 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 Mital. 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 that 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're 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're making. Worst is 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 SaaS company, but they got all this legacy on premises, they got 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? Could you have the cutting edge with customers? What are they looking at to do? 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. If 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 our 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. Whereas some data is assumed is going to keep continuing residing on premise, particularly where there are significant privacy considerations where there is basically kind of a risk or a heightened risk of cybersecurity, 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 in 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 backend transactional systems, data warehouses and databases, 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 part of the plan. But now you've got more regulations, just so Microsoft had an announcement this morning around having more privacy. So then you got international, you got clouds that have geography. So the complexity seems to increase. It's almost the N times N problem. What's your thoughts on that? I would say that I'm not necessarily kind of necessarily state 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 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 kind of 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, 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 they're software guys building abstraction layers. They've 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? Yeah. And the way, 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 that entire ecosystem. And frankly, they're not just a tools company focused around one aspect of that value chain. They're basically a platform company that has the ability to traverse across that entire value chain so that you could essentially access data, capitalize on the 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 kind of 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 are the best companies are doing to combat it? So absolutely right that there is a significant skills gaps and it's not necessarily something that's kind of getting mitigated. Frankly, that gap is increasing. And what we see kind of, I'll narrate this from a consulting and a systems integration standpoint. One of the areas that we are looking at to start closing some of this skills gap is the development and usage of what we call digital FDEs, 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 at a task. So we are taking a lot of the cookie cutter, repeatable kind of tasks as part of that value chain and we're automating it and building basically software bots that we in our language call digital FDEs so that a lot of that work can be taken upon by these digital FDEs versus we can take the limited pool of talented practitioners that we have, retrain, reskill, re-certify them in taking on 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 skill specific skill standpoint? So without necessarily kind of getting into the hard skill sets because frankly, technologies evolve, skill sets kind of develop, new platforms are basically kind of out there. The one area that I would kind of absolutely highlight is understanding the age of AI that we are living in and kind of as part of your education, paying attention to you 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 basically kind of use? When do I apply those algorithms and what is meaningful? In terms of kind of the application of the right data set or the selection of the right data set and married 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 basically algorithms comes into being and what is kind of important from a privacy ethics and bias standpoint? If you sort of kind of develop those skill sets and that understanding, it will actually serve you well in any circumstance and it will serve you well irrespective of the technology, irrespective of the vendor, irrespective of the underlying hard skill sets. That's terrific advice for all the budding technologists out there. 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.