 From Orlando, Florida, it's theCUBE. Covering SAP Sapphire Now 2018, brought to you by NetApp. Welcome to theCUBE, I'm Lisa Martin with Keith Townsend. In Orlando, at SAP Sapphire Now 2018, we're in the NetApp booth and we are having some great conversations, really understanding how SAP and their ecosystem of partners have really helped transform 390,000 plus customers. We're joined next by Sue Waite, who is one of the directors of the Global Center of Excellence for Database and Data Management at SAP. Sue, thanks for coming to theCUBE. Thank you very much for letting me join you. So SAP, 46 years young company, like I said, 390,000 customers and 25 plus industries. You guys have probably many centers of excellence. Give us a little bit of understanding of the COE for Database and Data Management. I'm happy to do so. So the team that I'm very, it's my pleasure to be a part of, focuses on helping our customers understand what are the new opportunities that are out there. Many customers are so driven by the day to day operations. How do we take that opportunity to do step back and look at what perhaps other competitors have done in their space or in completely different industries and what are new ways that they could be looking at approaching their business, approaching their engagements with their customers and helping them grow as well. And our Database and Data Management solutions are the platform that helps enable that in a truly comprehensive data management way. It sounds pretty symbiotic. Where they're actually, you're helping them, but customers are also helping you. Tell us maybe some examples like Data Hub, for example, of one of the things that maybe that symbiotic relationship helped to evolve. Yes, yes. So a little history in our Database and Data Management solutions, of course SAP HANA is a cornerstone to our core platforms. Very much a groundbreaking technology eight years ago in introducing a completely comprehensive platform. But one of the things we've learned as we've worked with our customers over time, is so many clients and in fact SAP itself has our different pockets of enterprise systems. We have our CRM applications, our ERP, our finance, our, you know, supply chain. But in today's environments, we have so much more information coming at us. There's the whole big data space. Everybody's trying to pull and collect information from of course social media feeds. That's the one everybody thinks of. Another new space is internet of things. Collecting information from sensors off their machines, from telemetry from where their trucks are, to facial recognition as people are coming into our stores, or image recognition as we're manufacturing sheet metal on the plant floor. It's amazing the amount of information that is now available to be collected and mined to bring further insight into business operations. So great, we can collect all of that fabulous new data and store it in Hadoop, or Amazon S3, or object stores. But how do we get at that information? Right, and extract valuable insights that they can then use to generate new products, new revenue streams, new businesses. Completely so. Yeah, a simple example is, well, not an example, but S4 and HANA, the journey to S4 and HANA starts mostly with BW. So with the original data warehouse and the capability that that brings to organizations, one of the first things that happens when you deploy BW on HANA is other businesses look up, other business units look up and say, hey, I want that capability, I want that instant analytics, that instant search. Yes. Talk to the evolution of that. After we go BW and the focus is still on analytics and data intelligence. And it should be, you know, it is about making important decisions in an instant now. I mean, everybody looks at their phone when we make a deposit. We expect to see that deposit instantaneously. The business needs to operate just as instantaneously. And with BW, it has a tremendously powerful system that works hand in hand, as you said, with S4, ERP and the whole business suite itself. But then the goal was as well to bring in this larger context from these other large data environments that are being captured in Hadoop or S3. So the genesis of the idea to help address that marrying up of data stored in our classic enterprise data warehouse, like BW, is the solution that we call Data Hub. And what Data Hub does, what's different about it is it truly is an umbrella solution that transcends the big data environment as well as the classic enterprise systems. And in doing so, one of the first problems was we have all this fabulous information collected in our data lake. How do we get to the information that's truly useful to combine with information in BW or even feed into S4 itself? So Data Hub helps pre-process, refine and enrich that information. And the key is doing so where the data lives. Let's not move petabytes of data around, just trying to derive intelligence from it. So Data Hub allows customers to pre-process, refine and enrich that data in their data lake itself. Get from petabytes of information to say gigabytes of data that is useful to combine with information in BW or within HANA or S4 or whatever other systems may be useful to bring that together. And the trick with all of that is having visibility into the information that truly lives within each of those systems, which is also something that Data Hub brings to the table because it has the ability to collect metadata. So information about the data that lives within each of those environments, so the data analysts who are bringing those data sets together can intelligently know, this is the data set I want, this is how I need to refine it and I want to combine it here and they can set that up through pipelines and orchestration within Data Hub. It is tremendously powerful in simplifying that end-to-end scenario and the whole goal is to make it easier for the business to get to those useful insights, really help me have a competitive differentiator because of the great set of information I can now bring together and bubble that up through our analytics tools. Yeah, access at speed, that was one of the things Husted Flatter talked about this morning is everything has to be real-time. We expect it as consumers, right? Yes. And then as consumers who are also business people, which many are, you also expect that. One of the things too that you reminded me of that Bill and McDermott talked about yesterday was customers in every industry need a 360 of their customers, right? But SAP is moving, it's away from the 360 just sales automation to really having a true, enabling a true 360 of the entire customer experience. And one of the things I liked yesterday was in a not creepy way, but we expect that and customers have to connect. If you can connect finance and procurement and supply chain and marketing and sales and extract those really valuable insights faster than your competition, that's what today's digital businesses need. One of the simplest statements I've heard that I think is so powerful is, understand more about your customers so that you can do more for your customers. That's what it's all about, truly providing that end service to help them achieve their goals and move to that. So let's talk about some of the, from a high level, some of the technology to make this capable. When you're talking about petabytes and petabytes of data, you can't move all the data. Different systems have different capabilities when it comes to data transformation. I love the insight that you provided that data analysts need to be aware of the metadata so that they can set up the transformations needed to get the reporting that they need. How does data hub enable the power of metadata to all these different systems, whether it's Hadoop, unstructured data, systems that we don't even control such as social media data. How does data hub bring all that metadata together? So one of the capabilities that enables that visibility into data content is due, what we call a data discovery mechanism and data hub includes metadata crawlers. So literally any time a data hub has a system that it's been authorized to connect to, we can then go out and collect the metadata about the information, the data itself that lives within those environments. And so it comes back and there's a repository within data hub that holds information about the tables, the column names and then things like data types as well as even basic profiling information such as minimum, maximum, how often value is showing up, cardinality, even the frequency of different values that are there down to the ability to even preview, literally look at the content within the tables. And so that's so powerful for the data analysts because they no longer have to go, literally crack open a file to look at the content, it's at their fingertips. And that's just an amazing tool that once they have that, then they can move on to the truly value added activity of how they want to refine and rich, mash up that information to get to those insights that are at their fingertips. With so many, the C-suite, like we've talked about force changing so dramatically, the CDO, the CIO, the CMO, the CXO, they all have needs, different needs, the needs for this data. Your customer conversations, where do you start at the C-suite in terms of, they've got all of this data that they know there's golden nuggets in there, how do we find it? And also exploit insights for marketing, for sales, for finance, for procurement, what's your, where do you start in terms of that conversation within a customer? Unite the C-suite to understand how they can team together? That is always the goal, of course. And it's important to understand each customer's individual, what their business is, what their market is, as well as that company themselves, what their goals are, what they're trying to achieve so that we can truly be, I know you've heard the term trusted advisor, but we really take that seriously because understanding what their challenges are and where they're trying to grow their business, along with the very technical aspects of which technologies they're using today and what roadblocks are they experiencing that are preventing them from achieving those goals? Of course, our objective is to help them cross those roadblocks, cross those bridges and if we can help with SAP solutions to achieve those goals, it's not about rip and replace, it's helping them bridge those challenges to reach those goals, and that's the role we play and I love what I do. So the Data Hub is a great example of a platform that can be expanded upon. Can you share about some of the successes that you've had with the ecosystem around Data Hub to extend, not just the analysts who can interact with Data Hub directly, but what we like to call bolt-on applications that extend the overall capability of whether it's analytics, AI, machine learning, the examples or automation, business process automation, what are some of the successes coming out of making Data Hub? I know it's only a year old, but what are making the Data Hub available to your ecosystem of partners? Yep, so some of the successes have been truly efficiency, obviously, but in that ability to bring those datasets together, for example, we've been working with one customer who's, I'll just say they're a manufacturer, and they have their own team of data scientists and they have petabytes of information they've been collecting in their data lake and we talked with them about Data Hub and what we were seeing, and they're like, love this story, but our data science team is really good. I think we've got this. They literally came back to us six months later and said it's a whole lot more work than we ever expected it would be, because in a classic environment, it's a lot of hand-coding, it's a lot of scripting, it's creating those predictive models, which is the like blood, that's why we hire data scientists, but they were spending so much time in data manipulation and trying to find the right information. They're like, please, yeah. They can't, they're not going to come back to us. It really seems like a great opportunity for the overall market to start adding value on top of Data Hub to basically shorten that time frame for the internal data scientists. They should be figuring out what questions to ask versus figuring out how to organize the data. Exactly so, that's why they're being paid the big bucks, let them do the job that we hired them to do. Well, Sue, you said you love your job and it's evident. Thank you so much for stopping by theCUBE and sharing what you're doing within the COE for Database and Database Management. So it's a great pleasure to speak with you this morning. With Data Hub, we can't wait to hear what's next for next year. All right, excellent. We want to thank you for watching theCUBE. I'm Lisa Martin with Keith Townsend from SAP Sapphire Now 2018. Thanks for watching.