 From London, England, extracting the signal from the noise. It's theCUBE, Cover, Discover 2015. Brought to you by Hewlett Packard Enterprise. Now your hosts, John Furrier and Dave Vellante. Okay, welcome back everyone. We are here live in London. This is Silicon Angles, theCUBE, our flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier, my co-host Dave Vellante. Our next guest is Martin. We show the SPP and General Manager, the Analyst and Data Management practice at Hewlett Packard Enterprise, HPE. That's the new HPE, go to hp.com. Martin, welcome to theCUBE. Thank you for having me here. Thanks for joining us. Thank you. So, very impressive to hear all the transformations. You are one of the, what they call transformation leads. Which means the lead executive around the key stakeholder area around empowering your enterprise with data-driven solutions. Organization, yes. I should know that better. I should have that right. But essentially, that's a big pillar. There's a lot of stuff underneath it. As well as leading the customer solution-oriented stuff around bringing all the HPE together. Together. Telling the story versus, not product speeds and feeds, but solutions. Absolutely. Can you share with us some of the high-level market forces that are driving this? Because is it just similar that they want simplification? Because there's one simplified story. Simplified delivery. Or tell us the main forces. So, you have heard about the concept of the idea economy, right? And why we have the four transformation themes. And at the end of the day, it's about disrupting or being disrupted. And data plays an essential role. I mean, you can see this with the startup, the unicorns that have data at the core, right? And the traditional companies have to think about how they're utilizing those assets. If you think about 90% of the data has been generated in the last, you know, two years, and there is no ending of this exponential growth. And we call this the data-driven organization. And you could say, hey Martin, this is not a new concept, right? We talked about the data-driven organization 20 years ago, right? We started and we have elected deliberately to call it the data-driven organization because what we could do 20 years ago or two years ago with the technology was something different than what we can do today. So think about where the exponential growth of data is. That's in the machine data, it's in the human data, next to the business data. And mining and harnessing all of this data to say, how can you disrupt the business? That's the name of the game of the idea economy. And we call it, go quickly from idea to insight to outcomes. And let me touch on this for a minute. Somehow, you know, organizations are not shy of ideas. But to say, how do I get to insight and prove whether there is a value in this idea and whether it's a good idea or a bad idea, right? That's the name of the game to go really, really fast from the idea to the insight. And this is something big companies have to build the muscle on, right? Because data-driven organization and startups having this at their core are pretty good in doing this. Bigger companies- Well, it takes away the fear of execution failure. Absolutely. And that's the big problem that's always been, well, slow down, great idea, but, you know. Exactly, and I'm a German. You know, failure normally in our DNA is not an option, but actually it is. If you go to Silicon Valley, this is how you build businesses and failing fast is something good if you have not invested a ton of money in order to say something good. So go quickly from idea to insight and say, is that a good idea or is it not a good idea? But then also go from insight to operation. It's not about empowering the 10 data scientists in a Fortune 50 company. What are you gaining if you're really empowering those people? But empowering, you know, the customer care person, the clerk in a shop making better decision, interacting better with your customers, right? This is how you operationalize the inside. So that's interesting, to be agile and iterating is kind of the failure, kind of catchphrase. And if I may, I love the data-driven organization because the big flip is, years ago, it was data is this mess that I have to manage and now it's an opportunity, so. Yeah, so agile and iterating, is you're iterating through failure, but it's experiments to a pretext of operational scale. Absolutely. So this is a nuance here, right? So failure at scale is not an option. Yeah, exactly. Failure in an experimental, how do you take 10 data scientists, learn machine learning, roll out. What could you do and then bring? Can you explain the difference there? Because that's the nuance that customers are now buying into. Yes. They're saying, okay, as long as I'm in Sandbox or Agile DevOps. Yes. But then scaling it to operational capacity. Yes. Talk about the difference between the two. So the difference is really, I mean, if you think about scaling, it's all about zero downtime, right? It needs to work 24 by seven and so on. Testing is a total different DNA. So you're absolutely right, you need to split posts to say there is a selected group of people and you need to bring them together from all sorts and forms from the organization. So it's not only the CIO, it's not only the line of business. And I give you examples, like I'm a German so I love automotive. So if you and I, we bring our car to a dealership, we explain something, right? I hear a strange noise in the car on the right-hand side. This is valuable information, right? And how do you mine this kind of information feed it back to the corporation to say, is that a systematic error of cars and how do I handle those? So from the first testing, is that a good idea to mine service records and how can I do this with technology like linguistic analytics and then scale it across so that every dealer and the corporation can detect failures faster? Those are the two kinds of always. So I got to ask you the other hard question which always comes up over the years. You're talking about innovation. Yes. DevOps, Agile, new mindset, the idea economy. You know, when I first heard Meg's speech and the around it was the Uber example, the Airbnb. You could be the next Google or Facebook to sell the dream, right? But that dream is really about how you can create new disruptions, create value. Yes. Great. But now when you bring it all together, you say, okay, the reality is I have compliance issues. So organic growth feels good, entrepreneurial, entrepreneurship as they call it. But now the reality is I got to have some compliance. I'm at big enterprise. I'm operating in Germany. Not in Germany. I got Ireland, I'm in the EU. I got India. I got King, Doc Frank. And so on. Just sovereignty issues, just compliance. So data management, which is your wheelhouse. How do you balance the two? Cause one is could be like top down. No, wait a minute restrictions. One's empowering. So how do you meet in the middle? What is enterprise's best practice for moving fast without breaking anything? Yes. You know what I'm saying? So what we have seen working at our customers and we help our customers is to create those sandbox environments. And for example, governance people as a best practice are good. I mean, if they want to drive the business and they are business minded, they will help you to say, where is the no go? And what do we need to implement versus what we can do? And for example, privacy law do differ vastly across certain regions and even countries. You talked about Germany. So the best practice is really to bring them all at a table in this fail fast approach to say, what can you do and what should you not do? And you're absolutely right. There is even the second element of bringing more and more data to the people. If you think about the millennials, my 24 year old cousin does not have any sympathy that he's not coming into a large enterprise and he doesn't have business data available at his fingertips, right? I mean, he's used of search and the internet and it's so easy and we need to implement exactly the same in organizations which then come with the governance start restriction, just having data lakes and critical information. What are you going to do if everybody has access to financial information one day prior of your earnings release, right? And so those kinds of things do not work. So best practice is to have the governance and the privacy people at the table being business minded to say, what can we do and what can we not do? And then implement the rules around it. So in your practice Martin, are you being brought in to help organizations become data-driven or are you helping data-driven organizations go one step further? Talk about that a little bit. Both, because you see the maturity and the appetite of organizations are vastly different. So there are organizations who have a clear call to action and you have a CEO mandate to really go fast and quick and then there are companies who are doing this more sensible. So we're seeing both and actually, it's not only my organization, but HP overall. And you will see that we're working with companies like BlaBlaCar, you will hear about this tomorrow. I don't want to steal the thunder, which is a true startup, right? To Uber, which is a unicorn, right? To Facebook, which is one of the most admired companies and our Fortune 50 companies. And they're all in various states and have different types of priorities, how fast they want to go. And if we're talking about operationalizing analytics and data, you need to also change how you do business. If you think about big companies that are not necessarily data companies like Facebook, they have manufacturing and so on, they have existing processes, right? And we've built applications around business processes for efficiency. Data is not necessarily the same concept. Data should not necessarily follow an existing business process. So you want to create a different environment, again, where people can test those ideas, what you can do differently, but then bring it back to the business process of how do you extract those kinds of information? So that's the name of the game. And this is what you're talking about before. You don't want to just enable a few data scientists. To me, that's decision support all over again. You've got a couple of analysts who have all the power. But they can't diffuse it throughout the organization. So operationalizing analytics is critical. So I wonder if you could talk about maybe how you're doing that, maybe even some examples. I mean, you've got all these data sources coming into my cloud or my data lake or whatever it is. I've got a data quality problem. I've got a transfer, ETL issue. Or however direction you want to go there. And then I want to embed those analytics into the citizens of the company. So that's sort of the nirvana. Is it happening today? Maybe some examples. How are you actually achieving that? So if we have a customer discussion, we have identified three areas how we help our customers the best. One is discover the value of your data. The second is build a data-centric foundation. And the last one is superior business outcome of a horizontal or specific vertical problem. It's more like a pre-packaged solution. I talked in the beginning a little bit about discover the value of your data, right? We bring technology together to our customers. You know, our purpose-built infrastructure. For example, if you want to do streaming analytics, we bring high-performance compute to you. We can do this in the cloud, in a private cloud. But some of our customers are saying they want to have it on-premise and then we ship everything on-premise. The first part is discover the value of your data, this fail fast and test very fast approach. Because again, companies need to build this muscle to say, I just want a test, but I'm not investing 100 million any longer in terms of testing an idea, right? So we bring everything to the customer in terms of how to do this or help our customers to do this repeatedly by themselves. We're building up data labs, digital labs and so on with our customers. So the three things were discover the value of the data. The second one was? Build your data-centric foundation. So here it's about the right infrastructure and compute power for your workloads. Because not every workload is equal, right? You go from low storage to high-performance compute and streaming analytics. Also our customers have existing legacy environments. If you're an SAP customer, you go for sure on SAP HANA, right? If you're not an SAP customer, you might select a total different environment. So how to help our customers to build the right infrastructure to not follow necessarily the business process but rather the data, the fluid data to say where is your hot and your cold data? Where should it reside? Because very often customers are also struggling with putting the workloads in the environments they have already in the DB2 or wherever. And that is not necessarily the right environment. So help our customers to build the right foundation to manage data. And the third one was? You know, superior business outcomes on pre-packaged solutions because there is a set of horizontal and vertical solutions that the entire industry are struggling with. And obviously we put them together. And not only we as HPE but also with our partners. For example, with FICO to do stress testing for the financial industry where we bring all of the compute power in order to have algorithms being run at the data and not move petabytes of data somewhere else. And FICO was all of their intelligence of being in the financial industry and doing stress testing for years. And you combine this all of a sudden financial industries can do stress testing in minutes rather than in days which is obviously extremely important to test new products before you bring them to market. I think you're really right on the money. Those three things are very powerful and very relevant. So, you know, I would give you a props on that. A little, a lot of stuff going on in those three things I just want to unpack that you mentioned it. Which is really important. I think you're right on the money. It's really critical. Pre-packaged applications. That's the apps that people want, the built-in security. That's what Sue was talking about. Also, there's also domain expertise within the app itself, right? But then the horizontal piece you mentioned on the foundation thing is important because you don't want data silos. It's the killer, it's the toxic problem. Silo data. It's an old way to do it. I mean, you got to have some data movement across in some sort of horizontally way. So, is that really the core thing? I mean, talk about what you're seeing in that area because this is a really interesting thing. They're not usually exclusive, right? I mean, you can mix and match that in any different ways. And again, it depends on the appetite of the customer. There are customers who want to set themselves up for the future in a different way. So, they start thinking about the foundation in a different way than a customer who wants to go in smaller pieces. And, but we help them to say, if you want to transform your business at a certain point of time, you need to do things differently. And how does that match to a step that you go in the future? So, on the foundation, to say, how do you have this fluid data level and foundation that does not necessarily follow your application and your business processes, but not every customer has immediately the appetite in order to go there. So, we help our customers to build a roadmap to say, keep that in mind. But very often, customers still want to do baby steps first, because you don't necessarily have the appetite. You don't want to over rotate and go ahead of your skis, if you will. And you need successes. So, very often, we help our customers to show the first successes. Here's a successful project where we were able to go from an idea to insight to operationalizing and then the company is getting more appetite to do this repeatedly. Because, you know, I mean, on the application level, you don't go immediately and say, we have a total agility everywhere in our application. And it goes hand in hand, obviously, with the cloud efforts, right? I mean, this is why you're going into the cloud. And that's the problem with the cloud. They get ahead of themselves. They try to bite off more than they can chew. Yes, absolutely. So, in your experience, however we want to define it subjectively, what percent of customers that you talk to visit, prospects, customers are data-driven, truly data-driven? Truly data-driven are, at this point of time, the data companies, honestly. And this is a little bit like the North Star for the other companies to say, if you are taking data very seriously, this is the way how you connect. Now, those data companies, most of the times have the luxury that they don't have physical supply chain in the back, right? They don't have the legacy baggage and so on, right? On the other side. The cloud-driven companies like Airbnb, Uber, you know. Exactly. But on the other side, you know, more traditional companies have the advantage of scale, the customer intimacy, right? You have, about me, I mean, I'm driving a certain auto brand for years. They should know a lot about me and about my preferences and so on. So, you can build this differently. So, come back to your question. Truly data-driven are the data-driven organization. If you were to ask my gut feeling, I would say there are 20% of the company really moving with a brutal force in terms of we need to do something different. Other companies are all testing, right? One of the questions that I have and ask and start a discussion like this is where do you test your ideas? And then you get very fast into a discussion whether companies are taking it very serious to test the new ideas, right? From idea to insight. If you don't have anything institutionalized, I think, you know, those companies need some help in order to get those kinds of ideas. That's how I ask. Final question. What's the show like here? Give some, share some insight into the, we all know it's the first day so we could ask you what the bumper sticker is. But it's not over yet. But what's the early view? I mean, you've been here in the ground planning with the other execs and the management team at HB. What's the main story from your standpoint here at the show? Share with the folks that are watching the event here. So I think you can see the transformation areas come to life. And I know you were in Las Vegas where we announced the transformation areas. And you know, I think here we are now really showing we're a Hewlett Packard enterprise and we take it very serious to help our customers in the idea economy to go in this direction. And it takes all the four pillars, right? I mean, we thought about it. And it's not like, you know, the cloud security, mobility and big data, how we called it in the past would not work together. And I think here you can start seeing how this all comes together to help our customers. Martin Riesau, senior vice president and channel manager. Thank you for all the data and the insights here on theCUBE. Superior outcome interview segment here. Thank you very much for sharing the great insights here on being data-driven here at HP Discover London. This is theCUBE. I'm John Furrier with Dave Vellante. We'll be right back after this short break. Thank you very much.