 We're back here live at Sapphire now, SAP's annual conference. We're live in Orlando, Florida. This is SiliconANGLE's theCUBE day one of three days of coverage. This is our wrap-up segment. I'm John Furrier, founder of SiliconANGLE. I'm here with the co-founder of Wikibon, David Flo here, I was going to say David Volante. He's in Las Vegas with ServiceNow, an amazing other company. And also Jeff Kelly, the number one big data analyst on the planet here at Wikibon. Jeff, David, thanks for coming on the wrap-up here. Let's break this down day one. So we saw the keynote, we saw the positioning, we saw McDermott, Hanna's front and center. Clearly they're driving the Hanna bus and that's going to be a disruptive enabler for themselves as well as the marketplace. So David, I want to start with you. What do you think of day one? It's good. The vibration is good. Large number of exhibitors here. The ecosystem is very, very strong indeed around SAP. Interesting to hear about SAP Hanna. It's still early days SAP Hanna. I'm not quite as bullish about the long-term success of that. The data in memory theme is very important. I personally think that the data in memory theme needs some refinement to be more data in non-volatile memory so that it can be lower cost and much larger amounts of data. Those are some refinements I'm sure that SAP are looking at and will add to it. But yes, a fun day, exciting day. And I think the demeanor of the SAP customers is that they're fundamentally satisfied with the status quo at the moment. Things are growing. But underneath it, I get this slightly uneasy feeling that maybe it's looking a little bit into the future and saying, are we on the right track? The Hanna is a good track, but is the whole of SAP ecosystem? Is that something that's reached its peak and it's going to be difficult to sustain the previous growth? Do you think they're putting a human face on Hanna by getting out all these people from the NBA? Obviously, great marketing. I mean, I've always been impressed with SAP. We're here in third year in a row where I've said, I love the show. They do a great job. Their messaging is on target. It's not Jeremy Burton style messaging. It's just a lot of messaging. A lot of individual. A lot of volume. Jeremy Burton at EMC is all about one message. They're putting out a lot of message, but it hangs together. It hangs together because it is about users. It's users solving business problems and it's very, very business focused. So it hangs together for that reason. People are doing it in order to become more efficient or to extend their reach or to enable new ways of doing business. So yes, I always love shows where the focus is on the end users themselves. If you remember at OpenStack, it was extremely good to have all of those users coming up talking about how they were using OpenStack. And here, everybody is talking about how they're using it, the business impact. Absolutely. I would agree with that. I think Big Data and Analytics lends itself to good user stories, right? I mean, there's some interesting correlations people can draw with a different data set. So it actually is an area of computing that lends itself to really bringing customers out front and center to tell their interesting stories and their journey. Things like we saw with the MBA on theCUBE today and other end users really driving new types of analytics and creating new capabilities. Not always the most sophisticated, but they're still new. I mean, the things like the MBA is doing, making analytics available to their end users. You know, this isn't necessarily rocket science, but it's extremely useful to the MBA as they try to continue to engage more with their user base. But I mean, the stories are compelling and you know, OpenStack, no offense to OpenStack, we were there, we loved that conference. We were really bullish on it, David, but you know, Rackspace stock is down and the analyst saying, what is OpenStack? So again, it's a much earlier, earlier stage. So in our last interview, we were talking to John Appleby from Blue Friend. He said, one of the biggest challenges he sees with HANA is that people just don't know what to do with it. It's compelling use cases, so they're in that formation stage. Would you agree with that or how would you comment on that? I think it's a little limited, it's the problem, because it works for data sets which are quite, relatively quite small. So if you've got a cube of data which is small and you can really iterate through it, it works brilliantly. But if you've got a cube of data where and you sometimes need to go and get some other data from somewhere else and combine that, and the effort to create that cube of data is pretty high as well. So if for use cases where it fits, it fits brilliantly, it's very nice indeed, it really works very well indeed, but it's a limited section of the total infrastructure. And if you really compare it with big data, like your views on this, it doesn't really meet the big data criteria of terabytes and petabytes of data. What was it? Well, you know, it's interesting because we had this debate internally when we were doing our market sizing earlier this year and I think there are use cases where it fits the criteria, but I agree with you, it's not particularly, from a volume perspective, it's nothing like Hadoop, it's not, you know, it doesn't compare there in terms of volume of data. And I agree with you, it's fairly, the use cases are focused on, I mean, think about the SAP customer base, the ERP customer base, and so the idea of, you know, SAP talking about essentially using HANA to improve the functionality, I should say performance of, you know, their core ERP business, but that's not big data. It's beneficial to their user base, but that's not big data. Where big data comes in is when you're starting to do some new types of analytics, some predictive analytics. So I think if you're using HANA to really drive more predictive forecasting type analytics and you're making decisions based on those analytics, particularly in real time, highly optimized to make real time decisions, you know, those are use cases that I think would fall under at least the mindset of big data, if not the strict technical definition. But, you know, again, I think a lot of the users here are more interested in HANA to kind of supercharge their existing ERP installations, which, you know, is again, very beneficial to them, but not particularly exciting from a big data analytics perspective. And I think that fits into my observation about the future. I mean, if you look at the very large amounts of data that are coming with the streams of data that are coming out, you know, we take those early people with making decisions in real time about what advert is going to be created or what price is going to be created or whatever it is, those are looking at huge volumes of data coming in and then looking at those streams and making real time decisions based on that. And that's not HANA's sweet spot at all. So, that's the sort of thing that I think is going to be the future of big data and real time analytics. And it's interesting to see that they're focusing on their ecosystem, but the internet of things is being pushed out way into the future. Let's talk about that. Let's drill down on that because I want to highlight that Sanjay Poonan's mobile press conference was impressive because he's an impressive guy, real big fans of Keeb alumni. But they're, you know, mobiles are tough enough to crack, right? You know, put a wrapper around something, you rebuild. Again, they're trying to put a good solution together, but his notable comment, David and Jeff, I want to get your, take a minute, you're both doing work on the infrastructure and on big data side about the internet of things. That's a North Star in their vision. Where are we with the internet of things and the industrial internet, things of that nature? We're hearing a lot about it with Pivotal. What is that, is it just puffery here? Is it, is it they have anything? Have you seen anything? And what's the state of the market? It really is the wave after the current wave. And there's really two key issues about the internet of things. It's sensors and it's software. Sensors to know what's going on and those sensors can be everything from being inside machines to being inside you or me. So tell us how our heart is going or how our lungs are functioning. So it's a potential for a huge amount of data to be coming in and with it is coming new opportunities, business opportunities, not only to improve current functions, but to bring out new ways of doing business. When we were at the EMC world, Paul Moritz was talking about going from selling engines to selling engine hours because with all that sensing, they would know the best way of being able to maintain that engine so that they could commit to selling the engine hours for so many hours over a period of time. A new way of doing business, similarly with hospitals, a new way of doing business, direct to the end user, to you or I, as opposed to through a hospital or through something else. So very different ways of doing business. And so where are we, Jeff? I want to get your comment, but I want to ask David first, where are we in the research? I know that you guys initiated coverage recently at Wikibon, which is all free content by the way, go to wikibon.org for free content. Is it true we've initiated coverage at Wikibon on internet of things, on industrial internet? Can you give an update on where that's at? Well, we're doing kind of our initial research right now. We're talking to a lot of end users. We're talking to some of the vendors in the space who are going to really help build the platforms to make this possible. But it's pretty early days. I think a lot of the initial wave of big data was focused on, in some senses, it was about social data, right? What can I do with all these tweets coming in? Which is interesting, but limited value, I think, overall, to the larger business world. And then we were talking a little bit about, I think people tend to talk about, what can I do with my own internal data? Things that I've got in my ERP system here at SAP or wherever, and maybe bringing in a few additional data sets. I think what the internet of things or industrial internet we're really talking about is an interconnected world of devices, industrial level machinery, that allows us to really orchestrate whole processes, if you will. So in healthcare, it's understanding that when a person with certain symptoms checks in or is admitted to a hospital, it kicks off a series of events that could be related to the machines that are going to be used to diagnose the patient. It could be related to the staffing of the nurses and doctors in that hospital. How that's going to impact the bottom line of the hospital. So it kicks off a number of events and analytics to really optimize those processes. And ultimately, the goal, if it's in healthcare, is to find efficiencies for the hospital to deliver better care for the patients. In some cases, that's going to be delivering that care or that information before the patient even gets to the hospital, hopefully preventing them. But in the world of aircraft, for instance, it's going to be understanding your fleet as a whole. It's going to be understanding individual jet engines and where they are in terms of maintenance and when they need to be upgraded or when you're pushing them too hard or not hard enough. And having all these systems interconnected so that they can make intelligence decisions in real time based on analysis of all the different data that's flowing through the system. So that's a very complex process. So what's the timetable on this? Obviously, you're talking a lot of customers and other data. When are we going to see something? Within the next four weeks, four to six weeks, we should be having the first push out of definition, if you like, of the internet of things and our first sort of... There you have it. If you're out there and you want to reach Wikibon, go to wikibon.org. There's a contact button there. Contact the office in Massachusetts or David Flores out in California. Get him on Twitter. You can find myself or David Vellante, Jeff Kelly, we're all on Twitter. It's easy to find with a cube as well. So go to wikibon.org, by the way, free research and go to siliconangle.com. This is a wrap up of day one. We're excited to close it out and thank you for watching and stay tuned tomorrow. We had coverage all day tomorrow and Thursday. It's the cube wrapping up day one. That's a wrap. We'll see you tomorrow.