 Okay, we're back here live. This is siliconangle.com's theCUBE. theCUBE is our flagship program. We go out to the events, extract a signal from the noise. You can find the videos on siliconangle.com live right now on demand on youtube.com slash siliconangle. For more research, go to wikibond.org. It's all free content. We've got research, we've got blogging, we've got publishing, we've got live TV. We are here in Las Vegas for IBM's information on demand conference with the center of the action. This is the beginning of big data week here at IBM in Vegas. This event is going to be talking about big data and all of what IBM's doing. We're going to New York for Strata, the Riley Stratoconference and Hadoop World and it's a big action packed event. I'm John Furrier, the founder of siliconangle.com and I'm joined with my co-host. I'm Dave Vellante of wikibond.org and we talk about a lot about how practitioners struggle to work together different data sources. Everybody talks about the three V's of big data but the real big ones really are the variety of data but also one that people don't talk much about is value and the practitioners that we talk about, talk about John and the wikibond community tell us it's really bringing together the structured and the unstructured data and identifying the data sources and then figuring out how to monetize that, how to get value out of it. So we're here with DataSkill. Nigel Hook is the CEO and president of DataSkill. They're an IBM partner. We're here at IBM IOD information on demand conference. We're going to talk about some of those themes and others. Nigel, welcome to theCUBE. Thank you. Thank you, Dave. Thank you for the opportunity to talk to you this morning. Yeah, so you heard me talk about what practitioners telling us. They really struggled to figure out those different data sources and really how to get value out of them. It seems like that's really what your firm is focused on helping customers do. Talk about DataSkill a little bit and then we'll get into it. Yeah, we were the company's 30 years old and we started out at developing custom software back in the early 80s and in the early 90s we got into business intelligence so we spent a lot of time two decades really taking advantage of structured data to provide better decision for our clients. So it occurred to us, you know, with this big hype around big data last year, it could we should be looking at this a little bit more. So a year ago we started looking at how that can bring value to our customers. And the area we saw is that, you look at what people say about unstructured data being 80% of all the data that's out there. So we're running analytics on just 20% effectively. So it's hard to drive full value on when you've only got effectively a quarter of a fifth of the information. So it's very difficult to do an analytics on the unstructured data because there's so many different forms that would be emails or documents or text voice. We, in partnering with IBM, we've been able to get technically enabled on the technology which really will enable us to take advantage of text. That's our focus right now. And there's a lot of text out there. And the area that we've seen the most value we can drive to during your question is in healthcare. There's so much data out there. The most accurate data is in the doctor's notes. Far more so than actually in the transactional data that's captured in the EMR system. That's assuming you can read the notes. The scribbles, you mean. That's right, the scribbles. You have technology to read scribbles? There's very good technology for that, yes. What our goal is is to intercept it before the actual writing. If we can actually take from the voice. Right. From that part. And that's been done for quite a while, right? The doctors, any clinician, right? Can record into like a dragon application. And someone can transcribe it into text format. The real key is taking that text and then extracting the knowledge out of that. That's where there's tremendous value. Both in the quality of outcomes for healthcare and also the streamlining of administrative costs as well. So there's advantages, both in the output, the quality of care, and in money savings. Talk a little bit more about that. So let's take each of those vectors. Let's start with the administrative cost, actually. Yes. So talk about how big data directly affects the bottom line. But, you know, I think everyone's familiar with the inefficiencies in healthcare, right? I mean, just morning, the one of the executives from Premier, I think it was, was talking about, you know, you go to any doctor, and the first thing you do is fill out a paper form, even though you may have been there a couple weeks before, right? And then you go to another doctor, you fill out another paper form, and it's just fraught with inefficiencies. In fact, I think healthcare has had smaller budgets around IT than any other industry when it comes to that. Although money has gone into treatment. And so I believe the healthcare is probably behind our industries. And so there's a lot of opportunity to take what's been learned and move it into that industry. I mean, even the whole investment into electronic healthcare records, which has been encouraged and incentivized by the present administration, to actually penalties that are going to be brought into Medicare payments if hospitals haven't adopted electronic medical record systems with meaningful use. And so from an administrative point of view, this is really helping the efficiency of the whole process. So, I mean, that seems like a straightforward thing to do in terms of just digitizing these records and obviously complying with HIPAA and the like. Is that so-called big data, or is it just electronic digitizing data? Very good question, very good question, because the way I see it is the three aspects of volume, velocity, and variety. What we're looking at is both the velocity, that's a lot of data very quickly having to be assimilated, then also the variety, because you're looking at all the other 80% of the data out there to take advantage of. And these are not just doctor's notes, they're medical scans, they're radiology reports, all of this information, and on the big scheme, IBM's working on taking genomic information for personalized healthcare, looking at drug discoveries, looking at research data. So you're looking at vast amounts of data, all of which you want to synthesize down to the point of care where the doctor could be getting some advice, some recommendations, some insight which typically is not available, other than what he's done himself with his own research into white papers, medical documents, et cetera. Nigel, can you help us, Kwanzaa, even if it's roughly, even if you have to gut feel it, what have you seen with customers in terms of how much they can save on administrative costs by going this direction, digitizing, the variety of data that you're talking about and the volumes of data and bringing that into electronic records. Are we talking about single digit percentage cuts, bigger than that? Much bigger than that, actually. How big? It varies by different health institutions, but we've seen 30, 40% gains there. I'll give you an example as well where this can kind of maybe quantify a little bit, I'll give you some example. And there is a lot of high incidents, I say, of re-admissions. An implementation IBM has done with a healthcare organization out of Dallas in Texas where they've looked at reducing the re-admissions for congestive heart failure, right? And if you have to re-admiss a patient within 30 days, it's very expensive when a little bit of extra care applied to the right patient in the right circumstance can save hundreds of thousands of dollars. And I'll give you an example of that, is that if a patient has been released, has an indicator that they shouldn't be released, but it's not on, you could say, the checklist of the outpatient nurse, then the person may be released, and then if they have to come back in again before the 30 days is up, very expensive, whereas a little bit of preventive care would prevent that. Another example being that some patients that get released in, it seems to be in low income housing don't always, although it's type of areas, and so maybe getting some care management for these type of people, you don't want to do it for everybody because it costs to be high. But to identify the patients that are in those high risk areas that may be not taking their medication, a little bit of outpatient care there, again, for maybe a few thousand dollars can prevent tens, 20 thousands of dollars for re-admission. Nigel, I want to ask you a question about the BI, Business Intelligence, Data Warehousing Market. We've had some startups on here inside theCUBE that say, oh, Data Warehousing's dying, it's going off a cliff. You know, democratization of data, Pauline, this is early from Intel, talking about democratization of data, or Tom from IBM talking about democratization of data. Obviously, Data Warehousing isn't going off a cliff, but it's changing. So could you share with the folks out there, Data Warehouse Business Intelligence, from your personal perspective, you've seen this movie since for many, many years evolve as paradigms come on with computing and software. Talk about big data, and today's paradigm, all the benefits of the Moore's Law and the computation, all the big data, the unstructured data. You know, we're back into a cycle again where you got specialty solutions and going on a general purpose. Kind of that is happening. So how does that, what is going on between the old way, new way, and how does the old way meet the new way? You know, I think the way I see it actually is, EDW's Data Warehouse is really just coming into their own. One of the reasons is because of the speed and simplicity of technology that IBM has with pure data for application, for analytics, powered by Netiza. But Netiza brand has done, we've seen, we've had experience how it's done an incredible job of simplifying the whole data warehousing maintenance, and then particularly in speed. When you look at the speed of use, Data Warehouses have gotten so big in certain instances and fairly slow, even though they are optimized within their technology for response. When you look at appliances now, but at take back to the next level, it really makes a difference. You know, being able to get analysis done in a matter of seconds or minutes as opposed to hours and days. And so the larger the data sets you have, we see that aspect being very useful. And so on the big data side, you know you've got the analysis you can do with Hadoop. And they're with streams taking the real time data in high volume. And I think there's different levels of cascading of analytics you can do, where you look on the real time data for patterns and trends. And when you see those, things you don't know, you've never identified before. Then you can look at them on a broader scale of data with Hadoop, right? And then when you've got the actual indicators, the key indicators you want, then you can transpose that data into the data warehouse and do some really heavy duty analytics and dive deeper into that data. You know, we're going to have Jeff Jonas on later today, Chief Data Scientist from IBM. So if you're watching, make sure you come back around three o'clock because he's a fantastic interview. But he and I talk about the puzzle pieces in the past and you know, figuring out the data. But one of the things that we've been talking about, SiliconANG, Wikibon is, people are basically making decisions, big decisions on data, but they don't know where it came from. You know, in some cases. So we've been kicking around this concept called data DNA, which we're talking about for the first time here on theCUBE. You know, where is the DNA of the data? Where did it come from? Where has it been? I mean, data is promiscuous, it's frictionless. All these are great things, right? But there's now changing the game of where the data came from. So can you talk about that? It's still an early concept for us and the market, but you got to know where the data came from. You do. And one of the things that you can't forget when you have all this big data and these exciting possibilities of making decisions on such a large corpus information is that you have to have trusted information, right? You've still got to have the data integration. You've got to have the data governance. All those things, which, you know, we've learned over the last 20, 30 years, still apply probably even more so now the volume is getting bigger. And you could say that some data requires a lot more integrity than others, right? And the way we look at it is until you get it into the data warehouse and do the structured analytics, the integrity is not quite as important because you're looking for general ideas and doing more synthesis rather than analysis. And so that's how we tell, look at it and when it comes to financial decisions or it comes to healthcare decisions where you need pinpoint accuracy, that's where I think you need it in a more of a structured form. So let's come back to that. We were talking about the business case before really being two vectors. One was the administrative cost. You talked about saving 30 to 40% on administrative cost. The second was healthcare outcomes. And that's what you're talking about here. And I would imagine that's really where you better get the data right. So talk about some examples in healthcare outcomes that you've seen. Well that's where IBM did a fantastic job with Watson. When we saw the Jeopardy show February of 2011, personally I was captivated by how they bought all these artificial intelligence technologies together essentially along with big data to be able to make much better decisions. And when you look at the decisions that a physician has to make, they've got incredible decisions and the body of knowledge has gotten so broad now. And so there's no one physician can have the knowledge of everything. And a lot of the larger hospitals are great ones like in oncology like MD Anderson. They'll have panels of physicians, right? So they have all these subspecialties that they can pass these symptoms by and see what the larger mindset can have a better decision. Well if you can imagine that taking that that panel of doctors and exponentially increasing it to all the doctors essentially on the planet, you can now look at saving people's lives because there may be some indication of a complication or an opportunity with another medication, another treatment protocol which can make a big difference. And one of the challenges is in the healthcare situation different hospitals have a set of treatment protocols that they have to kind of subscribe to, right? They have to prescribe. And those are there through tradition I believe and through insurance requirements. But they're not always the best for all the different types of symptoms that are out there. So what this big data is enabling now is for a larger, stepping back and seeing a larger viewpoint on treatment protocols outside of a standard set. And that's where the gains are going to be because you can't apply the same treatment to every person for a particular symptom because people are different. So you mentioned Watson. Are you actually using some of the Watson technologies in your system? It's called Ready for Watson. And the key parts of this are the NLP, the natural language processing, and the content analytics. That's the key part we're authorized in to sell and provide services on with IBM. And then when you look at that's really the key part of that's the understanding of what the physician's notes are pulling the extractive knowledge out of that and actually making meaning out of it. And that is all putting that in place, that whole infrastructure to support that and integrating that with the electronic medical records systems or all this structured data is, that's really on a pathway for Watson. The infrastructure we're building with IBM is going to have like a plug-in for when Watson becomes more available on the more of the advisory aspects of that. We're here with Nigel Hook with DataScale from San Diego. The best part of the country to live is the one that's always perfect. That's a marine layer, but overall a great place to live. I have a question for you. A little bit changing gears here because I'll say it's IOD and IBM's LAR, they service a lot of large customers. We were talking to a colleague just this morning who said, big data's BS for small businesses. And we're saying, you know, I can understand what you're saying, I appreciate that, but don't think so. I think it's actually an enabler with cloud and mobile. You now have an infrastructure for small medium-sized enterprises. Can you talk about that? What's your opinion on that? I mean, is big data and the tech and the solutions available and there for small medium-sized enterprise? And what can a small medium-sized enterprise who's not so savvy do to get into the big data benefits? Obviously, there's a lot of business value, we're going to talk more about that, but what's your take on that? That's a very good question, John. And I'll give you a bit of a point of view from where I came from. Early this year, we're only talking about how fast things are, right? Beginning of 2012. I'm a Vistage member and Vistage is a CEO organization. We meet monthly with 16 other CEOs in our group. And back in January, I'd been telling my fellow CEOs that we're making this big investment in big data. And I realized it was going to go into blank expressions. So I asked the other 15 CEOs there of small businesses, anywhere from 10 million to 40 million a year revenues, so small. I said, what have you heard about big data? And really back in January, nobody had. I mean, it was 100%, no one is really familiar with what big data was. So there was a lot of education that had to happen. That little subset I did a survey on is changed rapidly from Q2 to Q3. Now I see small businesses really understanding what's going on. And I think the first area that I've seen the uptick in is in marketing opportunity. Small companies, and I'm talking companies that may be 20, 30 million dollars of revenue are now looking at the big data out there as to how can we better understand what marketing programs, what demand gen, what trade shows like this, what steps are we having to do to turn a customer, to turn a transaction. And looking at all the different aspects of that. So I'm seeing that as the first uptick in that area. The second area I'm seeing traction in is in call centers. And with small businesses that have call centers as part of their business, trying to understand better what the conversation is between their call center operative and a customer. And even also looking at how to change that in real time, for doing real time sentiment analysis so they can see if the customer's happy. And by looking at the larger data sets, how important is that customer to you? What are they saying online about you? What's tweeting, what's on the blogs? And how can you, during that conversation, that really important call when you have a call center person which is maybe not on the highest level of a run? Make a decision. So where would you put the market then? Because so the question we're trying to get at is, first of all, I 100% agree with you. It's almost religious here. And so thank you, Wikibon. We love the prospects of big data. In fact, we said in our last cube that the big data revolution, the transformation so impactful, it's like the PC revolution and client server combined. Because and it's happening at a very accelerated rate. So the obvious question is people don't know what to do. So we're trying to figure out what is the first step of small medium-sized enterprise could do? I mean, is it move to the cloud, get rid of Microsoft Exchange? I mean, right now the biggest pain point for small businesses is the email problem. So I can't even get the big data if I can't fix email. So these are some things that we're hearing. So we're trying to get that roadmap down. And I think to your point, one of the things that is a common expression, right? There's a big data you start small and you can add just one extra data set. Whoever would be demographic data set. Another one, you know, there's a company here a part of IBM ESRI. Almost all decisions are made in a location, right? So what location information is going to impact the decision you're making or all the analysis you're doing on certain trends. And so I think of the idea is to start small. There's a lot of different public data sets out there as well that you can add to the whole mix of information you're going to make decisions on. Nigel, one final big question. We're going to have some big data here, big insights on theCUBE. Big question, knowing what you've lived through and you're living through now with your current company as the CEO, shoot the arrow forward next five years. What's going to be different in the marketplace in the IOD community and beyond? What is big data going to do for the society in the world? Personally, you know, that's a very large question, right? Because it's going to have impact which is going to be tremendous to mankind. Personally, where we really want to reap the advantages of big data is in healthcare and personalized healthcare. Five years from now, I see people wearing like a band or a watch or bracelet that's going to be taking 724, all their vitals as much as possible, right? The galvanic skin temperature, the blood pressure it can be looking at if we're looking at your pulse rate all the way. And then combining that with all of your genetic information, all of your medical data and being able to, using the Watson technology, being able to make recommendations to you ahead of a time, preventative of any potential problem. Predictive analytics. Hey, you're going to die. He goes, come on, call the ambulance. You know, this is later than sooner. This is serious stuff. I mean, see what's going on. The opportunity of that I think is going to be incredible especially as the baby boomer population is starting to get older and retire, right? The problems of healthcare are going to be huge based on that. So if we can simplify that, if we can make it more efficient, it's going to be terrific. Okay, Nigel, we're out of time. Thanks for that big insight. Totally agree with you. This is theCUBE, our flagship program for the events. Extract from the noise. Nigel Hook from Data Skill out of San Diego. Go check them out. They're really passionate about healthcare. Be right back with our next interview right after this short break.