 from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. Welcome back to theCUBE. We are live on our first day of coverage at our event, Big Data SV. This is our 10th Big Data event. We've done five here in Silicon Valley. We also do them in New York City in the fall. We have a great day of coverage. We're next to where the start of data conference is going on at Forger Tasting Room in Edery. Come on down, be part of our audience. We also have a great party tonight where you can network with some of our experts and analysts. And tomorrow morning, we've got a breakfast briefing. I'm Lisa Martin with my co-host Peter Burris and we're excited to welcome to theCUBE for the first time the CEO of Connecticut, Paul Appleby. Hey Paul, welcome. Hey, thanks, it's great to be here. We're excited to have you here and I saw something as a marketer and terms I grasp onto them. Connecticut is the insight engine for the extreme data economy. What is the extreme data economy and what are you guys doing to drive insight from it? Wow, how do I put that in a snapshot? Let me share with you my thoughts on this because the fundamental principles around data have changed. In the past, our businesses are really validated around data. We reported out how our businesses performed. We reported to our regulators. Over time, we drove insights from our data. But today, in this kind of extreme data world, in this world of digital business, our businesses need to be powered by data. So what are the ways, let me test this on you. So one of the ways that we think about it is that data has become an asset. Oh yeah. It's become an asset that now the business has to care for it, has to define it, care for it, feed it, continue to invest in it, find new ways of using it. Is that kind of what you're suggesting companies think about? Absolutely what we're saying. I mean, if you think about what Angela Merkel said at the World Economic Forum earlier this year, that she saw data as the raw material of the 21st century. And talking about Germany fundamentally shifting from being an engineering, manufacturing-centric economy to a data-centric economy. So this is not just about data powering our businesses, this is about data powering our economies. So let me build on that, if I may, because I think it gets to what many respects Connecticut's core value proposition is. And that is that data is a different type of an asset. Most assets are characterized by you apply it here or you apply it there. You can't apply it in both places at the same time. And it's one of the misnomers of the notion of data as fuels because fuel is still an asset that has certain specificities. You can't apply it to more places. But data you can't, which means that you can copy it, you can share it, you can combine it in interesting ways. But that means that to use data as an asset, especially given the velocity and the volume that we're talking about, you need new types of technologies that are capable of sustaining the quality of that data while making it possible to share it to all different applications. If I got that right, what does Connecticut do in that? You absolutely nailed it because what you talked about is a shift from predictability associated with data to unpredictability. We actually don't know the use cases that we're going to leverage for our data moving forward but you understand how valuable an asset it is. And I'll give you two examples of that. There's a company here based in the Bay Area, a really cool company called Liquid Robotics. And they build these autonomous aquatic robots and they carried a vast array of sensors and they were collecting data. And of course that's usually powerful to oil and gas exploration, to research, to shipping companies, and et cetera, et cetera, et cetera, even Homeland Security applications. But what they did, they were selling the robots and what they realized over time is that the value of their business wasn't the robots, it was the data. And that one piece of data has a totally different meaning to a shipping company than it does to a fisheries company but they could sell that exact same piece of data to multiple companies. Now, of course their business has grown on and scaled and I think they were acquired by Boeing. But what you're talking about is exactly where Connecticut sits. It's an engine that allows you to deal with the unpredictability of data, not only the sources of data but the uses of data and enables you to do that in real time. So Connecticut's technology was actually developed to meet some intelligence needs of the U.S. Army. My dad's a former Army Ranger, Airborne. So tell us a little bit about that and kind of the genesis of the technology. Yeah, it's a fascinating use case. If you think about it, we're all concerned globally about cyber threat, we're all concerned about terrorist threats. But how do you identify terrorist threats in real time? And the only way to do that is to actually consume vast amount of data whether it's drone footage or traffic cameras, whether it's mobile phone data or social data, but the ability to stream all of those sources of data and conduct the analytics on that in real time was really the genesis of this business. It was a research project with the Army and the NSA that was aimed at identifying terrorist threats in real time. But at the same time, you not only have to be able to stream all the data in and do an analytics on it, you also have to have interfaces and understandable approaches to acquiring the data so that, because I have a background, some background on that as well, to then be able to target the threat. So you have to be able to get the data in and analyze it, but also get it out to where it needs to be so an action can be taken. Yeah, and there are two big issues there. One issue is the interoperability of the platform and the ability for you to not only consume data in real time from multiple sources, but to push that out to a variety of platforms in real time. That's one thing. The other thing is to understand that in this world that we're talking about today, there are multiple personas that want to consume that data and many of them are not data scientists. They're not IT people. They're business people. They could be executives or they could be field operatives in the case of intelligence. So you need to be able to push this data out in real time onto platforms that they consume, whether it's via mobile devices or any other device for that matter. You also have to be able to build applications on it, right? Yeah, absolutely. So how does Connecticut facilitate that process? Because it looks more like a database. And which is, it's more than that, but it satisfies some of those conventions so developers have an affinity for it. Yeah, absolutely. So in the first instance, we provide tools ourselves for people to consume that data and to leverage the power of that data in real time in an incredibly visual way with a geospatial platform. But we also create the ability for it to interface with really commonly used tools. Because the whole idea, if you think about providing some sort of ubiquitous access to the platform, the easiest way to do that is to provide that through tools that people are used to using, whether that's something like Tableau, for example, or Esri if you want to talk about geospatial data. So the first instance, it's actually providing access in real time through platforms that people are used to using. And then, of course, by building our technology in a really, really open framework with a broadly published set of APIs, we're able to support not only the ability for our customers to build applications on that platform, and it could well be applications associated with autonomous vehicles. It could well be applications associated with Smart City. We're doing some incredible things with some of the biggest cities on the planet in leveraging the power of Big Data to optimize transportation, for example, in the city of London. It's those sorts of things that we're able to do with the platform. So it's not just about a database platform or an insights engine for dealing with these complex vast amounts of data, but also the tools that allow you to visualize and utilize that data. Turn that data into an action. Yeah, because the data is useless until you're doing something with it. And that's really, you know, if you think about the promise of things like Smart Grid. Collecting all of that data from all of those smart sensors is absolutely useless until you take an action that is meaningful for a consumer or meaningful in terms of the generational consumption of power. So Paul, when you're as the CEO, when you're talking to customers, we talk about chief data officer, chief information officer, chief information security officer. There's a lot data scientists, engineers, there's so many stakeholders. That need access to the data. As businesses transform, there's new business models that can come into development if, like you were saying, the data is evaluated and it's meaningful. What are the conversations that you're having? I guess I'm curious, maybe which personas are at the table when you're talking about the business values that this technology can deliver? Yeah, that's a really, really good question because the truth is there are multiple personas at the table. Now, we in the technology industry we're quite often guilty of only talking to the technology personas. But as I've traveled around the world, whether I'm meeting with the world's biggest banks, the world's biggest telcos, the world's biggest auto manufacturers, the people we meet more often than not are the business leaders and they're looking for ways to solve complex problems. How do you bring the connected car to life? How do you really bring it to life? You know, one car traveling around a city for a full day generates a terabyte of data. So what does that really mean when we start to connect the billions of cars that are in the marketplace in the framework of connected car and then ultimately in a world of autonomous vehicles? So for us, we're trying to navigate an interesting path. We're dragging the narrative out of just the technology-based narrative, speeds and feeds, algorithms and APIs into a narrative about, well, what does it mean for the pharmaceutical industry, for example? Because when you talk to pharmaceutical executives, the holy grail for the farmer industry is, how do we bring new and compelling medicines to market faster? Because, you know, the biggest challenge for them is the cycle times to bring new drugs to market. So we're helping companies like GSK shorten the cycle times to bring drugs to market. So they're the kinds of conversations that we're having. It's really about how we're taking data to power a transformational initiative in retail banking, in retail, in telco, in pharma, rather than a conversation about the raw technology. Now we always need to deal with the technologists, we need to deal with the data scientists and the IT executives, and that's an important part of the conversation. But you would have seen, in recent times, the conversation that we're trying to have is far more of a business conversation. So if I can build on that, so do you think, in your experience, and recognizing that you're a data management tool with some other tools that helps people use the data that gets into Connecticut, are we going to see the population of data scientists increase fast enough so executives don't have to become familiar with this new way of thinking, or executives going to actually adopt some of this new ways of thinking about the problem from a data risk perspective? I know which way I think. I'm curious which way you think. It's a loaded question, but I think if we are going to be in a world where business is powered by data, where our strategy is driven by data, our investment decisions are driven by data, and the new areas of business that we explore to create new paths to value driven by data, we have to make data more accessible. And if what you need to get access to the data is a whole team of data scientists, it kind of creates a barrier. Not knocking data scientists, but it does create an air gap. It makes the aperture. Yeah, absolutely, because every company I talk to says our biggest challenge is we can't get access to the data scientists that we need. So a big part of our strategy from the get go was to actually build a platform with all of these personas in mind. So it is built on the standard principles, common principles of a relational database built around ANSI standards. It's recognizable. And it's recognizable and consistent with the kinds of tools that executives have been using throughout their careers. Last question, we've got about 30 seconds left. No pressure. No pressure. You have said Connecticut's plan is to measure the success of the business by your customer's success. Absolutely. Where are you on that? We've begun that journey. I won't say we're there yet. We announced three weeks ago that we created a customer success organization. We put about 30% of the company's resources into that customer success organization. And that entire team is measured not on revenue, not on project delivered on time, but on value delivered to the customer. So we baseline where the customer's at. We agree what we're looking to achieve with each customer and we're measuring that team entirely against the delivery of those benefits to the customer. So it's a journey. We're on that journey, but we're committed to it. Exciting. Well, Paul, thank you so much for stopping by theCUBE for the first time. You're now a CUBE alumni. Oh, thank you. You've had a lot of fun. And we want to thank you for watching theCUBE. I'm Lisa Martin, live in San Jose with Peter Burris. We are at the Forger Tasting Room in Edery. Super cool place. Come on down, hang out with us today. We've got a cocktail party tonight. Well, you're sure to learn lots of insights from our experts. And tomorrow morning, we'll stick around. We'll be right back with our next guest after a short break.