you've got an executive summit, dinner last night, you're kind of like the sounding board, they go to you for the checkpoint, hey, does this story jive, what's going on internally, 'cause you're living through a transformation as well at Informatica. Your customers are going through a transformation as well. We're at this tipping point. What's your take so far of the conference, and is that still the case? Anything you'd like to share on that would be great. >> Yeah, I mean we're proud to have some of the world's best companies using our products to do meaningful and important things. And the scale that some of these companies are doing it at is just staggering. I met with someone last night at dinner and, at Allegis, the talent management organization, and they process and keep up to date 55 million resumes every day. And they extract the metadata from those resumes to match the right candidate to the right job. And you know, that's interesting for them as a company but the societal impact of that is significant. Imagine, I mean we're all starved for talent, and you're matching the right talent with the right opportunity more often than not, using the intelligence of the data, it's pretty interesting. And then of course, I know you had Andrew McIntyre from the Cubs on yesterday, I mean how can you not love that story of how an organization as great and renown as the Cubs is using data to transform it's business operation. It's really amazing. >> We had Bruce Chizen on who's Executive Chairman of the Board of Informatica, was on the board at Oracle, but Peter asked him an interesting question that I'll ask you. What's your definition of strategic data management? >> That's a good one, so the way I define it is, if the basis of your competition is on digital assets compared to physical assets. So we're no longer dealing with plant or machinery or even capital, it's digital assets. If that is the basis of your competition, then the data that you rely on is the very foundation of that. And then it becomes strategic just like money is strategic. And the access to talent is strategic. The ability to leverage the data within your company, about your company, is strategic, and you have to be able to do it on-prem, you have to be able to do it on the cloud, you have to be able to do it in the real world where most of us live, which is in both worlds. And that to me, that's what makes it strategic. >> But let me build on that Graeme, 'cause in many respects the whole concept of digital transformation is, oh let me step back. One of the premises of business is to try to reduce what's known in financial or economic worlds as an asset specificity. So traditionally we've looked at assets and said, this asset's going to be applied to that use, and this asset's going to be applied to that use, and if it's the use isn't needed or it's not being applied, you lose the value of the asset. One of the basic premises of digital business and business generally is how to we reduce asset specificity, and data let's us do that by turning an aircraft engine into a service, we have transformed the role that that asset plays in our customer's business. So you're absolutely right, it's the ratio of physical to digital assets, but all businesses have to find ways to reduce their asset specificity by adding digital on top of it so they can appropriate that asset to a lot of new purposes. Do you agree with that? >> Absolutely, so take, so I know you talked to Sally about the data leak. So take a user case like customer support. Who in a software company knows more about the customer, what product their running, what version of product their running, what they're using it for because of the connectors they have. Nobody in the company knows more about that than the customer support organization. But that asset, the most profitable use of that information, may be in marketing, because then we can help our customers adopt something more quickly, we can help them get value from it more quickly. And it helps us because it helps us focus our R&D effort where the customers are really using the product instead of having to guess. So I think you're spot on, if you can remove the constraint on the asset to be for who paid for it, for one particular purpose and make it available to the entire enterprise and outside the enterprise, then you really start to see the value. >> The thing that you mentioned about digital assets Peter, and the Wikibon team talk about this all the time in their research, digital assets, is the data. Whether it's content or whatever. Certainly we're in the content business, but... >> Peter: Well digital assets are data. >> Are data, exactly, and whether it's content or whatever aspect it is. So I've got to ask you... >> Software, software is a digital asset. >> Data is at the center of it all. So I've got to ask you, there's been a lot of artificial intelligence watching going on in the industry. I call it augmented intelligence because it's really not yet artificial by the strictest, purest definition, but machine learning is very relevant. We talked about IoT when you were last in our studio. How is it impacting your business and customer's business? Because that's the real proof in the pudding, if you will. And customers are trying to sift through the BS that they're hearing from other folks. I'm not saying that you guys are saying BS, but what's the acid test? How do you differentiate between smokescreen and real deal? >> I think it comes down to, like any other technology investment, is what is the business outcome that it generated? So if you're trying to... So humans make mistakes, if you're trying to eliminate human error from a process, a machine can execute that process more repeatably and more accurately than a human. It's not about reducing cost, that's only semi-interesting. It's about enabling outcomes that weren't possible before. So you think about healthcare industry. Everyone talks about self-driving cars and how safer it'll be if the cars aren't dependent on a human, but one thing I read recently is we kill more people in the US by prescribing the wrong drug or the wrong dosage than we do on the roads. So humans work hard, but they make mistakes. If we can have the machine do that job because a human can tell it how to do the job and it can learn over time, then you can eliminate that error. And we're able to do things that we can only imagine. >> Machines rarely get tired, they rarely lose attention, blah blah blah blah blah, and it's all those things, and that's where the augmentation is. And there will be the other forms of artificial intelligence, the algorithms have been around for a long time. The hardware now can support it, and the data is being generated to apply it. >> The data's available and the cost of compute is approaching zero. So we're able to do things that the government could only do before. >> Graeme, I want to get your thoughts on data integration. Certainly we saw yesterday the news with Google Spanner. You guys were one of three companies that was early on, before they announced their general release of Spanner Worldwide, the attributed database, horizontally scaled database. Big deal, but you guys were also on the front end of that as it says in their blog post, and you guys are really strong at data integration. What are some of the challenges that the customers face with integration? What are the key things? Because that seems to be, whether you go multi-cloud or hybrid-cloud today, which is a gateway to multi-cloud, which is happening pretty fast, data integration is pretty important. >> Yes, so as a CIO this is something that is a very hot topic for me, and it's not a new hot topic, it was a hot topic 15 years ago when we went nuts and deployed all these client server applications because they were cheap and easy. And then you had to think about, oh these different disconnected applications don't serve an end-to-end process anymore, now we have to stitch them all together. That was hard, but it was all on-prem and you had access to it all. >> Peter: It was all programed. >> Right, whereas now, like you said you've got Salesforce, you've got Workday, you've got Great People, you've got your on-prem stuff, you've got applications that you're hosting on someone's PAS cloud and the IAS cloud and the SAS cloud, but to execute an end-to-end business process to generate an outcome you have to tie it all together. So instead of thinking about... >> John: And it's not on-prem so you can't touch it, and it's not on, you don't have it. >> Right so you can't hand code that, you could, but I would argue that that would be an unintelligent way to do it, which is where Microservices API has come in. So you can leverage the R&D efforts that the great software vendors like Salesforce create for us. And then you use Microservices to plug into that instead of having an army of people hand-coding interfaces, which is what we used to do 15 years ago. >> That's the human error point. I mean, it could be spaghetti code, all kinds of errors could happen. >> But also the maintenance of that is just virtually impossible given the speed and the fact that human beings are now thinking about new ways of doing things. You just can't keep up with that. >> I mean the coding thing's a big deal. We used to call it, back in the day, spaghetti code cause it's like all this integrated purpose-built coding for one purpose to glue it together. >> Right and then you change one data element and you have to rewrite or retest the whole thing. >> John: A guy leaves or a girl leaves, it's a nightmare, right? With APIs and Microservices you're decoupling that. That's kind of what I think you're getting at, right? >> Exactly, and that's what the whole iPass space is about. You can decouple the user experience from the data and just have, what does a user have to do, and then Microservices and APIs will take care of the work behind the scenes between the applications and that really lets... There's this concept of a citizen integrator. So 15 years ago, it was kind of a modern thought to have business people write reports. I think it won't be long before we'll be able to give the business teams the ability to do integration between applications without depending on me. >> I was talking with a young developer the other day and I'm like, yeah you know your coding is like me doing PowerPoints. They're like, what do you mean, it's so easy. No, it's not that easy. >> Well we've been building macros, good or bad, inside for example things like Excel for a long time and one of the primary drivers, in fact of a lot of the BI stuff, was citizen coders building macros and said I need the data to make my little macro run. Now I don't want to say that that is... That's not what we're talking about, we're talking about something that's considerably more robust where we can be very very creative in thinking about how we might use the data. And then being able to discover it and find it and very quickly and with a low-code orientation being able to make the actual application happen that has consequential impact in the marketplace. So Graeme, you're in a company that's trying to help customers move through some of these transitions. You're in a crucial role because we know where the data is, we know how to integrate it. >> Graeme: You did? >> Well we're discovering where the data is, we have tools that's going to help us, we're learning how to integrate it. But one of the big challenges is to get the business to adopt new orientations to the role that data's going to play. That to me is one of the key roles of the CIO, having worked with a lot of CIOs over the years. For a very very simple example, agile development does not line up with annual budget finance. How are you with Informatica helping to acculturate executive teams to think through new processes, new approaches to doing these things so that the business is better able to use the data so that consequential action happens as these concepts of these great insights that you're generating? >> So the whole change in management effort is a huge and complex thing to overcome. But I have a personal passion about making sure that you always remind people why they're doing it. Too often as product people or technologists, we get into the how and the what and we forget the why. And as soon as it gets difficult people abandon because it starts to get too hard, it starts to get painful, and if they've lost sight of the big why they're not going to role their sleeves up and gut it out and get through the process. So that's the first thing you have to do is remind them that the prize at the end is worth the pain. And it will be painful because no longer are you optimizing just your function. You have to think about what happens upstream from you, what happens downstream from you, and try and optimize things at the enterprise level. And that's not how most people were brought up. It's not how their measured, it's not how their compensated, but that's what's really required if you're going to make that transformation I think end-to-end. >> But it's also, even our language, we talk about innovation in this industry as though it was synonymous with just creating something new. Certainly our research very strongly shows that there's a difference between inventing something which is an engineering act and innovating around something which is a social act. Exactly what you just said. How do we get people to adopt things and change behaviors and fully utilize something and embed it within their practices so that we get derivative innovation and all of the other stuff that we're looking for? >> Yeah there's no easy recipe. People are different so people require a different story in order to have them buy in. Some people are loss-framed people, where you got to explain here's what's going to be bad if you don't do this. Other people are gain-framed people where you can say if we can accomplish this, we'll be able to do these great things. And it would be great if everyone was the same and one story worked for everyone, but it doesn't. So it's almost a feet on the street. Go talk to people and just keep reminding everyone why you're doing this and why it's going to be worth it. >> Peter: A little bit of behavioral economics there. >> John: Graeme I want to ask you one final question. You mention client server and how it was easy on-prem in the old days, get your arms around things, which is the IT practice, you know? That's the way it was done. In the cloud, a little bit more complex. But to take that a little step further, I want to get your thoughts on something. You lived through the world of server sprawl. More servers, more glue, you get your arms around it but then it got bloated, IT got bloated. And that's one of the catalysts for going to the cloud is efficiencies, bottom-line costs. But now, top line revenue now is a mandate. So now we have SAS sprawl. So with APIs, a little bit more security concern, but your thoughts on the now we have a SASification happening or API economy. So you have a lot more APIs, there's Microservices coming on the scene, it's emerging very quickly, still emergent. Embryonic some will say, not so, but I think it's embryonic still. Okay server sprawl, client server, VM sprawl, now you got SAS sprawl. Your thoughts on this dynamic and how a CIO tackles that? >> Yes, so it's the modern equivalent of your legacy technical debt. So it's a modern mess instead of an old mess, but it's the same problem. You know, you have to stitch these applications together and it's made worse by the ease of consuming these SAS applications. So one business function can go off and buy an application that's just for them, and the adjacent business function goes off and buys another application that's just for them. And before you know where you are, you're single sign-on page has three pages because you've got so many applications that you're using to run your business. So I think we have to be more thoughtful and not make the same mistake that we made after 2000 when we went nuts on all these client server applications and make sure that we're thinking about the end-to-end business outcome. >> John: So the unification layer is what, Identity, is it the data? I mean how do you think about that just conceptually? >> Well I think you still need a sensible portfolio of applications. I don't advocate that you just go buy every great application that's out there. If your business doesn't compete based on the capability that that application provides, you've got no business innovating. Just be as good as the next guy. But if you compete based on something, go pick the very best application you can but deploy it thoughtfully. Make sure it's integrated, make sure it serves the end-to-end... >> Well I'm also fascinated by the role that Clair might play here at going and looking at the metadata associated with some of these SAS applications to help us identify patterns and utilization. I think Clair and the thing that was announced here actually could have an impact in thinking about some of these things. >> The Clairvoyant app is a great one, Clair, I mean... She, he, it's vendor neutral, that's a whole different story, only kidding. Final thought Graeme on this show? Just color perspective, what's your thought so far just on the show vibe for the folks who aren't here, what's it like? >> So when you and I met a couple weeks ago we talked about the fact that I'd just joined the company just after last year's show. So I have nothing to compare it to, but the energy level is phenomenal. The feedback from the customer's I've talked to just reinforces that we have really really important customers and we're really important to them. You know, the customers are the ones driving this digital transformation and we're proud to be helping them. And every conversation I've had with customers has really reinforced that and it's great, I can't wait to get back to the office. >> And as we say the KPI, the metric of the transformation of the world is not quadrants or category winners, it's customer wins. >> Graeme: Absolutely. >> And I think that's a great point. Graeme Thompson, Executive Vice President and Chief Information Officer of Informatica sharing his insight. He is an integral part of their transformation as well as his customers. Informatica World coverage with the Cube continues. I'm John Furrier with Peter Burris with Wikimon.com. We'll be back with more, stay with us after this short break. (electronic music)

>> Narrator: Live, from San Francisco, it's the Cube, covering Informatica World 2017. Brought to you by Informatica. (techno music) >> Hey, welcome back, everyone. Live here in San Francisco, this is the Cube's exclusive coverage of Informatica World 2017, our third year covering Informatica, and more to come. I'm John Furrier with Silicon Angle, the Cube. My co-host, Peter Burris, Head of Research for Silicon Angle Media, as well as General Manager of Wikibon.com, check out the great research at Wikibon. Some great stuff there on IOT, cloud ping data, great stuff. Of course, go to SiliconAngle.com for all the coverage YouTube.com/SiliconAngle for all the Cube videos. Our next guest is Bruce Chizen, board member of a lot of private companies, also Special Advisor at Informatica. You're on the board of Informatica, no? >> Executive Chair. >> John: Executive Chair of Informatica. Not only as Special Advisor, Executive Chair. Welcome back, good to see you. >> Great to be here. >> You were on last year, great to have you back. What a popular video. Jerry Held was on yesterday. Let's get some Board insights, so first question, when are you going public? (laughing) >> Good one. >> John: Warmed you up, and then, no. I mean the performance is doing well. Give us a quick update. >> Company's doing well. Q4 was a good quarter, Q1 was a good quarter. I think we will be positioned to do something late 2018, early 2019. A lot depends on how the company continues to do. A lot depends on the market. The private equity investors are in no hurry. >> John: Yeah. >> But it's always nice to have that option. >> So it's one of the things we, yeah, great option. Doing well. We heard that also from some of the management. We got O'Neil coming on, we'll press him on some of the performance side, but always had good products out, we talked about it last year. But the industry's going through a massive transformation. You've seen many waves over the years. The waves are hitting. What's your perspective right now? I mean, it's a pretty big wave. You got to get the surfboard out there, there's a set coming in. What's the big wave right now? >> So, data is driving every transformation within every organization. Any company that is not using and taking advantage of data will be left behind. You look at how companies like Amazon and Google and now a lot of our customers like Schwab and Tesla and others, the way they're using data, that will allow them to continue to either be successful in the case of a Schwab, or be a disruptor, like somebody like Tesla. Fortunately for us at Informatica, we are helping to drive that digital transformation. >> One of the things that I always observe, younger than you are, I've only seen a few waves in my day, but in the waves that were the most impactful in terms of creating wealth, and opportunity, and innovation, has had a cool and relevant factor. Meaning, if you go back to the PC days, it was cool and relevant. If you go back to mini computer, cool and relevant. And it goes on and on and on. And certainly internet, cool and relevant. But now, the, you mention Tesla. I'm testing driving one on Friday. My kids are like "Don't buy the Audi, buy the Tesla." This is my kids. So it's a cooler, it's a spaceship, it's cooler than the other cars. >> Bruce: Or an iPhone on wheels. >> Peter: (laughs) Exactly. A computer on wheels. >> So cool and relevant, talk about what is the cool and relevant thing right now. You talk about user experience, that's one. Data's changing it. So how is data being the cool and relevant trend? Point to some things that... >> If you look at what's happening from the chip on up, everything, everything will be intelligent. And I hate to use the term "internet of things," but the reality is everything will have intelligence. And that intelligent information will be able to be taken advantage of because of the scale of the cloud. Which means that any company will be able to take information, data, analyze it on the cloud, and then use it to do something with. And it's happening now. Fortunately, Informatica sits right in the middle of that, because they're the ones who could rationalize that data on behalf of their customers. 'Cause there's going to be a lot of it and somebody needs to govern it, secure it, homogenize it. >> John: You consider them an enabling platform? >> Absolutely, absolutely. I was joking, we just went through a rebranding exercise. And it's kind of cute, new logo, and it's kind of bold and sleek and it shows we'll have a leader, but it's a logo. But there's really around the messaging, we are finally getting across that we are the ones unleashing the power of data. That's what Informatica does. We'd just never really told anybody about it. We're very product focused, not really helping customers understand how uniquely positioned the company was. >> And it's also, you guys have done some things. Let's just go back and look at going private. Brought a new management team, have product chops again, we've talked about that in previous years. Last year in particular. So, okay, you have the wind at your back. Now you got Sally as a CMO, now you got to start being a humble braggart about the cool stuff you're doing. So which is marketing, basically. >> That's correct. >> John: But now, it's digital. >> Yeah. >> So, what's the Board conversation like, you say "Go, go build the brand!" >> So first of all, being private is great. (laughing) Because we get to do things you couldn't do as a public company. We're, a lot of our customers what to buy the products and solutions via subscription, that has huge impact to the P&L, especially in the short term. Cash flow's fine. So the PE guys are going okay, it's great, because we'll come out of this as a better company, and our customers like it because that's the way they want to buy products. So, that helps a lot. The conversation at the Board level has been, "Wow, we're number one in every category in which "we participate in. "Everything from big data to cloud integration "to traditional on-premise, to real-time streaming, "and, and, and data security." >> You're only one of three vendors in the Google general availabilities banner which went out yesterday. We covered that on Silicon Angle. >> We're number one there, we had AWS speak at our conference, we had Azure speak at our conference. All of the cloud guys love Informatica because we are the ones who are uniquely positioned to deal with all this data on behalf of their customers. As a private company, we're able to take advantage of that, spend some extra money on marketing. You know a lot of our customers know about us, but a lot more should know about us. So, part of coming out, having a new logo, having a new digital campaign, changing the website, that costs money. But as a private company, we get to do that. Because the fruits of those efforts will end up occurring a couple of years down the road, which is fine. >> So let me see if I can weave those two thoughts together in what I thought was an interesting way. Given that increasingly a lot of data's going to be in the cloud, and that's where the longer analysis is going to be required, that means a lot of the tools are going to have to be in the cloud. Amazon Marketplace is going to be a place where a lot of tools are going to be chosen. People are going to go into the Amazon Marketplace and see a lot of different options, including some that are free. They may not work as well, but they're free. You guys, what happens with marketing, and what's happening with that kind of a trend, is you need to buy, as customers, to choose tools that are actually going to work to serve or to solve the problem, to do the work that you need them to perform. And so what Sally Jenkins, the CMO, has done, with this new branding, is introduce the process of how do you buy us more customers to choose the right tool to do the right job? Does that make sense to you? >> It makes absolute sense, free is good. But be careful what you ask for. Sometimes you get what you pay for. You're talking about enterprise data. You want it to be governed, you want it to be secure. You want it to be accurate. >> John: Now there's laws coming out where you have to do it. >> You look at GTB... >> Peter: GDBPR. >> GDBPR in Europe, the privacy issues. You look at what's happening with Facebook, or what was reported today with France and how they're not happy with Facebook's privacy behaviors. It's an issue. It's an issue for anybody who does business anywhere, especially if you're a global company and you do business in Europe. You have to worry about corporate governance. Data security, data governance, data security. That's Informatica. The other thing is, while there will be some customers who will say "I'm going to AWS," there will be more customers who will either say "I have some legacy "systems that I'm going to leave on-premise, "and new projects will be in the cloud." Or they're going to say "I'm moving everything to "the cloud, but I don't want to be held hostage "by one cloud provider." And they're going to go with Amazon and Azure and Google and maybe Oracle, and, and, and. And again, because Informatica is Swiss, we're able to provide them with a solution that allows them to accomplish their data needs. >> Well, congratulations on the performance, I want to get that out of the way. But I want to ask a specific question on the historical, holistic picture of Informatica. Going back, what were the key bets that you guys made? 'Cause you guys sit around, and you got the private equity now coming to the table, they have expectations, but at the end of the day you've got to build a business. What were the key bets that is yielding the fruit that we're seeing? >> The number one bet was that the company had great products and a great R&D organization. We believed that, and fortunately, we got it right. Because if you don't have great products and passionate R&D organizations around the world, you can't make up for that. It doesn't make a difference how much you spend on marketing. At least not in the business that we're in. So that was number one bet, and that proved to play out well. The second thing was, this was a company that had done so well for so long that they never needed to change their business processes to behave like a billion, two billion, three billion, four billion dollar company. Many of their business processes were like that of a 200 million dollar company. And that's easier to fix. So things around back end, IT, legal, finance, go-to-market, marketing, sales. >> John: Less of a risk from an investment standpoint. >> That's correct. So that's what we believed, we were right And where we've been spending most of our energy and effort is helping the company, through the new management team, improve their business processes and their go-to-market. >> So we had a critical analysis yesterday during our wrap up session, and one of the comments I made, I want to get your reaction to this, was although impressive, your number one and all these Gartner Magic Quadrant categories, but that's an old scoreboard. If we're really living in digital transformation, those shouldn't really be a tell sign for what the performance of the new KBIs or the new metrics are. And so we were pontificating and analyzing what that would be, still unknown, we're going to see it. But Peter had a good point, he said "At the end "of the day, customer wins." >> Yeah, that was my reaction. It's like at the end of the day, all that matters do the customers.... >> What's the scoreboard look for customer wins? I know you were at the executive summit they had yesterday at the Intercontinental right around the corner. I had a chance to meet some of them at that dinner, some conversation. But I want to get your perspective. What is the vibe of the customers, what are those customer wins, and how does that translate into future growth for Informatica? >> Any customer who is looking at data, data management, strategically, is going with Informatica. >> Mmm hmm. >> There are a number of competitors that we have who try to compete with Informatica at the product level, and they end up doing okay through pricing, through better sales tactics, but when we have the opportunity to speak to the Chief Data Officer, the CIO, the CEO, they go with Informatica. It's the reason why Tesla went with Informatica on their project where they're trying to tie together the auto business with the solar business. Because if they get to know both sets of customers and are able to sync that up, one plus one will be greater than two for them, and that's why they did that deal. Or it's why Amazon has chosen our MDM solution for their sales operations. So you look at leading companies who are able to look at the enterprise level, at the strategic level, they are going with Informatica. That's why we know we're winning. >> So Bruce, give us three sentences, what is strategic data management? >> Strategic data management is being able to take reams and reams of data from all different platforms, traditional legacy, big data, real-time solutions, and data from the cloud and be able to look at it intelligently. Use artificial intelligence and machine learning to be able to analyze that data in a more intelligent way, and then act on it. >> So two questions on that point, I was going to ask about the AI washing going on in the industry. Every event now is like, "Oh my god, AI, we've got AI," but that's not really AI. What is AI, we call it augmented intelligence because you're really augmenting with the data, but even Google IO's got a little neural net throwback to the 80s, but what's your thoughts on how customers should look through the lens of b.s. to say, "Wow, that's the real AI, or the real "augmented intelligence." >> Does it do anything? That's ultimately the question that a Chief Data Officer or CIO or CEO...is something changing because of the artificial intelligence being applied? In the case of Informatica, we announced an AI platform called Clair, "clairvoyant," so artificial intelligence. What is Clair? It allows you to develop solutions like our enterprise information catalog, where an organization has thousands and thousands of databases, it's able to look at the metadata within those databases and then over time keep disclosing more and more data appropriate to the information that you're looking for. So then, if I'm an analyst or a businessperson, a marketing person, a sales person, I can take action on the right set of data. That's true artificial intelligence. >> Bruce, I want to get to one final point as we are winding down here. Again, you've seen many waves. But I want to talk about the companies that are trying to get through the transition of this transformation, Informatica certainly cleared the runway, they've got some things to work on, certainly brand-building. I see that as their air cover in many rising tide will float a lot of boats in the ecosystem. But there are companies where they have been in the infrastructure business and the cloud is one big infrastructure, selling boxes and whatnot. Other companies have traditional software models, download, whatever you want to call it, on-prem licenses, not subscriptions. They're working hard. Your advice to them if you are on their Board, or as a friend, what do you say to them, what do they got to do to get through this? And how should customers look at who's winning and who's losing, in terms of progress? >> The world of enterprise computing is moving to the cloud. Legacy systems will remain for a while. They need to figure out how to take their legacy solutions and make them relevant to the world of cloud computing. And if they can't do that, they should sell their company or get out of business. (laughing) >> And certainly data is the oil, it's the gold, it's the lifeblood of an organization. >> Of any organization. Even at Informatica, internally, we're using our own intelligent data platform to do our own marketing. Sally Jenkins is working closely with our CIO Graeme Thompson on working on solutions where we could help better understand what our customers want and need, so we can provide them with the right solution, leveraging our intelligent data leg. >> Bruce, thanks for coming on the Cube. Really appreciate your insight. Again, you've seen a lot of waves, you've been in the industry a long time, you have great Board presence, as well as other companies. Thanks for sharing the insight, and the data here on the Cube. A lot of insights and analytics being extracted here and sharing it with you. Certainly we're not legacy, we don't need to sell our business, we're doing great. If you haven't, make the transition. Good advice, thanks so much. >> Bruce: Great to be here. >> Bruce Chizen inside the Cube here. I'm John Furrier with Peter Burris. Stay with us for more coverage after this short break. (techno music)