 Live from Las Vegas, Nevada, it's theCUBE. Covering IBM World of Watson 2016. Brought to you by IBM. Here's your host, Dave Vellante. Welcome back to the Mandalay Bay, everybody. This is IBM World of Watson, and this is theCUBE, the worldwide leader in live tech coverage. Richard Wiedenbeck is here. He's the C-Device President and CIO of Emeritus. Welcome to theCUBE, good to see you. Thank you. So tell us about Emeritus. Break it down for the audience that doesn't really know your company. Right, and we're a company that doesn't spend a lot of time advertising. We're, what I would classify as a mid-sized median financial services firm, predominantly focused on insurance, life insurance annuities. We're the fourth largest provider of dental insurance in the U.S., most people don't know that. We're probably the 50th largest life insurance company, and although that seems like really far down the list, when you consider there are 1,100 life and annuity companies in the U.S., 50 is not too bad, but clearly not a spot we want to be. What you classify mid-sized median. We also do retirement plans, and then we have a broker dealer, a very common profile for the life insurance industry. We're really not that different. So the challenges would be obviously keeping the business growing, keeping the sellers happy, servicing the existing clients. Very much so. With the existing systems. Okay, so what's changing? I mean, everybody talks about this digital transformation and cognitive, you go to these events. How is that affecting your business, if at all? And forgive me for the cough drop. No worries. So I think there's a couple external pressures that are really hitting our industry. One is if you look at, there's enormous regulatory pressure coming in, right? The regulators continue to apply over and over again, a large amount of pressure to our industry and our business. We're also probably latter in the stages of coming around this age of the consumer, right? So life insurance predominantly was what they call sold, not bought, and therefore you had a financial advisor that sat across the table and talked to you, well, the new consumer wants to drive that dialogue themselves, right? And so you've got an entire network and industry established on it going through what we call a distribution channel. The rest of the organizations will call a sales channel and going through those various sales channels to get to the consumer. And now the consumer wants to be in charge of that. And that's a very huge shift to the way we're working. And then you put the regulatory pressures, the DOL is just coming out with this best interest clause around qualified money to kind of protect the 401K space. And you've got this huge regulatory burden. Couple that with a continued low interest rate environment, large insurance companies and even median size made a certain amount of money on the interest rate spread and you're in a 10-year low interest rate environment. So when you offer a product that has a guaranteed 3% interest, which 10 years ago wasn't very attractive, now looks really, really good, right? But the interest rates are to zero, you're upside down in those spots. And so those external pressures are there plus the consumer. And the consumers, most industries were allowed to kind of change their focus as they dictated it. And the expectations being set by banking, by Amazon, by wine.com, that expectation is coming from every place you go. So those are really starting to hit hard in the life insurance space. And you're seeing the life insurance industry, we heard the speech today around that speed of acceleration, how fast it's coming up. We used to have three, four, five years to respond. Now we have to figure out how to basically come up to speed on those in a year, 18 months. So that's the entire experience. You've got your building products, which are largely IT. Absolutely. You've got a distribution channel that you got to support. You got customers you got to support. So you've got these systems that are in place that were probably developed 15 years ago with the code base that reflects that. Okay, so now you fast forward this zero percent interest rate environment, all this modern technology. So how are you dealing with that? Obviously a big data challenge. How are you as the CIO dealing with that? Tell us your story. You come in and you see this set of systems very common in your business. What do you do? And I'll kind of dissect that into the data side of the world and analytics side of the world and just kind of the processing of the business, right? Because I think, and you can tell me where you want to kind of drive the conversation, right? Because both of them, we could spend probably, you know, hours just, you kind of go down those rat holes and you're down there for a while. On the data side, we looked at that. And when I joined as the CIO three and a half years ago, we had a disparate set of technologies and we were spending all of our time in the technology side of the organization and quite frankly in the business side of the organization trying to assemble pieces and parts, right? We're trying to find the next cool shiny object and we're going to buy that shiny object and we're going to stick it in and then we're going to go. So we took a step back and we said, we need to make a strategic decision here and also how do we leapfrog, right? We really said, how do we not pave that same cow path but how do we get ahead of it or catch up, right? Because the problem with being behind is if you take the same path everybody else took, you know, three years from now, you're still three years behind or five years behind or however far behind you are. So how do you jump over that? How do you leap over that? We took a step back and we said, look, we need to basically invest in what we would consider a strategic platform into end. And there's quite a few out there. When you start to do your scans and look to see who's out there, there's quite a few out there but it's not like 25, 30, or 50. You know, there's maybe five or six providers that really can give you kind of an end-to-end technology stack across. Yeah, unless you wanted to build it yourself which is just a nightmare. Which would be insane, right? I always like to say, why would you build what you can buy, right? And then you layer in cognitive computing. And when you layer in cognitive computing, if you ask me, and it's purely my opinion, there's three players that are going to be standing, right? I think we're going to see Microsoft, I think we're going to see Google, and I think we're going to see IBM. Those are the three players that I think we will see last men standing with, if you layer, cognitive in. That's not to say there's other players who have different parts of the solutions and I'm sure they'll all come in and argue. And in the enterprise. Right, I mean Apple might have some cool stuff and Amazon. Absolutely, yeah. But when you're talking about all the way from data prep positioning, data life cycle management, data management, data governance, business intelligence, somehow you got to bring all that together. And again, unique to us, I think we were far enough behind on all those fronts that it made sense for us to just go someplace different. So we looked and said, we chose IBM. It's convenient that I'm here at the IBM conference and talking. I'm not sure I would say that in every space on the planet but in this space, absolutely. We felt they were going to be one of the leaders and we might as well tie our head, tie our kind of wagon or hitch our wagon to one of those leaders. In this space being end-to-end analytics. End-to-end analytics, whatever we want to call it. Again, what I would call extracting value from your data, right? Because at the end, it's really about getting the value out of the data. And how do you keep focused on that, right? Instead of sitting down and looking at, well, we got a lot of people spend time figuring out how data moves around. We got a lot of people who spend time out of security. You got a lot of people spend time how to play with it. You got a lot, right? But at the end of the day, you got to keep that higher end focus on how am I going to get the value out of that data and how do I keep staying focused on that and keep that lens on all those activities and get the technology out of the way. I know as a CIO, that seems like a really crazy thing to say, right? Because we're supposedly in the job of technology. But really what we're in the job of is how do we get value out of technology and drive that value up into our business, right? And that moves us kind of in a different services plane where we should be sitting and understanding. I think I heard one of your earlier sessions where someone was saying, hey, we've got to go up and you've got to have a developer understand the business. They got to understand CapEx, OpEx, understand PNLs. You got to really understand your business so you can sit in a room and say, what can I do to help you? I can't actually do it for you all the way, but I got to find a way to help you get there so that you can get the value out of it as well. And so that's a very different job for technology, right? I mean, technology traditionally where the geek heads and the people in the closets, you know, making stuff work. So financial services generally and insurance specifically has always been a data-driven business. Absolutely. But when you talk to companies about what they have to go through, well, first of all, let me say that there was a period of time where data was often seen as a liability. Correct. Where the general counsel was sort of the tail wagging the dog, get rid of this stuff, work in process, delete it, okay? And then we started this new wave of, and then insurance companies have always understood how to get value out of data, but then there became so much more data and so much new opportunities that you had to reach this new digital channel. So part of that is understanding how you make money with data. The other part of that is understanding your data sources and potentially new data sources, social media, whatever. And the third is trusting that data. Absolutely. And you could go back, you could dial back 20 years and you had that same challenge. It's just at a different scale today. So I'm testing my little mental model here. You see your head nodding, so okay. But how did you sort of take those disciplines of understanding how to monetize data, of what data sources and trusting that data into this modern era? Yeah. And it's interesting, there's a comment I make when I talk to a lot of the senior business executives in our business lines. I always say, the CFO can tell you the value of this table, better than they can tell you the value of their data. And the argument is the data is infinitely worth a whole lot more value than the table is, right? Cause this is tangible and depreciable and goes there. So I think the question you're asking is at some point, I mean, you've got to get down and find a way. And so this is where I think things like, you know, your Watson analytics and a lot of your machine learning pieces really help. Cause what you're trying to do is let the data also guide you, right? So a lot of what we'll do is grab that data, put it together and then start to run a lot of these newer analytics on it to help the data start to give us part of the story, feed that back into a group of people who are starting to look at that, you know, the old form of hypothesis and then start to work that out. I think the key is there's two challenges to that and there's a key to that. One of the keys to that is how do you start to weed through and find the thing that will help you go do something, right? Cause what we see is all of that becomes a sea of data and a sea of information, you know, and it can overload you, right? And now you're starting to look for a needle in a stack of needles, right? Instead of a needle in a haystack. And it can overwhelm you. So how do you continue to make it easier for people to kind of go through that process? It really is a process of evolve and learn and evolve and learn and speed that cycle of insight up. And so what we're doing is we're actually setting up almost like playgrounds, right? I mean, all the tech terms are out there. I got a data lake. Okay, what's a data lake? But you got to create an isolated playground, throw the data in and let them take in and out, let them start to play with it and figure out what they don't know they know or don't know they don't know and allow that journey to move forward. So we're trying to create kind of these pockets and these playgrounds, but also keep those playgrounds isolated because at some point you come back to the trust and integrity factor. And then what we've done is our data governance has really been about what's the processes I put in place to allow that to come back into the organization more formally, right? Because you got to have a little bit of those controls so that I don't end up with 20 definitions of revenue. Right? And your job is to create that environment where they can have these little playgrounds or sandboxes and then present it in a business context to the LLB heads. And then once you get sort of confidence that you've done that the right way, then start to scale it. Exactly right. And enable the process to bring it back into more of a scale or more of an operation or more of a pieces. And we're still early stages, so we're testing a few of these. So we took data from our kind of provider networks and we started looking at consumer behavior patterns in our provider networks, which showed us insights, usage insights. How are they using different provider networks? And if you think about a lot of dentist offices they've got a contract with this provider going this way and a contract with that one. So you go to the dentist and which of the 60 contracts he's got is the one that's actually going to flow through. But we started looking at those usage patterns and then we did some high end kind of statistical robust modeling. And then we drove that back into our benefit schemes to allow our customers different treatment options, right? Which actually lowered your cost, improved your experience. But that's a very minimal, but what you're doing is taking that and that you go show somebody and say, this is an example, I use my moon base analogy, right? A lot of times when you're in this space of insight and analytics, you're talking about moon bases. And you're talking about moon bases to people who have never been to NASA, right? So you got to go show them a moon base. You got to go give them an example of a moon base and let them walk through it. And then they go, ah, that's what a moon. And now they start asking the right questions to, hey, well then how would I build an extension to my moon base? Or I mean, I could take that analogy and run with it for a long way. I love that analogy though, because in your days as a consultant, the technology was really mysterious. The business process was well known. Today it's the opposite. Exactly. The technology, like you said, there's five or six suppliers. They're pretty trustworthy companies. Obviously you chose IBM, very trustworthy. But the process, you're going to make it up as you go along. Absolutely, yeah. And learning as you go. And that I think is the key to how we're going to be successful and how we're going to adapt. So the technology is a collection of software components and some services as well. I mean, the knock on IBM is heavy services led. Is that still the case here? Or was it a combination of services and software? And it's interesting. And maybe because we've got a little bit of a green field in us, we're not so reliant on some of those big pieces. So we're finding that it's a nice blend. Everybody wants to sell you something. Everybody wants to sell you services. But we're finding that we're trying to find this balance of what we do and what we ask them to help us with. And we always take it through the lens of cost, risk, value, right? So we're not afraid of in out or cloud or not cloud. But it really is getting back. So we're seeing a nice mix there. They're not pushing a lot of services on us. But I think we're pretty strong about, hey, this is what we need from you and this is what we'll do. And we're also open to where we're wrong in that equation. Yeah, I mean, we've been working off the premise that IBM's got to codify in software its services expertise. So the company's like, you can absorb it, scale it and then afford to innovate. I think what we're experiencing is the integration points are the ones that matter, right? What do you mean by that? So we use their business glossary. So I'll give you a tangible example. We use their business glossary to get our data definitions in. It used to be, okay, I put that there, but then when I was using Cognos, I had to go re-scheme that in Cognos. It's like, I don't want to do that. I want you to do that. What I put here should move towards here, right? What we have in Cognos should be easily leverageable and extractable up in SPSS. We ought to be able to move around. I always like to say, don't make your suite of solutions my problem, right? I mean, make that stuff work together and do it. And we're finding that they're doing a really good job of that. Again, so far, I think we're early enough. Maybe we're not stress testing the parts of that that doesn't work. We were an early adopter of Watson Analytics and our average citizen user was really stumbling. And we fed a lot of that back and said, hey, so some of it is ease of use and some of it is integration. And I think those two together are the two where firms like IBM and the others need to focus on the most because it makes that usability work for us the best. And where does this all take place? In the cloud, on-prem, a little bit of both? I think a little bit of both. You know, I had this conversation with the IBM data cloud guys yesterday. I said, look, you know, we've got this combination of what I call data and then liability of data, right? So we live in a really secure, right? Everyone's afraid of security and everyone's afraid of breaches. We know that's a if-not-win proposition, right? And, but it's like, hey, we want you to use our cloud but we don't want to be liable for all the data if it gets lost. Well, you can't have that both ways, right? So we're finding that we've got to do a little bit of both. There's how do we protect it correctly? I think there's certain parts of the analytics that should be performed on-prem. So how do you hook all that together? I see that as that next challenge. I don't see those solutions readily available today. But I see that as the next challenge and it looks to me like people are thinking about it. But until then, yeah, we're going to say these things we're going to have to do on-prem and these things we're going to have to ship up to the cloud. But you really would like that to be ubiquitous and easy to just access and all those things around protection and security are built in no matter where you're doing it. But generally speaking, you're pretty comfortable with the technology. Now it's the hard part of the business process, the adoption. And I think the technology management process, right? Because you're managing differently. I mean, we all talk about the cloud, but it's not this amorphous, fluffy thing. I mean, it's a data center sitting somewhere that's running something. And so you've really got to care about how it does move from here to here, how it does get processed here versus there. And I think we have this great vision of I just access it, right? I mean, we have an internal cloud. We've worked real hard to emulate that cloud environment inside, but making two clouds jump across to each other? Yeah, with that same operating model, it's not trivial. It is not as easy. So that technology process on how you're going to manage it and how you change that technology process as the capabilities evolve, you want to do that as well. You don't want to build an old technology process that doesn't allow you to evolve with the speed of that change, because it will change. You can see that they're continuing to evolve it. All right, Richie, we got to leave it there. Thanks so much for coming on theCUBE. Share your knowledge. I hope you enjoyed it. All right, keep right there, but we'll be back with our next guest. This is theCUBE. 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