>> Announcer: From Berlin, Germany, it's the CUBE covering Dataworks Summit Europe 2018. Brought to you by Hortonworks. >> Welcome to the CUBE, we're here at Dataworks Summit 2018 in Berlin. I'm James Kobielus. I am the lead analyst for big data analytics on the Wikibon team of SiliconANGLE Media On the CUBE, we extract the signal from the noise and here at Dataworks Summit, the signal is big data analytics and increasingly the imperative for many enterprises is compliance with GDPR, the General Data Protection Regulation comes in five weeks, May 25th. There's more things going on so what I'm going to be doing today for the next 20 minutes or so is from Hortonworks I have Abhas Ricky who is the director of strategy and innovation. He helps customers, and he'll explain what he does, but at a high level, he helps customers to identify the value of investments in big data, analytics, big data platforms in their business. And Abhas, how do you justify the value of compliance with GDPR. I guess, the value would be avoid penalties for noncompliance, right? Can you do it as an upside as well? Is there an upside in terms of if you make an investment, and you probably will need to make an investment to comply, Can you turn this around as a strategic asset, possibly? Yeah, so I'll take a step back first. >> James: Like a big data catalog and so forth. >> Yeah, so if you look at the value part which you said, it's interesting that you mentioned it. So there's a study which was done by McKinsey which said that only 15% of executives can understand what is the value of a digital initiative, let alone big data initiative. >> James: Yeah. >> Similarly, Gardner says that if you look at the various portraits and if you look at various issues, the fundamental thing which executives struggle with identifying the value which they will get. So that is where I pitch in. That is where I come in and do a data perspective. Now if you look at GDPR specifically, one of the things that we believe, and I've done multiple blogs around that and webinars, GDPR should be treated at a business opportunity because of the fact that -- >> James: Any opportunity? Business opportunity. It shouldn't necessarily be seen as a compliance burden on costs or your balance sheets because of the fact, it is the one single opportunity which allows you to clean up your data supply chain. It allows you to look at your data assets with a holistic view, and if you create a transparent data supply chain, and your IT systems talk to each other. So some of the provisions, as you know, in addition to right to content, right to portability, etc. It is also privacy by design which says that you have to be proactive in defining your IT systems and architecture. It's not necessarily reactive. But guess what? If you're able to do that, you will see the benefits in other use cases like single view of customer or fraud or anti-money laundering because at the end of the day, all GDPR is allowing you to say is that where do you store your data, what's the lineage, what's the provenance? Can you identify what the personally identifiable information is for any particular customer? And can you use that to your effect as you go forward? So it's a great opportunity because to be able to comply with the provisions, you've got to take steps before that which is essentially streamlining your data operations which obviously will have a domino effect on the efficiency of other use cases. So I believe it's a business opportunity. >> Right, now part of that opportunity in terms of getting your arms around what data you have, when the GDPR is concerned, the customer has a right to withhold consent for you and the enterprise that holds that data to use that personal data of theirs which they own for various and sundry reasons. Many enterprises and many of Hortonworks customers are using their big data for things like AI and machine learning. Won't this compliance with GDPR limit their ability to seize the opportunity to build deep learning and so forth? What are customers saying about that? Is that going to be kind of a downer or a chilling effect on their investments in AI and so forth? >> So there's two elements around it. The first thing which you said, there are customers, there's machine learning in AI, yes, there are. But broadly speaking, before you're able to do machine learning and AI, you need to get your data sets onto a particular platform in a particular fashion, clean data, otherwise, you can't do AI or machine learning on top of it. >> James: Right. So the reason why I say it's an opportunity is that because you're being forced by compliance to get that data from every other place onto this platform. So obviously those capabilities will get enhanced. Having said, I do agree if I'm an organization which does targeting, retargeting of customers based on multiple segmentations and then one of the things is online advertisements. In that case, yes, your ability might get affected, but I don't think you'll get prohibited. And that affected time span will be only small because you just adapt. So the good thing about machine learning and AI is that you don't create rules, you don't create manual rules. They pick up the rules based on the patterns and how the data sets have been performing. So obviously once you have created those structures in place, initially, yes, you'll have to make an investment to alter your programs of work. However, going forward, it will be even better. Because guess what? You just cleaned your entire data supply chain. So that's how I would see that, yes, a lot of companies, ecommerce you do targeting and retargeting based on the customer DNA, based on their shopping profiles, based on their shopping ad libs and then based off that, you give them the next best offer or whatever. So, yes, that might get affected initially, but that's not because GDPR is there or not. That's just because you're changing your program software. You're changing the fundamental way by which you're sourcing the data, the way they are coming from and which data can you use. But once you have tags against each of those attributes, once you have access controls, once you know exactly which customer attributes you can touch and you cannot for the purposes, do you have consent or not, your life's even better. The AI tools or the machine learning algorithms will learn from themselves. >> Right, so essentially, once you have a tight ship in terms of managing your data in line with the GDPR strictures and so forth, it sounds like what you're saying is that it gives you as an enterprise the confidence and assurance that if you want to use that data and need to use that data, you know exactly how you've the processes in place to gain the necessary consents from customers. So there won't be any nasty surprises later on of customers complaining because you've got legal procedures for getting the consent and that's great. You know, one of the things, Abhas, we're hearing right now in terms of compliance requirements that are coming along, maybe not apart of GDPR directly yet, but related to it is the whole notion of algorithmic transparency. As you build machine learning models and these machine learning models are driven into working applications, being able to transparently identify if those models make, in particular, let's say autonomous action based on particular data and particular variables, and then there is some nasty consequences like crashing an autonomous vehicle, the ability, they call it explicably AI to roll that back and determine who's liable for that event. Does Hortonworks have any capability within your portfolio to enable more transparency into the algorithmic underpinnings of a given decision? Is that something that you enable in your solutions or that your partner IBM enables through DSX and so forth? Give us a sense whether that's a capability currently that you guys offer and whether that's something in terms of your understand, are customers asking for that yet or is that too futuristic? >> So I would say that it's a two-part question. >> James: Yeah. >> The first one, yes, there are multiple regulations coming in, like Vilica Financial Markets, there's Mid Fair, the BCBS, etc. and organizations have to comply. You've got the IFRS which span to brokers, the insurance, etc., etc. So, yes, a lot of organizations across industries are getting affected by compliance use cases. Where does Hortonworks come into the picture is to be able to be compliant from a data standpoint, A you need to be able to identify which of those data sources you need to implement a particular use case. B you need to get them to a certain point whereby you can do analytics on that And then there's the whole storage and processing and all of that. But also which you might have heard at the keynote today, from a cloud perspective, it's starting to get more and more complex because everyone's moving to the cloud which means, if you look at any large multi-national organization, most of them have a hybrid cloud structure because they work with two or three cloud vendors which makes the process even more complex because now you have multiple clusters, you have have on premise and you have multiple different IT systems who need to talk to each other. Which is where the Hortonworks data plan services come into the picture because it gives you a unified view of your global data assets. >> James: Yes. >> Think of it like a single pane of glass which whereby you can do security and governance across all data assets. So from those angles, yes, we definitely enable those use cases which will help with compliance. >> Making the case to the customer for a big data catalog along the lines of what you guys offer, in making the case, there's a lot of upfront data architectural work that needs to be done to get all you data assets into shape within the context of the catalog. How do they justify making that expense in terms of hiring the people, the data architects and so forth needed to put it all in shape. I mean, how long does it take before you can really stand up in your working data catalog in most companies? >> So again, you've asked two questions. First of all is how do they justify it? Which is where we say that the platform is a means to an end. It's enabling you to deliver use cases. So I look at it in terms of five key value drivers. Either it's a risk reduction or it's a cost reduction or it's a cost avoidance. >> James: Okay. >> Or it's a revenue optimization, or it's time to market. Against each one of these value drivers, or multiple of them or a combination of them, each of the use cases that you're delivering on the platform will lead you to benefits around that. My job, obviously, is to work with the customers and executes to understand what will that be to quantify the potential impact which will then form the basis and give my customer champions enough ammunition so that they can go back and justify those investments. >> James: Abhas, we're going to have to cut it short, but I'm going to let you finish your point here, but we have to end this segment so go ahead. >> That's fine. >> Okay, well, anyway, have had Abhas Ricky who is the director of strategy and innovation at Hortonworks. We're here at Dataworks Summit Berlin. And thank you very much Sorry to cut it short, but we have to move to the next guest. >> No worries, pleasure, thank you very much. >> Take care, have a good one. >> Thanks a lot, yes. (upbeat music)