>>from around the globe. It's the Cube with digital coverage of smart data. Marketplace is brought to You by Io Tahoe Digital transformation is really gone from buzzword to a mandate. Additional businesses, a data business. And for the last several months, we've been working with Iot Tahoe on an ongoing content. Serious, serious, focused on smart data and automation to drive better insights and outcomes, essentially putting data to work. And today we're gonna do a deeper dive on automating data Discovery. And one of the thought leaders in this space is a J ahora who is the CEO of Iot. Tahoe's once again joining Me A J Good to see you. Thanks for coming on. >>A great to be here, David. Thank you. >>So let's start by talking about some of the business realities. And what are the economics that air? That air driving, automated data Discovery? Why is that so important? >>Yeah, and on this one, David, it's It's a number of competing factors we've got. The reality is data which may be sensitive, so this control on three other elements are wanting to drive value from that data. So innovation, you can't really drive a lot of value without exchanging data. So the ability to exchange data and to manage those costs, overheads and data discovery is at the roots of managing that in an automated way to classify that data in sets and policies to put that automation in place. >>Yeah. Okay, look, we have a picture of this. We could bring it up, guys, because I want oh, A j help the audience. Understand? Unaware data Discovery fits in here. This is as we talked about this, a complicated situation for a lot of customers. They got a variety of different tools, and you really laid it out nicely here in this diagram. So take us through. Sort of where that he spits. >>Yeah. I mean, where at the right hand side, This exchange. You know, we're really now in a data driven economy that is, everything's connected through AP, eyes that we consume on mine free mobile relapse. And what's not a parent is the chain of activities and tasks that have to go into serving that data two and eight p. I. At the outset, there may be many legacy systems, technologies, platforms on premise and cloud hybrids. You name it. Andi across those silos. Getting to a unified view is the heavy lifting. I think we've seen Cem some great impacts that be I titles such as Power Bi I tableau looker on DSO on in Clear. Who had Andi there in our ecosystem on visualising Data and CEO's managers, people that are working in companies day to day get a lot of value from saying What's the was the real time activity? What was the trend over this month? First his last month. The tools to enable that you know, we here, Um, a lot of good things are work that we're doing with snowflake mongo db on the public cloud platforms gcpd as your, um, about enabling building those pay planes to feed into those analytics. But what often gets hidden is have you sauce that data that could be locked into a mainframe, a data warehouse? I ot data on DPA, though, that all of that together that is the reality of that is it's it's, um, it's a lot of heavy lifting It z hands on what that, um, can be time consuming on the issue There is that data may have value. It might have potential to have an impact on the on the top line for a business on outcomes for consumers. But you never any sure unless you you've done the investigation discovered it unified that Onda and be able to serve that through to other technologies. >>Guys have. You would bring that picture back up again because A. J, you made a point, and I wanna land on that for a second. There's a lot of manual curating. Ah, an example would be the data catalogue if they decide to complain all the time that they're manually wrangling data. So you're trying to inject automation in the cycle, and then the other piece that I want you to addresses the importance of AP eyes. You really can't do this without an architecture that allows you to connect things together. That sort of enables some of the automation. >>Yeah, I mean, I don't take that in two parts. They would be the AP eyes so virtual machines connected by AP eyes, um, business rules and business logic driven by AP eyes applications. So everything across the stack from infrastructure down to the network um, hardware is all connected through AP eyes and the work of serving data three to an MP I Building these pipelines is is often, um, miscalculated. Just how much manual effort that takes and that manual ever. We've got a nice list here of what we automate down at the bottom. Those tasks of indexing, labeling, mapping across different legacy systems. Um, all of that takes away from the job of a data scientist today to engineer it, looking to produce value monetize data on day two to help their business day to conceive us. >>Yes. So it's that top layer that the business sees, of course, is a lot of work that has to go went into achieving that. I want to talk about some of the key tech trends that you're seeing and one of the things that we talked about a lot of metadata at the importance of metadata. It can't be understated. What are some of the big trends that you're seeing metadata and others? >>Yeah, I'll summarize. It is five. There's trains now, look, a metadata more holistically across the enterprise, and that really makes sense from trying. Teoh look across different data silos on apply, um, a policy to manage that data. So that's the control piece. That's that lever the other side's on. Sometimes competing with that control around sense of data around managing the costs of data is innovation innovation, being able to speculate on experiment and trying things out where you don't really know what the outcome is. If you're a data scientist and engineer, you've got a hypothesis. And now, before you got that tension between control over data on innovation and driving value from it. So enterprise wide manage data management is really helping to enough. Where might that latent value be across that sets of data? The other piece is adaptive data governance. Those controls that that that stick from the data policemen on day to steer its where they're trying to protect the organization, protect the brand, protect consumers data is necessary. But in different use cases, you might want to nuance and apply a different policy to govern that data run of into the context where you may have data that is less sensitive. Um, that can me used for innovation. Andi. Adapting the style of governance to fit the context is another trend that we're seeing coming up here. A few others is where we're sitting quite extensively and working with automating data discovery. We're now breaking that down into what can we direct? What do we know is a business outcome is a known up front objective on direct that data discovery to towards that. And that means applying around with Dems run technology and our tools towards solving a known problem. The other one is autonomous data discovery. And that means, you know, trying to allow background processes do winds down what changes are happening with data over time flagging those anomalies. And the reason that's important is when you look over a length of time to see different spikes, different trends and activity that's really giving a day drops team the ability to to manage and calibrate how they're applying policies and controls today. There, in the last two David that we're seeing is this huge drive towards self service so reimagining how to play policy data governance into the hands off, um, a day to consumer inside a business or indeed, the consumer themselves. The South service, um, if their banking customer or healthcare customer and the policies and the controls and rules, making sure that those are all in place to adaptive Lee, um, serve those data marketplaces that, um when they're involved in creating, >>I want to ask you about the autonomous data discovering the adaptive data. Governance is the is the problem where addressing their one of quality. In other words, machines air better than humans are doing this. Is that one of scale that humans just don't don't scale that well, is it? Is it both? Can you add some color to that >>yet? Honestly, it's the same equation that existed 10 years ago, 20 years ago. It's It's being exacerbated, but it's that equation is how do I control both things that I need to protect? How do we enable innovation where it is going to deliver business value? Had to exchange data between a customer, somebody in my supply chains safely. And all of that was managing the fourth that leg, which is cost overheads. You know, there's no no can checkbook here. I've got a figure out. If only see io and CDO how I do all of this within a fixed budget so that those aspects have always been there. Now, with more choices. Infrastructure in the cloud, um, NPR driven applications own promise. And that is expanding the choices that a a business has and how they put mandated what it's also then creating a layer off management and data governance that really has to now, uh, manage those full wrath space control, innovation, exchange of data on the cost overhead. >>That that top layer of the first slide that we showed was all about business value. So I wonder if we could drill into the business impact a little bit. What do your customers seeing you know, specifically in terms of the impact of all this automation on their business? >>Yeah, so we've had some great results. I think view the biggest Have Bean helping customers move away from manually curating their data in their metadata. It used to be a time where for data quality initiatives or data governance initiative that be teams of people manually feeding a data Cavallo. And it's great to have the inventory of classified data to be out to understand single version of the trees. But in a having 10 15 people manually process that keep it up to date when it's moving feet. The reality of it is what's what's true about data today? and another few sources in a few months. Time to your business on start collaborating with new partners. Suddenly the landscape has changed. The amount of work is gonna But the, um, what we're finding is through automating creating that data discovery feeding a dent convoke that's releasing a lot more time for our CAS. Mr Spend on innovating and managing their data. A couple of others is around cell service data and medics moving the the choices of what data might have business value into the hands of business users and and data consumers to They're faster cycle times around generating insights. Um, we really helping that by automating the creation of those those data sets that are needed for that. And in the last piece, I'd have to say where we're seeing impacts. A more recently is in the exchange of data. There are a number of marketplaces out there who are now being compelled to become more digital to rewire their business processes. Andi. Everything from an r p a initiative. Teoh automation involving digital transformation is having, um, see iose Chief data officers Andi Enterprise architects rethink how do they how they re worthy pipelines? But they dated to feed that additional transformation. >>Yeah, to me, it comes down to monetization. Of course, that's for for profit in industry, from if nonprofits, for sure, the cost cutting or, in the case of healthcare, which we'll talk about in a moment. I mean, it's patient outcomes. But you know, the the job of ah, chief data officer has gone from your data quality and governance and compliance to really figuring out how data and be monetized, not necessarily selling the data, but how it contributes for the monetization of the company and then really understanding specifically for that organization how to apply that. And that is a big challenge. We chatted about it 10 years ago in the early days of a Duke. And then, you know, 1% of the companies had enough engineers to figure it out. But now the tooling is available, the technology is there and the the practices air there, and that really to me, is the bottom line. A. J is it says to show me the money. >>Absolutely. It's is definitely then six sing links is focusing in on the saying over here, that customer Onda, where we're helping there is dio go together. Those disparities siloed source of data to understand what are the needs of the patient of the broker of the if it's insurance? Ah, one of the needs of the supply chain manager If its manufacturing onda providing that 3 60 view of data, um is helping to see helping that individual unlock the value for the business. Eso data is providing the lens, provided you know which data it is that can God assist in doing that? >>And you know, you mentioned r p A. Before an r p A customer tell me she was a six Sigma expert and she told me we would never try to apply six segment to a business process. But with our P A. We can do so very cheaply. Well, what that means is lower costs means better employee satisfaction and, really importantly, better customer satisfaction and better customer outcomes. Let's talk about health care for a minute because it's a really important industry. It's one that is ripe for disruption on has really been up until recently, pretty slow. Teoh adopt ah, lot of the major technologies that have been made available, but come, what are you seeing in terms of this theme, we're using a putting data to work in health care. Specific. >>Yeah, I mean, healthcare's Havlat thrown at it. There's been a lot of change in terms of legislation recently. Um, particularly in the U. S. Market on in other economies, um, healthcare ease on a path to becoming more digital on. Part of that is around transparency of price, saying to be operating effectively as a health care marketplace, being out to have that price transparency, um, around what an elective procedure is going to cost before taking that that's that forward. It's super important to have an informed decision around there. So we look at the US, for example. We've seen that health care costs annually have risen to $4 trillion. But even with all of that on cost, we have health care consumers who are reluctant sometimes to take up health care if they even if they have symptoms on a lot of that is driven through, not knowing what they're opening themselves up to. Andi and I think David, if you are, I want to book, travel, holiday, maybe, or trip. We want to know what what we're in for what we're paying for outfront, but sometimes in how okay, that choice, the option might be their plan, but the cost that comes with it isn't so recent legislation in the US Is it certainly helpful to bring for that tryst price, transparency, the underlying issue there? There is the disparity. Different formats, types of data that being used from payers, patients, employers, different healthcare departments try and make that make that work. And when we're helping on that aspect in particular related to track price transparency is to help make that date of machine readable. So sometimes with with data, the beneficiary might be on a person. I've been a lot of cases now we're seeing the ability to have different systems, interact and exchange data in order to process the workflow. To generate online at lists of pricing from a provider that's been negotiated with a payer is, um, is really a neighboring factor. >>So, guys, I wonder if you bring up the next slide, which is kind of the Nirvana. So if you if you saw the previous slide that the middle there was all different shapes and presumably to disparage data, this is that this is the outcome that you want to get. Everything fits together nicely and you've got this open exchange. It's not opaque as it is today. It's not bubble gum band aids and duct tape, but but but described this sort of outcome the trying to achieve and maybe a little bit about what gonna take to get there. >>Yeah, that's a combination of a number of things. It's making sure that the data is machine readable. Um, making it available to AP eyes that could be our ph toes. We're working with technology companies that employ R P. A full health care. I'm specifically to manage that patient and pay a data. Teoh, bring that together in our data Discovery. What we're able to do is to classify that data on having made available to eight downstream tour technology or person to imply that that workflow to to the data. So this looks like nirvana. It looks like utopia. But it's, you know, the end objective of a journey that we can see in different economies there at different stages of maturity, in turning healthcare into a digital service, even so that you could consume it from when you live from home when telling medicine. Intellicast >>Yes, so And this is not just health care but you wanna achieve that self service doing data marketplace in virtually any industry you working with TCS, Tata Consultancy Services Toe Achieve this You know, if you are a company like Iota has toe have partnerships with organizations that have deep industry expertise Talk about your relationship with TCS and what you guys are doing specifically in this regard. >>Yeah, we've been working with TCS now for room for a long while. Andi will be announcing some of those initiatives here where we're now working together to reach their customers where they've got a a brilliant framework of business for that zero when there re imagining with their clients. Um, how their business cause can operate with ai with automation on, become more agile in digital. Um, our technology, the dreams of patients that we have in our portfolio being out to apply that at scale on the global scale across industries such as banking, insurance and health care is is really allowing us to see a bigger impact on consumer outcomes. Patient outcomes And the feedback from TCS is that we're really helping in those initiatives remove that friction. They talk a lot about data. Friction. Um, I think that's a polite term for the the image that we just saw with the disparity technologies that the legacy that has built up. So if we want to create a transformation, Um, having a partnership with TCS across Industries is giving us that that reach and that impacts on many different people's day to day jobs and knives. >>Let's talk a little bit about the cloud. It's It's a topic that we've hit on quite a bit here in this in this content Siri's. But But you know, the cloud companies, the big hyper scale should put everything into the cloud, right? But but customers are more circumspect than that. But at the same time, machine intelligence M. L. A. The cloud is a place to do a lot of that. That's where a lot of the innovation occurs. And so what are your thoughts on getting to the cloud? Ah, putting dated to work, if you will, with machine learning stuff you're doing with aws. What? You're fit there? >>Yeah, we we and David. We work with all of the cloud platforms. Mike stuffed as your G, c p IBM. Um, but we're expanding our partnership now with AWS Onda we really opening up the ability to work with their Greenfield accounts, where a lot of that data that technology is in their own data centers at the customer, and that's across banking, health care, manufacturing and insurance. And for good reason. A lot of companies have taken the time to see what works well for them, with the technologies that the cloud providers ah, are offered a offering in a lot of cases testing services or analytics using the cloud to move workloads to the cloud to drive Data Analytics is is a real game changer. So there's good reason to maintain a lot of systems on premise. If that makes sense from a cost from a liability point of view on the number of clients that we work with, that do have and we will keep their mainframe systems within kobo is is no surprise to us, but equally they want to tap into technologies that AWS have such a sage maker. The issue is as a chief data officer, I don't have the budget to me, everything to the cloud day one, I might want to show some results. First upfront to my business users Um, Onda worked closely with my chief marketing officer to look at what's happening in terms of customer trains and customer behavior. What are the customer outcomes? Patient outcomes and partner at comes I can achieve through analytics data signs. So I, working with AWS and with clients to manage that hybrid topology of some of that data being, uh, in the cloud being put to work with AWS age maker on night, I hope being used to identify where is the data that needs to bay amalgamated and curated to provide the data set for machine learning advanced and medics to have an impact for the business. >>So what are the critical attributes of what you're looking at to help customers decide what what to move and what to keep, if you will. >>Well, what one of the quickest outcomes that we help custom achieve is to buy that business blustery. You know that the items of data that means something to them across those different silos and pour all of that together into a unified view once they've got that for a data engineer working with a a business manager to think through how we want to create this application. There was the turn model, the loyalty or the propensity model that we want to put in place here. Um, how do we use predictive and medics to understand what needs are for a patient, that sort of innovation is what we're looking applying the tools such a sagemaker, uh, night to be west. So they do the the computation and to build those models to deliver the outcome is is across that value chain, and it goes back to the first picture that we put up. David, you know the outcome Is that a P I On the back of it, you've got the machine learning model that's been developed in That's always such as data breaks. But with Jupiter notebook, that data has to be sourced from somewhere. Somebody has to say that yet you've got permission to do what you're trying to do without falling foul of any compliance around data. Um, it'll goes back to discovering that data, classifying it, indexing it in an automated way to cut those timelines down two hours and days. >>Yeah, it's the it's the innovation part of your data portfolio, if you will, that you're gonna put into the cloud. Apply tools like sage maker and others. You told the jury. Whatever your favorite tool is, you don't care. The customer's gonna choose that and hear the cloud vendors. Maybe they want you to use their tool, but they're making their marketplaces available to everybody. But it's it's that innovation piece, the ones that you where you want to apply that self service data marketplace to and really drive. As I said before monetization. All right, give us your final thoughts. A. J bring us home. >>So final thoughts on this David is that at the moment we're seeing, um, a lot of value in helping customers discover that day the using automation automatically curating a data catalogue, and that unified view is then being put to work through our A B. I's having an open architecture to plug in whatever tool technology our clients have decided to use, and that open architecture is really feeding into the reality of what see Iose in Chief Data Officers of Managing, which is a hybrid on premise cloud approach. Do you suppose to breed Andi but business users wanting to use a particular technology to get their business outcome having the flexibility to do that no matter where you're dating. Sitting on Premise on Cloud is where self service comes in that self service. You of what data I can plug together, Dr Exchange. Monetizing that data is where we're starting to see some real traction. Um, with customers now accelerating becoming more digital, uh, to serve their own customers, >>we really have seen a cultural mind shift going from sort of complacency. And obviously, cove, it has accelerated this. But the combination of that cultural shift the cloud machine intelligence tools give give me a lot of hope that the promises of big data will ultimately be lived up to ah, in this next next 10 years. So a J ahora thanks so much for coming back on the Cube. You're you're a great guest. And ah, appreciate your insights. >>Appreciate, David. See you next time. >>All right? And keep it right there. Very right back. Right after this short break

>> from Santa Clara, California It's the Cube covering techno ovation. World Pitch Summit 2019 Brought to You by Silicon Angle Media. Now here's Sonia to Gari >> Hi and welcome to the Cube. I'm your host, Sonia to Gari, and we're here at Oracle's Agnew's campus covering techno vacations. World Pitch Summit 2019 a pitch competition in which girls from around the world developed mobile labs in order to create positive change in the world with us. Today we have teen LPs n from Spain. Welcome, and the team members are Paulo Fernandez Rosa's Sandra Cho Manual Gomez, Nouria, Peoria, the CIA, Fernandez and with the beyond Tovar. Welcome to the Cube. Thank you. So your app is called one and where tell us more about that. >> They will, when I'm were easy enough that detects anomalies when you go out to work or run am. It's to ensure woman's safety on it, obtains your location in real time. And if something happens, for example, if you stop or if you're in getting near to your destination, it calls the emergency contact or the emergency service's >> Wow, and so can you tell us how a user would would go through it. Step by step. >> Yes, A first of all you need to establish our contact am. So then you have two different Moz A the start mold, which is a for when you, for example, go running. And when do you stop the up? He takes that anomaly so it sends you a message in case off emergency it goes a the emergency contact on the other mode, it's they take me to a remote. So that's when you, for example, want to go home. And so you you don't follow your route. I am the only they up since you and alert. And in case of emergency, it's Cindy. Um, message to your contact. >> Wow. I feel like that could be really useful. Yes. Is that a big problem in Spain? >> Yes, it's He's actually well into feel better. Okay, Yeah, >> we saw this problem in our community on when they gave us the opportunity to try to help in some way. We thought while we can try to create this application on forgives on on it in our country, there have been a lot of women murdered on kidnapped ennui. A thought that it was something very >> very important. I'm That's amazing. So how did you all come up with this idea? >> A. Well, it'll be gone when we hear about their martyr off Laurel. Wilma It that made us a became aware with the magnitude of the problem, so am I. We wanted to do something that they will will be a helpful for us. So we did this >> application. Wow. And, um what problems or struggles as you go through creating this app? I >> am. Well, I think that the the worst think was the time because we had, like, a really short time to do this application to develop and to develop it because we started in February on, we had to We have a deadline in April. So for us, the time was the most difficult part. Also, the programming, the coding. But that that was because we had to learn coding. So yet the time was our our difficult >> part. If you get funding, where do you see this app in five years? >> Well, a We want to continue developing this up on improving it because we really need this up. We want to add new new languages and also introduce it in a iose to a iPhone users to use it also on in 50 years. We would like a this up to continue working about. Hopefully, maybe a this problem with disappear. >> That's great. Um, so tell us more about your experience at Tech Novation. How did you all meet? And why did you decide to join techno vacation? Tell >> me. So we discovered generation in the high school. Our technology teacher air showed as the contest, and we decided to join. And we're old friends. So it was a, like, easy to work because we already know each other. So am that's the best part. And we won't really wanted to do something that could be useful for us. So we decided to to start the Italians with that idea. >> That's awesome. What? What's been like the best experience a part of the experience so far? >> A this trip, actually, Yeah, it is being amazing. I am. It's actually one of the best rips off my life, and we're all having a great time here. >> That's also, um So, uh, thanks so much for coming on. We really appreciate it. And good luck for tonight. Thank you. This is team LPs n from Spain. Thanks so much for watching Stay tuned for more