 Live from Las Vegas, it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. Welcome back everyone to theCUBE's live coverage of Informatica World 2019 here in Sin City. I'm your host, Rebecca Knight. We are here with Aaron Varadarajan. He is the Vice President of AI and Analytics at Cognizant. Thank you so much for coming on theCUBE, Aaron. Wonderful, it's always great to meet you folks at theCUBE. You are a CUBE alum. I am a CUBE alum. This is probably the third or the fourth time that I'm on theCUBE. Excellent, excellent. Well, for those viewers who have not seen your previous clips, tell us a little bit about your role at Cognizant. So my role at Cognizant is focused on two primary things. One is to really get our customers ready for AI and truly compete in the digital world. And the second big focus for me is to get them there, right? And to me, it's all about the data. So many times we don't realize this that while, if you look at a lot of the fang players, you know, the digital natives, a born digital who really have leveraged machine learning and AI to disrupt the marketplace, they do it with data. And it's all about the data. So the big push that I'm working on these days is to help our clients create this new modern data platform that can truly help them leverage AI and disrupt the market where possible. So tell us what you, I mean, so we know that this journey is incredibly complex and there's a lot of layers, a lot of questions, hard questions that companies are wrestling with. Tell us, give us the lay of the land. What do you see are sort of the big dominant forces happening in AI and ML? I think the first place is companies are still trying to figure out where do they apply AI and ML. And I think that is where they need to start. Because if it is not designed and the initiative is not purposed around any sort of specific business area or business focus or business outcome, it becomes an engineering project that really doesn't see light of day. If you remember back in the days when Hadoop was big, Hadoop was almost like a solution trying to find a problem or a solution trying to find a problem, whichever way it is, right? And I think as opposed to taking a technology view, which has been the traditional approach that most of the CIO organizations have kind of used. In AI, even more so, there needs to be significant participation from the business to decide where are the opportunities for me to drive business value. So I've always told my clients that the place to start is where can I apply AI and machine learning? Because at the end of the day, it is just a technique, right? And the technique has to be focused on delivering true business outcomes and business value. So that is where I think our clients need to start. If you go back in time and remember the ERP days when people were implementing SAP and Oracle, there was this very strong focus on process optimization and process excellence. And how do I get a straight through process organization really create that process orchestration layer that could execute at excellence? I think that needs to be brought back today, but in a different light. And the light is now let me view my value chain, not just from a process orchestration standpoint, but where are the opportunities for me to leverage machine learning and AI to create very different outcomes within that process layer? And I think, sorry. I know I definitely want to go back to that, but I also want to remember that we are here at Informatica World and I want to make sure I ask you how you at Cognizant work with Informatica? So Informatica is a strategic partner of ours. And as I was saying, while you start with that outcome in mind and really say, you know what? These are the areas I want to drive business outcomes. It's very important you understand how data plays a role in delivering those outcomes. So that's where Informatica and our partnership really comes to fruition. You know that Informatica has been working very strong in the areas of metadata management, data governance, security. All of these are essential part of you knowing your data and knowing where your data is coming from, where is it going, who's using it, how is it being consumed, in what form and shape should it be delivered so that we can deliver business value is a key aspect of really leveraging AI and machine learning. And in AI and machine learning, the one thing that we have to be cognizant of, I mean pun intended, is the fact that when you're going to get the machine to start making decisions for you, the quality of your data has to be significantly higher than just a report that is inaccurate, right? Report inaccuracy, yes, you're going to get shouted at by the consumer of the report, but that's the only problem you face. But with AI and machine learning coming into play, if your data is not truly representative of the decision area that the machine is working on, then you're going to have a very bad outcome. This is a deep and philosophical issue. I mean, because if the data is shoddy or biased, there's a lot of problems that companies can get into. So where do you even start? How do you even work with a company to make sure that their data is the right data is pure? What do you think? So we've, interesting you asked that question, we've come up with this notion that even data has got IQ. We call it data IQ in cognizant. And it's a mathematical measure that we have come up with, which allows us to score a data's ability to perform in a given area of function. So it could be in the area of, let's say, sales effectiveness. We have a large retail company that is really trying to figure out how can they improve their store level information so that they can execute more sales orders with their customers, right? And their assumption is that they're working with a data set that can help them drive that outcome. How do they know that? Well, there's one way to find out, which is for you to experiment, test and learn and test and learn. But that's an arduous process, which is why a lot of the data science work that is happening today is, I would say probably 70 to 80% of the data science effort goes waste because there are experiments that fail. And this was- But is that a waste? I mean, so it failed, but you tried and you maybe had some learnings from it. I mean, right? So a lot of people keep saying that failure is a great teacher of- That's the Silicon Valley mantra right now. You can be smart about where you fail. True. If there are opportunities for you to prevent that failure, why wouldn't you? Okay, all right. And that's what we're looking at. So what I'm saying is that before you go into doing any data science experiment, what if I came back and told you that the data that you're working on is not going to be sufficient for you to deliver the outcome? Would it not be interesting? Exactly. So it's making sure that you at least are maximizing your chance of success by having the right data to begin with. If you're not even- It is, it is a failure for failure's sake if you're not even starting with the right data. Absolutely. And you know, the other thing that people don't realize is if you go and ask, if you just do what, you know, I'm going back to my industrial engineering days. If you go and do a simple time and motion study of data science, data scientists, I can guarantee you that 80% or 90% of their time is spent on just prepping the data. And only less than 10% or 15% on truly driving business value. So my question is you're spending big dollars on data science experiments where 80 to 90% of the time, the data scientists are prepping, looking at the data, is it the right skew? Does it got, is it the right features? Do I need to do some feature engineering? Do I need to normalize it? There are a whole bunch of data prep work that they do. My question is, what if we take that pain away from them? And that's what I call as data science freedom. And this is what we are promoting to our clients saying, what can you do with your data so that your data is ready for the data science folks? And today it's data science folks, tomorrow it's going to be, hopefully machine learning algorithms that can self model, right? Because a lot of people are talking about auto ML, which is the new buzzword, which is AI doing AI. And that's an area that we're heavily invested in, where you really want to make sure that the data going in is of the veracity and the complexity and the texture required for that outcome area. So that's where I think things like data IQ as a concept would really help our clients to know that hey, the data I'm working with has got the intrinsic intelligence in that outcome area for me to drive that particular business outcome that I'm working on. And that's where I think the magic lies. And that's where they'll see the value. That's where they'll see the value. So talk a little about the AI journey because that is, I mean, it's all intertwined, but how so many companies are coming to you, to Cognizant and saying, we know we need to do more of this. We want to make it real. How do we get there? And so what do you say? What's your advice? So I think I mentioned this right up front when we started the conversation. It all has to start with purpose. And without purpose, no AI project really succeeds. You'll end up creating a few bots. So in fact, when I just look out there in the world and look at the kind of work that is happening in machine learning and AI, many of the so-called AI projects, if you double click on them, are just bots. So we're doing some level of maybe process automation. We're trying to reduce labor content, bringing in bots. But are we truly driving change? I'm not saying that that's not a change. That is definitely a change, but it's more of an incremental change. It is not the kind of disruptive change that some of the FANG leaders that are showing, if you take Facebook, Amazon, the whole gamut of digital natives, they're truly disrupting the marketplace. Some of them are even able to do a million predictions, a second, to match demand, supply, and price. Now, that is how they are using it. Now, the question I think for our clients, for our enterprise clients, is to say, that's a great goal to have, but where do I start and how do I start? And it starts with, in my opinion, two or three big notions. One is, honestly ask yourself, how much of a change are you willing to make? Because if you have to compete and really leverage AI and machine learning, the way it has been designed to do so, you have to be willing to press the reset button. You have to be willing to destroy what you have today. And there is, I think Bill Baker back in the days, he was a SQL server guy. He was talking about this whole concept of what is known as scale up and scale out. And he was talking about it from the angle of managing a pet versus a managing cattle. So when you're managing a pet, a pet is a very unique component, like your mail server. So Bob, the mail server, if the mail server goes down, then all hell breaks to lose. And hopefully you have, you know, another alternate to Bob to manage the mail server. So it's more like a scale up model where you are looking at, hey, how do I manage high availability? As opposed to today's world where you have the opportunity to really look at things in a far more expansive manner. So if you have to do that, you can't be saying I have this on-prem data warehouse, right, which is running on XYZ. And I want to take that on-prem data warehouse and move it to the cloud and expect magic to happen. Because all you're doing is you're shifting your mess from your data center to somebody else's data center, which is called the cloud, right? So I think the big thing for clients to really understand is how much are they invested in this change? How are they willing to drive this change? And I'll tell you, it's not about the technology. And there are so many technology options today and we have got some really smart engineers who know how to engineer things. But the question is, what are you doing this for, right? If you want to compete in that paradigm, are you willing to let go of what you have today? And that is a big question that I would start with. An important question, but I want to sneak in one more question, and that is about the skills gap, because this is something that we hear so much about. So many companies facing, there's a dearth of qualified candidates who can do these jobs in data science and AI and ML. What are you seeing at Cognizant and what are you doing to remedy the problem? So I think it is an industry, it's definitely a challenge for the industry at large. And what we are starting to see is two things emerging. One is the new workforce coming into the market is better equipped because of the way the school systems have changed in the last few years. And I would say this is a global phenomenon, not just in North America or in Europe or in China or India, it's a global phenomenon. We're starting to see that undergrad students who come out of school today are better equipped to learn the new capabilities. That's number one, which is very heartening for us, right, in the whole talent space. What I have always believed in, and this is my personal view on this, right, what I've always believed in is that these skills will come into fashion and go out of fashion in months and days. It's about the kind of engineering approach you have that stays constant, right? If you look at any of the new technologies today, they all are based on some core standard principles. Yes, the semantics will change, the structure will change, but some of the engineering principles remain the same. So what we've been doing in Cognizant is really investing in our engineering talent. So we call it data engineering. And to us, data engineering means that if you're a data engineer, you can't tell me, you know, I will only work with A, B, or C technology, right? You should be in a position to work with all of these technologies. And you should be in a position to approach it from an engineering mindset as opposed to a skill or a tool-based mindset. And that's the change that we need. With, you know, fads coming in and out of Vogue, I think it's super important for all consultants in this space to be grounded on some core engineering principles. And that's what we are investing in very heavily. Well, it sounds like a sound investment. Well, thank you so much for coming on the show, Arun, I appreciate it. Thank you so much, it was a pleasure. I'm Rebecca Knight for theCUBE. You are watching theCUBE at Informatica World 2019. Stay tuned.