 From Cambridge, Massachusetts, it's The Cube, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. Welcome back, we're here to wrap up the MIT Chief Data Officer Information Quality, it's hashtag MITCDOIQ conference. You're watching The Cube. I'm Dave Vellante and Paul Gilan is my co-host. This is two days of coverage. We're wrapping up, this is our analysis of what's gone on here. Paul, let me kick it off. When we first started here, we talked about at our open, it was, we saw the Chief Data Officer role emerge from the back office, the information quality role. When in 2013, the CDOs 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 CDOs even said, oh yeah, security is actually part of our purview because all the cyber data, and so very, very wide scope. Even in some cases, some of the digital initiatives were sort of being claimed, the CDOs were staking their claim. The reality was, the CDO also emerged out of highly regulated industries, financial services, healthcare, government, and it really was this kind of wonky back office role, and so that's what it's become. And it's what it's become again. We're seeing that CDOs largely are not involved in a lot of the emerging AI 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 CIOs tell us that the CIO role was going to disappear, so you had both ends of the spectrum, but I feel as though that whatever it's called, CDO, data czar, chief analytics officer, head of data, analytics and governance, that role is here to stay, at least for a fair amount of time. And increasingly, issues of privacy and governance and at least the periphery of security are going to be supported by that CDO role. So that's kind of take away number one. Let me get your thoughts. I think there's a maturity process going on here. And what we saw really in 2016 through 2018 was sort of a celebration of the arrival of the CDO. And we're here, we've got power now, we've got an agenda. And that was a natural outcome of all this growth and 90% of organizations putting CDOs in place. I think what you're seeing now is a realization that oh my God, this is a mess. And what I heard this year was a lot less of this sort of crowing about the ascendance of CDOs and more about we've got a big integration problem, a big data cleansing problem, and we've got to get our hands down into the nitty gritty. And when you talk about, as you said, we hadn't hear so much this year about strategic initiatives 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 metadata to it, just getting it 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 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 it. Yeah, the second big theme, of course, we heard this from the former head of analytics at GSK on the opening keynote, is the traditional methods have failed. The enterprise data warehouse, and we've actually studied this a lot. My analogy is I often use snakes swallowing a basketball, having to build cubes. EDW practitioners would always, I used to call it chasing the chips until we'd come out with a new chip. Oh, we need that because we got to 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 actually saved the EDW business because reporting became part of compliance. The master data management piece, we've heard, you consistently, we've heard Mike Stonebreaker, who's obviously a technology visionary who was right on, it doesn't scale. This notion of de-duping everything just doesn't work and manually creating rules, it's just not the right approach. We also heard the top-down enterprise data model, doesn't work, it's too complicated, can't operationalize it. So what they do, they kick the can to governance. Hadoop was kind of a sidecar there, big data. That failed to live up to its promises. And so it's a big question as to whether or not AI will bring that level of automation. We heard from KPMG, certainly Mike Stonebreaker again said, and we heard this as well from Andy Palmer. They're using technology to automate and scale that the number one data science problem, which is they spend all their time wrangling data. You know, we'll see if that actually lives up to its promise. Well something we did hear today from several of our guests was about the promise of machine learning to automate this data cleanup process. And as Mark Ramsey kicked off the conference saying that all of these efforts to standardize data have failed in the past, this does look, he then showed how GSK had used some of the tools that were represented here using machine learning to actually clean up the data at GSK. 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 problem. Because really, organizing data, creating enterprise data models is a scale problem. And the only way you can solve that is with automation. Like Mike Stonebreaker is right on top of that. So there was optimism at this event. There was kind of an ooh, kind of a dismay at seeing all the data problems they have to clean up, but also promise that tools are on the way that can do that. Yeah, the reason I'm an optimist about this role is because data is such a hard problem. While there is that 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 DNA for it. So the role, that whole 360 thing we talked about, the traditional methods kind of failing, and then the third piece I 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 AI injecting itself into virtually every aspect, every corner of the industry. And again, I often joke, you know, same wine, new bottle. You know, 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 it was interesting, several of our guests 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 determine test outcomes, you can vet the quality 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. They're vetting, they're burning in machine learning in an environment that they can control. Right, so anyway, two solid days here. I think that this conference has really grown. When we first started here, it was about 130 people, I think. Right. And now it's 500 registrants this year. I think 600 is the sort of the goal for next year of moving venues. The Cube has been covering this all but one year since 2013. I hope to continue to do that. Paul, it was great working with you. Always great work with you, Dave. We could do more together. We're here at the Vertica's bringing back its conference, you and I did that together. So we had Colin Mahoney on. We had the Vertica Rockstars on, which was fun. Colin Mahoney, Mike Stonebreaker, Andy Palmer, and Chris Lynch, all kind of weighed in, which was great to get their perspectives into the days of MPP and how that's evolved, improving on traditional relational database and now, of course, Stonebreaker applying all these MI, same thing with AtScale, with Chris Lynch. So it was fun to watch those guys all Boston-based, East Coast folks. Some news, we just saw the news hit. President Trump holding up the Jedi contract as we've talked about. We've been following that story very closely. I've got some concerns over that. I think it's largely because he doesn't like Bezos. And the Washington Post. And the Washington Post, exactly. Here's this, America first. If the Pentagon says they need this to be competitive with China and AI, there's maybe some, where there's smoke, there's fire there. It's more important to stick it to Jeff Bezos. That's what it seems like. So we're watching that story very closely. I think it's a bad move for the executive branch to be involved in those types of decisions, but what do I know? Well, anyway, Paul, awesome working with you guys. Thanks, Andrew, appreciate you flying out. Sal, good job, Alex, Mike. Great team. Already wrapping up. So thank you for watching. Go to siliconangle.com for all the news. YouTube.com slash Silicon Angles, where we house our playlist. But theCUBE.net is the main site where we have all the events. It'll show you what's coming up next. We've got a bunch of stuff going on straight through the summer. And then of course, you know, VMworld is the big kickoff for the fall season. Go to wikibon.com for all the research. We're out. Thanks for watching Dave Vellante for Paul Gillan. We'll see you next time.