(orchestra music) >> Man: Live from Fisherman's Wharf in San Francisco, it's the Cube, covering IBM Chief Data Officer Strategy Summit, Spring 2017, brought to you by IBM. >> Welcome back everybody, Jeff Rick here at the Cube. We're in Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. It's a mouthful, but it's an ongoing series you know, it's not just one show. They're doing them on the east coast, west coast, and starting to take it all over the world. Really, a community of chief data officers coming together with the likes of their own, talking about common issues, best practices. And of course, IBM's got something to offer as well. So, we're excited to have our next guest, Ken Jacquier here. He's the Information Governance Practice from IBM. Welcome. >> Thank you. >> So, what have you been hearing in the hallways outside of the sessions? What's kind of the hot buzz topic? >> Well, actually everybody's pretty much talking about what came up in the sessions, it's all about the talent. How do these Chief Data Officers get the talent that they need to meet the mandate they've been given? >> It's not just automatically just like connect the data, via some APIs and the magic happens (laughs). >> Sometimes the people part is the hardest part. The technology's important, the machine learning is great, the algorithms are amazing, but it does come down to people. And there's some new skill sets that these chief data >> officers need in their people, so that's what they're talking about. >> So when you think about the talent, what kinds of jobs are talking about? We know the CDO job. >> Ken: Yeah. >> What kind of jobs are now underneath the CDO that are going to help the CDO get their job done? >> Yeah, absolutely. You've got the classic data scientist role that we are all talking about, we're all excited about because that can monetize the data. That's what gets the board's attention. So there's a lot of focus there. But a term that came up in the last session that I was in that I really liked was the data translator. And the point there was data scientists can be schooled in certain things, understand their algorithms, understand machine learning, but this really important skill set they're looking for is the data translator. >> So the business is looking to drive outcomes. The chief marking officer may have an objective. >> The vice president of sales has an objective. Supply chain needs to optimize. Who is the data translator that can get from this deep, difficult, often dirty data and translate it into what the business is trying to accomplish? It's a really cool role. >> Yeah, we've actually heard about this role pretty frequently, this concept very frequently when you come right down to it. And a lot of it pertains to who is in a position to understand data quality, how data transformation works, so that the outcome in fact is what's expected as opposed to just a consequence data wrong. >> Exactly. Two examples of that that I've heard today in the initial keynote session, it came up, that in this renaissance of data, we're going to look for people to bring the left side of their brain together and the right side of their brain together. In the last session, of the ladies at a large international bank, the chief data officer there, she said, "for me, honestly, even though this is difficult, "it's not about IQ, it's about EQ." I've got to have the people that can collaborate. I've got to have the people that can communicate both with the business and with the IT side. I mean, we all know that story, right. Such a challenge to pull IT and business together, >> but data is really forcing individually talented people to actually do that wherever they reside in the org chart. >> If you're the embed, you're the embed person from the CEO office working with that business unit, you've got to listen, you've got to convince them that you can help them, so it is really a softer skill. You know, the Da Vinci word has come up a couple of times. And what made Da Vinci so amazing is he had the science, but he also had the art, and the two are very, very connected. >> Exactly what we were talking about, exactly. And the listening skill is incredibly important as well. I mean a lot of times, there's so much emphasis in communication on getting your perspective out there. A lot of times in these situations, you're trying to express your view. Way underestimated skill, listening, how important that is for this stuff to work. >> So, your formal title is Information Governance Practice? >> Ken: Yes. >> Now, governance means a lot of things to a lot of people, and I don't want to put words in your mouth, but from my >> perspective, it means how are you going to ensure, put in place rules and mechanisms and methods to ensure that works get done around a particular set of issues. So, when we talk about talent, we talk about creativity, we also can talk about governance so that we in fact get the right set of practices put in place, so not that it >> runs by itself, but it runs at a high quality. >> So one of the things that you're doing with clients, to try to take talent and rules and turn it into an actual function that does (mumbles) business values. >> Yeah, it's a great question. So again, and if anybody's listening to this and they're talking about careers, or they're thinking about work coming up, or you're coming out of college, and you're like what would I want to do, think about this conversation we're having and the opportunity here. So, you just described I've got to drive business agility, and I've got to mitigate risk. Those sound like conflicting objectives. They can't be anymore. The talent has to come in. And what we're trying to help companies with is how do you build both a culture, but then also how do you bring in talent that can be excited, and creative, and innovative to drive that business agility, but respects the fact that if we don't take care of this data, important people can get in trouble. If we don't take care of this data, our clients can be in trouble, and our credibility can be damaged. But that has to be handled in tandem. It can't be two separate functions. In the past, a lot of times, we did have maybe an EIM organization that does the institutional, keep the data quality clean, and then there were innovation teams over here playing around building the new business model acquiring companies. In this new world, all this data's coming together, and you've got to be able to develop. So the word we like to use nowadays with our clients is the appropriate governments. With your financial data, you're still going to have that locked down. You're still going to have all those policies, all those business rules. That's got to be in place. But then, there's certain data that we can maybe not manage quite as tightly. We can create a landing zone where we brought in external data or third party data, and we can let marketing have a little more freedom with that. And we can be a little more creative and innovative and I don't think they have to be opposite perspectives. If they have the right architecture and the right processes, and the right governance, you can do both. >> Is it easy for someone who's had the lockdown governance for so long to start to open up their mind and think about ways that they can open it? Or does it have to come from an external point of view that looks at it from a different lens and isn't kind of locked down by the old paradigm? >> Yeah, that's a great question. And there were three R's that came up in the meeting today in terms of talent. It was recruit. So to your point, to some degree, we're going to have to recruit new folks with new paradigms. A lot of conversation in there about what an incredible opportunity for the millennials and the newer folks in the workforce if they don't have those paradigms. On the other hand, we have to still >> retain deep institutional knowledge of our data. So that might mean retraining existing skill sets, people that really know our databases, that really know where the most important data lives, but retrain them a little bit for this new environment. And then the third R was retain. So as we build these hybrid skill sets, people that are good on the business side, good on the IT side, we make that investment. How does an organization, how does a company retrain them? And for the HR professionals out there, for the senior VPs of HR, that's where you come in. You need to help these companies write job descriptions, build career paths, show people that they can work in these environments and still grow, both financially, professional, and career wise. Does that make sense? >> That makes a ton of sense, interesting challenge. I just interviewed a millennial speaker at the Professional Businesswoman's Conference, and he just flat out said, the new paradigm from his point of view as a 26 year old, is most people aren't staying on the job for more than six years. It's almost kind of built in life sabbatical every couple three or four years. So, the retention challenge is very difficult and for that generation, so much it's kind of the purposefulness. And if you can get the purposefulness in, big motivator behavior. >> Purposefulness, being a part of something bigger. >> So that's where this balance can come in. If I'm working to appropriately govern my financial data, but I'm also given an opportunity to work with the acquisitions team that's bringing an international flavor into my company, that can give that younger person a little bit of both, and help with that retention. >> One of the challenges though when we think about governance is to ensure as you said, that the rules were appropriate. >> Ken: Yes. >> One of the other things we've heard here and we certainly know about is data as an asset is different than other assets, in that it's not following the economic scarcity because it's so easy to copy, share, combine, recombine, everything else. >> Ken: Very good point. >> As you think about combining those two things, that appropriateness of data governance for financial data is different from the appropriateness of data governance for marketing data, when you combine them, which appropriateness wins? >> (laughing) >> That's a good question. So, ultimately-- >> Do we have an answer? Is that something we're discovering, is that one of the things that we need to better understand over time? What do you think? >> Yeah I do. And you used the keyword, understand. >> So, a very old terminology in our space is data profiling, of truly understanding your data and understanding where everything lives. That's never been more important than it is today. The right amount of tagging in your data links. So to do what you just described. The answer lies within truly understanding and inventorying what you have, and then you have at least an opportunity to strike that balance. But a lot of folks are skipping that step. So just moving data, they're replicating data, >> they're populating their data links in the Hadoop systems. You've got to have governance even that environment. >> Oh absolutely. And we're seeing that being one of the greatest challenges as people try to put together these analytic pipelines. Is to ensure that there's appropriate governance at each stage in the pipeline to ensure that the outcomes are both what they expected. They can be surprised, but at least it's relevant. And that they themselves are not breaking any laws or rules, or ethical or otherwise, associated with how the data gets used. >> I'd like your economic analogy, because I think that's what customers need to do, and that's what I try to help them with. >> Depending on what their business model is, they're going to understand some concept of a supply chain. But likely they don't understand what you just said, the concept of an information supply chain. So rather than try to explain it in geek speak, with IBM tooling, or all the things we typically do, I encourage customers to think about their perception of a supply chain. How does something move from a raw material to a sold product in their industry, whether it's finance, or whether they're building airplanes or whatever >> they're doing? And then, the customer can start to relate. Okay, my data's doing the same thing isn't it? And oh, I need to start thinking, I get that, my engineering brain and my process, and I have roles in the company. I have (mumbles) that their job is to work on my supply chain out in the factory, you're saying apply those types of approaches to a supply chain for data, what you just described. And once that light bulb starts to go off, there's an opportunity to do what you just said. >> Absolutely, in fact, we specifically talk to our clients about the notion first of, the role of data, first of all, data as an asset. In other words, something that has a consequential impact on a set of activities so you can put it into with other things in supply chain. But we also talk about the value chain. The role the data plays in the value chain. Whatever metaphor, both of those concepts are not broadly understood. Because data is so sharable, is so easily copied, too frequently, people say uh, it's really not an asset. Until they start making the wrong decision widely and repeatedly. So they have to think about it as an asset, they have to think about it as a value chain, and that's where the governance becomes so crucial. It's because if you're not putting in place good governance for your value chains, then you're not creating any value pretty quickly. >> And it's interesting if we think about it. So, data's an asset. Marketing people, software companies have been using that term for a long time. But now that we're at this stage and we have chief data officers, at the C-level folks reporting into the board that have this responsibility. So now the concept's a little better understood. So now the next step is what does that mean? What do I do with my typical assets? What do I do with my human resources assets? If I manage a fleet, what do I do with that fleet? So if something's truly an asset, what do I do? What do I do with it on the general ledger? What do I do from a staffing perspective? Where does it fit into to my overall operating model? And that's kind of what we're seeing unfold here. At an event like this, that's the level of conversation that's starting to happen. Not that it's a marketing buzzword anymore, but if it's true, organizationally, what have I done with other assets? Does that apply to my data as well if I'm using that statement? >> Alright, Ken, we're going to have to leave it there. I know you've got to run off to a session, but thanks for taking a few minutes out of your day. >> Thanks gentlemen. >> Alright, he's Ken. Peter, Jeff, you're watching the Cube at the IBM Chief Data Officer Strategy Summit 2017. Thanks for watching. (easy listening music) (percussive music)