 From Cambridge, Massachusetts, it's The Cube, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. Hi everybody, welcome back to Cambridge, Massachusetts. You're watching The Cube, the leader in tech coverage. We go out to the events, we extract the signal from the noise, and we're here at the MIT CDO IQ Conference. Chief Data Officer Information Quality Conference, it's the 13th year here at the Tang Building. We've outgrown this building, and it has to move next year. It's Fire Marshal Ful. Gokula Mishra is here. He is the Senior Director of Global Data and Analytics in supply chain. Formal, at? Formal Senior Director. Former, I'm sorry. Ah, former Senior Director of Global Data Analytics in supply chain at McDonald's. Oh, I didn't know that, I apologize, my friend. Well, welcome back to The Cube. We met when you were at Oracle, doing data. So you've left that, you're on to your next big day. Yes, thinking through it. Fantastic, and now, let's start with your career. You've had, so you just recently left McDonald's, I met you when you were at Oracle, so you cut over to the dark side for a while. And then before that, you've been a practitioner all your life, so take us through sort of your background. Yeah, I mean, my beginning was really with a company called Tata Burrows. Those days, we did not have a lot of work getting done in India. We used to send people to U.S., so I was one of the pioneers of the whole industry coming here and working on very interesting projects. But I was lucky to be working on mostly data analytics. Related work joined a great company called ZS Associates. I did my master's at Northwestern. In fact, my thesis was Intelligent Databases, so building AI into the databases. And from there on, I've been with Booz Allen, Oracle, HP, TransUnion, I used to run my own company, and Sierra Atlantic, which is part of Hitachi, and McDonald's. Awesome, so let's talk about use of data. It's evolved dramatically, as we know. One of the themes of this conference over the years has been sort of, I said yesterday, the Chief Data Officer role emerged from the ashes of kind of governance, kind of back office, information quality, compliance, and then ascended with the tailwind of the big data meme. And it's kind of come full circle. People are realizing actually to get value out of data, you have to have information quality. So those two worlds have collided together. And you've also seen the ascendancy of the Chief Digital Officer, who has really taken a front and center role in some of the more strategic and revenue generating initiatives. And in some ways, the Chief Data Officer has been a supporting role to that, providing the quality, providing the compliance, the governance, and the data modeling and analytics component of it. First of all, is that a fair assessment? And how do you see the way in which the use of data has evolved over the last 10 years? So to me, primarily, the use of data was, in my mind, mostly around financial reporting. So anything that companies needed to run their company, any metrics they needed, any data they needed. So if you look at all the reporting that used to happen, is primarily around metrics that are financials, whether it's around finances, around operations, finances around marketing effort, finances around reporting, if it's a public company reporting to the market. That's where the focus was. And so therefore, a lot of the data that was not needed for financial reporting was what we call nowadays dark data. This is data we collect, but don't do anything with it. Then, as the capability of the computing and the storage and new technologies and new techniques evolve, and are able to handle more variety and more volume of data, then people quickly realize how much potential they have in the other data, outside of the financial reporting data, that they can utilize to. So some of the pioneers leveraged that and actually improved a lot in their efficiency of operations, came out with innovation. GE comes to mind as one of the companies that actually leveraged data early on. And a number of other companies. Obviously, you look at today, data is defining some of the multi-billion dollar company and all they have is data. Well, Facebook, Google, Amazon, Microsoft, Apple, I mean, Apple obviously makes stuff, but those other companies, they're data companies, right? I mean, largely, and those five companies have the highest market value on the US stock exchange. They've surpassed all the other big leaders, even Berkshire Hathaway. And so now what is happening is, because the market changes, the forces that are changing the behavior of our consumers and customers, which I talked about, which is everyone now is digitally engaging with each other. What that does is all the experiences now are being captured digitally. All the services are being captured digitally. All the products are creating a lot of digital exhaust of data. And so now companies have to pay attention to engage with their customers and partners digitally. Therefore, they have to make sure that they're leveraging data and analytics in doing so. The other thing that has changed is the time to decision, to the time to act on the data insight that you get is shrinking and shrinking and shrinking. So a lot more decision making is now going real time. So therefore, you have a situation now, you have the capability, you have the technology, you have the data. Now you have to make sure that you convert that in what I call programmatic kind of data decision making. Obviously, there are people involved in more strategic decision making, so that's more manual, right? But at the operational level, it's going more programmatic decision making. Okay, I want to talk, by the way, I've seen a stat, and I don't know if you can confirm this, that 80% of the data that's out there today is dark data, in other words, data that's behind a firewall or not searchable, not open to Google's crawlers. So there's a lot of value there. I would say that that percent is declining over time as companies have realized the value of data. So more and more companies are removing the silos, bringing those dark data out. I think the key to that is companies being able to value their data. And as soon as they are able to value the data, they are able to leverage a lot of the data. I still believe there's a large percent still not used or accessed in companies. Well, and of course, you talked a lot about data monetization. Doug Laney is an expert in that topic. We had Doug on a couple of years ago when he just after he wrote in Phonomix, he was on yesterday. He's got a very detailed prescription as to, he makes the strong case as to why data should be valued like an asset. I don't think anybody really disagrees with that, but then he gave kind of a how to do it, which will somewhat make your eyes bleed, but it was really well thought out, as you know. But you talked a lot about data monetization. You talked about a number of ways in which data can contribute to monetization. Revenue, cost, reduction, efficiency, risk, and innovation. Revenue and cost is obvious. I mean, that's where the starting point is. Efficiency, it's interesting. I look at efficiency as kind of doing more with less. It's sort of a cost reduction, but explain why it's not in the cost bucket. It's something that's different. So it is, first starts with doing what we do today cheaper, better, faster. And doing more comes after that, because if you don't understand, and data is the way to understand how your current processes work, you will not take the first step. So if you take the first step, is to understand how can I do this process faster. And then you focus on cheaper, and then you focus on better. Of course, faster is because of some of the market forces and customer behavior that's driving you to do that process faster. Okay, and then the other one was risk reduction. I think that makes a lot of sense. Actually, let me go back. So one of the key pieces of efficiency is time to value. So if you can compress the time, or accelerate the time you get the value, that means more cash in, house faster, whether it's cost reduction or revenue. And the other aspect you look at is, can you automate more of the processes, and that way it can be faster? And that hits the income statement as well, because you're reducing headcount cost, and you're maybe not reducing headcount cost, but you're getting more out of headcount, you're reallocating them to more strategic initiatives. Everybody says that, but the reality is you hire less people because you just automated. And then risk reduction, so the degree to which you can lower your expected loss, right, that's just thinking in insurance terms. That's tangible value, certainly to large corporations, but even mid-sized and small corporations. Innovation I thought was a good one, right? But maybe you could give us an example of how in your career you've seen data contribute to innovation. So I'll give an example of oil and gas industry, right? If you look at speed of innovation in oil and gas industry, they were all paper-based. I don't know how much you know about drilling. A lot of the assets that goes into figuring out where to drill, how to drill, and actually drilling, and then taking the oil or gas out, and of course selling it to make money. A lot of those processes were paper-based, right? So if you can imagine trying to optimize a paper-based innovation, it's very hard. Not only that, it's very, very by itself, because it's on paper, it's in someone's drawer, or file. So it's siloed by design, right? And so one thing that the industry has gone through, they recognize that they have to optimize the processes to be better, to innovate, to find, for example, sale gas was a result output of digitizing the processes, because otherwise you can't drill faster, cheaper, better to leverage some of the sale gas drilling that they did. So the industry went through actually digitizing a lot of the paper assets. So they went from not having data to knowingly creating the data that they can use to optimize the process. And then in the process they're innovating new ways to drill the oil well, cheaper, better, faster. You know, early days of oil exploration in the US go back to the Osage Indian tribe in northern Oklahoma. And they brilliantly, when they got shuttled around, they pushed them out of Kansas, and they negotiated with the US government that they maintain the mineral rights. And so they became very, very wealthy. In fact, at one point they were the wealthiest per capita individuals in the entire world. And they used to hold auctions for various drilling rights. So it was all gut feel. All the oil barons would train in and they would have an auction. And it was just, again, it was gut feel as to which areas were the best. And then, of course, as they evolved, remember, it used to be, you drill a little hole, oh, no oil, drill a hole, no oil, drill a hole. Yeah, the expense was enormous, right? It can vary from 10 to 20 million dollars. Yeah, it's just a giant expense. And so now, today, fast forward to the century, and you're seeing much more sophisticated. Yeah, I can give you another example on pharmaceutical. They develop new drugs. It takes, it's a long process. So one of the initial processes to figure out what molecules they should be exploring in the next step. And you could have thousand different combinations of molecules that could treat a particular condition, right? And now, with digitization and data analytics, they're able to do this in a virtual world. Kind of creating a virtual lab where they can test out thousands of molecules. And then, once they can bring it down to a few more, then the physical aspect of that starts. Think about innovation really shrinking their process. I want to say that's about cloud. So I was using, you made the statement in your keynote that how many people out there think cloud is cheaper. And, or maybe you even said cheap, but cheaper, I inferred cheaper than on-prem. And so, it was a loaded question. So nobody put their hand up, they were afraid. But I put my hand up, because we don't have any, we don't have any IT. We used to have IT. It was a nightmare. So for us, it's better. But in your experience, I think I'm inferring correctly that you've meant cheaper than on-prem. And certainly, we talk to many practitioners who have large systems that, when they lift and shift to the cloud, they don't change their operating model. They don't really change anything. They get a bill at the end of the month, and they go, what did this really do for us? And I think that's what you mean. But that's important. So what I mean, let me make it clear, is that there are certain use cases that cloud is cheaper. And as you saw, that people did raise their hands and say, yeah, I have use cases where cloud is cheaper. I think you need to look at the whole thing, right? Cost is one aspect. The flexibility and agility of being able to do things is another aspect. For example, if you have a situation where your stakeholder want to do something for three weeks and they need five times the computing power and the data that they're buying from outside, to do that experiment, right? Now imagine doing that in a physical war. It's going to take a long time just to procure and get the physical box in. And then you own the asset after the fact. And then you'll be able to do it. In cloud, you can enable that. You can get GPUs depending on what problem you're trying to solve. That's another benefit. You can get the fit for purpose computing environment to that. And so there are a lot of flexibility, agility, all of that. It's a new way of managing it. So people need to pay attention to the cost because it will add to the cost. The other thing I will point out is that if you go to the public cloud because they make it cheaper because they have hundreds and thousands of this canned CPU, right? This is much computing power. This much memory. This much desk. This much connectivity. And they build thousands of them. And that's why it's cheaper, right? If your need is something that's very unique and they don't have it, you know, that's when it becomes a problem, right? Are they unique? More of those and the cost will be higher, right? So now we are getting to the IoT world. The volume of data is growing so much. And the type of processing that you need to do is becoming more real time. And you can't just move all this bulk of data and then bring it back and move the data back and forth. You need a special type of computing which is at what Amazon calls it, ads computing, right? And the industry is kind of trying to designing. So that is an example of hybrid computing evolving out of the cloud, out of the necessity that you need special purpose computing environment to deal with new situations and all of it can't be in the cloud. Yeah, I mean, I would argue, well, I guess, you know, I guess Microsoft with Azure Stack was kind of the first although not really now they're there. But I would say Oracle, your former company was the first one to say, okay, we're going to put the exact same infrastructure on-prem as that we have in the public cloud. Oracle, I would say it was the first to truly do that. They were doing hybrid computing. You now see Amazon without posts has done the same. Google kind of has similar approach as Azure. And so it's clear that hybrid is here to stay at least for some period of time. I think the cloud guys probably believe that ultimately it's all going to go to the cloud. You know, we'll see. It's going to be a long, long time before that happens. Okay, I'll give you last thoughts on this conference. Is this, you've been here before? Is this your first one? This is my first one. Okay, so your takeaways, your thoughts, things you might have learned? I am very impressed that, you know, I'm a practitioner, right, and finding so many practitioners coming from so many different backgrounds and industries. It's very, very enlightening to listen to their journey, their story, their learnings in terms of what works and what doesn't work. It is really invaluable. Yeah, I tell you this is always a highlight of our season. And Gokhale, thank you very much for coming on theCUBE. It's great to see you. Thank you. You're welcome. All right, keep it right there. We'll be back with our next guest, Dave Vellante. Paul Gillan is in the house. You're watching theCUBE from MIT. Right back.