(upbeat music) >> Hi and welcome to another Cube Conversation, where we go in depth with the thought leaders in the industry that are making significant changes to how we conduct digital business, and the likelihood of success with digital business transformations. I'm Peter Burris. Every organization today has some experience with the power of analytics, but they're also learning that the value of their analytic systems are, in part, constrained and determined by their access to core information. Some of the most important information that any business can start to utilize within their new advanced analytic systems, quite frankly, is that operational business information that the business has been using to run the business on for years. Now, we've looked at that as silos, and maybe it is, although partly that's in response to the need to have good policy, good governance, and good certainty and predictably in how the system behaves, and how secure it's going to be. So, the question is, how do we marry the new world of advanced analytics with the older, but, nonetheless, extremely valuable world of operational processing to create new types of value within digital business today? It's a great topic and we've got a great conversation. Tendü Yogurtçu is the CTO of Syncsort. Tendü, welcome back to theCube. >> Hi Peter, it's great to be back in theCube. >> Excellent. So, look, let's start with a quick update on Syncsort. How are you doing? What's going on? >> Oh, it's been really a exciting time at Syncsort. We have seen tremendous growth in the last three years. We quadrupled our revenue and also number of employees, tripled organic innovation and growth, as well as true acquisitions. So, we now have 7,000 plus customers in over 100 countries, and we still have the 84 of Fortune 100 serving large enterprises. It's been a really great journey. >> Well, so let's get into the specific distinction that you guys have. At Wikibon theCube, we've observed, we predicted that 1919, 2019, rather, 2019 was going to be the year that the enterprise asserted itself in the cloud. We had seen a lot of developers drive cloud forward, we've seen a lot of analytics drive cloud forward, but now as enterprises are entering into cloud in a big way, they're generating or bringing with them new types of challenges and issues that have to be addressed. So, when you think about where we are in the journey to more advanced analytics, better operational certainty, greater use of information, what do you think the chief challenges that customers face today are? >> Of course, as you mentioned, that everybody, every organization is trying to take advantage of the data, data is the core, and take advantage of the digital transformation to enable them for taking, getting more value out of their data. And, in doing so, they are moving into cloud, into hybrid cloud architectures. We have seen early implementations starting with the data lake. Everybody started creating this centralized data hub enabling advanced analytics and creating a data marketplace for their internal or external clients. And the early data lakes were utilizing Hadoop on on-premise architectures, now we are also seeing data lakes sometimes expanding over hybrid or cloud architectures. The challenges that these organizations also started realizing is around once I create this data marketplace, the access to the data, critical customer data, critical product data-- >> Order data. >> Order data, is a bigger challenge that I told that it will be in the pilot project because these critical data sets and core data sets often in financial services, banking, and insurance, and healthcare are environments, data platforms that these companies have invested over multiple decades. And I'm not referring to that as legacy because definition of legacy changes, these environments, platforms have been holding these critical data assets for decades successfully. >> We call them high value traditional applications because the traditional we know what they do, there's a certain operational certainty, and we've built up, you know, the organization around them to take care of those assets, but they still are very, very high value. >> Exactly, and making those applications and data available for next generation, next wave platforms is becoming a challenge for couple of different reasons. One, accessing this data, and accessing this data making sure the policies and the security and the privacy around these data stores are preserved when the data is available for advanced analytics, whether it's in the cloud or on-premise deployments. >> So, before you go to the second one, I want to make sure I understand that because it seems very, very important, that what you're saying is, if I may, the data is not just the ones and the zeros in the file, the data really needs to start being thought of as the policies, the governance, the security, and all the other attributes and elements, the metadata, if you will, has to be preserved as the data is getting used. >> Absolutely, and there are challenges around that because now you have to have skillsets to understand the data in those different types of stores, relational data warehouses, Mainframe, IBM i, SQL, Oracle, many different data owners and different teams in the organization, and then, you have to make sense of it and preserve the policies around each of these data assets while bringing it to the new analytics environments. And make sure that everybody is aligned with the access to privacy and the policies and the governance around that data. And also, mapping the metadata to the target systems, right? That's a big challenge because somebody who understands these data sets in a Mainframe environment is not necessarily understanding the cloud data stores or the new data formats, so how do you kind of bridge that gap and map into the target environment? >> And vice versa, right? >> Likewise, yes. >> This is where Syncsort starts getting really interesting because, as you noted, a lot of the folks in the Mainframe world may not have the familiarity of how the cloud works, and a lot of the folks, at least from a data standpoint, and a lot of folks in the cloud that have been doing things with object stores and whatnot, may not, in Hadoop, may not have the knowledge of how the Mainframe works. And so those two sides are seeing silos, but the reality is both sides have set up policies and governance models and security regimes and everything else because it works for the workloads that are in place on each side. >> Absolutely. >> So Syncsort's an interesting company because you guys have experience of crossing that divide. >> Absolutely, and we see both the next wave and existing data platforms as a moving, evolving target because these challenges have existed twenty years ago, ten years ago, it's just the platforms were different, the volume, the variety, complex was different, however, Hadoop, five, ten years ago was the next wave, now it's the cloud, blockchain will be the next platform that we have to still kind of adapt and make sure that we are advancing our data and creating value out of data. So that's accessing and preserving those policies is one challenge. And then the second challenge is that as you are making these data sets available for analytics or mission learning, data science applications, you're duplicating, standardizing, cleansing, making sure that you can deliver trusted data becomes a big challenge because if you train the models with the bad data, if you create the models with the bad data you have bad model and then bad data inside. So, mission learning and artificial intelligence depends on the data and the quality of the data, so it's not just bringing all enterprise data for analytics, it's also making sure that the data is delivered in a trusted way. That's a big challenge. >> Yeah, let me build on that if I may, Tendü, because a lot of these tools involve black box belief in what the tool's performing. >> Correct. >> So you really don't have a lot of visibility in the inner workings of how the algorithm is doing things. It's, you know, that's the way it is. So, in many respects, you're only real visibility into the quality of the outcome of these tools is visibility into the quality of data that's going into the building of these models. Have I got that right? >> Correct. And in mission learning, the effect of bad data it really multiplies because of the training of the model, as well as the insights. And with blockchain, in the future, it will also become very critical because once you load the data into blockchain platform, it's immutable. So, data quality comes at a higher price in some sense. So that's another big challenge. >> Which is to say that if you load bad data into a blockchain, it's bad forever. >> Yes, that's very true. So that's obviously another area that Syncsort, as we are accessing all of the enterprise data, delivering high quality data, discovering and understanding the data, and delivering the duplicated, standardized, enriched data to the mission learning and AI pipeline and analytics pipeline is an area that we are focused with our products. And the third challenge is that as you are doing it, the speed starts mattering because, okay, I created the data lake or the data hub, the next big use case we started seeing is that oh yeah, but I have twenty terabyte data, only ten percent is changing on a nightly basis, so how do I keep my data lake in sync? Not only that, I want to keep my data lake in sync, I also would like to feed that changed data and keep my downstream applications in sync. I want to feed the changed data to the micro services in the cloud. That speed of delivery started really becoming very critical requirement for the businesses. >> Speed and the targeting of the delivery. >> Speed of the targeting grid, exactly. Because I think the bottom line is you really want to create an architecture that you can be agnostic and also be able to deliver at the speed the business is going to require at different times. Sometimes it's near real time in a batch, sometimes it's real time and you have to feed the changes as quickly as possible to the consumer applications and the micro services in the cloud. >> Well, we've got a lot of CIOs who are starting to ask us questions about, especially as they start thinking about Kubernetes and Istio and other types of platforms that are intended to facilitate the orchestration and ultimately the management of how these container-based applications work. And we're starting to talk more about the idea of data assurance. Make sure the data is good, make sure it's high quality, make sure it's being taken care of, but also make sure that it's targeted where it needs to be, because you don't want a situation where you spin up a new cluster, which you could do very quickly with Kubernetes, but you haven't made the data available to that Kubernetes based application so that they can actually run. And a lot of CIOs and a lot of application development leaders and a lot of business people are now starting to think about that. How do I make sure the data is where it needs to be so that the applications run when they need to run? >> That's a great point, and going back to your kind of comment around the cloud and taking advantage of cloud architectures, one of the things we have observed is organizations looking at cloud in terms of scalable elasticity and reducing costs, dated lift and shift of applications, and not all applications can be taking advantage of cloud elasticity when you do that. Most of these applications are created for the existing on premise fixed architectures, so they are not designed to take advantage of that. And we are seeing a shift now, and the shift is around instead of trying to kind of lift and shift the existing applications, one, for new applications, let me try to adopt the technology assets, like you mentioned Kubernetes, that I can stay vendor agnostic for cloud vendors, but, more importantly, let me try to have some best practices in organization that new applications can be created to take advantage of the elasticity, even though they may not be running in the cloud yet. So some organizations refer to this as cloud native, cloud first, some different terms. And make the data, because the core asset here is always the data, make the data available, instead of going after the applications, make the data from these existing on premise and different platforms available for cloud. We are definitely seeing that shift. >> Yeah, and make sure and assure that that data is high quality, carries the policies, carries the governance, doesn't break the security models, all those other things. >> That is a big difference between how actual organizations ran into their Hadoop data lake implementations versus the cloud architectures now, because when initial Hadoop data lake implementations happened, it was dump all the data. And then, oh, I have to deal with the data quality now. >> No, it was also, oh, those Mainframe people just would, they're so difficult to work with, meanwhile, you're still closing the books on a monthly basis, on a quarterly basis, you're not losing orders, your customers aren't calling you on the phone angry, and that, at the end of the day, is what business has to do. You have to be able to extend what you can currently do with a digital business approach, and if you can replace certain elements of it, okay. But you can't end up with less functionality as you move forward into the cloud. >> Absolutely, and it's not just Mainframe, it's IBM i, it's the Oracle, it's the teradata, it's the DTSA, it's growing rapidly in terms of the complexity of that data infrastructure. And for cloud, we are seeing now a lot of pilots are happening with the cloud data warehouses, and trying to see if the cloud data warehouses can accommodate some of these hybrid deployments, and also we are seeing there's more focus, not after the fact, but more focus on data quality from day one. How am I going to insure that I'm delivering trusted data and populating the cloud data stores, or delivering trusted data to micro services in the cloud. There is a greater focus for both governance and quality. >> So, high quality data movement that leads to high quality data delivery in ways that the business can be certain that whatever derivative of work is done, remains high quality. >> Absolutely. >> Tendü Yogurtçu, thank you very much for being once again on theCube, it's always great to have you here. >> Thank you, Peter, it's wonderful to be here. >> Tendü Yogurtçu is the CTO of Syncsort, and, once again, I want to thank you very much for participating in this cloud, or this Cube Conversation, cloud on the mind, this Cube Conversation. Until next time. (upbeat music)