 Live from New York, it's theCUBE. Covering theCUBE, New York City, 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. Hey, welcome back everyone, this is theCUBE. Live in New York City for CUBE NYC, formerly Big Data NYC. Now it's turned from Big Data into much broader conversation, CUBE NYC is exploring all these surround data, data intelligence, cloud computing, DevOps, application developers, the whole range, all things data. I'm John Furrier here with Peter Burris, co-host analyst here in the session. Our next guest is Byron Banks, the Vice President of Product Marketing at SAP Analytics. No stranger to enterprise analytics. Welcome to theCUBE, thanks for joining us. Thank you for having us. So SAP is a brand doing business analytics for a long, long time, certainly powering the software for large enterprises, supply chain. You name it, you appeal. Everyone kind of knows the history of SAP. But you guys really have been involved in analytics. Hannah has been tailor made for some speed. We've been covering that. But now as the world turns into a cloud native, SAP has a global cloud platform that is multi-cloud driven. You guys kind of see this picture of a horizontally scalable computing environment. Analytics is a big, big piece of that. So what's going on with machine learning and AI and as analytical software and infrastructure need to be provisioned dynamically. This is an opportunity for people who love to get into the data. Absolutely. This is a great opportunity. What's the specific opportunity for us? We firmly believe that the era of optimization and digitization is over. It's not enough. It's certainly important. It has given a lot of benefits, but just overwhelming every user, every customer with more data, more optimization, faster data, better data, it's not enough. So we believe that the concept is switched to intelligence. So how do you make customers, how do you serve customers exactly what they need in the moment? How do you give them an offer that is relevant? Not spam them, give them a great offer. How do you motivate your employees to be the best at what they do, whether it's in HR or whether it's in sales? And we think technology is key to that. But at the end of the day, the customer, the organization is the driver. They are the driver, they know their business best. So what we want to do is be the pit crew, if you will, to use a racing analogy. If they're the driver of the race car, we want to bring the technology to them with some back practices and advice, because again we're SAP, we've been in the business for 45 years, so we have a very good perspective of what works based on the companies we see and serve over 300,000 of them. But it's really enabling them to be their best. And the customers that are doing the best, we call those intelligent enterprises. And that needs three components. It needs intelligent applications, what we call the intelligent suite. So how do we make an HR application that is great at retaining the best employees and also attracting great ones? How do we enable a sales system to be, give the best stoppers and do the best forecast? So all of that is the intelligent applications. The middle layer for that is called intelligent technologies. So how do we use these great technologies that we've been developing as an industry over the last three to five years? Things like big data, IOT, sensors, machine learning and analytics, that intelligent technology layer. How do we make that available? And then finally, it's the digital core, the digital platform for that. So how do we have this scalable platform, ideally in the cloud, that can pull data from both cloud sources, SAP sources, non-SAP sources, and give the right data to those applications and technologies in real time. I love the pit crew example of the race car on the track because you want to get as much data in the system as possible, because more data is more opportunities to understand and get insights. But at the end of the day, you want to make sure that the car not only runs well on the track and it's cost-effective, but it's performing. It actually wins the race or stays in the race. So customers want revenue. I mean, the big thing we're hearing is, okay, let's get some top-line benefit, not just good cost-effectiveness. So the objective of the customer and whatever that could be applications, it could be an insight into operational efficiency. The revenue piece of growth is a big part of the growth strategy for companies to have a data-centric system. This is part of the intelligence. But it's not just presenting the data. We introduced a product a couple of years ago and I promise this isn't going to be a marketing pitch, but I think it's very relevant to what you just said. So the SAP Analytics Cloud. That's one of those technologies I talked about, intelligent technologies. So it is modern, built from the ground for SaaS applications, cloud-based, built on the SAP Cloud Platform. And it has three major components. It has planning. So what are my KPIs? If I'm in HR, am I recruiting talent or am I retraining talent? What are my KPIs if I'm in sales? Am I trying to drive profitability or am I trying to track new customers? And if I'm in, again, in marketing, how effective are my campaigns? Tied to that is all the data visualization we can do so that we can mix and match data, discover new insights about our business, make it very, very easy, again, to connect to both SAP and non-SAP sources, and then provide the machine learning capabilities, all of that predictive capability. So now I'm not just looking at what happened in the past. I'm also looking at what's likely to happen in the next week. And the key point to all of that is when you open the application at start, the first thing it asks you is, what are you trying to do? What is the business problem you're trying to solve? It's a story, so it's designed from the get go to be very business outcome focused, not just show you 50 different data sources or 100 different data sources and then leave it to you to figure out what you should be doing. So it is designed to be very much a business outcome driven environment. So that, again, people like me, a marketer, can log on to that product and immediately start to work in campaigns and in the language that I want to work in, not in IT speak or Geek speak. Nothing wrong with Geek speak, but again. Yeah, I want to get into a conversation because one of the things we're very data driven as a media company, because we have data that's out there, consumption data, but some platforms don't have measurement capability. Like LinkedIn doesn't provide us any analytics. So there's data that's out there that I need, I want. That might be available down the road, but not today. So I want to get to that conversation around, okay, you can measure what you're looking at. So everything that's measurable, you got dashboards for, but there's some elusive gaps between what's available that could help the data model. These are future data sets or things that aren't yet instrumented properly. As new technology comes in with cloud native, the need for instrumentation is critical. How do you guys think about that from a product standpoint? Because customers aren't going to say, well, create a magic linkage between something that doesn't exist yet, but soon data will be existing. For instance, network effect or other things that might be important for people that aren't yet measurable, but might be in the future. They want to be set up for that. They don't want to foreclose that. Sure. Well, I think one of the balances we have as SAP, because we're a technology company, but we also, and we build a lot of great tools, but we also work a lot with our customers around business processes. So as I said, when we introduce our products, we don't want to give them just a black box, which is a bunch of feeds and speeds technologies that they need to figure it out. As we see patterns in our customers, we build an end-to-end process that is analytics driven, and we provide that back to our customers to give them a head start. But we have to have all of the capabilities in our solutions that allow them to build and extend in any way possible, because again, at the end of the day, they have a very unique business, but we want to give them a jumping off point so that they're not just staring at a blank screen. It's kind of like writing a speech. You don't want to start with just a blank screen. If you're in sales and marketing and you want to do a sales forecast, we will provide out of the box what we call embedded analytics, a fully complete dashboard that will take them through a guided workflow that says, hey, if you want to do a sales forecast, here's the data we think you want to pull. Do you want to pull that? Here's some additional inference we've seen from some of our machine learning algorithms based on what has happened in the last six weeks of selling and make a projection as to what we expect will happen. You give people started quickly. That's the whole goal. You give people started quickly. But we don't lock them in to only doing it the one way, the right way. We're not preaching. We want to give them the flexibility. But this is an important point because almost every decision at some point in time comes back to finance. Sure. So being able to extend your ability to learn something about data and act on data as measurements improve, you still want to be able to bring it back to what it means from a return standpoint. And that requires some agreement, not just some, a lot of agreement with the core financial system. And I think that this could be one of the big opportunities that you guys have is because knowing a lot about how the data works, where it is, sustaining that so that the transactional integrity remains the same, but you can reveal it through a lot of different analytic systems is a crucial element of this. Would you agree? I fully agree. And I think if you look at the analytics cloud that I talked about, the very first solution capability we built into it was planning. What are my KPIs that I'm trying to measure? Now, yes, of course, if you're in a business, it all turns into dollars or euros at the end of the day. But customer satisfaction, employee engagement, all of those things are incredibly important. So I do believe there is a way to put measurements, not always at a dollar value, that are important for what you're trying to do because it will ultimately translate into dollars down the road. All right, I want to get to the news. You guys have some hard news here in New York this week on your analytics and the stuff you're working on. What's the hard news? Absolutely, absolutely. So today we announced a bunch of updates to our analytics cloud platform. We've had it around for three or four years, thousands of customers, a lot of great innovation. And what we were doing today, what we announced today is the updates since our big annual conference in June this year. So we have built a number of machine learning capabilities that, again, speak in the language of the business user, give them the tools that allow them to quickly benefit from things like correlations, things like regressions, patterns we have seen in the data to guide them through a process where they can do forecasting, retainment, recruiting, maybe even looking for bias and unintended bias in things like campaigns or marketing campaigns. Give them a guided approach to that, speaking in their terms, using very natural language processing. So for example, we have things like smart insights where you can ask questions about give me the sales forecast for Japan. And you can say just type it that way and the analytic platform will start to construct and guide you through it and it will build all the queries it will give you, again, you're still in control, but it's a very guided process that says do you want to run a forecast? Here's how we recommend a forecast. Here's some variables we find very, very interesting that says, oh, in Japan, this product sold really well two quarters ago but it's not selling well this quarter. Maybe there's been a competitive action. Maybe we need to look at pricing. Maybe we need to retrain the sales organization. So it's giving them information, again, in a very guided business focus. And I think that's the key thing. Like data scientists, we love them. We want to use them in a lot of places but can't have data scientists involved in every single analytic that you're trying to do. They're just not enough. And I love the conversation because this exact conversation that goes down the road of DevOps, like conversation, automation, agility. These are themes that we're talking about at cloud platforms, not say data analytics. So it's now you bring data down. Hey, we're automating things. So it could look like a Siri or voice activated construct for interaction. And in their language, again, in the language that the Indian user wants to speak and it doesn't take the human out of it. It's actually making them better, right? We want to automate things and give recommendations so that you can automate things. A great example is like invoice matching. We have customers that use, spend hundreds of people, thousands of hours doing invoice matching because the address would line up or the purchaser had a transposed number in it. But using machine learning or using algorithms, we can automate all of that or go, hey, here's a pattern we see. Do you want us to automate this matching process for you? And customers that have implemented, they found 70% of the transactions could be automated. I think you're right on. I personally believe that humans are more valuable. Certainly in the media business that people think is sliding down, but humans, huge role. Now data and automation can surface and create value that humans can curate on top of. So the same with data. The human role is pretty critical in this because the synthesis is being helped by the computers, but the job's not going away. It's just shortcutting to the truth. And I think if you do it right, the actually machine learning can actually train the users on the job. I think about myself and I think about unintended bias, right? And you look at a resume that you put out or a job posting. If you use the term, I want somebody to lead a team, you will get a demographic profile of the people that apply to that job. If you use the term build a team, you'll get a different demographic profile. So I'm not saying one's better or the other, but me as a hiring manager, I'm not aware of that. I'm not totally on top of that. But if the tool is providing me information saying, hey, we've seen these keywords in your marketing campaign or in your recruiting or even in your customer support and the way you speak with your customers. And it's starting to see patterns just saying, hey, by the way, we know that if you use these kinds of terms, it's more likely to get this kind of a response. That helps me become a better marketer or be more appropriate in the way I get customers. So this too is your pit crew example, it's efficiency, all kinds of betterment. Absolutely. All right, thanks for coming on theCUBE. Appreciate the time for sharing the insights are on the SAP's news and your vision on analytics. Thanks for coming on, appreciate it. It's theCUBE live in New York City for CUBE NYC. I'm Chopra Peter Burr, stay with us. Day one continues, we're here for two days. All things data here in New York City, stay with us. We'll be right back.