>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> New data from ETR's soon to be released April survey, shows a clear deceleration in spending and a more cautious posture from technology buyers. Just this week, we saw sell side downgrades in hardware companies like Dell and HP and revised guidance from high flyer UiPath, citing exposures to Russia, Europe and certain sales execution challenges, but these headlines, we think are a canary in the coal mine. According to ETR analysis and channel checks in theCUBE, the real story is these issues are not isolated. Rather we're seeing signs of caution from buyers across the board in enterprise tech. Hello and welcome to this week's Wikibon CUBE insights powered by ETR. In this Breaking Analysis, we are the bearers of bad news. Don't shoot the messenger. We'll share a first look at fresh data that suggests a tightening in tech spending calling for 6% growth this year which is below our January prediction of 8% for 2022. Now, unfortunately the party may be coming to an end at least for a while. You know, it's really not surprising, right? We've had a two year record run in tech spending and meteoric rises in high flying technology stocks. Hybrid work, equipping and securing remote workers. The forced march to digital that we talk about sometimes. These were all significant tailwinds for tech companies. The NASDAQ peaked late last year and then as you can see in this chart, bottomed in mid-March of 2022, and it made a nice run up through the 29th of last month, but the mini rally appears to be in jeopardy with FED rate hikes, Russia, supply chain challenges. There's a lot of uncertainty so we should expect the C-suite to be saying, hey, wait slow down. Now we don't think the concerns are confined to companies with exposure to Russia and Europe. We think it's more broad based than that and we're seeing caution from technology companies and tech buyers that we think is prudent, given the conditions. You know, looks like the two year party has ended and as my ETR colleague Erik Bradley said, a little hangover shouldn't be a surprise to anybody. So let's get right to the new spending data. I'm limited to what I can share with you today because ETR is in its quiet period and hasn't released full results yet outside of its client base. But, they did put out an alert today and I can share this slide. It shows the expectation on spending growth from more than a thousand CIOs and IT buyers who responded in the most recent survey. It measures their expectations for spending. The key focus areas that I want you to pay attention to in this data are the yellow bars. The most recent survey is the yellow compared to the blue and the gray bars, which are the December and September '21 surveys respectively. And you can see a steep drop from last year in Q1, lowered expectations for Q2 in the far right, a drop from nearly 9% last September to around 6% today. Now you may think a 200 basis point downgrade from our prediction in January of 8% seems somewhat benign, but in a $4 trillion IT market, that's 80 billion coming off the income statements of some tech companies. Now the good news is that 6% growth is still very healthy and higher than pre pandemic spending levels. And the buyers we've talked to this week are saying, look, we're still spending money. We just have to be more circumspect about where and how fast. Now, there were a few other callouts in the ETR data and in my discussions today with Erik Bradley on this. First, it looks like in response to expected supply chain constraints that buyers pulled forward their orders late last year and earlier this year. You remember when we couldn't buy toilet paper, people started the stockpile and it created this rubber banding effect. So we see clear signs of receding momentum in the PC and laptop market. But as we said, this is not isolated to PCs, UiPath's earning guidance confirm this but the story doesn't end there. This isn't isolated to UiPath in our view, rather it's a more based slowdown. The other big sign is spending in outsourced IT which is showing a meaningful deceleration in the last survey, showing a net score drop from 13% in January to 6% today. Net score remember is a measure of the net percentage of customers in the survey that on balance are spending more than last survey. It's derived by subtracting the percent of customers spending less from those spending more. And there's a, that's a 700 basis point drop in three months. This isn't a market where you can't hire enough people. The percent of companies hiring has gone from 10% during the pandemic to 50% today according to recent data from ETR. And we know there's still an acute skills shortage. So you would expect more IT outsourcing, but you don't see that in the data, it's down. And as this quote from Erik Bradley explains, historically, when outsourced IT drops like this, especially in a tight labor market, it's not good news for IT spending. All right, now, the other interesting callout from ETR were some specific company names that appear to be seeing the biggest change in spending momentum. Here's the list of those companies that all have meaningful exposure to Europe. That's really where the focus was. SAP has big exposure to on-premises installations and of course, Europe as well. ServiceNow has European exposure and also broad based exposure in IT in across the globe, especially in the US. Zoom didn't go to the moon, no surprise there given the quasi return to work and Zoom fatigue. McAfee is a bit of a concern because security seemed to be one of those areas, when you look at some of the other data, that is per actually insulated from all the spending caution. Of course we saw the Okta hack and we're going to cover that next week with hopefully some new data from ETR, but generally security's been holding up pretty well. You look at CrowdStrike, you look at Zscaler in particular. Adobe's another company that's had a nice bounce in the last couple of weeks. Accenture, again, speaks to that outsourcing headwinds that we mentioned earlier. And now the Google Cloud platform is a bit of a concern. It's still elevated overall, you know but down and well down in Europe. Under that magic, you know we often show that magic 40% dotted line, that red dotted line of net score anything above that we cite as elevated. Well, some important callouts to hear that you see companies that have Euro exposure. And again, we see this as just not confined to Europe and this is something we're going to pay close attention to and continue to report on in the next several weeks and months. All right, so what should we expect from here? The Ark investment stocks of Cathie Wood fame have been tracking in a downward trend since last November, meaning, you know, these high PE stocks are making lower lows and higher, sorry, lower highs and lower lows since then, right? The trend is not their friend. Investors I talk to are being much more cautious about buying the dip. They're raising cash and being a little bit more patient. You know, traders can trade in this environment but unless you can pay attention to in a minute by minute you're going to get whipsawed. Investors tell me that they're still eyeing big tech even though Apple has been on a recent tear and has some exposure with supply change challenges, they're looking for maybe entry points in, within that chop for Apple, Amazon, Microsoft, and Alphabet. And look, as I've been stressing, 6% spending growth is still very solid. It's a case of resetting the outlook relative to previous expectations. So when you zoom out and look at the growth in data, getting digital right, security investments, automation, cloud, AI containers, all the fundamentals are really strong and they have not changed. They're all powering this new digital economy and we believe it's just prudence versus a shift in the importance of IT. Now, one point of caution is there's a lot of discussion around a shift in global economies. Supply chain uncertainty, persistent semiconductor shortages especially in areas like, you know driver ICs and boring things like parts for displays and analog and micro controllers and power regulators. Stuff that's, you know, just not playing nice these days and wreaking havoc. And this creates uncertainty, which sometimes can pick up momentum in a snowballing effect. And that's something that we're watching closely and we're going to be vigilant reporting to you when we see changes in the data and in our forecast even when we think our forecast are wrong. Okay, that's it for today. Thanks to Alex Merson who does the production and podcasts for Breaking Analysis and Stephanie Chan who provides background research. Kristen Martin and Cheryl Knight, and all theCUBE writers they help get the word out, and thanks to Rob Hof, our EIC over at SiliconANGLE. Remember I publish weekly on wikibon.com and siliconangle.com. These episodes are all available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcasts. etr.ai that's where you can get access to all this survey data and make your own cuts. It's awesome, check that out. Keep in touch with me. You can email me at dave.vellante@siliconangle.com. You can hit me up on LinkedIn. This is Dave Vellante for theCUBE insights powered by ETR. Be safe, stay well, and we'll see you next time. (gentle music)

(uplifting orchestral music) >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at the Palo Alto studios, having theCUBE Conversation, a little break in the action of the conference season before things heat up, before we kind of come to the close of 2018. It's been quite a year. But it's nice to be back in the studio. Things are a little bit less crazy, and we're excited to talk about one of the really hot topics right now, which is edge computing, fog computing, cloud computing. What do all these things mean, how do they all intersect, and we've got with us today David King. He's the CEO of FogHorn Systems. David, first off, welcome. >> Thank you, Jeff. >> So, FogHorn Systems, I guess by the fog, you guys are all about the fog, and for those that don't know, fog is kind of this intersection between cloud, and on prem, and... So first off, give us a little bit of the background of the company and then let's jump into what this fog thing is all about. >> Sure, actually, it all dovetails together. So yeah, you're right, FogHorn, the name itself, came from Cisco's invented term, called fog computing, from almost a decade ago, and it connoted this idea of computing at the edge, but didn't really have a lot of definition early on. And so, FogHorn was started actually by a Palo Alto Incubator, just nearby here, that had the idea that hey, we got to put some real meaning and some real meat on the bones here, with fog computing. And what we think FogHorn has become over the last three and a half years, since we took it out of the incubator, since I joined, was to put some real purpose, meaning, and value in that term. And so, it's more than just edge computing. Edge computing is a related term. In the industrial world, people would say, hey, I've had edge computing for three, 40, 50 years with my production line control and also my distributed control systems. I've got hard wired compute. I run, they call them, industrial PCs in the factory. That's edge compute. The IT roles come along and said, no, no, no, fog compute is a more advanced form of it. Well, the real purpose of fog computing and edge computing, in our view, in the modern world, is to apply what has traditionally been thought of as cloud computing functions, big, big data, but running in an industrial environment, or running on a machine. And so, we call it as really big data operating in the world's smallest footprint, okay, and the real point of this for industrial customers, which is our primary focus, industrial IoT, is to deliver as much analytic machine learning, deep learning AI capability on live-streaming sensor data, okay, and what that means is rather than persisting a lot of data either on prem, and then sending it to the cloud, or trying to stream all this to the cloud to make sense of terabytes or petabytes a day, per machine sometimes, right, think about a jet engine, a petabyte every flight. You want to do the compute as close to the source as possible, and if possible, on the live streaming data, not after you've persisted it on a big storage system. So that's the idea. >> So you touch on all kinds of stuff there. So we'll break it down. >> Unpack it, yeah. >> Unpack it. So first off, just kind of the OT/IT thing, and I think that's really important, and we talked before turning the cameras on about Dr. Tom from HP, he loves to make a big symbolic handshake of the operations technology, >> One of our partners. >> Right, and IT, and the marriage of these two things, where before, as you said, the OT guys, the guys that have been running factories, you know, they've been doing this for a long time, and now suddenly, the IT folks are butting in and want to get access to that data to provide more control. So, you know, as you see the marriage of those two things coming together, what are the biggest points of friction, and really, what's the biggest opportunity? >> Great set of questions. So, quite right, the OT folks are inherently suspicious of IT, right? I mean, if you don't know the history, 40 plus years ago, there was a fork in the road, where in factory operations, were they going to embrace things like ethernet, the internet, connected systems? In fact, they purposely air gapped an island of those systems 'cause they was all about machine control, real-time, for safety, productivity, and uptime of the machine. They don't want any, you can't use kind of standard ethernet, it has to be industrial ethernet, right? It has to have time bound and deterministic. It can't be a retry kind of a system, right? So different MAC layer for a reason, for example. What did the physical wiring look like? It's also different cabling, because you can't have cuts, jumps in the cable, right? So it's a different environment entirely that OT grew up in, and so, FogHorn is trying to really bring the value of what people are delivering for AI, essentially, into that environment in a way that's non-threatening to, it's supplemental to, and adds value in the OT world. So Dr. Tom is right, this idea of bringing IT and OT together is inherently challenging, because these were kind of fork in the road, island-ed in the networks, if you will, different systems, different nomenclature, different protocols, and so, there's a real education curve that IT companies are going through, and the idea of taking all this OT data that's already been produced in tremendous volumes already before you add new kinds of sensing, and sending it across a LAN which it's never talked to before, then across a WAN to go to a cloud, to get some insight doesn't make any sense, right? So you want to leverage the cloud, you want to leverage data centers, you want to leverage the LAN, you want to leverage 5G, you want to leverage all the new IT technologies, but you have to do it in a way that makes sense for it and adds value in the OT context. >> I'm just curious, you talked about the air gapping, the two systems, which means they are not connected, right? >> No, they're connected with a duct, they're connected to themselves, in the industrial-- >> Right, right, but before, the OT system was air gapped from the IT system, so thinking about security and those types of threats, now, if those things are connected, that security measure has gone away, so what is the excitement, adoption scare when now, suddenly, these things that were separate, especially in the age of breaches that we know happen all the time as you bring those things together? >> Well, in fact, there have been cyber breaches in the OT context. Think about Stuxnet, think about things that have happened, think about the utilities back keys that were found to have malwares implanted in them. And so, this idea of industrial IoT is very exciting, the ability to get real-time kind of game changing insights about your production. A huge amount of economic activity in the world could be dramatically improved. You can talk about trillions of dollars of value which the McKenzie, and BCG, and Bain talk about, right, by bringing kind of AI, ML into the plant environment. But the inherent problem is that by connecting the systems, you introduce security problems. You're talking about a huge amount of cost to move this data around, persist it then add value, and it's not real-time, right? So, it's not that cloud is not relevant, it's not that it's not used, it's that you want to do the compute where it makes sense, and for industrial, the more industrialized the environment, the more high frequency, high volume data, the closer to the system that you can do the compute, the better, and again, it's multi-layer of compute. You probably have something on the machine, something in the plant, and something in the cloud, right? But rather than send raw OT data to the cloud, you're going to send processed intelligent metadata insights that have already been derived at the edge, update what they call the fleet-wide digital twin, right? The digital twin for that whole fleet of assets should sit in the cloud, but the digital twin of the specific asset should probably be on the asset. >> So let's break that down a little bit. There's so much good stuff here. So, we talked about OT/IT and that marriage. Next, I just want to touch on cloud, 'cause a lot of people know cloud, it's very hot right now, and the ultimate promise of cloud, right, is you have infinite capacity >> Right, infinite compute. >> Available on demand, and you have infinite compute, and hopefully you have some big fat pipes to get your stuff in and out. But the OT challenge is, and as you said, the device challenge is very, very different. They've got proprietary operating systems, they've been running for a very, very long time. As you said, they put off boatloads, and boatloads, and boatloads of data that was never really designed to feed necessarily a machine learning algorithm, or an artificial intelligence algorithm when these things were designed. It wasn't really part of the equation. And we talk all the time about you know, do you move the compute to the data, you move the data to the compute, and really, what you're talking about in this fog computing world is kind of a hybrid, if you will, of trying to figure out which data you want to process locally, and then which data you have time, relevance, and other factors that just go ahead and pump it upstream. >> Right, that's a great way to describe it. Actually, we're trying to move as much of the compute as possible to the data. That's really the point of, that's why we say fog computing is a nebulous term about edge compute. It doesn't have any value until you actually decide what you're trying to do with it, and what we're trying to do is to take as much of the harder compute challenges, like analytics, machine learning, deep learning, AI, and bring it down to the source, as close to the source as you can, because you can essentially streamline or make more efficient every layer of the stack. Your models will get much better, right? You might have built them in the cloud initially, think about a deep learning model, but it may only be 60, 70% accurate. How do you do the improvement of the model to get it closer to perfect? I can't go send all the data up to keep trying to improve it. Well, typically, what happens is I down sample the data, I average it and I send it up, and I don't see any changes in the average data. Guess what? We should do is inference all the time and all the data, run it in our stack, and then send the metadata up, and then have the cloud look across all the assets of a similar type, and say, oh, the global fleet-wide model needs to be updated, and then to push it down. So, with Google just about a month ago, in Barcelona, at the IoT show, what we demonstrated was the world's first instance of AI for industrial, which is closed loop machine learning. We were taking a model, a TensorFlow model, trained in the cloud in the data center, brought into our stack and referring 100% inference-ing in all the live data, pushing the insights back up into Google Cloud, and then automatically updating the model without a human or data scientist having to look at it. Because essentially, it's ML on ML. And that to us, ML on ML is the foundation of AI for industrial. >> I just love that something comes up all the time, right? We used to make decisions based on the sampling of historical data after the fact. >> That's right, that's how we've all been doing it. >> Now, right, right now, the promise of streaming is you can make it based on all the data, >> All the time. >> All the time in real time. >> Permanently. >> This is a very different thing. So, but as you talked about, you know, running some complex models, and running ML, and retraining these things. You know, when you think of edge, you think of some little hockey puck that's out on the edge of a field, with limited power, limited connectivity, so you know, what's the reality of, how much power do you have at some of these more remote edges, or we always talk about the field of turbines, oil platforms, and how much power do you need, and how much compute that it actually starts to be meaningful in terms of the platform for the software? >> Right, there's definitely use cases, like you think about the smart meters, right, in the home. The older generation of those meters may have had very limited compute, right, like you know, talking about single megabyte of memory maybe, or less, right, kilobytes of memory. Very hard to run a stack on that kind of footprint. The latest generation of smart meters have about 250 megabytes of memory. A Raspberry Pi today is anywhere from a half a gig to a gig of memory, and we're fundamentally memory-bound, and obviously, CPU if it's trying to really fast compute, like vibration analysis, or acoustic, or video. But if you're just trying to take digital sensing data, like temperature, pressure, velocity, torque, we can take humidity, we can take all of that, believe it or not, run literally dozens and dozens of models, even train the models in something as small as a Raspberry Pi, or a low end x86. So our stack can run in any hardware, we're completely OS independent. It's a full up software layer. But the whole stack is about 100 megabytes of memory, with all the components, including Docker containerization, right, which compares to about 10 gigs of running a stream processing stack like Spark in the Cloud. So it's that order of magnitude of footprint reduction and speed of execution improvement. So as I said, world's smallest fastest compute engine. You need to do that if you're going to talk about, like a wind turbine, it's generating data, right, every millisecond, right. So you have high frequency data, like turbine pitch, and you have other conceptual data you're trying to bring in, like wind conditions, reference information about how the turbine is supposed to operate. You're bringing in a torrential amount of data to do this computation on the fly. And so, the challenge for a lot of the companies that have really started to move into the space, the cloud companies, like our partners, Google, and Amazon, and Microsoft, is they have great cloud capabilities for AI, ML. They're trying to move down to the edge by just transporting the whole stack to there. So in a plant environment, okay, that might work if you have massive data centers that can run it. Now I still got to stream all my assets, all the data from all of my assets to that central point. What we're trying to do is come out the opposite way, which is by having the world's smallest, fastest engine, we can run it in a small compute, very limited compute on the asset, or near the asset, or you can run this in a big compute and we can take on lots and lots of use cases for models simultaneously. >> I'm just curious on the small compute case, and again, you want all the data-- >> You want to inference another thing, right? >> Does it eventually go back, or is there a lot of cases where you can get the information you need off the stream and you don't necessarily have to save or send that upstream? >> So fundamentally today, in the OT world, the data usually gets, if the PLC, the production line controller, that has simple KPIs, if temperature goes to X or pressure goes to Y, do this. Those simple KPIs, if nothing is executed, it gets dumped into a local protocol server, and then about every 30, 60, 90 days, it gets written over. Nobody ever looks at it, right? That's why I say, 99% of the brown field data in OT has never really been-- >> Almost like a security-- >> Has never been mined for insight. Right, it just gets-- >> It runs, and runs, and runs, and every so often-- >> Exactly, and so, if you're doing inference-ing, and doing real time decision making, real time actual with our stack, what you would then persist is metadata insights, right? Here is an event, or here is an outcome, and oh, by the way, if you're doing deep learning or machine learning, and you're seeing deviation or drift from the model's prediction, you probably want to keep that and some of the raw data packets from that moment in time, and send that to the cloud or data center to say, oh, our fleet-wide model may not be accurate, or may be drifting, right? And so, what you want to do, again, different horses for different courses. Use our stack to do the lion's share of the heavy duty real time compute, produce metadata that you can send to either a data center or a cloud environment for further learning. >> Right, so your piece is really the gathering and the ML, and then if it needs to go back out for more heavy lifting, you'll send it back up, or do you have the cloud application as well that connects if you need? >> Yeah, so we build connectors to you know, Google Cloud Platform, Google IoT Core, to AWS S3, to Microsoft Azure, virtually any, Kafka, Hadoop. We can send the data wherever you want, either on plant, right back into the existing control systems, we can send it to OSIsoft PI, which is a great time series database that a lot of process industries use. You could of course send it to any public cloud or a Hadoop data lake private cloud. You can send the data wherever you want. Now, we also have, one of our components is a time series database. You can also persist it in memory in our stack, just for buffering, or if you have high value data that you want to take a measurement, a value from a previous calculation and bring it into another calculation during later, right, so, it's a very flexible system. >> Yeah, we were at OSIsoft PI World earlier this year. Some fascinating stories that came out of-- >> 30 year company. >> The building maintenance, and all kinds of stuff. So I'm just curious, some of the easy to understand applications that you've seen in the field, and maybe some of the ones that were a surprise on the OT side. I mean, obviously, preventative maintenance is always towards the top of the list. >> Yeah, I call it the layer cake, right? Especially when you get to remote assets that are either not monitored or lightly monitored. They call it drive-by monitoring. Somebody shows up and listens or looks at a valve or gauge and leaves. Condition-based monitoring, right? That is actually a big breakthrough for some, you know, think about fracking sites, or remote oil fields, or mining sites. The second layer is predictive maintenance, which the next generation is kind of predictive, prescriptive, even preventive maintenance, right? You're making predictions or you're helping to avoid downtime. The third layer, which is really where our stack is sort of unique today in delivering is asset performance optimization. How do I increase throughput, how do I reduce scrap, how do I improve worker safety, how do I get better processing of the data that my PLC can't give me, so I can actually improve the performance of the machine? Now, ultimately, what we're finding is a couple of things. One is, you can look at individual asset optimization, process optimization, but there's another layer. So often, we're deployed to two layers on premise. There's also the plant-wide optimization. We talked about wind farm before, off camera. So you've got the wind turbine. You can do a lot of things about turbine health, the blade pitch and condition of the blade, you can do things on the battery, all the systems on the turbine, but you also need a stack running, like ours, at that concentration point where there's 200 plus turbines that come together, 'cause the optimization of the whole farm, every turbine affects the other turbine, so a single turbine can't tell you speed, rotation, things that need to change, if you want to adjust the speed of one turbine, versus the one next to it. So there's also kind of a plant-wide optimization. Talking about time that's driving, there's going to be five layers of compute, right? You're going to have the, almost what I call the ECU level, the individual sub-system in the car that, the engine, how it's performing. You're going to have the gateway in the car to talk about things that are happening across systems in the car. You're going to have the peer to peer connection over 5G to talk about optimization right between vehicles. You're going to have the base station algorithms looking at a micro soil or macro soil within a geographic area, and of course, you'll have the ultimate cloud, 'cause you want to have the data on all the assets, right, but you don't want to send all that data to the cloud, you want to send the right metadata to the cloud. >> That's why there are big trucks full of compute now. >> By the way, you mentioned one thing that I should really touch on, which is, we've talked a lot about what I call traditional brown field automation and control type analytics and machine learning, and that's kind of where we started in discrete manufacturing a few years ago. What we found is that in that domain, and in oil and gas, and in mining, and in agriculture, transportation, in all those places, the most exciting new development this year is the movement towards video, 3D imaging and audio sensing, 'cause those sensors are now becoming very economical, and people have never thought about, well, if I put a camera and apply it to a certain application, what can I learn, what can I do that I never did before? And often, they even have cameras today, they haven't made use of any of the data. So there's a very large customer of ours who has literally video inspection data every product they produce everyday around the world, and this is in hundreds of plants. And that data never gets looked at, right, other than training operators like, hey, you missed the defects this day. The system, as you said, they just write over that data after 30 days. Well, guess what, you can apply deep learning tensor flow algorithms to build a convolutional neural network model and essentially do the human visioning, rather than an operator staring at a camera, or trying to look at training tapes. 30 days later, I'm doing inference-ing of the video image on the fly. >> So, do your systems close loop back to the control systems now, or is it more of a tuning mechanism for someone to go back and do it later? >> Great question, I just got asked that this morning by a large oil and gas super major that Intel just introduced us to. The short answer is, our stack can absolutely go right back into the control loop. In fact, one of our investors and partners, I should mention, our investors for series A was GE, Bosch, Yokogawa, Dell EMC, and our series debuted a year ago was Intel, Saudi Aramco, and Honeywell. So we have one foot in tech, one foot in industrial, and really, what we're really trying to bring is, you said, IT, OT together. The short answer is, you can do that, but typically in the industrial environment, there's a conservatism about, hey, I don't want to touch, you know, affect the machine until I've proven it out. So initially, people tend to start with alerting, so we send an automatic alert back into the control system to say, hey, the machine needs to be re-tuned. Very quickly, though, certainly for things that are not so time-sensitive, they will just have us, now, Yokogawa, one of our investors, I pointed out our investors, actually is putting us in PLCs. So rather than sending the data off the PLC to another gateway running our stack, like an x86 or ARM gateway, we're actually, those PLCs now have Raspberry Pi plus capabilities. A lot of them are-- >> To what types of mechanism? >> Well, right now, they're doing the IO and the control of the machine, but they have enough compute now that you can run us in a separate module, like the little brain sitting right next to the control room, and then do the AI on the fly, and there, you actually don't even need to send the data off the PLC. We just re-program the actuator. So that's where it's heading. It's eventually, and it could take years before people get comfortable doing this automatically, but what you'll see is that what AI represents in industrial is the self-healing machine, the self-improving process, and this is where it starts. >> Well, the other thing I think is so interesting is what are you optimizing for, and there is no right answer, right? It could be you're optimizing for, like you said, a machine. You could be optimizing for the field. You could be optimizing for maintenance, but if there is a spike in pricing, you may say, eh, we're not optimizing now for maintenance, we're actually optimizing for output, because we have this temporary condition and it's worth the trade-off. So I mean, there's so many ways that you can skin the cat when you have a lot more information and a lot more data. >> No, that's right, and I think what we typically like to do is start out with what's the business value, right? We don't want to go do a science project. Oh, I can make that machine work 50% better, but if it doesn't make any difference to your business operations, so what? So we always start the investigation with what is a high value business problem where you have sufficient data where applying this kind of AI and the edge concept will actually make a difference? And that's the kind of proof of concept we like to start with. >> So again, just to come full circle, what's the craziest thing an OT guy said, oh my goodness, you IT guys actually brought some value here that I didn't know. >> Well, I touched on video, right, so without going into the whole details of the story, one of our big investors, a very large oil and gas company, we said, look, you guys have done some great work with I call it software defined SCADA, which is a term, SCADA is the network environment for OT, right, and so, SCADA is what the PLCs and DCSes connect over these SCADA networks. That's the control automation role. And this investor said, look, you can come in, you've already shown us, that's why they invested, that you've gone into brown field SCADA environments, done deep mining of the existing data and shown value by reducing scrap and improving output, improving worker safety, all the great business outcomes for industrial. If you come into our operation, our plant people are going to say, no, you're not touching my PLC. You're not touching my SCADA network. So come in and do something that's non-invasive to that world, and so that's where we actually got started with video about 18 months ago. They said, hey, we've got all these video cameras, and we're not doing anything. We just have human operators writing down, oh, I had a bad event. It's a totally non-automated system. So we went in and did a video use case around, we call it, flare monitoring. You know, hundreds of stacks of burning of oil and gas in a production plant. 24 by seven team of operators just staring at it, writing down, oh, I think I had a bad flare. I mean, it's a very interesting old world process. So by automating that and giving them an AI dashboard essentially. Oh, I've got a permanent record of exactly how high the flare was, how smoky was it, what was the angle, and then you can then fuse that data back into plant data, what caused that, and also OSIsoft data, what was the gas composition? Was it in fact a safety violation? Was it in fact an environmental violation? So, by starting with video, and doing that use case, we've now got dozens of use cases all around video. Oh, I could put a camera on this. I could put a camera on a rig. I could've put a camera down the hole. I could put the camera on the pipeline, on a drone. There's just a million places that video can show up, or audio sensing, right, acoustic. So, video is great if you can see the event, like I'm flying over the pipe, I can see corrosion, right, but sometimes, like you know, a burner or an oven, I can't look inside the oven with a camera. There's no camera that could survive 600 degrees. So what do you do? Well, that's probably, you can do something like either vibration or acoustic. Like, inside the pipe, you got to go with sound. Outside the pipe, you go video. But these are the kind of things that people, traditionally, how did they inspect pipe? Drive by. >> Yes, fascinating story. Even again, I think at the end of the day, it's again, you can make real decisions based on all the data in real time, versus some of the data after the fact. All right, well, great conversation, and look forward to watching the continued success of FogHorn. >> Thank you very much. >> All right. >> Appreciate it. >> He's David King, I'm Jeff Frick, you're watching theCUBE. We're having a CUBE conversation at our Palo Alto studio. Thanks for watching, we'll see you next time. (uplifting symphonic music)

>>Live from Las Vegas, Nevada. Extracting the signal from the noise. It's the queue covering IBM edge 2015 brought to you by IBM. >>Hello everyone. Welcome to the cube. I'm John furrier. We are here in Las Vegas for a special presentation inside the cube. A special announcement. We have mayor AC Borden, mayor of Memphis and Jen Crozer who's the vice president at IBM alliances and Alliance. Welcome to the cube. So mayor Memphis, I'll see renounced city, great culture. Um, smarter cities is a big thing right now. So talk about why Memphis, why IBM, why are you here? What's the big announcement? What's happening in Memphis? >>Well, it's a great day for Memphis in addition to the Grizzlies had slipped that in there, but uh, one, uh, of, of, of just the handful of cities that are receiving what are known as IBM smart cities challenge grants, we pick a challenge. We have, uh, they help us come up with a solution to it. And it's not some abstract idea. In our case, it's how do we weed out the non-emergency calls from the true emergency calls and our EMS service? 120,000, over 120,000 calls a year, about 25,000 of them are not truly emergency calls. So what that does is it takes valuable time and resources away from those true emergency, a true emergency calls. It should be attended to on a priority basis. >>So I know that you have a Twitter handle and you've got a lot of followers. Is the tech culture in Memphis emerging describes the folks that they, what's it like in Memphis from a tech perspective? Are there people who have moved over or there's rabbit. I know there's a lot of folks in town really talk about the tech community. >>Even in my generation, I'm on there just to do a little quality checking. Also on a double analysis. I'm still in this from Zinn. Uh, we're one of the three cities that will received the, uh, Twitter grants, which will allow us to access us and get that data there and use it as we make decisions. So that's really going to be unique for Memphis. So yes, Memphis is a up to date. >>Jen, I gotta ask you because one of the things that's near and dear to our heart in the cube is technology for the advancement of better signal, not noise, whether that's society, education, the Twitter data, and we've talked to in heat you saw about this is that it's the signal of the humans. Um, and this notion of smarter cities is bringing technology to impact the human lives, not just making people get an iWatch or what are, there's some real benefits. Talk about the grants, talk about what IBM is doing because this is real important stuff. I mean, smarter planets to marketing slogan, but the end of the day technology can help people and talk about how that's part of the grant and, and why Memphis and what are these guys doing that's unique. That could be a great case study for others. We started building a smarter planet at one of the things we had to think about was what was the acupressure >>points that would have the biggest ripple effects. And it's cities, right? More than half of the world's population lives in cities. And that's growing by a multiplier every day. And so that's where we wanted to start and we've been really gratified when we started smarter cities challenge, which is a pro bono program. Give us your toughest problem. We will send you a team of six IBM executives for three weeks to help you solve it for free. We've had over 600 mayors apply and we've delivered more than 115 teens >>and in Memphis. I got to ask the question about how you look at the, the governing process now with mobile computing, you can hear everything. They're talking back in real time and it might not be as organized. Certainly tweeting all over the place and kind of getting that data is really key. What's your vision >>that that's the key. We know Memphis, we know what information we have with that. We have what in the world do you do with it? So what better partner than IBM? We know Memphis, but IBM knows the world. We're not the only one who's faced this challenge. So with this team of experts, the IBM professionals who will be owned the ground there, they will then say, here's what you have. Here's the best way to use it. Here's what they did in Rome. Here's what they did in Berlin, London, New York or wherever. So the key is not how much information do you have but what in the world can you do with it in real day to day solutions to those everyday problems. And let me point this out. This is much more than just technology with the process we're going to employ in Memphis using nurses perhaps as dispatchers so that they can ask a few more questions when the call comes in are perhaps helping us set up a system in which nurses will go to the homes of the individuals who we call frequent flyers who often call when, it's not true any emergency but this is because life is on the line here and you really have to have the ability to analyze in real time and apply the right solution. >>And this is why IBM's expertise on a worldwide basis is so critical. >>We always talk about, we always talk about two aspects of real time near real time, which is people get today it's close enough, but when you're in a self driving car maybe or an emergency situation, you want real time. So that's really the key here. Yeah, >>that's the gay real time information being employed in a real real life situation. And that's what any emergency call that's. >>So I've got to get under the hood a little bit cause we like to go a little bit into the engine of, of the, of the local environment. I mean it, people who know life today, they got their cell phones, they think it's easy to call nine one one. It's not that easy. You have these old systems and the cell towers are connected to the municipal networks and you've got a lot of volume of calls coming in. That's a challenge for the local, the technology team and with this new system that's going to clear it up. So, so talk how you guys go from this clogged, you know, traffic calls to really segmenting the emergencies from the nonverbal. >>Again, that's another critical point. We're confident this is going to work and it will somewhat declaw if that's a word unclog because I experienced just without the grant shows us that we could weed out so many of the other calls. They will not be coming in to your nine one one. So that's, that's a big, big help right there is to make sure if we could weed out 25,000 calls, which is what we had last year. We're not truly emergency calls, you wouldn't speak in terms of a Claude nine one one system. >>I was talking to a friend, they're like, give me an example of some of this clog networks that I go, well imagine your phone going off a million times a night. The notifications, cause we're in a notification economy that you have to kind of weed through that. So how are you guys using the data? What's the technology? Can you give some specifics to what's being implemented, the team and how the local resources inter interact with IBM? >>Well I think, you know, the mayor's called out this one source of data that he's getting and mayors we know are getting multiple strings. So we have our intelligent operations center that IBM uses to create dashboards for mayors to see real time data about several different industries or sources or areas that are important to them. But I think that your point about the humans talking is a really critical one. And I want to come back to that because it's easy for us to fixate on the technology. And I think one of the things we've seen in this program is the technology enabling city leaders to hear their constituents in new ways, what they're saying and what they're not saying. And also for them to communicate back with them and close the loop on feedback as policies and programs are inactive. And the thing about the presence of IBM is kind of like a good housekeeping. It will open up Memphis to resources from other national groups. As a matter of fact, we're already using funds from another entity to set up our dashboards for performance in all areas, including of the nine one one calls. So IBM is like this huge magnet. But once folks see, Hey, IBM is in there, others who come in and say, we're going to help Memphis as it develops this system. So >>may I have to ask you a question. If as automation and technology helps abstract away a lot of the manual clogged data and understanding the signal from the noise, what's relevant, what's real time, you have a lot more contextual visibility into your environment and the people. How would you envision the future organization of the government and education and, and uh, police, fire, et cetera, working together? What's the preferred future in your mind's eye? As technology rolls out? The preferred future will be >>the, that when we come up with an innovation like this will be a non event. It would just be, it ought to be the order of the day. Uh, government sometimes kind of lags behind. No, we want to get to the point where we're leading. Uh, quite frankly, my vision is that this soon will become a non event. It will become the order of the day. Uh, humans are citizens will not be afraid of, Oh, I bet not call. I'm going to get a computer on the end of the line or they got a gadget down. They're just going to try to innovate me and see if I'm going to say it would be the order of the day. That's, that's what we're working forward and what we are emphasizing here is not what we are taking away but what we are bringing in. Additionally for this technology, we will actually be able to have a good diagnosis, a good case record built on what we call the frequent flyers. We know the people who call every two weeks, but they will feel so much better when two days before they usually call. A nurse will show up and say, came to check on you and that's what's coming out of this will be customers. This will be the new norm >>because is work. This is already that they're happy people, happy customers, happy voters. Hey, you nailed it. Barack Obama had put in for the first time a data scientist on the white house, DJ Patel, a former entrepreneur, former venture capitalist. Data science is a big deal. Now. Um, are you guys seeing that role coming into the local presence as well? Yes, >>and it's so critical to government and the private sector. If you come up with an item that's not reducing the profit margin, you just shut it down. We can't do that in government that week. Every service we provide, we're locked into that. I cannot say, well the police department where we are, we're not breaking even on that. Let's just shut that down. We won't run three shifts. We'll cut out that third shift. So we have a mandate. It's an imperative. What we're doing here is not an option. This is an absolutely essential. >>So you're excited for the grant. What's next after the announcement? What do you guys be doing together? We've got 16 cities around the world who will be getting these teams. So it's time to schedule them and get started and have the grant now, how many mayors applied and what was the numbers again? Over the life of the program, over 600 mayors have applied for this. This year it was just over a hundred and we are sending teams to 16 cities this year. Well, you guys can get that technology go and get some more music pumping through the world. That's a great place and I'll see the technology, help them. This is a citizen. Thanks for, for sharing the great story. Congratulations, mr mayor. Thanks for joining us on the cube. We right back here in Las Vegas. You watching the cube? I'm John. We'll be right back.

>> From Orlando, Florida, it's theCUBE. Covering SAP SAPPHIRE NOW 2018. Brought to you by NetApp. >> Welcome to theCUBE, Lisa Martin with Keith Townsend. We are just wrapping up day two at SAP SAPPHIRE 2018. Keith, this event is enormous. We were just comparing our step goals. This event size is 16 American football fields. Enormous, 20,000 people. I think, combined, we have around 15,000 steps today. >> That sounds about right. >> Quite a few of them go to your longer legs than mine but this event is really been incredible, the energy that SAP's CEO Bill McDermott kicked off with yesterday morning has really been carried through this event and with our guests on the show for the last two days. >> No, we did 23, 24 interviews and every last one of them was high-energy. The guests were extremely excited about the products, the solutions, and the problems they're solving for, not just enterprise, but for society. I thought that was a really great theme of the guests today specifically. >> It's amazing, and you talk about, you know, the impact on society and SAP wants to be one of the top world's most valuable brands like Apple, Google, Coca Cola, who are all customers of SAP's and who all sell products that we can interact with, that we can taste, you know, Mercedes Benz, we can drive. They've got this invisible software product. They've been around for 46 years. And to your point, the stories that we have heard about how these invisible product, products, are transforming industries, are saving lives, was really something that I did not expect. >> Well when you make a great product that impact lives or... I compare it to making great content. theCUBE makes great content, that content would be found, people would take notice, you make a great product that impacts people's lives. It's no wonder that SAP is near the top of that brand recognition, brand value, 17th on the list. If they continue to do that, if they become the product, the ERP solution that you can talk to and you can ask a question, you know, not just business questions of what were the numbers the last quarter for Chicago, but you can ask a question, you know what, where is the best place to take my family to live in Eastern Europe during the summer months? That becomes value-add that people wouldn't be able to ignore. >> They've done a tremendous job building this partner ecosystem. There were hundreds of partner sessions alone. We've heard from a lot of their partners. We're in the NetApp booth, thanks to NetApp for having theCUBE here. NetApp is a customer and a partner of SAP and we heard a lot about how SAP is transforming to the cloud dramatically with the help of this massive partner ecosystem. >> You know what, we've had Microsoft, Fujitsu, SAP, NetApp, Nvidia, the list goes on and on of customers and partnerships of examples of companies that have come together and they've been consistent. In some areas, obviously Microsoft competes with SAP. In some areas, Microsoft competes with NetApp. But they recognize that without these alliances, without these partnerships, they can't solve these large, complex problems of ridding parts of Africa with mosquitoes. SAP can't do that by themselves. Microsoft can't do that by themselves. And this week was a great acknowledgement and a example of how the ecosystem works. >> They also talked a lot at this event about the intelligent enterprise where it's, you know, it's not just about digital transformation as table stakes. Companies that do it well have, or are working towards getting, this true 360-degree view of the customer which is essential. They talked about enabling that via certain things that they're leading in, or pioneering, which is connecting the demand chain and the supply chain. They really talked about enabling this new, this current SAP that's built for this fourth generation customer experience. Our lives as consumers have dramatically influenced business. We expect to have the ability to, you know, try and buy an app if we want it, right? And they're using that model very well to give customers in many industries, they have 390,000 customers, choice and flexibility. And the partner ecosystem is just part of that flexibility that they have to give. And they do a great job of listening to their customers who really are helping with a lot of the co-development in a very symbiotic way. >> Yeah, SAP is reentering this people-centric view of ERP, CRM, of data, saying that their relationship is about people. Bill McDermott spent a lot of time talking about trust. One of the reasons why people trust the brand of theCUBE is because we're on the ground, we're talking to the users, we're talking to the people. People can reach out and touch and feel you, there's a personal relationship between that brand and the community. The same thing with, got the same feel for what SAP is trying to do of, you know, obviously with over 20,000 people, I dunno if the number is 21,000, 22,000, but more than 20,000 people, a million people online watching the event, SAP the serious about this C/4HANA move, of being able to say, you know what, we are going to create a ecosystem of trust. We talked about trust with the app center and being able to validate applications on the platform. SAP has long been one of those companies that's serious about their partnerships and validation and certification of platforms. So whether it's HCI, storage with NetApp, the deep relationship with NetApp, SAP is going to put its brand upfront and say that if you're going to engage with one of our partnerships, there's a transient trust that goes from SAP to their partners. >> And we talked with a number of folks working in different groups within SAP focused on the customer. This morning we had on their Chief Customer, a guy from their Chief Customer Office who talked about these, kinda top 100 strategic accounts that they partner with who then also they take that information, those learnings and don't just improve the technologies but they also use them to influence much greater than a hundred customers. They're strategically utilizing that data. We talked yesterday with one of the gentlemen running the SAP four, S/4HANA community rather, and the Leonardo community and the amount of engagement that they have in that community, especially in Leonardo which has only been around for a year. The customer engagement is key but also their reaction to it, and I would say even, I think we heard a lot of how they're being proactive with creating content and enabling their customers to be able to learn at the same time as they're learning from their customers. >> Yeah some hero numbers that we heard this week: 6,000 people in that HANA, the S/4HANA community. While the Customer Success Group focuses on the top 100 customers, there were, I think 38,000 people following the Twitter account, so there's obviously outreached stretch. The Leonardo and S/4 communities have created a thousand videos on how-to. So obviously the impact of and the reach of SAP has ambitions of not just raising brand awareness and getting into that Top 10 with Apple and Google, they also have the ambitions of becoming a platform, a ecosystem. You know, we look at Microsoft as kinda one of the ultimate platform companies. Microsoft partners make more money off of Windows than Microsoft makes off of Windows. SAP seems to have the same goal of their partners, there's a hundred partners on the show floor, that should generate more revenue than SAP which would be impressive. SAP, I looked the other day, $136 billion market capital, not a small company at all. >> So you have an interesting perspective, for many reasons, but one you've run large SAP infrastructures before. And here you are now at SAPPHIRE from the press and media, the analyst perspective. What are some of the things that really surprised you in all of your experience as a user of SAP to now covering it from this angle. >> You know what, I don't know if it was a year ago. It was not even a full year, my anniversary for running my company is August. So less than a year ago I ran SAP for a large pharmaceutical. And we're in the throes of selecting where our next platform was gonna be hosted. Cloud was a possibility and it is amazing how the conversations have changed from my peers a year ago, or a year and a half or even a year ago, to now to how readily acceptable customers are of running mission-critical, the core of the business, 77% of the world's transactions, we heard today, goes through SAP, how willing customers are at running those work goals in the cloud. Second piece, which was probably a proof point, how much SAP has improved SAP in the cloud. SAP has marketed SAP HANA and SAP as cloud-ready applications, it was more of something that you... I took legacy application, I installed it on VMs in the cloud, cloud-ready. No we've given examples from the hyperscalers, specifically Google, of how, and Microsoft of how, customers are coming whipping their credit card up, spinning up instances of HANA, spinning them down. Google talked about how you can migrate your whole ECC on HANA to the cloud within 30 minutes to two hours, amazing movement in cloud. I think it's by far my biggest surprise coming to this show. I didn't expect SAP to accelerate their cloud adoption as fast as they have. >> I'm curious to your thoughts too about simplicity, simplicity of message, you know, what's their best-run businesses campaign? Best-run businesses run on SAP. Simplicity has long been part of their messaging. As we look at the SAP cloud platform and some of the announcements there today and you look at, they've got Ariba, and Concur, and Fieldglass, and SuccessFactors, with the C/4 announcement from yesterday, what is your impression on, have they been able to sort of simplify and kind of reduce customer confusion in terms of this breadth of products and technologies that SAP now delivers? >> You know, SAP is a big company and they have a lot of products. They've been around for 46 years. You know, we didn't talk about any legacy database stuff. They still own Siebel so they still own a traditional database company. It's easier said than done to simplify the message. When you come to... You know, we talked to interviewee after interviewee, customers are still overwhelmed when they look at a overall problem. They can even identify SAP as the potential partner to solve it, but 300 products is still 300 products. It's very... You can help simplify the message by throwing those products in categories, sales force, which product you lead with, so new customers, you know, sales force will help you with that. Traditional customers that don't have deep relationships with their sales force and solution providers, maybe, I think there's still a little difficulty around understanding the messaging around all of 300 products. I mean, it's 300 products. >> Well, there's always work to be done and well we have... There was a lot of product announcements, a lot of energy, and evangelicalism that you and I heard consistently throughout the event and on-set here. A third area that I think really struck me is, SAP has been very vocal about having an initiative to raise the profile of women in technology. They did an excellent job of getting women onstage during both keynote sessions, yesterday and today. From their CMO, Alicia Tillman, to Lindsey Vonn and a whole suite of women Olympic athletes that were yesterday in the general session, to some of the women that were doing some of these outstanding demos and I, I really tip my hat to SAP because for being as large and as lengthy of an incumbent as they are, they're really able to focus on some of these key areas and we at theCUBE love to cover that because it's something that really needs consistent awareness. >> Well, I dunno if people would notice but we probably, both of us, are very vested in diversity and Silicon Valley, in general, is always appreciated when companies go, not just acknowledge the challenge of diversity, it is a very, very difficult problem. It's probably one of the most difficult problems in our industry. So to actually put some meat on a bone, announce the problem, announce the challenge, and go forth and put, you know, obviously, extremely capable women and minorities in the forefront. >> Yeah. Well Keith, always a pleasure hosting with you. Thanks so much for working with me the last couple of days, it's been-- >> I always enjoy it. >> I do too. It's really been a really fun, energetic show so thanks for all of your help. >> Thank you. >> Keith and I wanna thank you for watching theCUBE. Lisa Martin for Keith Townsend, we're from SAP SAPPHIRE 2018. Thanks for watching. (energetic music)

>> From Orlando, Florida, it's theCUBE. Covering SAP Sapphire Now 2018, brought to you by NetApp. >> Welcome back to theCUBE, I am Lisa Martin, with Keith Townsend. We have been here all day at SAP Sapphire 2018. Keith, this venue in Orlando is so huge. It's the equivalent of 16 American football fields. >> Yeah, probably should not have worn a pair of new shoes. >> No, but you did close your rings, so it's a trade-off, right? >> It's a trade-off, yeah. >> So, the keynote this morning started out with a bang. Bill McDermott, the CEO of SAP, is probably the most energetic, evangelical, C-level I've ever seen on stage. You really could feel the excitement, the momentum. They also followed that with some great announcements. You know, they've been saying for awhile, being pretty bullish about wanting to not just disrupt the Sierra market, but wanting to become one of the world's most valuable brands. They wanna be up there with the Apples, and the Googles, and Coca-Cola and Mercedes-Benz, who all have products that we all see, and touch, and feel, and buy. And they announced that the brands e-rankings just came out the other day, that they're number 17, up four spots from last year. So, their momentum is, they're really putting their money where their mouth is. >> Yeah, so SAP is the cash register of the world. 70% of the world's transactions go through SAP, but most of us don't see it. So, it's amazing to see that they're ranked number 17 on those brands that are very, you know, if you told somebody you worked for SAP, they'd be like, oh, okay, I think I might have heard of that. >> Right. >> Or, I've heard that that was the reason why manufacturing is down, because the SAP system was down. So, it is a bold statement to say that you're gonna go from that, to a household name. Interestingly enough, part of that is becoming an ecosystem. So, becoming a platform. What we've heard today was a lot of talk about how SAP is transforming from a product company. You know, a point-of-sale system is one thing, but to say that you've built a ecosystem, and a platform around that, is the goal that I think I heard today from the stage floor. >> And you're right, you talk about, you know, them becoming a household name, with a product that's basically invisible to most people who probably use it. They have amassed 390,000 customers in 46 years. They've been around for a long time. This event, though, is massive. The partner area alone is huge. There's probably more than 20,000 people not just that are here, in Orlando, but, he said, Bill McDermott, a million people engaging with SAP Sapphire via the online experience. That's enormous. But to your point, it's all really fundamentally due to the partnerships, the systems integrators, the technology partners and more who have helped them on their transformation. >> Yeah, we had KPIT on, they said the guest has been on 20 Sapphires for 20 years, the event has gone on for 25 years in some form. He remembered, initially, they might have had one or two sessions. They have 12, KPIT has 12 sessions this year at the Sapphire 2018. There's a huge ecosystem of partners, here on the show floor. Over 500, I think, sessions in general. We had the VP of Community for S/4. They have 1,000 how-to videos on how to just do basic things in S/4. Huge community, huge event. SAP is starting to make end rolls and becoming, again, not just a products company, but an ecosystem company, I think. Sapphire in Orlando is a great example of how they're expanding the brand. >> Yes, and in fact, on the brand part, you know, that's one of the things that their CMO, Alicia Tillman, who was on main stage this morning, that's something that I've heard her talk about before. She's been the CMO for about nine months now, and she said, you know, and marketers will know, campaigns and messaging will change every quarter, six months, and that is fine. It's the brand narrative that they really started to work on at SAP. So, you're seeing this "Best-run companies run on SAP", it's sharing the value of what SAP can deliver with their partner ecosystem, in terms of how it's helping customers transform their businesses, transform industries, save lives. They've done a very focused job on showing how this invisible technology is really revolutionizing the world. They're now going, you know, full-force, embedding A.I., and really being quite bold, they're saying. I loved what Bill McDermott had on the slide this morning, of augmented intelligence. And there's always a lot of concern with A.I, right? Jobs being replaced. And he talked about what he, and some of the other world leaders, were talking about. And I liked augmented intelligence, to augment humanity, this connection of humans and machines working together. They're really being quite bold, and focused, in that area. I'm just curious what your take was from an advanced analytics A.I. perspective. >> So, there's a lot of talk around advanced A.I. analytics. At the end of the day, it's about actual business results. We're here in the booth of NetApp, who has done a great job, frankly, of transforming their image from a storage company in the middle of a transformation to being known as a data-driven company. So, NetApp has gone through a similar change that SAP is looking to do, from a brand perspective. Reasonably enough, we had the CIO, Bill, from NetApp, that talked about that transformation, and how data is a key part of their own transformation, internally. And, how SAP could probably hold NetApp up as a great example of a company that's using the predecessor to C/4HANA, which was just announced, on the staged hypers of taking data, analyzing that data, applying A.I, machine learning, more like machine learning in reality. Machine learning to that data, and then getting insights, so that humans can make better decisions. >> Right. You know, on that front, one of the themes I heard today, Keith, from not just Bill Miller, the CIO of NetApp, who was on here with us earlier, but some of their other partners, NetApp and SAP's partners, all talk about their own transformations, internally, as essential for them to become intelligent enterprises, which is a lot of what SAP's talking about. But I also thought that was quite valuable, from an external perspective, to hear NetApp talk so candidly about their transformation, and share that with their customers who are in similar positions. I think, when vendors will, say, drink their own champagne, and there's real proof there in the pudding. I think that's tremendously valuable for these brands. And we've just heard that kind of consistently throughout the day today, of companies that are showing how they're transforming to then help their customers also transform. >> So, one of the things that we like to ask on theCUBE is not just about current customer base, but, what new customers are you attracting? So, one of the interesting conversations is one of the last ones we had with WorkSpan, and how they're a small company, and they started out the gate with SAP, and how the brand has gone beyond this, oh, this is a manufacturing, supply chain, you must be a Fortune 500 company to even consider rolling it out to. You know what? We're a brand new company, providing a data-driven product, and out of the gate, we're selecting a S/4HANA and the platform to create this new product that's consumed by not necessarily technologists, that powers an alliance platform to find and curate business alliances. I thought that was an extremely interesting interview that shows the power of expanding beyond just a focus on traditional enterprise, but the power of data. And once you've become a platform, how you can power your partner ecosystem. >> I thought that was a great example, as well, of a company that's only been in business for three years, less than four years. How they saw this gap in the market, where they said, you know, we're surrounded by alliance partners of SAP's in this 16 football fields location that we're in. And WorkSpan found that 60 to 75% of announced alliances fail. Huge opportunity for them to then get in from a systematic perspective and align, you know, two companies' marketing automation systems, for example, and sales automation systems. And they really saw this big opportunity to, like you were saying, create an entirely new product, and probably create a new market as a result. I thought that was a really modern example of an idea that saw a huge gap, and can be transformative. I asked Ahmed, after we stopped rolling the cameras, all right, so you found 60 to 75% of these announced alliances fail, typically. What does WorkSpan think you can do to bring that number down? And he said, within two years, we wanna get that down to about 30%. >> Wow. That is an amazing stat. So, let's look at the companies that are digitally transforming. So we had two guests that I want to highlight, one with Mike McGivney from SAP SuccessFactors, which is SAP's people-focused cloud, and then Wolfgang Hopfes, the head of SAP Business for EMEA. And they're on a unique challenge. SAP has been around for 46 years, and in IT years, that's like, you know, 1,000. So, there's a lot of technical debt, that companies are now paying for. You know, back in the nineties, early 2000s, customizing SAP was all the rage. Now, customers are faced with, they have to digitally transform their organizations, how do they do so? Well, it's not so easy to move from a customized SAP to S/4. Bill trumpeted the numbers of 1,800 SAP HANA customers, which is great, well over a billion dollars in sales for an in-memory database. However, SAP has over 300,000 customers. So there's a lot of opportunity, but a lot of challenge. So, the ecosystem of partners, Fujitsu, NetApp, other infrastructure companies looking to help simplify the infrastructure so that technologists within these customer organizations can focus on the higher stack of those larger business challenges of basically pulling apart what they've built. Bill from NetApp shared how difficult their transformation was from their CRM to >> Hypers? >> Hypers. He called it painful, a painful six months. And what we saw today, I think, was a reality check. A lot of enterprises have a lot of pain ahead of them. >> Well, it's pain in a number of areas, and one of them is cultural. And I really thought, you know, you say, SAP being 46 years old is like, 1,000 in IT, or dog years. They're like the Gandalf of IT, right? But one of the things that I found quite remarkable is 46 year-old history, 390,000 customers. But clearly, they have been able to evolve their culture to be able to support what their customers need, and go from just being a supply chain procurement-focused type of business. And I thought that was really quite compelling, to see how they must have had to transform their culture, so that they can help businesses transform. They make it look easy, with the messaging and the momentum, but that was something that for a company that's an incumbent like that, is a bit of, you might say, even a model for how to do that right. >> Yeah, we talked to Joe Lazar, he's the SAP VP of Global Technology Partners. He talked about how SAP likes to be pushed to be a little uncomfortable by their partners, and we asked him the tough questions. You know, there's been tweets and there's been announcements from all the ACI vendors. I've talked to customer after customer that says, you know what, S/4HANA on HCI is what we want. A very quotable comment that he made was, we're not doing S/4 on HANA because we want to, we're doing S/4 on HANA because customers demand it. So, SAP is definitely listening to customer demand, S/4 on HANA is one of those things. You know, he tried to stay away from the bad word of certified on 4HANA, and validated, and focused on solutions, but SAP has a little ways to go. And that's kind of a, you talk to any HCI customer, validated and certified 4HANA is a bad word today, but SAP understands it and they're moving to certify the platform for HCI, so I thought that was a great example of them listening to customers and continuing to transform over the years. >> You're absolutely right. In fact, you know, if you look up digital transformation, one of the first pillars that you're gonna see is you gotta become customer-centric. And we really heard that a lot today. Even NetApp, when you were talking with Bill Miller about ONTAP in the cloud, going it's okay guys, maybe we have to listen to our customers. If we don't we won't be in business. That's a hallmark of an enterprise that is digitally transforming. >> Yeah, I'd argue that Dave Hitts was the one who forced that, that kind of cultural change. You had to bring in the founder to talk to the engineers and that had very engineer-driven thinking And I think Dave was very direct, like you know, we have to make the change or we won't be in business. The pendulum has changed to cloud. The SAP, which is not by any stretch of the mind, was never designed to run in the cloud, but they're adopting the technology for what customers are demanding. There's an AWS booth here, Fujitsu was the first one to say that, you know what, if customers need fail-fast environments, that's exactly where they should go, and put S/4 implementations, and then steady states should be moved to RMPRAM or private dating center or hosted solutions. So, the ecosystem seems to be embracing this change. >> Definitely. Anything that you're particularly looking forward to tomorrow for Day 2? >> You know what? I love talking to customers, so I'm looking forward to more customer conversations, talking about how is this being used? We haven't really talked a lot about Leonardo much. So, you know, IoT, A.I., how are these things that get a lot of press being perceived by actual customers? How are they being implemented? What's their true adoption rate? >> Awesome. Well, I look forward to hosting with you tomorrow, Keith. Thanks so much. >> I appreciate it. >> Thanks for watching. Keith and I have been at SAP Sapphire, bringing you some hopefully great informative content. From the NetApp booth, Lisa Martin for Keith Townsend. Thanks for watching.

>> From Orlando, Florida, it's theCUBE, covering SAP SAPPHIRE NOW 2018. Brought to you by NetApp. >> Hey, welcome to theCUBE. I am Lisa Martin with Keith Townsend, and we are live in the NetApp booth at SAP SAPPHIRE 2018. We are joined by Robert Stumpf, Senior Director of IT, Enterprise Solutions Delivery. Welcome to theCUBE! >> Thank you, thank you. >> So we're here in the NetApp booth at SAPPHIRE NOW. As they said in the keynote this morning, they're expecting a million people to engage with SAP SAPPHIRE this week. >> Yes. >> Think, I've heard rumblings there's about 20+ thousand people here in attendance. >> Yeah. >> Huge event, huge show, lots of announcements. Let's talk about NetApp and SAP as partners. Specifically in the context of the Next-Gen Data Center, bringing cloud-ready solutions to business application. What are you guys doing there with SAP? >> Sure, I can talk a little bit about that. The NetApp solutions fit into the Next-Generation Data Center in a variety of different ways. We have the All FAS Flash that really is the core of our product base and is really the workhorse of all the hardcore applications, gives you really a strong performance in the storage area. Then we have the Cloud Volumes with when you want to scale out to hyper scaler, and you can use the Cloud Volumes abilities there. And then when you look at our HCI components, it is capable of giving you a lot more of the container-based compute power, so we fit into a variety of different components there. >> So, Robert, we're at SAP. And SAP hasn't been traditionally known as a cloud-aware application. Tell us, from the NetApp perspective, what's changed with SAP over the years that now, you can comfortably talk about SAP as a cloud-aware application? >> So SAP's moving a long way in that direction. You saw it this morning in the keynote that they were talking about the C4, their customer-focused applications. That's really kind of putting a framework on top of all of the customer engagements, and making the customer the center of everything. So they're moving a lot in that direction. We at NetApp have implemented their Hybris platform, their cloud for customer application. We just went live with that last year, so we're on that journey with SAP as well. >> So, as we talk about that, what makes the application, or what make applications in general cloud-aware? >> Okay, when you look at making something cloud-aware, you want to really look at the architecture that you have underneath it. So you'll build something that has a lot more automation in it, a lot more scalable, where you don't have to, the scalability's built into the framework, like you're leveraging. In the case of our NetApp support site, which we just completely re-architected and went live last month, we have built that on what's called a MEAN stack, so that's where the Mongo database and the back-end that's a NoSQL database, and then on top of an Angular node.js, which gives you much more robust framework for you to be able to scale-out your application. So with it being a website, and your volume can go up and down, so you want to be able to scale the application without needing people to get involved in that scaling, so they will just fire up new containers as needed as the volume increases, and it's a lot more robust in architecture. >> So if we look at Hybris and we look at NetApp products and solutions, that framework and architecture. Can you paint a picture for us what NetApp solutions and products are cloud-aware? >> Sure, the cloud-aware applications, really you need to look at the complete stack of the Next-Generation Data Center, which is really embodying the on-prem data center, your hyperscaler cloud data centers, and then a private cloud if you so wish to build one. So the Next-Generation Data Center takes advantage of the All FAS Flash on your on-prem solution, so you've got your performance, high-performance scalability. Then your Cloud Volumes allows you to move your data between your on-prem out to the hyperscaler as you need to, and the HCI component gives you that container-based compute array that allows the applications to scale. Also, you can leverage StorageGRID, which is much more of an object-based data base, which is something that you'll use extensively on cloud-aware applications. >> So, thanks Keith. So one of the things that was announced this morning, you mentioned C/4HANA where Bill McDermott was sort-of expected to announce what SAP was going to be doing that's gonna help differentiate them. They want more share from Salesforce and Oracle. He made kind of some aloof references to that, but one of the things that he talked about was: companies need, in this day and age, speed obviously, but to move away from a 360-degree view of sales automation to an actual 360-degree view of the customer. I'd love to get your insight on NetApp and SAP as partners together. Are you seeing any particular industries leading here? We think of manufacturing, maybe automotive oil and gas, but I'm just wondering from NetApp's perspective, are you seeing any industries that are really leading-edge here in evolving to a Next-Gen Data Center that enables this 360-degree view? >> There's a variety of different industries that are doing that. If you take a look at applications like Netflix and Amazon Prime, those applications are architectured to be scalable and to be much more robust, and they are much more focused on the customer. And because you don't have outages, right? They don't take the system offline when they're doing an upgrade to their capabilities. When was the last time you heard of Netflix going offline for twelve hours to do an upgrade? So, these applications are built much more robustly around that, and that's what one thing that we are looking to do at NetApp with the Hybris implementation that we did with SAP, and we're also upgrading our back office CRM system to their CRM on HANA on-prem, and we're gonna be taking advantage of the Hybris capabilities there to give that full picture of the customer. We'll be heavily engaged with SAP on their C4 journey and making sure that we are a part of that as well. >> So it's great that you brought up Netflix as an example that continues to be operating an environment that has this huge back-end automated with technology. SAP traditionally hasn't been considered a technology that you could upgrade on the fly. I've managed an SAP environment where we can only take twelve hours of downtime a year because mission critical, it's very difficult to get that time. >> Yes. >> How has the NetApp data fabric story played into making that a possibility in your own environment and customers' environments? >> Okay, we leverage a lot of the NetApp storage on our on-prem system. I'm in the exact place, same situation as you were talking about. We have a lot of mission critical customers that are on our support application. I have to give 90-days notice to take the system down for any longer than four hours at a time, so I'm in that very similar situation. So we leverage a lot of the NetApp technologies to make sure that the applications are available when I'm doing the upgrades, and we can do rapid copies of the data that's in there, make sure it's all robust. Our data, failover database, failover systems, are set up that way so that they take advantage of the snapshots that we got from the application, and we're working with SAP. The SAP Hybris application is actually built on top of NetApp storage, and we're working very closely with SAP to re-architect our applications, to take advantage of the capabilities that NetApp storage brings to the equation. >> So none of this coming into its own in this hybrid cloud model that's been around 26 years, right, long time. But now, it's everything you see. You mentioned Netflix, and I don't know anybody on the planet that would survive if Netflix went down for an hour, let alone twelve. So speed, access to data, but this evolution of NetApp, I'm interested, and you know now again in this hybrid cloud model, you guys made your name from building network attached to storage on-prem data centers, the announcement with Google Platform just last week. Talk to us about some of the evolution from NetApp, from your perspective, from the storage perspective, into really facilitating this hybrid cloud model. >> Sure, we are really at the forefront of that because at the end of the day, it's all about the data. Right, your application can run wherever you want, but wherever your data is is really the key. And that's the framework that we're putting in place is to make your data a lot more mobile. So if you want to keep the data on-premise, then you can keep it on-premise. If you want to move it out next to the hyperscaler, you can burst it out, you can use the Cloud Volumes and migrate the data. So the NetApp picture, the story is really in making your data much more mobile and moving it to the location of choice for any particular workload that you're looking for. >> So, we can't have a discussion in 2018 about data without talking about privacy and security. What's the relationship in ensuring that NetApp and SAP is one, media requirements in GDPR, we have to talk about GDPR, we have to talk about security. How is NetApp securing data and ensuring that in-users' and organizations' data stay private? >> That's a very good question, right? It's definitely a challenge that a lot of companies are struggling with, and the tools that NetApp provides with our storage systems are paramount, security is paramount, and that's something that we're very much focused on in making sure that your data is your data, and the specific components of the data that you want to keep on-premise, which you want to keep as much more secure, then you can keep that on the NetApp All FAS Flash storage systems, and then you protect it as if it's in your own kingdom. But then the data that's a little bit more lax on the security sites, then you can push that out onto the hyperscalers and use the NetApp Cloud Volumes to have it outside of your on-premise. You know, it's like your own firewall. >> So one of the basic things as a ONTAP customer that ONTAP customers depend on and the private data centers, this ability to encrypt data on the fly. Now that we look at, you know we see ONTAP in the cloud, do we get that same basic capability to encrypt data on the fly or encrypt data while it's in transit? How do I know my data is protected from an encryption perspective? >> You get the same capabilities when you're using the on-cloud tools that we provide, so there's no real difference in that, and that's the beauty behind that. You're using the same storage management tools for your Cloud Volumes as you would be for your on-premise systems. >> I want to ask a question on competition. There's a lot of co-opetition that's going on just at SAPPHIRE alone. With what you talked about about how NetApp is leveraging Hybris, you mentioned, to really kind of get towards that model of connecting supply chain with demand, getting that full view of customers, SAP partners with probably all of your competitors. So how is what NetApp is doing internally to digitally transform, how do you see it as giving NetApp that competitive edge against the other guys? >> Okay, the way that we look at our competitive edge at NetApp from an application standpoint is really focusing on keeping our core capabilities very, very vanilla. So in the implementation with Hybris, we were very much focused on not customizing the application. But because at the end of the day, you sell stuff, you build stuff, you manufacture it, and you support it. So those are the core capabilities, and we've kept that very vanilla as much as possible within the implementation. Where we differentiate, that's where we customize. So our application landscape is much more focused on customizing for the differentiating capabilities, and that's the component that's specific to NetApp and how we do business. And that's the way that we go about differentiating ourselves from our competitors. So we use the core capabilities of all the enterprise applications that we have, that we purchase such as Hybris, and then we go build our custom solutions that are differentiated, that really searches our ASUP, AutoSupport system, that gets what's embedded right from day one, that's a custom-built application, it's very proprietary, it's really the keys to the kingdom for our organization. And that's something that's very, very integral as part of the NetApp culture. >> So, let's talk about some lessons learned from that. One of the pain points for many SAP customers is they look at capability like ECC on HANA, really want it, but they've customized their environment too much, so making that switch is extremely difficult for them. What have you learned as a team that says, you know what, the best way to stay in line with SAP and follow that roadmap for mission critical applications that are both stable and differentiating, you should follow these basic policies from a hygiene perspective. >> Sure, we actually went through that last year with our project where we replaced our Sales Force Automation system, and we implemented C4, C4C Hybris. So the key to that is really getting the executive sponsorship bought-in to making sure that you're adhering to the vanilla applications and not customizing it. So we were very fortunate where we had Henri Richard and Bill Miller, our CIO. They were the executive sponsors of the project, and they were adamant that we would not customize the application, and we went through, it took us six months to replace our CRM system for an office CRM system. Very proud of that project. It was an incredible painful journey to go through, but the benefits that we got out of the end of it are phenomenal because we were in that situation where we had an overly-custom SAS application that was running our sales organization that really wasn't meeting the needs of the business. Now we have a much more agile implementation that's on top of SAP's Hybris platform, and we're taking advantage of the new capabilities they introduce, rather than focusing on our own customizations. >> That's a great summary. I think you articulated very well what, one of the themes was from Bill McDermott's keynote this morning, is making things simple, is not an easy thing to do, but it's critical. There are so many-- >> It's totally critical. >> business outcomes that come out of that, not just stream-learning processes, improving sales and marketing and connecting them together, but really affecting revenue, profit, share, et cetera. So Robert, thanks so much for stopping by theCUBE and chatting with Keith and me today about what you guys are doing with SAP. >> Great, thank you, thank you for your time. >> We want to thank you. You're watching theCUBE: Lisa Martin with Keith Townsend from SAP SAPPHIRE 2018, thanks for watching! (light percussive music)