>> Announcer: Live from Orlando Florida. It's the Cube. Covering Pentaho World 2017. Brought to you by Hitachi Vantara. >> Welcome back to the Cube's live coverage of Pentaho World brought to you by Hitachi Vantara. I'm your host, Rebecca Knight along with my co-host, Jim Kobielus. We are joined by Geo Thomas. He is the director of It at Benefits Science a healthcare insurance analytics company. Thanks so much for coming on the Cube, Geo. >> Thank-you, thanks for having me. >> So Benefits Science is a company launched out of MIT, tell our viewers a little bit more about the company. >> Okay, so Benefits Science is a healthcare data analytic company which co-founded by MIT (mumbles). Doctor (mumbles) and Doctor Stephen so far and we have one more partner. We do data analytics on the healthcare side and we work with employers and the brokers to analyze the data and give them dashboards and workbooks, and so that's what we mainly do. And we, yeah. >> So, as you said, you work with employers to save them healthcare dollars. Can you get into the nitty-gritty a little bit more. >> That's exactly right, so what we do is we empower employers to manage their employee benefits. Providing them the data analytic tools and other optimization tools, and we give them a very fine clear picture of how these plans are performing, and how they can optimize their plans in the near future by giving plan optimization tools and (mumbles) algorithms and things like that. >> You refer this as a manage service for your clients or do you provide specifically licensed software that helps them do this for themselves? From their own premises. >> We are a Cloud platform, and we provide our platform as a sub-lease for our clients. So, we get the data from them and we provide data analytic tool by mashing of this data and they use our platform to see those reports and insights and things like that. >> So, healthcare data is a really special kind of complicated when it comes to data because there's so many security and privacy issues related to it, how do you go about it managing this kind of data? >> Healthcare data is a very complex, very huge and we can't expect what comes next and there a lot of regulations and there are a lot of security issues, so we take all these with upmost priority. So, our company is a SOC1, SOC2, certified company. Which covers a lot of regulations by itself. Our employee's, Benefits Science employees, are really very much aware of these heap of rules. And they are all certified. We have lots of internal an external audits and regulations throughout the place so that would cover all this compliance issues, mainly. >> From an operational standpoint, how are you managing the day-to-day, day-in and day-out, do you provide a data warehouse within which you load it and then from which you do the analysis? What's the sense for how you architected your environment and then where how Pentaho plays into the overall picture? >> We take the data. Once we get the data, we measure the data. So, how we do those, we use Pentahos, and then two and two. Because it gives us a very standardized methodology to process this data, so we identify the PHP data. We sample it, scramble it, and then we do the (mumbles). And once the data element is done, and nobody touches any of those PA jobs or the jobs which we created with Pentaho, and we run this in a very secure environment in which we put all this transformed data into a data analytical platform. >> When you say scramble, you're referring to masking and anonmyzing the data? >> Correct, yes. >> That's what I assumed, you tell me, that's required by HIPA, that you do it that way? >> Yes, that's correct, yeah, yeah. So, we don't take all the data for the development. We take only the sample data, and then we scramble it and we (mumbles) all this information. >> So, what kind of results have you seen in your company since using Pentaho? >> So, I started in almost one year back and when we started, we had 20 tenants. Now, we have 200 tenants, so that's the summary of recently of what I'm seeing because Pentaho gives us lot of flexibility to standardize and make proper checks and balances throughout the data pipeline and we had created very huge test framework which can run automatically. So, all these things would benefit us to board a client because right now, onboarding a client would take less than a week. >> When you say test run automatically what sort of test are you referring to? >> So, we create test scripts, and we created a test suit framework by using Pentaho Jobs. And we schedule that. That test suit what we do is every, whenever any tenant comes in, developers can create N number of test cases and plug that in. So, it is growing and that will run automatically. Along with the PA jobs. So, that gives us a number of outputs and checks and balances and depending on the results we board the client. >> Saving healthcare dollars, spending healthcare dollars. This is really part of the national conversation. How much does Benefits Science really feel a responsibility to weigh-in on these issues. We heard a lot from the CEO this morning about how Pentaho really views its guiding principles as doing good in the world and bettering society. >> The double bottom line. >> Very true, very true, because as Benefits Science company our vision, our motto is not to just built some software and give to customers and get some money. Our vision is to help people or employers reduce the healthcare cost, so. Our data scientists built this great plan optimization tool or (mumbles) to provide employers to look at, "Okay, these "are the large claimant details, which means we might have "to go and find out the reasons and work with them "to reduce the cost." So, we are giving all the tools for them and another thing is the data (mumbles) analyzer our users love it, because we provided a simplified cube for them to drag and drop and create the reports and they can easily drag a couple of data elements and come up with, "Okay, these are the paid amounts "which we paid last month, and this has to go down." So, they can come up with their own strategies to make it down, at least, for the next year and on. >> In terms of user's being able to, in a self-service basis define their views and their reports. Do you take that intelligence that you gained from users and then bring that back into the basic service in terms of adjusting the data model? The set of canned reports or dashboards you provide? What do you do in that regard? >> Yeah, so we have a custom insight reports. Which will give pretty good idea about what this data meant to be for the customers. Like drag dashboards or large claimants or quality measures so things like that. We also have another data science group works on this AI tools or machine-learning algorithms to provide more predictive analysis. So, that would give users a different perspective of, "Okay, if we do this, we can reduce the cost." >> Is that WECA or? >> No, we are using. That's another thing I want to go back and tell them. There is a WECA here, we probably have to start using it. So, right now, we are not, right now we are using RN Python. There's something called (mumbles). So, that's what we use. >> What are some challenges that you are facing right now? What is keeping you up at night? What do you want the next versions of Pentaho to solve for you? >> I'm Director of IT, so I care about IT more than the business. So, my challenge is always how I can board more clients within a short span of time. The scalability, the security, how we can make it compliant. So, I was listening to that ATO, what are the new things coming in ATO? One of the main thing I was looking at is the scalability that is there is something called Worker Naught, that's got announced in ATO. Which you can scale as a docker, and you can spin off as many dockers as you want, and it will work by itself. That's fantastic, I'm really looking forward to get that scalability into our system. >> So, you're saying your IT environment. Your focused now more and more on a Cloud data environment that takes the application functionality and wraps it as containers? So, that's where you're going? And then you're saying that, I don't want to put words in your mouth, what you're doing is consistent with where Pentaho's going with their overall product platform? >> We are hosting an (mumbles) Cloud with Pentaho. So, Pentaho is also going into that direction. Makes me very happy because we are really looking forward to get that working in the Cloud. The thing is the. The Worker Naught, what they're talking about? Is what we were thinking of implementing on our own. So, now they have their own Worker Naught which we can just take and put it there. So, that's very good news. >> I wanted to ask you about the talent shortage in technology because that is something that the CEO talked about, Karen Perlich talked about, too. Is this real dearth of talent in data science. There was a piece in the New York Times just the other day that talked about how data scientists just a PHD can come out and make a half a million dollars in Silicon Valley. What do you think will be the real change and will get more and more graduates into this field. It seems as though the money should be enticement enough. >> That's a million dollar question though. We are in the same boat. >> You're a Massachusetts' based company, it should be. >> Even with that, we are finding a lot of difficulties to get some good data scientists. Because the moment you pass out as data scientist they're asking half a million, so. >> Literally I saw an article the other day. A good data scientist in Silicon Valley can fetch upwards of a half a million per year, so. Imagine in other regions, and now Massachusetts has no shortage of educated, smart people, but still. >> They have that level, then yes. These tools would help, and. Building that artificial intelligence on top of these tools would help, definitely, to have some sort of, not depending on data scientists so much. That even others can do those kind of things. >> So, you might not need the talent in a way. >> I'm looking forward to that because I was listening to your session in the morning. Very impressed with that because that's where I'm also trying to see where the world is heading to. >> So, you make recommendations to your clients about how they should start structure their healthcare insurance plans or employees. Do you have a capability right now within Benefits Science to basically embed a recommendation engine of that sort to help advisors on your staff to work with clients to recommend the right set of options or approaches pulling from the data, that's already there? >> Yes, that's already there. So, we provide recommendations for clients by using these algorithms. So, we have this plan optimization tool. Which will give you, if you do such and such things this is going to go down in the next year. Or there is a plan designed data. So, whenever an enrollment happens the main thing that they look at is what plan they have to sell at for their set of employees. So, every case is unique. So, we put a lot of historical data information and we put those machine-learning algorithms in there and then we come up with. We clean that model with all this data and we predict for each tenant. So, we have that right now. >> Geo, thanks so much for coming on the Cube. It's been really fun talking to you. >> Thanks for having me. >> I'm Rebecca Knight for Jim Kobielus. We will have more from the Cube's live coverage of Pentaho World, just after this. (calm electronica music)