 From the SiliconANGLE Media Office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. Hi, I'm Stu Miniman, and this is a CUBE conversation from our Boston area studios. The ecosystem around data and analytics definitely isn't becoming any simpler today. Joining me for this segment is Drew Clark who's the Chief Strategy Officer at CLIC and Drew. You know, let's start there. We talk about, you know, the wave of big data. A lot of them have, you know, wrapped themselves around the cloak of AI today. You know, you've got machine learning in there. So, help kind of give us a little bit about, you know, where CLIC fits into that ecosystem and differentiates itself from this very diverse ecosystem. Yeah, sure. And I get that question a lot, Stu, is who is CLIC and what makes us unique? And as a strategy individual and professional, I spend a lot of time talking, working with customers, other looking at companies. And I always come back to it as like, what is that core kind of part? You know, every company comes from something. And, you know, and then how does it fit into the landscape? So, I use actually our history to explain a little bit about who we are. So, we're 25 years ago, or 25 years old. And our very first customer was Tetra Pak, which make cardboard boxes of all different sizes. So, you think about Amazon when you order something and you get it showed up, it shows up at your desktop or your door. It's in a different size box. Well, Tetra Pak had a problem of their salespeople were selling inventory they didn't have. But, you know, and they needed to be able to sell what they had, but they also wanted to make sure they showed what they did not have. So, they signed on and, you know, had a project with CLIC. And this is in Sweden. And they developed a product, which was really a product configurator tied with a visualization to it. So, what they had to answer on a business question was, tell me what products are and are not available and be able to dynamically make selections as, you know, sales rep is answering the questions. So, that was the genesis of our own kind of product. So, we had a choice back then to say, do we stay in a product configurator space? Or do we kind of move into the visualization kind of analytics? And so, we took that unique kind of package, what we call the associative engine, with the visual kind of piece. And we went and started on the business intelligence or the analytics journey. And where we've kind of evolved that as a company is we took that in, you know, great examples. Another customer a couple of years ago, there was the tsunami in Japan. You remember that? You know, when that happened? So, one of our customers was in a consumer products. And they had a lot of supply or ingredients that came out of Japan. And, you know, they also knew that, okay, the tsunami hit big impact on their supply chain. And they had to actually make an announcement. They had earnings on Wall Street and they were like needed to be able to outline to their investors within the week to say, well, is this a big impact? Is this not a big impact on, you know, our forward looking kind of revenues? And they tried answering a question on using traditional analytics. You know, show me what products were impacted by the tsunami. And that's a first order question is, as you know, it's kind of, well, it's an easy question to ask. Well, now you're going down into the ingredients, you're looking at where the data is in the supply chain and you come back with an answer as these are the ones that are impacted. The next question that the business asked was, okay, tell me what products were not affected. And now think about that is not question going through every single row. Oh, and tell me what the inventory is and can we run campaigns and sales where we know we're either A, gonna miss our revenue numbers or we're gonna hit them. And they used the click, they tried a different kind of traditional way of answering a question they couldn't answer because they got stuck at that first. It was click that actually answered and helped them answer the second question, show what products were not affected and do we have inventory and they would be able to make the decision. And so that's where we start. What we make us unique is this combination of analytics and visual kind of interface. And that's been kind of our core differentiator in the market from 25 years ago to where we are today. Yeah, and boy, that industry has changed quite a lot. I think about data visualization. We used to do infographics years ago, just how do I tell a story with that data? There's the creative things you can do with it, but as well as us as humans, we look at all of those data points out there and most of the time it's not static. I love people when they're sharing, it's like, okay, let me give you charts for something over a hundred year period and you can watch it, ebb and flow and change and the like, so there's so many technology 25 years ago, you know, cloud had many different terms. You know, I can argue I've worked with plenty of people that we had the XSPs back in the 90s and the pre-cloud things, but there's some challenges we've been trying to solve and then some major breakthroughs we've had with some of these journeys and these technology waves. So bring us up to today as we talk about things like speed and scale and agility impacting what we're doing. It's gotta be, you've got the why and the core, but the how and the what has changed dramatically. Stu, you really are kind of a technical kind of guy at heart, right? So one of the things that you said at the beginning there where you talked about looking at an infographic and the human kind of component of how do you look at this information and how do you understand it? It's getting bigger and harder to understand and one of the things that we firmly believe in is the human being is an integral part of the decision-making process. And so you think about a scatterplot with 30,000 data points, how do you actually make sense of it? And we spent a lot of time about the human brain and how it looks at information on this kind of big data scale. And we're a predator as a human, we're binocular and we look for certain things. And so we spent a lot of time around that kind of visual interface and I think Stephen Fugh writes about this, Edward Tufti, his documentation around kind of how do you present information in a great way? Well, you take that 30,000 kind of data points on a scatterplot and well, bringing it forward in our technology, we show density and heat because that's what we look for and we look for patterns and we look for outliers as a predator or as kind of an individual. And so we present the information in a way that a human is kind of wired to receive it. But underneath, and this is where I think your second part was going, underneath is like, how do you keep that elegance and but responding to kind of now compute and infrastructure and all the sides? Yeah, and I guess I always worried is, we talk about garbage in, garbage out sometimes. How do I make sure I've got good data? How do I make sure the algorithms learning, that there was a tool that was, oh, let me train this AI on Twitter. And what they got back, they had to turn it off really quick because it became a troll and then much worse and the language was awful. So sometimes if you just let the data run wild that doesn't understand what's going on, how do you balance that? Make sure we're getting good decisions and good information and we say, if you automate a bad process, you haven't done a good thing. Right, right. Well, and that comes through, I think a number of layers from automation. There's kind of the data, getting it from the raw source, getting it ready for the analytical kind of consumption and is it a machine? Is it a human? Is it a human augmented with kind of the intelligence? And as you progress through this kind of data, kind of journey of bringing the data into, now the common terms are data lakes and data swamps and well, how do you find the right information and where do you put the right kind of governance and governance not being a bad word but governance being a, I'm confident that information is correct. And so, you know, you see the introduction of data catalogs, so much like a card catalog in a library if you're old enough to actually remember that. I know they're doing this system. Okay, there you go. So I'm old, I was a page. I was when I first paying job was to put books back in the library and you know, you don't want to be able to find the right information and then know that it's been curated, been set up, but it doesn't have to be written all out. You want to have that progressive, kind of bringing of that information for the user to be able to do that. And as you kind of fan out from the central, that raw data out to kind of where the analytics users are kind of engaging and working with it, that governance allows for that confidence but then you need to know that you're scaling and the speed, you don't want to wait. You know, if you're had a request, you know, the decision like, just even what happened to that customer tsunami happened, I have earnings on a set day in days from an event. I can't wait a month to come up with the answer. I need that speed, I need that faster. All right, so who's the one that inside the customer that you know, works on this? You know, we've all heard that, you know, there's skill gaps out there. You know, years ago, it was like, okay, we're going to build this giant army of data scientists. You know, it's not like we're saying we don't need data scientists, but you know, we don't have enough time to train enough PhDs to fill the job. So is that, you know, where are we today? You know, where do the customers fit organizationally? And you know, if you can't get in a little bit to the kind of where the product touches them. Sure, so what you bring up is the, like a great interview or a broad question, so many different ways we can go with this. And I come back to the idea of, what a lot of people come and talk about is the citizen data scientist, but it's really about data literacy. And you know, these are individuals who need to be comfortable working with data and how do you actually have that kind of confidence level of, when I'm looking at it, do I know is it real? Am I, you know, having the right kind of conversation? Just recently I had the opportunity to see a number of presentations by college seniors who were kind of presenting their senior theses on how they're kind of working with, or on a particular theme. And I was in this behavioral sciences and leadership kind of department is at the United States Military Academy at West Point. And when you think about leadership and you think about behavioral sciences and you think about a lot of the softer side of it, but every one of these cadets had data and you can see them looking at the empirical data, looking at the R coefficients, is this noise, is this signal, what's causation versus correlation? What you see is this language of kind of data literacy in the curriculum. And you flash forward and you go look at every department in a company and you see people who are coming in who understand that there's data that can be used to be informing my decision. So I don't need to wait for this white lab coat PhD on data science, but you know, it's like, well, you know, is there causation? Is there correlation? So marketing, finance, sales, we're seeing this at that data citizen kind of at the edges in a company and it's coming out of the universities. Yeah, no, I was at a conference recently and the analyst up on the keynote stage says, you wanna teach your team machine learning? Get a summer intern that's taken the courses and have them spend a week training you up on it. So excellent, so sounds like, if somebody wants to get started with click, relatively low bar, I don't have to go through some six month training class to be able to start getting some business value and rolling this down. Yeah, exactly. Stu, you can go right on our website and you can sign up, start to use our product right in the cloud. If you want to put it on a desktop, you can do that. And when you just drag in your first kind of data files and I encourage you to actually bring in a complicated kind of data set. Don't go with a simple Excel file. You know, a lot of companies can do bars, charts and graphs, what you really want to do is bring in two different data sets and they kind of bring it into and remember the associative engine of bringing different data together and it's the second and the third question that you really are looking for those insights. And so you can very quickly assemble the information. You don't need to go back and learn what a left outer join is because our engine takes care of that for you. You want to understand what's going on, it's transparent and then you start finding insights within kind of minutes of being able to use that. Yeah, well, if you go back to the guide to the galaxy, sometimes the answer is easy. I have to know the right questions. Yeah, I should be able to ask. All right, Drew, I want to give you the final takeaway for this piece. Okay, so if you're thinking about dealing with any data and you want to kind of answer not just the question but it's usually the second and the third and you want to have a speed of use, you can do that with our platform but think about it really in that concept of kind of data literacy and you want that right information for the individuals to read and write. That's okay and it's easy. It's analyzing and arguing and that's where the competitive advantage. So take a look at that. All right, Drew Clark, really appreciate the updates on click and be sure to check out thecube.net. It's a nice little search bar on top. You can search by company, search by person, actually a lot of the key metadata you can search from that. Thousands of videos in there, never registration or to be able to get it. So I'm Stu Miniman and thanks as always for watching theCUBE.