>> Live from Las Vegas, it's theCUBE, covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host Rebecca Knight, along with my co-host John Furrier. We're joined by Ali Ghodsi, he is the CEO of Databricks, thank you so much for coming on, for returning to theCUBE. You're a CUBE veteran. >> Yes, thank you for having me. >> So I want to pick up on something that you said up on the main stage, and that is that every enterprise on the planet wants to add AI capabilities, but the hardest part of AI is not AI, it's the data. >> Yeah. >> Can you riff on that a little bit for our viewers? Elaborate? >> Yeah, actually, the interesting part is that, if you look at the company that succeeded with AI, the actual AI algorithms they're using, are actually algorithms from the 70s, you know, they're actually developed in the 70s, that's 50 years ago. So then how come they're succeeding now? When actually the same algorithms weren't working in the 70s, so people gave up on them. Like, these things called neural nets, right? Now they're en vogue and they're, you know, super successful. The reason is you have to apply orders of magnitude more data. If you feed those algorithms that we thought were broken orders of magnitude more data, you actually get great results, but that's actually hard. You know, dealing with petabyte scale data and cleaning it, making sure that it's actually the right data for the task at hand is not easy. So that's the part that people are struggling with. >> I saw you up on stage, I'm like ah, Ali's here, Databricks is here, that's awesome. Psyched that you stopped by theCUBE. Been a while. I wanted to get a quick update, 'cause you guys have been on a tear, doing some great work at Cal, we were just told before we came on camera. But what are you doing here? What's the, is there any announcements or news with Informatica? What's the story? >> Yeah, it's, we're doing partnership around Delta Lake, which is our next generation engine that we built, so we're super excited about that. It integrates with all of the Informatica platform. So their ingestion tools, their transformation tools, and the catalog that they also have. So we think together, this can actually really help enterprises make that transition into the AI era. >> So you know, we've been followers, our 10th year, so remember when we were in the cloud era office of Mike Olsen and Amr Awadallah when we first started and now, Hadoop movement started, and then the cloud came along. Right when you guys started your company, the cloud growth took off. You guys were instrumental in changing the equation in dealing with data, data lakes, whatever they're calling it back then. So now, data, holistically, is a systems architecture. On premise it's a huge challenge, cloud native, well no real challenge, people love that. Data feeds AI, lot of risk taking, lot of reward. We're seeing the SaaS business explode, Zoom communications. The list goes on and on. Do you know, enterprise that's trying to be SAS is hard. So you can't just take data from an enterprise and make it SaaS-ified. You really got to think differently. What are you guys doing? How have you guys evolved and vectored into that challenge, because this is where your core value proposition initially started change. Take us through that Databricks story and how you're solving that problem today. >> Yeah, it's a great question. Really what happened is that people started collecting a lot of our data about a decade ago. And the promise was, you can do great things with this. There are all these aspirational use cases around machine learning, real time, it's going to be amazing. Right? So people started collecting it. They started storing one petabytes, two petabytes, and they kept going back to their boss and saying this project is real successful I now have five petabytes in it. But at some point the business said, okay that's great but what can you do with it? What business problems are you actually addressing? What are you solving? And so, in the last couple years there's been a push towards let's prove the value of these data lakes. And actually, many of these projects are falling short. Many are failing. And the reason is, people have just been dumping this data into data lakes without thinking about, the structure, the quality, how it's going to be used. The use cases have been an afterthought. So the number one thing in the top of mind for everyone right now is how do we make these data lakes that we have successful so we can prove some business value to our management? Towards this, this is the main problem that we're focusing on. Towards this, we built something called Delta Lake. It's something you situate on top of your data lake. And what it does is it increases the quality, the reliability, the performance, and the scale of your data lake. >> (John) So it's like a filter. >> Yeah. >> The cream rises to the top. >> (Ari) Exactly. >> Let's the sludge, the data swamp stay below the clean water, if you will. >> Exactly actually you nailed it. So basically, we look at the data as it comes in, filter as you said, and then look at, if there's any quality issues we then put it back in the data lake. It's fine, it can stay there. We'll figure out how to get value out of it later. But if it makes it into the Delta Lake, it will have high quality. Right? So that's great. And since we're anyway already looking at all the data as it's coming in, we might as well also store a lot of inducees and a lot of things that let us performance optimize it later on. So that, later, when people are actually trying to use that data they get really high performance, they get really good quality. And we also added asset transactions to it so that now you're also getting all those transactional use cases working on your existing data lake. >> I saw, at my daughter's graduation in Cal Berkley this weekend and yesterday, people around with Databricks backpacks. Very popular in academic. You guys got the young generation coming in. What's the update on the company? How many employees? What's the traction? Give us a quick business update. >> Yeah we're about 800 employees now. About 100 people in Europe, I would say, and maybe 40-50 people in Asiapac. We're expanding the ME and the Asia business. >> (John) Growth mode. >> Yeah, growth mode. So it's expanding as fast as possible. I mean, I actually, as a CEO, I try to always, slow the hiring down to make sure that we keep the quality bars. So that's actually top of mind for me. But yeah we're-- >> (John) You did Delta Lake on that one. >> Yeah (laughing) >> Exactly. Yeah and we're super excited about working with these universities. We get a lot of graduate students from top universities-- >> And Cal had the first ever class in college of data analytics, what was that? Data analytics are the first inagaural class graduated. Shows how early it is. >> Yeah, yeah, yeah. And actually used Databricks, the community edition, for a class of over a thousand students at Cal used the platform. So they're going to be trained in data science as they come out. >> So I want to ask about that because as you said you're trying to slow down the hiring to make sure that you are maintaining a high bar for your new hires. But yet, I'm sure there's a huge demand because you are in growth mode. So what are you doing? You said you're working with universities to make sure that the next generation is trained up and is capable of performing at Databricks. So tell us more about those efforts. >> Yeah I mean, so, obviously university recruiting is big for us. Cal, I think Databricks has the longest line of all the companies that come there on the career fair day. So, we work very closely with these universities. I think, next generation, as they come out, this generation that's coming out today actually is data science trained. So it's a big difference. There is a huge skills gap out there. Every big enterprise you talk tells you my biggest problem is actually, I don't have skilled people. Can you help me hire people? I say, hey we're not in the recruiting business. But, the good news is, if you look at the universities, they're all training thousands and thousands of data scientists every year now. I can tell you just at Cal, because, I happpen to be on the faculty there, is, almost every applicant now, to grad school, wants to do something AI related. Which has actually led to, if you look at all the programs in universities today, people used to do networking, professors used to do networking, say we do intelligent networks. People who do databases say, we do intelligent databases. People who do systems research say, hey we do intelligent systems, right? So what that means is, in a couple years you'll have lots of students coming out and these companies, that are now struggling hiring, then will be able to hire this talent and will actually succeed better with these AI projects. >> As they say in Berkley, nothing like a good revolution once in a while. AI is kind of changing everyone over. I got to ask you for the young kids out there, and parents who have kids either in elementary school or high school, everyone is trying to figure out, and there's no yet clear playbook, we're starting to see first generation training, but is there a skill set, because there's a range in surface area, you got hardcore coding to ethics, and everything in between from visualization, multiple dimensions of opportunities. What skills do you that people could hone or tweak that may not be on a curriculum that they could get, or pieces of different curriculums in school that would be a good foundation for folks learning and wanting to jump in to data and data value, whether it's coding to ethics? >> Yeah, just looking at my own background and seeing how, what I got to learn in school, the thing that was lacking, compared to what's needed today, is statistics. Understanding of statistics, statistical knowledge, That I think, it's going to be pervasive. So I think, 10, 15 years from now, no matter which field you're in, actually whatever job you have, you have to have some basic level of statistical understanding 'cause the systems you're working with will be, they'll be spitting out statistics and numbers and you need to understand what is false positives, what is this, what is the sample, what is that? What do these things mean? So that's one thing that's definitely missing and actually it's coming, that's one. The second is computing will continue being important. So, in the intersection of those two is, I think a lot of those jobs. >> In all fields, we were talking about earlier, biology, everything's intersecting, biochemistry to whatever right? >> (Ali) Yeah. >> I got to ask you about, well I'm a little old school, I'm 53 years old but I remember when I broke into the business coding, I used to walk into departments, they were called DP, data processing. So we're getting into the data processing world now, you've got statistics, you've got pipeline, these are data concepts. So I got to ask you as companies that are in the enterprise may be slower to move to the cutting edge like you guys are, they got to figure out where to store the data. So can you share your opinion or view on how customers are thinking and how they maybe should be architecting data on premise, in the cloud. Certainly cloud's great, if you're getting cloud native for pure SAS, and born in the cloud like a start-up. But if you're a large enterprise, and you want to be SAS-like, to have all that benefit, take the risk with the reward of being agile, you got to have data because if you don't the data into the machine learning or AI, you're not going to have good AI. So you need to get that data feeding in fast. And if it's constrained with regulation compliance you're screwed. So what's your view on this? Where should it be stored? What's your opinion? >> Yeah, we've had the same opinion for five, six years, right? Which is the data belongs in the cloud. Don't try to do this yourself. Don't try to do this on prem. Don't store it in, at Duke, it's not built for this. Store it in the cloud. In the cloud, first of all, you get a lot of security benefits that the cloud vendors are already working on. So that's one good thing about it. Second, you get it, it's realiable. You get the 10, 11 lines of availability, so that's great, you get that. Start collecting data there. Another reason you want to do it in the cloud is that a lot of the data sets that you need to actually get good quality results, are available in the cloud. Often times what happens with AI is, you build a predictive model, but actually, it's terrible. It didn't work well. So you go back, and then the main trick, the first tricks you use to increase the quality is actually augmenting that data with other data sets. You might purchase those data sets from other vendors. You don't want to be shipping hard drives around or, you know, getting that into your data center. Those will be available in the cloud, so you can augment that data. So we're big fans of storing your data in data lakes, in the cloud. We obviously believe that you need to make that data high quality and reliable. With that we believe the Delta Lake platform, open-source project that we created is a great vehicle for that. But I think moving to the cloud is the number one thing. >> (John) And hybrid works with that if you need to have something on premise? >> In my opinion the two worlds are so different, that it's hard. You hear a lot of vendors that say we're the hybrid solution that works on both and so on. But the two models are so different, fundamentally, that it's hard to actually make them work well. I have not yet seen a customer yet or enterprise. You see a lot of offerings, where people say hybrid is the way. Of course, a lot of on prem vendors are now saying, hey, we're the hybrid solution. I haven't actually seen that be successful to be frank. Maybe someone will crack that nut but-- >> I think it's an operational question to see who can make it work. Ali, congratulations on all your success. Great to see you. >> Yeah it's been great having you on the show. >> Thank you so much for having me. >> You are watching theCUBE, Informatica 2019. I'm Rebecca Knight, for John Furrier, stay tuned.

(orchestral music) >> Hello everyone. Welcome to the special CUBE conversation here in theCUBE's Palo Alto studio. I'm John Furrier. Where conversation around venture capital, entrepreneurship, crypto currencies, block chain, and more, Jonathan Ebinger our friend with BRV, formerly Blue Run Ventures, but BRV for short, sounds better, welcome to theCUBE. >> Thanks John, looking forward to it. >> Great to see you, we've known each other for a long time and you've been a great investor, your firm has done a lot of great stuff, deals are really famous deals, but also you dig into the companies and you really stand by your portfolio companies, but you've also done a lot of work in China. >> Yes. >> So you have a good landscape of what's going on. What's the, what's going on in China? >> Well China is really expanding in ways which we had not foreseen when we first started investing there almost 15 years ago. We were really active for five to 10 years, investing in companies that initially were considered copycat companies, you can't really use that term anymore. In fact what's happening more and more, you're seeing Chinese ideas coming to the United States. Businesses like We Chat are being copied as fast as they can, you're seeing Snapchat, Messenger and so forth, they're quickly trying to amalgamate as many assets as they can within their viewership much like we're seeing in a lot of the other Chinese analogs over there. It's exciting to see, it's very much an arms race. >> It's been interesting to watch. We were at the Ali Baba Cloud Conference last year, at the end of last year, it's interesting the innovation and entrepreneurial thirst has really changed. If you go back just 10 years ago when you guys were first getting in there, I remember the conversations were what's going on in China, it's very developmental but what's going on 10 years ago, they are dominating the mobile space, they're mobile usage is really much different makeup in how they do startups, the apps. How much of that has influenced some of their success just the demand? >> Always on, location always available, it opens up a whole new level of communication services. The idea of the larger screen format, people used to think in the United States, these large devices coming out of Korea first and then China, we thought these would never play in the United States, now Apple 10, larger screen size, it makes sense, it's mobile first right from the get go for a now billion plus users. >> So BRV, how many active portfolio companies do you guys have and what's the profile that you're looking for for entrepreneurs, what are some of the kind of companies? >> We're about 45 active companies right now. We're putting about, we're putting money in about 10 new companies a year at this point. We have a very disciplined approach of investing in Series A style companies, Series A of course means a lot of different things to people, but generally, we like to put $3 to $5 million to work early on and then follow on. >> How much do take for that, just a third? >> Typical in the 20%-25% range. There's a lot of companies out there that still fit that profile. Of course you're seeing some super sized Series A's that happen, we don't play in those but for the traditional software companies, evaluations are really right in our sweet spot. >> How big is the fund now, just what's the number in terms of capital? >> We're in fund six, we're just over $150 million. >> And you got to save some for follow on rounds. >> Exactly. >> Talk about the changes in venture capital because what's interesting, I had a conversation with Greg Sands with Costanoa Ventures, another great investor, formerly I think the first employee of Netscape I think or the business plan. Great guy, he talked about the dynamics of, you don't need that much cash anymore because if you can get unit economic visibility into what the business is working, you can do so much more with that and I'm calling it the hourglass effect, you get through that visibility, you're in control, you own your own destiny, versus the old Silicon Valley model which seems to be fading away, which is hey, what do you need? $40 million, or here's $100 million. That really limits your exit options and sometimes you can drown in your own capital. Talk about that dynamic. >> You're seeing the $40 million rounds with businesses that are much more capital intensive and that's coming back in vogue now but for the most part, I agree with what Greg's saying and this whole advent of seed funds and super seed funds and angel funds and so forth has been really great for the traditional series A investor. A lot of that early fundamental and foundational work is being done and then when the series A comes, it's more about expansion so we're effectively getting what was a Series B type stage company now we're investing in Series A. We're saying hey, this product works, there's product market fit, let's put dollars to work to really grow the market. >> So you're saying Series B was a kind of prove the business model, shifted down to the A because the cost to get there is lower and hence that's opened up a seed round lower in numbers, so it just shifts down a little bit. >> It really has, it really has and that plays into our sweet spot. We really like working on business models, distribution strategies, things like that. >> And what kind of startups do you want to invest in? What are some of the categories? >> Love financial services, we like health tech, we're doing education, we're really pretty omnivorous when it comes to the sector. What we're looking for is really businesses that are using data, real time data to disrupt the numbers. >> So you're not sector driven, you're disruption oriented. >> That's right. >> Okay let's talk about disruption, my favorite trend. Obviously I love the China dynamic because you're not sure what it is, but it's really doing well so you can't ignore it and they're innovative and they're hustling hard and they've got massive numbers. Block chain, we're super excited about, we love crypto, we think it's the biggest wave coming out there, so a lot of my smart, entrepreneurial friends are jumping on their surfboards literally and jumping out into those waves and there's a lot of action there. At the same time, people are saying, stay away from that crypto thing, it's a scam. Kind of a different perspective, what's your thoughts on that? >> If you look at, you separate the cryptocurrencies from block chain, I think it becomes a lot more clear. Block chain is for real. Tracking provenance on transactions, real estate transactions, multinational transactions, makes a lot of sense, dovetails nicely with security, so there's a real business there. You saw the announcement with IBM and Mersk the other day, what they are taking enterprise level block chain into their whole supply chain. I think that's really important. We have a company in the category called pay stand which is doing the same sort of thing with smaller size businesses, just accelerating the whole process on accounts receivable, taking working capital. >> And they're doing block chain for that? >> Yes block chain is an option, we're not forcing people onto block chain, but the idea of hey, let's give people more cost effective ways to transact, get rid of the paper checks, get rid of the invoicing and just join the modern world, much like you use Venmo if you and I are going to exchange money. >> That's pay stand, that's one of your hot companies. >> Yeah it is, absolutely. >> So are they using block chain or not? >> They are, yes. >> Okay, because it's a physical asset, it's kind of a supply chain thing? >> They use it to track the funds themselves, unlike a credit card where you have to pay a big fee or ACH which you can't really get proof of funds, with their block chain technology, you can be sure that you have the funds available and you get it instantly. >> Let's talk about use cases that you think out there, I'd like you to just weigh in on use cases for block chain that a mainstream person that's not in the tech business would understand, because they say, is it real or not? I agree block chain is legit, what are some use cases that would highlight that? >> I think if you've ever been involved in real estate, bought a home, things like that, just tracking title insurance, you're going all the way back if you live in California, you're going all the way back to pre-statehood days, you have to track the provenance of that land all the way through. You're paying title insurance, title insurance is a business you don't really need if you have accurate provenance tracking through block chain. I think that's one most of us can understand. Obviously bills of weighting with things coming over on ships. That's natural and right now things get held up in port because people are trying to find a clipboard before you can sign off on who, is this bill of weighting actually clean, that stuff can be done automatically with 2D barcodes, block chain usage. >> Certainly with perishable goods too, we learned that with IBM's example. >> Sure. >> Okay let's get into the hot companies you got going on. Name some of the hot investments that you've done. >> Sure, well I talked about pay stand a minute ago, really excited about them, another one we really like is a company called aerobotics. I know you're a fan of autonomous flying. If you think about drones and everyone knows DJI and they're a great company, that's one to one, one person flying one drone, that's not scalable obviously, it scales at one to one. With autonomous flying, you can have a whole army of drones out doing your business, whether they're doing site exploration, checking for chemical spills, looking at traffic and so forth. The company is now operating in three continents, it's just, if you think about what a drone is, effectively it's a flying cell phone. It's a cell phone that goes around, takes pictures, transmits data back, we know something about cell phones at BRV, we've been investing in this category for a long time so when we say aerobotics come along, we said this is just a natural extension of real time data, cellular technology, and location based services. >> You guys don't get a lot of credit as much as you should, in my opinion on that, you guys were very early on the mobile, mobile connectivity side and mobile footprint and device and software. That's playing well into the hottest trend that we see, that's not the sexiest trend, that's IOT. >> Absolutely. >> Because drones are certainly, industrial IOT is a big one. Instrumenting physical plants, equipment, and IOT in general the edge of the network. What's your thoughts on IOT and how would you, how do you see that evolving? It's more than just the edge of the network issue, it's bigger. >> It is, well of course the devices and sensors are important. I think a lot of that's been commoditized. The business that we've been seeing develop and there's a lot of folks, they've moved from analytics of the web to analytics of IOT, so there's a lot of interesting companies coming in the analytic space. We're not playing in that as much, we tend to like to invest in companies that are big enough that you need to have analytics for them. We like companies that have proprietary control of analytics versus necessarily running analytics for company X. >> So you're not poopooing IOT per se, just that from an investment thesis standpoint, it's not on your radar yet. >> That's right, they're either too capital intensive for us as a firm or you're basically managing someone else's data. I want to be in companies that we're managing our own data for a proprietary advantage. >> That's really what I was going to get to next, the role of data driven, so we've lived in dupe world, theCUBE started in 2010 in the offices of Cloud Air actually and people don't know the history and it's been interesting, Hadoop was supposed to save the world, the data, but it really started the data trend, the data driven trend, Mike Olsen, Amar Omadala and the team over there really nailed it but it didn't turn into be just Hadoop, it's everything so we're seeing that now become a bumper sticker, data driven marketer, I'm a data driven executive, I'm a data driven interviewer, all that stuff, what does it actually mean? What does data driven mean to you? >> Data is, there's big data and then there's actionable data obviously people talk about exhaust, the data coming off, we really got started with, as you know, we were investors in Waze, awful lot of data coming out of your cell phone, extracting just the important pieces of it are really what's important. We're investors in a company called Cabbage which looks at every transaction a small business makes to determine their credit worthiness. It's really the science. People talk about data scientists, what do they actually do? What they're actually doing is separating out the wheat from the chaff because it's just a crush of data. I saw your interview with Andy Jazzy to other day from AWS, the amount of data that's being stored, it's almost unfathomable but the important people. >> They have a lot of data. You'd like to invest in them now. >> Exactly, but that's really the thing, it's being able to separate the good data from the bad. >> You look at Amazon, I was talking to Jesse and he didn't really go there because he was kind of on message but when I talked with Swami who runs the AI group over there, we were talking about, I said to him straight up, I'm like, you're running a lot of workloads on your cloud, I'm sure you have data on those workloads. Just the impact of what they could do with that data. This is the virtuous cycle that their business model is made up of, but it's changing the game for what they can become. The thing that we're seeing in the data world is, sometimes the outcome might not be what you think because if you can use the data effectively, it's a competitive advantage, not a department. >> Right and you have to really stay true to your commitment to data. What we've seen happen is when companies, if you've been around for 10 years or so, you start to trust your gut, that's important, but it can also not lead you to see obvious conclusions because the world changes. >> And also committing to data also means from a practitioner's standpoint, investing in the tech, investing in things to be data driven, not just to say it. >> Exactly. >> Okay so what's the future for you guys? What are you looking at next year, what are some of the things you'd like to accomplish for investment opportunities, besides getting all the hot deals, you did Waze, that was an amazing deal, one of my favorite products, how did that go down? How many people passed on Waze? >> I don't know how many people passed, but we were lucky, they wanted to bring us in to the initial syndicate, they wanted to have some folks who understood. >> But it wasn't that obvious though at the beginning. What was the original pitch? >> The initial pitch was that they were going to have folks have the dash devices, the product would sit on your dashboard and they were going to be using it to map Eastern Europe because Eastern Europe was just coming into the Western world and they didn't really have good roads and good maps. We thought, that's interesting but they probably also don't have smartphones, so why don't we come across the Atlantic and let's make this thing work in the US and then from there, the rest took off country by country we were the number one navigation app in I think 150 countries at one point. >> What's the biggest thing that you've learned over the past few years in the industry that's different now I mean obviously there's some context that I'll share which is obviously the big cloud players are becoming bigger, scale's a big thing, you got Google, you got Microsoft and Amazon, you've got Facebook's out there as well. Then you get the political climate. You go to Washington D.C. and New York, Silicon Valley is not really talked highly about these days on the hill in Washington, yet GovCloud is completely changing the game of how the government is going to work with massive innovations and efficiencies, literally overnight, it's almost weird. >> It is and it isn't. If you look at it through a longer term horizon, Silicon Valley is again at the forefront, we're really the first ones with more transparency in the industry, all the different movements which are really important and all the conversations that are happening are important and they're happening here first. I think you're starting to see a ripple effect, you're seeing it going through entertainment, you're going to see it in the government, industry after industry I think is going to start to have to be more open as Silicon Valley has led the way on that. >> That's a great point. Take a minute to describe the folks out there watching that aren't from here, what is Silicon Valley about in your opinion? >> Silicon Valley is, of course it's more than a mindset, but folks who are here are here on purpose. They come here intentionally. There are very few people that I know who were born and raised here, so they're coming here because they want to be part of a shared ethos around success, around success, around shared values and competition so it's a very healthy environment, I came, I used to live in Washington D.C. and I couldn't be happier to be 3000 miles away. >> If you're a technology entrepreneur, this is where all the sports and action is, as I always say, we always love sports analogies. Okay, I got to ask you about the VC situation around ICOs, initial coin offerings are being talked about as an alternative to fundraising, there's some security options on token sales as a utility, the SEC has started to put some guidelines down on what that looks like, but the general sentiment is, it's a new way to raise money and some people are doing private rounds with venture capital and doing token sales through ICOs. You see some hybrids, but for the most part, the hard core I don't want to say right or left wing, is there a wing of the political spectrum, but the hard core ICO guys are like, this is all about disrupting the VC community and you're a VC, so you got to take that a little bit personal but the point is, what do you think about that? Is that talked about? >> I think that's good salesmanship. The VC industry such as it is, you can fit every VC into one section of Stanford stadium. There just aren't that many VCs to really go after. We're a small group of folks. I think that going after maybe disrupting the way folks are raising money through Kickstarter and things like that, that's all great. We're not going to stop it, we're going to embrace it. I think that there's plenty of different ways to raise capital, I have no compunction about those things. >> Do you think it's more of a democratization trend or a new asset class, so you don't see it disrupting the VCs per se, but if it's only a handful of VCs that could fit into Stanford Stadium, for instance, then certainly there's more options, it's a dilution. >> I think you look at it as it's just an alternative financing method, do I take debt, do I take equity, do I take venture, do I take friends and family? It's just one more arrow in the quiver of the entrepreneur, I think you have to be smart about it because thinking that you're going to get the same level of attention from an investor in your ICO that you are going to get from a series A investor who owns 20% of your company, those are two very different value propositions. >> So you see a lot of pitches and sometimes, you have to say no a lot and that's the way the game is, but a lot of times, you want the best deals. But the founders' side of the table, they're looking at the VC, I need money. So that's one of the options, what they really want is a value added partner, so what's your current take on what that means these days? Sometimes it means a firm, sometimes it means a partner, sometimes it means the community. How are you guys looking at BRV as value add versus the worst case scenario which is value subtract, you just want to have that be positive. >> I see that written about venture too. >> I know, some people experienced it. >> I think it helps that we've been around now for almost 20 years, we got started in '98 so you have to look at our body of work and the continuum of investments and founders and CEOs and CTOs that we've invested in. There's hundreds and hundreds of people who have taken money from BRV, and so that's one of the real positives about this current state we're in is that there's so much transparency. The fact that we are, I like to think we're good actors and have been for a long time, that comes out, now through our words but through the words of. >> What would they say about you guys? What would your entrepreneurs say about BRV? >> Aside from using buzzwords like value add, they say, they know their industry, they're not afraid to ask for help, they try to call problems when they see it, things like that. >> You stand by your companies. >> Absolutely. >> Awesome, well what's your favorite trend that you're personally interested in? >> I think you have to go after health care right now. It is just such a big market right now. People have been nibbling all different sides of it right now, there's been folks who are trying to expedite processing, there's actual innovations happening on the medical side, I think there is just, technology is just now starting to get into that, technology has gotten into education. >> How about the startup you guys funded that's related to the health care field. >> Yes, we're in a company called Hello Heart which is really at the confluence of a number of trends. It starts off, what Hello Heart is, it's a personal blood pressure cuff for you as an employee of a big company, more and more companies are starting to self insure. If you're a big enough company, 10,000 plus employees or even fewer, you're going to want to self insure to save money but also, your employees get very much more comfortable with you as an employer, you care about my well being, so it's a very virtuous cycle for the employees. >> So companies themselves insuring their own employees. >> Absolutely. >> They have to be super big, this company. >> This is just one component of a self insured business. You also, of course you still have access to doctors and stuff, I'm not making the pitch for being self insured as a company, I'm just saying that. >> But that's a trend. >> It's absolutely a trend and you're seeing a lot of what I would call point solutions stepping in, whether it's psychiatric, whether it's opioid help, whether it's working on heart conditions, these are all different point solutions which are being amalgamated together to help companies which are self insuring. >> So is Hello Heart for consumers or for business? >> It's sold to businesses but individual employees have it so they can keep track of their blood pressure. >> But I can't buy one if I wanted one? >> Not today, but I'll make sure I can get one to you. >> I need one, get all of our employees instrumented. >> Exactly. >> Drug tested all that stuff going on. People worry about the privacy, that's something I would be concerned with, putting. >> That's taken a really fast pendulum swing. A few years ago, Generation X was privacy, there is no privacy, the default was, location is always on, that's just flipped 180 degrees in the last few years. >> Well Jonathan, thanks for coming into this CUBE conversation, I want to ask you one final question, one thing we're passionate about is women in tech and underserved minorities, obviously Silicon Valley has to do a better job, it's out on the table, and it's working but we're still seeing a lot more work to be done, we're seeing titles not being at the right level, but pay's getting there in some places but titles aren't, some paying still below for women, still a lot more to do, what are you guys doing for the women in tech trend, how are you guys looking at that? Certainly it's a sensitive topic these days, but more importantly, it's one that's super important to society. >> It is, I think like a lot of things that have long term value, it's really about your actions versus your words, so our firm has two out of the five investment professionals are female, one of the last three CEO's we've founded is a female CEO, we have technologists, we have marketing people, we have CEO's that are females it's very much of a cross the board, sex, race and so forth. >> You guys are indiscriminate, a good deal's a good deal. >> Exactly right. >> It's about making money, VC's are in the business of making money, a lot of people don't understand, you guys have a job to do but you do a good job. >> We're in the business of making money but our investors for the most part are not for profits. Large universities, our biggest investor is the Red Cross, so when we do well, the Red Cross does well and the country does well. >> You're mission driven at this point. >> Exactly. >> Is that by design or is that just, your selection? >> We're delighted with our LP's, it's important that we have synergies aside from just finances with our investors. >> That's super well, I appreciate you coming on, I think it's super great that you're tying society benefits into money making and entrepreneurship, great stuff Jonathan Ebinger here on theCUBE, BRV check them out, great VC firm here in Silicon Valley. It's a CUBE conversation, we're talking about startups and entrepreneurship I'm John Furrier, thanks for watching. (dramatic music)

>> Hello, Wikibon community, and welcome to our 2017 predictions for the technology industry. We're very excited to be able to do this, today. This is one of the first times that Wikibon has undertaken something like this. I've been here since about April, 2016, and it's certainly the first time that I've been part of a gathering like this, with so many members of the Wikibon community. Today I'm joined with, or joined by, Dave Vellante, who's our co-CEO. So I'm the Chief Research Officer, here, and you can see me there on the left, that you can see this is from our being on TheCube at big data, New York City, this past September, and there's Dave on the right-hand side. Dave, you want to say hi? >> Dave: Hi everybody; welcome. >> So, there's a few things that we're going to do, here. The first thing I want to note is that we've got a couple of relatively simple webinar housekeeping issues. The first thing to note is everyone is muted. There is a Q&A option. You can hit the tab and a window will pop up and you can ask questions there. So if you hear anything that requires an answer, something we haven't covered or you'd like to hear again, by all means, hit that window, ask the question, and we'll do our best to get back to you. If you're a Wikibon customer, we'll follow up with you shortly after the call to make sure you get your question answered. If, however, you want to chat with your other members of the community, or with either Dave or myself, you want to comment, then there's also a chat option. On some of the toolbars, it's listed under the More button. So if you go to the More button, and you want to chat, you can probably find that there. Finally, we're also recording the webinar, and we will turn this into a Wikibon deliverable for the overall community. So, very excited to be doing this. Now, Dave, one of the things that we note on this slide is that we have TheCube in the lower left-hand corner. Why don't you take us through a little bit about who we are and what we're doing? >> Okay, great; thanks, Peter. So I think many of you or most of you know that SiliconANGLE Media Inc is sort of the umbrella company, and underneath SiliconAngle, we have three brands: the Wikibon research brand, which was started in the 2007 time frame. It's a community of IT practitioners. TheCube is, some people call it the ESPN of tech. We'll do 100 events this year, and we extensively use TheCUBE as a data-gathering mechanism and a way to communicate to our community. We've got some big shows coming up, pretty much every week, but of course we've got Amazon Reinvent coming up, and we'll be in London with HPE Discover. And so, we cover the world and cover technology, particularly in the enterprise, and then there's the SiliconANGLE publishing team, headed up by Rob Hoaf. It was founded by my co-CEO John Ferrier, and Rob Hoaf, former Business Week, is now leading that team. So those are the three main brands. We've got a new website coming out this month, on SiliconANGLE, so really excited about that and just thank the community for all your feedback and participation, so Peter, back to you. >> Thank you, Dave, so what you're going to hear today is what the analyst team here at Wikibon has pulled together for what we regard as some of the most interesting things that we think are going to happen over the next two years. Wikibon has been known for looking at disruptive technologies, and so while the focus, from a practical standpoint, in 2017, we do go further out. What is the overarching theme? Well, the overarching theme of our research and our conversations with the community is very simple. It's: put more data to work. The industry has developed incredible tools to gather data, to do analysis on data, to have applications use data and store data. I could go on with that list. But the data tends to be quite segmented and quite siloed to a particular application, a particular group, or a particular other activity. And the goal of digital business, in very simple terms, is to find ways to turn that data into an asset, so that it can be applied to other forms of work. That data could include customer data, operational data, financial data, virtually any data that we can imagine. And the number of sources that we're going to have over the next few years are going to be astronomical. Now, what we want to do is we want to find ways so that data can be freed up, almost like energy, in a physical sense, to dramatically improve the quality of the work that a firm produces. Whether it's from an engagement standpoint, or a customer experience standpoint, or actual operations, and increasingly automation. So that's the underlying theme. And as we go through all of these predictions, that theme will come out, and we'll reinforce that message during the course of the session. So, how are we going to do this? The first thing we're going to do is we're going to have six predictions that focus in 2017. Those six predictions are going to answer crucial questions that we're getting from the community. The first one is: what's driving system architecture? Are there new use cases, new applications, new considerations that are going to influence not only how technology companies create the systems and the storage and the networking and the database, and the middleware and the applications, but also how users are going to evolve the way they think about investing? The second one is: do micro-processor options matter? Through 20 years now, we've pretty much focused on one, limited class of micro-processor, the X386, er, the X86 architecture. But will these new workloads drive opportunities or options for new micro-processors? Do we have to worry about that? Thirdly, all this data has to be stored somewhere. Are we going to continue to store it, limited only on HDDs, or are other technologies going to come into vogue? Fourthly, in the 2017 time frame, we see the cloud, a lot's happening, professional developers have flocked to it, enterprises are starting to move to it in a big way, what does it mean to code in the cloud? What kinds of challenges are we going to face? Are they technological? Are they organizational, institutional? Are they sourcing? Related to that, obviously, is Amazon's had enormous momentum over the past few years. Do we expect that to continue? Is everybody else going to be continuing to play catch-up? And the last question for 2017 that we think is going to be very important is this notion of big data complexity. Big data has promised big things, and quite frankly has, except in some limited cases, been a little bit underwhelming. As some would argue, this last election showed. Now, we're going to move, after those six predictions, to 2022, where we'll have three predictions that we're going to focus on. One is: what is the new IT mandate? Is there a new IT mandate? Is it going to be the same old, same old, or is IT going to be asked to do new things? Secondly, when we think about Internet of Things, and we think about Augmented Reality or virtual reality, or some of these other new ways of engaging people, is that going to draw out new classes of applications? And then finally, after years of investing heavily in mobile applications, in mobile websites, and any number of other things, and thinking that there was this tight linkage where mobile equaled digital engagement, we're starting to see that maybe that's breaking, and we have to ask the question: is that all there is to digital engagement, or is there something else on the horizon that we're going to have to do? The last prediction, in 2027, we're going to take a stab here and say: will we all work for AI? So, these are the questions that we hear frequently from our clients, from our community. These are the predictions we're going to attend to and address. If you have others, let us know. If there's other things that you want us to focus on, let us know, but here's where we're starting. Alright. So let's start with 2017. What's driving system architecture? Our prediction for 2017 regarding this is very simple. The IoT edge use cases begin shaping decisions in system and application architecture. Now, the right-hand side, if you look at that chart, you can see a very, very important result of the piece of research that David Foyer recently did. And it shows IoT edge options, three-year costs. From left to right, moving all the data into the cloud over a normal data communications, telecommunications circuit, in the middle, moving that data into a central location, namely using cellular network technologies, which have different performance and security attributes, and then finally, keeping 95 percent of the data at the edge, processing it locally. We can see that the costs are overwhelming, favoring being smarter by how we design these applications and keeping more of that data local. And in fact, we think that so long as data and communications costs remain what they are, that there's going to be an irrevokeable pressure to alter key application architectures and ways of thinking to keep more of that crossing at the edge. The first point to note, here, is it means that data doesn't tend to move to the center as much as many are predicting, but rather, the cloud moves to the edge. The reason for that is that data movement isn't free. That means we're going to have even more distributed, highly autonomous apps, so none of those have to be managed in ways that sustain the firm's behavior in a branded, consistent way. And very importantly, because these apps are going to be distributed and autonomous, close to the data, it ultimately means that there's going to be a lot of operational technology players that impact the key decisions, here, that we're going to see made as we think about the new technologies that are going to be built by vendors and in the application architectures that are going to be deployed by users. >> So, Peter, let me just add to that. I think the key takeaway there is, as you mentioned, and I just don't want it to get lost, is 95 percent of the data, we're predicting, will stay at the edge. That's a much larger figure than I've seen from other firms or other commentary, and that's substantial, that's significant, it says it's not going to move. It's probably going to sit on flash, and the analytics will be done at the edge, as opposed to this sort of first bar, being cloud only. That 95 percent figure has been debated. It's somewhat controversial, but that's where we are today. Just wanted to point that out. >> Yeah, that's a great point, Dave. And the one thing to note, here, that's very important, is that this is partly driven by the cost of telecommunications or data communications, but there also are physical realities that have to be addressed. So, physics, the round trip times because of the speed of light, the need for greater autonomy and automation on the edge, OT and the decisions and the characteristics there, all of these will contribute strongly to this notion of the edge is increasingly going to drive application architectures and new technologies. So what's going to power those technologies? What's going to be behind those technologies? Let's start by looking at the CPUs. Do micro-processor options matter? Well, our prediction is that evolution in workloads, the edge, big data, which we would just, for now, put AI and machine learning, and cognitive underneath many of those big data things, almost as application forms, creates an opening for new micro-processor technologies, which are going to start grabbing market share from x86 servers in the next few years. Two to three percent next year, in 2017. And we can see a scenario where that number grows to double digits in the next three or four years, easily. Now, these micro-processors are going to come from multiple sources, but the factors driving this are, first off, the unbelievable explosion in devices served. That it's just going to require more processing power all over the place, and the processing power has to become much more cost-effective and much more tuned specifically to serving those types of devices. Data volumes and data complexity is another reason. Consumer economics is clearly driving a lot of these factors, has been for years, and it's going to continue to do so. But we will see new, ARM-based processors and other, and GPUs for big data apps, which have the advantage of being also supported in many of the consumer applications out there, driving this new trend. Now, the other two factors. Moore's Law is not out of room. We don't want to suggest that, but it's not the factor that it used to be. We can't presume that we're going to get double the performance out of a single class of technology every year or so, and that's going to remove any and all other types of micro-processor sets. So there's just not as much headroom. There's going to be an opportunity now to drive at these new workloads with more specialized technology. And the final one is: the legacy software issue's never going to go away; it's a big issue, it's going to remain a big issue. But, these new workloads are going to create so much new value in digital business settings, we believe, that it will moderate the degree to which legacy software keeps a hold on the server marketplace. So, we expect a lot of ARM-based servers that are lower cost, tuned and specialized, supporting different types of apps. A lot of significant opportunity for GPUs for big data apps, which do a great job running those kinds of graph-based data models. And a lot of room, still, for RISC in pre-packaged HCI solutions. Which we call: single managed entities. Others call: appliances. So we see a lot of room for new micro-processors in the marketplace over the next few years. >> I guess I'll add to that, and I'll be brief, just in the interest of time, the industry has marched to the cadence of Moore's Law for, as we know, many, many decades, and that's been the fundamental source of innovation. We see the innovation curve shifting and changing to become combinatorial, a combination of technologies. Peter mentioned GPU, certainly visualization's in there. AI, machine learning, deep learning, graph databases, combining to be the fundamental driver of innovation, going forward, so the answer here is: yes, they matter. Workloads are obviously the key. >> Great, Dave. So let's go to the next one. We talked about CPUs, well now, let's talk about HDDs. And more broadly, storage. So the prediction is that anything in a data center that physically moves gets less useful and loses share of wallet. Now, clearly that includes tape, but now it's starting to include HDDs. In our overall enterprise systems, storage systems revenue forecast, which is going to be published very, very shortly, we've identified that we think that the revenue attributable to HDD-based enterprise storage systems is going to drop over the next few years, while flash-based enterprise storage system revenue rises dramatically. Now, we're talking about storage system revenue here, Dave. We're not just talking about the HDDs, themselves. The HDD market starts, continues to grow, perhaps not as fast, partly because, even as the performance side of the HDD market starts to fade a bit, replaced by flash, that bulk, volume part of the HDD marketplace starts to substitute for tape. So, why is this happening? One of the main reasons it's happening is because the storage revenue, the storage systems revenue is very strongly influenced by software. And those software revenues are being bundled into the flash-based systems. Now, there's a couple reasons for this. First off, as we've predicted for quite some time, we do see a flash-only data center option on the horizon. It's coming well into focus. Number two is that, the good news is flash-based products are starting to come down and also are in sight of HDD-based products at the performance level. But very importantly, and here's one of the key notions of the value of data, and finding new ways to increase the use of data: flash, our research shows, offers superior business value, too, precisely because you can make so many copies of it and have a single set of data serve so many different applications and so many users, at scales that just aren't possible with traditional, HDD-based enterprise storage systems. Now, this applies to labor, too, Dave, doesn't it? >> Yeah, so a couple of points here. Yes, labor being one of those, sort of, areas that Peter's talking about are, ah, in jeopardy. We see about $200 billion over the next 10 years shifting from what we often refer to as non-differentiated IT labor, in provisioning and networking configuration and laying cable, et cetera, shifting from where it is today in services and/or on-prem IT labor, to vendor R&D or the cloud. So that's a very important point. I think I just wanted to add some color to what you were talking about before when you talked about HDD revenue continuing to grow, I think you were talking about, specifically, in the enterprise, in this storage systems view. And the other thing I want to add is, Peter, referenced sort of the business value of flash, as you, many of you know, David Floyer and Wikibon predicted, very early on, the impact that flash would have on spinning disk, and not only because of cost related to compression and de-duplication, but also this notion that Peter's talking about, of data sharing. The ability of development organizations to use the same data and minimize the number of copies. Now, the thing to watch, here, and kind of the wildcard is the hyperscale model. Hyperscalers, as we know, are consuming many, many, you know, exabytes and petabytes of data. They do things differently than is done in the enterprise, so that's something that we're watching very closely in terms of that model, that model being the hyperscale model, how it mimics or how it doesn't mimic what traditionally has occurred in the enterprise and how that will affect adoption of both flash and spinning disk. But as Peter said, we'll be releasing this data very shortly, and you'll be able to dig into it with is. >> And very importantly, Dave, in response to one of the comments in the chat, we're not talking about duplication of data everywhere, we're talking about the ability to provide logical and effective copies to single-data sources, so that, just because you can just drive a lot more throughput. So, that's the HDD. Now, let's turn to some of this notion of coding the cloud. What are we going to do with code in the cloud? Well our prediction is that the new cloud development stack, which is centered on containers and APIs, matures rapidly, but institutional habits in development constrain change. Now, why do we say that? I want to draw your attention to the graphic on the right-hand side. Now, this is what we think the mature, or the maturing cloud development stack looks like. As you can see, it's a lot of notions of containers, a lot of notions of other types of technologies. We'll see APIs interspersed throughout here as a primary way of getting to some of these container-based applications, services, microservices, et cetera, but this same, exact chart could be mapped back to SOA from 10 years ago, and even from some of the distributed computing environments that were put forward 20 years ago. The challenge here is that a sizable percentage, and we're estimating about 80 percent of in-house development, is still set up to work the old way. And so long as development organizations are structured to build monolithic apps or take care of monolithic apps, they will tend to create monolithic apps, with whatever technology's available to them. So, while we see these stacks becoming more vogue and more in use, we may not see, in 2017, shops being able to take full advantage of them. Precisely because the institutional work forms are going to change more slowly. Now, big data will partly contravene these habits. Why? Because big data is going to require quite different development approaches, because of the complexity associated of analytic pipelines, building analytic pipelines, managing data, figuring out how to move things from here to there, et cetera; there's some very, very complex data movement that takes place within big data applications. And some of these new application services, like Cognitive, et cetera, will require some new ways of thinking about how to do development. So, there will be a contravening force here, which we'll get to, shortly, but the last one is: ultimately, we think time-to-value metrics are going to be key. As KPI's move from project cost and taking care of the money, et cetera, and move more towards speed, as Agile starts to assert itself, as organizations start to, not only, build part of the development organization around Agile, but also Agile starts bleeding into other management locations, like even finance, then we'll start to see these new technologies really start asserting themselves and having a big impact. >> So, I would add to that, this notion of the iron triangle being these embedded processes, which as we all know, people, processes, and technology, people and process are the hardest to change, I'm interested, Peter, in your thoughts on, you hear a lot about Waterfall versus Agile; how will organizations, sort of, how will that affect organizations, in terms of their ability to adopt some of these, you know, new methodologies like Agile and Scrum? >> Well, the thing we're saying is the technology's going to happen fast, the Agile processes are being well-adopted, and are being used, certainly, in development, but I have had lots of conversations with CIOs, for example, over the last year and a half, two years ago, where they observed that they're having a very difficult time with reconciling the impedance mismatch between Agile development and non-Agile budgeting. And so, a lot of that still has to be worked out, and it's going to be tied back to how we think about the value of data, to be sure, but ultimately, again, it comes back to this notion of people, Dave, if the organization is not set up to fully take advantage of these new classes of technologies, if they're set up to deliver and maintain more monolithic applications, then that's what's going to tend to get built, and that's what's going to get, and that's what the organization is going to tend to have, and that's going to undermine some of the new value propositions that these technologies put forward. Well, what about the cloud? What kind of momentum does Amazon have? And our prediction for 2017 is that Amazon's going to have yet another banner year, but customers are going to start demanding a simplicity reset. Now, TheCUBE is going to be at Amazon Reinvent with John Ferrier and Steve Minnamon are going to be up there, I believe, Dave, and we're very excited. There's a lot of buzz happening about Reinvent. So follow us up there, through TheCUBE at Reinvent. But what I've done on the right-hand side is sent you a piece of Wikibon research. What we did is we wrote up, and we did an analysis of all of the AWS cases put forward, on their website, about how people are using AWS, and there's well over 650, or at least there were when we looked at it, and we looked at about two-thirds of them, and here's what we came up with. Somewhere in the vicinity of 80 percent, or so, of those cases are tied back to firms that we might regard as professional software delivery organizations. Whether they're stash or business services or games, provided games, or social networks. There's a smaller piece of the pie that's dedicated to traditional enterprise-type class of customers. But that's a growing and important piece, and we're not diminishing it at all, but the broad array of this pie chart, folks are relatively able to hire the people and apply the skills and devote the time necessary to learn some of the more complex, 75-plus Amazon services that are now available. The other part of the marketplace, the part that's moving into Amazon, the promise of Amazon is that it's simple, it's straightforward, and it is. Certainly more so than other options, but we anticipate that there will have to be a new type of, and Amazon's going to have to work even harder to simplify it, as it tries to attract more of that enterprise crowd. It's true that the flexibility of Amazon is certainly spawning complexity. We expect to see new tools, in fact, there are new tools on the market from companies like Appfield, for example, for handling and managing AWS billing and services, and that is, our CIOs are telling us, they're actually very helpful and very powerful in helping to manage those relationships, but the big issue here is that other folks, like VM Ware, have done research to suggest that the average shop has two to three big cloud relationships. That makes a lot of sense to us. As we start adding hybrid cloud into this and the complexities of inter-cloud communication and inter-cloud orchestration starts to become very real, that's going to even add more complexity, overall. >> So I'd add to that, just in terms of Amazon momentum, obviously those of you who follow what I read, you know, have been covering this for quite some time, but to me, the marginal economics of Amazon's model continue to be increasingly attractive. You can see it in the operating profits. Amazon's gap, operating profits, are in the mid-20s. 25, 26 percent. Just to give you a sense, EMC, who's an incredibly profitable company, its gap operating profits are in the teens. Amazon's non-gap operating profits are into 30 percent, so it's an incredibly profitable company. The more it grows, the more profitable it gets. Having said that, I think we agree with what Peter's saying in terms of complexity; think about API creep in Amazon. And different proprietary APIs for each of the data services, whether it's Kinesis or EC2 or S3 or Dynamo DB or EMR, et cetera, so the data complexity and the complexity of the data pipeline is growing, and I think that opens the door for the on-prem folks to at least mimic the public cloud experience to a great degree; as great a degree as possible. And you're seeing people, certainly, companies do that in their marketing, and starting to do that in the solutions that they're delivering. So by no means are we saying Amazon takes over the world, despite, you know, the momentum. There's a window open for those that can mimic, to the large extent, the public cloud capabilities. >> Yeah, very important point there. And as we said earlier, we do expect to see the cloud move closer to the edge, and that includes on-prem, in a managed way, as opposed to presuming that everything ends up in the cloud. Physics has something to say about that, as do some of the costs of data movement. Alright, so we've got one more 2017 prediction, and you can probably guess what it is. We've spent a lot of years and have a pretty significant place in spin big data, and we've been pretty aggressive about publishing what we think is going to happen in big data, or what is happening in big data, over the last year or so. One of the reasons why we think Amazon's momentum is going to increase is precisely because we think it's going to become a bigger target for big data. Why? Because big data complexity is a serious concern in many organizations today. Now, it's a serious concern because the spoke nature of the tools that are out there, many of which are individually extremely good, means that shops are spending an enormous amount of time just managing the underlying technology, and not as much time as they need to learning about how to solve big data problems, doing a great job of piloting applications, demonstrating to the business the financial returns are there. So as a result of this bespoked big data tool aggregates, we get multi-source, and we need to cobble it together from a lot of different technology sources, a lot of uncoordinated software and hardware updates that dramatically drive up the cost of on-prem administration. A lot of conflicting commitments, both from the business as well as from the suppliers, and very, very complex contracts. And as a result of that, we think that that's been one of the primary reasons why there's been so many pilot failures and why big data has not taken off the way that it probably should have. We think, however, that in 2017, we're going to see, and here's our prediction, we're going to see failure rates for big data pilots drop by 50 percent, as big vendors, IBM, Microsoft, AWS, and Google, amongst the chief ones, and we'll see if Oracle gets into that list, bring pre-packaged, problem-based analytic pipelines to market. And that's what we mean by this concept, here, of big data, single-managed entities. The idea that we can pull together, a company can pull together, or that it can pull together all the various elements necessary to provide the underlying infrastructure so that a shop can focus more time making sure that they understand the use-case, they understand how to go get the data necessary to serve that use-case, and understand how to pilot and deploy the application, because the underlying hardware and system software is pre-packaged and used. Now, we think that these, the SMEs, that are going to be most successful will be ones that are not predicated only on more proprietary software, but utilize a lot of open-source software. The ones that we see that are most successful today are in fact combining the pre-packaging of technology with the availability, or access, to the enormous value that the open-source market continues to build as it constructs new tools and delivers them out to big data applications. Ultimately, you've seen this before, or you've heard this before, from us: time-to-value becomes the focus. Similar to development, and we think that's one of the convergences that we have, here. We think that big data apps, or app patterns, will start to solidify. George Gilbert's done some leading-edge research on what some of those application patterns are going to be, and how those application patterns are going to drive analytic pipeline decisions, and very important, the process of building out the business capabilities necessary to build out the repeatable big data services to the business. Now, very importantly, some of these app patterns are going to be, are going to look like machine learning, cognitive AI, in many respects, all of these are part of this use-case to app trend that we see. So, we think that big data's kind of an umbrella for all of those different technology classes. It's going to be a lot of marketing done that tries to differentiate machine learning, cognitive AI. Technically, there are some differences, but from our perspective, they're all part of the effort of trying to ensure that we can pull together the technology in a more simple way so that it can be applied to complex business problems more easily. One more point I'll note, Dave, is that, and you adjust that world a lot, so I'd love to get your comments on this, but one of the more successful single-managed entities out there is, in fact, Watson from IBM, and it's actually a set of services and not just a device that you buy. >> Yeah, so a couple comments, there. One is that you can see the complexity in the market data, and we've been covering big data markets for a long time now, and there were two things that stood out when we started covering this. One is that software, as a percentage of the total revenue, is much lower than you would expect, in most markets. And that's because of the open-source contribution and the, you know, the multi-year collapse that we've seen in infrastructure software pricing. Largely due to open-source and cloud. The other piece of that is professional services, which have dominated spending within big data, because of the complexity. I think you're right, when you look at what happened at World of Watson and, you know, what IBM's trying to do, and others, in your prediction, there, are putting together a full, end-to-end data pipeline to do, you know, ingest and data wrangling and collaboration between data scientists, data engineers, and application developers and data quality people, and then bringing in the analytics piece. And essentially, you know, what many companies have done, and IBM included, they've cobbled together sets of tools and they've sort of layered on a way to interact with those tools, so the integration has still been slow in coming, but that's where the market is headed, so that we actually can build commercial, off the shelf applications. There's been a lack of those applications. I remember, probably four years ago, Mike Olsen at a (unintelligible) predicted: this will be the year of the big data app. And it still has not happened, so, and until it does, that complexity is going to reign. >> Yeah, and so it, again, as we said earlier, we anticipate that the big data, the need for developers to become more a part of the big data ecosystem, and the need for developers to get more value out of some of the other new cloud stacks are going to come together and will reinforce each other over the course of the next 24 to 36 months. So those were our 2017 predictions. Now let's take a look at our 2022 predictions, and we've got three. The first one is we do think a new IT mandate's on the horizon. Consistent with all these trends we've talked about, the idea of new ways of thinking about infrastructure and application architecture, based on the realities of the edge, new ways of thinking about how application developers need to participate in the value equation activities of big data, new ways of organizing to try to take greater advantage of the new processes, new technologies for development. We think, very strongly, that IT organizations will organize work to generate greater value from data assets by engineering proximity of applications and data. What do we mean by that? Well, proximity can mean physical proximity, but it also is something that we mean in terms of governance, tool similarity, infrastructure commonality, we think that over the next four to five years, we'll see a lot of effort to try to increase the proximity of not only data assets from a data standpoint, or the raw data, but also understanding from an infrastructure, governance skillset, et cetera, standpoint. So that we can actually do a better job of, again, generating more work out of our data by finding new and interesting ways of weaving together systems of records, big analytics, IOT, and a lot of other new application forms we see on the horizon, including one that I'll talk about in a second. Data value becomes a hot topic. We're going to have to do a better job, as a community, of talking about how data is valuable. How it creates (unintelligible) in the business, how it has to be applied, or has to be thought of as a source of value, in building out those systems. We talked earlier about the notion of people, process, and technology, well, we have to add to that: data. Data needs to be an asset that gets consumed as we think about how business changes. So data value's going to become a hot topic, and it's something we're focused on, as to what it means. We think, as Dave mentioned earlier, it's going to catalyze a true private cloud solutions for legacy applications. Now, I know Dave, you're going to want to talk about, in a second, what this might need. For example, things like the Amazon, VM Ware recent announcement. But it also means that strategic sourcing becomes reality. The idea of just going after the cheapest solution, or cost-optimized solution, which, don't get me wrong, don't get us wrong, is not going to go away, but it means that increasingly we're going to focus on new sourcing arrangements that facilitate creating greater proximity for those crucial aspects that make our shop run. >> Okay, so a couple of thoughts there, Peter. You know, there's a lot of talk, a couple years ago, and it's slowly beginning to happen, of bringing transaction and analytic systems together. What that oftentimes means is somebody takes their mainframe for the transactions and sticks it in finneban pipe into an exodata. I don't think that's what everybody envisioned when you started to sort of discuss that mean. So that's sort of happening slowly. But it's something that we're watching. This notion of data value, and shifting from, really a process economy to a data, or an insight, economy is something that's also occurring. You're seeing the emergence of the chief data officer. And our research shows that there are five things a chief data officer must do to really get started. The first is to understand data value, and how data contributes to the monetization of their company. So not monetizing the data, per se, and I think that's a mistake that a lot of people made, early on, is trying to figure out how to sell their data, but it's really to understand how data contributes to value for your organization. The second piece is how to access that data, who gets access to that data, and what data sources you have. And the third is the quality and trust of that data. And those are sequential things that our research shows a chief data officer has to do. And then the other, sort of parallel items, are relationship with the line of business and re-skilling. And those are complicated issues for most organizations to undertake, and something that's going to take, you know, many, many years to play out. The vast majorities of customers that we talk to say their data-driven, but aren't necessarily data-driven. >> Right, so, the one other thing I wanted to mention, Dave, is that we did some research, for example, on the VM Ware, Amazon relationship, and the reason why we were positive on it is quite simple. That it provides a path for VM Ware's customers, with their legacy applications running under VM Ware, to move those applications and the data associated with those applications, if they choose to, closer to some of the new, big data applications that are showing up in Amazon. So there's an example of this notion of making it more proximate, making applications and data more proximate, based on physics, based on governance, based on overall tooling and skilling, and we anticipate that that's going to become a new design center for a lot of shops over the course of the next few years. Now, coming to this notion of a new design center, the next thing we want to note is that, IoT, the Internet of Things, plus augmented reality, is going to have an impact on the marketplace. We got very excited about IoT, simply by thinking about the things, but our perspective is, increasingly, we have to recognize that people are going to always be a major feature, and perhaps the greatest value-creating feature, of systems. And augmented reality is going to emerge as a crucial actuator for the Internet of Things, and people. And that's kind of what we mean, is that augmented reality becomes an actuator for people. As will Chat Box and other types of technologies. Now, an actuator in an IoT sense is the devices or set of capabilities that take the results of models and actually turn that into a real-world behavior. So, if we think about this virtuous cycle that we have on the right-hand side, the internet, these are the three capabilities that we think people or firms are going to have to build out. They're going to have to build out an Internet of Things and People that are capable of capturing data, and turning analogue data into digital data, so that it can be moved into these big data applications. Again, with machine learning and AI and cognitive, sort of being part of that or underneath that umbrella, so that, then, we can build more models, more insights, more software that then translates into what we're calling systems of enaction. Or systems of "enaction", not "inaction". Systems of enaction. Businesses still serve customers, and these systems of enaction are going to generate real-world outcomes from these models and insights, and these real-world outcomes will certainly be associated with things, but they will also be associated with human being and people. And as a consequence of this, this we think is so powerful and is going to be so important over the course of the next five years that we anticipate that we will see a new set of disciplines focused on social discovery. Historically, in this industry, we've been very focused on turning insights or discovery about physics into hardware. Well, over the next few years, and Dave mentioned moving from the process to some new economy, we're going to see an enormous investment in understanding the social dynamics of how people work together and turn that into software. Not just how accountants do things, but how customers and enterprises come together to make markets happen, and through that social discovery, create these systems of enaction so that businesses can successfully, can successfully attend to and deliver the promises and the, ah, and the predictions that they're making through their other parts of their big data applications. >> So, Peter, you've pointed out many times that the big change, relative to processes, and historically, in the IT business, we've known what the processes are. The technology was sort of unknown, and mysterious. That's flipped. It's now, really the process is the unknown piece. That's the mysterious part. The technology is pretty well-understood. I think, as it relates to what you're talking about here with IoT and AR, what people tell us, the practitioners that are struggling with this, first of all, there's so much analogue data that people are trying to digitize, the other piece is there's a limited budget that folks have, and they're trying to figure out, alright, do I spend it on getting more data, and will that improve my data, increase my observation space? Or do I spend it on better models, and improving my models and iterating? And that's a trade-off that people have to make, and of course the answer is "both", but how those funds are allocated is something that organizations are really trying to better understand. There's a lot of trial and error going on. Because obviously, more data, in theory anyway, means you can make better decisions. But it's that iteration of that model, that trial and error and constant improvement, and both of those take significant resources. And budgets are still tight. >> Very true, Dave, and in fact, George Gilbert's research with the community is starting to demonstrate that more of the value's going to come from the models, as opposed to the raw data. We need the raw data to get to the models, but more of the value's going to come from the models. So that's where we think more people are going to focus their time and attention. Because the value will be in the insights and the models. But to go back to your point: where do you put your money? Well, you got to run these pilots, you got to keep up with your competitors, you got to serve customers better, so you're going to have to build all these models, sometimes in a bespoked way. But George is publishing an enormous amount of research right now that's very valuable to a lot of our community members that really shows how that pipeline, how those analytic pipelines or the capabilities associated with those analytic pipelines are starting to become better understood. So that we can actually start getting experience and generating efficiencies or generating a scale out of those analytic pipelines. And that's going to be a major feature underlying this basic trend. Now, this notion of people is really crucial, because as we think about the move to the Internet of Things and People, we have to ask ourselves: has digital engagement really, fully considered what it means to engage people throughout their customer journey? And the answer is: no, it hasn't. We believe that by 2022, IT will be given greater responsibility for management of demand chains. Working to unify customer journey designs and operations across all engagement functions. And by engagement functions, we mean marketing, sales, we mean product, we mean service, we mean fulfillment. That doesn't mean that they all report to IT. Don't mean that, at all. But it means that IT is going to have to, again, find ways to apply data from all these different sources so that it can, in fact, simplify and unify and bring together consistent design and operations so that all these functions can be successful and support reorganization if necessary, because the underlying systems provide that degree of unity and focus on customer success. Now, this is in strong opposition to the prediction made a few years ago, that marketing was going to emerge as the be-all and end-all, that's going to spend more than IT. That was silly, it hasn't happened, and you'd have to redefine marketing very aggressively to see that actually happening. But, when we think about this notion of putting more data to work, the first thing we note, and this is what all the digital natives have shown us, the data can transform a product into a service. That is the basis for a lot of the new business models we're talking about, a lot of these digital native business models and successes that they've had, and we think it's going to be a primary feature of the IT mandate to help business understand how data, more data can be put to work, transforming products into services. It also means, at a tactical level, that mobile applications have been way too focused on solving the seller's problems. We want to caution folks, don't presume that because your mobile application has gotten lost in some online store somewhere that that means that digital engagement's a failure. No, it means that you have to focus digital engagement on providing value throughout the customer journey, and not just from the problem to the solution, where the transaction for money takes place. Too much mobile applications, or too many mobile applications have been focused, in a limited way, on the marketers' problem within the business, of trying to generate, trying to generate awareness and demand. And it has to be, mobile has to be applied in a coherent and comprehensive way, across the entire journey. And ultimately, I hate to say this, but we think collaboration's going to make a comeback. But collaboration to serve customers. So the business can collaborate better inside, but in support of serving the customers. Major, major feature of what we think is going to happen over the course of the next couple years. >> I think the key point there is we all, there's many mobile apps that we love, and utilize, but there are a lot that are not so great. And the point that we've made to the community, quite often, is that it used to be that the brands had all the power, they had all the information, there was an asymmetry of information, the customer, the consumer didn't really know much about pricing. The web, obviously, has leveled that playing field and what many brands are trying to do is recreate that asymmetry and maybe got over their skis a little bit, before providing value to the customers. And I think your point is that, to the extent that you can provide value to that customer, that information advantage will come back to you. But you can't start with that information advantage. >> Great point, Dave. But it also means that we need to, that IT needs to look at the entire journey and see transactions and the discover, evaluate, buy, apply, use and fix throughout this entire journey and find ways of designing systems that provide value to customers at all times and in all places. So the demand chain notion, which historically has been focused on trying to optimize the value that the buyer gets in the buy process, at a cost-effective way, that notion of demand chain has to be applied to the entire engagement lifecycle. Alright, so that's 2022. Let's take a crack at our big prediction for 2027. And it's at, ah, it's on a lot of people's minds. Will we all work for AI? There've been a lot of studies done, over the course of the past year, year and a half, that have been kind of suggested that 47 percent of jobs are going to go away, for example. And that's not, that's not the only high end. Actually, folks have suggested much more, over the next 10, 15 years. Now, if you take a look at the right-hand side, you see a robot thinker. Now, you may not know this, but when The Thinker was actually first, when Rodan first constructed The Thinker, what he was envisioning was actually someone looking down into the seven levels of Hell as described by Dante. And I think that a lot of people would agree that the notion of no work is a Hell for a lot of people. We don't think that that's going to happen in the same way that most folks do. We believe that AI technology advances will far outpace the social advances. Some tasks will be totally replaced, but most jobs will only be partially replaced. We have to draw a clear distinction between the idea that a job performs only this or that task, as opposed to a job or an individual, an employee, as part of a complex community that ensures that a business is capable of serving customers. It doesn't mean we're not going to see more automation, but automation is going to focus mostly on replacing tasks. And to the degree that that task sets a particular job is replaced, then those jobs will be replaced. But ultimately, there's going to be a lot of social friction that gates how fast this happens. One of the key reasons for the social friction is something in behavioral economics that's known as loss avoidance. People are more afraid of losing something than they are of gaining something. And, whether it's a union or whether it's regulations or any number of other factors, that's going to gate the rate at which this notion that AI crushes employment occurs. AI will tend to compliment, not substitute for labor. And that's been a feature of technology for years. It doesn't, again, mean that some tasks and some task sets, sort of those in line with jobs, aren't replaced; there will be people put out of work as a consequence of this. But for the most part, we will see AI tend to compliment, not fully substitute for most jobs. Now this creates, also, a new design consideration. Historically, as technologists, we've looked at what can be done with technology, and we've asked: can we do it? And if the answer is "yes", we tend to go off and do it. And now, we need to start asking ourselves: should we do it? And this is not just a moral imperative. This has other implications, as well. So, for example, the remarkably bad impact that a lot of automated call centers have had on customer service from a customer experience standpoint. This has to become one of the features of how we think about bringing together, in these systems of enaction, all the different functions that are responsible for serving a customer. Asking ourselves: well, we can do it, from a technical standpoint, but should we do it from a customer experience, from a community relations, and even from a, ah, from a cultural imperative standpoint, as we move forward? >> Okay, I'll be brief, because we're wrapping up here, but first of all, machines have always replaced humans. When, largely with physical tasks, now we're seeing that occur with cognitive tasks. People are concerned, as Peter said. The middle class is obviously under fire. The median income in the United States has dropped from $55,000 in 1999 to just above $50,000 today. So, something's going on, and clearly you can look around and see whether it's an an airport with kiosks or billboards, electronic machines and cognitive functions are replacing human functions. Having said that, we're sanguine, because the, the story I'll tell is that the greatest chess player in the world is not a machine. When Deep Blue beat Gary Kasparov, what Gary Kasparov did is he started a competition to collaborate with other, you know, human chess players with machines, to beat the machine, and they succeeded at that, so this, again, I come back to this combination of technologies. Combinatorial technologies are really what's going to drive the innovation curve over the next, we think, 20 to 50 years. So, it's something that is far out there, in terms of our predictions, but it's also something that is relevant to the society, and obviously the technology industry. So thank you, everybody. >> So, we have one more slide, and it's Conclusions Slide, so let me hit these really quick, and before I do so, let me note that George, our big data analyst is George Gilbert. George Gilbert: G-I-L-B-E-R-T. Alright, so, very quickly, tech architecture question, we think edge IoT is going to have a major effect in how we think about architecture of the future. Micro-processor options? Yup, new micro-processor options are going to have an impact in the marketplace. Whither HDDs? For the performance side of storage, flash is coming on strong. Code in the cloud? Yes, the technologies are great, but development has to change its habits. Amazon momentum? Absolutely going to continue. Big data complexity? It's bad and we have to find ways to make it simpler so that we can focus more on the outcomes and the results, as opposed to the infrastructure and the tooling. 2022, new IT mandate? Drive the value of that data. Get more value out of your data. The Internet of Things and People is going to become the proper way of thinking about how these new systems of enaction work, and we anticipate that demand chain management is going to be crucial to extending the idea of digital engagement. Will we all work for AI? Dave just mentioned, as we said, there's going to be dislocation, there's going to be tasks that are replaced, but not by 2027. Alright, so thank you very much for your time, today. Here is how you can contact Dave and myself. We will be publishing this, the slides and this broadcast. Wikibon's going to deliver three coordinated predictions talks over the course of the next two days, so look for that. Go up to SiliconANGLE, we're up there a fair amount. Follow us on Twitter, and we want to thank you very much for staying with us during the course of this session. Have a great day.