>> live from San Francisco, California. It's the key. You covering the IBM chief Data officer? Someone brought to you by IBM. >> We're back at the IBM CDO conference. Fisherman's Worf Worf in San Francisco. You're watching the Cube, the leader in life tech coverage. My name is David Dante. Glenn Finches. Here's the global leader of Big Data Analytics and IBM, and we're pleased to have Mark Clare. He's the head of data enablement at AstraZeneca. Gentlemen, welcome to the Cube. Thanks for coming on my mark. I'm gonna start with this head of data Data Enablement. That's a title that I've never heard before. And I've heard many thousands of titles in the Cube. What is that all about? >> Well, I think it's the credit goes to some of the executives at AstraZeneca when they recruited me. I've been a cheap date officer. Several the major financial institutions, both in the U. S. And in Europe. Um, AstraZeneca wanted to focus on how we actually enable our business is our science areas in our business is so it's not unlike a traditional CDO role, but we focus a lot more on what the enabling functions or processes would be >> So it sounds like driving business value is really the me and then throw. Sorry. >> I've always looked at this role in three functions value, risk and cost. So I think that in any CDO role, you have to look at all three. I think the you'd slide it if you didn't. This one with the title. Obviously, we're looking at quite a bit at the value we will drive across the the firm on how to leverage our date in a different way. >> I love that because you can quantify all three. All right, Glenn. So you're the host of this event. So awesome. I love that little presentation that you gave. So for those you didn't see it, you gave us pay stubs and then you gave us a website and said, Take a picture of the paste up, uploaded, and then you showed how you're working with your clients. Toe. Actually digitize that and compress all kinds of things. Time to mortgage origination. Time to decision. So explain that a little bit. And what's that? What's the tech behind that? And how are people using it? You know, >> for three decades, we've had this OCR technology where you take a piece of paper, you tell the machine what's on the paper. What longitudinal Enter the coordinates are and you feed it into the hope and pray to God that it isn't in there wrong. The form didn't change anything like that. That's what that's way. We've lived for three decades with cognitive and a I, but I read things like the human eye reads things. And so you put the page in and the machine comes back and says, Hey, is this invoice number? Hey, is this so security number? That's how you train it as compared to saying, Here's what it So we use this cognitive digitization capability to grab data that's locked in documents, and then you bring it back to the process so that you can digitally re imagine the process. Now there's been a lot of use of robotics and things like that. I'm kind of taken existing processes, and I'm making them incrementally. Better write This says look, you now have the data of the process. You can re imagine it. However, in fact, the CEO of our client ADP said, Look, I want you to make me a Netflix, not a blood Urbach Blockbuster, right? So So it's a mind shift right to say we'll use this data will read it with a I will digitally re imagine the process. And it usually cuts like 70 or 80% of the cycle time, 50 to 75% of the cost. I mean, it's it's pretty groundbreaking when you see it. >> So markets ahead of data neighborhood. You hear something like that and you're not. You're not myopically focused on one little use case. You're taking a big picture of you doing strategies and trying to develop a broader business cases for the organization. But when you see an example like that and many examples out there, I'm sure the light bulbs go off. So >> I wrote probably 10 years cases down while >> Glenn was talking about you. You do get tactical, Okay, but but But where do you start when you're trying to solve these problems? >> Well, I look att, Glenn's example, And about five and 1/2 years ago, Glenn was one I went to had gone to a global financial service, firms on obviously having scale across dozens of countries, and I had one simple request. Thio Glenn's team as well as a number of other technology companies. I want cognitive intelligence for on data in Just because the process is we've had done for 20 years just wouldn't scale not not its speed across many different languages and cultures. And I now look five and 1/2 years later, and we have beginning of, I would say technology opportunities. When I asked Glenn that question, he was probably the only one that didn't think I had horns coming out of my head, that I was crazy. I mean, some of the leading technology firms thought I was crazy asking for cognitive data management capabilities, and we are five and 1/2 years later and we're seeing a I applied not just on the front end of analytics, but back in the back end of the data management processes themselves started automate. So So I look, you know, there's a concept now coming out day tops on date offices. You think of what Dev Ops is. It's bringing within our data management processes. It's bringing cognitive capabilities to every process step, And what level of automation can we do? Because the, you know, for typical data science experiment 80 to 90% of that work Estate engineering. If I can automate that, then through a date office process, then I could get to incite much faster, but not in scale it and scale a lot more opportunities and have to manually do it. So I I look at presentations and I think, you know, in every aspect of our business, where we clear could we apply >> what you talk about date engineering? You talk about data scientist spending his or her time just cleaning the wrangling data, All the all the not fun stuff exactly plugging in cables back in the infrastructure date. >> You're seeing horror stories right now. I heard from a major academic institution. A client came to them and their data scientists. They had spent several years building. We're spending 99% of their time trying to cleanse and prep data. They were spend 90% cleansing and prepping, and of the remaining 10% 90% of that fixing it where they fix it wrong and the first time so they had 1% of their job doing their job. So this is a huge opportunity. You can start automating more of that and actually refocusing data science on data >> science. So you've been a chief data officer number of financial institutions. You've got this kind of cool title now, which touches on some of the things a CDO might do and your technical. We got a technical background. So when you look a lot of the what Ginny Rometty calls incumbents, call them incumbent Disruptors two years ago at Ivy and think they've got data that has been hardened, you know, in all these projects and use cases and it's locked and people talk about the silos, part of your role is to figure out Okay, how do we get that data out? Leverage. It put it at the core. Is that is that fair? >> Well, and I'm gonna stay away from the word core cause to make core Kenan for kind of legacy processes of building a single repositories single warehouse, which is very time consuming. So I think I can I leave it where it is, but find a wayto to unify it. >> Not physically, exactly what I say. Corny, but actually the court, that's what we need >> to think about is how to do this logically and cream or of Ah unification approach that has speed and agility with it versus the old physical approaches, which took time. And resource is >> so That's a that's a computer science problem that people have been trying to solve for years. Decentralized, distributed, dark detectors, right? And why is it that we're now able Thio Tap your I think it's >> a perfect storm of a I of Cloud, the cloud native of Io ti, because when you think of I o. T, it's a I ot to be successful fabric that can connect millions of devices or millions of sensors. So you'd be paired those three with the investment big data brought in the last seven or eight years and big data to me. Initially, when I started talking to companies in the Valley 10 years ago, the early days of, um, apparatus, what I saw or companies and I could get almost any of the digital companies in the valley they were not. They were using technology to be more agile. They were finding agile data science. Before we call the data signs the map produce and Hadoop, we're just and after almost not an afterthought. But it was just a mechanism to facilitate agility and speed. And so if you look at how we built out all the way up today and all the convergence of all these new technologies, it's a perfect storm to actually innovate differently. >> Well, what was profound about my producing in the dupe? It was like leave the data where it is and shipped five megabytes a code two upended by the data and that you bring up a good point. We've now, we spent 10 years leveraging that at a much lower cost. And you've got the cloud now for scale. And now machine intelligence comes in that you can apply in the data causes. Bob Pityana once told me, Data's plentiful insights aren't Amen to that. So Okay, so this is really interesting discussion. You guys have known each other for a couple of couple of decades. How do you work together toe to solve problems Where what is that conversation like, Do >> you want to start that? >> So, um, first of all, we've never worked together on solving small problems, not commodity problems. We would usually tackle something that someone would say would not be possible. So normally Mark is a change agent wherever he goes. And so he usually goes to a place that wants to fix something or change something in an abnormally short amount of time for an abnormally small amount of money. Right? So what's strange is that we always find that space together. Mark is very judicious about using us as a service is firm toe help accelerate those things. But then also, we build in a plan to transition us away in transition, in him into full ownership. Right. But we usually work together to jump start one of these wicked, hard, wicked, cool things that nobody else >> was. People hate you. At first. They love you. I would end the one >> institution and on I said, OK, we're going to a four step plan. I'm gonna bring the consultants in day one while we find Thailand internally and recruit talent External. That's kind of phases one and two in parallel. And then we're gonna train our talent as we find them, and and Glenn's team will knowledge transfer, and by face for where, Rayna. And you know, that's a model I've done successfully in several organizations. People can. I hated it first because they're not doing it themselves, but they may not have the experience and the skills, and I think as soon as you show your staff you're willing to invest in them and give them the time and exposure. The conversation changes, but it's always a little awkward. At first, I've run heavy attrition, and some organizations at first build the organizations. But the one instance that Glen was referring to, we came in there and they had a 4 1 1 2 1 12 to 15 year plan and the C I O. Looked at me, he says. I'll give you two years. I'm a bad negotiator. I got three years out of it and I got a business case approved by the CEO a week later. It was a significant size business case in five minutes. I didn't have to go back a second or third time, but we said We're gonna do it in three years. Here's how we're gonna scale an organization. We scaled more than 1000 person organization in three years of talent, but we did it in a planned way and in that particular organization, probably a year and 1/2 in, I had a global map of every data and analytics role I need and I could tell you were in the US they set and with what competitors earning what industry and where in India they set and in what industry And when we needed them. We went out and recruited, but it's time to build that. But you know, in any really period, I've worked because I've done this 20 plus years. The talent changes. The location changes someone, but it's always been a challenge to find him. >> I guess it's good to have a deadline. I guess you did not take the chief data officer role in your current position. Explain that. What's what. What's your point of view on on that role and how it's evolved and how it's maybe being used in ways that don't I >> mean, I think that a CDO, um on during the early days, there wasn't a definition of a matter of fact. Every time I get a recruiter, call me all. We have a great CDO row for first time I first thing I asked him, How would you define what you mean by CDO? Because I've never seen it defined the same way into cos it's just that way But I think that the CDO, regardless of institutions, responsibility end in to make sure there's an Indian framework from strategy execution, including all of the governance and compliance components, and that you have ownership of each piece in the organization. CDO most companies doesn't own all of that, but I think they have a responsibility and too many organizations that hasn't occurred. So you always find gaps and each organization somewhere between risk costs and value, in terms of how how they're, how the how the organization's driving data and in my current role. Like I said, I wanted to focus. We want the focus to really be on how we're enabling, and I may be enabling from a risk and compliance standpoint, Justus greatly as I'm enabling a gross perspective on the business or or cost management and cost reductions. We have been successful in several programs for self funding data programs for multi gears. By finding and costs, I've gone in tow several organizations that it had a decade of merger after merger and Data's afterthought in almost any merger. I mean, there's a Data Silas section session tomorrow. It'd be interesting to sit through that because I've found that data data is the afterthought in a lot of mergers. But yet I knew of one large health care company. They've made data core to all of their acquisitions, and they was one the first places they consolidated. And they grew faster by acquisition than any of their competitors. So I think there's a There's a way to do it correctly. But in most companies you go in, you'll find all kinds of legacy silos on duplication, and those are opportunities to, uh, to find really reduce costs and self fund. All the improvements, all the strategic programs you wanted, >> a number inferring from the Indian in the data roll overlaps or maybe better than gaps and data is that thread between cost risk. And it is >> it is. And I've been lucky in my career. I've report toe CEOs. I reported to see Yellows, and I've reported to CEO, so I've I've kind of reported in three different ways, and each of those executives really looked at it a little bit differently. Value obviously is in a CEO's office, you know, compliance. Maurizio owes office and costs was more in the c i o domain, but you know, we had to build a program looking >> at all three. >> You know, I think this topic, though, that we were just talking about how these rules are evolving. I think it's it's natural, because were about 5 2.0. to 7 years into the evolution of the CDO, it might be time for a CDO Um, and you see Maur CEOs moving away from pure policy and compliance Tomb or value enablement. It's a really hard change, and that's why you're starting to Seymour turnover of some of the studios because people who are really good CEOs at policy and risk and things like that might not be the best enablers, right? So I think it's pretty natural evolution. >> Great discussion, guys. We've got to leave it there, They say. Data is the new oil date is more valuable than oil because you could use data to reduce costs to reduce risk. The same data right toe to drive revenue, and you can't put a gallon of oil in your car and a quart of oil in the car quarter in your house of data. We think it's even more valuable. Gentlemen, thank you so much for coming on the cues. Thanks so much. Lot of fun. Thanks. Keep right, everybody. We'll be back with our next guest. You're watching the Cube from IBM CDO 2019 right back.

>> live from San Francisco, California It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> We're back. You're watching the Cube, the leader in life Tech coverage. My name is Dave Volant Day, and we're covering the IBM Chief Data officer event hashtag IBM CDO is the 10th year that IBM has been running This event on the New Cube has been covering this for the last I'd say four or five years. Beth rottenness here. She's the distinguished engineer and principal data scientist. Cognitive within GTS Large Service's organization within IBM. Bet thanks so much for coming on the Cube. >> Absolutely. Thank you for having me. >> So you're very welcome. So really interesting sort of title. I'm inferring a lot. Um, and you're sexually transforming lives through data and analytics. Talk about your role a little bit. >> So my role is to infuse workforce transformation with cognitive. I typically we go from I think you've heard the ladder to a I. But as we move up that ladder and we can actually >> apply artificial intelligence and NLP, which is a lot of what I'm doing, >> it is it's instrumental in being able to see human beings in a lot more dimensions. So when we classify humans by a particular job role skill set, we often don't know that they have a passion for things like coding or anything else. And so we're really doing a lot more where we're getting deeper and being able to match your supply and demand in house as well as know when we have a demand for someone. And this person almost meets that demand. Based on all the different dimensionality that weaken dio, >> we can >> put them into this specific training class and then allow them to go through that training class so that we can upgrade the entire upscale and reschedule the entire work force. >> So one of the challenges you're working on is trying to operationalize machine intelligence and obviously starts with that training and skill level so well, it's not easy company the size of of I B M E. You're starting the GTS group, which probably has an affinity, at least conceptually, for transformation. That's what you guys do for your clients. So how's that going? You know, where are you in that journey? >> I think that we're in the journey and we're doing really well. I think that a lot of our people and the people who are actually working on the ground, we're talking to our clients every single day. So people on the helped us, they're talking to clients and customers. They understand what that client is doing. They understand the means, the troops, the mores, the language of the customer, of the organization of the customer, in the client, giving those people skills to understand what they can do better. To help solve our client's problems is really what it's all about. So understanding how we can take all of the unstructured data, all of like the opportunity for understanding what skills those people have on the ground and then being able to match that to the problems that our clients and customers are having. So it's a great opportunity. I think, that I've been in GTS my entire career and being an I t. I think that you understand this is where you store or create or, you know, manage all of the data in an entire enterprise organization, being able to enable and empower the people to be ableto upscale and Reese kill themselves so that they can get access to that so that we can do better for our clients and customers. >> So when you think about operations, folks, you got decades of skills that have built up you. D. B A is, you got network engineers, you got storage administrators. You know the VM add men's, you know, Unix. Add men's, I mean and a lot of those jobs. Air transforming clearly people don't want to invest is much in heavy lifting and infrastructure deployment, right? They want to go up the stack, if you will. So my question is, as you identify opportunities for transformation, I presume it's a lot of the existing workforce that you're transforming. You're not like saying, Okay, guys, you're out. What is gonna go retrain or bring in new people? Gonna retrain existing folks? How's that going? What's their appetite for that? Are they eagerly kind of lining up for this? You could describe that dynamic. >> I think the bits on the ground, they're very hungry. Everyone is so, so, so hungry because they understand what's coming on. They listen to the messages, they're ready. We were also in flexing. I'm sure you've heard of the new collar program were influencing a lot of youth as early professional hires. I have 2 16 year olds in the 17 year old on my team as interns from a P Tech program in Boulder, and getting that mix in that diversity is really all what it's about. We need that diversity of thought. We need that understanding of how we can start to do these things and how people can start to reach for new ways to work. >> All right, so I love this top of the cube we've we've covered, you know, diversity, women in tech. But so let's talk about that a little bit. You just made a statement that you need that diversity. Why is it so important other than it's the right thing to do? What's the what's the business effect of bringing diversity to the table? >> I think that would. We're searching for information truth if you want. If you want to go there, you need a wide variance of thought, the white of your variance, the more standard you're me, and it's actually a mathematical theory. Um, so this is This is something that is part of our truth. We know that diversity of thoughts we've been working. I run and sponsor the LGBT Q Plus group. I do women's groups in the B A R G's and then we also are looking at neuro diversity and really understanding what we can bring in as far as like, a highly diverse workforce. Put them all together, give them the skills to succeed. Make sure that they understand that the client is absolutely the first person that they're looking at in the first person that they're using Those skills on enable them to automate, enable them to stop doing those repeatable tasks. And there's so much application of a I that we can now make accessible so that people understand how to do this at every single level. >> So it's a much wider scope of an observation space. You're sort of purposefully organizing. So you eliminate some of that sampling bias and then getting to the truth. As you say, >> I think that in order to come up with ethical and explainable, aye, aye, there's definitely a way to do this. We know how to do it. It's just hard, and I think that a lot of people want to reach for machine learning or neural nets that spit out the feature without really understanding the context of the data. But a piece of data is an artifact of a human behavior, so you have to trace it all the way back. What process? What person who put it there? Why did they put it there? What was that? When we when we look at really simple things and say, Why are all these tickets classified in this one way? It's because when you observe the human operator, they're choosing the very first thing human behaviors put data in places or human behaviors create machines to put data in places. All of this can be understood if we look at it in a little bit of a different way. >> I thought I had was. So IBM is Business is not about selling ads, so there's no one sent to future appropriate our data to sell advertising. However, if we think about IBM as an internal organism, there's certain incentive structures. There's there's budgets, there's resource is, and so there's always incentives to game the system. And so it sounds like you're trying to identify ways in which you can do the right thing right thing for the business right for people and try to take some of those nuances out of the equation. Is that >> so? From an automation perspectively build digital management system. So all the executives can go in a room and not argue about whose numbers are correct, and they can actually get down to the business of doing business. From the bottoms up perspective, we're enabling the workforce to understand how to do that automation and how to have not only the basic tenets of data management but incorporate that into a digital management system with tertiary and secondary and triangulation and correlations so that we have the evidence and we can show data providence for everything that we're doing. And we have this capability today we're enabling it and operational izing. It really involves a cultural transformation, which is where people like me come in. >> So in terms of culture, so incentives drive behavior, how have you thought through and what are you doing in terms of applying new types of incentives? And how's that working? >> So when we start to measure skills were not just looking at hard skills. We're looking at soft skills, people who are good collaborators, people who have grit, people who are good leaders, people who understand how to do things over and over and over again in a successful manner. So when you start measuring your successful people, you start incentivizing the behaviors that you want to see. And when you start measuring people who can collaborate globally in global economies that that is our business as IBM, that is who we want to see. And that's how we're incentivizing the behaviors that we want to. D'oh. >> So when I look at your background here, obviously you're you're a natural fit for this kind of transformation. So you were You have an anthropology background language. Your data scientist, you do modeling. >> I always say I'm a squishy human data scientist, but I got to work with a huge group of people to create the data science profession with an IBM and get that accredited through open group. And that's something we're very passionate about is to give people a career past so that they know where their next step is. And it's all about moving to growth and sustainable growth by making sure that the workforce knows how value they are by IBM and how valuable they are by our clients. What does >> success look like to you? >> I think success is closer than we think. I think that success is when we have everybody understanding everybody, understanding what it's like to pick up the phone and answer a customer service call from our client and customer and be able to empathize and sympathize and fix the problem. We have 350,000 human beings. We know somebody in some circle that can help fix a client's problem. I think success looks like being able to get that information to the right people at the right time and give people a path so that they know that they're on the boat together, all rowing together in order to make our clients successful. >> That's great. I love the story. Thanks so much for coming on the hearing. You're very welcome. Keep it right there, but we'll be back with our next guest is a day. Violante. We're live from Fisherman's. More for the IBM CDO Chief Data officer event. Right back sticker The cube dot net is where the

(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)