 from Berlin, Germany. It's theCUBE, covering DataWorks Summit Europe 2018. Brought to you by Hortonworks. Welcome to theCUBE. We're here at DataWorks Summit 2018 in Berlin. I'm James Kobielus. I'm the lead analyst for Big Data Analytics on the Wikibon team of SiliconANGLE Media. We are on theCUBE, 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 I have, from Hortonworks, I have Abbas Ricky, who's 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 Abbas, how do you justify the value of compliance with GDPR? The value, I guess the value would be avoid penalties for non-compliance, right? Can you do it as an upside as well? Or is there an upside in terms of if you make an investment, you probably will need to make an investment to comply? Can you turn this around as a strategic asset possible for the investment? So I'll take a step back first. The point which you meant- Like a Big Data Catalog and so forth. Yeah, so if you look at the value part which he said, it's interesting that you mention 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. Similarly Gardner says that if you look at the various squadrons and if you look at various issues, the fundamental thing which executives struggle with is identifying the value which they will get. So that is where I pitch in and that is where I come in on a day-to-day 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 with GDPR should be treated as a business opportunity because of the fact that- An opportunity. Business opportunity. It shouldn't be necessarily seen as a compliance burden on cost 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 write to content and write to portability, et cetera is also privacy by design which says that you have to be proactive in your 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 a personally identifiable information is for any particular customer and can you use that to affect 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 dominant 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 your GDPR is concerned the customer has a right to withhold consent for you an enterprise that holds that data to use that personal data of theirs which they own for various and sundry reasons. Many enterprises, many 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 there going to be a downer or a chilling effect on their investments in AI and so forth? So there are two elements around it. The first thing which you said that our customers do is machine learning and AI, yes they are. But broadly speaking, before you're able to do machine learning and AI you need to get your data sets onto your particular platform in a particular fashion, clean data. Otherwise you can't do AI or machine learning on top of it. 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 that, 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 it'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 on how the data and the data sets have been performing. So obviously once you've created those structures in place, initially, yes, you'll have to make an investment to alter your programs of work. However, going forward, it'll be even better because guess what, you've just cleaned your entire data supply chain. So that's how I would see that, yes, a lot of companies, e-commerce, you do targeting and retargeting based on the customer DNA, based on the shopping profiles, based on the shopping habits. And then based on that, you give them the next best offer or whatever. So yes, that might get affected initially but that's not because TDPR is there or not. That's just because you're changing your programs of work, you're changing the fundamental way by which you're sourcing the data and where they're 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 having you have content or not, your life's even better. The AI tools or the algorithms, machine learning algorithms will learn from themselves. 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, need to use that data, you know exactly how you've got the processes in place to gain the necessary consents from customers. So there won't be any nasty surprises later on of customers complain because you've got procedures, legal procedures for getting the consent, that's great. You know, one of the things Abbas we're hearing right now in terms of compliance requirements that are coming along, maybe that part 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 a particular, say autonomous action based on particular data and particular variables. And then there's some nasty consequences like crashing an autonomous vehicle. The ability, they call it explicable 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 for whether that's a capability currently that you guys offer and whether that's something that in terms of your understanding that our customers asking for that yet or is that too futuristic? So I would say that it's a two-part question. The first one, yeah, yes, there are multiple regulations coming in. Like if you look at financial markets, it's MIFI, the BCBS, et cetera, and organizations have to comply. You've got the IFRS, which spans across sales codes, insurance, et cetera, et cetera. 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 are 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 them. And then there's the whole storage and processing all of that. But also, which you might have heard at the keynote today, from a cloud's 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 multinational organization, most of them have a hybrid cloud strategy because they work with two or three cloud vendors, which makes the process even more complex because now you have multiple clusters, you 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. Think of it like a single pane of glass, which whereby you can do security and governance across all your 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. Isn't it, you know, making the case, there's a lot of upfront data architectural work that needs to be done to get all your data assets in shape within the context of the catalog. How do they justify making that expense in terms of hiring the people who 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 a 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, 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 executives to understand what will that be to quantify the potential impact, and which will then form the basis and give my customer champions enough ammunition so that they can go back and justify those investments. Now, Abbas, we're going to have to cut it short, but I'll let you finish your point here, but we have to end the segment, so go ahead. That's fine. So okay, well anyway, we haven't had Abbas Ricky, who's the Director of Strategy and Innovation Board, works. We're here at DataWorks Summit, Berlin. And thank you very much. I'm going 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.