>> 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.