 Live from San Jose in the heart of Silicon Valley. It's theCUBE, covering DataWorks Summit 2018. Brought to you by Hortonworks. Welcome back to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Cindy Mikey. She is the VP Industry Solutions and GM Insurance and Healthcare at Hortonworks. Thanks so much for coming on theCUBE, Cindy. Thank you, look forward to it. So before the cameras are rolling, we were talking about the business case for data, for data analytics. So walk our viewers through how you think about the business case and your approach to sort of selling it. Yeah, so when you think about data and analytics, I mean, as industries, we've been very good, sometimes at doing kind of like the operational reporting. To me, that's looking in the rear view mirror. Some things already happened, but when you think about data and analytics and especially big data, it's about what questions haven't I've been able to answer. And a lot of companies, when they embark on it, they're like, let's do it for technology's sake, but from a business perspective, we as our industry GMs, we were out there working with our customers, it's like, what questions can't you answer today? And how can I look at existing data, new data sources to actually help me answer questions? I mean, we were talking a little bit about the usage of sensors and so forth around telematics in the insurance industry, connected home, connected lives, connected cars. Those are some types of concepts in other industries. We're looking at industrial internet of things. So how do I actually make the operations more efficient? How do I actually deploy time series analysis to actually help us become more profitable? And that's really where companies are about, I think in our keynote this morning, we were talking about new communities and what does that mean? How do we actually leverage data to either monetize new data sources or make us more profitable? So you're a former insurance CFO, so let's delve into that use case a little bit and talk about the questions that I haven't asked yet. What are some of those and how are companies putting this to work? Yeah, so the insurance industry, it's kind of frustrating sometimes where as an insurance company, you sit there and go, you always monitor what's your combined ratio, especially if you're a property casualty company and you go, yeah, but that tells me information like once a month, but I was actually with a chief marketing officer recently and she's like, she came from the retail industry and she goes, I need to understand what's going on in my business on any given day and so how can we leverage better real-time information to say what customers are we interacting with, what customers should we not be interacting with and then the last thing insurance companies want to do is go out and say, we want you as a customer and then you decline their business because they're not risk worthy. So that's where we're seeing the insurance industry and I'll focus a lot on insurance here, but it's how do we leverage data to change that customer engagement process, look at connected ecosystems and it's a good time to be, well, fundamentally in the insurance industry, we're seeing a lot of use cases, but also in the retail industry, new data opportunities that are out there, we talked a little bit before the interview started on shrinkage and the retail industries, especially in the food, any type of consumer type packages, we're starting to see the usage of sensors to actually help companies move fresh food around to reduce their shrinkage, you know, we've got a... I'm sorry, let's define shrinkage because I'm not even sure I understand. It's not that like your apple is getting smaller and it's drying up, it refers to perishable goods and you explained it. Right, so you're actually looking at how do we make sure that my produce or items that are perishable, you know, I want to minimize the amount of inventory write-offs that I have to do. So that would be the shrinkage and this one major retail chain is they have a lot of consumer goods that they're actually saying, you know what, their shrinkage was pretty high. So they're now using sensors to help them monitor should we, do we need to move certain types of produce? Do we need to look at food before it expires, you know, to make sure that we're not doing an inventory write-off? When you say sensors in this country, are you referring to cameras taking photos of the produce or are you referring to other types of? It's actually both. Like chemical analysis or whatever it might be, I don't know. Yeah, so it's actually a little bit about this. How do I actually, you know, looking at certain types of products? So, you know, we all know when you walk into a grocery store or some type of department store, there's cameras all over the place. So it's not just looking at security, but it's also looking at, you know, are those goods moving? And so, you know, you can't move people around the store, but I can actually, you know, use the visualization. And now with deep machine learning, you can actually look at that and say, you know what, those bananas are getting a little ripe. We need to, like, move those, or we need to, you know, help turn the inventory. So, and then there's also things with barcoding, you know, when you think of things that are on the shelf. So, how do I look at those barcodes? Because in the past, you would have taken somebody down the aisle, they would have, like, checked that. But no, now we're actually, you know, looking up the barcodes and saying, do we need to move this? Do we need to put these things on sale? At this conference, we're hearing just so much excitement and talk about data as the new oil. And it is an incredible strategic asset, but you were also saying that it can become a liability. Talk about the point at which it becomes a liability. It becomes a liability when, one, we don't know what to do with it, or we make decisions off of data, data. So, you think about, you know, I'll give you an example in the healthcare industry. You know, medical procedures have changed so immensely. The advancement in technology, precision medicine. But if we're making healthcare decisions on medical procedures from 10 years ago, so you really need to say, how do I leverage, you know, newer data sets? So over time, if you make your algorithms based upon data that's 10, 20 years old, it's good in certain things, but you know, you can make some bad business decisions if the data's not recent. So that's when I talk about the liability aspect. Okay, okay. And then thinking about how you talk with, collaborate with customers, what is your approach in the sense of how you help them think through their concerns, their anxieties? So, a lot of times it's really kind of understanding what's their business strategy? You know, what are their financial, what are their operational goals? And you say, you know, what can we look at from a data perspective, both data that we have today or data that we can acquire from new data sources to help them actually achieve their business goals? And you know, specifically in the insurance industry, we focus on, you know, top line growth was growing your premium or decreasing your combined ratio. So what are the types of data sources and the analytical use cases that we can actually, you know, use? See the exact same thing in manufacturing, so. And have customer attitudes evolved over time since you've been in the industry? I mean, how would you describe their mindsets right now? I think we still have some industries that we struggle with, but it's actually, you know, I mentioned healthcare. The way we're seeing data being used in the healthcare industry, I mean, it's about precision medicine. You look at genomics research. It says that if people, like 58% of the world's population would actually do a genomics test if they could actually use that information. So it's interesting to see. So the struggle is with people's concern about privacy encroachment, is that the primary? There's a little bit of that and companies are saying, you know, I want to make sure that it's not being used against me, but you know, there was actually a recent article in Best Review, which is of an insurance trade magazine that says, you know, if I have, actually have a genomics test, can the insurance industry use that against me? So I mean, there's still a little bit of, you know. Which is a legitimate concern. It is, it is, absolutely. And then also, you know, we see globally with just, you know, the Global, or the General Data Protection Act, so GDPR. You know, how are companies using my information and data? So, you know, consumers have to be comfortable with the type of data, but you know, outside of the consumer side, there's so much data in the industry. And you made the comment about, you know, data is the new oil. I have a thing against, with that is, but we don't use oil straight in a car. We don't use crude putting in a car. So unless we do something with it, which is the analytical side, then that's where we get the business insights. So data for data sake is just data. It's the business insights is what's really important. Looking ahead at Hortonworks 5, 10 years from now, I mean, how much will your business account for the total business of Hortonworks, do you think, in the sense of, you know, as you've said, this is healthcare and insurance represents such huge potential possibilities and opportunities for the company. I mean, where do you see the trajectory? The trajectory, I believe, is really in those analytical apps. So we were working with a lot of partners that are like, you know, how do I accelerate, you know, those business value? Because like I said, it's like we're not just into data management. We're in the data age. And what does that mean? It's like turning those things into business value and I've got to be able to, I think from an industry perspective, you know, we're working with the right partners and then also customers, you know, because they lack some of the skill sets. So who can actually accelerate, you know, the time to value of, you know, using data for profitability? Is your primary focus area helping regulated industries with their data analytics challenges and using IoT? Or does it also cover unregulated? Unregulated as well. And are there analytics requirements different between regulated and unregulated in terms of the underlying capabilities they require in terms of predictive modeling or governance or so forth? And how does Hortonworks just differentiate your response to those needs? Yeah, so it varies a little bit based upon the regulations. I mean, even if you look at life sciences, life sciences is very, very regulated on how long do I have to keep the data, you know, how can I actually use the data? So if you look at those industries that maybe aren't regulated as much, so we'll get away from financial services, highly regulated across all different areas. But I'll also look at, say, business insurance, not as much regulated as like you and I as consumers because insurance companies can use any type of data to actually do the pricing and doing the underwriting and the actual claims. So still regulated based upon the solvency but not regulated on how we use it to evaluate risk. Manufacturing, definitely some regulation in there from a work safety perspective, but you can use the data to optimize your yields, you know, however you see fit. So we see a mixture of everything but I think, you know, from a Hortonworks perspective is being able to share data across multiple industries because we talk about connected ecosystems and connected ecosystems are really going to change business of the future. So how so? I mean, and especially bringing it back to this conference, to DataWorks and the main stage this morning we heard so much about these connected communities and really it's all about the ecosystem. What do you see as the biggest change going forward? So you look at, and I'll give you the context of the insurance industry, you look at companies like Erity, which is the division of all state, what they're doing, actually working with the car manufacturers. So at some point in time, you know, the automotive industry, you know, General Motors tried this 20 years ago that didn't quite get it with OnStar and GMAC insurance. Now you actually have the opportunity with, you know, maybe I'm the front man for the insurance industry. So I can now start to collect the data from the vehicle. I'm using that for driving of the vehicle, but I can also use it to help a driver, you know, make it safer driving. And optimize their experience of actually driving, make it more pleasant as well as safer. Right. So there's many layers of what can be done now with the same data. Some of those uses in Pingeon are related to regulated concern or mandatory concerns. And some are purely for competitive differentiation like the whole issue of experience. Right. Well, and you think about certain aspects, the insurance industry just has, you know, a negative connotation. And we have an image challenge on, you know, what data can and cannot be used. So, but a lot of people opt in to an automotive manufacturer and share that type of data. So moving forward, who's to say, with the connected ecosystem, I still have the insurance company in the background doing all the underwriting, but my distribution channel is now the car dealer. I love it. Great. That's a great note to end on. Yeah. Thanks so much for coming on. Thank you, Cindy. I'm Rebecca Knight for James Kobielus. We will have more from the Cube's live coverage of DataWorks in just a little bit.