 It's theCUBE, covering Sapphire Now 2017. Brought to you by SAP Cloud Platform and HANA Enterprise Cloud. Hey, welcome back everybody. Jeff Frick here with theCUBE with our ongoing coverage of SAP Sapphire 2017 out down in Orlando. Really exciting day today, day two, because we got to see Hossel Plotner, got up and gave his keynote. Jordan joined by George Gilbert. George, great to see you. I know you've known Hossel for years and years and years. So impressions of the keynote. God, there's so much stuff that we can dig into and I'm looking forward to it. Hossel almost never disappoints because he's just got a richness of history and a vision. He goes all the way back to the beginning. He was probably the sort of, the technical visionary from the very beginning. He was the guy who took them from the first super integrated mainframe ERP package, all the way to the client server age with R3 and now beyond into sort of in memory, cloud ready and with machine learning and IoT baked in. But he really speaks like a developer. You can really tell that he likes the technology. He understands the technology. He's kind of a no BS guy. Some of the Q and A afterwards, people were trying to trip them up and challenging them on stuff and he would either say, I don't know or I don't believe that or here's our impression. I really, you could tell he's a humble guy, smart guy and really has a grasp of what the heck is going on here. So let's jump into it. So many themes we can talk about but the one that started out early in the conversation was he literally said, we need to get as quickly to the cloud as possible. Here's this coming from a guy who built the company based on on-prem ERP heavy lifting. And even he said today, 2017, we need to get to the cloud as quickly as possible. I think there are a few things going on behind there. When you unpack it, one is they did start building for the cloud in the early 2000s and it was meant to be a product for the mid-market. In fact, actually, its first objective wasn't to be cloud ready. The first objective was to be highly configurable so that you could bend it to the needs of many customers without customizing it because typically with the customizations, it made it very difficult to upgrade. And in making it configurable first and sort of cloud ready second, they kind of accomplished neither but they learned a lot and so they started on sort of this next version which was okay, we're going to take an in-memory database which we're building from the ground up because Oracle wasn't building it at the time. And then we're going to build SAP sort of the ERP from scratch on top of this new database because database was so high performance that they didn't have to separate analytics from transactions the way traditionally you had to do in all applications. So they could simplify the app then in simplifying it, they could make it easier to run in the cloud. And now just like Oracle, just like Microsoft, they now build cloud first and on-prem second because by building in cloud first, it sort of simplifies the assumptions that you have to make. Right, and he talked quite a bit about the so much effort now is around the integration connectors to get stuff in and out of this thing. And that's a big focus, he said. It's not that we're ignoring it, it's just a big, hard, hairy problem that we're attacking. Yeah, and this is interesting because, and there's a lot of history behind this. Oracle in the 90s, up until about the late 90s, their greatest success was in their industry-specific applications where they took different modules from different vendors and stitched them together. And that was how they built a special solution for consumer packaged goods companies. But it turned out that that wasn't really workable because the different modules for the different vendors sort of upgraded at different rates so there was no way coherently to integrate them and tie them together. And SAP had said that all along. They were like, this wasn't going to work. So fast forward to the last five plus years SAP started buying products from a bunch of different vendors. Ariba, success factors, concur, hybrids. And so you're like, aren't they doing the same thing Oracle did 10 years, 15 years before? But no, and this is what Hasa was talking about today, which was once those apps are in the cloud, you only have to build the integration points once. It's not like when it's on every customer's data center, you have to build integrations that work for every version that every customer has. And so I think that's what he was talking about. You put it all in the cloud, you integrate it once. Another thing that he talked about, he really, he spoke in tweets, if anyone goes to my Twitter feed, I was basically like bang, bang, bang, and as he was talking, he talked about databases and databases in the cloud. Nobody cares, right? It's a classic thing we hear over and over. We presume it works, we just want it to work. You know, it should just work. Nobody really cares what the underlying database is. But he was, in those cases, referring to these purchased apps, concur, SuccessFactors, Ariba, Hybris. He was like, some of them work on SQL Server, some of them work on Oracle, but you know what? Until we get around to upgrading them to HANA, it doesn't matter because you, the customer, don't know that. If they were on prem and you ought to support all those different databases, it might be a different story. But he's like, we'd rather give you the functionality that's baked into them now than get around to upgrading the databases later. Another theme that came up, and he actually referenced the conversation with Michael Dell from yesterday's keynote, about kind of the evolution of compute horsepower. And, you know, he had CPUs and CPUs kind of topped out. Then you have, you know, multi-core CPUs. Now we have GPUs that he said you can put tens or hundreds of thousands on the board at one time. And basically, you know, he's smart guy. He's down the road a few steps from, you know, delivering today's product, saying that, you know, we were basically living in an era of unlimited free compute and, you know, kind of asymptotically approaching, but that's where we are. And how does that really change the way that we look now at new application development? I thought that was a pretty interesting thing. Yeah, and sort of big advances in software architecture come from when you have a big change in the relative cost of compute memory network storage. And so, as you were saying, cost of compute, you know, is approaching zero, but at the same time, the cost of memory relative to, you know, storage is coming way down. So not only do you have these really beefy clusters with lots of compute, but you also have lots of memory. And so he was talking about something like putting 16 terabytes of memory, you know, in a server and putting 64 servers in a cluster and all of a sudden, I can't do that math, being that I was a humanities major, but all of a sudden, you're talking about huge, you know, databases where you can crunch through this stuff very, very fast because it's all, you have lots of processors running in parallel and you have lots of memory. And it's pretty interesting. He made an interesting statement. He used a sailor reference. He said, you know, we are through the big waves and now we're in the smooth water and really saying that, you know, all this kind of heavy lifting and now that this cloud architecture is here and we have this phenomenal compute and store technology that you can kind of take a breath and really kind of refresh a look out into the future as to how do we build modern apps that have intelligence with basically unlimited resources and how does that change the way that we go forward? I thought that was an interesting point of view, especially because he has been at it for decades. You know, I think he was probably looking back to some of the arrows he had in his back from having done an in-memory database essentially before anyone else did for mission critical apps. And I think when he's saying we're out of the choppy water and into the smooth water because we now have the hardware that lets us run, you know, essentially these very resource-intensive databases and the apps on them so that we no longer have to worry, are we overtaxing the infrastructure? Is it too expensive, you know, to outfit the hardware for a customer? And so his, when he talks about rethinking the apps, he's like, okay, so we don't have to have separate analytical systems from the transaction systems and not only that, we can simplify because we don't have to have what he's calling aggregates. In other words, we don't have to, we don't sort of let's say take an order and all the line items in an order and then pre-aggregate sort of all the orders. It's like we do that on the fly and that simplifies things a lot. And then not only that, because we have all this memory, we can do like machine learning very inexpensively. So a whole nother chapter in his keynote was about kind of modern software design. And a lot of really interesting things, especially in the context of SAP, which was a big monolithic application, hard to learn, hard to understand, hard to manage, I remember a startup that we were at and we were using as a core, you know, B2C commerce engine and to add 16 colors of shirts times 10 neck sizes and 10 sleeve sizes was just a nightmare. And you're not going to ask some merchant that works at Macy's to put that into the system. But he talked about intelligent design, which is pretty interesting. We're hearing that more and more, a lot of work done over at Stanford on intelligent design. He's talking about no manuals. He's like, if I can't figure it out, I need to understand it. He talked about intelligent applications that continue to learn as the applications get more data. And specifically the fact that machines don't get bored testing, you know, hundreds or thousands of even millions of scenarios and grinding through those things to get the intelligence to start to learn about what's going on. So a very different kind of an application, both development delivery approach than kind of what we think of historically as our three. Yeah, like the design thinking was they have this new UI called Fiori. And I mean, if you go back 10, 15 years, let's say when they started, 15 years when they started trying to put browser-based user interfaces on what was a client-server system, they had tens and tens of thousands of forms-based screens and they had to convert them one by one to work in a browser. And I think what he's saying now is they can mock up these prototypes in a simple tool and they can essentially recreate the UI. It's not going to be the exact sort of same forms but they can recreate the UI to the entire system so that it's much more accessible. On the machine learning front, he was talking about one example was like matching up invoices that you're going to have to pay and so that you're going to train the system with all these invoices. It learns how to essentially do that, the OCR, recognize the text and it gets smarter to the point where it can do 95% of it without... Human interaction. Yeah, human interaction. You know, it's interesting when we were at service now last week as well and they are using AI to do relatively mundane tasks that people don't want to do, that machines are good at. Things like categorization and assignment and things that are relatively straightforward processes but very time consuming. And again, if you can get to a 70% solution, 80% solution, 90% solution to free people up to do other things on the stuff that's relatively routine. If the invoice matches the anticipated bill in the system, pay it. Somebody really have to look at it. So I thought that was really interesting. Something I want to dig in with you, he talked a lot about data, where the data lives, data gravity and he even said that he fought against data warehousing in the 90s and lost. A lot of real passionate conversation about where is data and how should apps interact with data and he's really against like this data replication in a data lake and moving this stuff all around but having it, you know, kind of central. I want to just get your thoughts on kind of that history. What do you think he means now and where's that going? That's a great question and there's a lot of history behind that. Not everyone would remember but there was an article in Fortune Magazine in the late 90s where it described him getting up in a small conference of software CEOs, enterprise software CEOs and he said basically, we're going to grind you into dust because everything comes in our system integrated and if you leave it up to the customer to try and stitch all this stuff together, it's going to be a nightmare and that was back when everyone was thinking, you know, one company can't do it all and the reality was that was the point in time where we really had given you know, go, pass, go, collect $200 to every best of breed little software vendor and it did prove out over, you know, the next decade that the fewer integration points there were that it meant much lower cost of ownership for the customer and not only lower cost of ownership but better business process integration because you had the end-to-end integration. I bring this up because, well actually I was there when he said it but I bring it up because he's essentially saying the same thing now which is we'll put all the machine learning technology, you know, the building blocks in SAP and if you need any, you know, sort of contextual data, bring it into our system, you don't want to take our data out and put it into all these other machine learning programs because there's security issues, there's, you know, again, the breakdown in the business process integration. He did acknowledge that with data warehouses if you have, you know, hundreds of other sources, yes, you're going to need an external data warehouse but I think that he's going to find with machine learning the greatest value with the data that you use in machine learning is when you're always adding richer and richer contextual data and that contextual data means you're getting it from other sources. Right, right. And I don't think he's going to win this battle in terms of keeping most of it within SAP. So it kind of brings up this other kind of intersection that he talked about. You know, in now delivering SAP as a cloud application, he said now we have to learn how to run our application, not our customers, a very different way of looking at the world. The other thing that piggybacks off of what you just said is we've seen this trend towards configuration, not customization, right? And it used to be probably, you know, back in the days if you had the big SIs they love customization because it's a huge project multi-year, I used to talk to one of our central partners, like how do you manage a multi-year SAP project when most of the people that started it probably aren't even there when the day you finish it. But he had a specific quote I wanted to call out. Now what you just said is that he said only our customers have the data, the desire, and the domain knowledge to make the most out of it. So it's a really interesting recognition that yes you want customers to have this configuration option but we keep hearing more and more it's config, not customization for upgrades and all these other things which now when you go to a cloud-based application that becomes significant. You don't want customizations because that just complicates everything. You can't and I don't know if he said this today. I guess he must have said it today but basically when you're in the cloud, the, I forgot the terminology for the different instances but when you're in like the SAP cloud you can only configure. There's essentially a set of greater constraints on you when you go to the other end of the spectrum, let's say you run it in your own data center, you can customize it but when you're running it essentially sharing infrastructure, you're constrained, you're much more constrained and they build it for that environment first. Right, but at the same time they've got the data and again this has come up with other SaaS companies that we've talked to is hopefully they're out of the box. Business process covers 90% of the basics and I think there's been a realization on the business analyst side that we think we're special but really most of the time order to cash is order to cash. So if you got to tweak your own internal process to match best of breed, do it. You're much better off than trying to shape that computing system to fill your little corner cases. It's funny that you mentioned that because what happened in the 90s was that by far the biggest influencers in the purchase decision and the overall life cycle of the app were the big system integrators and they could typically collect $10 in implementation and change management fees for every dollar of license that went to the software vendors. So they had a huge sort of incentive to tell the customer, well you really should customize this around your particular needs because they made all the money off that. So another huge theme, again it was such a great keynote. We watch a lot of keynotes and I have a very high bar for what I consider a great keynote. This was a great keynote by a smart guy who knows his stuff and the kind of history but another thing was just really about AI. And he talked a little bit which I thought was great. Nobody talks about the fact that airplanes have been flying themselves for a very long time. And so it is coming and I think he even said maybe this is the age of AI but there always have to be some humans involved. It's not a complete handover of control but it is coming and it's coming very, very quickly. I actually thought that they were a little further behind than might be expected for considering that it's been years now that people sort of in software have seen this coming. But they have in the dozens of sort of applications or functions right now that are machine learning enabled but if you look out at their roadmap where they get to predictive accounting, customer behavior segmentation, profile completeness in sales, solution recommenders, model training infrastructure for the base software foundation, they have a pretty rich roadmap but I guess I would have thought it would be a little farther along but then Oracle isn't really any farther along. Workday has done some work for HR and for whatever reason, I think the enterprise application vendors, I think they found this challenging for two reasons. On the technical side, machine learning is very different from the traditional analytics they did which was really essentially OLAP, business intelligence. This requires the data scientists and the white lab coats and instead of backward looking business intelligence, this forward looking predictive analytics. The other thing is I think you sell this stuff differently which is when it was business intelligence you're basically selling reporting on what happened to department heads or function leaders whereas when you're selling predictive capabilities, it's a little more transformative and you're not selling efficiency which is what these applications have always, that's been their value proposition. You're selling transformational outcomes which is a different sort of selling motion. It's funny I heard a funny quote the other day. We used to look backwards with a sample of the data and now we're in real time with all the data. Very different situation and forward looking as well with the predictive. That's a great quote. Again, he touched on so many things but one of the things he brought up is Tesla. He actually said he has two Teslas or he has a second Tesla and there was a question and answer afterwards really about the Tesla, not as a technology platform and he poked fun at Germans. He said Germans have problems with simplicity and he referenced I presume a Mercedes or a Porsche in all the perfectly ergonomically placed buttons and switches. He goes he's sitting at Tesla and it just all comes up on the touchscreen and if you want to do an update overnight, they update your software and now you have the newer version of the car versus the Mercedes where it takes them three years to redesign the buttons and switches. I thought that was interesting and then one of the Q and A people said what about the buying experience? If you've known anyone who's ever bought a Tesla it's a very different experience in buying a car and how does that really apply to selling software and it was pretty interesting. He said we're not there yet but he has clearly grasped on it's a new world and it's a new way to interact with the customers kind of like his no manuals comment that Tesla is defining a new way to buy a car experience a car, upgrade a car. At the same time he got the crazy mode, fanatical mode I can ever ludicrous mode so that he could stop it and tell the Porsche guys that you're falling behind for every single day. So I thought that you know really interesting bring that kind of a consumer play and a kind of a cutting edge automotive example into what was historically a pretty stodgy enterprise software space. You know it's funny I'm listening when you're saying that and that was almost like the day one objective from Salesforce which was we want an enterprise app like Siebel but we want an eBay like or Yahoo like experience and that did change the experience for buying it and for operating it and I think that was almost 20 years ago where that was Mark Benioff's objective and he's saying okay it's easier to do that for CRM but it's now time to bring that to ERP. Right, the other thing he brought in which I was happy being a Bay Area resident is the Sharks because he's a part owner of San Jose Sharks obviously it's SAP center now also known as the Shark Tank and used to be owned by another technology company but he made just a funny thing. I like hockey so I should like SAP because and he was talking about the analysis of how often the logos come up on the telecast etc but the thing that struck me is he said the analysis is actually now faster than the game. Pretty interesting way to think about this data in flow in that the analysis coming out of the game that feeds Vegas it feeds all these stat lines it feeds fantasy it feeds all this stuff it feeds the advertising purchase and the ROI on my logo is it in the corner is it on the ice is it in the middle is actually moving faster than the hockey game and hockey is a pretty fast game very different world in which we live even on the mark the Martek side. And that was that was an example of one of the machine learning type apps because I think in their case they were using I think Google image recognition technology you know to parse out essentially all the logos and see what type of impact your brand made relative to your purchase. Yeah I mean I could go on and on I have so many notes again I live tweeted a lot of it. You know he's just such a humble guy he's a smart guy he comes at it with the technology background but you know he said we're a little bit slower than we'd like he talked about some things taking longer than he thought they would but he also now sees around the corner that we are very quickly going to be in this age of infinite compute and we are very we are already in an age of no one's reading manuals and it just seemed very you know kind of customer-centric we're no longer the super smart Germans that will do it our way or the highway and you will adapt your process to us but really kind of customer-centric point of view design thinking talked about sharing their road map as far out in advance as possible. I think specifically when he got a question on design thinking he's like you know the studies show that a collaborative effort yields better results. It's no longer we're the smartest guy in the rooms and we're going to do it this way and you're going to adapt so really progressive. And he talked about with concur he said their UI is so easy that you really don't need a manual. In fact if you do you know you've failed and I think what he's you know trying to say is we're going to take that iterative prototyping capability agile development you know and extend it to the rest of the you know ERP family and that with their Fiori UI and the tools that build that those screens that it'll make that possible. You can have a little cap you know we don't spend enough investment on design in UI because it is such an important piece of the puzzle. But George we're running out of time here I want to give you the last word you've been paying attention to SAP for a very long time. So Hosso's terrific but then Hosso gets off the stage and he said I don't run the company anymore I only make recommendations. As you look at SAP and you know Bill McDermott was yesterday are they changing you know are they just stuck in an innovators dilemma because they just make so much money on their historical business or are they really changing what's your you know kind of take as they develop where they are now and what do you see kind of going forward for SAP? Well it's a really good question and I would say I look at the value of the business processes that they are either augmenting or automating I hesitate to say automate because as he said you still want the pilot you know in the cockpit. In proximity to the right. And he was like look when we do the invoice matching it's not like we're going to get it 100% right we're going to get it I think he was saying like in the labs right now it's like 94% right. So we're going to make you more productive we're not going to eliminate that job. But when you know when you're doing invoice matching that's not a super high value you know business process if you're doing something where you're predicting churn and making a next best you know offer to a customer that's a higher value process or if you have a multi-touch point commerce you know solution where you can track the customer you know whether it's mobile whether it's whether he's coming via chat whether he's in a store and you're able to you know see his history or her history and you know what's most appropriate given their context at any one moment that's higher value and then it's super high value to be able to take that back upstream towards okay here's where the inventory is I have some in this store I can't fulfill that clothing item directly from the store but I can fulfill it from this one or you know I have it in another warehouse when you have that level of sort of awareness and integration that's high value. Yeah but I want to push back a little bit on you George because I do think the invoice match if he can automatically match 94% of the invoices that is tremendous value because that that I just think it's so creative when you apply this machine learning to tasks that feel relatively mundane but if you're speeding your cash flow along if you get 94% of your invoice is done one day faster and you're a multimillion dollar business what is the dollar the direct dollar impact to the bottom line like immediately it's huge and then you can iterate and move into other processes I just I think the what's termed a low value transaction is actually a lot higher value than people give a credit it's just like again another one we hear about all the time automation of password reset some of these service desks password reset I heard a stat and one of them was like 70% of the calls are password reset so if you can automate password reset sounds kind of silly and mundane oh my gosh it's like 70% of your calls it's humongous I hear what you're saying let me let me give you another counter example which was and he I think he brought this up I don't know if it was today or when Dell Michael Dell spoke which was the Dell's revolution wasn't that they were more efficient than doing what compact did it's that they had a different business model which was specifically they got paid before they even procured or assembled the components and paid for them right no they had no inventory carry so in fact that meant that meant they're working capital they're working capital needs like we're negative in fact the bigger they got the more money they collected right before they had to spend it and that's a different business model that wasn't automating the invoice matching that was we have such good systems that we don't even have to pay for them and then assemble the stuff until after the customer gave us their credit card and I think those are the things that new types of applications can make possible well we see it time and time again it's all about scale it's all about finding inefficiencies and there's a lot more inefficiencies around than people give credit as Uber showed with a lot of cars that sit in driveways and Amazon and the public clouds are showing with a lot of inefficient not used utilization in private data centers so the themes go on and on and they're pretty universal so exciting keynote any last comment before we sign off on for today? I guess we want to take a close look at Oracle next and see how their roadmap looks like in terms of applying these new technologies IoT machine learning blockchain because you know all of these can remake how you build a business. Yeah all right well that's George Gilbert from Wikibon I'm Jeff Frick from theCUBE we are covering ongoing coverage of SAP Sapphire 2017 thanks for watching we'll be back with more after this short break thanks.