(gentle music) >> Narrator: "TheCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> Hello everybody, welcome back to day four of "theCUBE's" coverage of MWC '23. We're here live at the Fira in Barcelona. Wall-to-wall coverage, John Furrier is in our Palo Alto studio, banging out all the news. Really, the whole week we've been talking about the disaggregation of the telco network, the new opportunities in telco. We're really excited to have AT&T and Snowflake here. Dave Whittington is the AVP, at the Chief Data Office at AT&T. Roddy Tranum is the Assistant Vice President, for Channel Performance Data and Tools at AT&T. And Phil Kippen, the Global Head Of Industry-Telecom at Snowflake, Snowflake's new telecom business. Snowflake just announced earnings last night. Typical Scarpelli, they beat earnings, very conservative guidance, stocks down today, but we like Snowflake long term, they're on that path to 10 billion. Guys, welcome to "theCUBE." Thanks so much >> Phil: Thank you. >> for coming on. >> Dave and Roddy: Thanks Dave. >> Dave, let's start with you. The data culture inside of telco, We've had this, we've been talking all week about this monolithic system. Super reliable. You guys did a great job during the pandemic. Everything shifting to landlines. We didn't even notice, you guys didn't miss a beat. Saved us. But the data culture's changing inside telco. Explain that. >> Well, absolutely. So, first of all IoT and edge processing is bringing forth new and exciting opportunities all the time. So, we're bridging the world between a lot of the OSS stuff that we can do with edge processing. But bringing that back, and now we're talking about working, and I would say traditionally, we talk data warehouse. Data warehouse and big data are now becoming a single mesh, all right? And the use cases and the way you can use those, especially I'm taking that edge data and bringing it back over, now I'm running AI and ML models on it, and I'm pushing back to the edge, and I'm combining that with my relational data. So that mesh there is making all the difference. We're getting new use cases that we can do with that. And it's just, and the volume of data is immense. >> Now, I love ChatGPT, but I'm hoping your data models are more accurate than ChatGPT. I never know. Sometimes it's really good, sometimes it's really bad. But enterprise, you got to be clean with your AI, don't you? >> Not only you have to be clean, you have to monitor it for bias and be ethical about it. We're really good about that. First of all with AT&T, our brand is Platinum. We take care of that. So, we may not be as cutting-edge risk takers as others, but when we go to market with an AI or an ML or a product, it's solid. >> Well hey, as telcos go, you guys are leaning into the Cloud. So I mean, that's a good starting point. Roddy, explain your role. You got an interesting title, Channel Performance Data and Tools, what's that all about? >> So literally anything with our consumer, retail, concenters' channels, all of our channels, from a data perspective and metrics perspective, what it takes to run reps, agents, all the way to leadership levels, scorecards, how you rank in the business, how you're driving the business, from sales, service, customer experience, all that data infrastructure with our great partners on the CDO side, as well as Snowflake, that comes from my team. >> And that's traditionally been done in a, I don't mean the pejorative, but we're talking about legacy, monolithic, sort of data warehouse technologies. >> Absolutely. >> We have a love-hate relationship with them. It's what we had. It's what we used, right? And now that's evolving. And you guys are leaning into the Cloud. >> Dramatic evolution. And what Snowflake's enabled for us is impeccable. We've talked about having, people have dreamed of one data warehouse for the longest time and everything in one system. Really, this is the only way that becomes a reality. The more you get in Snowflake, we can have golden source data, and instead of duplicating that 50 times across AT&T, it's in one place, we just share it, everybody leverages it, and now it's not duplicated, and the process efficiency is just incredible. >> But it really hinges on that separation of storage and compute. And we talk about the monolithic warehouse, and one of the nightmares I've lived with, is having a monolithic warehouse. And let's just go with some of my primary, traditional customers, sales, marketing and finance. They are leveraging BSS OSS data all the time. For me to coordinate a deployment, I have to make sure that each one of these units can take an outage, if it's going to be a long deployment. With the separation of storage, compute, they own their own compute cluster. So I can move faster for these people. 'Cause if finance, I can implement his code without impacting finance or marketing. This brings in CI/CD to more reality. It brings us faster to market with more features. So if he wants to implement a new comp plan for the field reps, or we're reacting to the marketplace, where one of our competitors has done something, we can do that in days, versus waiting weeks or months. >> And we've reported on this a lot. This is the brilliance of Snowflake's founders, that whole separation >> Yep. >> from compute and data. I like Dave, that you're starting with sort of the business flexibility, 'cause there's a cost element of this too. You can dial down, you can turn off compute, and then of course the whole world said, "Hey, that's a good idea." And a VC started throwing money at Amazon, but Redshift said, "Oh, we can do that too, sort of, can't turn off the compute." But I want to ask you Phil, so, >> Sure. >> it looks from my vantage point, like you're taking your Data Cloud message which was originally separate compute from storage simplification, now data sharing, automated governance, security, ultimately the marketplace. >> Phil: Right. >> Taking that same model, break down the silos into telecom, right? It's that same, >> Mm-hmm. >> sorry to use the term playbook, Frank Slootman tells me he doesn't use playbooks, but he's not a pattern matcher, but he's a situational CEO, he says. But the situation in telco calls for that type of strategy. So explain what you guys are doing in telco. >> I think there's, so, what we're launching, we launched last week, and it really was three components, right? So we had our platform as you mentioned, >> Dave: Mm-hmm. >> and that platform is being utilized by a number of different companies today. We also are adding, for telecom very specifically, we're adding capabilities in marketplace, so that service providers can not only use some of the data and apps that are in marketplace, but as well service providers can go and sell applications or sell data that they had built. And then as well, we're adding our ecosystem, it's telecom-specific. So, we're bringing partners in, technology partners, and consulting and services partners, that are very much focused on telecoms and what they do internally, but also helping them monetize new services. >> Okay, so it's not just sort of generic Snowflake into telco? You have specific value there. >> We're purposing the platform specifically for- >> Are you a telco guy? >> I am. You are, okay. >> Total telco guy absolutely. >> So there you go. You see that Snowflake is actually an interesting organizational structure, 'cause you're going after verticals, which is kind of rare for a company of your sort of inventory, I'll say, >> Absolutely. >> I don't mean that as a negative. (Dave laughs) So Dave, take us through the data journey at AT&T. It's a long history. You don't have to go back to the 1800s, but- (Dave laughs) >> Thank you for pointing out, we're a 149-year-old company. So, Jesse James was one of the original customers, (Dave laughs) and we have no longer got his data. So, I'll go back. I've been 17 years singular AT&T, and I've watched it through the whole journey of, where the monolithics were growing, when the consolidation of small, wireless carriers, and we went through that boom. And then we've gone through mergers and acquisitions. But, Hadoop came out, and it was going to solve all world hunger. And we had all the aspects of, we're going to monetize and do AI and ML, and some of the things we learned with Hadoop was, we had this monolithic warehouse, we had this file-based-structured Hadoop, but we really didn't know how to bring this all together. And we were bringing items over to the relational, and we were taking the relational and bringing it over to the warehouse, and trying to, and it was a struggle. Let's just go there. And I don't think we were the only company to struggle with that, but we learned a lot. And so now as tech is finally emerging, with the cloud, companies like Snowflake, and others that can handle that, where we can create, we were discussing earlier, but it becomes more of a conducive mesh that's interoperable. So now we're able to simplify that environment. And the cloud is a big thing on that. 'Cause you could not do this on-prem with on-prem technologies. It would be just too cost prohibitive, and too heavy of lifting, going back and forth, and managing the data. The simplicity the cloud brings with a smaller set of tools, and I'll say in the data space specifically, really allows us, maybe not a single instance of data for all use cases, but a greatly reduced ecosystem. And when you simplify your ecosystem, you simplify speed to market and data management. >> So I'm going to ask you, I know it's kind of internal organizational plumbing, but it'll inform my next question. So, Dave, you're with the Chief Data Office, and Roddy, you're kind of, you all serve in the business, but you're really serving the, you're closer to those guys, they're banging on your door for- >> Absolutely. I try to keep the 130,000 users who may or may not have issues sometimes with our data and metrics, away from Dave. And he just gets a call from me. >> And he only calls when he has a problem. He's never wished me happy birthday. (Dave and Phil laugh) >> So the reason I asked that is because, you describe Dave, some of the Hadoop days, and again love-hate with that, but we had hyper-specialized roles. We still do. You've got data engineers, data scientists, data analysts, and you've got this sort of this pipeline, and it had to be this sequential pipeline. I know Snowflake and others have come to simplify that. My question to you is, how is that those roles, how are those roles changing? How is data getting closer to the business? Everybody talks about democratizing business. Are you doing that? What's a real use example? >> From our perspective, those roles, a lot of those roles on my team for years, because we're all about efficiency, >> Dave: Mm-hmm. >> we cut across those areas, and always have cut across those areas. So now we're into a space where things have been simplified, data processes and copying, we've gone from 40 data processes down to five steps now. We've gone from five steps to one step. We've gone from days, now take hours, hours to minutes, minutes to seconds. Literally we're seeing that time in and time out with Snowflake. So these resources that have spent all their time on data engineering and moving data around, are now freed up more on what they have skills for and always have, the data analytics area of the business, and driving the business forward, and new metrics and new analysis. That's some of the great operational value that we've seen here. As this simplification happens, it frees up brain power. >> So, you're pumping data from the OSS, the BSS, the OKRs everywhere >> Everywhere. >> into Snowflake? >> Scheduling systems, you name it. If you can think of what drives our retail and centers and online, all that data, scheduling system, chat data, call center data, call detail data, all of that enters into this common infrastructure to manage the business on a day in and day out basis. >> How are the roles and the skill sets changing? 'Cause you're doing a lot less ETL, you're doing a lot less moving of data around. There were guys that were probably really good at that. I used to joke in the, when I was in the storage world, like if your job is bandaging lungs, you need to look for a new job, right? So, and they did and people move on. So, are you able to sort of redeploy those assets, and those people, those human resources? >> These folks are highly skilled. And we were talking about earlier, SQL hasn't gone away. Relational databases are not going away. And that's one thing that's made this migration excellent, they're just transitioning their skills. Experts in legacy systems are now rapidly becoming experts on the Snowflake side. And it has not been that hard a transition. There are certainly nuances, things that don't operate as well in the cloud environment that we have to learn and optimize. But we're making that transition. >> Dave: So just, >> Please. >> within the Chief Data Office we have a couple of missions, and Roddy is a great partner and an example of how it works. We try to bring the data for democratization, so that we have one interface, now hopefully know we just have a logical connection back to these Snowflake instances that we connect. But we're providing that governance and cleansing, and if there's a business rule at the enterprise level, we provide it. But the goal at CDO is to make sure that business units like Roddy or marketing or finance, that they can come to a platform that's reliable, robust, and self-service. I don't want to be in his way. So I feel like I'm providing a sub-level of platform, that he can come to and anybody can come to, and utilize, that they're not having to go back and undo what's in Salesforce, or ServiceNow, or in our billers. So, I'm sort of that layer. And then making sure that that ecosystem is robust enough for him to use. >> And that self-service infrastructure is predominantly through the Azure Cloud, correct? >> Dave: Absolutely. >> And you work on other clouds, but it's predominantly through Azure? >> We're predominantly in Azure, yeah. >> Dave: That's the first-party citizen? >> Yeah. >> Okay, I like to think in terms sometimes of data products, and I know you've mentioned upfront, you're Gold standard or Platinum standard, you're very careful about personal information. >> Dave: Yeah. >> So you're not trying to sell, I'm an AT&T customer, you're not trying to sell my data, and make money off of my data. So the value prop and the business case for Snowflake is it's simpler. You do things faster, you're in the cloud, lower cost, et cetera. But I presume you're also in the business, AT&T, of making offers and creating packages for customers. I look at those as data products, 'cause it's not a, I mean, yeah, there's a physical phone, but there's data products behind it. So- >> It ultimately is, but not everybody always sees it that way. Data reporting often can be an afterthought. And we're making it more on the forefront now. >> Yeah, so I like to think in terms of data products, I mean even if the financial services business, it's a data business. So, if we can think about that sort of metaphor, do you see yourselves as data product builders? Do you have that, do you think about building products in that regard? >> Within the Chief Data Office, we have a data product team, >> Mm-hmm. >> and by the way, I wouldn't be disingenuous if I said, oh, we're very mature in this, but no, it's where we're going, and it's somewhat of a journey, but I've got a peer, and their whole job is to go from, especially as we migrate from cloud, if Roddy or some other group was using tables three, four and five and joining them together, it's like, "Well look, this is an offer for data product, so let's combine these and put it up in the cloud, and here's the offer data set product, or here's the opportunity data product," and it's a journey. We're on the way, but we have dedicated staff and time to do this. >> I think one of the hardest parts about that is the organizational aspects of it. Like who owns the data now, right? It used to be owned by the techies, and increasingly the business lines want to have access, you're providing self-service. So there's a discussion about, "Okay, what is a data product? Who's responsible for that data product? Is it in my P&L or your P&L? Somebody's got to sign up for that number." So, it sounds like those discussions are taking place. >> They are. And, we feel like we're more the, and CDO at least, we feel more, we're like the guardians, and the shepherds, but not the owners. I mean, we have a role in it all, but he owns his metrics. >> Yeah, and even from our perspective, we see ourselves as an enabler of making whatever AT&T wants to make happen in terms of the key products and officers' trade-in offers, trade-in programs, all that requires this data infrastructure, and managing reps and agents, and what they do from a channel performance perspective. We still ourselves see ourselves as key enablers of that. And we've got to be flexible, and respond quickly to the business. >> I always had empathy for the data engineer, and he or she had to service all these different lines of business with no business context. >> Yeah. >> Like the business knows good data from bad data, and then they just pound that poor individual, and they're like, "Okay, I'm doing my best. It's just ones and zeros to me." So, it sounds like that's, you're on that path. >> Yeah absolutely, and I think, we do have refined, getting more and more refined owners of, since Snowflake enables these golden source data, everybody sees me and my organization, channel performance data, go to Roddy's team, we have a great team, and we go to Dave in terms of making it all happen from a data infrastructure perspective. So we, do have a lot more refined, "This is where you go for the golden source, this is where it is, this is who owns it. If you want to launch this product and services, and you want to manage reps with it, that's the place you-" >> It's a strong story. So Chief Data Office doesn't own the data per se, but it's your responsibility to provide the self-service infrastructure, and make sure it's governed properly, and in as automated way as possible. >> Well, yeah, absolutely. And let me tell you more, everybody talks about single version of the truth, one instance of the data, but there's context to that, that we are taking, trying to take advantage of that as we do data products is, what's the use case here? So we may have an entity of Roddy as a prospective customer, and we may have a entity of Roddy as a customer, high-value customer over here, which may have a different set of mix of data and all, but as a data product, we can then create those for those specific use cases. Still point to the same data, but build it in different constructs. One for marketing, one for sales, one for finance. By the way, that's where your data engineers are struggling. >> Yeah, yeah, of course. So how do I serve all these folks, and really have the context-common story in telco, >> Absolutely. >> or are these guys ahead of the curve a little bit? Or where would you put them? >> I think they're definitely moving a lot faster than the industry is generally. I think the enabling technologies, like for instance, having that single copy of data that everybody sees, a single pane of glass, right, that's definitely something that everybody wants to get to. Not many people are there. I think, what AT&T's doing, is most definitely a little bit further ahead than the industry generally. And I think the successes that are coming out of that, and the learning experiences are starting to generate momentum within AT&T. So I think, it's not just about the product, and having a product now that gives you a single copy of data. It's about the experiences, right? And now, how the teams are getting trained, domains like network engineering for instance. They typically haven't been a part of data discussions, because they've got a lot of data, but they're focused on the infrastructure. >> Mm. >> So, by going ahead and deploying this platform, for platform's purpose, right, and the business value, that's one thing, but also to start bringing, getting that experience, and bringing new experience in to help other groups that traditionally hadn't been data-centric, that's also a huge step ahead, right? So you need to enable those groups. >> A big complaint of course we hear at MWC from carriers is, "The over-the-top guys are killing us. They're riding on our networks, et cetera, et cetera. They have all the data, they have all the client relationships." Do you see your client relationships changing as a result of sort of your data culture evolving? >> Yes, I'm not sure I can- >> It's a loaded question, I know. >> Yeah, and then I, so, we want to start embedding as much into our network on the proprietary value that we have, so we can start getting into that OTT play, us as any other carrier, we have distinct advantages of what we can do at the edge, and we just need to start exploiting those. But you know, 'cause whether it's location or whatnot, so we got to eat into that. Historically, the network is where we make our money in, and we stack the services on top of it. It used to be *69. >> Dave: Yeah. >> If anybody remembers that. >> Dave: Yeah, of course. (Dave laughs) >> But you know, it was stacked on top of our network. Then we stack another product on top of it. It'll be in the edge where we start providing distinct values to other partners as we- >> I mean, it's a great business that you're in. I mean, if they're really good at connectivity. >> Dave: Yeah. >> And so, it sounds like it's still to be determined >> Dave: Yeah. >> where you can go with this. You have to be super careful with private and for personal information. >> Dave: Yep. >> Yeah, but the opportunities are enormous. >> There's a lot. >> Yeah, particularly at the edge, looking at, private networks are just an amazing opportunity. Factories and name it, hospital, remote hospitals, remote locations. I mean- >> Dave: Connected cars. >> Connected cars are really interesting, right? I mean, if you start communicating car to car, and actually drive that, (Dave laughs) I mean that's, now we're getting to visit Xen Fault Tolerance people. This is it. >> Dave: That's not, let's hold the traffic. >> Doesn't scare me as much as we actually learn. (all laugh) >> So how's the show been for you guys? >> Dave: Awesome. >> What're your big takeaways from- >> Tremendous experience. I mean, someone who doesn't go outside the United States much, I'm a homebody. The whole experience, the whole trip, city, Mobile World Congress, the technologies that are out here, it's been a blast. >> Anything, top two things you learned, advice you'd give to others, your colleagues out in general? >> In general, we talked a lot about technologies today, and we talked a lot about data, but I'm going to tell you what, the accelerator that you cannot change, is the relationship that we have. So when the tech and the business can work together toward a common goal, and it's a partnership, you get things done. So, I don't know how many CDOs or CIOs or CEOs are out there, but this connection is what accelerates and makes it work. >> And that is our audience Dave. I mean, it's all about that alignment. So guys, I really appreciate you coming in and sharing your story in "theCUBE." Great stuff. >> Thank you. >> Thanks a lot. >> All right, thanks everybody. Thank you for watching. I'll be right back with Dave Nicholson. Day four SiliconANGLE's coverage of MWC '23. You're watching "theCUBE." (gentle music)

>> from Cambridge, Massachusetts. It's three Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back. We're here to wrap up the M I T. Chief data officer officer, information quality. It's hashtag m i t CDO conference. You're watching the Cube. I'm David Dante, and Paul Gill is my co host. This is two days of coverage. We're wrapping up eyes. Our analysis of what's going on here, Paul, Let me let me kick it off. When we first started here, we talked about that are open. It was way saw the chief data officer role emerged from the back office, the information quality role. When in 2013 the CEO's that we talked to when we asked them what was their scope. We heard things like, Oh, it's very wide. Involves analytics, data science. Some CEOs even said Oh, yes, security is actually part of our purview because all the cyber data so very, very wide scope. Even in some cases, some of the digital initiatives were sort of being claimed. The studios were staking their claim. The reality was the CDO also emerged out of highly regulated industries financialservices healthcare government. And it really was this kind of wonky back office role. And so that's what my compliance, that's what it's become again. We're seeing that CEOs largely you're not involved in a lot of the emerging. Aye, aye initiatives. That's what we heard, sort of anecdotally talking to various folks At the same time. I feel as though the CDO role has been more fossilized than it was before. We used to ask, Is this role going to be around anymore? We had C I. Ose tell us that the CEO Rose was going to disappear, so you had both ends of the spectrum. But I feel as though that whatever it's called CDO Data's our chief analytics off officer, head of data, you know, analytics and governance. That role is here to stay, at least for for a fair amount of time and increasingly, issues of privacy and governance. And at least the periphery of security are gonna be supported by that CD a role. So that's kind of takeaway Number one. Let me get your thoughts. >> I think there's a maturity process going on here. What we saw really in 2016 through 2018 was, ah, sort of a celebration of the arrival of the CDO. And we're here, you know, we've got we've got power now we've got an agenda. And that was I mean, that was a natural outcome of all this growth and 90% of organizations putting sea Dios in place. I think what you're seeing now is a realization that Oh, my God, this is a mess. You know what I heard? This year was a lot less of this sort of crowing about the ascendance of sea Dios and Maura about We've got a big integration problem of big data cleansing problem, and we've got to get our hands down to the nitty gritty. And when you talk about, as you said, we had in here so much this year about strategic initiatives, about about artificial intelligence, about getting involved in digital business or customer experience transformation. What we heard this year was about cleaning up data, finding the data that you've got organizing it, applying meditator, too. It is getting in shape to do something with it. There's nothing wrong with that. I just think it's part of the natural maturation process. Organizations now have to go through Tiu to the dirty process of cleaning up this data before they can get to the next stage, which was a couple of three years out for most of >> the second. Big theme, of course. We heard this from the former head of analytics. That G s K on the opening keynote is the traditional methods have failed the the Enterprise Data Warehouse, and we've actually studied this a lot. You know, my analogy is often you snake swallowing a basketball, having to build cubes. E D W practitioners would always used to call it chasing the chips until we come up with a new chip. Oh, we need that because we gotta run faster because it's taking us hours and hours, weeks days to run these analytics. So that really was not an agile. It was a rear view mirror looking thing. And Sarbanes Oxley saved the E. D. W. Business because reporting became part of compliance thing perspective. The master data management piece we've heard. Do you consistently? We heard Mike Stone Breaker, who's obviously a technology visionary, was right on. It doesn't scale through this notion of duping. Everything just doesn't work and manually creating rules. It's just it's just not the right approach. This we also heard the top down data data enterprise data model doesn't works too complicated, can operationalize it. So what they do, they kick the can to governance. The Duke was kind of a sidecar, their big data that failed to live up to its promises. And so it's It's a big question as to whether or not a I will bring that level of automation we heard from KPMG. Certainly, Mike Stone breaker again said way heard this, uh, a cz well, from Andy Palmer. They're using technology toe automate and scale that big number one data science problem, which is? They spend all their time wrangling data. We'll see if that if that actually lives up >> to his probable is something we did here today from several of our guests. Was about the promise of machine learning to automate this day to clean up process and as ah Mark Ramsay kick off the conference saying that all of these efforts to standardize data have failed in the past. This does look, He then showed how how G s K had used some of the tools that were represented here using machine learning to actually clean up the data at G S. K. So there is. And I heard today a lot of optimism from the people we talked to about the capability of Chris, for example, talking about the capability of machine learning to bring some order to solve this scale scale problem Because really organizing data creating enterprise data models is a scale problem, and the only way you can solve that it's with with automation, Mike Stone breaker is right on top of that. So there was optimism at this event. There was kind of an ooh, kind of, ah, a dismay at seeing all the data problems they have to clean up, but also promised that tools are on the way that could do that. >> Yeah, The reason I'm an optimist about this role is because data such a hard problem. And while there is a feeling of wow, this is really a challenge. There's a lot of smart people here who are up for the challenge and have the d n a for it. So the role, that whole 360 thing. We talked about the traditional methods, you know, kind of failing, and in the third piece that touched on, which is really bringing machine intelligence to the table. We haven't heard that as much at this event. It's now front and center. It's just another example of a I injecting itself into virtually every aspect every corner of the industry. And again, I often jokes. Same wine, new bottle. Our industry has a habit of doing that, but it's cyclical, but it is. But we seem to be making consistent progress. >> And the machine learning, I thought was interesting. Several very guest spoke to machine learning being applied to the plumbing projects right now to cleaning up data. Those are really self contained projects. You can manage those you can. You can determine out test outcomes. You can vet the quality of the of the algorithms. It's not like you're putting machine learning out there in front of the customer where it could potentially do some real damage. There. They're vetting their burning in machine, learning in a environment that they control. >> Right, So So, Amy, Two solid days here. I think that this this conference has really grown when we first started here is about 130 people, I think. And now it was 500 registrants. This'd year. I think 600 is the sort of the goal for next year. Moving venues. The Cube has been covering this all but one year since 2013. Hope to continue to do that. Paul was great working with you. Um, always great work. I hope we can, uh we could do more together. We heard the verdict is bringing back its conference. You put that together. So we had column. Mahoney, um, had the vertical rock stars on which was fun. Com Mahoney, Mike Stone breaker uh, Andy Palmer and Chris Lynch all kind of weighed in, which was great to get their perspectives kind of the days of MPP and how that's evolved improving on traditional relational database. And and now you're Stone breaker. Applying all these m i. Same thing with that scale with Chris Lynch. So it's fun to tow. Watch those guys all Boston based East Coast folks some news. We just saw the news hit President Trump holding up jet icon contractors is we've talked about. We've been following that story very closely and I've got some concerns over that. It's I think it's largely because he doesn't like Bezos in The Washington Post Post. Exactly. You know, here's this you know, America first. The Pentagon says they need this to be competitive with China >> and a I. >> There's maybe some you know, where there's smoke. There's fire there, so >> it's more important to stick in >> the eye. That's what it seems like. So we're watching that story very closely. I think it's I think it's a bad move for the executive branch to be involved in those type of decisions. But you know what I know? Well, anyway, Paul awesome working with you guys. Thanks. And to appreciate you flying out, Sal. Good job, Alex Mike. Great. Already wrapping up. So thank you for watching. Go to silicon angle dot com for all the news. Youtube dot com slash silicon angles where we house our playlist. But the cube dot net is the main site where we have all the events. It will show you what's coming up next. We've got a bunch of stuff going on straight through the summer. And then, of course, VM World is the big kickoff for the fall season. Goto wicked bond dot com for all the research. We're out. Thanks for watching Dave. A lot day for Paul Gillon will see you next time.

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to Cambridge, Massachusetts. Everybody watching the Cuban leader live tech coverage. We here covering the M I t CDO conference M I t CEO Day to wrapping up. Bob Parr is here. He's a partner in principle at KPMG, and he's joined by Streetcar Krishna, who is the managing director of data science. Aye, aye. And innovation at KPMG. Gents, welcome to the Cube. Thank >> thank you. Let's start with your >> roles. So, Bob, where do you focus >> my focus? Ah, within KPMG, we've got three main business lines audit tax, an advisory. And so I'm the advisory chief date officer. So I'm more focused on how we use data competitively in the market. More the offense side of our focus. So, you know, how do we make sure that our teams have the data they need to deliver value? Uh, much as possible working concert with the enterprise? CDO uh, who's more focused on our infrastructure, Our standards, security, privacy and those >> you've focused on making KPMG better A >> supposed exactly clients. OK, >> I also have a second hat, and I also serve financial service is si Dios as well. So Okay, so >> get her out of a dual role. I got sales guys in >> streetcar. What was your role? >> Yeah, You know, I focus a lot on data science, artificial intelligence and overall innovation s o my reaction. I actually represent a centre of >> excellence within KPMG that focuses on the I machine learning natural language processing. And I work with Bob's Division to actually advance the data site off the store because all the eye needs data. And without data, there's no algorithms, So we're focusing a lot on How do we use a I to make data Better think about their equality. Think about data lineage. Think about all of the problems that data has. How can we make it better using algorithms? And I focused a lot on that working with Bob, But no, it's it's customers and internal. I mean, you know, I were a horizontal within the form, So we help customers. We help internal, we focus a lot on the market. >> So, Bob, you mentioned used data offensively. So 10 12 years ago, it was data was a liability. You had to get rid of it. Keep it no longer than you had to, because you're gonna get soon. So email archives came in and obviously thinks flipped after the big data. But so what do you What are you seeing in terms of that shift from From the defense data to the offensive? >> Yeah, and it's it's really you know, when you think about it and let me define sort of offense versus defense. Who on the defense side, historically, that's where most of CEOs have played. That's risk regulatory reporting, privacy, um, even litigation support those types of activities today. Uh, and really, until about a year and 1/2 ago, we really saw most CEOs still really anchored in that I run a forum with a number of studios and financial service is, and every year we get them together and asked him the same set of questions. This was the first year where they said that you know what my primary focus now is. Growth. It's bringing efficiency is trying to generate value on the offensive side. It's not like the regulatory work's going away, certainly in the face of some of the pending privacy regulation. But you know, it's It's a sign that the volume of use cases as the investments in their digital transformations are starting to kick out, as well as the volumes of data that are available. The raw material that's available to them in terms of third party data in terms of the the just the general volumes that that exist that are streaming into the organization and the overall literacy in the business units are creating this, this massive demand. And so they're having to >> respond because of getting a handle on the data they're actually finding. Word is, they're categorizing it there, there, >> yeah, organizing that. That is still still a challenge. Um, I think it's better with when you have a very narrow scope of critical data elements going back to the structure data that we're talking it with the regulatory reporting when you start to get into the three offense, the generating value, getting the customer experience, you know, really exploring. You know that side of it. There's there's a ton of new muscle that has to be built new muscle in terms of data quality, new muscle in terms of um, really more scalable operating model. I think that's a big issue right now with Si Dios is, you know, we've got ah, we're used to that limited swath of CDs and they've got Stewardship Network. That's very labor intensive. A lot of manual processes still, um, and and they have some good basic technology, but it's a lot of its rules based. And when you do you think about those how that constraints going to scale when you have all of this demand. You know, when you look at the customer experience analytics that they want to do when you look at, you know, just a I applied to things like operations. The demand on the focus there is is is gonna start to create a fundamental shift >> this week are one of things that I >> have scene, and maybe it's just my small observation space. But I wonder, if you could comment Is that seems like many CBO's air not directly involved in the aye aye initiatives. Clearly, the chief digital officer is involved, but the CDO zehr kind of, you know, in the background still, you see that? >> That's a fantastic question, and I think this is where we're seeing some off the cutting it change that is happening in the industry. And when Barbara presenter idea that we can often civilly look at data, this is what it is that studios for a long time have become more reactive in their roles. And that is that is starting to come forefront now. So a lot of institutions were working with are asking What's the next generation Roll off a CDO and why are they in the background and why are they not in the foreground? And this is when you become more often they were proactive with data and the digital officers are obviously focused on, you know, the transformation that has to happen. But the studios are their backbone in order to make the transformation. Really. And if the CDO started, think about their data as an asset did as a product did us a service. The judicial officers are right there because those are the real, you know, like the data data they're living so CDO can really become from my back office to really become a business line. We've >> seen taking the reins in machine learning in machine learning projects and cos you work with. Who >> was driving that? Yeah. Great question. So we are seeing, like, you know, different. I would put them in buckets, right? There is no one mortal fits all. We're seeing different generations within the company's. Some off. The ones were just testing out the market. There's two keeping it in their technology space in their back office. Take idea and, you know, in in forward I d let me call them where they are starting to experiment with this. But you see, the mature organizations on the other end of the spectrum, they are integrating action, learning and a I right into the business line because they want to see ex souls having the technology right by their side so they can lead leverage. Aye, aye. And machine learning spot right for the business right there. And that is where we're seeing know some of the new models. Come on. >> I think the big shift from a CDO perspective is using a i to prep data for a That's that's fundamentally where you know, where the data science was distributed. Some of that data science has to come back and free the integration for equality for data prepping because you've got all this data third party and other from customer streaming into the organization. And you know, the work that you're doing around, um, anomaly detection is it transcends developing the rules, doing the profiling, doing the rules. You know, the very manual, the very labor intensive process you've got to get away from that >> is used in order for this to be scale goes and a I to figure out which out goes to apply t >> clean to prepare the data toe, see what algorithms we can use. So it's basically what we're calling a eye for data rather than just data leading into a I. So it's I mean, you know, you developed a technology for one off our clients and pretty large financial service. They were getting closer, like 1,000,000,000 data points every day. And there was no way manually, you could go through the same quality controls and all of those processes. So we automated it through algorithms, and these algorithms are learning the behavior of data as they flow into the organization, and they're able to proactively tell their problems are starting very much. And this is the new face that we see in in the industry, you cannot scale the traditional data governance using manual processes, we have to go to the next generation where a i natural language processing and think about on structure data, right? I mean, that is, like 90% off. The organization is unstructured data, and we have not talked about data quality. We have not talked about data governance. For a lot of these sources of information, now is the time. Hey, I can do it. >> And I think that raised a great question. If you look at unstructured and a lot of the data sources, as you start to take more of an offensive stance will be unstructured. And the data quality, what it means to apply data quality isn't the the profiling and the rules generation the way you would with standard data. So the teams, the skills that CEOs have in their organizations, have to change. You have to start to, and, you know, it's a great example where, you know, you guys were ingesting documents and there was handwriting all over the documents, you know, and >> yeah, you know, you're a great example, Bob. Like you no way would ask the client, like, you know, is this document gonna scanned into the system so my algorithm can run and they're like, Yeah, everything is good. I mean, the deal is there, but when you then start scanning it, you realize there's handwriting and the information is in the handwriting. So all the algorithms breakdown now >> tribal knowledge striving Exactly. >> Exactly. So that's what we're seeing. You know, if I if we talk about the digital transformation in data in the city organization, it is this idea dart. Nothing is left unseen. Some algorithm or some technology, has seen everything that is coming into. The organization has has has a para 500. So you can tell you where the problems are. And this is what algorithms do. This scale beautifully. >> So the data quality approaches are evolving, sort of changing. So rather than heavy, heavy emphasis on masking or duplication and things like that, you would traditionally think of participating the difficult not that that goes away. But it's got to evolve to use machine >> intelligence. Exactly what kind of >> skill sets people need thio achieve that Is it Is it the same people or do we need to retrain them or bring in new skills. >> Yeah, great question. And I can talk from the inspector off. Where is disrupting every industry now that we know, right? But we knew when you look at what skills are >> required, all of the eye, including natural language processing, machine learning, still require human in the loop. And >> that is the training that goes in there. And who do you who are the >> people who have that knowledge? It is the business analyst. It's the data analyst who are the knowledge betters the C suite and the studios. They are able to make decisions. But the day today is still with the data analyst. >> Those s Emmys. Those sm >> means So we have to obscure them to really start >> interacting with these new technologies where they are the leaders, rather than just waiting for answers to come through. And >> when that happens now being as a data scientist, my job is easy because they're Siamese, are there? I deploy the technology. They're semi's trained algorithms on a regular basis. Then it is a fully fungible model which is evolving with the business. And no longer am I spending time re architect ing my rules. And like my, you know, what are the masking capabilities I need to have? It is evolving us. >> Does that change the >> number one problem that you hear from data scientists, which is the 80% of the time >> spent on wrangling cleaning data 10 15 20% run into sm. He's being concerned that they're gonna be replaced by the machine. Their training. >> I actually see them being really enabled now where they're spending 80% of the time doing boring job off, looking at data. Now they're spending 90% of their time looking at the elements future creative in which requires human intelligence to say, Hey, this is different because off X, >> y and Z so let's let's go out. It sounds like a lot of what machine learning is being used for now in your domain is clean things up its plumbing. It's basic foundation work. So go out. Three years after all that work has been done and the data is clean. Where are your clients talking about going next with machine learning? Bob, did you want? >> I mean, it's a whole. It varies by by industry, obviously, but, um but it covers the gamut from, you know, and it's generally tied to what's driving their strategies. So if you look at a financial service is organization as an example today, you're gonna have, you know, really a I driving a lot of the behind the scenes on the customer experience. It's, you know, today with your credit card company. It's behind the scenes doing fraud detection. You know, that's that's going to continue. So it's take the critical functions that were more data. It makes better models that, you know, that that's just going to explode. And I think they're really you can look across all the functions, from finance to to marketing to operations. I mean, it's it's gonna be pervasive across, you know all of that. >> So if I may, I don't top award. While Bob was saying, I think what's gonna what What our clients are asking is, how can I exhilarate the decision making? Because at the end of the day on Lee, all our leaders are focused on making decisions, and all of this data science is leading up to their decision, and today you see like you know what you brought up, like 80% of the time is wasted in cleaning the data. So only 20% time was spent in riel experimentation and analytics. So your decision making time was reduced to 20% off the effort that I put in the pipeline. What if now I can make it 80% of the time? They're I put in the pipeline, better decisions are gonna come on the train. So when I go into a meeting and I'm saying like, Hey, can you show me what happened in this particular region or in this particular part of the country? Previously, it would have been like, Oh, can you come back in two weeks? I will have the data ready, and I will tell you the answer. But in two weeks, the business has ran away and the CDO know or the C Street doesn't require the same answer. But where we're headed as as the data quality improves, you can get to really time questions and decisions. >> So decision, sport, business, intelligence. Well, we're getting better. Isn't interesting to me. Six months to build a cube, we'd still still not good enough. Moving too fast. As the saying goes, data is plentiful. Insights aren't Yes, you know, in your view, well, machine intelligence. Finally, close that gap. Get us closer to real time decision >> making. It will eventually. But there's there's so much that we need to. Our industry needs to understand first, and it really ingrained. And, you know, today there is still a fundamental trust issues with a I you know, it's we've done a lot of work >> watch Black box or a part of >> it. Part of it. I think you know, the research we've done. And some of this is nine countries, 2400 senior executives. And we asked some, ah, a lot of questions around their data and trusted analytics, and 92% of them came back with. They have some fundamental trust issues with their data and their analytics and and they feel like there's reputational risk material reputational risk. This isn't getting one little number wrong on one of the >> reports about some more of an >> issue, you know, we also do a CEO study, and we've done this many years in a row going back to 2017. We started asked them okay, making a lot of companies their data driven right. When it comes to >> what they say they're doing well, They say they're day driven. That's the >> point. At the end of the day, they making strategic decisions where you have an insight that's not intuitive. Do you trust your gut? Go with the analytics back then. You know, 67% said they go with their gut, So okay, this is 2017. This industry's moving quickly. There's tons and tons of investment. Look at it. 2018 go down. No, went up 78%. So it's not aware this issue there is something We're fundamentally wrong and you hit it on. It's a part of its black box, and part of it's the date equality and part of its bias. And there's there's all of these things flowing around it. And so when we dug into that, we said, Well, okay, if that exists, how are we going to help organizations get their arms around this issue and start digging into that that trust issue and really it's the front part is, is exactly what we're talking about in terms of data quality, both structured more traditional approaches and unstructured, using the handwriting example in those types of techniques. But then you get into the models themselves, and it's, you know, the critical thing she had to worry about is, you know, lineage. So from an integrity perspective, where's the data coming from? Whether the sources for the change controls on some of that, they need to look at explain ability, gain at the black box part where you can you tell me the inferences decisions are those documented. And this is important for this me, the human in the loop to get confidence in the algorithm as well as you know, that executive group. So they understand there's a structure set of processes around >> Moneyball. Problem is actually pretty confined. It's pretty straightforward. Dono 32 teams are throwing minor leagues, but the data models pretty consistent through the problem with organizations is I didn't know data model is consistent with the organization you mentioned, Risk Bob. The >> other problem is organizational inertia. If they don't trust it, what is it? What is a P and l manage to do when he or she wants to preserve? Yeah, you know, their exit position. They attacked the data. You know, I don't believe that well, which which is >> a fundamental point, which is culture. Yes. I mean, you can you can have all the data, science and all the governance that you want. But if you don't work culture in parallel with all this, it's it's not gonna stick. And and that's, I think the lot of the leading organisations, they're starting to really dig into this. We hear a lot of it literacy. We hear a lot about, you know, top down support. What does that really mean? It means, you know, senior executives are placing bats around and linking demonstrably linking the data and the role of data days an asset into their strategies and then messaging it out and being specific around the types of investments that are going to reinforce that business strategy. So that's absolutely critical. And then literacy absolutely fundamental is well, because it's not just the executives and the data scientists that have to get this. It's the guy in ops that you're trying to get you. They need to understand, you know, not only tools, but it's less about the tools. But it's the techniques, so it's not. The approach is being used, are more transparent and and that you know they're starting to also understand, you know, the issues of privacy and data usage rights. That's that's also something that we can't leave it the curb. With all this >> innovation, it's also believing that there's an imperative. I mean, there's a lot of for all the talk about digital transformation hear it everywhere. Everybody's trying to get digital, right? But there's still a lot of complacency in the organization in the lines of business in operation to save. We're actually doing really well. You know, we're in financial service is health care really hasn't been disrupted. This is Oh, it's coming, it's coming. But there's still a lot of I'll be retired by then or hanging. Actually, it's >> also it's also the fact that, you know, like in the previous generation, like, you know, if I had to go to a shopping, I would go into a shop and if I wanted by an insurance product, I would call my insurance agent. But today the New world, it's just a top off my screen. I have to go from Amazon, so some other some other app, and this is really this is what is happening to all of our kind. Previously that they start their customers, pocketed them in different experience. Buckets. It's not anymore that's real in front of them. So if you don't get into their digital transformation, a customer is not going to discount you by saying, Oh, you're not Amazon. So I'm not going to expect that you're still on my phone and you're only two types of here, so you have to become really digital >> little surprises that you said you see the next. The next stage is being decision support rather than customer experience, because we hear that for CEOs, customer experience is top of mind right now. >> No natural profile. There are two differences, right? One is external facing is absolutely the customer internal facing. It's absolutely the decision making, because that's how they're separating. The internal were, says the external, and you know most of the meetings that we goto Customer insight is the first place where analytics is starting where data is being cleaned up. Their questions are being asked about. Can I master my customer records? Can I do a good master off my vendor list? That is where they start. But all of that leads to good decision making to support the customers. So it's like that external towards internal view well, back >> to the offense versus defense and the shift. I mean, it absolutely is on the offense side. So it is with the customer, and that's a more directly to the business strategy. So it's get That's the area that's getting the money, the support and people feel like it's they're making an impact with it there. When it's it's down here in some admin area, it's below the water line, and, you know, even though it's important and it flows up here, it doesn't get the VIN visibility. So >> that's great conversation. You coming on? You got to leave it there. Thank you for watching right back with our next guest, Dave Lot. Paul Gillen from M I t CDO I Q Right back. You're watching the Cube

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to Cambridge, Massachusetts. Everybody, You're watching the Cube, the leader in live tech coverage, and we're covering the M I t CDO conference M I t. CDO. My name is David Monty in here with my co host, Paul Galen. Mike Stone breakers here. The legend is founder CTO of Of Tamer, as well as many other companies. Inventor Michael. Thanks for coming back in the Cube. Good to see again. Nice to be here. So this is kind of ah, repeat pattern for all of us. We kind of gather here in August that the CDO conference You're always the highlight of the show. You gave a talk this week on the top 10. Big data mistakes. You and I are one of the few. You were the few people who still use the term big data. I happen to like it. Sad that it's out of vogue already, but people associated with the doo doop it's kind of waning, but regardless, so welcome. How'd the talk go? What were you talking about. >> So I talked to a lot of people who were doing analytics. We're doing operation Offer operational day of data at scale, and they always make most of them make a collection of bad mistakes. And so the talk waas a litany of the blunders that I've seen people make, and so the audience could relate to the blunders about most. Most of the enterprise is represented. Make a bunch of the blunders. So I think no. One blunder is not planning on moving most everything to the cloud. >> So that's interesting, because a lot of people would would would love to debate that, but and I would imagine you probably could have done this 10 years ago in a lot of the blunders would be the same, but that's one that wouldn't have been there. But so I tend to agree. I was one of the two hands that went up this morning, and vocalist talk when he asked, Is the cloud cheaper for us? It is anyway. But so what? Why should everybody move everything? The cloud aren't there laws of physics, laws of economics, laws of the land that suggest maybe you >> shouldn't? Well, I guess 22 things and then a comment. First thing is James Hamilton, who's no techies. Techie works for Amazon. We know James. So he claims that he could stand up a server for 25% of your cost. I have no reason to disbelieve him. That number has been pretty constant for a few years, so his cost is 1/4 of your cost. Sooner or later, prices are gonna reflect costs as there's a race to the bottom of cloud servers. So >> So can I just stop you there for a second? Because you're some other date on that. All you have to do is look at a W S is operating margin and you'll see how profitable they are. They have software like economics. Now we're deploying servers. So sorry to interrupt, but so carry. So >> anyway, sooner or later, they're gonna have their gonna be wildly cheaper than you are. The second, then yet is from Dave DeWitt, whose database wizard. And here's the current technology that that Microsoft Azure is using. As of 18 months ago, it's shipping containers and parking lots, chilled water in power in Internet, Ian otherwise sealed roof and walls optional. So if you're doing raised flooring in Cambridge versus I'm doing shipping containers in the Columbia River Valley, who's gonna be a lot cheaper? And so you know the economies of scale? I mean, that, uh, big, big cloud guys are building data centers as fast as they can, using the cheapest technology around. You put up the data center every 10 years on dhe. You do it on raised flooring in Cambridge. So sooner or later, the cloud guys are gonna be a lot cheaper. And the only thing that isn't gonna the only thing that will change that equation is For example, my lab is up the street with Frank Gehry building, and we have we have an I t i t department who runs servers in Cambridge. Uh, and they claim they're cheaper than the cloud. And they don't pay rent for square footage and they don't pay for electricity. So yeah, if if think externalities, If there are no externalities, the cloud is assuredly going to be cheaper. And then the other thing is that most everybody tonight that I talk thio including me, has very skewed resource demands. So in the cloud finding three servers, except for the last day of the month on the last day of the month. I need 20 servers. I just do it. If I'm doing on Prem, I've got a provision for peak load. And so again, I'm just way more expensive. So I think sooner or later these combinations of effects was going to send everybody to the cloud for most everything, >> and my point about the operating margins is difference in price and cost. I think James Hamilton's right on it. If he If you look at the actual cost of deploying, it's even lower than the price with the market allows them to their growing at 40 plus percent a year and a 35 $40,000,000,000 run rate company sooner, Sooner or >> later, it's gonna be a race to the lot of you >> and the only guys are gonna win. You have guys have the best cost structure. A >> couple other highlights from your talk. >> Sure, I think 2nd 2nd thing like Thio Thio, no stress is that machine learning is going to be a game is going to be a game changer for essentially everybody. And not only is it going to be autonomous vehicles. It's gonna be automatic. Check out. It's going to be drone delivery of most everything. Uh, and so you can, either. And it's gonna affect essentially everybody gonna concert of, say, categorically. Any job that is easy to understand is going to get automated. And I think that's it's gonna be majorly impactful to most everybody. So if you're in Enterprise, you have two choices. You can be a disrupt or or you could be a disruptive. And so you can either be a taxi company or you can be you over, and it's gonna be a I machine learning that's going going to be determined which side of that equation you're on. So I was a big blunder that I see people not taking ml incredibly seriously. >> Do you see that? In fact, everyone I talked who seems to be bought in that this is we've got to get on the bandwagon. Yeah, >> I'm just pointing out the obvious. Yeah, yeah, I think, But one that's not quite so obvious you're is a lot of a lot of people I talked to say, uh, I'm on top of data science. I've hired a group of of 10 data scientists, and they're doing great. And when I talked, one vignette that's kind of fun is I talked to a data scientist from iRobot, which is the guys that have the vacuum cleaner that runs around your living room. So, uh, she said, I spend 90% of my time locating the data. I want to analyze getting my hands on it and cleaning it, leaving the 10% to do data science job for which I was hired. Of the 10% I spend 90% fixing the data cleaning errors in my data so that my models work. So she spends 99% of her time on what you call data preparation 1% of her time doing the job for which he was hired. So data science is not about data science. It's about data integration, data cleaning, data, discovery. >> But your new latest venture, >> so tamer does that sort of stuff. And so that's But that's the rial data science problem. And a lot of people don't realize that yet, And, uh, you know they will. I >> want to ask you because you've been involved in this by my count and starting up at least a dozen companies. Um, 99 Okay, It's a lot. >> It's not overstated. You estimated high fall. How do you How >> do you >> decide what challenge to move on? Because they're really not. You're not solving the same problems. You're You're moving on to new problems. How do you decide? What's the next thing that interests you? Enough to actually start a company. Okay, >> that's really easy. You know, I'm on the faculty of M i t. My job is to think of news new ship and investigate it, and I come up. No, I'm paid to come up with new ideas, some of which have commercial value, some of which don't and the ones that have commercial value, like, commercialized on. So it's whatever I'm doing at the time on. And that's why all the things I've commercialized, you're different >> s so going back to tamer data integration platform is a lot of companies out there claim to do it day to get integration right now. What did you see? What? That was the deficit in the market that you could address. >> Okay, great question. So there's the traditional data. Integration is extract transforming load systems and so called Master Data management systems brought to you by IBM in from Attica. Talent that class of folks. So a dirty little secret is that that technology does not scale Okay, in the following sense that it's all well, e t l doesn't scale for a different reason with an m d l e t l doesn't scale because e t. L is based on the premise that somebody really smart comes up with a global data model For all the data sources you want put together. You then send a human out to interview each business unit to figure out exactly what data they've got and then how to transform it into the global data model. How to load it into your data warehouse. That's very human intensive. And it doesn't scale because it's so human intensive. So I've never talked to a data warehouse operator who who says I integrate the average I talk to says they they integrate less than 10 data sources. Some people 20. If you twist my arm hard, I'll give you 50. So a Here. Here's a real world problem, which is Toyota Motor Europe. I want you right now. They have a distributor in Spain, another distributor in France. They have a country by country distributor, sometimes canton by Canton. Distribute distribution. So if you buy a Toyota and Spain and move to France, Toyota develops amnesia. The French French guys know nothing about you. So they've got 250 separate customer databases with 40,000,000 total records in 50 languages. And they're in the process of integrating that. It was single customer database so that they can Duke custom. They could do the customer service we expect when you cross cross and you boundary. I've never seen an e t l system capable of dealing with that kind of scale. E t l dozen scale to this level of problem. >> So how do you solve that problem? >> I'll tell you that they're a tamer customer. I'll tell you all about it. Let me first tell you why MGM doesn't scare. >> Okay. Great. >> So e t l says I now have all your data in one place in the same format, but now you've got following problems. You've got a d duplicated because if if I if I bought it, I bought a Toyota in Spain, I bought another Toyota in France. I'm both databases. So if you want to avoid double counting customers, you got a dupe. Uh, you know, got Duke 30,000,000 records. And so MGM says Okay, you write some rules. It's a rule based technology. So you write a rule. That's so, for example, my favorite example of a rule. I don't know if you guys like to downhill downhill skiing, All right? I love downhill skiing. So ski areas, Aaron, all kinds of public databases assemble those all together. Now you gotta figure out which ones are the same the same ski area, and they're called different names in different addresses and so forth. However, a vertical drop from bottom to the top is the same. Chances are they're the same ski area. So that's a rule that says how to how to put how to put data together in clusters. And so I now have a cluster for mount sanity, and I have a problem which is, uh, one address says something rather another address as something else. Which one is right or both? Right, so now you want. Now you have a gold. Let's call the golden Record problem to basically decide which, which, which data elements among a variety that maybe all associated with the same entity are in fact correct. So again, MDM, that's a rule's a rule based system. So it's a rule based technology and rule systems don't scale the best example I can give you for why Rules systems don't scale. His tamer has another customer. General Electric probably heard of them, and G wanted to do spend analytics, and so they had 20,000,000 spend transactions. Frank the year before last and spend transaction is I paid $12 to take a cab from here here to the airport, and I charged it to cost center X Y Z 20,000,000 of those so G has a pre built classification system for spend, so they have parts and underneath parts or computers underneath computers and memory and so forth. So pre existing preexisting class classifications for spend they want to simply classified 20,000,000 spent transactions into this pre existing hierarchy. So the traditional technology is, well, let's write some rules. So G wrote 500 rules, which is about the most any single human I can get there, their arms around so that classified 2,000,000 of the 20,000,000 transactions. You've now got 18 to go and another 500 rules is not going to give you 2,000,000 more. It's gonna give you love diminishing returns, right? So you have to write a huge number of rules and no one can possibly understand. So the technology simply doesn't scale, right? So in the case of G, uh, they had tamer health. Um, solve this. Solved this classification problem. Tamer used their 2,000,000 rule based, uh, tag records as training data. They used an ML model, then work off the training data classifies remaining 18,000,000. So the answer is machine learning. If you don't use machine learning, you're absolutely toast. So the answer to MDM the answer to MGM doesn't scale. You've got to use them. L The answer to each yell doesn't scale. You gotta You're putting together disparate records can. The answer is ml So you've got to replace humans by machine learning. And so that's that seems, at least in this conference, that seems to be resonating, which is people are understanding that at scale tradition, traditional data integration, technology's just don't work >> well and you got you got a great shot out on yesterday from the former G S K Mark Grams, a leader Mark Ramsay. Exactly. Guys. And how they solve their problem. He basically laid it out. BTW didn't work and GM didn't work, All right. I mean, kick it, kick the can top down data modelling, didn't work, kicked the candid governance That's not going to solve the problem. And But Tamer did, along with some other tooling. Obviously, of course, >> the Well, the other thing is No. One technology. There's no silver bullet here. It's going to be a bunch of technologies working together, right? Mark Ramsay is a great example. He used his stream sets and a bunch of other a bunch of other startup technology operating together and that traditional guys >> Okay, we're good >> question. I want to show we have time. >> So with traditional vendors by and large or 10 years behind the times, And if you want cutting edge stuff, you've got to go to start ups. >> I want to jump. It's a different topic, but I know that you in the past were critic of know of the no sequel movement, and no sequel isn't going away. It seems to be a uh uh, it seems to be actually gaining steam right now. What what are the flaws in no sequel? It has your opinion changed >> all? No. So so no sequel originally meant no sequel. Don't use it then. Then the marketing message changed to not only sequel, So sequel is fine, but no sequel does others. >> Now it's all sequel, right? >> And my point of view is now. No sequel means not yet sequel because high level language, high level data languages, air good. Mongo is inventing one Cassandra's inventing one. Those unless you squint, look like sequel. And so I think the answer is no sequel. Guys are drifting towards sequel. Meanwhile, Jason is That's a great idea. If you've got your regular data sequel, guys were saying, Sure, let's have Jason is the data type, and I think the only place where this a fair amount of argument is schema later versus schema first, and I pretty much think schema later is a bad idea because schema later really means you're creating a data swamp exactly on. So if you >> have to fix it and then you get a feel of >> salary, so you're storing employees and salaries. So, Paul salaries recorded as dollars per month. Uh, Dave, salary is in euros per week with a lunch allowance minds. So if you if you don't, If you don't deal with irregularities up front on data that you care about, you're gonna create a mess. >> No scheme on right. Was convenient of larger store, a lot of data cheaply. But then what? Hard to get value out of it created. >> So So I think the I'm not opposed to scheme later. As long as you realize that you were kicking the can down the road and you're just you're just going to give your successor a big mess. >> Yeah, right. Michael, we gotta jump. But thank you so much. Sure appreciate it. All right. Keep it right there, everybody. We'll be back with our next guest right into the short break. You watching the cue from M i t cdo Ike, you right back

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. Everybody. This is the Cube. The leader in live coverage. My name is David wanted. I'm here with my co host, Paul Gill, in this day to coverage of the M I t cdo I Q conference. A lot of acronym stands for M I. T. Of course, the great institution. But Chief Data officer information quality event is his 13th annual event. Lars to Maria's here is the managing partner of Brass Rat Capital. Cool name Lars. Welcome to the Cube. Great. Very much. Glad I start with a name brass around Capitol was That's >> rat is reference to the M I t school. Okay, Beaver? Well, he is, but the students call it a brass rat, and I'm third generation M i t. So it's just seen absolutely appropriate. That is a brass rods and capital is not a reference to money, but is actually referenced to the intellectual capital. They if you have five or six brass rats in the same company, you know, we Sometimes engineers arrive and they could do some things. >> And it Boy, if you put in some data data capital in there, you really explosions. We cause a few problems. So we're gonna talk about some new regulations that are coming down. New legislation that's coming down that you exposed me to yesterday, which is gonna have downstream implications. You get ahead of this stuff and understand it. You can really first of all, prepare, make sure you're in compliance, but then potentially take advantage for your business. So explain to us this notion of open government act. >> Um, in the last five years, six years or so, there's been an effort going on to increase the transparency across all levels of government. Okay, State, local and federal government. The first of federal government laws was called the the Open Data Act of 2014 and that was an act. They was acted unanimously by Congress and signed by Obama. They was taking the departments of the various agencies of the United States government and trying to roll up all the expenses into one kind of expense. This is where we spent our money and who got the money and doing that. That's what they were trying to do. >> Big picture type of thing. >> Yeah, big picture type thing. But unfortunately, it didn't work, okay? Because they forgot to include this odd word called mentalities. So the same departments meant the same thing. Data problem. They have a really big data problem. They still have it. So they're to G et o reports out criticizing how was done, and the government's gonna try and correct it. Then in earlier this year, there was another open government date act which said in it was signed by Trump. Now, this time you had, like, maybe 25 negative votes, but essentially otherwise passed Congress completely. I was called the Open as all capital O >> P E >> n Government Data act. Okay, and that's not been implemented yet. But there's live talking around this conference today in various Chief date officers are talking about this requirement that every single non intelligence defense, you know, vital protection of the people type stuff all the like, um, interior, treasury, transportation, those type of systems. If you produce a report these days, which is machine, I mean human readable. You must now in two years or three years. I forget the exact invitation date. Have it also be machine readable. Now, some people think machine riddle mil means like pdf formats, but no, >> In fact, what the government did is it >> said it must be machine readable. So you must be able to get into the reports, and you have to be able to extract out the information and attach it to the tree of knowledge. Okay, so we're all of sudden having context like they're currently machine readable, Quote unquote, easy reports. But you can get into those SEC reports. You pull out the net net income information and says its net income, but you don't know what it attaches to on the tree of knowledge. So, um, we are helping the government in some sense able, machine readable type reporting that weaken, do machine to machine without people being involved. >> Would you say the tree of knowledge You're talking about the constant >> man tick semantic tree of knowledge so that, you know, we all come from one concept like the human is example of a living thing living beast, a living Beeston example Living thing. So it also goes back, and they're serving as you get farther and farther out the tree, there's more distance or semantic distance, but you can attach it back to concept so you can attach context to the various data. Is this essentially metadata? That's what people call it. But if I would go over see sale here at M I t, they would turn around. They call it the Tree of Knowledge or semantic data. Okay, it's referred to his semantic dated, So you are passing not only the data itself, but the context that >> goes along with the data. Okay, how does this relate to the financial transparency? >> Well, Financial Transparency Act was introduced by representative Issa, who's a Republican out of California. He's run the government Affairs Committee in the House. He retired from Congress this past November, but in 2017 he introduced what's got referred to his H R 15 30 Um, and the 15 30 is going to dramatically change the way, um, financial regulators work in the United States. Um, it is about it was about to be introduced two weeks ago when the labor of digital currency stuff came up. So it's been delayed a little bit because they're trying to add some of the digital currency legislation to that law. >> A front run that Well, >> I don't know exactly what the remember soul coming out of Maxine Waters Committee. So the staff is working on a bunch of different things at once. But, um, we own g was asked to consult with them on looking at the 15 30 act and saying, How would we improve quote unquote, given our technical, you know, not doing policy. We just don't have the technical aspects of the act. How would we want to see it improved? So one of the things we have advised is that for the first time in the United States codes history, they're gonna include interesting term called ontology. You know what intelligence? Well, everyone gets scared by the word. And when I read run into people, they say, Are you a doctor? I said, no, no, no. I'm just a date. A guy. Um, but an intolerant tea is like a taxonomy, but it had order has important, and an ontology allows you to do it is ah, kinda, you know, giving some context of linking something to something else. And so you're able Thio give Maur information with an intolerant that you're able to you with a tax on it. >> Okay, so it's a taxonomy on steroids? >> Yes, exactly what? More flexible, >> Yes, but it's critically important for artificial intelligence machine warning because if I can give them until ology of sort of how it goes up and down the semantics, I can turn around, do a I and machine learning problems on the >> order of 100 >> 1000 even 10,000 times faster. And it has context. It has contacts in just having a little bit of context speeds up these problems so dramatically so and it is that what enables the machine to machine? New notion? No, the machine to machine is coming in with son called SP R M just standard business report model. It's a OMG sophistication of way of allowing the computers or machines, as we call them these days to get into a standard business report. Okay, so let's say you're ah drug company. You have thio certify you >> drugged you manufactured in India, get United States safely. Okay, you have various >> reporting requirements on the way. You've got to give extra easy the FDA et cetera that will always be a standard format. The SEC has a different format. FERC has a different format. Okay, so what s p r m does it allows it to describe in an intolerant he what's in the report? And then it also allows one to attach an ontology to the cells in the report. So if you like at a sec 10 Q 10 k report, you can attach a US gap taxonomy or ontology to it and say, OK, net income annual. That's part of the income statement. You should never see that in a balance sheet type item. You know his example? Okay. Or you can for the first time by having that context you can say are solid problem, which suggested that you can file these machine readable reports that air wrong. So they believe or not, There were about 50 cases in the last 10 years where SEC reports have been filed where the assets don't equal total liabilities, plus cheryl equity, you know, just they didn't add >> up. So this to, >> you know, to entry accounting doesn't work. >> Okay, so so you could have the machines go and check scale. Hey, we got a problem We've >> got a problem here, and you don't have to get humans evolved. So we're gonna, um uh, Holland in Australia or two leaders ahead of the United States. In this area, they seem dramatic pickups. I mean, Holland's reporting something on the order of 90%. Pick up Australia's reporting 60% pickup. >> We say pick up. You're talking about pickup of errors. No efficiency, productivity, productivity. Okay, >> you're taking people out of the whole cycle. It's dramatic. >> Okay, now what's the OMG is rolling on the hoof. Explain the OMG >> Object Management Group. I'm not speaking on behalf of them. It's a membership run organization. You remember? I am a >> member of cold. >> I'm a khalid of it. But I don't represent omg. It's the membership has to collectively vote that this is what we think. Okay, so I can't speak on them, right? I have a pretty significant role with them. I run on behalf of OMG something called the Federated Enterprise Risk Management Group. That's the group which is focusing on risk management for large entities like the federal government's Veterans Affairs or Department offense upstairs. I think talking right now is the Chief date Officer for transportation. OK, that's a large organization, which they, they're instructed by own be at the, um, chief financial officer level. The one number one thing to do for the government is to get an effective enterprise worst management model going in the government agencies. And so they come to own G let just like NIST or just like DARPA does from the defense or intelligence side, saying we need to have standards in this area. So not only can we talk thio you effectively, but we can talk with our industry partners effectively on space. Programs are on retail, on medical programs, on finance programs, and so they're at OMG. There are two significant financial programs, or Sanders, that exist once called figgy financial instrument global identifier, which is a way of identifying a swap. Its way of identifying a security does not have to be used for a que ce it, but a worldwide. You can identify that you know, IBM stock did trade in Tokyo, so it's a different identifier has different, you know, the liberals against the one trading New York. Okay, so those air called figgy identifiers them. There are attributes associated with that security or that beast the being identified, which is generally comes out of 50 which is the financial industry business ontology. So you know, it says for a corporate bond, it has coupon maturity, semi annual payment, bullets. You know, it is an example. So that gives you all the information that you would need to go through to the calculation, assuming you could have a calculation routine to do it, then you need thio. Then turn around and set up your well. Call your environment. You know where Ford Yield Curves are with mortgage backed securities or any portable call. Will bond sort of probabilistic lee run their numbers many times and come up with effective duration? Um, And then you do your Vader's analytics. No aggregating the portfolio and looking at Shortfalls versus your funding. Or however you're doing risk management and then finally do reporting, which is where the standardized business reporting model comes in. So that kind of the five parts of doing a full enterprise risk model and Alex So what >> does >> this mean for first? Well, who does his impact on? What does it mean for organizations? >> Well, it's gonna change the world for basically everyone because it's like doing a clue ends of a software upgrade. Conversion one's version two point. Oh, and you know how software upgrades Everyone hates and it hurts because everyone's gonna have to now start using the same standard ontology. And, of course, that Sarah Ontology No one completely agrees with the regulators have agreed to it. The and the ultimate controlling authority in this thing is going to be F sock, which is the Dodd frank mandated response to not ever having another chart. So the secretary of Treasury heads it. It's Ah, I forget it's the, uh, federal systemic oversight committee or something like that. All eight regulators report into it. And, oh, if our stands is being the adviser Teff sock for all the analytics, what these laws were doing, you're getting over farm or more power to turn around and look at how we're going to find data across the three so we can come up consistent analytics and we can therefore hopefully take one day. Like Goldman, Sachs is pre payment model on mortgages. Apply it to Citibank Portfolio so we can look at consistency of analytics as well. It is only apply to regulated businesses. It's gonna apply to regulated financial businesses. Okay, so it's gonna capture all your mutual funds, is gonna capture all your investment adviser is gonna catch her. Most of your insurance companies through the medical air side, it's gonna capture all your commercial banks is gonna capture most of you community banks. Okay, Not all of them, because some of they're so small, they're not regularly on a federal basis. The one regulator which is being skipped at this point, is the National Association Insurance Commissioners. But they're apparently coming along as well. Independent federal legislation. Remember, they're regulated on the state level, not regularly on the federal level. But they've kind of realized where the ball's going and, >> well, let's make life better or simply more complex. >> It's going to make life horrible at first, but we're gonna take out incredible efficiency gains, probably after the first time you get it done. Okay, is gonna be the problem of getting it done to everyone agreeing. We use the same definitions >> of the same data. Who gets the efficiency gains? The regulators, The companies are both >> all everyone. Can you imagine that? You know Ah, Goldman Sachs earnings report comes out. You're an analyst. Looking at How do I know what Goldman? Good or bad? You have your own equity model. You just give the model to the semantic worksheet and all turn around. Say, Oh, those numbers are all good. This is what expected. Did it? Did it? Didn't you? Haven't. You could do that. There are examples of companies here in the United States where they used to have, um, competitive analysis. Okay. They would be taking somewhere on the order of 600 to 7. How 100 man hours to do the competitive analysis by having an available electronically, they cut those 600 hours down to five to do a competitive analysis. Okay, that's an example of the type of productivity you're gonna see both on the investment side when you're doing analysis, but also on the regulatory site. Can you now imagine you get a regulatory reports say, Oh, there's they're out of their way out of whack. I can tell you this fraud going on here because their numbers are too much in X y z. You know, you had to fudge numbers today, >> and so the securities analyst can spend Mme. Or his or her time looking forward, doing forecasts exactly analysis than having a look back and reconcile all this >> right? And you know, you hear it through this conference, for instance, something like 80 to 85% of the time of analysts to spend getting the data ready. >> You hear the same thing with data scientists, >> right? And so it's extent that we can helped define the data. We're going thio speed things up dramatically. But then what's really instinct to me, being an M I t engineer is that we have great possibilities. An A I I mean, really great possibilities. Right now, most of the A miles or pattern matching like you know, this idea using face shield technology that's just really doing patterns. You can do wonderful predictive analytics of a I and but we just need to give ah lot of the a m a. I am a I models the contact so they can run more quickly. OK, so we're going to see a world which is gonna found funny, But we're going to see a world. We talk about semantic analytics. Okay. Semantic analytics means I'm getting all the inputs for the analysis with context to each one of the variables. And when I and what comes out of it will be a variable results. But you also have semantics with it. So one in the future not too distant future. Where are we? We're in some of the national labs. Where are you doing it? You're doing pipelines of one model goes to next model goes the next mile. On it goes Next model. So you're gonna software pipelines, Believe or not, you get them running out of an Excel spreadsheet. You know, our modern Enhanced Excel spreadsheet, and that's where the future is gonna be. So you really? If you're gonna be really good in this business, you're gonna have to be able to use your brain. You have to understand what data means You're going to figure out what your modeling really means. What happens if we were, You know, normally for a lot of the stuff we do bell curves. Okay, well, that doesn't have to be the only distribution you could do fat tail. So if you did fat tail descriptions that a bell curve gets you much different results. Now, which one's better? I don't know, but, you know, and just using example >> to another cut in the data. So our view now talk about more about the tech behind this. He's mentioned a I What about math? Machine learning? Deep learning. Yeah, that's a color to that. >> Well, the tech behind it is, believe or not, some relatively old tech. There is a technology called rd F, which is kind of turned around for a long time. It's a science kind of, ah, machine learning, not machine wearing. I'm sorry. Machine code type. Fairly simplistic definitions. Lots of angle brackets and all this stuff there is a higher level. That was your distracted, I think put into standard in, like, 2000 for 2005. Called out. Well, two point. Oh, and it does a lot at a higher level. The same stuff that already f does. Okay, you could also create, um, believer, not your own special ways of a communicating and ontology just using XML. Okay, So, uh, x b r l is an enhanced version of XML, okay? And so some of these older technologies, quote unquote old 20 years old, are essentially gonna be driving a lot of this stuff. So you know you know Corbett, right? Corba? Is that what a maid omg you know, on the communication and press thing, do you realize that basically every single device in the world has a corpus standard at okay? Yeah, omg Standard isn't all your smartphones and all your computers. And and that's how they communicate. It turns out that a lot of this old stuff quote unquote, is so rigidly well defined. Well done that you can build modern stuff that takes us to the Mars based on these old standards. >> All right, we got to go. But I gotta give you the award for the most acronyms >> HR 15 30 fi G o m g s b r >> m fsoc tarp. Oh, fr already halfway. We knew that Owl XML ex brl corba, Which of course >> I do. But that's well done. Like thanks so much for coming. Everyone tried to have you. All right, keep it right there, everybody, We'll be back with our next guest from M i t cdo I Q right after this short, brief short message. Thank you

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back >> to M I. T. Everybody watching the Cube, The leader in live tech coverage. My name is Dave Volonte here with Paul Guillen. My co host, Tom Davenport, is here is the president's distinguished professor at Babson College. Huebel? Um, good to see again, Tom. Thanks for coming on. Glad to be here. So, yeah, this is, uh let's see. The 13th annual M I t. Cdo lucky. >> Yeah, sure. As this year. Our seventh. I >> think so. Really? Maybe we'll offset. So you gave a talk earlier? She would be afraid of the machines, Or should we embrace them? I think we should embrace them, because so far, they are not capable of replacing us. I mean, you know, when we hit the singularity, which I'm not sure we'll ever happen, But it's certainly not going happen anytime soon. We'll have a different answer. But now good at small, narrow task. Not so good at doing a lot of the things that we do. So I think we're fine. Although as I said in my talk, I have some survey data suggesting that large U. S. Corporations, their senior executives, a substantial number of them more than half would liketo automate as many jobs as possible. They say. So that's a little scary. But unfortunately for us human something, it's gonna be >> a while before they succeed. Way had a case last year where McDonald's employees were agitating for increasing the minimum wage and tThe e management used the threat of wrote of robotics sizing, hamburger making process, which can be done right to thio. Get them to back down. Are you think we're going to Seymour of four that were maybe a eyes used as a threat? >> Well, I haven't heard too many other examples. I think for those highly structured, relatively low level task, it's quite possible, particularly if if we do end up raising the minimum wage beyond a point where it's economical, pay humans to do the work. Um, but I would like to think that, you know, if we gave humans the opportunity, they could do Maur than they're doing now in many cases, and one of the things I was saying is that I think companies are. Generally, there's some exceptions, but most companies they're not starting to retrain their workers. Amazon recently announced they're going to spend 700,000,000 to retrain their workers to do things that a I and robots can't. But that's pretty rare. Certainly that level of commitment is very rare. So I think it's time for the companies to start stepping up and saying, How can we develop a better combination of humans and machines? >> The work by, you know, brain Nelson and McAfee, which is a little dated now. But it definitely suggests that there's some things to be concerned about. Of course, ultimately there prescription was one of an optimist and education, and yeah, on and so forth. But you know, the key point there is the machines have always replace humans, but now, in terms of cognitive functions, but you see it everywhere you drive to the airport. Now it's Elektronik billboards. It's not some person putting up the kiosks, etcetera, but you know, is you know, you've you've used >> the term, you know, paid the cow path. We don't want to protect the past from the future. All right, so, to >> your point, retraining education I mean, that's the opportunity here, isn't it? And the potential is enormous. Well, and, you know, let's face it, we haven't had much in the way of productivity improvements in the U. S. Or any other advanced economy lately. So we need some guests, you know, replacement of humans by machines. But my argument has always been You can handle innovation better. You can avoid sort of race to the bottom at automation sometimes leads to, if you think creatively about humans and machines working as colleagues. In many cases, you remember in the PC boom, I forget it with a Fed chairman was it might have been, Greenspan said, You can see progress everywhere except in the product. That was an M. I. T. Professor Robert Solow. >> OK, right, and then >> won the Nobel Prize. But then, shortly thereafter, there was a huge productivity boom. So I mean is there may be a pent up Well, God knows. I mean, um, everybody's wondering. We've been spending literally trillions on I t. And you would think that it would have led toe productivity, But you know, certain things like social media, I think reduced productivity in the workplace and you know, we're all chatting and talking and slacking and sewing all over the place. Maybe that's is not conducive to getting work done. It depends what you >> do with that social media here in our business. It's actually it's phenomenal to see political coverage these days, which is almost entirely consist of reprinting politicians. Tweets >> Exactly. I guess it's made life easier for for them all people reporters sitting in the White House waiting for a press conference. They're not >> doing well. There are many reporters left. Where do you see in your consulting work your academic work? Where do you see a I being used most effectively in organizations right now? And where do you think that's gonna be three years from now? >> Well, I mean, the general category of activity of use case is the sort of someone's calling boring I. It's data integration. One thing that's being discussed a lot of this conference, it's connecting your invoices to your contracts to see Did we actually get the stuff that we contracted for its ah, doing a little bit better job of identifying fraud and doing it faster so all of those things are quite feasible. They're just not that exciting. What we're not seeing are curing cancer, creating fully autonomous vehicles. You know, the really aggressive moonshots that we've been trying for a while just haven't succeeded at what if we kind of expand a I is gonna The rumor, trawlers. New cool stuff that's coming out. So considering all these new checks with detective Aye, aye, Blockchain new security approaches. When do you think that machines will be able to make better diagnoses than doctors? Well, I think you know, in a very narrow sense in some cases, that could do it now. But the thing is, first of all, take a radiologist, which is one of the doctors I think most at risk from this because they don't typically meet with patients and they spend a lot of time looking at images. It turns out that the lab experiments that say you know, these air better than human radiologist say I tend to be very narrow, and what one lab does is different from another lab. So it's just it's gonna take a very long time to make it into, you know, production deployment in the physician's office. We'll probably have to have some regulatory approval of it. You know, the lab research is great. It's just getting it into day to day. Reality is the problem. Okay, So staying in this context of digital a sort of umbrella topic, do you think large retail stores roll largely disappeared? >> Uh, >> some sectors more than others for things that you don't need toe, touch and feel, And soon before you're to them. Certainly even that obviously, it's happening more and more on commerce. What people are saying will disappear. Next is the human at the point of sale. And we've been talking about that for a while. In In grocery, Not so not achieve so much yet in the U. S. Amazon Go is a really interesting experiment where every time I go in there, I tried to shoplift. I took a while, and now they have 12 stores. It's not huge yet, but I think if you're in one of those jobs that a substantial chunk of it is automata ble, then you really want to start looking around thinking, What else can I do to add value to these machines? Do you think traditional banks will lose control of the payment system? Uh, No, I don't because the Finn techs that you see thus far keep getting bought by traditional bank. So my guess is that people will want that certainty. And you know, the funny thing about Blockchain way say in principle it's more secure because it's spread across a lot of different ledgers. But people keep hacking into Bitcoin, so it makes you wonder. I think Blockchain is gonna take longer than way thought as well. So, you know, in my latest book, which is called the Aye Aye Advantage, I start out talking by about Tamara's Law, This guy Roy Amara, who was a futurist, not nearly as well known as Moore's Law. But it said, You know, for every new technology, we tend to overestimate its impact in the short run and underestimated Long, long Ryan. And so I think a I will end up doing great things. We may have sort of tuned it out of the time. It actually happens way finally have autonomous vehicles. We've been talking about it for 50 years. Last one. So one of the Democratic candidates of the 75 Democratic ended last night mentioned the chief manufacturing officer Well, do you see that automation will actually swing the pendulum and bring back manufacturing to the U. S. I think it could if we were really aggressive about using digital technologies in manufacturing, doing three D manufacturing doing, um, digital twins of every device and so on. But we are not being as aggressive as we ought to be. And manufacturing companies have been kind of slow. And, um, I think somewhat delinquent and embracing these things. So they're gonna think, lose the ability to compete. We have to really go at it in a big way to >> bring it. Bring it all back. Just we've got an election coming up. There are a lot of concern following the last election about the potential of a I chatbots Twitter chat bots, deep fakes, technologies that obscure or alter reality. Are you worried about what's coming in the next year? And that that >> could never happen? Paul. We could never see anything deep fakes I'm quite worried about. We don't seem. I know there's some organizations working on how we would certify, you know, an image as being really But we're not there yet. My guess is, certainly by the time the election happens, we're going to have all sorts of political candidates saying things that they never really said through deep fakes and image manipulation. Scary? What do you think about the call to break up? Big check. What's your position on that? I think that sell a self inflicted wound. You know, we just saw, for example, that the automobile manufacturers decided to get together. Even though the federal government isn't asking for better mileage, they said, We'll do it. We'll work with you in union of states that are more advanced. If Big Tak had said, we're gonna work together to develop standards of ethical behavior and privacy and data and so on, they could've prevented some of this unless they change their attitude really quickly. I've seen some of it sales force. People are talking about the need for data standard data protection standards, I must say, change quickly. I think they're going to get legislation imposed and maybe get broken up. It's gonna take awhile. Depends on the next administration, but they're not being smart >> about it. You look it. I'm sure you see a lot of demos of advanced A I type technology over the last year, what is really impressed you. >> You know, I think the biggest advances have clearly been in image recognition looking the other day. It's a big problem with that is you need a lot of label data. It's one of the reasons why Google was able to identify cat photos on the Internet is we had a lot of labeled cat images and the Image net open source database. But the ability to start generating images to do synthetic label data, I think, could really make a big difference in how rapidly image recognition works. >> What even synthetic? I'm sorry >> where we would actually create. We wouldn't have to have somebody go around taking pictures of cats. We create a bunch of different cat photos, label them as cat photos have variations in them, you know, unless we have a lot of variation and images. That's one of the reasons why we can't use autonomous vehicles yet because images differ in the rain and the snow. And so we're gonna have to have synthetic snow synthetic rain to identify those images. So, you know, the GPU chip still realizes that's a pedestrian walking across there, even though it's kind of buzzed up right now. Just a little bit of various ation. The image can throw off the recognition altogether. Tom. Hey, thanks so much for coming in. The Cube is great to see you. We gotta go play Catch. You're welcome. Keep right. Everybody will be back from M I t CDO I Q In Cambridge, Massachusetts. Stable, aren't they? Paul Gillis, You're watching the Cube?

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. In Cambridge, Massachusetts. Everybody. You're watching the cube. The leader in live tech coverage. This is M I t CDO I Q the chief data officer and information quality conference. Someday Volonte with my co host, Paul Galen. Professor Dr Stewart, Mad Nick is here. Longtime Cube alum. Ah, long time professor at M i. T soon to be retired, but we're really grateful that you're taking your time toe. Come on. The Cube is great to see you again. >> It's great to see you again. It's been a long time. She worked together and I really appreciate the opportunity to share our spirits. Hear our mighty with your audience. Well, it's really been fun >> to watch this conference evolved were full and it's really amazing. We have to move to a new venue >> next year. I >> understand. And data we talk about the date explosion all the time, But one of the areas that you're focused on and you're gonna talk about today is his ethics and privacy and data causes so many concerns in those two areas. But so give us the highlight of what you're gonna discuss with the audience today. We'll get into >> one of things that makes it so challenging. It is. Data has so many implications. Tow it. And that's why the issue of ethics is so hard to get people to reach agreement on it. We're talking people regarding medicine and the idea big data and a I so know, to be able to really identify causes you need mass amounts of data. That means more data has to be made available as long as it's Elsa data, not mine. Well, not my backyard. If he really So you have this issue where on the one hand, people are concerned about sharing the data. On the other hand, there's so many valuable things would gain by sharing data and getting people to reach agreement is a challenge. Well, one of things >> I wanted to explore with you is how things have changed you back in the day very familiar with Paul you as well with Microsoft, Department of Justice, justice, FTC issues regarding Microsoft. And it wasn't so much around data was really around browsers and bundling things today. But today you see Facebook and Google Amazon coming under fire, and it's largely data related. Listen, Liz Warren, last night again break up big tech your thoughts on similarities and differences between sort of the monopolies of yesterday and the data monopolies of today Should they be broken up? What do you thought? So >> let me broaden the issue a little bit more from Maryland, and I don't know how the demographics of the audience. But I often refer to the characteristics that millennials the millennials in general. I ask my students this question here. Now, how many of you have a Facebook account in almost every class? Facebook. You realize you've given away a lot of nation about yourself. It it doesn't really occurred to them. That may be an issue. I was told by someone that in some countries, Facebook is very popular. That's how they cordoned the kidnappings of teenagers from rich families. They track them. They know they're going to go to this basketball game of the soccer match. You know exactly what I'm going after it. That's the perfect spot to kidnap them, so I don't know whether students think about the fact that when they're putting things on Facebook than making so much of their life at risk. On the other hand, it makes their life richer, more enjoyable. And so that's why these things are so challenging now, getting back to the issue of the break up of the big tech companies. One of the big challenges there is that in order to do the great things that big data has been doing and the things that a I promises do you need lots of data. Having organizations that can gather it all together in a relatively systematic and consistent manner is so valuable breaking up the tech companies. And there's some reasons why people want to do that, but also interferes with that benefit. And that's why I think it's gonna be looked at real Kim, please, to see not only what game maybe maybe breaking up also what losses of disadvantages we're creating >> for ourselves so example might be, perhaps it makes United States less competitive. Visa VI China, in the area of machine intelligence, is one example. The flip side of that is, you know Facebook has every incentive to appropriate our data to sell ads. So it's not an easy, you know, equation. >> Well, even ads are a funny situation for some people having a product called to your attention that something actually really want. But you never knew it before could be viewed as a feature, right? So, you know, in some case of the ads, could be viewed as a feature by some people. And, of course, a bit of intrusion by other people. Well, sometimes we use the search. Google, right? Looking >> for the ad on the side. No longer. It's all ads. You know >> it. I wonder if you see public public sentiment changing in this respect. There's a lot of concerns, certainly at the legislative level now about misuse of data. But Facebook user ship is not going down. Instagram membership is not going down. Uh, indication is that that ordinary citizens don't really care. >> I know that. That's been my I don't have all the data. Maybe you may have seen, but just anecdotally and talking to people in the work we're doing, I agree with you. I think most people maybe a bit dramatic, but at a conference once and someone made a comment that there has not been the digital Pearl Harbor yet. No, there's not been some event that was just so onerous. Is so all by the people. Remember the day it happened kind of thing. And so these things happen and maybe a little bit of press coverage and you're back on your Facebook. How their instagram account the next day. Nothing is really dramatic. Individuals may change now and then, but I don't see massive changes. But >> you had the Equifax hack two years ago. 145,000,000 records. Capital one. Just this week. 100,000,000 records. I mean, that seems pretty Pearl Harbor ish to me. >> Well, it's funny way we're talking about that earlier today regarding different parts of the world. I think in Europe, the general, they really seem to care about privacy. United States that kind of care about privacy in China. They know they have no privacy. But even in us where they care about privacy, exactly how much they care about it is really an issue. And in general it's not enough to move the needle. If it does, it moves it a little bit about the time when they show that smart TVs could be broken into smart. See, TV sales did not Dutch an inch. Not much help people even remember that big scandal a year ago. >> Well, now, to your point about expects, I mean, just this week, I think Equifax came out with a website. Well, you could check whether or not your credentials were. >> It's a new product. We're where we're compromised. And enough in what has been >> as head mind, I said, My wife says it's too. So you had a choice, you know, free monitoring or $125. So that way went okay. Now what? You know, life goes >> on. It doesn't seem like anything really changes. And we were talking earlier about your 1972 book about cyber security, that many of the principles and you outlined in that book are still valid today. Why are we not making more progress against cybercriminals? >> Well, two things. One thing is you gotta realize, as I said before, the Cave man had no privacy problems and no break in problems. But I'm not sure any of us want to go back to caveman era because you've got to realize that for all these bad things. There's so many good things that are happening, things you could now do, which a smartphone you couldn't even visualize doing a decade or two ago. So there's so much excitement, so much for momentum, autonomous cars and so on and so on that these minor bumps in the road are easy to ignore in the enthusiasm and excitement. >> Well and now, as we head into 2020 affection it was. It was fake news in 2016. Now we've got deep fakes. Get the ability to really use video in new ways. Do you see a way out of that problem? A lot of people looking a Blockchain You wrote an article recently, and Blockchain you think it's on hackable? Well, think again. >> What are you seeing? I think one of things we always talk about when we talk about improving privacy and security and organizations, the first thing is awareness. Most people are really small moment of time, aware that there's an issue and it quickly pass in the mind. The analogy I use regarding industrial safety. You go into almost any factory. You'll see a sign over the door every day that says 520 days, his last industrial accident and then a sub line. Please do not be the one to reset it this year. And I often say, When's the last time you went to a data center? And so assign is at 50 milliseconds his last cyber data breach. And so it needs to be something that is really front, the mind and people. And we talk about how to make awareness activities over companies and host household. And that's one of our major movements here is trying to be more aware because we're not aware that you're putting things at risk. You're not gonna do anything about it. >> Last year we contacted Silicon Angle, 22 leading security experts best in one simple question. Are we winning or losing the war against cybercriminals? Unanimously, they said, we're losing. What is your opinion of that question? >> I have a great quote I like to use. The good news is the good guys are getting better than a firewall of cryptographic codes. But the bad guys are getting batter faster, and there's a lot of reasons for that well on all of them. But we came out with a nautical talking about the docking Web, and the reason why it's fascinating is if you go to most companies if they've suffered a data breach or a cyber attack, they'll be very reluctant to say much about unless they really compelled to do so on the dock, where they love to Brent and reputation. I'm the one who broke in the Capital One. And so there's much more information sharing that much more organized, a much more disciplined. I mean, the criminal ecosystem is so much more superior than the chaotic mess we have here on the good guys side of the table. >> Do you see any hope for that? There are service's. IBM has one, and there are others in a sort of anonymous eyes. Security data enable organizations to share sensitive information without risk to their company. You see any hope on the collaboration, Front >> said before the good guys are getting better. The trouble is, at first I thought there was an issue that was enough sharing going on. It turns out we identified over 120 sharing organizations. That's the good news. And the bad news is 120. So IBM is one and another 119 more to go. So it's not a very well coordinated sharing. It's going just one example. The challenges Do I see any hope in the future? Well, in the more distant future, because the challenge we have is that there'll be a cyber attack next week of some form or shape that we've never seen before and therefore what? Probably not well prepared for it. At some point, I'll no longer be able to say that, but I think the cyber attackers and creatures and so on are so creative. They've got another decade of more to go before they run out of >> Steve. We've got from hacktivists to organized crime now nation states, and you start thinking about the future of war. I was talking to Robert Gates, aboutthe former defense secretary, and my question was, Why don't we have the best cyber? Can't we go in the oven? It goes, Yeah, but we also have the most to lose our critical infrastructure, and the value of that to our society is much greater than some of our adversaries. So we have to be very careful. It's kind of mind boggling to think autonomous vehicles is another one. I know that you have some visibility on that. And you were saying that technical challenges of actually achieving quality autonomous vehicles are so daunting that security is getting pushed to the back burner. >> And if the irony is, I had a conversation. I was a visiting professor, sir, at the University of Niece about a 12 14 years ago. And that's before time of vehicles are not what they were doing. Big automotive tele metrics. And I realized at that time that security wasn't really our top priority. I happen to visit organization, doing really Thomas vehicles now, 14 years later, and this conversation is almost identical now. The problems we're trying to solve. A hider problem that 40 years ago, much more challenging problems. And as a result, those problems dominate their mindset and security issues kind of, you know, we'll get around him if we can't get the cot a ride correctly. Why worry about security? >> Well, what about the ethics of autonomous vehicles? Way talking about your programming? You know, if you're gonna hit a baby or a woman or kill your passengers and yourself, what do you tell the machine to Dio, that is, it seems like an unsolvable problem. >> Well, I'm an engineer by training, and possibly many people in the audience are, too. I'm the kind of person likes nice, clear, clean answers. Two plus two is four, not 3.94 point one. That's the school up the street. They deal with that. The trouble with ethic issues is they don't tend to have a nice, clean answer. Almost every study we've done that has these kind of issues on it. And we have people vote almost always have spread across the board because you know any one of these is a bad decision. So which the bad decision is least bad. Like, what's an example that you used the example I use in my class, and we've been using that for well over a year now in class, I teach on ethics. Is you out of the design of an autonomous vehicle, so you must program it to do everything and particular case you have is your in the vehicle. It's driving around the mountain and Swiss Alps. You go around a corner and the vehicle, using all of senses, realize that straight ahead on the right? Ian Lane is a woman in a baby carriage pushing on to this onto the left, just entering the garage way a three gentlemen, both sides a road have concrete barriers so you can stay on your path. Hit the woman the baby carriage via to the left. Hit the three men. Take a shop, right or shot left. Hit the concrete wall and kill yourself. And trouble is, every one of those is unappealing. Imagine the headline kills woman and baby. That's not a very good thing. There actually is a theory of ethics called utility theory that says, better to say three people than to one. So definitely doing on Kim on a kill three men, that's the worst. And then the idea of hitting the concrete wall may feel magnanimous. I'm just killing myself. But as a design of the car, shouldn't your number one duty be to protect the owner of the car? And so people basically do. They close their eyes and flip a coin because they don't want anyone. Those hands, >> not an algorithmic >> response, doesn't leave. >> I want to come back for weeks before we close here to the subject of this conference. Exactly. You've been involved with this conference since the very beginning. How have you seen the conversation changed since that time? >> I think I think it's changing to Wei first. As you know, this record breaking a group of people are expecting here. Close to 500 I think have registered s o much Clea grown kind of over the years, but also the extent to which, whether it was called big data or call a I now whatever is something that was kind of not quite on the radar when we started, I think it's all 15 years ago. He first started the conference series so clearly has become something that is not just something We talk about it in the academic world but is becoming main stay business for corporations Maur and Maur. And I think it's just gonna keep increasing. I think so much of our society so much of business is so dependent on the data in any way, shape or form that we use it and have >> it well, it's come full circle. It's policy and I were talking at are open. This conference kind of emerged from the ashes of the back office information quality and you say the big date and now a I guess what? It's all coming back to information. >> Lots of data. That's no good. Or that you don't understand what they do with this. Not very healthy. >> Well, doctor Magic. Thank you so much. It's a >> relief for all these years. Really Wanna thank you. Thank you, guys, for joining us and helping to spread the word. Thank you. Pleasure. All right, keep it right, everybody. Paul and >> I will be back at M I t cdo right after this short break. You're watching the cue.

>> Live from Las Vegas, it's theCUBE covering Informatica World 2018, brought to you by Informatica. >> Hey, welcome back, everyone. I'm John Furrier here in theCUBE with Peter Burris, my cohost for the next few days. Live coverage from Informatica World 2018 here in Las Vegas. Our next guest is Graeme Thompson, senior vice president and CIO, chief information officer, for Informatica. He handles all the CIO roles, as well, inside the company and also speaks with a lot of their customers, who are also CIOs. Graeme, great to see you again, thanks for coming back on theCUBE. So last year, we had the conversation around your role as a CIO, but also, you're doing a lot of stuff internally, certainly using your own product, but you're spending a lot of time with customers, and a lot of those customers can either be project guys, application developers, CXOs, CDOs, CIOs. You interface a lot of customers, what's changed in the marketplace with respect to the CXO, chief something officer, 'cause there's been movement. Your thoughts? >> Yeah, definitely. So as I talk to POs and our customers, it's very clear that data integration, data management has moved beyond just trying to get a big project done. It's no longer about deploying ERP or CRM in the cloud and needing to move data around, the data management part is really in service of something much greater, and it may be getting close with your customer, it may be using the data that's generated in your company and about your company to generate insight, so the next best action to improve the operational efficiency of the company or more importantly, to improve the customer experience that they have as they deal with your company, so it's moved way beyond just getting a project done, and it's now a strategic thing in service of something higher-level, and that higher-level thing is usually on the radar of the board and the CEO. >> So our research suggests that we're moving from a world of process first, in the IT organization, to data first. Is that too far a stretch, as far as you're concerned? >> Not for some companies. When you look at the most valuable companies in the world today, companies like Microsoft, Facebook, Apple, Amazon, you could argue they're data companies. The product that they generate is data, the thing they use to compete upon is data, so for those companies, I don't think that's a stretch at all. For everyone else, they need to figure out what they're going to do about that and figure out whether they're going to try and catch up or whether they're going to try and disrupt, but I think for the world's best companies, you're seeing them more and more focused on the data, and the process is turning into just a thing that generates that data, which is the thing that generates the value. >> We've been seeing companies becoming more data companies, and Peter's research and the team has been showing digital business is about data assets, and Facebooks and the Amazons, they're obvious examples, we see them as hyperscalers, but there's going to be, the end-user customers, the traditional enterprises, they're now becoming service providers. They got cloud, they got multi-cloud, they're going to have an IOT edge, they have a bigger set of complexities around this horizontally-scalable digital business architecture. So your point about projects, in old days, easy, you ship, connect everyone, they log in, they do their job, and then they go out and sell to customers. >> Peter: But you still are. >> Well, I mean straightforward, known, right? It was a known enterprise, you had a perimeter. Now you have digital channel, you have more challenges. How do you look at that, and where does Informatica fit in that conversation with the CIO and the CEO, who have to report to the board level and say, we got to manage our security, we got to do all this stuff, how do you guys fit into that new world? >> Yeah, so the thing that differentiates Informatica from everyone else, frankly, is the fact that we look at it holistically, and we cover everything from discovering the data. If it's an asset, you have to know that you have it, so you got to go discover it, you got to be able to catalog it, so you can keep control of what you have. You need to know the lineage of it, where does it get created, where does it get moved to, who has access to it, with GDPR going in effect later this week. It's increasingly important for us to know who has access to the data, it's increasingly important to manage the lifecycle of that data so that you know where it's being created, used, moved. You have to secure it. If your aspiration is to have a true enterprise data lake, you got to make sure that the identity governance is in place, you got to make sure that information that may be HR-related isn't accessible by people who don't have the privilege to see it in the HR application. So that's the discovery, the cataloging. Then you have to clean it, master it, and looking at an MVM solution for getting a true 360 degree view of your customer or your product or your supplier. And then there's the analytics part, which is often the prize at the end. If you can get all the data into your data lake, and potentially with a data warehouse on the back of that, in the cloud, and then you can choose the presentation layer that you love the most and use that to serve up self-service analytics for your customers. So we're different in that we look at all of it. We've got a lot of nimble competitors that do one thing very well, and if our customer is trying to just get a project done, my advice to them is go to an RFP and pick whatever one you like, but if this is really strategic for you, you need to pick someone that they can do all of it and do it all well in a way that's going to be scalable and independent from the big software providers. >> But I want to come back, Graeme, to this, 'cause I think there's one more thing I want to test you on, this is kind of the basis for my comment earlier about moving to a data-first as opposed to a process-first world. Because I think it also, you have to be able to discover it, catalog it, be able to audit it, all those other things. But you also have to be able to deliver it and deliver it with a high degree of certainty that it's the right data at the right time. Historically, application developers started with a process and they presume that the data would be associated with that process. Now we're starting with these assets that are very, very high value, and we're looking for new ways to leverage those assets. It kind of has a different mindset, doesn't it? >> It really does, and that's the fun part, quite honestly. If you think about, the data used to be hostage to its process, the process used to be hostage to the application that it was executed in, and now we're opening up all these opportunities where you can take, just in our company, you can take usage information and make it available to our customer support organization, so they can proactively help our customer adopt the product. We can tell which features the customer may be using and not using to help focus our adoption efforts and really help the customer get more value from the product. That's an opportunity that was either unknown or very difficult to take advantage of when you were just looking at the process of fulfilling an order, delivering the cloud environment to the customer and then 12 months later, going back and trying to renew it. It's now a connected lifecycle of the customer's experience with your product, and it's all based on the data. The applications and the processes are just the things that generate it. >> What's changed, go back, 'cause you mentioned, that's an awesome example, the old way, with process, it now seems like the data is freed up. What changed, what was the catalyst from going, you know, stuck in the process, slave to the process, slave to the app, to what you just referred to, which seems like the outcome people want to get to, which is create data so that people can innovate on it. What's changed? >> Yeah, so I think as individuals, as humans, our expectations have changed. We now know that it's reasonable to expect that if I have an interaction with one part of your company on a Monday, the other part of your company, who I interact with on a Thursday, should know about it. I think as consumers, we've become conditioned to really expect that, and just like we now see in the B-to-C world, folks are expecting it in the B-to-B world. So you've got higher expectations, and then the capabilities to do it didn't really exist before. And now, with all these different, you've got all your different applications in the cloud, you've still got applications on premise, and there's an expectation and now the capability to do analytics on all of it, there's an expectation that information about you is known and used to improve your experience as a customer when you're dealing with these businesses. >> But the whole notion of data as an asset requires different governance, different people. We're strong believers that actually, you can measure the degree to which a company is on its digital transformation journey by the degree to which it has in fact institutionalized work around data or changed that or organized. When you look at the CIO role and how the CIO role is going to change or is changing and is going to change more, as a consequence of this, increasing focus on data as an asset within the business, what are you doing, what do you expect to be doing, what are you counseling other CIOs to do? >> Yeah, so that's a good one. When I talk to POs, I ask them, I try and create an analogy between the data as an asset and money as an asset, so I would ask them, "If you were to take your CFO, say, and ask them, "'Do you know where all your money is?' "They'll say, 'Of course I do.'" "'Do you know which currency your money is stored in? "'Do you know where it's physically? "'Do you know who has access to it? "'Do you have a governance process in place to try "'and figure out the most profitable use of that asset?' "And they'll go, "'Yeah, of course my CFO knows that.'" I say, "Okay, swap the word money for data, "and you as a CIO, can you answer yes "to any of those questions?" And you get a reaction of, "Oh, I believe I should, but I can't." A lot of companies say that data is an asset, but they're really not operating that way. They don't have the governance around it, they don't have the control around it, they don't have the governance in place to make sure they're using it in the most profitable way to get that return that you suggested. So I think that's definitely where we're moving, and some of the world's best companies are definitely going in that direction. >> That is exactly one of the things we were just talking about on our intro here this morning around the CIO and the CEOs don't know where their data is, and I think the GDPR is, I'm not a big fan of it, with all the technical challenges, and ultimately, it's a signal in my opinion, but ultimately, it's going to happen. But I think it's a signal to your point. You need to know about your data, not treat it as some fenced-off storage thing, and the storage administrator, where is it all, and the guy left, who's running it now, where's the data, what's the schema. These are all technical storage questions. >> Yeah, it's not the stuff on the storage assets, is the bottom line. >> That paradigm is over. You're talking about something that's fundamental, strategic business aspect, so I think this is a new generation. So with that, I want to ask you, you had talked before camera that you have a CDO that reports to you, a change for Informatica. Can you explain that decision, why a chief data officer reports to the CIO, why you guys came to that conclusion, and as a result of that, what's happening? >> Yeah, so we're going through a transformation in our company, as we move from being a traditional software company that sells license and maintenance, to being a cloud and subscription company. The processes and the systems you need to be a subscription company, to be a good one, are very different, right? It's a connected, end-to-end process all the way from how you generate your product, your go-to-market strategy, all the way through how you fulfill it, how you drive adoption and value creation with your customer, and ultimately, how you renew it and sell more. That's a different process than a traditional, ship it and forget it license company. So as we go through this transformation, we are solving a lot of the governance problems, we're solving a lot of the system of record and data quality problems, and we need to make sure that once we're done with each part of the project, it doesn't get broken again. In software companies, IT people are really good at fixing things, but they're not always really good at keeping it fixed. So the time was right for us to create this new position, and we debated where it should report, but we believe that as an action-orientated, get-stuff-done function, it has to be collocated with the team who are delivering the new applications and the new processes, and for the moment, that's within the CIO function. >> Are they going to be tracking this notion of asset, tracking like, if you treat data like an asset, like money, are you guys down the road on that? How are you viewing it internally, as you guys roll out the CDO relationship with you, and obviously, we're making it strategic, obviously, you guys know that, you're in the data business. Where are you on that question that you asked rhetorically for yourselves? >> Graeme: Yeah, so-- >> John: Do you know where your data is and-- >> Yeah, I mean, we're very, very fortunate to have unfettered access to all of our products, so we're very proud of our intelligent data lake deployment, which is on Azure Cloud. As more and more of ours and any other customer's workload move to the cloud, it makes more and more sense to have analytics there. People are questioning the wisdom of bringing all the data back on prem just to do analytics. The only people making money out of that are AT&T and Verizon, there's got to be a better way. So that's one thing that would be under the purview of the CDO, and that would be to enable self-service analytics across the company, get IT out of the way of generating the presentation layer of reporting, and enable the great and talented people throughout the company to do that. So that would be the analytics side. And then obviously, cataloging and securing is something we have the best solutions in the industry, so those solutions are deployed, and that'll help us with our GDPR compliance, but it'll also help us make sure that we know what we have and we have a process in place to at least consider what the most profitable use of that data asset would be elsewhere in the company. >> John: So you feel good about it. >> Yeah. >> Alright, so for the people that can't answer that question, a CIO, "Hmm, you know what, that's a good question, "I should know this." What do they do next, what's the next step of action that a CIO should take when they go, "Oh, no, I can't answer that question." They might have their hands on some fingertips of data, but ultimately, the strategic question is what do I do next? Obviously, call Informatica, I mean, do I do an audit? >> Hopefully, you'll call us, but if you take the vendor and the technology out of it, if you were trying to figure out how much money you had, you would put a process in place to go discover it all. >> John: Count it. >> So the equivalent there is cataloging, so our enterprise data catalog product is the fastest-growing product we've ever had in the company, and what that does is allows Google for your data. You can search for where all your customer data lives, you can search for where all your product data lives, you can figure out where it moves, and that is the first thing that I would advise a CIO to do, is figure out what you have, where it's stored, where it moves to, where it's used, and who has access to it, and if you have that, then at least you've got a shot at figuring out how to, you still need intelligent people to figure out where the most profitable use of it would be, but at least you know what you have, where it is, and who has access to it. And then when someone wants to come and ask you about the GDPR and your compliance level, if you can show them that, then at least it's clear that you have an objective to comply with the regulation. >> And they're going to be pretty lenient from what we hear, but they're going to want to see people making steps for compliance, and it's a moving train with GDPR. Again, we're going to go in deep on this this week. Graeme Thompson, senior vice president and CIO. Thanks for coming on theCUBE, great to see you again, let's keep in touch, love to explore the CDO relationship with the CIO, I think that's cutting-edge, congratulations. Know where your data is, how much it's worth. If you know where your money is and how much it's worth, you don't want to lose your data, you want to make sure you're leveraging it. It's theCUBE coverage here at Informatica World, I'm John Furrier, Peter Burris, more live coverage after this short break. (techno music)

>> Announcer: Live from Washington DC, it's the Cube, covering .NEXT Conference. Brought to you by Nutanix. (electronic music) >> Welcome back to .NEXT everybody. This is the Cube, the leader in live tech coverage. We go out to the events and extract the signal from the noise. My name is Dave Vellante, and I'm with my cohost, Stu Miniman. This is day two of .NEXT. Dr. John Bates is here. He's the CEO of TestPlant, and author of Thingalytics. Sir, welcome to the Cube. >> Thanks. >> Nice to have you on. >> Nice to be here. >> Thingalytics, everybody's talking about things. >> This thing, that thing, the refrigerator, the iode things. What's Thingalytics? >> Well, things, i.e. connected devices, sensors and so on. They're not very interesting unless you actually do something with them. So you search through all that data that's coming out for the opportunities and threats to your business, for example, and then you act on it, while you've got time and perhaps, beat your competitor. So, Thingalytics is about smart, big data analytics, and the internet of things coming together. >> Okay, and what's the premise of the book? >> Well the premise of the book is, you know, everybody thinks, I mean if it's one message from it, it's IoT is not so hard to get into. So get started. You know, start small, and here's some lessons of how you can do it. And here's some stories from different industries of how thought leaders, you know, like Coca Cola, or GE, or many different companies, Medtronic, in different industries have actually got started and really been extremely disruptive in what they've done. >> And is this getting started, is this all for companies, or are you seeing individuals that can also participate? >> You know, I do have a chapter in there about the Smarthome. So, obviously that's the aspect where the individual is going to come. But you know, I think it's really the real winner in this will be the industrial and the enterprise, Internet of Things. I guess that the key message is for business leaders. >> Do you think that given that there's, the internet of things requires things, and there's so many things that are installed by these big, industrial companies that the whole IoT thing will be maybe less of a disruption than it will be an evolution of companies like GE, and Siemens and Hitachi, and guys like that. Is that a reasonable premise, or will we see a whole new wave of companies? Certainly we'll see startups come in, but will they attack these big industrial giants, that have been around for a hundred years? >> You know, this is a really great question, and I think that, at the moment, the opportunity is in the hands of the big buyer. You know, keynoting at .NEXT, Bill McDermott coming in to do his presentation. I sold my IoT platform company to SAP. And why, for example has SAP got an amazing opportunity? Because they've got all these applications, they've done an amazing job of taking ERP and adding a whole load of applications: financial planning, supply chain, business networks. But those applications model the real world. But they're not connected to the real world. So what happens when you take a model of a financial model about the value of a factory or a mine, and connect it to the real world. Suddenly, it's not theoretical. It actually is calculating in real time, the value of those assets. The supply chain is really about that. So, SAP is an unbelievable opportunity. IBM has an unbelievable opportunity. GE has an unbelievable opportunity. But it's going to be how they execute, and is someone going to come in, and do something unbelievably disruptive we haven't even thought about. So, those guys need to make all the running right now to really protect themselves. >> I wonder if you could comment on this. I see some of the execution risks as what Jeffrey Immelt said, "I went to bed an industrial giant," "and woke up a software company." >> John: (laughs) Yes. >> Wow, it's hard to be a successful software company. So, is that one of the many execution risks? Are there others? >> I think you're absolutely right. I mean, if you take GE for example, my friend, Bill Ruh. He's the chief digital officer, the CDO of GE Digital. >> Dave: We know him, yeah, sure. >> Yeah, he's awesome. Completely new business, but it's really hard. I think that's taken longer than they expected to build up that Predix platform. And are they going to be the people, it depends what business you're in. If you're the business of buying aircraft engines, then rather than buying an aircraft engine, you want to buy engine as a service. So that's the kind of the thing that maybe you'll buy from GE, or maybe it's one of GE's partners and GE provides the infrastructure. But I think they've learned that's really much harder than they thought. And I think everybody's sort of discovering that. It's not so much the thingalytics, I've realized, it's the thingonomics, the economics of the internet things. That's the really important thing to get right. >> We actually worked with GE when they were coming out with the Industrial Internet, and we did a lot of interviews. There's some of these barriers that we're going to hit along the way. As a matter of fact, at Wikibon, our team that works on it, they call it the Internet of Things and People because there's so much that needs to happen to be able to move forward. Some of them are just old industrial things, some of them are regulations, some of them are the mindsets. How do you see some of these, what do you see as some of the major barriers, and how do we knock them down to be able to accelerate this even more? >> Absolutely. Well, first, you're absolutely right. One of the key barriers is a cultural barrier, or a, oh, that's just too hard, getting back to why did I write Thingalytics. And I think it's a question of people have just got to get started, not try and boil the ocean, and try and get some successful projects going. But definitely there's a cultural thing, and you just have to get those people together that think differently. And there's a reason why this new role of the Chief Digital Officer was created, but you can have many Chief Digital Officers throughout your company, just sort of get them together with that thought. One of the other things I can bring up that is really, really hard and why I went from being in the core of the IoT platform world into a company that's a software testing company, when you're going to launch this stuff, how do you, de-risk it, how do you make sure, in this world where there's all these sensors at the edge, all these strange mobile devices on the front end, and the cloud in the middle, how do you make sure you test that? It's a really complicated distributed architecture, that requires completely new technology. You don't even own the code, so how do you test that? So there's a whole load of issues there, but I think you have to put at the heart of it, think differently, think digitally. >> So what's the company you sold to SAP? Tell us about that. >> So the company's called Plat.One, and it was one of the leaders in platforms, software platforms, to enable Internet of Things application. So the idea is that you're going to build an Internet of Things application. You could start and hardwire, start writing some code and hardwire against all these devices and sensors, but then you start shipping your applications. What about if you made the wrong decisions? What about if you spent years just writing all the integrations to your factory floor, or your logistics networks? So, there's a whole load of common protocols out there, in machine to machine, and they call it a new Internet of Things protocols. Plat.One, new and could talk to all these protocols and make machines talk to each other. It could virtualize that, so that you disconnect those protocols from the application you write. So you're modeling things like, in a Smart city, truck and streetlamps, rather than bits and bytes. So then when you change the implementation from one city to another, you're future-proofed. And then graphical tools to model and plug them together, and a platform that manages microservices at the edge and the cloud. So you're managing an adaptive platform that you can place logic, depending on what it is. And that enabled SAP to rapidly roll out ITOs. >> And your company had customers? >> Yeah, a lot of customers, people like, you know, a great customer, Pirelli. Pirelli, obviously a tire manufacturer as you know them, but what they can do, if they plug sensors into their tires and have telematics boxes on tops of trucks or vehicles, suddenly they can go to the fleet management markets and sell them big data analytics because they know where the trucks are, they know how they're being driven, and what's more, rather than selling you a tire, they could lease you a tire as a service because they can track it, they know how much use you've got out of it. Unbelievable new thingonomic models. So, that's an example, flextronics, T-Systems, we had a whole lot of interesting smart cities using it, logistics, manufacturers. So yeah, it was a great, but early stage company, and you have to ask yourself the question, can you, as a small company, win, or would you be better off partnering with an SAP with that unbelievable reach? >> One of the things, I've got a networking background, we hear all these new protocols and the maturity there, there's the security risk there. I hear the fleet of trucks that was like, oh wait, I might turn off these sensors or do something malicious. The surface area has just grown by orders of magnitude. How do we address this as the industry? What is some of the advice you're giving for this? >> You're absolutely right, 'cause when we were talking about the issues earlier, that's a corker, isn't it, you know, the security of it. And as a Tesla owner, it was great when hackers tried to hack into the Tesla and they couldn't. All they could do was make the horn go beep. Which you can do from your app on your phone, anything that was publicly there, but couldn't take control of the car. That was great, that was nice. But with all this highly distributed model, you've got to be able to have end-to-end security. So in Plat.One for example, we had the ability to have role-based, end-to-end security right from the application to the device. And that was part of the platform, so you got that for free. But you've got to make sure that's the case in your applications. >> What's the opportunity for jobs in the growing IoT economy? >> You know, IoT giveth and IoT taketh away. (Dave laughs) We're all thinking let's bring more jobs back to America, which is a political thing at the moment. But are these jobs are going to be replaced by robots? I mean, is there a global issue, which is, are these jobs going to be replaced by robots, and by algorithims? The answer is yes, but on the other hand, are more jobs going to be created? Are people going to become much more productive? So I think humans are going to become more productive, for sure, for things like smart factories, smart cities, and life's going to get better in smart cities, but yeah, we're also going to lose jobs. I draw an analogy to trading, financial markets trading, where we used to have traders in the pits waving pieces of paper, then it went to Bloomburg terminals where people entered their trades automatically, then it went to algorithmic trading and high frequency trading where algorithms run it. Still humans involved, but less and less. But the humans are more productive and more coordinated. >> Hey, what if we put a 30% tax on all IoT-related initiatives, that would help preserve jobs. (John laughs) So tell-- >> Wouldn't slow down innovation or corporate profit or anything like that. >> Hey, here's an idea for you, Since we're in Washington I thought I'd throw out some good ideas. >> (laughs) Yes, exactly, very topical. >> So, tell us about your software testing company, TestPlant. >> So, the reason I was really excited to join TestPlant is there's this new world, you put IoT together with the mobile world and the cloud world, and you have the world of digital. How do you make sure that in this new digital enterprise that everybody's going to compete in, that you're, how do you make sure you're doing well, and how do you make sure your stuff works, and how do you make sure you're beating your competitors? So, TestPlant's all about end-to-end testing of the digital experience. It's taking testing to a new level, 'cause if you think about testing, it used to be about, does your code work? Now, it's about, are you offering up an unbelievable, delightful digital experience to your customers, because testing now has become a profit center. It's the differentiator between you doing an amazing job of launching an app and getting five stars in the app store, or crashing and burning because something's gone down, or there's a usability issue or there's a problem. So that's what we do, we test applications using artificial intelligence through the eye of the user, we actually, our algorithms actually use the applications and connect to the APIs and can take control and automate the testing process and discover these business metrics and show customers what good really is. >> So John, you were the founder of Plat.One, is that right? >> So I was an early joiner of Plat.One, I was the CEO, I wasn't the founder, we have two amazing founders. >> Okay, but you helped do the initial raise? >> Yes, exactly, and I took it from an early interesting technology to the company that got bought by SAP >> Made it viable, and sellable, you're an investor, I heard you say. >> John: Yes. Okay, now you're an author, you're CEO now of an more established company, right? >> John: Yes. >> Jack-of-all-trades here, well, maybe that's not a fair term, but you do a lot of different things. What are your thoughts on which things you enjoy the most, where do you see all of this headed? >> Well-- >> Polymath is the word I was looking for. (John laughs) >> Well, I started off actually as a professor, a university professor, and I took some of my research and started my first company. I loved building a start-up from scratch, and taking that as a first streaming analytics or real-time analytics company, and I then spent over a decade as a C-level executive in public software companies. But I haven't had so much fun as what I'm doing right now. It's beautiful, it's sort of mid-sized, really great private equity, backers, the Carlyle group, so I love what I'm doing right now, it's definitely my favorite gig, so far, I think that's the nice sweet spot for me. >> That's great, well, John, we love having big brains in the Cube, Stu and I, and it rubs off a little bit, at least we think it does, so thanks very much for coming on. >> John: Thank you gentlemen. >> You're welcome, alright, keep it right there, buddy. We'll be back with our next guest. We're live from Nutanix NEXTconf, this is the Cube.

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

(upbeat music) >> Covering InterConnect 2017, brought to you by IBM. >> Welcome back. We're here live in Las Vegas from Mandalay Bay for the IBM InterConnect 2017, this is Cube's exclusive coverage with SiliconANGLE media. I'm John Furrier, my co-host Dave Vellante here all week. We missed our kickoff this morning on day two and, because the keynotes went long with Ginni Rometty. Great star line up, you had Marc Benioff, the CEO of AT&T, and CEO of H&R Block, which I love their ad with Mad Men's guy in there. Dave let's wrap up day two. Big day, I mean traffic on the digital site, ibmgo.com was off the charts and the site just performed extremely well, excited about that. Also the keynote from the CEO of IBM, Ginni, really kind of brings us themes we've been talking about on theCUBE. I want to get your reaction to that, which is social good is now a purpose that's now becoming a generational theme, and it's not just social good in terms of equality of pay for women, which is great and of course more STEM, it's everything, it's society's global impact but also the tagline is very tight. Enterprise strong, has a Boston strong feeling to it. Enterprise strong, data first, cognitive to the core, pretty much hits their sweet spot. What did you think of her keynote presentation? >> I thought Ginni Rometty nailed it. I've always been a huge fan of hers, I first met her when she was running strategy, and you know the question you used to always get because IBM 19 quarters of straight declining revenue, how long is Ginni going to get? How long is Ginni going to get? You know when is her tenure going to be up? My answer's always been the same. (laughs) Long enough to prove that she was right. And I think, I just love her presentation today, I thought she was on, she was engaging, she's a real pro and she stressed the innovation that IBM is going through. And this was the strategy that she laid out, you know, five, six years ago and it's really coming to fruition and it was always interesting to me that she never spoke at these conferences and she didn't speak at these conferences 'cause the story was not great you know, it was coming together the big data piece or the analyst piece was not formed yet. >> So you think she didn't come to these events because the story wasn't done? >> Yeah, I think she was not-- >> That is not a fact, you believe that. >> No, this is my belief. She was not ready to showcase you know, the greatness of IBM and I said about a year ago, I said you watch this whole strategy is coming together. You are going to see a lot more of Ginni Rometty than you've seen in the past. You started to see her on CNBC much more, we saw her at the Women in Tech Conference, at the Grace Hopper Conference, we saw her at World of Watson and now we see her here at InterConnect and she's very good on stage. She's extremely engaging, I thought she was good at World of Watson, I thought she was even better today. And a couple of notable things, took a swipe at both AWS and maybe a little bit at HPE, I'm not so sure that they worry about HPE. Sam Palmisano, before he left on a Wall Street Journal interview, said "I don't worry about HPE, they don't invest in RND. "I worry about Oracle." But nonetheless, she said, it's not just a new way, cloud is not just a new way to deliver IT. Right that's the Amazon you know. >> HP. >> And certainly new way of you style by IT. >> You style by IT. >> Is Meg's line. She also took a swipe at Google basically saying, look we're not taking your data to inform some knowledge draft that we're going to take your IP and give it to the rest of the world. We're going to protect your data, we're going to protect your models. They're really making a strong statement in that regard which I think is really important for CIOs and CDOs and CEOs today. Thoughts? >> I agree. I first of all am a big fan of Ginni, I always kind of question whether she came in, I never put it together like you intuitively around her not seeing the story but you go to all the analyists thing, so I think that's legit I would say that I would buy that argument. Here's what I like. Her soundbite is enterprise strong, data first, cognitive to the core. It's kind of gimmicky, but it hits all their points. Enterprise strong is core in the conversations with customers right now. We see it in theCUBE all the time. Certainly Google Nexus was one event we saw this clearly. Having enterprise readiness is not easy and so that's a really tough code to crack. Oracle and Microsoft have cracked that code. So has IBM of the history. Amazon is getting faster to the Enterprise, some of the things they are doing. Google has no clue on the Enterprise, they're trying to do it their way. So you have kind of different dimensions. So that's the Enterprise, very hard to do, table stakes are different than having pure cloud native all the time 100%, lift and shift, rip and replace, whatever you want to call it. Data First is compelling because they have a core data strategy analytics but I thought it was interesting that they had this notion of you own your own data, which implies you're renting everything else, so if you're renting everything else, infrastructure (laughs) and facilities and reducing the cost of doing business, the only thing you really got is data, highlighted by Blockchain. So Blockchain becomes a critical announcement there. Again, that was the key announcement here at the show is Blockchain. IOT kind of a sub-text to the whole show but it's supported through the Data First. And finally Cognitive to the Core is where the AI is going to kind of be the shiny, silly marketing piece with I am Watson, I'm going to solve all your health problems. Kind of showing the futuristic aspect of that but under the hood there is machine learning, under that is a real analytics algorithms that they're going to integrate across their business whether it's a line of business in verticals, and they're going to cross pollinate data. So I think those three pillars, she is a genius (laughs) in strategy 'cause she can hit all three. What I just said is a chockfull of strategy and a chockfull execution. If they can do that then they will have a great run. >> So I go back to Palmisano's statement before Ginni took over and it was a very candid interview that he gave. And as they say, you look at when he left IBM, it was this next wave was coming like a freight train that was going to completely disrupt IBM's business, so it was, it's been a long turn around and they've done it with sort of tax rates, (laughs) stock buybacks, and all kinds of financial engineering that have held the company's stock price up, (laughs) and cash flow has been very strong and so now I really believe they're in a good position. You know to get critical for just a second, yes there's no growth but look who else isn't growing. HPE's not growing, Oracle's not growing, Tennsco's not growing, Cisco's not growing, Microsoft's not growing. The only two companies really in the cartel that are growing showing any growth really are Intel a little bit and SAP. The rest of the cartel is flat (laughs) to down. >> Well they got to get on new markets and I mean the thing is new market penetration is interesting so Blockchain could be an enabler. I think it's going to be some resistance to Blockchain, my gut tells me that but the innovative entrepreneur side of me says I love Blockchain. I would be all over Blockchain if I was an entrepreneur because that really would change the game on identity and value and all that great stuff. That's a good opportunity to take the data in. >> Well the thing I like is IBM's making bets, big bets, Blockchain, quantum computing, we'll see where that goes, cloud, clearly we could talk about, you know you said it (laughs) InterConnect two or three years ago you know SoftLayer's kind of hosting. True, but Blu makes the investments hoping-- >> SoftLayer's is not all Blu makes. >> That's right, well yeah so but any rate, the two billion dollar bet that they made on SoftLayer has allowed them to go to clients and say we have cloud. Watson, NAI, Analytics, IOT these are big bets which I think are going to pay off. You know, we'll see if quantum pays off in the year term, we'll see about Blockchain, I think a lot of the bets they've been making are going to pay off, Stark, et cetera. >> So let's talk about theCUBE interviews Dave, what got your attention? I'll start while you dig up something good from your notes. I loved Willie Tejada talked about this, they're putting in these clouds journey pieces which is not a best practice it's not a reference architecture but it's actually showing the use cases of people who are taking a cross functional journey of architecture and cloud solutions. I love the quantum computing conversation we had with believe it or not the tape person. And so from the tape whatever it was, GS. >> GS8000. >> GS8000. >> It's a storage engineering team. >> But in terms of key points, modernizing IOT relevance was a theme that popped out at me. It didn't come out directly. You start to see IOT be a proof point of operationalizing data. Let me explain, IOT right now is out there. People are focused on it because it's got real business impact, because it's either facilities, it's industrial or customer connected in some sort. That puts the pressure to operationalize that data, and I think that flushes out all the cloud washing and all the data washing, people who don't have any solutions there. So I think the operationalizing of the data with IOT is going to force people to come out with real solutions. And if you don't, you're gone, so that's, you're dead. The cultural issue is interesting. Trust as now table stakes in the equation of whether it's product trusts, operational trusts, and process trusts. That's something I saw very clearly. And of course I always get excited about DevOps and cloud native, as you know. And some of the stuff we did with data as an asset from the chief data architect. >> A couple I would add from yesterday, Indiegogo who I thought had a great case study, and then Mohammed Farooq, talking about cloud brokering. 60% of IBM's business is still services. Services is very very important. And I think that when I look at IBM's big challenge, to me, John, it's when you take that deep industry expertise that they have that competes with Accenture and ENY and Deloitte and PWC. Can you take that deep industry expertise and codify it in software and transform into a more software-oriented company? That's what IBM's doing, trying to do anyway, and challenging. To me it's all about differentiation. IBM has a substantially differentiated cloud strategy that allows them not to have to go head to head with Amazon, even though Amazon is a huge factor. And the last thing I want to say is, it's what IBM calls the clients. It's the customers. They have a logo slide, they bring up the CEOs of these companies, and it's very very impressive, almost in the same way that Amazon does at its conferences. They bring up great customers. IBM brings in the C-Suite. They're hugging Ginni. You know, it was a hug fest today. Betty up on stage. It was a pretty impressive lineup of partners and customers. >> I didn't know AT&T and IBM were that close. That was a surprise for me. And seeing the CEO of AT&T up there really tees it out. And I think AT&T's interesting, and Mobile World Congress, one of the things that we covered at that event was the over the top Telco guys got to get their act together, and that's clear that 5G and wireless over the top is going to power the sensors everywhere. So the IOT on cars, for instance, and life, is going to be a great opportunity for, but Telco has to finally get a business model. So it's interesting to see his view of digital services from a Telco standpoint. The question I have for AT&T is, are they going to be dumped pipes or are they actually going to move up the stand and add value? Interesting to see who's the master in that relationship. IBM with cognitive, or AT&T with the pipes. >> And, you know, you're in Silicon Valley so you hear all the talk from the Silicon Valley elites. "Oh well, Apple and Amazon "and Google and Facebook, "much better AI than Watson." I don't know, maybe. But IBM's messaging-- >> Yes. >> Okay, so yes, fine. But IBM's messaging and positioning in the enterprise to apply their deep industry knowledge and bring services to bear and solve real problems, and protect the data and protect the models. That is so differentiable, and that is a winning strategy. >> Yeah but Dave, everyone who's doing-- >> Despite the technical. >> Anyone who's doing serious AI attempts, first of all, this whole bastardized definition. It's really machine learning that's driving it and data. Anyone who's doing any serious direction to AI is using machine learning and writing their own code. They're doing it on their own before they go to Watson. So Watson is not super baked when it comes to AI. So what I would say is, Watson has libraries and things that could augment traditional custom-built AI as a kernel. Our 13-year-old guest Tanmay was on. He's doing his own customizing, then bring it to Watson. So I don't see Watson being a mutually exclusive, Watson or nothing else. Watson right now has a lot of things that adds to the value but it's not the Holy Grail for all things AI, in my opinion. The innovation's going to come from the outside and meet up with Watson. That to me is the formula. >> Going back to Mohammed Farooq yesterday, he made the statement, roughly, don't quote me on these numbers, I'll quote myself, for every dollar spent on technology, 10 dollars are going to be spent on services. That's a huge opportunity for IBM, and that's where they're going to make Watson work. >> If I'm IBM and Watson team, and I'm an executive there and engineering lead, I'm like, look it, what I would do is target the fusion aspect of connecting with their customers data. And I think that's what they're kind of teasing out. I don't know if they're completely saying that, but I want to bring my own machine learning to the table, or my own custom stuff, 'cause it's my solution. If Watson can connect with that and handshake with the data, then you got the governance problem solved. So I think Seth, the CDO, is kind of connecting the dots there, and I think that's still unknown, but that's the direction that I see. >> And services, it remains critical because of the complexity of IBM's portfolio, but complexity has always been the friend of services. But at the same time, IBM's going to transform its services business and become more software-like, and that is the winning formula. At the end of the day, from a financial perspective, to me it's cash flow, cash flow, cash flow. And this company is still a cash flow cow. >> So the other thing that surprised me, and this is something we can kind of end the segment on is, IBM just reorganized. So that's been reported. The games, people shift it a little bit, but it's still the same game. They kind of consolidated the messaging a little bit, but I think the proof point is that the traffic for on the digital side, for this show, is 2X World of Watson. The lines to get into keynotes yesterday and today were massive. So there's more interest in InterConnect than World of Watson. >> Well we just did. >> Amazing, isn't it? >> Well then that was a huge show, so what that means is, this is hitting an interest point. Cloud and data coming together. And again, I said it on the intro yesterday. IOT is the forcing function. That to me is bringing the big data world. We just had Strata Hadoop and R event at BigDataSV. That's not Hadoop anymore, it's data and cloud coming together. And that's going to be hitting IOT and this cognitive piece. So I think certainly it's going to accelerate at IBM. >> And IBM's bringing some outside talent. Look at Harry Green who came from Thomas Cook, Michelle Peluso. Marketing chops. They sort of shuffled the deck with some of their larger businesses. Put Arvind Krishna in charge. Brought in David Kenny from the Weather Company. Moved Bob Picciano to the cognitive systems business. So as you say, shuffle things around. Still a lot of the same players, but sometimes the organization-- >> By the way, we forgot to talk about Don Tapscott who came on, my favorite of the day. >> Another highlight. >> Blockchain Revolution, but we interviewed him. Check out his book, Blockchain can be great. Tomorrow we got a big lineup as well. We're going to have some great interviews all day, going right up to 5:30 tomorrow for day three coverage. This is theCUBE, here at the Mandalay Bay for IBM InterConnect 2017. I'm John Furrier and Dave Vellante. Stay with us, join us tomorrow, Wednesday, for our third day of exclusive coverage of IBM InterConnect 2017, thanks for watching.