 From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. Hi, I'm Stu Miniman and welcome to a special edition of CUBE Conversations here in our Boston area studio. Happy to welcome to the program, first of all, to my right, a first-time guest of the program, Drew Clark, who's the Chief Strategy Officer at CLIC, and welcome back to the program, Itamar Ankaryon, who's a Senior Vice President of Enterprise Data Integration, now with CLIC, but a new title due to the acquisition of Atunity. So thanks so much for joining us, gentlemen. Great to be here. All right, so Drew, Atunity, we've had on the program anytime. We have at CLIC on the program, but maybe for audience, just give us a quick level set on CLIC and the acquisition is some exciting news. So let's start there and we'll get into it. Sure, thanks, Stu. And CLIC, we're a 25-year-old company in the business analytics space. A lot of people know about our products. CLIC View, CLIC Sense. We have 50,000 customers around the world and from large companies to kind of small organizations. Yeah, all right, so we talk a lot about data on our program. When I look through some of the CLIC documentation, it resonated with me a bit because when we talk about digital transformation on our program, the key thing that differentiates the most between the old way of doing things, the modern is I need to be data-driven, I need to make my decision, the analytics piece of that. So, Itamar, let's start there and talk about, other than the logo on your card changes, what's the same, what's different going forward for you? Well, first of all, we're excited about this merger and the opportunity that we see in the market because there's a huge demand for data, predominantly for doing new types of analytics, business intelligence, data is fueling digital transformation. And part of the main challenge customers have, organizations have is making more data available faster and putting it in the hands of the people who need it. So on our part, coming from a community, we spend the last few years innovating and creating technologies that help organizations modernize how they create new data architectures to support faster data, more agility in terms of enabling data for analytics. And now together with CLIC, we can continue to expand that. And then at the end of the day, provide more data to more people. So, Drew, it's interesting. There's been no shortage of data out there. For decades been talking about the data growth, but actually getting access to our data, it's in silos more than ever, it's spread out all over the day. We say the challenge of our time is really building distributed architectures and data is really all over the place. And customers, there's stats all over the place as to how much is searchable, how much is available, how much is usable. So, explain a little bit the challenge you're facing and how you're helping move customers along that journey. Well, what you bring up, Stu, is the idea of data and analytics for decision-making. And really it's about that decision-making to go faster and get into that right language into the right individuals. And we really believe in this concept of data literacy. And data literacy was said, I think well, between two professors who co-authored a white paper. One professor was from MIT, the other one was from Everson College, a communication school. Data literacy is kind of the ability to read, understand, analyze and argue with data. And the more you can actually get that working inside an organization, the better you have from a decision-making and the better competitive advantage you have. You're even going to win, you're going to accomplish your mission. And now with what you said, the proliferation of data, it gets harder and where do you find it? And you need it in real time. And that's where the acquisition of attunity comes in. Okay, I need to ask a follow-up on that. So one of my favorite events I ever did with two other MIT professors. Yes, we're Boston area, we're quoting a lot of MIT professors here. But Andy McAfee and Eric Brunyolson talked about racing with the machine because it's oh great, you know, who's the best chess player out there? Was it the human grandmaster or was it the computer? And the studies were actually, is if you put the grandmaster with the computer, they could actually beat either the best computer or the best person. So when you talk about the data and analytics, everybody's looking at the AI and the ML pieces is like, okay, how do these pieces go together? How does that fit into the data literacy piece, the people and the machine learning, I'm assuming is part of it. Yeah, well what you bring up is the idea of kind of augmenting the human. And we believe very much around the cognitive, kind of interface of kind of the technology, the software with kind of the person and that decision making point. And so what you'll see around our own kind of perspective is that we were part of a second generation BI of like self service. And we've moved rapidly into this third generation which is the cognitive kind of augmentation of the decision maker, right? And so when you say this data literacy is arguing with data, well, how do you argue when you actually have the updated machine learning kind of recommendations, but it's still human making that decision. And that's an important kind of component of our kind of late, our own kind of technology that we bring to the table. But with the tunerty, that's the data side needs to be there faster and more effective. Yeah, so Inamar, please, you know, fill us in on that data is the, you know, in big data, we talked about the three Vs. So, you know, where are we today and how do I be able to, you know, get and leverage all of that data? So that's exactly where we've been focused over the last few years. And as we've worked with customers that were focused on building new data lakes, new data warehouses, looking at the cloud, building basically more than new foundations for enabling the organization to use way more data than they ever used before. So it goes back to the volume, at least one V out of the three Vs you mentioned. And the other one of course is the velocity and how fast it is. And we've actually come to see that there are, in a sense, two dimensions to velocity that come together. One is how timely is the data you're using? And one of the big changes we're seeing in the market is that the user expectation and the business need for real-time data is becoming ever more critical. If we used to talk to customers that talked about real-time data because when they ask for data, they get a response very quickly, but it's last week's data. Well, that's not, doesn't cut it. So what we're seeing is that first of all, the dimension of getting data that is real-time data represents the data as it's currently at. Second one is how quickly you can actually make that happen. So because business dynamics change much faster now, the speed of change in the industry accelerates. Customers need the ability to put solutions together, make data available, to answer business questions really faster. They cannot do it in the order of month and years. They need to do it in the order of days, sometimes even hours. And that's where our solutions come in. Yeah, it's interesting. My background's on the infrastructure side. I spent a lot of time in the cloud world and you talk about what we need for real-time. Well, it used to be, I rolled out a server that took weeks or months, then a VM, it reduced in time. Now we're in containerized and Kubernetes world and we're now talking a much sort of timeframe. And it's like, oh, if you show me the way something was an hour ago, oh my gosh, that's not the way the world is. And I think for years, we've talked to the two world. What is real-time and how do I really define that? And the answer we usually came up, it is getting the right information in the right place to the right person. Or in the sales standpoint, it's like, I need that information to save that client or get what they need. So are we still, some of those terms, scale in real-time, short of require context, but where does that fit into your customer discussions? Well, two parts there is you bring up, I think what you were saying is absolutely still true. Right data, right person, right time. It gets harder though with just the volumes of data. Where is it? And how do you find it? How do you make sure that it's the right pieces to the right place? And you brought up the evolution of just the compute infrastructure and analytics likes to be close to the data. But if you have data everywhere, how do you make sure that part works? And we've been investing in a lot of our own cloud analytics infrastructure is now done on a microservices basis. So it's running on Kubernetes clusters. It can work in whatever cloud compute infrastructure you want, be it Amazon or Azure or Google or kind of your local kind of platform data centers. But you need that kind of small piece tied to the right kind of data on the side. And so that's where you see a great match between the two solutions. And when you, in the second part is the response from our customers. And after the acquisition was announced was tremendous. I had one customer who works in a manufacturing space was like, this is exactly what I was looking to do from an analytics basis. I needed more data real time. And I was looking at a variety of solutions. She said, thank you very much. You made my kind of life a little easier. I can narrow down to one particular platform. So we have manufacturing companies. We have military kind of units and organizations to healthcare organizations. I've had just countless kind of feedback coming in along that same kind of question. All right, NMR, for the Atunity customers, what does this mean for them coming into the Click family? First of all, it means for them that we have a much broader opportunity to serve them. Click is a much bigger company. We have more resources we can put to bear to both continue and enhance the Atunity offering as well as creating integrations with other products such as the Click Data Catalyst, which I've click acquired several months ago, and there's a great synergy between those products, the Atunity product and the Click Data Catalyst to provide a much more comprehensive, modern enterprise data integration platform. And then beyond that to create also synergies with other Click Analytics products. So again, while the Click Data Integration platform consisting of Atunity and Click Data Catalyst will be independent and provide solutions for any data platform, analytic platform, cloud platform, as it already does today. We'll continue to invest in it. There's also opportunities to create unique synergies with some of our ClickSat technologies such as the Associative Big Data Index and some others to provide more value, especially at scale. All right, so Drew, please expand on that a little bit if you can. There's so many pieces. I know we're going to spend a little bit of time going deeper in some of the other ones, but when you talk to your customers, when you talk to your partners, what do you want to make sure? What their key takeaways are. Right, so there was a couple of important points that you made on the data integration platform. And so that's the combination of the Atunity products plus the data catalysts, which was co-wired through Podium data. Both of those kind of components are available and will continue to be available for our customers to use on whatever analytics platform. So we have customers who use data for data science and they want to work in R and Python and their own kind of machine learning or working with platforms like Data Robots. And they'll be able to continue to do that with that same speed. They also could be using another kind of analytical visualization tool. And we actually have a number of customers who do that and will continue to support that. So that's the first point I think you made up which is an important one. The second is, while we do think there is some value with using ClickSense with the platform and we've been investing on a platform called the Associative Big Data Index. And that sounds like a very complicated piece but what we've done is taken our kind of unique kind of value proposition as an analytical company which is the ability to work with data and ask questions of it and have the answers come to you very quickly is to be able to take that same associative experience that people use in our product and bring it down to the data lake. And that's where you start to see that same kind of what people love about ClickView and ClickSense and brought into the data lake. And that's where DataMar was bringing up from a scale kind of perspective. So you have both kinds of opportunities. All right, well, Drew and Inbar really appreciate you sharing the importance of these coming together. We're gonna spend some more time digging into the individual pieces there. I might be able to say, okay, or we passed the data lakes as it got to a data swamp or a data ocean because there are lots of sources of data and the lake I always say is it seems a little bit more pristine than the average IT environment is. So, but thank you so much and look forward to having some more conversations with you. Thanks Drew. All right. And be sure to check out thecube.net for all our videos. I'm Stu Miniman. Thanks so much for watching.