 Live from the Mandalay Convention Center in Las Vegas, Nevada, it's the queue at IBM Insight 2014. Here is your host, Dave Vellante. Welcome back to IBM Insight 2014, this is Dave Vellante and we're here at the Mandalay Bay Hotel in Las Vegas. Alvaro Chavez-Torres is here. He's with Remac Insurance and to my left is Shankar Ramamurthy who's with IBM. He's involved in Big Data Strategy, helping clients really implement Big Data. Gentlemen, welcome to the queue. Thank you very much. Thank you very much. Alvaro, I want to start with Remac. Maybe you could just describe the company, you know, what you guys are all about. Okay, I will spend a few minutes on this. Remac was founded in 1895. We're a 180 company. Older than IBM. Yes, totally older than IBM. We're operating in Peru. And to have an idea of the size of the company, by the end of 2014, we will be issuing premiums of around $1.3 billion. We're also vertically integrated with the healthcare business. We own four hospitals and nine medical centers and we are part of the Breca Holding. The Breca Holding consists basically in, participates in several businesses. Basically in the mining industry, efficient finance services, insurance services, real estate and tourism. Just for you to know, as a whole, the Breca Holding has a direct impact in around 10% of the Peruvian GDP. All right, and Shankar, you are involved in helping clients apply Big Data Analytics processes. Maybe describe your role a little bit and then we'll get into the Remac case study. Sure, absolutely. So, our view in IBM is that the combination of Big Data Analytics, along with cloud and mobile and social, what we call engagement with mobile social, is fundamentally transforming the global economy. And every industry and every enterprise, whether they're operating primarily in the digital world or primarily in the physical world, is being profoundly impacted by this trend. And what we've done in IBM is invested tens of billions of dollars in helping our clients by creating capability, helping our clients with their transformation journey. I am the global managing partner for the consulting part of that business. What we've done is we've brought together what historically used to be a strategy consulting practice with what we call BAO, Business Analytics and Optimization, brought it all together to really focus on the types of issues executives are dealing with. Increasingly, the power of cloud and analytics and mobile and social is an issue that transcends the CIO. It's an issue that's of importance to the CEO, to the chief operations officer, human resource officer and so on. And what we've done is brought that power of that transformation to bear along with our software capability and helping clients like Remak and others address the industry transformation and take advantage of the power of big data and analytics. So, Alvaro, you hear this story a lot, right? Companies understand or the pundits say the world is being transformed. Many companies do understand some don't. You guys came to the realization that something had to change, you had to become more whatever buzzword you want to use, data driven, more analytical. How did it all start? Maybe describe some of the drivers. What types of people in the organization really brought this? Was it a bottoms up? Was it a top down from the board room? Can you describe that? As a leading company in the Peruvian market with more than 30% of the market share for the last 10 years, we were conscious that the insurance industry was evolving in a different way. That is incorporating big data analytics and the process of making decisions is shortened and Al is tied for the purpose of aiming to get exponential results. So, for Remak, it was not easy because of the good results that the company has to start envisioning change, transformation, because everybody was comfortable with it. And we started talking with IBM and we spent several weeks at the beginning of 2013 trying to understand each of the processes that we wanted to be transformed. And we attached to that process KPI specifically to capture the benefits of this transformation. Then after, we had to convince not only our board members, but the top management of the company and as well the 4,000 employees of Remak. It wasn't easy. At the beginning, as I tell, it wasn't easy, but now we have everybody on board and the results are starting to come one year, I mean, on our first year. So, it wasn't top down or bottom up, it was kind of middle out, is that right? Yes, it was a middle out strategy, that's correct. Okay, and the driver for that change was what? You saw opportunity, you saw risk, you saw the competition, why? Yes, we're not aiming that a global company comes to Peru and then start moving on. What we decided is, as is common, your loss ratio in the insurance business, loss ratio impacts directly on the bottom line results. So, all of these events and processes are part of this program have a deep impact on our loss ratio, basically. So, this will turn Remak, a player, for a different league in terms of, in order any other competition comes, they won't catch up easily because the speed of the company will be a lot more than it is actually. So, loss ratio is, I guess, what a risk met? It's a percentage of the premiums. Right, but you have to forecast, right? Looking forward to risk of taking on a certain... Yes, I mean, the way it was focused, the transformation process has to do with our claims, with our insurance strategy, on the way we analyze data for our new business and our renewals, we're also tackling fraud and leakage in each of the processes in the different risks so that all as a compound impacts our loss ratio. Yeah, so I would think risk, fraud, and marketing, and sales would be the three obvious applications for data. Does it include those three? Yes, of course, and there's also something very important to mention. The information that we are getting now and the tools that are applied to the capturing value out of the information makes us better outcomes in most of our decisions in how to take risk and what's the opportunity that we can easy find in our market. So why IBM? Well, IBM, in comparison to other consultants, is taking the risk with us. That is, all the benefits that we're going to capture out of this process, it's shared in terms of IBM is being a partner of RIMAC in this journey. So the decision had to do with, they're not only robust in the way they approach the market in terms of the consultancy that they bring up to the table, but also the tools that they're implementing can help us widely and after all, better than any other consultant firm. Okay, so Shankar, someone like Alvaro comes to you, let's take this specific case and says, okay, we have a problem and opportunity, we want your help, where do you start? So typically what we tend to do is try and come up with a proof of concept. In other words, we are so comfortable with the power of big data and analytics and our ability to apply models to business problems to unlock economic value, that we try and prove within a relatively short period of time that we can unlock a lot of economic value. So rather than try and do a large engagement, we would say, let's take a part of your problem and let's prove to you the economic value. And when we- What's a short period of time? Could be six weeks, it depends on, so, you know. Yeah, that's short, right. It is short. If you get an answer in six weeks, you got my attention, right? Right, so in a short period of time, demonstrate that there is value to be unlocked. Now the actual transformation program, like Alvaro was saying, is a long journey, right? But if you know that you can unlock economic value and then you take the monies unlocked, whether it be in claims or underwriting or fraud, you know, risk management, and then apply the monies unlocked to the actual transformation journey, which has got lots of pieces to it. There's a change management, there is a technology piece, there's a process for engineering, there is the way you communicate with external world and the multiple stakeholders. A number of things to be done in the actual journey. But if you can unlock the economic value based on big data and analytics, it then makes that journey easier because it becomes a self-funded program. And in VR, like Alvaro was suggesting, very comfortable with the economic value we can unlock, so we are willing to put skin in the game and work with our clients in partnership to create the economic value. Over, you mentioned KPI, as you mentioned, one loss ratio. You have to be careful, I would imagine, not have, try to optimize every KPI under the sun. There are some knobs that you can turn to optimize your business. So what are we talking about? We're talking about a handful of KPIs, dozens, hundreds, thousands, what? Give us a sense of the scope. It's very simple. Each of the processes that are being transformed has a specific KPI, for example. In terms of fraud and leakage, out of all the claims that we process, for example, in health, there are around 2,000, 200,000 transactions a month. And out of that, we're capturing a specific KPI on each of the type of claims that we pay in terms of, well, now we're doing things in a linear way and only analyzing, portionally, proportionally, a piece of all those transactions, but now we're applying this technology to the whole process. So those 200,000 transactions a month are being analyzed, deeply analyzed, and we're capturing any possible fraud activity on those transactions. Are you trying to optimize pricing or whether or not to take a deal? No. As long as we capture value in our loss ratio, we can price better and be more competitive. Right, okay, so it's all about pricing to get the deal if it's profitable. Of course. Or pricing appropriately, where you've got a loss ratio that's too high. And at some point, we will be able to transfer this benefit to our customers. It's going to be transparent. So, okay, so you start with the proof of concept. Where do you start? Do you start with the business process, the application portfolio, some combination? So it depends, right? So one of the very clear things that's happening is every line of business executive is increasingly open to actually leveraging the power of big data analytics. So it could be, I'll give you an example. It could be the CHRO, the Chief Human Resource Officer. It could be the CFO, it could be the Chief Risk Officer. It could be the Chief Operations Officer. It could be any CXO might be interested in leveraging Chief Marketing Officer, the Chief Sales Officer. Any part of the business might be a logical place in which you interact with the business executive to unlock economic value. It turns out, increasingly, the CIO, who used to be the Chief Data Officer, the CIO who used to be the primary person involved in technology, is now becoming an advocate and a partner with the line of business executives and unlocking economic value. So the way we have structured ourselves in our consulting practice, we've got about 20,000 people in the strategy analytics, consulting practice worldwide, the way we've organized ourselves is by what we call domain, meaning the various CXOs and by industry. So, in the case of Remac, as an example, we bring people who understand the insurance industry along with people who understand technology deeply and specific domains. So you bring that combination together along with the power of IBM software and IBM research and Watson, we are able to do extraordinary things for our clients. Okay, so it's role-based and industry-based. Obviously, the industry here is insurance. And where did you start from a domain? So, again, it depends on the client. In the case of Remac, the domain is all around operations and underwriting and risk, right? That's where the material opportunities, you know, exist and you look at the processes in those areas, you unlock a lot of economic value and put in place a transformation journey. Okay, so it's underwriting and risk trying to understand, but starting with the underwriting risk but looking at the claims data, right? So you can then go back to the systems in the business processes around underwriting and risk. Now, those business processes are pretty hardened. You've got, I don't know how many decades of business process layered, built up, and you've got technology systems supporting that. That's correct. That are also very hardened and they work. Of course. Right, so did you get a lot of friction in terms of? Before moving into this process, we spent five years optimizing our legacy and core systems in the company. Modernizing those systems. Yes, modernizing for five years. And after that, we decided that it was about time to capture the benefit out of those efforts. That's why we moved into the transformation with IBM. Okay, so that modernization was what? An application portfolio rationalization, looking at business process. Basically, what we changed was the structure of the databases for all, I mean, we integrated the customer's view and all the risks involved with our customers. I mean, now, Remac is considered a customer-centric company. So you had a product view before, or potentially something like that. That's correct. And the customer would call for one product to go, I don't know, and it's very frustrating for the customers. We all have, consumers can understand that. That's correct. So you fixed that problem. Okay, so now you got a better view of the customer, but you weren't optimizing on underwriting and risk in your view. We didn't have the tools and the technology to have a better usage of our information. So you weren't worried that it would necessitate a change in your underlying technology infrastructure. Or maybe it did. I don't know. Are you building on top of that? Or are you having to? Because you've just modernized your application portfolio and your technology infrastructure. Are you worried they have to throw that out and start over again? Or why were you confident that you wouldn't have to do that? Because we, I mean, the way our IT architecture is built is based on AII standard. Yeah, okay. So everybody's doing that. So there was no means of potential errors. And as I mentioned, we decided to build things on top of those new legacies. Do you, as part of this, do you have a chief data officer? Of course, we have one. That's interesting. You say, of course, many companies don't. Of course, insurance, more likely to have one. So when did that CDO role emerge? Well, it emerged a couple of years ago and all this data governance process that we run during the last two years was very important for that purpose as well. Right, and I wonder if we could, can we generalize by industry or maybe just in general? Taking the industries that are more, most likely to have a CDO, say finance, insurance, healthcare maybe, government, what percent of the clients that you talk to have a chief data officer? Well, the title might slightly vary, but the data's all right. Yeah, so in financial services, pretty much every large enterprise has increasingly got a chief data officer because in some instances, regulators are mandating it as well. So close to 100%. Yeah, I mean, it's increasingly becoming the norm. Certainly, in the mature markets, it's pretty much the norm. There are other parts of the world where it's, where it's still a wall. And they might not be called a chief data officer, but there's someone responsible. There's somebody who's responsible for governance, data architecture, data quality. But over time, that role becomes much broader, right? That person ends up becoming the advocate for bringing the power of big data and analytics to bear on enterprise information and not just enterprise information. Combine that enterprise information, the structured data, with a whole bunch of unstructured data that's outside the enterprise, right? So whether it be information from social media or public domain, bring all that together to unlock the economic value. And that becomes a virtuous cycle. So any industry that's primarily dealing with bits, so healthcare, there's a lot of information, financial services, telecommunications industry. When there's a whole bunch of industries that are primarily dealing with information, information is a real asset. Those industries have got the equivalent of a chief data officer, increasingly, and they recognize information as a genuine competitive weapon. That's not to say that industries that operate in the physical world don't. Well, we're like, gas, I mean, they're increasingly data. They massively, they massively data oriented. But you're right, I mean, the Adam businesses are, there's less pressure than the... They have a bit more time. They have the luxury of time, but it's catching up with them as well. And this individual reports to whom? The CDO, is it the... To the Chief Operating Officer? Yeah, not the CIO. So the CIO and the CDO report to the CIO. In our case, the COO and the CIO are the same person. They're one and the same. Oh, really? Oh, interesting. That's fascinating. I've had a lot of CIOs tell me that their role is morphing and they're having to choose. Do I become a Chief Data Officer, a Chief Operating Officer, or a Chief Technical Officer? You know, it's interesting. It was the modernization project was a prerequisite to this, I presume. Of course, it was. Any other sort of things that people should think about? Change management, basically. As I mentioned this morning, it wasn't easy to move all the company because of their results. I mean, nobody was envisioning, why do we have to change? So, the first thing we have done was thoroughly analyze the process, place the KPIs where you have to be, but secondly and more important, communication. We communicated everybody in the company, why do we need to change? Why is change important? And what's the outcome of change? Because people get scared, you know. Every time you change your applications, every time you do new things in your square meter of work, people start thinking, what's going to come next? Is there a benefit for me? Where is it? So, those answers to those questions were specifically delivered by our CEO to all the employees in the company. So, Shankar, the proof of concept was, if I understand it correctly, essentially a business case around a high value area where you can unlock economic value is essentially what you described. So, how'd you do that? Did you sample data? Did you take your benchmarks from other, you know, like companies? Yeah, so it's a combination of things, right? So, it turns out that there are a lot of repeatable patterns within industries and across geographies. There are certain repeatable patterns that we come up with multiple times. It's based on the tens of thousands of projects that we do around the world. And there are some, you know, for example, if with RIMAC, you know, claims is an opportunity, turns out that in many insurance companies by applying very sophisticated analytical techniques, you can actually unlock value in claims. You know, or if you're a CHRO, there are opportunities by applying analytics to the talent, to the people dimension, you can unlock economic value. I'll give you some examples, all right? So, we did this piece of work for this theater chain to understand what the value drivers were and how to unlock economic value. It turns out that 100% of the profitability for this theater chain came not from selling movie tickets, but from selling popcorn and confectionery, right? Of course, right? But you had the data to prove it. Now, yeah, and? I got four kids, so I know that well. You know, the movie ticket space for the infrastructure and it keeps the lights on and so on, but the profitability comes from the confectionery and the popcorn. Turns out that the profitability of a particular theater was directly dependent on the turnover of the employees in that store, right? Wow. Now, once the enterprise understood the insight, they were able to put in place, they had 300% turnover a year in their confectionery and popcorn store, right? If you can reduce that turnover, so when you compare the best theaters with the rest, the empirical data was stocked, so they put in place mechanisms to reduce turnover, right? Better job environment, benefits. Benefits, promotion opportunities, exactly. Significant impact on profitability. So you think about that pattern, right? So now we are able to take that pattern and apply it to multiple retail industries where employee turnover is high. So we can go with confidence and say, you know what, give us your data, we're going to prove to you that there's economic value. You know, doing that alone is not enough, but that unlocks the economic value that we can then take in the case of Remac, they've already done the transformation of the technology portfolio, modernization. And some other clients, we need to unlock the economic value to help them modernize. So we're out of time, but I wanted to just get a sense of the timing of the projects. When did this start and what's the timeframe look like? I'm intrigued by the whole self-funding mechanism. That sounds like it's critical, but yes, this started this year, 2014, and it will last for five years. And at the end of each year, we're going to renew our vows and check on the KPIs for all of the processes that are going to be transformed. And the intent is to make itself funding. Of course. And then the profit that you make goes back into, where, bottom line, organization, is there a gain sharing? How does that work? Yes, there is a bottom line and a gain share between us. Awesome. Great, well gentlemen, thanks very much for coming to theCUBE and sharing your story. Really appreciate it. Cheers. All right, keep it right there, everybody. We'll be back to wrap day one from IBM Insight. This is theCUBE, right back.