(upbeat music) >> Hi, I'm Peter Burris and welcome to another CUBE Conversation from our wonderful studios in Palo Alto, California. Great conversation today, we got Christian Rodatus, who is the CEO of Datameer, here to talk about some of the trends within the overall analytic space. One of the most important things happening in technology today. Christian, welcome back to theCube! >> Good morning, Peter, thanks for having me today. >> It's great to have you here. Hey, let's start with, kind of some of the preliminaries. What's happening at Datameer? >> Well we've been around for nine years now, which is a lot of time in a very agile technology space. And I actually just came back from an Investiere offsite that was arranged from one of our biggest investors. And everything is centering around the cloud, right? We were trotting along within the Hadoop ecosystem, the big data ecosystem over the past couple years and since, 12, 15 months, the transition and the analytics market and how it's transforming from on premise to the cloud in a hybrid way as well has been stunning, right? And we're faced with a challenge in innovating in those spaces and making our product relevant for on premise deployment, for cloud deployments, and various different cloud platforms, and in a hybrid fashion as well. And we've been traditionally working with customers that have been laggards in terms of cloud adoption because we do a lot of business and financial services, and insurance, healthcare, telecommunications, but even in those industries over the past year, it has been stunning how they are accelerate cloud adoption, how they move analytic workloads to the cloud. >> Well, actually, they all sound like sometimes leaders in the analytics world, even if they're laggards in the cloud. And there's something of a relationship there. People didn't want to do a lot of their analytics because they were doing analytics in some of the most strategic, sensitive data, and they felt pressured to not give that off to a company that they felt perhaps, or an industry that's a little bit less ready from infrastructure standpoint. But our research shows pretty strongly that we're seeing a push to adoption, precisely because so much of that ecosystem got wrapped up in the infrastructure and never got to the possible value of analytics. So is that helping to force this along, do you think, the idea of-- >> Absolutely, right, if you look at the key drivers, and there was some other analyst research that I read this week. Why are people being moderated moving analytic workloads into the cloud? It's really less cost, it's really business agility. How do they become independent from IT and procure services across the organization in a very simple, easy, and fast fashion? And then there's a lot of fears associated with it. It's data governance, it's security, it's data privacy, is what these industries that we predominately work in are concerned with, right, and we provide a solution framework that actually helps them to transition those on premise analytic workloads into the cloud and still get the enterprise grade features that they're used to from an on premise solution deployment. >> Yeah, so in other words, a lot of businesses confuse failure to deal with big data infrastructure as failure to do big data. >> That's correct. >> I want to build on something you've just said, specifically the governance issue, because I think you're absolutely right. There's an enormous lack of understanding about what really constitutes data governance. It used to be, oh, data governance is what the data administrator does when they do modeling, and who gets to change the model, and who owns the model, and who gets to, all that other stuff. We're talking about something fundamentally different as we embed more deeply some of these analytics directly into high value business activities that are being utilized or performed by high cost business executives. >> Absolutely. >> How does data governance play out, and I'm going to ask you specifically, what are you guys doing that makes data governance more accessible, more manageable, within Datameer customers? >> So I think there's two key features to a solution that's important. So number one, we have very much a self-service aspect to it, so we're pushing abilities to model and create views on the big data assets that are persisting in the data lakes, towards a business user, right? But we do this in a very governed way, right? We can provide barefold data lineage, we can audit every single step, how the data's being sourced, how it's being manipulated on the way, and provide an audit trail, which is very important for many of the customers that we work with. And we really bring this into the hands of the business users without much IT interference. They don't have to work on models to be built and so on and so forth, and this is really what helps them build rapid analytic applications that provide a lot of value and benefits for their business processes. >> So you talked about how you're using governance, or the ability to provide a manageable governance regime, to open up the aperture on the utilization of some of these high value analytics frameworks by broader numbers of individuals within the organization. That seems to me to be a pretty significant challenge for a lot of businesses. It's not enough to just have a ivory tower group of data scientists be good at crafting data, understanding data, and then advising people what actions to take based on that data. It seems it has to be more broadly diffused within the organization, what do you think? >> So this is clearly the trend, and as these analytics services move to the cloud, you will see this even more so, right? You will have created data assets and you provide access control for certain using groups that can see and work with this data, but then you need to provide a solution framework that enables these customers to consume this in a very seamless and an easy way. This is basically what we are doing. We're going to push it down to the end user and give them the ability to work on complex analytical problems using our framework in a governed way, in a fast way, in a very iterative analytic workflow. A lot of our customers say they have analytic, or they pursue analytic problems that are of investigative nature, and you cannot do this if you rely on IT to build new new models to delay the process-- >> Or if you only rely on IT. >> And only rely on IT, right? They want to do this on their own and create their own views, depending on their analytic workflow in a very rapid, rapid way. And so we support this in a highly governed way that can do this in a very fast and rapid fashion, and as it moves to the cloud, it provides some of the even more opportunities to do so. >> So as CEO of Datameer, you're spending a lot time with customers. Are there some patterns that you're seeing customers, in addition to buy Datameer, but are there some patterns in addition to what you just described that the successful companies are utilizing to facilitate this fusion? Are they training people more? >> Yep. Are they embedding this more deeply into other types of applications or workflows? What are some of those patterns of success that you're seeing amongst your customers? >> So that's a very interesting question, right, because a lot of big data initiatives within companies fail for the lack of an option. So they build these big data lakes and ramp up cloud services, and they never really see adoption. And so the successful customers we work with, they have a couple of things they do differently than others. They have a centralized, serious type of organization, usually, that facilitates and promotes and educates people on number one, the data assets being available through the organization, about the tool sets that are being used, and amongst one of them, obviously, is Datameer within our customers, and they facilitate constant education and experience sharing across the organization for the user of big data assets throughout the organization. And these companies, they see adoption, right? And it spreads throughout the organization. It has increasing momentum and adoption across various business departments from many eye value use cases. >> So we've done a lot of research. I myself have spent a lot of time on questions of technology adoption, questions within the large enterprises. And you actually described it fails to adopt, and from adoption standpoint, it's called they abandon. >> Absolutely true. >> One of the things that often catalyzes whether or not someone continues to adopt, or a group determines to abandon, is a lack of understanding of what the returns are, what kind of returns these changes of behavior are initiating or instantiating. I've always been curious why a lot of these software tools don't do a good job of actually utilizing data about utilization, from a big data standpoint, to improve the adoption of big data. Are you seeing any effort made by companies to use Datameer to help businesses better adopt Datameer? >> Well, I haven't seen that yet. I see this more with our OEN customers. So we've got OEN customers that analyze the cloud consumption with their customers and provide analytics on users across the organization. I see these things, and from our standpoint, we facilitate this process by providing use case discovery workshops, so we have a services organization that helps our customers to see the light, literally, right, to understand what's the nature of the data assets available. How can they leverage for specific use case, high value use case, implementations, experience sharing, what other customers are doing, what kind of high value application are they going after in a specific industry, and things like this. We do lunch and learns with our customers. We just recently did one with a big healthcare provider and the interest is definitely there. You get 200 people in a room for a lunch and learn meeting, and everyone's interesting, how they can make their life easier and make better business decisions based on the data assets that are available throughout the organization. >> That's amazing, when a lunch and learn meeting goes from 20 people to 200 people, it really becomes much more focused on learn. One of the questions I have related to this is that you've got a lot of experience in the analytics space, more than big data, and how the overall analytics space has evolved over the years. We have some research, pretty strong to suggest that it's time to start thinking about big data not as a thing unto itself, but as part of an aggregate approach to how enterprises should think about analytics. What do you think? How do you think an enterprise should start to refashion its understanding of the role that big data plays in a broader understanding of analytics? >> Back in the earlier days, when my career come from the EDW road, and then all the large enterprises had EDWs and they tried to build a centralized repository of data assets-- >> Highly modeled. >> Highly modeled, a lot of work to set up, structured, highly modeled, extreme complex to modify and service a new application regressed from business users, and then came the Hadoop data lake base, big data approach there. It said dump the data in, and this is where we were a part, within where we became very successful in providing a tool framework that allows customers to build virtue of use into these data assets in a very rapid fashion, driven by the business user community. But to some extent, these data lakes have also had issues in servicing the bread and butter BI user community throughout the organization, and the EDW never really went away, right, so now we have EDWs, we have data lakes that service different analytic application requirements throughout the organization. >> And new reporting systems. >> And even reporting systems. And now the third wave is coming by moving workloads into the cloud, and if you look into the cloud, the wealth of available solutions to a customer becomes even more complex, as cloud vendors themselves build out tons of different solutions to service different analytical needs. The marketplaces offer hundreds of solutions of third party vendors, and the customers try to figure out how all these things can be stitched together and provide the right services for the right business user communities throughout the organization. So what we see moving forward will be a hybrid approach that will retain some of the on premise EDW and data lake services, and those will be combined with multi-cloud services. So there always will not be a single cloud service, and we're already seeing this today. One of our customers is Sprint Pinsight, the advertising business of the Sprint. Telecommunications companies say they have a massive Hadoop on premise data lake, and then they do all the preprocessing of the ATS data from their network, with Datameer on premise, and we condensed down the data assets from a daily volume of 70 terabytes to eight, and this gets exposed to a secret cloud base dataware service for BI consumption throughout the organization. So you see these hybrid, very agile services emerging throughout our customer base, and I believe this will be the future-- >> Yeah, one of the things we like about the concept, or the approach of virtual view, is precisely that. It focuses in on the value that the data's creating, and not the underlying implementation, so that you have greater flexibility about whether you treat it as a big data approach, or EDW approach, or whether you put it here, or whether you put it there. But by focusing on the outcome that gets delivered, it allows a lot of flexibility in the implementation you employed. >> Absolutely, I agree. >> Phenomenal, Christian Rodatus, CEO of Datameer, thanks again for being on theCUBE! >> Thanks so much. I appreciate it, thanks, peter. >> We'll be back.