>> From the SiliconANGLE Media office in Boston, Massachesetts, 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 Clarke who's the chief strategy officer at Qlik. And Drew let's start there, we talk about the wave of big data, a lot of them have wrapped themselves around the cloke of AI today, you've got machine learning in there. So help kinda give us a little bit about where Qlik 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 Qlik and what makes us unique. And as a strategy, individual and professional, I spend a lot of time talking, working with customers that are looking at companies and I always come back to it like, what is that core kinda part? Every company comes from something 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 Tetrapak which make cardboard boxes, of all different sizes, so if you think about Amazon when you order something and you get it showed up at your, it shows up at your desktop or your door, it's in a different size box. Well Tetrapak had a problem of their sales people were selling inventory they didn't have. 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 had a project with Qlik. And this is in Sweden, and they developed a product which is really a product configurator tied with a visualization to it. So what they had the answer on a business question was, tell me what products are and are not available and be able to dynamically make selections as sales reps were answering the questions. So that was the genesis of our own kinda product, so we had a choice back then to say, do we stay in a product configurator space, or do we move into the visualization analytics? And so we took that unique kinda package, what we call the associative engine with the visual kinda piece and we went and started on the business intelligence or the analytics journey. And where we've kinda evolved that as a company is we took that, and another great example is another customer a couple of years ago there was the tsunami in Japan, do you remember that Stu? >> Of course. >> When that happened. So one of our customers was in the consumer products and they had a lot of supply or ingredients that came out of Japan. And they also knew that, okay, the tsunami hit, big impact on there supply chain, and they had to actually make an announcement, they had earnings on Wall Street, and they 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 our forward looking revenues? And they tried answering the question using traditional analytics, you know, show me what products were impacted by the tsunami, and that's a first order question, as you know 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 that says these are the ones that are impacted. The next question that the business asks 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 'em. And they used the Qlik, they tried a different kinda traditional way of answering a question, they couldn't answer it 'cause they get stuck at that first. It was Qlik that actually entered 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 that decision. And so that's where we start, what we makes us unique is this combination of analytics and visual kinda interface. And that's been kinda our core differentiator in the market from 25 years ago to where we are today. >> Yeah, and boy that history has changed quite a lot. Think about data visualization, we used to do infographics many 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 times it's not static, I love people when they're sharing, it's like okay, let me give you charts for something over a 100 year period, and you can watch it ebb and flow and change in the like, so there's so many technology. 25 years ago, cloud had many different terms, I can argue I've worked with plenty of people that we had the XSP back in the 90s and the pre-cloud things. But there's some challenges that 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 to, we talk about things like speed and scale and agility impacting what we're doing, it's got to 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 kinda guy at heart, right, so one of the things you said at the beginning there where you talked about looking at an infographic and the human kinda component of, how do you look at this information, how do you understand it? It's getting bigger and harder to understand. 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 scatter plot with 30,000 data points, how do you actually make sense of it? And we spend a lot of time about the human brain and how it looks at information on this kinda big data scale and we're a predator as a human, we're binocular and we look for certain things, and so we spend a lot of time around that kinda visual interface. And I think Steven Few writes about this, Edward Tufte and his documentation around kinda how do you present information in a great way. Well, you take that 30,000 data points on a scatterplot, and well bringing it forward in our technology we show density in heat because that's what we look for. And we look for patterns, and we look for outliers as a predator as kind of an individual. And so we present the information in a way that a human is kinda wired to receive it, but underneath, and this is where I think you're second part was going, underneath is like how do you keep that elegance and responding to a 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 algorithm is learning? 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 the algorithm doesn't understand what's going on. How do you balance that and 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 that comes through a number of layers from automation there's kinda the data, getting it from the raw source, getting it ready for the analytical consumption, and is it a machine, is it a human, is it a human augmented with kinda the intelligence? And as you progress through this data journey of bringing the data into now the common terms are data lakes and data swamps. How do you find the right information and where do you put the right kinda governance? And governance, not being a bad word, but governance being a, I'm confident that information is correct. And so 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 the Dewey Decimal System. >> Okay, there you go so I was a page, that was one of my first paying job was to put books back in the library. And you want to be able to find the right information and 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 kinda where the analytics users are kinda 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 if you had a request. The decision just like, 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. >> Alright, so who's the one inside the customer that work's on this, you know, we've all heard that there's skill gaps out there. Years ago it was like, okay we're going to build this giant army of data scientists. It's not like we're saying we don't need data scientists but we don't have enough time to train enough PhD's to fill the jobs. So where are we today, where do the customers fit organizationally, and if you can get into a little bit of where the product touches them? >> Sure, so what you bring up is the. Great interviewer, 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 this citizen data scientist, but it's really about data literacy. And these are individuals who need to be comfortable working with data, and how do you actually have that confidence level of when I'm looking at it do I know is it real? Am I having the right conversation? Just recently I had the opportunity to see a number of presentations by college seniors who were presenting their senior thesis' on how they're working on a particular theme. And I was in this behavioral sciences and leadership department, it was at the United States Military Academy at Westpoint. 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 everyone 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 data literacy in the curriculum and you flash forward and you look at every department in a company and you see people are coming in who understand 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. It's like well, is there causation is there correlation? So marketing, finance, sales, we're seeing this at that data citizen at the edges in a company and it's coming out of the universities. >> Yeah I was at a conference recently and the analysts up on the keynote stage says, you want to 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 someone wants to get started with Qlik, 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 out. >> Yeah, exactly. Stu, you can go right on our website and you can sign up and start to use our product right in the cloud. If you want to put it on a desktop you can do that. And you just drag in your first data files and I encourage you to actually bring in a complicated dataset. Don't go with a simple excel file, a lot of companies can do bars, charts, and graphs. But what you really want to do is bring in two different datasets and bring it into, and remember the associative engine of bringing different data together? And it's the second and third question that you're really 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 joint 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 minutes of being able to use that. >> Yeah well if you go back to the Hitchhikers Guide to the Galaxy, sometimes the answer's easy, I have to know the right questions to be able to ask. Alright, 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 answer not just the question, but it's usually the second and 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 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. >> Alright, well Drew Clarke really appreciate the updates on Qlik and be sure to check out theCUBE.net. There'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 for in there. Thousands of videos in there. Never a registration to be able to get it. So I'm Stu Miniman and thanks as always for watching theCUBE. (upbeat music)