>> 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 here in Las Vegas. I'm your host Rebecca Knight, along with my co-host John Furrier. We are here with Nitin Mittal, he is the Principal Analytics and Cognitive Offering at Deloitte. Thank you so much for coming direct from Boston. >> Thank you. Thank you for inviting me. >> So first of all, tell us a little bit about your role at Deloitte. >> Yeah so, as the Analytics and Cognitive Offering leader at Deloitte Consulting, the practice that I run in Deloitte Consulting, we help a lot of our clients with basically their data management needs, their data modernization needs and also basically how to capitalize on the data they have. Particularly as it relates to the analytics and the insights that they could generate and more so using AI approaches and machine learning techniques. And that's a lot of the work that we do and the business that I lead in Deloitte Consulting. >> So what you just described I think in technology world parlance is now digital transformation and that is using data to transform business models and approaches. I want to hear, where you think most companies are in terms of this journey? I mean are they still in the planning stage? Are they in the execution stage? Where do you see things? >> Frankly I would say depending on the industry that you work in, they are in different states of maturity. Frankly I would say financial services companies and life sciences pharmaceutical companies, they are a bit ahead in terms of their level of understanding along that digital transformation spectrum, as well as the effort and the investments that they are making versus if you take many of the consumer companies, unless you happen to be an e-commerce company like Amazon, many of them are basically still kind of catching up and trying to understand how do they cross what is called the digital divide which is customers coming into their retail stores versus customers actually going to their web properties and expressing an intent or trying to basically buy a particular kind of product. How do they actually sort of correlate that? So depending on the industry, depending on frankly the market that you're in, you will absolutely see a variance and you'll have companies along that entire spectrum, from companies who are just starting and trying to understand what do they do with their data to companies who are a lot more progressive, who inherently understand and comprehend the data that they have but are more focused on how do we capitalize on that data for the purposes of insights. >> The digital transformation data is a big part of it. People want to be like a SAS company where they've got all this legacy on premises, they get Cloud-native activity kind of coming together. Where should customers store their data? This becomes a big question we hear a lot. What are you guys doing at the edge of the network, you know the cutting edge with customers. What are they looking at doing? Are they architecting it? Where are they in figuring out where data sits, how data feeds the machine learning, how machine learning feeds the AI, all this requires data. And it's not addressable. You can't get it to the app, there's a problem. What are you seeing on where the data should be stored? >> A very very big debate in companies right now and frankly a lot of the architectural discussions that take place are all with respect to basically exactly the nature and the intent and the spirit of the question that you just kind of asked. More and more frankly what we see is a discussion along the lines of a hybrid cloud architecture. Where is some data is assumed it's going to keep continue residing on premise, particularly where there are significant privacy considerations, where there is basically kind of a risk or a heightened risk of cyber security et cetera. That type of data still is resident on premise. But more and more if you take like customer data, sales data, supply chain data, a lot of that is moving to a cloud based environment. But what we also see in that mix is that many companies at least at this point of time, have not necessarily gone down the path of choosing a singular cloud platform. They still have basically a multitude of cloud platforms, whether they are public cloud or frankly a private cloud and you see kind of data moving to those cloud environments too. So in a sense we are going from a world where data has been fragmented and siloed, in many of the back end transactional systems, data warehouses and data basis, to well a lot more consolidated in a cloud environment but not necessarily a singular, unified view of that data because data is still, to some degree, getting fragmented in a multitude of cloud environments. >> Is the regulations create more constraints then, because what you're saying is is that privacy and compliance and risk which we've all known about, it's been a part of the plan, but now you got more regulations. Just saw Microsoft had an announcement this morning around having more privacy so then you got Internash, you've got clouds, you have geography, so the complexity seems to increase. Its almost the N times N problem. What's your thoughts on that? >> I would say that, I won't necessarily state the constraints but it's certainly a very prominent consideration and I kind of talked about this yesterday at the conference as well. It actually goes beyond privacy. It actually includes three different things. Privacy is a big consideration, but then there's also basically the topic of ethics, particularly in the age of AI, in terms of what constitute ethics because we as humans we are given basically the macro environment that we live in, our upbringing, our morals, and kind of our general know-how, essentially have a ethical code and a set of principles that we follow. The same needs to be embraced by intelligent machines. Ethics is becoming another topic. And a third topic around algorithmic bias. Frankly all, whether it's privacy, whether it's ethics, whether it's algorithmic bias, those are becoming prominent topics for consideration and something which consciously have to looked in, or looked upon in the context of data management, in the context of basically analytics, in the context of the processes that are being applied, and in the context of the systems that are being architected. >> It's not just software level abstractions it's societal level, human input-- >> Exactly. >> kind of blends it in, we'll get to that in the skills question later. But okay real quick, how does Informatica address this because their software guys, building abstraction layers, they got now Compute in the cloud. As the world changes so fast, how do customers implement a solution to solve these complexities? What's your take on the Informatica story? >> As one of probably the most significant systems integration partner for Informatica, the way that we have always kind of viewed Informatica and why frankly I view our partnership has excelled in the marketplace and at many clients, is because we actually see Informatica as an entire ecosystem around the topic and domain of data. Whether it comes around basically data extraction, data integration, data management, master data management, data governance, data privacy as well as basically intelligent insight generation, we literally see Informatica as having the platform, having the products, having the solutions that address a multitude of needs across the entire ecosystem and frankly, they're not just a tools company focused around one aspect of that value chain. They are basically a platform company that has the ability to traverse across that entire value chain so that you could essentially access data, capitalize on that data, generate insights of that data and use advanced machine learning and artificial intelligence techniques to actually get a better competitive edge in the marketplace against your competitors and in the market that you're working in. >> So you've just painted this portrait of this exceedingly complex landscape, where companies are wrestling with all these really hard questions. Do we have the right people in place who are trying to answer these questions? I mean the skills gap is well documented. What do you think the best companies are doing to combat it? >> So absolutely right that there is a significant skills gap and it's not necessarily something that's kind of getting really better. Frankly that gap is increasing and we see kind of, I'll narrate this from a consulting and a systems integration standpoint. One of the areas that we're looking at to start closing some of this skills gap, is the development and usage of what we call digital FDE's which is we know we've got a limited pool of essentially highly talented practitioners and team members and human beings as part of our practice but we need to have them focus on some of the higher value added task. So we're taking a lot of the cookie cutter, repeatable kind of tasks as part of that value chain and we're automating it, building basically software bots that we in our language call digital FDE's so that a lot of that work can be taken upon by these digital FDE's versus we can take the limited pool of talented practitioners that we have, retrain, reskill, recertify them, and taking some of the more complex activities that we have to undertake for our clients. >> Love that strategy but I got to ask you, for all the graduates that are graduating college, in high school, the other question to follow up on that is what specific skills, what do I need to know to solve the data, be in the data business. Is there a certain playbook you see? A certain success formula from a skills specific skills standpoint? >> So without necessarily kind of getting into the hard skills sets because frankly, technologies evolve, skills sets kind of develop, new platforms are basically kind of out there, the one area that I would absolutely highlight is understanding the age of AI that we are living in and as part of your eduction, paying attention to and focusing on how do I deal with data? How should it be architected? How should it be classified? How should it be categorized? What are the appropriate algorithms to use? When do I apply those algorithms and what is meaningful in terms of the application of the right data set, or the selection of the right data set, and marry it with the algorithm to generate the meaningful insights. Understanding basically the age of AI, and what that entails and how does the role of data change, how does the role of algorithms comes into being, and what is important from a privacy, ethics and biased standpoint. If you develop those skill sets and that understanding, it will actually serve you well in any circumstance. And it will serve you well irrespective the technology, irrespective of the vendor, irrespective of the underlying hard skill sets. >> That's terrific advice for all the budding technologists out there. Nimin thank you so much for coming on the program. >> Thank you. Thank you for having me. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE at Informatica World 2019. (upbeat music)