>> Live from Las Vegas, its theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to the theCUBE's live coverage of Informatica World 2019 here in Sin City. I'm your host Rebecca Knight. We're here with Arun Varadarajan. He is the vice president of AI and anaylsitcs at Cognizant. Thank you so much for coming on theCUBE Arun. >> Wonderful its also great to meet you folks at theCUBE. >> You are Cube alarm. >> I am a Cube alarm. This is probably the third or fourth time that I'm on theCUBE. >> Excellent. Well for those viewers who have not seen your previous clips tell us a little bit about your role at Cognizant. >> 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. The second big focus for me is to get them there. To me it's all about the data. So many times we don't realize this that if you look at a lot of the FANG players. The digital natives are born digital who really have leveraged machine learning and AI to disrupt the market place. They do it with data. 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've-- 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. >> Yes. >> Give us the lay of the land. What do you see as 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. 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. I think as opposed to taking a technology view which has been the traditional approach that most of the CIO organizations have used. In AI even more so, there needs to be significant participation for 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 values. 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. 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 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. >> Informatica is a strategic partner of ours and as I was saying, while you start with that outcome in mind and really say 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's coming from, where is it going, who is 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. In AI and machine learning the one thing that we have to be cognizant of, 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 because if the data is shoddy or biased there is 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? >> Interesting you ask 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 or function. So it could be in the area of sales effectiveness. Look 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. 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 seventy to 80% of the data science effort goes waste because there are experiments that fail. This was-- >> But is that a waste? So it failed, but you tried and you maybe had some learnings from it, right? >> So a lot of people keep saying that failure is a great teacher of-- >> That's the Silicon Valley mantra right now. >> Well you can be smart about where you fail. >> True. >> Right. >> Good point. >> If there are opportunities for you to prevent that failure why wouldn't you? >> Okay. All right. >> 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 that 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. 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 is if you go and ask-- If you just do it, 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 eighty to 90% of the time the data scientists are prepping. Looking at the data, is it the right skew, has it the right features, do I need to do some feature engineering, do you denormalize 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? 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? Today it's data science folks, tomorrow it's going to be hopefully machine learning algorithms that can self model because a lot of people are talking about auto ML which is the new buzz-word, 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. That's where I think the magic lies. >> 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, it's all intertwined but 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? 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. Without purpose no AI project really succeeds. You'll end up creating a few bots. In fact when I 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 are 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. There 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 right. If you take Facebook, Amazon, the whole gamut of digital natives, they are truly disrupting the market place. 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? 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 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 loose and hopefully you have 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 X Y Z, 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. >> 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? I'll tell you it's not about the technology. There are so many technology options today and we have got some really smart engineers who know how to engineer things. The question is, what are you doing this for? Are you willing, if you want to compete in that paradigm, are you willing to let go of what you have tody? 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 a, there is 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'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've always believed in, and this is my personal view on this, 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 I will only work with A, B, or C technology. 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 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. 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. (lighthearted music)