(dramatic music) >> Narrator: Live from Fisherman's Wharf in San Francisco. It's theCube. Covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back, everybody. Jeff Frank here with theCube. We are wrapping up day one at the IBM CEO Strategy Summit Spring 2017 here at the Fisherman's Wharf Hyatt. A new venue for us, never been here. It's kind of a cool venue. Joined by Peter Burris, Chief Research Officer from Wikibon, and we're excited to have practitioners. We love getting practitioners on. So we're joined by this segment by Bruce Tyler. He's a VP Data Analytics for IBM Global Business Services. Bruce, nice to see you. >> Thank you. >> And he's brought along Fawad Butt, the Chief Data Governance Officer for Kaiser Permanente. Welcome. >> Thank you, thank you. >> So Kaiser Permanente. Regulated industry, health care, a lot of complex medical issues, medical devices, electronic health records, insurance. You are in a data cornucopia, I guess. >> It's data heaven all the way. So as you mentioned, Kaiser is a vertically integrated organization, Kaiser Permanente is. And as such the opportunity for us is the fact that we have access to a tremendous amount of data. So we sell insurance, we run hospitals, medical practices, pharmacies, research labs, you name it. So it's an end to end healthcare system that generates a tremendous amount of dataset. And for us the real opportunity is to be able to figure out all the data we have and the best uses for it. >> I guess I never really thought of it from the vertical stack perspective. I used to think it was just the hospital, but the fact that you have all those layers of the cake, if you will, and can operate within them, trade data within them, and it gives you a lot of kind of classic vertical stack integration. That fits. >> Very much so. And I didn't give you the whole stack. I mean, we're actually building a medical school in Southern California. We have a residency program in addition to everything else we've talked about. But yeah, the vertical stack does provide us access to data and assets related to data that are quite unique. On the one side, it's a great opportunity. On the other side, it has to be all managed and protected and served in the best interest of our patrons and members. >> Jeff: Right, right. And just the whole electronic health records by themselves that people want access to that, they want to take them with. But then there's all kinds of scary regulations around access to that data. >> So the portability, I think what you're talking about is the medical record portability, which is becoming a really new construct in the industry because people want to be able to move from practitioner to practitioner and have that access to records. There are some regulation that provide cover at a national scale but a lot of this also is impacted by the states that you're operating in. So there's a lot of opportunities where I can tell some of the regulation in this space over time and I think that will, then we'll see a lot more adoption in terms of these portability standards which tend to be a little one off right now. >> Right, right. So I guess the obvious question is how the heck do you prioritize? (laughter) You got a lot of things going on. >> You know, I think it's really the standard blocking/tackling sort of situation, right? So one of the things that we've done is taken a look at our holistic dataset end to end and broken it down into pieces. How do you solve this big problem? You solve it by piecing it out a little bit. So what we've done is that we've put our critical dataset into a set of what we call data domains. Patient, member, providers, workers, HR, finance, you name it. And then that gives us the opportunity to not only just say how good is our data holistically but we can also go and say how good is our patient data versus member data versus provider data versus HR data. And then not only just know how good it is but it also gives us the opportunity to sort of say, "Hey, there's no conceivable way we can invest "in all 20 of these areas at any given point." So what's the priority that aligns with business objectives and goals? If you think about corporate strategy in general, it's based on customers and demand and availability and opportunities but now we're adding one more tool set and giving that to our executives. As they're making decisions on investments in longer term, and this isn't just KP, it's happening across industries, is that the data folks are bringing another lens to the table, which is to say what dataset do we want to invest in over the course of the next five years? If you had to choose between 20, what are the three that you prioritize first versus the other. So I think it's another lever, it's another mechanism to prioritize your strategy and your investments associated with that. >> But you're specifically focused on governance. >> Fawad: I am. >> In the health care industry, software for example is governed by a different set of rules as softwares in other areas. Data is governed by a different set of rules than data is governed in most other industries. >> Fawad: Correct. >> Finance has its own set of things and then some others. What does data governance mean at KP? Which is a great company by the way. A Bay Area company. >> Absolutely. >> What does it mean to KP? >> It's a great question, first of all. Every data governance program has to be independent and unique because it should be trying to solve for a set of things that are relevant in that context. For us at KP, there are a few drivers. So first is, as you mentioned, regulation. There's increased regulation. There's increased regulatory scrutiny in pressure. Some things that have happened in financial services over the last eight or ten years are starting to come and trickle in to the healthcare space. So there's that. There's also a changing environment in terms of how, at least from an insurance standpoint, how people acquire health insurance. It used to be that your employer provided a lot of that, those services and those insurances. Now you have private marketplaces where a lot of people are buying their own insurance. And you're going from a B2B construct to a B2C construct in certain ways. And these folks are walking around with their Android phones or their iPhones and they're used to accessing all sorts of information. So that's the customer experience that you to to deliver to them. So there's this digital transformation that's happening that's driving some of the need around governance. The other areas that I think are front and center for us are obviously privacy and security. So we're custodians of a lot of datasets that relate to patients' health information and their personal information. And that's a great responsibility and I think from a governance standpoint that's one of the key drivers that define our focus areas in the governance space. There are other things that are happening. There's obviously our mission within the organization which is to deliver the highest coverage and care at the lowest cost. So there's the ability for us to leverage our data and govern our data in a way which supports those two mission statements, but the bigger challenge in nuts and bolts terms for organizations like ours, which are vertically integrated, is around understanding and taking stock of the entire dataset first. Two, protecting it and making sure that all the defenses are in place. But then three, figuring out the right purposes to use this, to use the data. So data production is great but data consumption is where a lot of the value gets captured. So for us some of the things that data governance facilitates above all is what data gets shared for what purposes and how. Those are things that an organization of our size deliver a tremendous amount of value both on the offensive and the defensive side. >> So in our research we've discovered that there are a lot of big data functions or analytic functions that fail because they started with the idea of setting up the infrastructure, creating a place to put the data. Then they never actually got to the use case or when they did get to the use case they didn't know what to do next. And what a surprise. No returns, lot of costs, boom. >> Yep. >> The companies that tend to start with the use case independently individual technologies actually have a clear path and then the challenge is to accrete knowledge, >> Yes. >> accrete experience and turn it into knowledge. So from a governance standpoint, what role do you play at KP to make sure that people stay focused in use cases, that the lessons you learn about pursuing those use cases then turn to a general business capability in KP. >> I mean, again, I think you hit it right on the head. Data governance, data quality, data management, they're all great words, right? But what do they support in terms of the outcomes? So from our standpoint, we have a tremendous amount of use cases that if we weren't careful, we would sort of be scatterbrained around. You can't solve for everything all at once. So you have to find the first set of key use cases that you were trying to solve for. For us, privacy and security is a big part of that. To be able to, there's a regulatory pressure there so in some cases if you lose a patient record, it may end up costing you $250,000 for a record. So I think it's clear and critical for us to be able to continue to support that function in an outstanding way. The second thing is agility. So for us one of the things that we're trying to do with governance and data management in general, is to increase our agility. If you think about it, a lot of companies go on these transformation journeys. Whether it's transforming HR or trying to transform their finance functions or their business in general, and that requires transforming their systems. A lot of that work, people don't realize, is supported and around data. It's about integrating your old data with the new business processes that you're putting out. And if you don't have that governance or that data management function in place to be able to support that from the beginning or have some maturity in place, a lot of those activities end up costing you a lot more, taking a lot longer, having a lower success rate. So for us delivering value by creating additional agility for a set of activities that as an organization, we have committed to, is one for of core use cases. So we're doing a transformation. We're doing some transformation around HR. That's an area where we're making a lot of investments from a data governance standpoint to be able to support that as well as inpatient care and membership management. >> Great, great lessons. Really good feedback for fellow practitioners. Bruce, I want to get your perspective. You're kind of sitting on the other side of the table. As you look at the experience at Kaiser Permanente, how does this equate with what you're seeing with some of your other customers, is this leading edge or? >> Clearly on point. In fact, we were talking about this before we came up and I'm not saying that you guys led, we led the witness here but really how do you master around the foundational aspects around the data, because at the end of the day it's always about the data. But then how do you start to drive the value out of that and go down that cognitive journey that's going to either increase value onto your insights or improve your business optimization? We've done a healthy business within IBM helping customers go through those transformation processes. I would say five years ago or even three years ago we would start big. Let's solve the data aspect of it. Let's build the foundational management processes around there so that it ensures that level of integrity and trusted data source that you need across an organization like KP because they're massive because of all the different types of business entities that they have. So those transformation initiatives, they delivered but it was more from an IT perspective so the business partners that really need to adopt and are going to get the value out of that were kind of in a waiting game until that came about. So what we're seeing now is looking at things around from a use case-driven approach. Let's start small. So whether you're looking at trying to do something within your call center and looking at how to improve automation and insights in that spec, build a proof of value point around a subset of the data, prove that value, and those things can typically go from 10 to 12 weeks, and once you've demonstrated that, now how do can you scale? But you're doing it under your core foundational aspects around the architecture, how you're going to be able to sustain and maintain and govern the data that you have out there. >> It's a really important lesson all three of you have mentioned now. That old method of let's just get all the infrastructure in place is really not a path to success. You getting hung up, spend a lot of money, people get pissed off and oh by the way, today your competitors are transforming right around you while you're >> Unless they're also putting >> tying your shoes. >> infrastructure. >> Unless they're also >> That's right. (laughter) >> tying their shoes too. >> Build it and they will come sounds great, but in the data space, it's a change management function. One of my favorite lines that I use these days is data management is a team sport. So this isn't about IT, or this isn't just about business, and can you can't call business one monolith. So it's about the various stakeholders and their needs and your ability to satisfy them to the changes you're about to implement. And I think that gets lost a lot of times. It turns into a technical conversation around just capability development versus actually solving and solutioning for that business problem set that are at hand. >> Jeff: Yeah. >> Peter: But you got to do both, right? >> You have to. >> Bruce: Absolutely, yeah. >> Can I ask you, do we have time for another couple of questions? >> Absolutely. >> So really quickly, Fawad, do you have staff? >> Fawad: I do. >> Tell us about the people on your staff, where they came from, what you're looking for. >> So one of the core components of data governance program are stewards, data stewards. So to me, there are multiple dimensions to what stewards, what skills they should have. So for stewards, I'm looking for somebody that has some sort of data background. They would come from design, they would come from architecture, they would come from development. It doesn't really matter as long as they have some understanding. >> As long as you know what a data structure is and how you do data monitoring. >> Absolutely. The second aspect is that they have to have an understanding of what influence means. Be able to influence outcomes, to be able to influence conversations and discussions way above their pay grade, so to be able to punch above your weight so to speak in the influence game. And that's a science. That's a very, very definitive science. >> Yeah, we've heard many times today that politics is an absolute crucial game you have to play. >> It is part of the game and if you're not accounting for it, it's going to hit you in the face when you least expect it. >> Right. >> And the third thing is, I look for people that have some sort of an execution background. So ability to execute. It's great to be able to know data and understand data and go out and influence people and get them to agree with you, but then you have to deliver. So you have to be able to deliver against that. So those are the dimensions I look at typically when I'm looking at talent as it relates particularly to stewardship talent. In terms of where I find it, I try to find it within the organization because if I do find it within the organization, it gives me that organizational understanding and those relationship portfolios that people bring to the table which tend to be part of that influence-building process. I can teach people data, I can teach them some execution, I can't teach them how to do influence management. That just has to-- >> You can't teach them to social network. >> Fawad: (laughing) That's exactly right. >> Are they like are the frustrated individuals that have been seen the data that they're like (screams) this is-- >> They come from a lot of different backgrounds. So I have a steward that is an attorney, is a lawyer. She comes from that background. I have a steward that used to be a data modeler. I have a steward that used to run compliance function within HR. I have a steward that comes from a strong IT background. So it's not one formula. It's a combination of skills and everybody's going to have a different set of strengths and weaknesses and as long as you can balance those out. >> So people who had an operational role, but now are more in an execution setup role. >> Fawad: Yeah, very much so. >> They probably have a common theme, though, across them that they understand the data, they understand the value of it, and they're able to build consensus to make an action. >> Fawad: That's correct. >> That's great. That's perfect close. They understand it and they can influence, and they can get to action. Pretty much sums it up, I think so. All right. >> Bruce: All right thank you. >> Well, thanks a lot, Bruce and Fawad for stopping by. Great story. Love all the commercials on the Warriors, I'm a big fan and watch KNBR. (laughter) But really a cool story and thanks for sharing it and continued success. >> Thank you for the opportunity. >> Absolutely. All right, with Peter Burris, I'm Jeff Frank. You're watching theCube from the IBM Chief Data Officer Strategy Summit Spring 2017 from Fisherman's Wharf, San Francisco. We'll be right back after this short break. Thanks for watching. (electronic music)