(gentle upbeat music) >> Announcer: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (bright upbeat music) >> We're back in Barcelona at the Fira. My name is Dave Vellante. I'm here with David Nicholson. We're live at MWC23, day four of the coverage. The show is still rocking. You walk the floor, it's jamming. People are lined up to get in the copter, in the right. It's amazing. Planes, trains, automobiles, digitization of analog businesses. We're going to talk private wireless here with Dell. Sarvesh Sharma, the Global Director for Edge and Private Mobility Solutions practice at Dell. And John McCready is a Senior Director for 5G Solutions and product management at Dell Technologies. Guys, good to see you. >> Likewise, likewise. >> Good to see you too. >> Private wireless. It's the buzz of the show. Everybody's talking about it. What's Dell's point of view on that? >> So Dell is, obviously, interested entering the private wireless game, as it's a good part of the overall enterprise IT space. As you move more and more into the different things. What we announced here, is sort of our initial partnerships with some key players like Airspan and expedo and AlphaNet. Players that are important in the space. Dell's going to provide an overall system integration solution wrap along with our Edge BU as well. And we think that we can bring really good solutions to our enterprise customers. >> Okay, I got to ask you about AlphaNet. So HPE pulled a little judo move they waited till you announced your partnership and then they bought the company. What, you know, what's your opinion on that? You going to, you going to dump AlphaNet, you're going to keep 'em? >> No. >> We're open Ecosystem. >> Yeah, it's an open ecosystem. We announce these are our initial partners, you know we're going to announce additional partners that was always the case. You know, there's a lot of good players in this space that bring different pros and cons. We got to be able to match the solution requirements of all our customers. And so we'll continue to partner with them and with others. >> Good, good answer, I like that. So some of these solutions are sort of out of the box, others require more integration. Can you talk about your, the spectrum of your portfolio? >> So I'm glad you brought up the integration part, right? I mean, if you look at private wireless, private mobility it is not a sell by itself. At the end of the day what the enterprise wants is not just private mobility. They're looking for an outcome. Which means from an integration perspective, you need somebody who can integrate the infrastructure stack. But that's not enough. You need somebody who can bring in the application stack to play and integrate that application stack with the enterprises IT OT. And that's not enough. You need somebody to put those together. And Dell is ideally suited to do all of this, right? We have strong partners that can bring the infrastructure stack to play. We have a proven track record of managing the IT and the enterprise stack. So we are very excited to say, "Hey, this is the sweet spot for us. And if there was a right to win the edge, we have it." >> Can you explain, I mean, people might be saying, well, why do I even need private wireless? I got Wi-Fi. I know it's kind of a dumb question for people who are in the business, but explain to folks in the audience who may not understand the intersection of the two. >> So, yeah, so I think, you know, wireless is a great techno- pardon me, Wi-Fi is a great technology for taking your laptop to the conference room. You know, it's effectively wireless LAN Where private 5G and before that private LTE had come into play is where there's a number of attributes of your application, what you're using it for, for which Wi-Fi is not as well suited. And so, you know, that plays out in different verticals in different ways. Either maybe you need a much higher capacity than Wi-Fi, better security than Wi-Fi, wider coverage like outdoor, and in many cases a more predictable reliability. So cellular is just a different way of handling the wireless interface that provides those attributes. So, you know, I think at the beginning, the first several years, you know Wi-Fi and 5G are going to live side by side in the enterprise for their different roles. How that plays out in the long term? We'll see how they each evolve. >> But I think anybody can relate to that. I mean, Wi-Fi's fine, you know, we have our issues with Wi-Fi. I'm having a lot of issues with Wi-Fi this week, but generally speaking, it works just fine. It's ubiquitous, it's cheap, okay. But I would not want to run my factory on it and rely on it for my robots that are shipping products, right? So that really is kind of the difference. It's really an industry 4.0 type. >> Yeah, exactly. So I mean, manufacturing's an important vertical, but things of energy and mining and things like that they're all outdoor, right? So you actually need the scale that comes, with a higher power technology, and even, you know just basic things like running cameras in a retail store and using AI to watch for certain things. You get a much better latency performance on private 5G and therefore are able to run more sophisticated applications. >> So I could be doing realtime inference. I can imagine Dave, I got an arm processor I'm doing some realtime inference AI at the Edge. You know, you need something like 5G to be able to do that, you can't be doing that over Wi-Fi. >> Yeah >> You nailed it. I mean that's exactly the difference, right? I mean if you look at Wi-Fi, it grow out from a IT enabled mode, right? You got to replace an ethernet. It was an IT extension. A LAN extension. Cellular came up from the mode of, "Hey, when I have that call, I need for it to be consistent and I need for it to be always available," right? So it's a different way of looking at it. Not to say one is better, the other is not better. It's just a different philosophy behind the technologies and they're going to coexist because they meet diverse needs. >> Now you have operators who embrace the idea of 5G obviously, and even private 5G. But the sort of next hurdle to overcome for some, is the idea of open standards. What does the landscape look like right now in terms of those conversations? Are you still having to push people over that hump, to get them beyond the legacy of proprietary closed stacks? >> Yeah, so I think I look, there are still people who are advocating that. And I think in the carrier's core networks it's going to take a little longer their main, you know macro networks that they serve the general public. In the private network though, the opportunity to use open standard and open technology is really strong because that's how you bring the innovation. And that's what we need in order to be able to solve all these different business problems. You know, the problems in retail, and healthcare and energy, they're different. And so you need to be able to use this open stack and be able to bring different elements of technology and blend it together in order to serve it. Otherwise we won't serve it. We'll all fail. So that's why I think it's going to have a quicker path in private. >> And the only thing to add to that is if you look at private 5G and the deployment of private LTE or private 5G, right? There is no real technology debt that you carry. So it's easy for us to say, "Hey, the operators are not listening, they're not going open." But hey, they have a technical debt, they have 2G, 3G, 4G, 5G, systems, right? >> Interviewer: Sure. >> But the reason we are so excited about private 5G and private 4G, is right off the bat when we go into an enterprise space, we can go open. >> So what exactly is Dell's role here? How do you see, obviously you make hardware and you have solutions, but you got to open ecosystems. You got, you know, you got labs, what do you see your role in the ecosystem? Kind of a disruptor here in this, when I walk around this show. >> Well a disruptor, also a solution provider, and system integrator. You know, Sarvesh and I are part of the telecom practice. We have a big Edge practice in Dell as well. And so for this space around private 5G, we're really teamed up with our cohort in the Edge business unit. And think about this as, it's not just private 5G. It's what are you doing with it? That requires storage, it requires compute, it requires other applications. So Dell brings that entire package. There definitely are players who are just focused on the connectivity, but our view is, that's not enough. To ask the enterprise to integrate that all themself. I don't think that's going to work. You need to bring the connectivity and the application to storage compute the whole solution. >> Explain Telecom and and Edge. They're different but they're like cousins in the Dell organization. Where do you guys divide the two? >> You're saying within Dell? >> Yeah, within Dell. >> Yeah, so if you look at Dell, right? Telecom is one of our most newest business units. And the way it has formed is like we talk Edge all the time, right? It's not new. Edge has always been around. So our enterprise Edge has always been around. What has changed with 5G is now you can seamlessly move between the enterprise Edge and the telecom Edge. And for that happen you had to bring in a telecom systems business unit that can facilitate that evolution. The next evolution of seamless Edge that goes across from enterprise all the way into the telco and other places where Edge needs to be. >> Same question for the market, because I remember at Dell Tech World last year, I interviewed Lowe's and the discussion was about the Edge. >> John: Yep. >> What they're doing in their Edge locations. So that's Edge. That's cool. But then I had, I had another discussion with an agriculture firm. They had like the massive greenhouses and they were growing these awesome tomatoes. Well that was Edge too. It was actually further Edge. So I guess those are both Edge, right? >> Sarvesh: Yeah, yeah, yeah. >> Spectrum there, right? And then the telecom business, now you're saying is more closely aligned with that? >> Right. >> Depending on what you're trying to do. The appropriate place for the Edge is different. You, you nailed it exactly, right. So if you need wide area, low latency, the Edge being in the telecom network actually makes a lot of sense 'cause they can serve wide area low latency. If you're just doing your manufacturing plant or your logistics facility or your agricultural growing site, that's the Edge. So that's exactly right. And the tech, the reason why they're close cousins between telecom and that is, you're going to need some kind of connectivity, some kind of connectivity from that Edge, in order to execute whatever it's you're trying to do with your business. >> Nature's Fresh was the company. I couldn't think of Nature's Fresh. They're great. Keith awesome Cube guest. >> You mentioned this mix of Wi-Fi and 5G. I know it's impossible to predict with dates certain, you know, when this, how's this is going to develop. But can you imagine a scenario where at some point in time we don't think in terms of Wi-Fi because everything is essentially enabled by a SIM or am I missing a critical piece there, in terms of management of spectrum and the complicated governmental? >> Yeah, there is- >> Situation, am I missing something? It seems like a logical progression to me, but what am I missing? >> Well, there is something to be said about spectrum, right? If you look at Wi-Fi, as I said, the driver behind the technology is different. However, I fully agree with you that at some point in time, whether it's Wi-Fi behind, whether it's private 5G behind becomes a moot point. It's simply a matter of, where is my data being generated? What is the best technology for me to use to ingest that data so I can derive value out of that data. If it means Wi-Fi, so be it. If it means cellular, so be it. And if you look at cellular right? The biggest thing people talk about SIMs. Now if you look at 5G standard. In 5G standard, you have EAPTLS, which means there is a possibility that SIMs in the future go away for IoT devices. I'm not saying they need to go away for consumer devices, they probably need to be there. But who's to say going ahead for IoT devices, they all become SIM free. So at that point, whether you Wi-Fi or 5G doesn't matter. >> Yeah, by the way, on the spectrum side people are starting to think about the concept. You might have heard this NRU, new radio unlicensed. So it's running the Wi-Fi standard, but in the unlicensed bands like Wi-Fi. So, and then the last piece is of course you know, the cost, the reality it stays 5G still new technology, the endpoints, you know, what would go in your laptop or a sensor et cetera. Today that's more expensive than Wi-Fi. So we need to get the volume curve down a little bit for that to really hit every application. I would guess your vision is correct. >> David: Yep >> But who can predict? >> Yeah, so explain more about what the unlicensed piece means for organizations. What does that for everybody? >> That's more of a future thing. So you know, just- >> No, right, but let's put on our telescope. >> Okay, so it's true today that Wi-Fi traditionally runs in the bands that have been licensed by the government and it's a country by country thing, right? >> Dave: Right. >> What we did in the United States was CBRS, is different than what they've done in Germany where they took part of the Zurich C-band and gave it to the enterprises. The telco's not involved. And now that's been copied in Japan and Korea. So it's one of the complications unfortunately in the market. Is that you have this different approach by regulators in different countries. Wi-Fi, the unlicensed band is a nice global standard. So if you could run NR just as 5G, right? It's another name for 5G, run that in the unlicensed bands, then you solve the spectrum problem that Dave was asking about. >> Which means that the market really opens up and now. >> It would be a real enabler >> Innovation. >> Exactly. >> And the only thing I would add to that is, right, there are some enterprises who have the size and scale to kind of say, "Hey, I'm going the unlicensed route. I can do things on my own." There are some enterprises that still are going to rely on the telcos, right? So I don't want to make a demon out of the telcos that you own the spectrum, no. >> David: Sure. >> They will be offering a very valuable service to a massive number of small, medium enterprises and enterprises that span regional boundaries to say, hey we can bring that consistent experience to you. >> But the primary value proposition has been connectivity, right? >> Yes. >> I mean, we can all agree on that. And you hear different monetization models, we can't allow the OTT vendors to do it again. You know, we want to tax Netflix. Okay, we've been talking about that all week. But there may be better models. >> Sarvesh: Yes. >> Right, and so where does private network fit into the monetization models? Let's follow the money here. >> Actually you've brought up an extremely important point, right? Because if you look at why haven't 5G networks taken off, one of the biggest things people keep contrasting is what is the cost of a Wi-Fi versus the cost of deploying a 5G, right? And a portion of the cost of deploying a 5G is how do you commercialize that spectrum? What is going to be the cost of that spectrum, right? So the CSPs will have to eventually figure out a proper commercialization model to say, hey listen, I can't just take what I've been doing till date and say this is how I make. Because if you look at 5G, the return of investment is incremental. Any use case you take, unless, let's take smart manufacturing, unless the factory decides I'm going to rip and replace everything by a 5G, they're going to introduce a small use case. You look at the investment for that use case, you'll say Hmm, I'm not making money. But guess what? Once you've deployed it and you bring use case number two, three, four, five, now it starts to really add value. So how can a CSP acknowledge that and create commercial models to enable that is going to be key. Like one of the things that Dell does in terms of as a service solution that we offer. I think that is a crucial way of really kick starting 5G adoption. >> It's Metcalfe's Law in this world, right? The first telephone, not a lot of value, second, I can call one person, but you know if I can call a zillion now it's valuable. >> John: Now you got data. >> Yeah, right, you used a phrase, rip and replace. What percentage of the market that you are focusing on is the let's go in and replace something, versus the let's help you digitally transform your business. And this is a networking technology that we can use to help you digitally transform? The example that you guys have with the small breweries, a perfect example. >> Sarvesh: Yeah. >> You help digitize, you know, digitally transform their business. You weren't going in and saying, I see that you have these things connected via Wi-Fi, let's rip those out and put SIMs in. >> No. >> Nope, so you know- >> That's exactly right. It's enabling new things that either couldn't be achieved before or weren't. So from a private 5G perspective, it's not going to be rip and replaced. As I said, I think we'll coexist with Wi-Fi, it's still got a great role. It's enabling those, solving those business problems that either hadn't been solved before or could not be solved with other technology. >> How are you guys using AI? Everybody's talking about ChatGPT. I love ChatGPT, we use it all the time. Love it, hate it, you know, whatever. It's a fun topic. But AI generally is here in a way that it wasn't when the enterprise disaggregated. >> John: Right. >> So there's AI, there's automation, there's opportunities there. How do they fit into private 5G? >> So if you look at it, right, AI, AI/ML is actually crucial to value extraction from that data, because all private 5G is doing is giving you access to that precious data. But that data by itself means nothing, right? You get access to the data, extracting value out of the data that bring in business value is all going to be AI/ML. Whether it's computer vision, whether it's data analytics on the fly so that you can, you know do your closed loop controls or what have you. All of these are going to be AI/ML models. >> Dave: Does it play into automation as well? >> Absolutely, 'cause they drive the automation, right? You learn your AI models, drive their automation. Control, closed loop control systems are a perfect example of their automation. >> Explain that further. Like give us an example. >> So for example, let's say we're talking about a smart manufacturing, right? So you have widgets coming down the pipe, right? You have your computer vision, you have your AI/ML model that says, "Hey, I'm starting to detect a consistent error in the product being manufactured. I'm going to close loop that automation and either tweak the settings of the machine, shut down the machine, open a workflow, escalate it for human intervention." All that automation is facilitated by the AI/ML models >> And that, and by the way, there's real money in that, right? If you're making your power and you're making it wrong, you don't detect it for hours, there's real money in fixing that >> Right. >> So I've got a, I've got an example albeit a slight, not even slightly, but a tragic one. Let's say you have a train that's rolling down the tracks at every several miles or so, temperature readings are taken from bearings in the train. >> Sarvesh: Yes, yes. >> Wouldn't it be nice to have that be happening in real time? >> Sarvesh: Yes. >> So it doesn't reach that critical point >> Yes. >> Where then you have a derailment. >> Yes. >> Yeah, absolutely. >> I mean, those are, it's doesn't sound sexy in terms of "Hey, what a great business use case that we can monetize." >> John: Yeah. >> But I'll bet you in hindsight that operator would've loved to have that capability. >> John: Yeah. >> Sarvesh: Right. >> To be able to shut the train down and not run. >> That's a great example where the carrier is actually, probably in a good position, right? Cause you got wide area, you want low latency. So the traditional carriers would be able in great position to provide that exact service. Telemetry is another great example. We've been talking about other kinds of automation, but just picking up measurements and so on. The other example of that is in oil and gas, right? As you've got pipelines running around you're measuring pressure, temperature, you detect a leak, >> David: Right. >> in minutes, not weeks. >> David: Right. >> So there's a lot of good examples of things like that >> To pick up in a point, Dave. You know, it's like you look at these big huge super tankers, right? They have big private networks on that super tanker to monitor everything. If on this train we had, you know, we hear about so many Edges, let's call one more the rolling Edge. >> Yeah. >> Right, that, that Edge is right on that locomotive tracking everything with AI/ML models, detecting things, warning people ahead of time shutting it down as needed. And that connectivity doesn't have to be wired. It can be a rolling wireless. It potentially could be a spectrum that's you know, open spectrum in the future. Or as you said, an operator could facilitate that. So many options, right? >> Yeah, got to double down on this. Look, I know 'cause I've been involved in some of these projects. Amusement park operators are doing this for rides. >> John: Yes. >> Sarvesh: Yep. >> So that they can optimize the amount of time the ride is up, so they can shorten lines >> Yes. >> So that they can get people into shops to buy food and souvenirs. >> John: Yes. >> Certainly we should be able to do it to protect infrastructure. >> Sarvesh: Absolutely. >> Right, so- >> But I think the ultimate point you're making is, it's actually quite finally segmented. There's so many different applications. And so that's why again, we come back to what we started with is at Dell, we're bringing the solution from Edge, compute, application, connectivity, and be able to bring that across all these different verticals and these different solutions. The other amusement park example, by the way, is as the rides start to invest in virtual reality, so you're moving, but you're seeing something, you need some technology like 5G to have low latency and keep that in sync and have a good experience on the ride. >> To 5G and beyond, gents. Thanks so much for coming on theCUBE. >> All right, thank you Dave. >> It was great to have you. >> Thank, thank you guys. >> Great to meet you guys. Thank you very much. >> Great, all right. Keep it right there. For David Nicholson and Dave Vellante, This is theCUBE's coverage of MWC23. Check out siliconangle.com for all the news. theCUBE.net is where all these videos live. John Furrier is in our Palo Alto office, banging out that news. Keep it right there. Be right back after this short break. (gentle upbeat music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante last week's aws re invent brought into focus the degree to which cloud computing generally and aws specifically have impacted the technology landscape from making infrastructure orders of magnitude simpler to deploy to accelerating the pace of innovation to the formation of the world's most active and vibrant infrastructure ecosystem it's clear that aws has been the number one force for change in the technology industry in the last decade now going forward we see three high-level contributors from aws that will drive the next 10 years of innovation including one the degree to which data will play a defining role in determining winners and losers two the knowledge assimilation effect of aws's cultural processes such as two pizza teams customer obsession and working backwards and three the rise of super clouds that is clouds that run on top of hyperscale infrastructure that focus not only on i.t transformation but deeper business integration and digital transformation of entire industries hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll review some of the takeaways from the 10th annual aws re invent conference and focus on how we see the rise of super clouds impacting the future of virtually all industries one of the most poignant moments for me was a conversation with steve mullaney at aw aws re invent he's the ceo of networking company aviatrix now just before we went on the cube nick sterile one of aviatrix's vcs looked up at steve and said it's happening now before i explain what that means this was the most important hybrid event of the year you know no one really knew what the crowd would be like but well over twenty 000 people came to reinvent and i'd say at least 25 to 26 000 people attended the expo and probably another 10 000 or more came without badges to have meetings and side meetings and do networking off the expo floor so let's call it somewhere between thirty to forty thousand people physically attended the reinvent and another two hundred thousand or more online so huge event now what nick sterile meant by its happening was the next era of cloud innovation is upon us and it's happening in earnest the cloud is expanding out to the edge aws is bringing its operating model its apis its primitives and services to more and more locations yes data and machine learning are critical we talk about that all the time but the ecosystem flywheel was so evident at this year's re invent more so than any other re invent partners were charged up you know there wasn't nearly as much chatter about aws competing with them rather there was much more excitement around the value that partners are creating on top of aws's massive platform now despite aggressive marketing from competitive hyperscalers other cloud providers and as a service or on-prem slash hybrid offerings aws lead appears to be accelerating a notable example is aws's efforts around custom silicon far more companies especially isvs are tapping into aws's silicon advancements we saw the announcement of graviton 3 and new chips for training and inference and as we've reported extensively aws is now on a curve a silicon curve that will outpace x86 vis-a-vis performance price performance cost power consumption and speed of innovation and its nitro platform is giving aws and its partners the greatest degree of optionality in the industry from cpus gpus intel amd and nvidia and very importantly arm-based custom silicon springing from aws's acquisition of annapurna aws started its custom silicon journey in 2008 and is and it has invested massive resources into this effort other hyperscalers notably microsoft google and alibaba which have the scale economics to justify such custom silicon efforts are just recently announcing initiatives in this regard others who don't have the scale will be relying on third-party silicon providers a perfectly reasonable strategy but because aws has control of the entire stack we believe it has a strategic advantage in this respect silicon especially is a domain where to quote andy jassy there is no compression algorithm for experience b on the curve matters a lot and the biggest story in my view this past week was the rise of the super clouds in his 2020 book with steve hamm frank slootman laid out the case for the rise of data cloud a title which i've conveniently stolen for this breaking analysis rise of the super cloud thank you frank in his book slootman made a case for companies to put data at the center of their organizations rather than organizing just around people for example the idea is to create data networks while people of course are critical organizing around data and enabling people to access and share data will lead to the democracy democratization of data and network effects will kick in this was essentially metcalfe's law for data bob metcalf was the inventor of ethernet ethernet he put forth that premise when we we both worked or the premise when we both worked for pat mcgovern at idg that the value of a network is proportional to the square of the number of its users or nodes on the network thought of another way the first connection isn't so valuable but the billionth connection is really valuable slootman's law if i may says the more people that have access to the data governed of course and the more data connections that can be shared or create sharing the more value will be realized from that data exponential value in fact okay but what is a super cloud super cloud is an architecture that taps the underlying services and primitives of hyperscale clouds to deliver incremental value above and beyond what's available from the public cloud provider a super cloud delivers capabilities through software consumed as services and can run on a single hyperscale cloud or span multiple clouds in fact to the degree that a super cloud can span multiple clouds and even on-premises workloads and hide the underlying complexity of the infrastructure supporting this work the more adoption and the more value will be realized now we've listed some examples of what we consider to be super clouds in the making snowflake is an example we use frequently frequently building a data cloud that spans multiple clouds and supports distributed data but governs that data centrally somewhat consistent with the data mesh approach that we've been talking about for quite some time goldman sachs announced at re invent this year a new data management cloud the goldman sachs financial cloud for data with amazon web services we're going to come back to that later nasdaq ceo adina friedman spoke at the day one keynote with adam silipsky of course the new ceo of aws and talked about the super cloud they're building they didn't use that term that's our term dish networks is building a super cloud to power 5g wireless networks united airlines is really in my view they're porting applications to aws as part of its digital transformation but eventually it will start building out a super cloud travel platform what was most significant about the united effort is the best practices they're borrowing from aws like small teams and moving fast but many others that we've listed here are on a super cloud journey just some of the folks we talked to at reinvent that are building clouds on top of clouds that are shown here cohesity building out a data management cloud focused on data protection and governance hashicorp announced its ipo at a 13 billion valuation building an it automation super cloud data bricks chaos search z-scaler z-scaler is building a security super cloud and many others that we spoke with at the event now we want to take a moment to talk about castles in the cloud it's a premise put forth by jerry chen and the team at greylock it's a really important piece of work that is building out a data set and categorizing the various cloud services to better understand where the cloud giants are investing where startups can participate and how companies can play in the castles that are being built that have been built by the hyperscalers and how they can cross the moats that have been dug and where innovation opportunities exist for other companies now frequently i'm challenged about our statements that there really are only four hyperscalers that exist in the world today aws microsoft google and alibaba while we recognize that companies like oracle have done a really excellent job of improving their clouds we don't consider companies like oracle ibm and other managed service providers as hyperscalers and one of the main data points that we use to defend our thinking is capex investment this was a point that was made in castles in the cloud there are many others that we look at elder kpi size of ecosystem partner acceleration enablement for partners feature sets etc but capex is a big one here's a chart from platform nomics a firm that is obsessed with cl with capex showing annual capex spend for five cloud companies amazon google microsoft ibm and oracle this data goes through 2019 it's annual spend and we've superimposed the direction for each of these companies amazon spent more than 40 billion dollars on capex in 2020 and will spend more than 50 billion this year sure there are some warehouses for the amazon retail business in there and there's other capital expenses in these numbers but the vast majority spent on building out its cloud infrastructure same with google and microsoft now oracle is at least increasing its cap x it's going to spend about 4 billion but it's de minimis compared to the cloud giants and ibm is headed in the other direction it's choosing to invest for instance 34 billion dollars in acquiring red hat instead of putting its capital into a cloud infrastructure look that's a very reasonable strategy but it underscores the gap okay another metric we look at is i as revenue here's an updated chart that we showed last month in our cloud update which at the time excluded alibaba's most recent quarter results so we've updated that very slight change it wasn't really material so you see the four hyperscalers and by the way they invested more than a hundred billion dollars in capex last year it's gonna be larger this year they'll collectively generate more than 120 billion dollars in revenue this year and they're growing at 41 collectively that is remarkable for such a large base of revenue and for aws the rate of revenue growth is accelerating it's the only hyperscaler that can say that that's unreal at their size i mean they're going to do more than 60 billion dollars in revenue this year okay so that's why we say there are only four hyperscalers but so what there are so many opportunities to build on top of the infrastructure that the three u.s giants especially are building as folks are really cautious about china at the moment so let's take a look at what some of the companies that we've been following are doing in the super cloud arena if you will this chart shows some etr data plotting net score or spending momentum on the vertical axis and market share or presence in the etr data set on the horizontal axis most every name on the chart is building some type of super cloud but let me start as we often do calling out aws and azure i guess they're already super clouds but they're not building necessarily on top of of of other people's clouds and there are a little bit you know microsoft does some of that certainly google's doing some of that amazon really bringing its cloud to the edge at this point it's not participating in multi-cloud actively anyway aws and azure they stand alone as the cloud leaders and you can debate what's included in azure in our previous chart on revenue attempts to strip out the microsoft sas business but this is a customer view they see microsoft as a cloud leader which it is so that's why its presence on the horizontal axis and its momentum is is you know very large and very strong stronger than even in aws in this view even though it's is revenue that we showed earlier microsoft is significantly smaller but they both have strong momentum on the vertical axis as shown by that red horizontal line anything above that remember is considered considered elevated that 40 percent or above now google cloud it's well behind these two to we kind of put a red dotted line around it but look at snowflake that blue circle i mean i realize we repeat ourselves often but snowflake continues to hold a net score in the mid to high 70s it held 80 percent for a long time it's getting much much bigger it's so hard to hold that and in 165 mentions in the survey which you can see in the inserted table it continues to expand its market's presence on the horizontal axis now all the technology companies that we track of all of them we feel snowflake's vision and execution on its data cloud and that strategy is most is the most prominent example of a super cloud truly every tech company every company should be paying attention to snowflakes moves and carving out unique value propositions for their customers by standing on the shoulders of cloud giants as ceo ed walsh likes to say now on the left hand side of the chart you can see a number of companies that we spoke with that are in various stages of building out their super clouds data bricks dot spot data robots z z scalar mentioned hashi you see elastic confluent they're all above the forty percent line and somewhat below that line but still respectable we see vmware with tanzu cohesity rubric and veeam and many others that we didn't necessarily speak with directly at reinvent and or they don't show up in the etr dataset now we've also called out cisco dell hpe and ibm we didn't plot them because there's so much other data in there that's not apples to apple but we want to call them up because they all have different points of view and are two varying degrees building super clouds but to be honest these large companies are first protecting their respective on-prem turf you can't blame them those are very large install basis now they're all adding as a service offerings which is cloud-like i mean they're behind way behind trying to figure out you know things like billing and they don't nearly have the ecosystem but they're going to fight rightly they're going to fight hard and compete with their respective portfolios with their channels and their vastly improved simplicity but when you speak to customers at re invent and these are not just startups we're talking to we're talking about customers of these enterprise tech companies these customers want to build on aws they look at aws as cloud and that is the cloud that they want to write to now they want to connect they're on-prem but they're still largely different worlds when you when you talk to these customers now they'll fully admit they can't or won't move everything out of their data centers but the vast vast majority of the customers i spoke with last week at reinvent have much more momentum around moving towards aws they're not repatriating as everybody's talking about or not everybody but many are talking about and yeah there's some recency bias because we just got back but the numbers that we shared earlier don't lie the trend is very clear now these large firms that we mentioned these incumbents in the tech industry these big enterprise tech giants they're starting to move in the super cloud direction and they will have much more credibility around multi-cloud than the hyperscalers but my honest view is that aws's lead is actually accelerating the gap in my opinion is not closing now i want to come back and dig into super cloud a little bit more around 2010 and 2011 we collaborated with two individuals who really shaped our thinking in the big data space peter goldmaker was a cell side analyst at common at the time and abi abhishek meta was with bank of america and b of a was transforming its data operations and avi was was leading that now peter was you know an analyst sharp and less at the time he said you know it's going to be the buyers of big data technology and those that apply big data to their operations who would create the most value he used an example of sap he said look you you couldn't have chosen that sap was going to lead an erp but if you could have figured out who which companies were going to apply erp to their business you would have made a lot of money investing so that was kind of one of his investment theses now he posited that the companies that would apply the big data technology the buyers if you will would create far more value than the cloud errors or the hortonworks or a collection of other number of big data players and clearly he was right in that regard now abi mehta was an example of that and he posited that ecosystems would evolve within vertical industries around data kind of going back to frank slootman's premise that in putting data at the core and that would power the next generation of value creation via data machine learning and business transformation and he was right and that's what we're seeing with the rise of super cloud now after the after the first reinvent we published a post seen on the right hand side of this chart on wikibon about the making of a new gorilla aws and we said the way to compete would be to take an industry focus or one way to compete with take an industry focus and become best to breed within that industry and we aligned really with abbey meta's point of view that industry ecosystems would evolve around data and offer opportunities for non-hyperscalers to compete now what we didn't predict at the time but are now seeing clearly emerge is that these super clouds are going to be built on top of aws and other hyperscale clouds makes sense goldman's financial cloud for data is taking a page out of aws it's pointing its proprietary data algorithms tools and processes at its clients just like amazon did with its technology and it's making these assets available as a service on top of the aws cloud a super cloud for financial services if you will they are relying on aws for infrastructure compute storage networking security and other services like sagemaker to power that super cloud but they're bringing their own ip to the table nasdaq and dish similarly bringing forth their unique value and as i said as i said earlier united airlines will in our view eventually evolve from migrating its apps portfolio to the cloud to building out a super cloud for travel what about your logo what's your super cloud strategy i'm sure you've been thinking about it or perhaps you're already well down the road i'd love to hear how you're doing it and if you see the trends the same or differently as we do okay that's it for now don't forget these episodes are all available as podcasts wherever you listen all you do is search breaking analysis podcast you definitely want to check out etr's website at etr.plus for all the survey data remember we publish a full report every week on wikibon.com and siliconangle.com you can email me if you want to get in touch with david.velante at siliconangle.com you can dm me at devolante on twitter you can comment on our linkedin posts this is dave vellante for the cube insights powered by etr have a great week stay safe be well and we'll see you next time [Music] you

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Snowflake is not going to grow into its valuation by stealing the croissant from the breakfast table of the on-prem data warehouse vendors. Look, even if snowflake got 100% of the data warehouse business, it wouldn't come close to justifying its market cap. Rather Snowflake has to create an entirely new market based on completely changing the way organizations think about monetizing data. Every organization I talk to says it wants to be, or many say they already are data-driven. why wouldn't you aspire to that goal? There's probably nothing more strategic than leveraging data to power your digital business and creating competitive advantage. But many businesses are failing, or I predict, will fail to create a true data-driven culture because they're relying on a flawed architectural model formed by decades of building centralized data platforms. Welcome everyone to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, I want to share some new thoughts and fresh ETR data on how organizations can transform their businesses through data by reinventing their data architectures. And I want to share our thoughts on why we think Snowflake is currently in a very strong position to lead this effort. Now, on November 17th, theCUBE is hosting the Snowflake Data Cloud Summit. Snowflake's ascendancy and its blockbuster IPO has been widely covered by us and many others. Now, since Snowflake went public, we've been inundated with outreach from investors, customers, and competitors that wanted to either better understand the opportunities or explain why their approach is better or different. And in this segment, ahead of Snowflake's big event, we want to share some of what we learned and how we see it. Now, theCUBE is getting paid to host this event, so I need you to know that, and you draw your own conclusions from my remarks. But neither Snowflake nor any other sponsor of theCUBE or client of SiliconANGLE Media has editorial influence over Breaking Analysis. The opinions here are mine, and I would encourage you to read my ethics statement in this regard. I want to talk about the failed data model. The problem is complex, I'm not debating that. Organizations have to integrate data and platforms with existing operational systems, many of which were developed decades ago. And as a culture and a set of processes that have been built around these systems, and they've been hardened over the years. This chart here tries to depict the progression of the monolithic data source, which, for me, began in the 1980s when Decision Support Systems or DSS promised to solve our data problems. The data warehouse became very popular and data marts sprung up all over the place. This created more proprietary stovepipes with data locked inside. The Enron collapse led to Sarbanes-Oxley. Now, this tightened up reporting. The requirements associated with that, it breathed new life into the data warehouse model. But it remained expensive and cumbersome, I've talked about that a lot, like a snake swallowing a basketball. The 2010s ushered in the big data movement, and Data Lakes emerged. With a dupe, we saw the idea of no schema online, where you put structured and unstructured data into a repository, and figure it all out on the read. What emerged was a fairly complex data pipeline that involved ingesting, cleaning, processing, analyzing, preparing, and ultimately serving data to the lines of business. And this is where we are today with very hyper specialized roles around data engineering, data quality, data science. There's lots of batch of processing going on, and Spark has emerged to improve the complexity associated with MapReduce, and it definitely helped improve the situation. We're also seeing attempts to blend in real time stream processing with the emergence of tools like Kafka and others. But I'll argue that in a strange way, these innovations actually compound the problem. And I want to discuss that because what they do is they heighten the need for more specialization, more fragmentation, and more stovepipes within the data life cycle. Now, in reality, and it pains me to say this, it's the outcome of the big data movement, as we sit here in 2020, that we've created thousands of complicated science projects that have once again failed to live up to the promise of rapid cost-effective time to insights. So, what will the 2020s bring? What's the next silver bullet? You hear terms like the lakehouse, which Databricks is trying to popularize. And I'm going to talk today about data mesh. These are other efforts they look to modernize datalakes and sometimes merge the best of data warehouse and second-generation systems into a new paradigm, that might unify batch and stream frameworks. And this definitely addresses some of the gaps, but in our view, still suffers from some of the underlying problems of previous generation data architectures. In other words, if the next gen data architecture is incremental, centralized, rigid, and primarily focuses on making the technology to get data in and out of the pipeline work, we predict it's going to fail to live up to expectations again. Rather, what we're envisioning is an architecture based on the principles of distributed data, where domain knowledge is the primary target citizen, and data is not seen as a by-product, i.e, the exhaust of an operational system, but rather as a service that can be delivered in multiple forms and use cases across an ecosystem. This is why we often say the data is not the new oil. We don't like that phrase. A specific gallon of oil can either fuel my home or can lubricate my car engine, but it can't do both. Data does not follow the same laws of scarcity like natural resources. Again, what we're envisioning is a rethinking of the data pipeline and the associated cultures to put data needs of the domain owner at the core and provide automated, governed, and secure access to data as a service at scale. Now, how is this different? Let's take a look and unpack the data pipeline today and look deeper into the situation. You all know this picture that I'm showing. There's nothing really new here. The data comes from inside and outside the enterprise. It gets processed, cleanse or augmented so that it can be trusted and made useful. Nobody wants to use data that they can't trust. And then we can add machine intelligence and do more analysis, and finally deliver the data so that domain specific consumers can essentially build data products and services or reports and dashboards or content services, for instance, an insurance policy, a financial product, a loan, that these are packaged and made available for someone to make decisions on or to make a purchase. And all the metadata associated with this data is packaged along with the dataset. Now, we've broken down these steps into atomic components over time so we can optimize on each and make them as efficient as possible. And down below, you have these happy stick figures. Sometimes they're happy. But they're highly specialized individuals and they each do their job and they do it well to make sure that the data gets in, it gets processed and delivered in a timely manner. Now, while these individual pieces seemingly are autonomous and can be optimized and scaled, they're all encompassed within the centralized big data platform. And it's generally accepted that this platform is domain agnostic. Meaning the platform is the data owner, not the domain specific experts. Now there are a number of problems with this model. The first, while it's fine for organizations with smaller number of domains, organizations with a large number of data sources and complex domain structures, they struggle to create a common data parlance, for example, in a data culture. Another problem is that, as the number of data sources grows, organizing and harmonizing them in a centralized platform becomes increasingly difficult, because the context of the domain and the line of business gets lost. Moreover, as ecosystems grow and you add more data, the processes associated with the centralized platform tend to get further genericized. They again lose that domain specific context. Wait (chuckling), there are more problems. Now, while in theory organizations are optimizing on the piece parts of the pipeline, the reality is, as the domain requires a change, for example, a new data source or an ecosystem partnership requires a change in access or processes that can benefit a domain consumer, the reality is the change is subservient to the dependencies and the need to synchronize across these discrete parts of the pipeline or actually, orthogonal to each of those parts. In other words, in actuality, the monolithic data platform itself remains the most granular part of the system. Now, when I complain about this faulty structure, some folks tell me this problem has been solved. That there are services that allow new data sources to really easily be added. A good example of this is Databricks Ingest, which is, it's an auto loader. And what it does is it simplifies the ingestion into the company's Delta Lake offering. And rather than centralizing in a data warehouse, which struggles to efficiently allow things like Machine Learning frameworks to be incorporated, this feature allows you to put all the data into a centralized datalake. More so the argument goes, that the problem that I see with this, is while the approach does definitely minimizes the complexities of adding new data sources, it still relies on this linear end-to-end process that slows down the introduction of data sources from the domain consumer beside of the pipeline. In other words, the domain experts still has to elbow her way into the front of the line or the pipeline, in this case, to get stuff done. And finally, the way we are organizing teams is a point of contention, and I believe is going to continue to cause problems down the road. Specifically, we've again, we've optimized on technology expertise, where for example, data engineers, well, really good at what they do, they're often removed from the operations of the business. Essentially, we created more silos and organized around technical expertise versus domain knowledge. As an example, a data team has to work with data that is delivered with very little domain specificity, and serves a variety of highly specialized consumption use cases. All right. I want to step back for a minute and talk about some of the problems that people bring up with Snowflake and then I'll relate it back to the basic premise here. As I said earlier, we've been hammered by dozens and dozens of data points, opinions, criticisms of Snowflake. And I'll share a few here. But I'll post a deeper technical analysis from a software engineer that I found to be fairly balanced. There's five Snowflake criticisms that I'll highlight. And there are many more, but here are some that I want to call out. Price transparency. I've had more than a few customers telling me they chose an alternative database because of the unpredictable nature of Snowflake's pricing model. Snowflake, as you probably know, prices based on consumption, just like AWS and other cloud providers. So just like AWS, for example, the bill at the end of the month is sometimes unpredictable. Is this a problem? Yes. But like AWS, I would say, "Kill me with that problem." Look, if users are creating value by using Snowflake, then that's good for the business. But clearly this is a sore point for some users, especially for procurement and finance, which don't like unpredictability. And Snowflake needs to do a better job communicating and managing this issue with tooling that can predict and help better manage costs. Next, workload manage or lack thereof. Look, if you want to isolate higher performance workloads with Snowflake, you just spin up a separate virtual warehouse. It's kind of a brute force approach. It works generally, but it will add expense. I'm kind of reminded of Pure Storage and its approach to storage management. The engineers at Pure, they always design for simplicity, and this is the approach that Snowflake is taking. Usually, Pure and Snowflake, as I have discussed in a moment, is Pure's ascendancy was really based largely on stealing share from Legacy EMC systems. Snowflake, in my view, has a much, much larger incremental market opportunity. Next is caching architecture. You hear this a lot. At the end of the day, Snowflake is based on a caching architecture. And a caching architecture has to be working for some time to optimize performance. Caches work well when the size of the working set is small. Caches generally don't work well when the working set is very, very large. In general, transactional databases have pretty small datasets. And in general, analytics datasets are potentially much larger. Is it Snowflake in the analytics business? Yes. But the good thing that Snowflake has done is they've enabled data sharing, and it's caching architecture serves its customers well because it allows domain experts, you're going to hear this a lot from me today, to isolate and analyze problems or go after opportunities based on tactical needs. That said, very big queries across whole datasets or badly written queries that scan the entire database are not the sweet spot for Snowflake. Another good example would be if you're doing a large audit and you need to analyze a huge, huge dataset. Snowflake's probably not the best solution. Complex joins, you hear this a lot. The working set of complex joins, by definition, are larger. So, see my previous explanation. Read only. Snowflake is pretty much optimized for read only data. Maybe stateless data is a better way of thinking about this. Heavily right intensive workloads are not the wheelhouse of Snowflake. So where this is maybe an issue is real-time decision-making and AI influencing. A number of times, Snowflake, I've talked about this, they might be able to develop products or acquire technology to address this opportunity. Now, I want to explain. These issues would be problematic if Snowflake were just a data warehouse vendor. If that were the case, this company, in my opinion, would hit a wall just like the NPP vendors that proceeded them by building a better mouse trap for certain use cases hit a wall. Rather, my promise in this episode is that the future of data architectures will be really to move away from large centralized warehouses or datalake models to a highly distributed data sharing system that puts power in the hands of domain experts at the line of business. Snowflake is less computationally efficient and less optimized for classic data warehouse work. But it's designed to serve the domain user much more effectively in our view. We believe that Snowflake is optimizing for business effectiveness, essentially. And as I said before, the company can probably do a better job at keeping passionate end users from breaking the bank. But as long as these end users are making money for their companies, I don't think this is going to be a problem. Let's look at the attributes of what we're proposing around this new architecture. We believe we'll see the emergence of a total flip of the centralized and monolithic big data systems that we've known for decades. In this architecture, data is owned by domain-specific business leaders, not technologists. Today, it's not much different in most organizations than it was 20 years ago. If I want to create something of value that requires data, I need to cajole, beg or bribe the technology and the data team to accommodate. The data consumers are subservient to the data pipeline. Whereas in the future, we see the pipeline as a second class citizen, with a domain expert is elevated. In other words, getting the technology and the components of the pipeline to be more efficient is not the key outcome. Rather, the time it takes to envision, create, and monetize a data service is the primary measure. The data teams are cross-functional and live inside the domain versus today's structure where the data team is largely disconnected from the domain consumer. Data in this model, as I said, is not the exhaust coming out of an operational system or an external source that is treated as generic and stuffed into a big data platform. Rather, it's a key ingredient of a service that is domain-driven and monetizable. And the target system is not a warehouse or a lake. It's a collection of connected domain-specific datasets that live in a global mesh. What is a distributed global data mesh? A data mesh is a decentralized architecture that is domain aware. The datasets in the system are purposely designed to support a data service or data product, if you prefer. The ownership of the data resides with the domain experts because they have the most detailed knowledge of the data requirement and its end use. Data in this global mesh is governed and secured, and every user in the mesh can have access to any dataset as long as it's governed according to the edicts of the organization. Now, in this model, the domain expert has access to a self-service and obstructed infrastructure layer that is supported by a cross-functional technology team. Again, the primary measure of success is the time it takes to conceive and deliver a data service that could be monetized. Now, by monetize, we mean a data product or data service that it either cuts cost, it drives revenue, it saves lives, whatever the mission is of the organization. The power of this model is it accelerates the creation of value by putting authority in the hands of those individuals who are closest to the customer and have the most intimate knowledge of how to monetize data. It reduces the diseconomies at scale of having a centralized or a monolithic data architecture. And it scales much better than legacy approaches because the atomic unit is a data domain, not a monolithic warehouse or a lake. Zhamak Dehghani is a software engineer who is attempting to popularize the concept of a global mesh. Her work is outstanding, and it's strengthened our belief that practitioners see this the same way that we do. And to paraphrase her view, "A domain centric system must be secure and governed with standard policies across domains." It has to be trusted. As I said, nobody's going to use data they don't trust. It's got to be discoverable via a data catalog with rich metadata. The data sets have to be self-describing and designed for self-service. Accessibility for all users is crucial as is interoperability, without which distributed systems, as we know, fail. So what does this all have to do with Snowflake? As I said, Snowflake is not just a data warehouse. In our view, it's always had the potential to be more. Our assessment is that attacking the data warehouse use cases, it gave Snowflake a straightforward easy-to-understand narrative that allowed it to get a foothold in the market. Data warehouses are notoriously expensive, cumbersome, and resource intensive, but they're a critical aspect to reporting and analytics. So it was logical for Snowflake to target on-premise legacy data warehouses and their smaller cousins, the datalakes, as early use cases. By putting forth and demonstrating a simple data warehouse alternative that can be spun up quickly, Snowflake was able to gain traction, demonstrate repeatability, and attract the capital necessary to scale to its vision. This chart shows the three layers of Snowflake's architecture that have been well-documented. The separation of compute and storage, and the outer layer of cloud services. But I want to call your attention to the bottom part of the chart, the so-called Cloud Agnostic Layer that Snowflake introduced in 2018. This layer is somewhat misunderstood. Not only did Snowflake make its Cloud-native database compatible to run on AWS than Azure in the 2020 GCP, what Snowflake has done is to obstruct cloud infrastructure complexity and create what it calls the data cloud. What's the data cloud? We don't believe the data cloud is just a marketing term that doesn't have any substance. Just as SAS is Simplified Application Software and iOS made it possible to eliminate the value drain associated with provisioning infrastructure, a data cloud, in concept, can simplify data access, and break down fragmentation and enable shared data across the globe. Snowflake, they have a first mover advantage in this space, and we see a number of fundamental aspects that comprise a data cloud. First, massive scale with virtually unlimited compute and storage resource that are enabled by the public cloud. We talk about this a lot. Second is a data or database architecture that's built to take advantage of native public cloud services. This is why Frank Slootman says, "We've burned the boats. We're not ever doing on-prem. We're all in on cloud and cloud native." Third is an obstruction layer that hides the complexity of infrastructure. and fourth is a governed and secured shared access system where any user in the system, if allowed, can get access to any data in the cloud. So a key enabler of the data cloud is this thing called the global data mesh. Now, earlier this year, Snowflake introduced its global data mesh. Over the course of its recent history, Snowflake has been building out its data cloud by creating data regions, strategically tapping key locations of AWS regions and then adding Azure and GCP. The complexity of the underlying cloud infrastructure has been stripped away to enable self-service, and any Snowflake user becomes part of this global mesh, independent of the cloud that they're on. Okay. So now, let's go back to what we were talking about earlier. Users in this mesh will be our domain owners. They're building monetizable services and products around data. They're most likely dealing with relatively small read only datasets. They can adjust data from any source very easily and quickly set up security and governance to enable data sharing across different parts of an organization, or, very importantly, an ecosystem. Access control and governance is automated. The data sets are addressable. The data owners have clearly defined missions and they own the data through the life cycle. Data that is specific and purposely shaped for their missions. Now, you're probably asking, "What happens to the technical team and the underlying infrastructure and the cluster it's in? How do I get the compute close to the data? And what about data sovereignty and the physical storage later, and the costs?" All these are good questions, and I'm not saying these are trivial. But the answer is these are implementation details that are pushed to a self-service layer managed by a group of engineers that serves the data owners. And as long as the domain expert/data owner is driving monetization, this piece of the puzzle becomes self-funding. As I said before, Snowflake has to help these users to optimize their spend with predictive tooling that aligns spend with value and shows ROI. While there may not be a strong motivation for Snowflake to do this, my belief is that they'd better get good at it or someone else will do it for them and steal their ideas. All right. Let me end with some ETR data to show you just how Snowflake is getting a foothold on the market. Followers of this program know that ETR uses a consistent methodology to go to its practitioner base, its buyer base each quarter and ask them a series of questions. They focus on the areas that the technology buyer is most familiar with, and they ask a series of questions to determine the spending momentum around a company within a specific domain. This chart shows one of my favorite examples. It shows data from the October ETR survey of 1,438 respondents. And it isolates on the data warehouse and database sector. I know I just got through telling you that the world is going to change and Snowflake's not a data warehouse vendor, but there's no construct today in the ETR dataset to cut a data cloud or globally distributed data mesh. So you're going to have to deal with this. What this chart shows is net score in the y-axis. That's a measure of spending velocity, and it's calculated by asking customers, "Are you spending more or less on a particular platform?" And then subtracting the lesses from the mores. It's more granular than that, but that's the basic concept. Now, on the x-axis is market share, which is ETR's measure of pervasiveness in the survey. You can see superimposed in the upper right-hand corner, a table that shows the net score and the shared N for each company. Now, shared N is the number of mentions in the dataset within, in this case, the data warehousing sector. Snowflake, once again, leads all players with a 75% net score. This is a very elevated number and is higher than that of all other players, including the big cloud companies. Now, we've been tracking this for a while, and Snowflake is holding firm on both dimensions. When Snowflake first hit the dataset, it was in the single digits along the horizontal axis and continues to creep to the right as it adds more customers. Now, here's another chart. I call it the wheel chart that breaks down the components of Snowflake's net score or spending momentum. The lime green is new adoption, the forest green is customers spending more than 5%, the gray is flat spend, the pink is declining by more than 5%, and the bright red is retiring the platform. So you can see the trend. It's all momentum for this company. Now, what Snowflake has done is they grabbed a hold of the market by simplifying data warehouse. But the strategic aspect of that is that it enables the data cloud leveraging the global mesh concept. And the company has introduced a data marketplace to facilitate data sharing across ecosystems. This is all about network effects. In the mid to late 1990s, as the internet was being built out, I worked at IDG with Bob Metcalfe, who was the publisher of InfoWorld. During that time, we'd go on speaking tours all over the world, and I would listen very carefully as he applied Metcalfe's law to the internet. Metcalfe's law states that the value of the network is proportional to the square of the number of connected nodes or users on that system. Said another way, while the cost of adding new nodes to a network scales linearly, the consequent value scores scales exponentially. Now, apply that to the data cloud. The marginal cost of adding a user is negligible, practically zero, but the value of being able to access any dataset in the cloud... Well, let me just say this. There's no limitation to the magnitude of the market. My prediction is that this idea of a global mesh will completely change the way leading companies structure their businesses and, particularly, their data architectures. It will be the technologists that serve domain specialists as it should be. Okay. Well, what do you think? DM me @dvellante or email me at david.vellante@siliconangle.com or comment on my LinkedIn? Remember, these episodes are all available as podcasts, so please subscribe wherever you listen. I publish weekly on wikibon.com and siliconangle.com, and don't forget to check out etr.plus for all the survey analysis. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching. Be well, and we'll see you next time. (upbeat music)

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone and welcome this week's episode of theCUBE Insights powered by ETR. And welcome to the Trillionaire's Club. In this Breaking Analysis, I want to look at how the big tech companies have really changed the recipe for innovation in the Enterprise. And as we enter the next decade, I think it's important to sort of reset and re-look at how innovation will determine the winners and losers going forward, including not only the sellers of technology but how technology applied will set the stage for the next 50 years of economic growth. Here's the premise that I want to put forth to you. The source of innovation in the technology business has been permanently altered. There's a new cocktail of innovation, if you will, that will far surpass Moore's Law in terms of it's impact on the industry. For 50 years we've marched to the cadence of that Moore's Law, that is the doubling of transistor counts every 18 months, as shown in the left-hand side of this chart. And of course this translated as we know, into a chasing of the chips, where by being first with the latest and greatest microprocessor brought competitive advantage. We saw Moore's Law drive the PC era, the client server era, and it even powered the internet, notwithstanding the effects of Metcalfe's Law. But there's a new engine of innovation or what John Furrier calls the "Innovation Cocktail," and that's shown in the right-hand of this slide where data plus machine intelligence or AI and Cloud are combinatorial technologies that will power innovation for the next 20 plus years. 10 years of gathering big data have put us in a position to now apply AI. Data is plentiful but insights are not and AI unlocks those insights. The Cloud brings three things, agility, scale, and the ability to fail quickly and cheaply. So, it's these three elements and how they are packaged and applied that will in my view determine winners and losers in the next decade and beyond. Now why is this era now suddenly upon us? Well I would argue there are three main factors. One is cheap storage and compute combined with alternative processor types, like GPUs that can power AI. And the era of data is here to stay. This next chart from Dave Moschella's book, "Seeing Digital," really underscores this point. Incumbent organizations born in the last century organized largely around human expertise or processes or hard assets like factories. These were the engines of competitive advantage. But today's successful organizations put data at the core. They live by the mantra of data driven. It is foundational to them. And they organize expertise, processes and people around the data. All you got to do to drive this point home is look at the market caps of the top five public companies in the U.S. Stock Market, Apple, Microsoft, Google, Amazon, and Facebook. I call this chart the Cuatro Comas! as a shout out to Russ Hanneman, the crazy billionaire supporting, was a supporting character in the Silicon Valley series. Now each of these companies, with the exception of Facebook, has hit the trillion dollar club. AWS, like Mr. Hanneman, hit the trillion dollar club status back in September 2018 but fell back down and lost a comma. These five data-driven companies have surpassed big oil and big finance. I mean, the next closest company is Berkshire at 566 billion. And I would argue that if it hadn't been for the fake news scandal, Facebook probably would be right there with these others. Now, with the exception of Apple, these companies, they're not highly valued because of the goods they pump out, rather, and I would argue even in the case of Apple, their highly valued because they're leaders in digital and in the best position to apply machine intelligence to massive stores of data that they've collected. And they have massive scale, thanks to the Cloud. Now, I get that the success of some of these companies is largely driven by the consumer but the consumerization of IT makes this even more relevant, in my opinion. Let's bring in some ETR data to see how this translates into the Enterprise tech world. This chart shows market share from Microsoft, AWS, Apple iPhone, and Google in the Enterprise all the way back to 2010. Now I get that the iPhone is a bit of a stretch here but stick with me. Remember, market share in ETR terms is a measure of pervasiveness in the data set. Look at how Microsoft has held it's ground. And you can see the steady rise of AWS and Google. Now if I superimpose traditional Enterprise players like Cisco, IBM, or Hewlett or even Dell, that is companies that aren't competing with data at the core of their business, you would see a steady decline. I am required to black out January 2020 as you probably remember, but that data will be out soon and made public shortly after ETR exits its self-imposed quiet period. Now Apple iPhone is not a great proxy but Apple, they're not an Enterprise tech company, but it's data that I can show but now I would argue again that Apple's real value and a key determinate of their success going forward, lies in how it uses data and applies machine intelligence at scale over the next decade to compete in apps and digital services, content, and other adjacencies. And I would say these five leaders and virtually any company in the next decade, this applies. Look, digital means data and digital businesses are data driven. Data changes how we think about competition. Just look at Amazon's moves in content, grocery, logistics. Look at Google in automobiles, Apple and Amazon in music. You know, interestingly Microsoft positions this as a competitive advantage, especially in retail. For instance, touting Walmart as a partner, not a competitor, a la Amazon. The point is, that digital data, AI, and Cloud bring forth highly disruptive possibilities and are enabling these giants to enter businesses that previously were insulated from the outsiders. And in the case of the Cloud, it's paying the way. Just look at the data from Amazon. The left bar shows Amazon's revenue. AWS represents only 12% of the total company's turnover. But as you can see on the right-hand side, it accounts for almost half of the company's operating income. So, the Cloud is essentially funding Amazon's entrance into all these other businesses and powering its scale. Now let's bring in some ETR data to show what's happening in the Enterprise in the terms of share shifts. This chart is a double-Y axis that shows spending levels on the left-hand side, represented by the bars, and the average change in spending, represented by the dots. Focus for a second on the dots and the percentages. Container orchestrations at 29% change. Container platforms at 19.7%. These are Cloud-native technologies and customers are voting with their wallets. Machine learning and AI, nearly 18% change. Cloud computing itself still in the 16% range, 10 plus years on. Look at analytics and big data in the double digits still, 10 years into the big data movement. So, you can see the ETR data shows that the spending action is in and around Cloud, AI, and data. And in the red, look at the Moore's Law techs like servers and storage. Now, this isn't to say that those go away. I fully understand you need servers, and storage, and networking, and database, and software to power the Cloud but this data shows that right now, these discreet cocktail technologies are gaining spending momentum. So, the question I want to leave you with is, what does this mean for incumbents? Those that are not digital-natives or not born in the Cloud? Well, the first thing I'd point out is that while the trillionaires, they look invincible today, history suggests that they are not invulnerable. The rise of China, India, open-source, peer-to-peer models, open models, could coalesce and disrupt these big guys if they miss a step or a cycle. The second point I would make is that incumbents are often too complacent. More often than not, in my experience, there is complacency and there will be a fallout. I hear a lot of lip service given to digital and data driven but often I see companies that talk the talk but they don't walk the walk. Change will come and the incumbents will be disrupted and that is going to cause action at the top. The good news is that the incumbents, they don't have to build the tech. They can compete with the disruptors by applying machine intelligence to their unique data sets and they can buy technologies like AI and the Cloud from suppliers. The degree to which they are comfortable buying from these supplies, who may also be competitors, will play out over time but I would argue that building that competitive advantage sooner rather than later with data and learning to apply machine intelligence and AI to their unique businesses, will allow them to thrive and protect their existing businesses and grow. These markets are large and the incumbents have inherent advantages in terms of resources, relationships, brand value, customer affinity, and domain knowledge that if they apply and transform from the top with strong leadership, they will do very, very well in my view. This is Dave Vellante signing out from this latest episode of theCUBE Insights powered by ETR. Thanks for watching everybody. We'll see you next time and please feel free to comment. In my LinkedIn, you can DM me @dvellante and don't forget we turned this into a podcast so check that out at your favorite podcast player. Thanks again.

>> From our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to the special. Keep conversation here in Palo Alto, California. I'm John for a co host of the Cube. Were Arsenal was a C I S O. C. So for Microsoft also corporate vice President, Chief information security. Thanks for joining me today. >> Thank you. >> Appreciate it. Thanks. So you have a really big job. You're a warrior in the industry, security is the hardest job on the planet. >> And hang in sight >> of every skirt. Officer is so hard. Tell us about the role of Microsoft. You have overlooked the entire thing. You report to the board, give us an overview of what >> happens. Yeah. I >> mean, it's you know, obviously we're pretty busy. Ah, in this world we have today with a lot of adversaries going on, an operational issues happening. And so I have responsibility. Accountability for obviously protecting Microsoft assets are customer assets. And then ah, And for me, with the trend also responsibility for business continuity Disaster recovery company >> on the sea. So job has been evolving. We're talking before the camera came on that it's coming to CEO CF roll years ago involved to a business leader. Where is the sea? So roll now in your industry is our is a formal title is it establishes their clear lines of reporting. How's it evolved? What's the current state of the market in terms of the sea? So it's roll? >> Yeah, the role is involved. A lot. Like you said, I think like the CIA or twenty years ago, you know, start from the back room of the front room and I think the, you know, one of things I look at in the role is it's really made it before things. There's technical architecture, there's business enablement. There's operational expert excellence. And then there's risk management and the older ah, what does find the right word? But the early see so model was really about the technical architecture. Today. It's really a blend of those four things. How do you enable your business to move forward? How do you take calculated risks or manage risks? And then how do you do it really effectively and efficiently, which is really a new suit and you look at them. You'LL see people evolving to those four functions. >> And who's your boss? Would you report to >> I report to a gentleman by the name of a curtain. Little Benny on DH. He is the chief digital officer, which would be a combination of Seo did officer and transformation as well as all of Microsoft corporate strategy >> and this broad board visibility, actually in security. >> Yeah, you >> guys, how is Microsoft evolved? You've been with the company for a long time >> in the >> old days ahead perimeters, and we talk about on the Cube all the time. When a criminalist environment. Now there's no perimeter. Yeah, the world's changed. How is Microsoft evolved? Its its view on security Has it evolved from central groups to decentralize? How is it how how was it managed? What's the what's the current state of the art for security organization? >> Well, I think that, you know, you raise a good point, though things have changed. And so in this idea, where there is this, you know, perimeter and you demanded everything through the network that was great. But in a client to cloak cloud world, we have today with mobile devices and proliferation or cloud services, and I ot the model just doesn't work anymore. So we sort of simplified it down into Well, we should go with this, you know, people calls your trust, I refer to It is just don't talk to strangers. But the idea being is this really so simplified, which is you've got to have a good identity, strong identity to participate. You have to have managed in healthy device to participate, to talk to, ah, Microsoft Asset. And then you have to have data in telemetry that surrounds that all the time. And so you basically have a trust, trust and then verify model between those three things. And that's really the fundamental. It's really that simple. >> David Lava as Pascal senior with twenty twelve when he was M. C before he was the C E O. V M. Where he said, You know his security do over and he was like, Yes, it's going to be a do over its opportunity. What's your thoughts on that perspective? Has there been a do over? Is it to do over our people looking at security and a whole new way? What's your thoughts? >> Yeah, I mean, I've been around security for a long time, and it's there's obviously changes in Massa nations that happened obviously, at Microsoft. At one point we had a security division. I was the CTO in that division, and we really thought the better way to do it was make security baked in all the products that we do. Everything has security baked in. And so we step back and really change the way we thought about it. To make it easier for developers for end users for admin, that is just a holistic part of the experience. So again, the technology really should disappear. If you really want to be affected, I think >> don't make it a happy thought. Make it baked in from Day one on new product development and new opportunity. >> Yeah, basically, shift the whole thing left. Put it right in from the beginning. And so then, therefore, it's a better experience for everyone using it. >> So one of things we've observed over the past ten years of doing the Cube when do first rolled up with scene, you know, big data role of date has been critical, and I think one of the things that's interesting is, as you get data into the system, you can use day that contextually and look at the contextual behavioral data. It's really is create some visibility into things you, Meyer may not have seen before. Your thoughts and reaction to the concept of leveraging data because you guys get a lot of data. How do you leverage the data? What's the view of data? New data will make things different. Different perspectives creates more visibility. Is that the right view? What's your thoughts on the role of Data World Data plays? >> Well, they're gonna say, You know, we had this idea. There's identity, there's device. And then there's the data telemetry. That platform becomes everything we do, what there's just security and are anomalous behavior like you were talking about. It is how do we improve the user experience all the way through? And so we use it to the service health indicator as well. I think the one thing we've learned, though, is I was building where the biggest data repositories your head for some time. Like we look at about a six point five trillion different security events a day in any given day, and so sort of. How do you filter through that? Manage? That's pretty amazing, says six point five trillion >> per day >> events per day as >> coming into Microsoft's >> that we run through the >> ecosystem your systems. Your computers? >> Yeah. About thirty five hundred people. Reason over that. So you can Certainly the math. You need us. Um, pretty good. Pretty good technology to make it work effectively for you and efficiently >> at RC A Heard a quote on the floor and on the q kind of echoing the same sentiment is you can't hire your way to success in this market is just not enough people qualified and jobs available to handle the volume and the velocity of the data coming in. Automation plays a critical role. Your reaction to that comment thoughts on? >> Well, I think I think the cure there, John, those when you talk about the volume of the data because there's what we used to call speeds and feeds, right? How big is it? And I used to get great network data so I can share a little because we've talked, like from the nineties or whatever period that were there. Like the network was everything, but it turns out much like a diverse workforce creates the best products. It turns out diverse data is more important than speeds and feeds. So, for example, authentication data map to, you know, email data map to end point data map. TEO SERVICE DATA Soon you're hosting, you know, the number of customers. We are like financial sector data vs Healthcare Data. And so it's the ability Teo actually do correlation across that diverse set of data that really differentiates it. So X is an example. We update one point two billion devices every single month. We do six hundred thirty billion authentications every single month. And so the ability to start correlating those things and movement give us a set of insights to protect people like we never had before. >> That's interesting telemetry you're getting in the marketplace. Plus, you have the systems to bring it in >> a pressure pressure coming just realized. And this all with this consent we don't do without consent, we would never do without consent. >> Of course, you guys have the terms of service. You guys do a good job on that, But I think the point that I'm seeing there is that you guys are Microsoft. Microsoft got a lot of access. Get a lot of stuff out there. How does an enterprise move to that divers model because they will have email, obviously. But they have devices. So you guys are kind of operating? I would say tear one of the level of that environment cause you're Microsoft. I'm sure the big scale players to that. I'm just an enterprising I'm a bank or I'm an insurance company or I'm in oil and gas, Whatever the vertical. Maybe. What do I do if I'm the sea? So they're So what does that mean, Diversity? How should they? >> Well, I think they have a diverse set of data as well. Also, if they participate, you know, even in our platform today, we you know, we have this thing called the security graph, which is an FBI people can tap into and tap into the same graph that I use and so they can use that same graph particular for them. They can use our security experts to help them with that if they don't have the all the resource and staff to go do that. So we provide both both models for that to happen, and I think that's why a unique perspective I should think should remind myself of which is we should have these three things. We have a really good security operations group we have. I think that makes us pretty unique that people can leverage. We build this stuff into the product, which I think is good. But then the partnership, the other partners who play in the graph, it's not just us. So there's lots of people who play on that as well. >> So like to ask you two lines of questions. Wanting on the internal complex is that organizations will have on the external complexity and realities of threats and coming in. How do they? How do you balance that out? What's your vision on that? Because, you know, actually, there's technology, his culture and people, you know in those gaps and capabilities on on all three. Yeah, internally just getting the culture right and then dealing with the external. How does a C so about his company's balance? Those realities? >> Well, I think you raised a really good point, which is how do you move the culture for? That's a big conversation We always have. And that was sort of, you know, it's interesting because the the one side we have thirty five hundred people who have security title in their job, But there's over one hundred thousand people who every day part of their job is doing security, making sure they'LL understand that and know that is a key part we should reinforce everyday on DSO. But I think balancing it is, is for me. It's actually simplifying just a set of priorities because there's no shortage of, you know, vendors who play in the space. There's no shortage of things you can read about. And so for us it was just simplifying it down and getting it. That simplifies simplified view of these are the three things we're going to go do we build onerous platform to prioritize relative to threat, and then and then we ensure we're building quality products. Those five things make it happen. >> I'd like to get your thoughts on common You have again Before I came on camera around how you guys view simplification terminal. You know, you guys have a lot of countries, the board level, and then also you made a common around trust of security and you an analogy around putting that drops in a bucket. So first talk about the simplification, how you guys simplifying it and why? Why is that important? >> You think we supply two things one was just supplying the message to people understood the identity of the device and making sure everything is emitting the right telemetry. The second part that was like for us but a Z to be illustrative security passwords like we started with this technology thing and we're going to do to FAA. We had cards and we had readers and oh, my God, we go talk to a user. We say we're going to put two FAA everywhere and you could just see recoil and please, >> no. And then >> just a simple change of being vision letters. And how about this? We're just going to get rid of passwords then People loved like they're super excited about it. And so, you know, we moved to this idea of, you know, we always said this know something, know something new, how something have something like a card And they said, What about just be something and be done with it? And so, you know, we built a lot of the capability natively into the product into windows, obviously, but I supported energies environment. So I you know, I support a lot of Mac clinics and IOS and Android as well So you've read it. Both models you could use by or you could use your device. >> That's that. That's that seems to be a trend. Actually, See that with phones as well as this. Who you are is the password and why is the support? Because Is it because of these abuses? Just easy to program? What's the thought process? >> I think there's two things that make it super helpful for us. One is when you do the biometric model. Well, first of all, to your point, the the user experience is so much better. Like we walk up to a device and it just comes on. So there's no typing this in No miss typing my password. And, you know, we talked earlier, and that was the most popular passwords in Seattle with Seahawks two thousand seventeen. You can guess why, but it would meet the complexity requirements. And so the idea is, just eliminate all that altogether. You walk up machine, recognize you, and you're often running s o. The user experience is great, but plus it's Actually the entropy is harder in the biometric, which makes it harder for people to break it, but also more importantly, it's bound locally to the device. You can't run it from somewhere else. And that's the big thing that I think people misunderstanding that scenario, which is you have to be local to that. To me, that's a >> great example of rethinking the security paradigm. Exactly. Let's talk about trust and security. You you have an opinion on this. I want to get your thoughts, the difference between trust and security so they go hand in hand at the same time. They could be confused. Your thoughts on this >> well being. You can have great trust. You can, so you can have great security. But you generally and you would hope that would equate like a direct correlation to trust. But it's not. You need to you build trust. I think our CEO said it best a long time ago. You put one bucket of water, one bucket. Sorry, one truffle water in the bucket every time. And that's how you build trust. Over time, my teenager will tell you that, and then you kick it over and you put it on the floor. So you have to. It's always this ratcheting up bar that builds trust. >> They doing great you got a bucket of water, you got a lot of trust, that one breach. It's over right, >> and you've got to go rebuild it and you've got to start all over again. And so key, obviously, is not to have that happen. But then, that's why we make sure you have operational rigor and >> great example that just totally is looking Facebook. Great. They have massive great security. What really went down this past week, but still the trust factor on just some of the other or societal questions? >> Yeah, >> and that something Do it. >> Security. Yeah, I think that's a large part of making sure you know you're being true. That's what I said before about, you know, we make sure we have consent. We're transparent about how we do the things we do, and that's probably the best ways to build trust. >> Okay, so you guys have been successful in Microsoft, just kind of tight the company for second to your role. It's pretty well documented that the stock prices at an all time high. So if Donatella Cube alumni, by the way, has been on the cue before he he took over and clear he didn't pivot. He just said we'd go in the cloud. And so the great moves, he don't eat a lot of great stuff. Open source from open compute to over the source. And this ship has turned and everything's going great. But that cheering the cloud has been great for the company. So I gotta ask you, as you guys move to the cloud, the impact to your businesses multi fold one products, ecosystem suppliers. All these things are changing. How has security role in the sea? So position been impact that what have you guys done? How does that impact security in general? Thoughts? >> Yeah, I think we obviously were like any other enterprise we had thousands of online are thousands of line of business applications, and we did a transformation, and we took a method logical approach with risk management. And we said, Okay, well, this thirty percent we should just get rid of and decommission these. We should, you know, optimize and just lifting shifting application. That cloud was okay, but it turns out there's massive benefit there, like for elasticity. Think of things that quarterly reporting or and you'll surveys or things like that where you could just dynamically grow and shrink your platform, which was awesome linear scale that we never had Cause those events I talk about would require re architectures. Separate function now becomes linear. And so I think there is a lot of things from a security perspective I could do in a much more efficient must wear a fish. In fact, they're then I had to have done it before, but also much more effective. I just have compute capability. Didn't have I have signal I didn't have. And so we had to wrap her head around that right and and figure out how to really leverage that. And to be honest, get the point. We're exploited because you were the MySpace. I have disaster and continent and business. This is processed stuff. And so, you know, everyone build dark fiber, big data centers, storage, active, active. And now when you use a platform is a service like on that kind of azure. You could just click a Bach and say, I want this thing to replicate. It also feeds your >> most diverse data and getting the data into the system that you throw a bunch of computer at that scale. So What diverse data? How does that impact the good guys and the bad guys? That doesn't tip the scales? Because if you have divers date and you have his ability, it's a race for who has the most data because more data diversity increases the aperture and our visibility into events. >> Yeah, I you >> know, I should be careful. I feel like I always This's a job. You always feel like you're treading water and trying to trying to stay ahead. But I think that, um, I think for the first time in my tenure do this. I feel there's an asymmetry that benefits. They're good guys in this case because of the fact that your ability to reason over large sets of data like that and is computed data intensive and it will be much harder for them like they could generally use encryption were effectively than some organization because the one the many relationship that happens in that scenario. But in the data center you can't. So at least for now, I feel like there's a tip This. The scales have tipped a bit for the >> guy that you're right on that one. I think it's good observation I think that industry inside look at the activity around, from new fund adventures to overall activity on the analytics side. Clearly, the data edge is going to be an advantage. I think that's a great point. Okay, that's how about the explosion of devices we're seeing now. An explosion of pipe enabled devices, Internet of things to the edge. Operational technologies are out there that in factory floors, everything being I P enables, kind of reminds me of the old days. Were Internet population you'd never uses on the Internet is growing, and >> that costs a lot >> of change in value, creation and opportunities devices. Air coming on both physical and software enabled at a massive rate is causing a lot of change in the industry. Certainly from a security posture standpoint, you have more surface area, but they're still in opportunity to either help on the do over, but also create value your thoughts on this exploding device a landscape, >> I think your Boston background. So Metcalfe's law was the value the net because the number of the nodes on the network squared right, and so it was a tense to still be true, and it continues to grow. I think there's a huge value and the device is there. I mean, if you look at the things we could do today, whether it's this watch or you know your smartphone or your smart home or whatever it is, it's just it's pretty unprecedented the capabilities and not just in those, but even in emerging markets where you see the things people are doing with, you know, with phones and Lauren phones that you just didn't have access to from information, you know, democratization of information and analysis. I think it's fantastic. I do think, though, on the devices there's a set of devices that don't have the same capabilities as some of the more markets, so they don't have encryption capability. They don't have some of those things. And, you know, one of Microsoft's responses to that was everything. Has an M see you in it, right? And so we, you know, without your spirit, we created our own emcee. That did give you the ability to update it, to secure, to run it and manage it. And I think that's one of the things we're doing to try to help, which is to start making these I, O. T or Smart devices, but at a very low cost point that still gives you the ability because the farm would not be healed Update, which we learn an O. T. Is that over time new techniques happen And you I can't update the system >> from That's getting down to the product level with security and also having the data great threats. So final final talk Tracking one today with you on this, your warrior in the industry, I said earlier. See, so is a hard job you're constantly dealing with compliance to, you know, current attacks, new vector, new strains of malware. And it's all over the map. You got it. You got got the inbound coming in and you got to deal with all that the blocking and tackling of the organization. >> What do you What do >> you finding as best practice? What's the what if some of the things on the cso's checklist that you're constantly worried about and or investing in what some of >> the yeah, >> the day to day take us through the day to day life >> of visited a lot? Yeah, it >> starts with not a Leslie. That's the first thing you have to get used to, but I think the you know again, like I said, there's risk Manager. Just prioritize your center. This is different for every company like for us. You know, hackers don't break and they just log in. And so identity still is one of the top things. People have to go work on him. You know, get rid of passwords is good for the user, but good for the system. We see a lot in supply chain going on right now. Obviously, you mentioned in the Cambridge Analytical Analytics where we had that issue. It's just down the supply chain. And when you look at not just third party but forthe party fifth party supply and just the time it takes to respond is longer. So that's something that we need to continue to work on. And then I think you know that those are some of the other big thing that was again about this. How do you become effective and efficient and how you managed that supply chain like, You know, I've been on a mission for three years to reduce my number of suppliers by about fifty percent, and there's still lots of work to do there, but it's just getting better leverage from the supplier I have, as well as taking on new capability or things that we maybe providing natively. But at the end of the day, if you have one system that could do what four systems going Teo going back to the war for talent, having people, no forces and versus one system, it's just way better for official use of talent. And and obviously, simplicity is the is the friend of security. Where is entropy is not, >> and also you mentioned quality data diversity it is you're into. But also there's also quality date of you have quality and diverse data. You could have a nice, nice mechanism to get machine learning going well, but that's kind of complex, because in the thie modes of security breaches, you got pre breached in breech post breach. All have different data characteristics all flowing together, so you can't just throw that answer across as a prism across the problem sets correct. This is super important, kind of fundamentally, >> yeah, but I think I >> would I would. The way I would characterize those is it's honestly, well, better lessons. I think I learned was living how to understand. Talk with CFO, and I really think we're just two things. There's technical debt that we're all working on. Everybody has. And then there's future proofing the company. And so we have a set of efforts that go onto like Red Team. Another actually think like bad people break them before they break you, you know, break it yourself and then go work on it. And so we're always balancing how much we're spending on the technical, that cleanup, you know, modernizing systems and things that are more capable. And then also the future proofing. If you're seeing things coming around the corner like cryptography and and other other element >> by chain blockchain, my supply chain is another good, great mechanism. So you constantly testing and R and D also practical mechanisms. >> And there in the red team's, which are the teams that attacking pen everything, which is again, break yourself first on this super super helpful for us >> well bred. You've seen a lot of ways of innovation have been involved in multiple ways computer industry client server all through the through the days, so feel. No, I feel good about this you know, because it reminds me and put me for broken the business together. But this is the interesting point I want to get to is there's a lot of younger Si SOS coming in, and a lot of young talent is being attractive. Security has kind of a game revived to it. You know, most people, my friends, at a security expert, they're all gamers. They love game, and now the thrill of it. It's exciting, but it's also challenging. Young people coming might not have experience. You have lessons you've learned. Share some thoughts over the years that scar either scar tissue or best practices share some advice. Some of the younger folks coming in breaking into the business of, you know, current situation. What you learned over the years it's Apple Apple. But now the industry. >> Yeah, sadly, I'd probably say it's no different than a lot of the general advice I would have in the space, which is there's you value experience. But it turns out I value enthusiasm and passion more here so you can teach about anybody whose passion enthusiastic and smart anything they want. So we get great data people and make them great security people, and we have people of a passion like you know, this person. It's his mission is to limit all passwords everywhere and like that passion. Take your passion and driver wherever you need to go do. And I >> think the nice >> thing about security is it is something that is technically complex. Human sociology complex, right? Like you said, changing culture. And it affects everything we do, whether it's enterprise, small, medium business, large international, it's actually a pretty It's a fasten, if you like hard problem. If you're a puzzle person, it's a great It's a great profession >> to me. I like how you said Puzzle. That's I think that's exactly it. They also bring up a good point. I want to get your thoughts on quickly. Is the talent gap is is really not about getting just computer science majors? It's bigger than that. In fact, I've heard many experts say, and you don't have to be a computer scientist. You could be a lot of cross disciplines. So is there a formula or industry or profession, a college degree? Or is it doesn't matter. It's just smart person >> again. It depends if your job's a hundred percent. Security is one thing, but like what we're trying to do is make not we don't have security for developers you want have developed to understand oppa security and what they build is an example on DSO. Same with administrators and other components. I do think again I would say the passion thing is a key piece for us, but But there's all aspects of the profession, like the risk managers air, you know, on the actuarial side. Then there's math people I had one of my favorite people was working on his phD and maladaptive behavior, and he was super valuable for helping us understand what actually makes things stick when you're trying to train their educate people. And what doesn't make that stick anthropologist or super helpful in this field like anthropologist, Really? Yeah, anthropologist are great in this field. So yeah, >> and sociology, too, you mentioned. That would think that's a big fact because you've got human aspect interests, human piece of it. You have society impact, so that's really not really one thing. It's really cross section, depending upon where you want to sit in the spectrum of opportunity, >> knowing it gives us a chance to really hire like we hire a big thing for us has been hard earlier in career and building time because it's just not all available. But then also you, well, you know, hire from military from law enforcement from people returning back. It's been actually, it's been a really fascinating thing from a management perspective that I didn't expect when I did. The role on has been fantastic. >> The mission. Personal question. Final question. What's getting you excited these days? I mean, honestly, you had a very challenging job and you have got attend all the big board meetings, but the risk management compliance. There's a lot of stuff going on, but it's a lot >> of >> technology fund in here to a lot of hard problems to solve. What's getting you excited? What what trends or things in the industry gets you excited? >> Well, I'm hopeful we're making progress on the bad guys, which I think is exciting. But honestly, this idea the you know, a long history of studying safety when I did this and I would love to see security become the air bags of the technology industry, right? It's just always there on new president. But you don't even know it's there until you need it. And I think that getting to that vision would be awesome. >> And then really kind of helping move the trust equation to a whole other level reputation. New data sets so data, bits of data business. >> It's total data business >> breath. Thanks for coming on the Q. Appreciate your insights, but also no see. So the chief information security officer at Microsoft, also corporate vice president here inside the Cuban Palo Alto. This is cute conversations. I'm John Career. Thanks for watching. >> Thank you.

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCube! (funky electronic music) Now, here's your host, Dave Vellante! >> Welcome, everybody, to this Cube conversation here in our Marlborough offices. I am very excited today, I spent a number of years at IDC, which, of course, is owned by IDG. And there's a new book out, relatively new, called Future Forward: Leadership Lessons from Patrick McGovern, the Visionary Who Circled the Globe and Built a Technology Media Empire. And it's a great book, lotta stories that I didn't know, many that I did know, and the author of that book, Glenn Rifkin, is here to talk about not only Pat McGovern but also some of the lessons that he put forth to help us as entrepreneurs and leaders apply to create better businesses and change the world. Glenn, thanks so much for comin' on theCube. >> Thank you, Dave, great to see ya. >> So let me start with, why did you write this book? >> Well, a couple reasons. The main reason was Patrick McGovern III, Pat's son, came to me at the end of 2016 and said, "My father had died in 2014 and I feel like his legacy deserves a book, and many people told me you were the guy to do it." So the background on that I, myself, worked at IDG back in the 1980s, I was an editor at Computerworld, got to know Pat during that time, did some work for him after I left Computerworld, on a one-on-one basis. Then I would see him over the years, interview him for the New York Times or other magazines, and every time I'd see Pat, I'd end our conversation by saying, "Pat, when are we gonna do your book?" And he would laugh, and he would say, "I'm not ready to do that yet, there's just still too much to do." And so it became sort of an inside joke for us, but I always really did wanna write this book about him because I felt he deserved a book. He was just one of these game-changing pioneers in the tech industry. >> He really was, of course, the book was even more meaningful for me, we, you and I started right in the same time, 1983-- >> Yeah. >> And by that time, IDG was almost 20 years old and it was quite a powerhouse then, but boy, we saw, really the ascendancy of IDG as a brand and, you know, the book reviews on, you know, the back covers are tech elite: Benioff wrote the forward, Mark Benioff, you had Bill Gates in there, Walter Isaacson was in there, Guy Kawasaki, Bob Metcalfe, George Colony-- >> Right. >> Who actually worked for a little stint at IDC for a while. John Markoff of The New York Times, so, you know, the elite of tech really sort of blessed this book and it was really a lot to do with Pat McGovern, right? >> Oh, absolutely, I think that the people on the inside understood how important he was to the history of the tech industry. He was not, you know, a household name, first of all, you didn't think of Steve Jobs, Bill Gates, and then Pat McGovern, however, those who are in the know realize that he was as important in his own way as they were. Because somebody had to chronicle this story, somebody had to share the story of the evolution of this amazing information technology and how it changed the world. And Pat was never a front-of-the-TV-camera guy-- >> Right. >> He was a guy who put his people forward, he put his products forward, for sure, which is why IDG, as a corporate name, you know, most people don't know what that means, but people did know Macworld, people did know PCWorld, they knew IDC, they knew Computerworld for sure. So that was Pat's view of the world, he didn't care whether he had the spotlight on him or not. >> When you listen to leaders like Reed Hoffman or Eric Schmidt talk about, you know, great companies and how to build great companies, they always come back to culture. >> Yup. >> The book opens with a scene of, and we all, that I usually remember this, well, we're just hangin' around, waitin' for Pat to come in and hand out what was then called the Christmas bonus-- >> Right. >> Back when that wasn't politically incorrect to say. Now, of course, it's the holiday bonus. But it was, it was the Christmas bonus time and Pat was coming around and he was gonna personally hand a bonus, which was a substantial bonus, to every single employee at the company. I mean, and he did that, really, literally, forever. >> Forever, yeah. >> Throughout his career. >> Yeah, it was unheard of, CEOs just didn't do that and still don't do that, you were lucky, you got a message on the, you know, in the lunchroom from the CEO, "Good work, troops! Keep up the good work!" Pat just had a really different view of the culture of this company, as you know from having been there, and I know. It was very familial, there was a sense that we were all in this together, and it really was important for him to let every employee know that. The idea that he went to every desk in every office for IDG around the United States, when we were there in the '80s there were probably 5,000 employees in the US, he had to devote substantial amount-- >> Weeks and weeks! >> Weeks at a time to come to every building and do this, but year after year he insisted on doing it, his assistant at the time, Mary Dolaher told me she wanted to sign the cards, the Christmas cards, and he insisted that he ensign every one of them personally. This was the kind of view he had of how you keep employees happy, if your employees are happy, the customers are gonna be happy, and you're gonna make a lot of money. And that's what he did. >> And it wasn't just that. He had this awesome holiday party that you described, which was epic, and during the party, they would actually take pictures of every single person at the party and then they would load the carousel, you remember the 35-mm. carousel, and then, you know, toward the end of the evening, they would play that and everybody was transfixed 'cause they wanted to see their, the picture of themselves! >> Yeah, yeah. (laughs) >> I mean, it was ge-- and to actually pull that off in the 1980s was not trivial! Today, it would be a piece of cake. And then there was the IDG update, you know, the Good News memos, there was the 10-year lunch, the 20-year trips around the world, there were a lot of really rich benefits that, you know, in and of themselves maybe not a huge deal, but that was the culture that he set. >> Yeah, there was no question that if you talked to anybody who worked in this company over, say, the last 50 years, you were gonna get the same kind of stories. I've been kind of amazed, I'm going around, you know, marketing the book, talking about the book at various events, and the deep affection for this guy that still holds five years after he died, it's just remarkable. You don't really see that with the CEO class, there's a couple, you know, Steve Jobs left a great legacy of creativity, he was not a wonderful guy to his employees, but Pat McGovern, people loved this guy, and they st-- I would be signing books and somebody'd say, "Oh, I've been at IDG for 27 years and I remember all of this," and "I've been there 33 years," and there's a real longevity to this impact that he had on people. >> Now, the book was just, it was not just sort of a biography on McGovern, it was really about lessons from a leader and an entrepreneur and a media mogul who grew this great company in this culture that we can apply, you know, as business people and business leaders. Just to give you a sense of what Pat McGovern did, he really didn't take any outside capital, he did a little bit of, you know, public offering with IDG Books, but, really, you know, no outside capital, it was completely self-funded. He built a $3.8 billion empire, 300 publications, 280 million readers, and I think it was almost 100 or maybe even more, 100 countries. And so, that's an-- like you were, used the word remarkable, that is a remarkable achievement for a self-funded company. >> Yeah, Pat had a very clear vision of how, first of all, Pat had a photographic memory and if you were a manager in the company, you got a chance to sit in meetings with Pat and if you didn't know the numbers better than he did, which was a tough challenge, you were in trouble! 'Cause he knew everything, and so, he was really a numbers-focused guy and he understood that, you know, his best way to make profit was to not be looking for outside funding, not to have to share the wealth with investors, that you could do this yourself if you ran it tightly, you know, I called it in the book a 'loose-tight organization,' loose meaning he was a deep believer in decentralization, that every market needed its own leadership because they knew the market, you know, in Austria or in Russia or wherever, better than you would know it from a headquarters in Boston, but you also needed that tightness, a firm grip on the finances, you needed to know what was going on with each of the budgets or you were gonna end up in big trouble, which a lot of companies find themselves in. >> Well, and, you know, having worked there, I mean, essentially, if you made your numbers and did so ethically, and if you just kind of followed some of the corporate rules, which we'll talk about, he kind of left you alone. You know, you could, you could pretty much do whatever you wanted, you could stay in any hotel, you really couldn't fly first class, and we'll maybe talk about that-- >> Right. >> But he was a complex man, I mean, he was obviously wealthy, he was a billionaire, he was very generous, but at the same time he was frugal, you know, he drove, you know, a little, a car that was, you know, unremarkable, and we had buy him a car. He flew coach, and I remember one time, I was at a United flight, and I was, I had upgraded, you know, using my miles, and I sat down and right there was Lore McGovern, and we both looked at each other and said right at the same time, "I upgraded!" (laughs) Because Pat never flew up front, but he would always fly with a stack of newspapers in the seat next to him. >> Yeah, well, woe to, you were lucky he wasn't on the plane and spotted you as he was walking past you into coach, because he was not real forgiving when he saw people, people would hide and, you know, try to avoid him at all cost. And, I mean, he was a big man, Pat was 6'3", you know, 250 lbs. at least, built like a linebacker, so he didn't fit into coach that well, and he wasn't flying, you know, the shuttle to New York, he was flyin' to Beijing, he was flyin' to Moscow, he was going all over the world, squeezing himself into these seats. Now, you know, full disclosure, as he got older and had, like, probably 10 million air miles at his disposal, he would upgrade too, occasionally, for those long-haul flights, just 'cause he wanted to be fresh when he would get off the plane. But, yeah, these are legends about Pat that his frugality was just pure legend in the company, he owned this, you know, several versions of that dark blue suit, and that's what you would see him in. He would never deviate from that. And, but, he had his patterns, but he understood the impact those patterns had on his employees and on his customers. >> I wanna get into some of the lessons, because, really, this is what the book is all about, the heart of it. And you mentioned, you know, one, and we're gonna tell from others, but you really gotta stay close to the customer, that was one of the 10 corporate values, and you remember, he used to go to the meetings and he'd sometimes randomly ask people to recite, "What's number eight?" (laughs) And you'd be like, oh, you'd have your cheat sheet there. And so, so, just to give you a sense, this man was an entrepreneur, he started the company in 1964 with a database that he kind of pre-sold, he was kind of the sell, design, build type of mentality, he would pre-sold this thing, and then he started Computerworld in 1967, so it was really only a few years after he launched the company that he started the Computerworld, and other than Data Nation, there was nothing there, huge pent-up demand for that type of publication, and he caught lightning in a bottle, and that's really how he funded, you know, the growth. >> Yeah, oh, no question. Computerworld became, you know, the bible of the industry, it became a cash cow for IDG, you know, but at the time, it's so easy to look in hindsight and say, oh, well, obviously. But when Pat was doing this, one little-known fact is he was an editor at a publication called Computers and Automation that was based in Newton, Massachusetts and he kept that job even after he started IDC, which was the original company in 1964. It was gonna be a research company, and it was doing great, he was seeing the build-up, but it wasn't 'til '67 when he started Computerworld, that he said, "Okay, now this is gonna be a full-time gig for me," and he left the other publication for good. But, you know, he was sorta hedging his bets there for a little while. >> And that's where he really gained respect for what we'll call the 'Chinese Wallet,' the, you know, editorial versus advertising. We're gonna talk about that some more. So I mentioned, 1967, Computerworld. So he launched in 1964, by 1971, he was goin' to Japan, we're gonna talk about the China Stories as well, so, he named the company International Data Corp, where he was at a little spot in Newton, Mass.-- >> Right, right. >> So, he had a vision. You said in your book, you mention, how did this gentleman get it so right for so long? And that really leads to some of the leadership lessons, and one of them in the book was, sort of, have a mission, have a vision, and really, Pat was always talking about information, about information technology, in fact, when Wine for Dummies came out, it kind of created a little friction, that was really off the center. >> Or Wine for Dummies, or Sex for Dummies! >> Yeah, Sex for Dummies, boy, yeah! >> With, that's right, Ruth Westheimer-- >> Dr. Ruth Westheimer. >> But generally speaking, Glenn, he was on that mark, he really didn't deviate from that vision. >> Yeah, no, it was very crucial to the development of the company that he got people to, you know, buy into that mission, because the mission was everything. And he understood, you know, he had the numbers, but he also saw what was happening out there, from the 1960s, when IBM mainframes filled a room, and, you know, only the high priests of data centers could touch them. He had a vision for, you know, what was coming next and he started to understand that there would be many facets to this information about information technology, it wasn't gonna be boring, if anything, it was gonna be the story of our age and he was gonna stick to it and sell it. >> And, you know, timing is everything, but so is, you know, Pat was a workaholic and had an amazing mind, but one of the things I learned from the book, and you said this, Pat Kenealy mentioned it, all American industrial and social revolutions have had a media company linked to them, Crane and automobiles, Penton and energy, McGraw-Hill and aerospace, Annenberg, of course, and TV, and in technology, it was IDG. >> Yeah, he, like I said earlier, he really was a key figure in the development of this industry and it was, you know, one of the key things about that, a lot publications that came and went made the mistake of being platform or, you know, vertical market specific. And if that market changed, and it was inevitably gonna change in high tech, you were done. He never, you know, he never married himself to some specific technology cycle. His idea was the audience was not gonna change, the audience was gonna have to roll with this, so, the company, IDG, would produce publications that got that, you know, Computerworld was actually a little bit late to the PC game, but eventually got into it and we tracked the different cycles, you know, things in tech move in sine waves, they come and go. And Pat never was, you know, flustered by that, he could handle any kind of changes from the mainframes down to the smartphone when it came. And so, that kind of flexibility, and ability to adjust to markets, really was unprecedented in that particular part of the market. >> One of the other lessons in the book, I call it 'nation-building,' and Pat shared with you that, look, that you shared, actually, with your readers, if you wanna do it right, you've gotta be on the ground, you've gotta be there. And the China story is one that I didn't know about how Pat kind of talked his way into China, tell us, give us a little summary of that story. >> Sure, I love that story because it's so Pat. It was 1978, Pat was in Tokyo on a business trip, one of his many business trips, and he was gonna be flying to Moscow for a trade show. And he got a flight that was gonna make a stopover in Beijing, which in those days was called Peking, and was not open to Americans. There were no US and China diplomatic relations then. But Pat had it in mind that he was going to get off that plane in Beijing and see what he could see. So that meant that he had to leave the flight when it landed in Beijing and talk his way through the customs as they were in China at the time with folks in the, wherever, the Quonset hut that served for the airport, speaking no English, and him speaking no Chinese, he somehow convinced these folks to give him a day pass, 'cause he kept saying to them, "I'm only in transit, it's okay!" (laughs) Like, he wasn't coming, you know, to spy on them on them or anything. So here's this massive American businessman in his dark suit, and he somehow gets into downtown Beijing, which at the time was mostly bicycles, very few cars, there were camels walking down the street, they'd come with traders from Mongolia. The people were still wearing the drab outfits from the Mao era, and Pat just spent the whole day wandering around the city, just soaking it in. He was that kind of a world traveler. He loved different cultures, mostly eastern cultures, and he would pop his head into bookstores. And what he saw were people just clamoring to get their hands on anything, a newspaper, a magazine, and it just, it didn't take long for the light bulb to go on and said, this is a market we need to play in. >> He was fascinated with China, I, you know, as an employee and a business P&L manager, I never understood it, I said, you know, the per capita spending on IT in China was like a dollar, you know? >> Right. >> And I remember my lunch with him, my 10-year lunch, he said, "Yeah, but, you know, there's gonna be a huge opportunity there, and yeah, I don't know how we're gonna get the money out, maybe we'll buy a bunch of tea and ship it over, but I'm not worried about that." And, of course, he meets Hugo Shong, which is a huge player in the book, and the home run out of China was, of course, the venture capital, which he started before there was even a stock market, really, to exit in China. >> Right, yeah. No, he was really a visionary, I mean, that word gets tossed around maybe more than it should, but Pat was a bonafide visionary and he saw things in China that were developing that others didn't see, including, for example, his own board, who told him he was crazy because in 1980, he went back to China without telling them and within days he had a meeting with the ministry of technology and set up a joint venture, cost IDG $250,000, and six months later, the first issue of China Computerworld was being published and within a couple of years it was the biggest publication in China. He said, told me at some point that $250,0000 investment turned into $85 million and when he got home, that first trip, the board was furious, they said, "How can you do business with the commies? You're gonna ruin our brand!" And Pat said, "Just, you know, stick with me on this one, you're gonna see." And the venture capital story was just an offshoot, he saw the opportunity in the early '90s, that venture in China could in fact be a huge market, why not help build it? And that's what he did. >> What's your take on, so, IDG sold to, basically, Chinese investors. >> Yeah. >> It's kind of bittersweet, but in the same time, it's symbolic given Pat's love for China and the Chinese people. There's been a little bit of criticism about that, I know that the US government required IDC to spin out its supercomputer division because of concerns there. I'm always teasing Michael Dow that at the next IDG board meeting, those Lenovo numbers, they're gonna look kinda law. (laughs) But what are your, what's your, what are your thoughts on that, in terms of, you know, people criticize China in terms of IP protections, etc. What would Pat have said to that, do you think? >> You know, Pat made 130 trips to China in his life, that's, we calculated at some point that just the air time in planes would have been something like three and a half to four years of his life on planes going to China and back. I think Pat would, today, acknowledge, as he did then, that China has issues, there's not, you can't be that naive. He got that. But he also understood that these were people, at the end of the day, who were thirsty and hungry for information and that they were gonna be a player in the world economy at some point, and that it was crucial for IDG to be at the forefront of that, not just play later, but let's get in early, let's lead the parade. And I think that, you know, some part of him would have been okay with the sale of the company to this conglomerate there, called China Oceanwide. Clearly controversial, I mean, but once Pat died, everyone knew that the company was never gonna be the same with the leader who had been at the helm for 50 years, it was gonna be a tough transition for whoever took over. And I think, you know, it's hard to say, certainly there's criticism of things going on with China. China's gonna be the hot topic page one of the New York Times almost every single day for a long time to come. I think Pat would have said, this was appropriate given my love of China, the kind of return on investment he got from China, I think he would have been okay with it. >> Yeah, and to invoke the Ben Franklin maxim, "Trading partners seldom wage war," and so, you know, I think Pat would have probably looked at it that way, but, huge home run, I mean, I think he was early on into Baidu and Alibaba and Tencent and amazing story. I wanna talk about decentralization because that was always something that was just on our minds as employees of IDG, it was keep the corporate staff lean, have a flat organization, if you had eight, 10, 12 direct reports, that was okay, Pat really meant it when he said, "You're the CEO of your own business!" Whether that business was, you know, IDC, big company, or a manager at IDC, where you might have, you know, done tens of millions of dollars, but you felt like a CEO, you were encouraged to try new things, you were encouraged to fail, and fail fast. Their arch nemesis of IDG was Ziff Davis, they were a command and control, sort of Bill Ziff, CMP to a certain extent was kind of the same way out of Manhasset, totally different philosophies and I think Pat never, ever even came close to wavering from that decentralization philosophy, did he? >> No, no, I mean, I think that the story that he told me that I found fascinating was, he didn't have an epiphany that decentralization would be the mechanism for success, it was more that he had started traveling, and when he'd come back to his office, the memos and requests and papers to sign were stacked up two feet high. And he realized that he was holding up the company because he wasn't there to do this and that at some point, he couldn't do it all, it was gonna be too big for that, and that's when the light came on and said this decentralization concept really makes sense for us, if we're gonna be an international company, which clearly was his mission from the beginning, we have to say the people on the ground in those markets are the people who are gonna make the decisions because we can't make 'em from Boston. And I talked to many people who, were, you know, did a trip to Europe, met the folks in London, met the folks in Munich, and they said to a person, you know, it was so ahead of its time, today it just seems obvious, but in the 1960s, early '70s, it was really not a, you know, a regular leadership tenet in most companies. The command and control that you talked about was the way that you did business. >> And, you know, they both worked, but, you know, from a cultural standpoint, clearly IDG and IDC have had staying power, and he had the three-quarter rule, you talked about it in your book, if you missed your numbers three quarters in a row, you were in trouble. >> Right. >> You know, one quarter, hey, let's talk, two quarters, we maybe make some changes, three quarters, you're gone. >> Right. >> And so, as I said, if you were makin' your numbers, you had wide latitude. One of the things you didn't have latitude on was I'll call it 'pay to play,' you know, crossing that line between editorial and advertising. And Pat would, I remember I was at a meeting one time, I'm sorry to tell these stories, but-- >> That's okay. (laughs) >> But we were at an offsite meeting at a woods meeting and, you know, they give you a exercise, go off and tell us what the customer wants. Bill Laberis, who's the editor-in-chief at Computerworld at the time, said, "Who's the customer?" And Pat said, "That's a great question! To the publisher, it's the advertiser. To you, Bill, and the editorial staff, it's the reader. And both are equally important." And Pat would never allow the editorial to be compromised by the advertiser. >> Yeah, no, he, there was a clear barrier between church and state in that company and he, you know, consistently backed editorial on that issue because, you know, keep in mind when we started then, and I was, you know, a journalist hoping to, you know, change the world, the trade press then was considered, like, a little below the mainstream business press. The trade press had a reputation for being a little too cozy with the advertisers, so, and Pat said early on, "We can't do that, because everything we have, our product is built, the brand is built on integrity. And if the reader doesn't believe that what we're reporting is actually true and factual and unbiased, we're gonna lose to the advertisers in the long run anyway." So he was clear that that had to be the case and time and again, there would be conflict that would come up, it was just, as you just described it, the publishers, the sales guys, they wanted to bring in money, and if it, you know, occasionally, hey, we could nudge the editor of this particular publication, "Take it a little bit easier on this vendor because they're gonna advertise big with us," Pat just would always back the editor and say, "That's not gonna happen." And it caused, you know, friction for sure, but he was unwavering in his support. >> Well, it's interesting because, you know, Macworld, I think, is an interesting case study because there were sort of some backroom dealings and Pat maneuvered to be able to get the Macworld, you know, brand, the license for that. >> Right. >> But it caused friction between Steve Jobs and the writers of Macworld, they would write something that Steve Jobs, who was a control freak, couldn't control! >> Yeah. (laughs) >> And he regretted giving IDG the license. >> Yeah, yeah, he once said that was the worst decision he ever made was to give the license to Pat to, you know, Macworlld was published on the day that Mac was introduced in 1984, that was the deal that they had and it was, what Jobs forgot was how important it was to the development of that product to have a whole magazine devoted to it on day one, and a really good magazine that, you know, a lot of people still lament the glory days of Macworld. But yeah, he was, he and Steve Jobs did not get along, and I think that almost says a lot more about Jobs because Pat pretty much got along with everybody. >> That church and state dynamic seems to be changing, across the industry, I mean, in tech journalism, there aren't any more tech journalists in the United States, I mean, I'm overstating that, but there are far fewer than there were when we were at IDG. You're seeing all kinds of publications and media companies struggling, you know, Kara Swisher, who's the greatest journalist, and Walt Mossberg, in the tech industry, try to make it, you know, on their own, and they couldn't. So, those lines are somewhat blurring, not that Kara Swisher is blurring those lines, she's, you know, I think, very, very solid in that regard, but it seems like the business model is changing. As an observer of the markets, what do you think's happening in the publishing world? >> Well, I, you know, as a journalist, I'm sort of aghast at what's goin' on these days, a lot of my, I've been around a long time, and seeing former colleagues who are no longer in journalism because the jobs just started drying up is, it's a scary prospect, you know, unlike being the enemy of the people, the first amendment is pretty important to the future of the democracy, so to see these, you know, cutbacks and newspapers going out of business is difficult. At the same time, the internet was inevitable and it was going to change that dynamic dramatically, so how does that play out? Well, the problem is, anybody can post anything they want on social media and call it news, and the challenge is to maintain some level of integrity in the kind of reporting that you do, and it's more important now than ever, so I think that, you know, somebody like Pat would be an important figure if he was still around, in trying to keep that going. >> Well, Facebook and Google have cut the heart out of, you know, a lot of the business models of many media companies, and you're seeing sort of a pendulum swing back to nonprofits, which, I understand, speaking of folks back in the mid to early 1900s, nonprofits were the way in which, you know, journalism got funded, you know, maybe it's billionaires buying things like the Washington Post that help fund it, but clearly the model's shifting and it's somewhat unclear, you know, what's happening there. I wanted to talk about another lesson, which, Pat was the head cheerleader. So, I remember, it was kind of just after we started, the Computerworld's 20th anniversary, and they hired the marching band and they walked Pat and Mary Dolaher walked from 5 Speen Street, you know, IDG headquarters, they walked to Computerworld, which was up Old, I guess Old Connecticut Path, or maybe it was-- >> It was actually on Route 30-- >> Route 30 at the time, yeah. And Pat was dressed up as the drum major and Mary as well, (laughs) and he would do crazy things like that, he'd jump out of a plane with IDG is number one again, he'd post a, you know, a flag in Antarctica, IDG is number one again! It was just a, it was an amazing dynamic that he had, always cheering people on. >> Yeah, he was, he was, when he called himself the CEO, the Chief Encouragement Officer, you mentioned earlier the Good News notes. Everyone who worked there, at some point received this 8x10" piece of paper with a rainbow logo on it and it said, "Good News!" And there was a personal note from Pat McGovern, out of the blue, totally unexpected, to thank you and congratulate you on some bit of work, whatever it was, if you were a reporter, some article you wrote, if you were a sales guy, a sale that you made, and people all over the world would get these from him and put them up in their cubicles because it was like a badge of honor to have them, and people, I still have 'em, (laughs) you know, in a folder somewhere. And he was just unrelenting in supporting the people who worked there, and it was, the impact of that is something you can't put a price tag on, it's just, it stays with people for all their lives, people who have left there and gone on to four or five different jobs always think fondly back to the days at IDG and having, knowing that the CEO had your back in that manner. >> The legend of, and the legacy of Patrick J. McGovern is not just in IDG and IDC, which you were interested in in your book, I mean, you weren't at IDC, I was, and I was started when I saw the sort of downturn and then now it's very, very successful company, you know, whatever, $3-400 million, throwin' off a lot of profits, just to decide, I worked for every single CEO at IDC with the exception of Pat McGovern, and now, Kirk Campbell, the current CEO, is moving on Crawford del Prete's moving into the role of president, it's just a matter of time before he gets CEO, so I will, and I hired Crawford-- >> Oh, you did? (laughs) >> So, I've worked for and/or hired every CEO of IDC except for Pat McGovern, so, but, the legacy goes beyond IDG and IDC, great brands. The McGovern Brain Institute, 350 million, is that right? >> That's right. >> He dedicated to studying, you know, the human brain, he and Lore, very much involved. >> Yup. >> Typical of Pat, he wasn't just, "Hey, here's the check," and disappear. He was goin' in, "Hey, I have some ideas"-- >> Oh yeah. >> Talk about that a little. >> Yeah, well, this was a guy who spent his whole life fascinated by the human brain and the impact technology would have on the human brain, so when he had enough money, he and Lore, in 2000, gave a $350 million gift to MIT to create the McGovern Institute for Brain Research. At the time, the largest academic gift ever given to any university. And, as you said, Pat wasn't a guy who was gonna write a check and leave and wave goodbye. Pat was involved from day one. He and Lore would come and sit in day-long seminars listening to researchers talk about about the most esoteric research going on, and he would take notes, and he wasn't a brain scientist, but he wanted to know more, and he would talk to researchers, he would send Good News notes to them, just like he did with IDG, and it had same impact. People said, "This guy is a serious supporter here, he's not just showin' up with a checkbook." Bob Desimone, who's the director of the Brain Institute, just marveled at this guy's energy level, that he would come in and for days, just sit there and listen and take it all in. And it just, it was an indicator of what kind of person he was, this insatiable curiosity to learn more and more about the world. And he wanted his legacy to be this intersection of technology and brain research, he felt that this institute could cure all sorts of brain-related diseases, Alzheimer's, Parkinson's, etc. And it would then just make a better future for mankind, and as corny as that might sound, that was really the motivator for Pat McGovern. >> Well, it's funny that you mention the word corny, 'cause a lot of people saw Pat as somewhat corny, but, as you got to know him, you're like, wow, he really means this, he loves his company, the company was his extended family. When Pat met his untimely demise, we held a crowd chat, crowdchat.net/thankspat, and there's a voting mechanism in there, and the number one vote was from Paul Gillen, who posted, "Leo Durocher said that nice guys finish last, Pat McGovern proved that wrong." >> Yeah. >> And I think that's very true and, again, awesome legacy. What number book is this for you? You've written a lot of books. >> This is number 13. >> 13, well, congratulations, lucky 13. >> Thank you. >> The book is Fast Forward-- >> Future Forward. >> I'm sorry, Future Forward! (laughs) Future Forward by Glenn Rifkin. Check out, there's a link in the YouTube down below, check that out and there's some additional information there. Glenn, congratulations on getting the book done, and thanks so much for-- >> Thank you for having me, this is great, really enjoyed it. It's always good to chat with another former IDGer who gets it. (laughs) >> Brought back a lot of memories, so, again, thanks for writing the book. All right, thanks for watching, everybody, we'll see you next time. This is Dave Vellante. You're watchin' theCube. (electronic music)

>> Live from Barcelona, Spain. It's the cue covering Sisqo. Live Europe. Brought to you by Cisco and its ecosystem partners. >> Welcome back to sunny Barcelona. Everybody watching the Cube, the leader and live tech coverage. We go out to the events, we extract the signal from the noise we hear There's our third day of coverage that Sisqo live. Barcelona David Lot. John Furrier. This here stew Minutemen all week. John, we've been covering this show. Walter Wall like a canon ae is here is a distinguished engineer and product line. CTO for Cisco Analytics. Welcome to the Cube. You see you again. Welcome back to the Cube. I should say thank you very much. So tell us about your role. You're focused right now on malware encryption. We want to get into that, but but set it up with your roll >> first. Well, I'm trying to raise the cost to the bad guy's hiding in your network. I mean, basically it's it. It it's an economics thing because one there's a lot of places for them to hide. And and they they are innovating just as much as we are. And so if I can make it more expensive for them to hide and operate. Then I'm doing my job. And and that means not only using techniques of the past but developing new techniques. You know, Like I said, it's It's really unlike a regular job. I'm not waiting for the hard drive to fail or a power supply to fail. I have an active adversary that's smart and well funded. So if I if I shipped some innovation, I forced them to innovate and vice versa. >> So you're trying to reduce their our ally and incentives. >> I want to make it too expensive for them to do business. >> So what's the strategy there? Because it's an arms race. Obviously wanted one one. You know, Whitehead over a black hat, kind of continue to do that. Is it decentralized to create more segments? What is the current strategies that you see to make it more complex or less economically viable to just throw resource at a port or whatever? >> There's sort of two dimensions that are driving change one. You know they're trying to make a buck. Okay? And and, you know, we saw the ransomware stuff we saw, you know, things that they did to extract money from a victim. Their latest thing now is they've They've realized that Ransomware wasn't a recurring revenue stream for them. Right? And so what's called crypto jacking is so they essentially have taking the cost structure out of doing crypto mining. You know, when you do crypto mining, you'll make a nickel, maybe ten cents, maybe even twenty cents a day. Just doing this. Mathematical mining, solving these puzzles. And if you had to do that on your own computer, you'd suck up all this electricity and thing. You'd have some cost structure, right and less of a margin. But if you go on, you know, breach a thousand computers, maybe ten thousand, maybe one hundred thousand. Guess what, right you? Not one you're hiding. So guess what? Today you make a nickel tomorrow, you make another nickel. So, you know, if you if you go to the threat wall here, you'd be surprised this crypto mining activity taking place here and nobody knows about it. We have it up on the threat wall because we can detect its behavior. We can't see the actual payload because all encrypted. But we have techniques now. Advanced Analytics by which we can now call out its unique behaviour very distinctly. >> Okay, so you're attacking this problem with with data and analytics. Is that right? What? One of the ingredients of your defense? >> Yeah. I mean, they're sort of Ah, three layer cake There. You first. You have? You know, I always say all telemetry is data, but not all data. Is telemetry. All right? So when you when you go about looking at an observation or domain, you know, Inhumans, we have sight. We have hearing these air just like the network or the endpoint. And there's there's telemetry coming out of that, hopefully from the network itself. Okay, because it's the most pervasive. And so you have this dilemma tree telling you something about the good guys and the bad guys and you, you perform synthesis and analytics, and then you have an analytical outcome. So that's sort of the three layer cake is telemetry, analytics, analytical outcome. And what matters to you and me is really the outcome, right? In this case, detecting malicious activity without doing decryption. >> You mentioned observation. Love this. We've been talking to Cuba in the past about observation space. Having an observation base is critical because you know, people don't write bomb on a manifest and ship it. They they hide it's it's hidden in the network, even their high, but also the meta data. You have to kind extract that out. That's kind of where you get into the analytics. How does that observation space gets set up? Happened? Someone creating observation special? They sharing the space with a public private? This becomes kind of almost Internet infrastructure. Sound familiar? Network opportunity? >> Yeah. You know, there's just three other. The other driver of change is just infrastructure is changing. Okay. You mean the past? Go back. Go back twenty years, you had to rent some real estate. You gotto put up some rocks, some air conditioning, and you were running on raw iron. Then the hyper visors came. Okay, well, I need another observation. A ll. You know, I meet eyes and ears on this hyper visor you got urbanity is now you've got hybrid Cloud. You have even serve Ellis computing, right? These are all things I need eyes and ears. Now, there that traditional methods don't don't get me there so again, being able to respect the fact that there are multiple environments that my digital business thrives on. And it's not just the traditional stuff, you know, there's there's the new stuff that we need to invent ways by which to get the dilemma tree and get the analytical >> talkabout this dynamic because we're seeing this. I think we're just both talking before we came on camera way all got our kind of CS degrees in the eighties. But if you look at the decomposition of building blocks with a P, I's and clouds, it's now a lot of moving to spare it parts for good reasons, but also now, to your point, about having eyes and ears on these components. They're all from different vendors, different clouds. Multi cloud creates Mohr opportunities. But yet more complexity. Software abstractions will help manage that. Now you have almost like an operating system concept around it. How are you guys looking at this? I'll see the intent based networking and hyper flex anywhere. You seeing that vision of data being critical, observation space, etcetera. But if you think about holistically, the network is the computer. Scott McNealy once said. Yeah, I mean, last week, when we are this is actually happening. So it's not just cloud a or cloud be anon premise and EJ, it's the totality of the system. This is what's happening >> ways. It's it's absolutely a reality. And and and the sooner you embrace that, the better. Because when the bad guys embrace it verse, You have problems, right? And and you look at even how they you know how they scale techniques. They use their cloud first, okay, that, you know their innovative buns. And when you look at a cloud, you know, we mentioned the eyes and ears right in the past. You had eyes and ears on a body you own. You're trying to put eyes in here on a body you don't own anymore. This's public cloud, right? So again, the reality is somebody you know. These businesses are somewhere on the journey, right? And the journey goes traditional hyper visor. You have then ultimately hybrid multi clouds. >> So the cost issue comes back. The play of everything sass and cloud. It's just You start a company in the cloud versus standing up here on the check, we see the start of wave from a state sponsored terrorist organization. It's easy for me to start a threat. So this lowers the cost actually threat. So that lowers the IQ you needed to be a hacker. So making it harder also helps that this is kind of where you're going. Explain this dynamic because it's easy to start threats, throw, throw some code at something. I could be in a bedroom anywhere in the world. Or I could be a group that gets free, open source tools sent to me by a state and act on behalf of China. Russia, >> Of course, of course, you know, software, software, infrastructures, infrastructure, right? It's It's the same for the bad guys, the good guys. That's sort of the good news and the bad news. And you look at the way they scale, you know, techniques. They used to stay private saying, You know, all of these things are are valid, no matter what side of the line you sit on, right? Math is still math. And again, you know, I just have Ah, maybe a fascination for how quickly they innovate, How quickly they ship code, how quickly they scale. You know, these botnets are massive, right? If you could get about that, you're looking at a very cloud infrastructure system that expands and contracts. >> So let's let's talk a little more about scale. You got way more good guys on the network than bad guys get you. First of all, most trying to do good and you need more good guys to fight the bad guys up, do things. Those things like infrastructure is code dev ops. Does that help the good guys scale? And and how so? >> You know it does. There's a air. You familiar with the concept called The Loop Joe? It was It was invented by a gentleman, Colonel John Boyd, and he was a jet fighter pilot. Need taught other jet fighter pilots tactics, and he invented this thing called Guadalupe and it's it's o d a observe orient decide. And at all right. And the quicker you can spin your doodle ooh, the more disoriented your adversary ISS. And so speed speed matters. Okay. And so if you can observe Orient, decide, act faster, then your adversary, you created almost a knowledge margin by which they're disoriented. And and the speed of Dev ops has really brought this two defenders. They can essentially push code and reorient themselves in a cycle that's frankly too small of a window for the adversary to even get their bearings right. And so speed doesn't matter. And this >> changing the conditions of the test, if you will. How far the environment, of course, on a rabbit is a strategy whether it's segmenting networks, making things harder to get at. So in a way, complexity is better for security because it's more complex. It costs more to penetrate complex to whom to the adversary of the machine, trying very central data base. Second, just hack in, get all the jewels >> leave. That's right, >> that's right. And and again. You know, I think that all of this new technology and and as you mentioned new processes around these technologies, I think it's it's really changing the game. The things that are very deterministic, very static, very slow moving those things. They're just become easy targets. Low cost targets. If you will >> talk about the innovation that you guys are doing around the encryption detecting malware over encrypted traffic. Yeah, the average person Oh, encrypted traffic is totally secure. But you guys have a method to figure out Mel, where behavior over encrypted, which means the payload can't be penetrated or it's not penetrated. So you write full. We don't know what's in there but through and network trav explain what you're working on. >> Yeah. The paradox begins with the fact that everybody's using networks now. Everything, even your thermostat. You're probably your tea kettle is crossing a network somewhere. And and in that reality, that transmission should be secure. So the good news is, I no longer have to complain as much about looking at somebody's business and saying, Why would you operate in the clear? Okay, now I say, Oh, my God, you're business is about ninety percent dot Okay, when I talked about technology working well for everyone, it works just as well for the bad guys. So I'm not going to tell this this business start operating in the clear anymore, so I can expect for malicious activity. No, we have to now in for malicious activity from behavior. Because the inspection, the direct inspection is no longer available. So that we came up with a technique called encrypted Traffic analytics. And again, we could have done it just in a product. But what we did that was clever was we went to the Enterprise networking group and said, if I could get of new telemetry, I can give you this analytical outcome. Okay? That'll allow us to detect malicious activity without doing decryption. And so the network as a sensor, the routers and switches, all of those things are sending me this. Richard, it's Tellem aji, by which I can infer this malicious activity without doing any secret. >> So payload and network are too separate things contractually because you don't need look at the payload network. >> Yeah. I mean, if you want to think about it this way, all encrypted traffic starts out unencrypted. Okay, It's a very small percentage, but everything in that start up is visible. So we have the routers and switches are sending us that metadata. Then we do something clever. I call it Instead of having direct observation, I need an observational derivative. Okay, I need to see its shape and size over time. So at minute five minute, fifteen minute thirty, I can see it's timing, and I can model on that timing. And this is where machine learning comes in because it's It's a science. That's just it's day has come for behavioral science, so I could train on all this data and say, If this malware looks like this at minute, five minute, ten minute fifteen, then if I see that exact behavior mathematically precise behaviour on your network, I can infer that's the same Mallory >> Okay, And your ability you mentioned just you don't have to decrypt that's that gives you more protection. Obviously, you're not exposed, but also presumably better performance. Is that right, or is that not affected? >> A lot? A lot better performance. The cryptographic protocols themselves are becoming more and more opaque. T L s, which is one of the protocols used to encrypt all of the Web traffic. For instance, they just went through a massive revision from one dot two two version one not three. It is faster, It is stronger. It's just better. But there's less visible fields now in the hitter. So you know things that there's a term being thrown around called Dark Data, and it's getting darker for everyone. >> So, looking at the envelope, looking at the network of fact, this is the key thing. Value. The network is now more important than ever explain why? Well, >> it connects everything right, and there's more things getting connected. And so, as you build, you know you can reach more customers. You can You can operate more efficiently, efficiently. You can. You can bring down your operational costs. There's so many so many benefit. >> FBI's also add more connection points as well. Integration. It's Metcalfe's law within a third dimension That dimension data value >> conductivity. I mean, the message itself is growing exponentially. Right? So that's just incredibly exciting. >> Super awesome topic. Looking forward to continuing this conversation. Great. Great. Come. Super important, cool and relevant and more impactful. A lot more action happening. Okay, Thanks for sharing that. Great. It's so great to have you on a keeper. Right, everybody, we'll be back to wrap Day three. Francisco live Barcelona. You're watching the Cube. Stay right there.

>> Narrator: Live from downtown San Francisco, it's theCUBE, covering IBM Chief Data Officer Strategy Summit 2018, brought to you by IBM. >> We're back in San Francisco, we're here at the Parc 55 at the IBM Chief Data Officer Strategy Summit. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante and IBM's Chief Data Officer Strategy Summit, they hold them on both coasts, one in Boston and one in San Francisco. A couple times each year, about 150 chief data officers coming in to learn how to apply their craft, learn what IBM is doing, share ideas. Great peer networking, really senior audience. John Thomas is here, he's a distinguished engineer and director at IBM, good to see you again John. >> Same to you. >> Thanks for coming back in theCUBE. So let's start with your role, distinguished engineer, we've had this conversation before but it just doesn't happen overnight, you've got to be accomplished, so congratulations on achieving that milestone, but what is your role? >> The road to distinguished engineer is long but today, these days I spend a lot of my time working on data science and in fact am part of what is called a data science elite team. We work with clients on data science engagements, so this is not consulting, this is not services, this is where a team of data scientists work collaboratively with a client on a specific use case and we build it out together. We bring data science expertise, machine learning, deep learning expertise. We work with the business and build out a set of tangible assets that are relevant to that particular client. >> So this is not a for-pay service, this is hey you're a great customer, a great client of ours, we're going to bring together some resources, you'll learn, we'll learn, we'll grow together, right? >> This is an investment IBM is making. It's a major investment for our top clients working with them on their use cases. >> This is a global initiative? >> This is global, yes. >> We're talking about, what, hundreds of clients, thousands of clients? >> Well eventually thousands but we're starting small. We are trying to scale now so obviously once you get into these engagements, you find out that it's not just about building some models. There are a lot of challenges that you've got to deal with in an enterprise setting. >> Dave: What are some of the challenges? >> Well in any data science engagement the first thing is to have clarity on the use case that you're engaging in. You don't want to build models for models' sake. Just because Tensorflow or scikit-learn is great and build models, that doesn't serve a purpose. That's the first thing, do you have clarity of the business use case itself? Then comes data, now I cannot stress this enough, Dave, there is no data science without data, and you might think this is the most obvious thing, of course there has to be data, but when I say data I'm talking about access to the right data. Do we have governance over the data? Do we know who touched the data? Do we have lineage on that data? Because garbage in, garbage out, you know this. Do we have access to the right data in the right control setting for my machine learning models we built. These are challenges and then there's another challenge around, okay, I built my models but how do I operationalize them? How do I weave those models into the fabric of my business? So these are all challenges that we have to deal with. >> That's interesting what you're saying about the data, it does sound obvious but having the right data model as well. I think about when I interact with Netflix, I don't talk to their customer service department or their marketing department or their sales department or their billing department, it's one experience. >> You just have an experience, exactly. >> This notion of incumbent disruptors, is that a logical starting point for these guys to get to that point where they have a data model that is a single data model? >> Single data model. (laughs) >> Dave: What does that mean, right? At least from an experienced standpoint. >> Once we know this is the kind of experience we want to target, what are the relevant data sets and data pieces that are necessary to make their experience happen or come together. Sometimes there's core enterprise data that you have in many cases, it has been augmented with external data. Do you have a strategy around handling your internal, external data, your structured transactional data, your semi-structured data, your newsfeeds. All of these need to come together in a consistent fashion for that experience to be true. It is not just about I've got my credit card transaction data but what else is augmenting that data? You need a model, you need a strategy around that. >> I talk to a lot of organizations and they say we have a good back-end reporting system, we have Cognos we can build cubes and all kinds of financial data that we have, but then it doesn't get down to the front line. We have an instrument at the front line, we talk about IOT and that portends change there but there's a lot of data that either isn't persisted or not stored or doesn't even exist, so is that one of the challenges that you see enterprises dealing with? >> It is a challenge. Do I have access to the right data, whether that is data at rest or in motion? Am I persisting it the way I can consume it later? Or am I just moving big volumes of data around because analytics is there, or machine learning is there and I have to move data out of my core systems into that area. That is just a waste of time, complexity, cost, hidden costs often, 'cause people don't usually think about the hidden costs of moving large volumes of data around. But instead of that can I bring analytics and machine learning and data science itself to where my data is. Not necessarily to move it around all the time. Whether you're dealing with streaming data or large volumes of data in your Hadoop environment or mainframes or whatever. Can I do ML in place and have the most value out of the data that is there? >> What's happening with all that Hadoop? Nobody talks about Hadoop anymore. Hadoop largely became a way to store data for less, but there's all this data now and a data lake. How are customers dealing with that? >> This is such an interesting thing. People used to talk about the big data, you're right. We jumped from there to the cognitive It's not like that right? No, without the data then there is no cognition there is no AI, there is no ML. In terms of existing investments in Hadoop for example, you have to absolutely be able to tap in and leverage those investments. For example, many large clients have investments in large Cloudera or Hortonworks environment, or Hadoop environments so if you're doing data science, how do you push down, how do you leverage that for scale, for example? How do you access the data using the same access control mechanisms that are already in place? Maybe you have Carbros as your mechanism how do you work with that? How do you avoid moving data off of that environment? How do you push down data prep into the spar cluster? How do you do model training in that spar cluster? All of these become important in terms of leveraging your existing investments. It is not just about accessing data where it is, it's also about leveraging the scale that the company has already invested in. You have hundred, 500 node Hadoop clusters well make the most of them in terms of scaling your data science operations. So push down and access data as much as possible in those environments. >> So Beth talked today, Beth Smith, about Watson's law, and she made a little joke about that, but to me its poignant because we are entering a new era. For decades this industry marched to the cadence of Moore's law, then of course Metcalfe's law in the internet era. I want to make an observation and see if it resonates. It seems like innovation is no longer going to come from doubling microprocessor speed and the network is there, it's built out, the internet is built. It seems like innovation comes from applying AI to data together to get insights and then being able to scale, so it's cloud economics. Marginal costs go to zero and massive network effects, and scale, ability to track innovation. That seems to be the innovation equation, but how do you operationalize that? >> To your point, Dave, when we say cloud scale, we want the flexibility to do that in an off RAM public cloud or in a private cloud or in between, in a hybrid cloud environment. When you talk about operationalizing, there's a couple different things. People think that, say I've got a super Python programmer and he's great with Tensorflow or scikit-learn or whatever and he builds these models, great, but what happens next, how do you actually operationalize those models? You need to be able to deploy those models easily. You need to be able to consume those models easily. For example you have a chatbot, a chatbot is dumb until it actually calls these machine learning models, real time to make decisions on which way the conversation should go. So how do you make that chatbot intelligent? It's when it consumes the ML models that have been built. So deploying models, consuming models, you create a model, you deploy it, you've got to push it through the development test staging production phases. Just the same rigor that you would have for any applications that are deployed. Then another thing is, a model is great on day one. Let's say I built a fraud detection model, it works great on day one. A week later, a month later it's useless because the data that it trained on is not what the fraudsters are using now. So patterns have changed, the model needs to be retrained How do I understand the performance of the model stays good over time? How do I do monitoring? How do I retrain the models? How do I do the life cycle management of the models and then scale? Which is okay I deployed this model out and its great, every application is calling it, maybe I have partners calling these models. How do I automatically scale? Whether what you are using behind the scenes or if you are going to use external clusters for scale? Technology is like spectrum connector from our HPC background are very interesting counterparts to this. How do I scale? How do I burst? How do I go from an on-frame to an off-frame environment? How do I build something behind the firewall but deploy it into the cloud? We have a chatbot or some other cloud-native application, all of these things become interesting in the operationalizing. >> So how do all these conversations that you're having with these global elite clients and the challenges that you're unpacking, how do they get back into innovation for IBM, what's that process like? >> It's an interesting place to be in because I am hearing and experiencing first hand real enterprise challenges and there we see our product doesn't handle this particular thing now? That is an immediate circling back with offering management and development. Hey guys we need this particular function because I'm seeing this happening again and again in customer engagements. So that helps us shape our products, shape our data science offerings, and sort of running with the flow of what everyone is doing, we'll look at that. What do our clients want? Where are they headed? And shape the products that way. >> Excellent, well John thanks very much for coming back in theCUBE and it's a pleasure to see you again. I appreciate your time. >> Thank you Dave. >> All right good to see you. Keep it right there everybody we'll be back with our next guest. We're live from the IBM CDO strategy summit in San Francisco, you're watching theCUBE.

>> Announcer: Live from downtown San Francisco, it's theCUBE covering IBM Chief Data Officer Strategy Summit, 2018 brought to you by IBM. >> Welcome back to San Francisco everybody. We're here covering the IBM CDO strategy summit. You're watching theCUBE, the leader and live tech coverage hashtag IBM CDO. Beth Smith is here, she's the general manager at Watson data and AI at IBM and of course Inderpal Bandari who's the global chief data officer at IBM. Folks, welcome back to theCUBE. It's great to see you both again. >> Good to be back. >> So I love these shows, they're intimate, practitioner really they're absorbing everything. You're getting some good questions, some good back and forth but Beth share with us what you're hearing from customers. What matters for enterprises right now in the context of the cognitive enterprise, the AI enterprise. >> So you know customers are looking at how did they get the benefit? They recognize that they've got a lot of valuable data, data that we haven't always called data. Sometimes it's documents and journals and other kinds of really unstructured things and they want to determine how can they get value from that and they look out and compare it to maybe consumer things and recognize they don't have the same volume of that. So it's important for customers, how do they get started and I would tell you that most of them start with a small project, they see value with that quickly they then say, okay how do we increment and grow from that. >> So Inderpal you had said I think I got this right, this is your fourth CDO gig. You're not new to this rodeo. Were you the first healthcare CDO is that right? >> I was. >> Dave: Okay you got it all started. >> There were four of us at that time. >> Okay so forth and four okay I did get that right. So you obviously bring a lot of experience here and one of the things you stressed today in your talk is you basically want to showcase IBM so you're applying sort of data enterprise data strategies to IBM and then you showcase that to your clients. Talk about that a little bit. >> Yeah I mean if you think about it, we are the quintessential complex enterprise. We're global, we're far-flung, we have literally thousands of products. We acquire companies, we move forward at a global scale and also we are always competing at a global scale. So there literally is that complexity that enterprises face which all our customers who are the large enterprises have to also deal with. So given all that we felt that the best way to talk about an AI enterprise is to use ourselves as a showcase. >> Okay Beth, I got to ask you about Watson's law. Okay so we had Moore's law we all know what that is. Metcalfe's law the network effect, Watson's law and I have a noodling on this a little bit. We're entering a new era which I think is underscored by... and names matter. We use a parlance in our industry whether it's cloud or a big data or internet or whatever it is and so we're trying to sort of figure out what this new era is like. What do you envision as Watson's law. I'd love to have a little riff on that. >> So first of all as we look at all those things and compare them back, they're all about opportunities to scale and how things changed because of a new scaling effect. So I would argue that the one we're in now, which we like to call Watson's law the future will determine what it's actually called is about scaling knowledge and applying knowledge so it's about how to use AI machine learning applied to data, all forms of data and turn that into knowledge and that's what's going to separate people and I would tell you that's also going to come back and give the incumbents an opportunity because the incumbents are strong in their industries, in their domains, they can leverage the data that they have, the knowledge and experience they have and then use that for how do they disrupt and really become the disruptors of the future. >> So okay what about the math behind this? I'm kind of writing down some notes as you were talking so my version of Watson's law and love your comment is innovation in the future and the current is going to be a function of the data, your ability to apply AI or cognitive to that data and then your ability to your point scale, the cloud economics. Does that make sense to you guys, is that where innovation is going to come? >> It's true but I have to go back at this point Dave of knowledge so I think you take data and you take AI or machine learning and those are sort of your ingredients. The scaling factor is going to be on knowledge and how does that ultimately get applied. Cloud again gives us an ingredient if you will that can be applied to it but the thing that'll make the difference on it, just like networking was in the past and opened up opportunities around the internet is that in the other will be how folks use knowledge. It's almost like you could think of it as a learning era and how that's not just going to be about individuals but about companies and business models etc. >> So the knowledge comes from applying cognitive to the data and then being able to scale it. Okay and then Inderpal, how do I address the access issue? I've got many if not most incumbents data are in silos. The marketing department has data, the sales department has data, the customer service department has data. How do you as a CDO address that challenge? >> Well what you've got to do is use the technology to actually help you address that challenge. So building data lakes is a good start for both structured and unstructured data where you bring data that's traditionally been siloed together but that's not always possible. Sometimes you have to let the data be where they are but you at least have to have a catalog that tells you where all the data is so that an intelligent system can then reason about that when working with somebody on a particular use case actually help them find that data and help them apply it and use it. >> So that's a metadata challenge correct? >> It's a metadata challenge in the AI world because the metadata challenge has always been there but now you have the potential to apply AI to not just create metadata but then also to apply it effectively to help business users and subject matter experts who are not data experts find the right data and work it. >> You guys make a big deal about automating some of this stuff up front as the point of creation or use automating. Classification is a good example. How are you solving that problem from a technology perspective? >> Well some of our core Watson capabilities are all about classification and then customers use that. It can be what I will call a simple use case of email classification and routing. We have a client in France that has 350,000 emails a week and they use Watson for that level of classification. You look at all sorts of different kinds of ticketing things you look at AI assistants and it comes down to how do you really understand what the intent is here and I believe classification is one of the fundamental capabilities in the whole thing. >> Yeah it's been a problem that we've been trying to solve in this industry for a while kind of pre AI and you really there's not a lot you can do if you don't have good classification if you can't automate it then you can't scale. >> That's right. >> So from a classification standpoint, I mean it's a fundamental always been fundamental problem. Machines have gotten much better at it with the AI systems and so forth but there's also one aspect that's quite interesting which is now you have open loop systems so you're not just classifying based on data that was historically present and so in that sense you're confined to always repeat your mistakes and so forth. You hear about hedge funds that implode because their models are no longer applicable because there's a Black Swan event. Now with the AI systems you have the opportunity to tap the realtime events as they're going and actually apply that to the classification as well. So when Beth talks about the different APIs that we have available to do classification, we also have NLP APIs that allow you to bring to bare this additional stuff that's going on and go from a closed-loop classification to an open-loop one. >> So I want to ask you about the black box problem. If you think about AI, I was saying this in my intro, I know when I see a dog but if I have to describe how I actually came to that conclusion, it's actually quite difficult to do and computers can show me here's a dog or I joked in Silicon Valley. I don't know if you guys watch that show Silicon Valley. Hot dog or not so your prescription at IBM is to make a white box, open that up, explain to people which I think is vitally important because when line of business people get in the room. like how'd you get to that answer and then it's going to be it's going to actually slow you down if you have arguments but how do you actually solve that black box problem? >> It's a much harder problem obviously but there are a whole host of reasons as to why you should look at it that way. One is we think it's just good business practice because when people are making business decisions they're not going to comply unless they really understand it. From my previous stint at IBM when I was working with the coaches of the NBA, they would not believe what patterns were being put forward to them until such time as we tied it to the video that showed what was actually going on. So it's that same aspect in terms of being able to explain it but there's also fundamentally more important reasons as well. You mentioned the example of looking at a dog and saying that's a dog but not being able to describe it. AI systems have that same issue. Not only that what we're finding is that you can take an AI system and you can just tweak a little bit of the data so that instead of recognizing it as a dog now it's completely fooled and it will recognize it as a rifle or something like that. Those are adversarial examples. So we think that taking this white box approach sets us up not just tactically and from a business standpoint but also strategically from a technical standpoint because if a system is able to explain it, describe it and really present its reasoning, it's not going to be fooled that easily either. >> Some of the themes that we hear from IBM, you own your own data, the Facebook blowback has actually been an unbelievable tailwind for that meme and most of the data in the world is not publicly searchable. So build on those themes and talk about how IBM is helping its customers take advantage of those two dynamics. >> So they kind of go hand-in-hand in the sense that because customers have most of the data behind their firewall if you will, within their business walls it means it's unlikely that it's annotated and labeled and used for a lot of these systems so we're focusing on how do we put together techniques to allow systems to learn more with less data. So that goes hand-in-hand with that. That's also back to the point of protecting your data because as we protect your data, you and your competitor we cannot mix that data together to improve the base models that are a part of it so therefore we have to do techniques that allow you to learn more with less data. One of the simplest thing is around the customization. I mean we're coming up on two years since we provided the capability to do custom models on top of visual recognition, Watson visual recognition. The other guys have been bragging about it in the last four to five months. We've been doing it in production with clients, will be two years in July so you'd say okay, well what's that about? We can end up training a base model that understands some of the basics around visual type things like your dog example and some other things but then give you the tools to very quickly and easily create your custom one that now allows you to better understand equipment that may be natural to you or how it's all installed or agricultural things or rust on a cell phone tower or whatever it may be. I think that's another example of how this all comes about to say that's the part that's important to you as a company, that's part that has to be protected that also has to be able to learn with you only spending a few days and a few examples to train it, not millions and billions. >> And that base layer is IBM, but the top layer is client IP and you're guaranteeing the clients that my IP won't seep into my competitors. >> So our architecture is one that separates that. We have hybrid models as a part of it and that piece that as you said is the client piece is separate from the rest of it. We also do it in such a way that you could see there could be a middle layer in there as well. Let's call it industry or licensed data so maybe it comes from a third party, it's not owned by the client but it's something that's again licensed not public as a part of it. That's a part of what our architecture is-- >> And you guys, we saw the block diagrams in there. You're putting together solutions for clients and it's a combination of your enterprise data architecture and you actually have hardware and software components that you can bring to bear here. Can you describe that a little bit? >> Go ahead, it's your implementation. >> Yeah so we've got again the perfect example of a large enterprise. There's significant on-prem implementations, there's private cloud implementations, there's public cloud implementations. You've got to bridge all that and do it in a way that makes it seamless and easy for an enterprise to adopt so we've worked through all that stuff. So we've learned things the hard way about well if you're truly going to do an AI data lake, you better have it on flash. For that reason we have it on flash on-prem but also on the cloud, our storage is on flash and so we were able to make those types of decisions so that we've learned through this, we want to share that with our clients. How do you involve deep learning in this space, well it's going to be proximate to your data lake so that the servers can get to all that data and run literally thousands and thousands of experiments in time that it's going to be useful for the decision. So all those hard learnings we are packaging that in the form of these showcases. We're bringing that forward but right now it's around hybrid cloud and the bridge so that because we want to talk about everything and then going forward it's all public cloud as we leverage that for the elasticity of the computer. >> Well IBM if you can do it there you can do it anywhere. It's a highly complex organization. So it's been what two years in for you now two? >> Little over two years. >> So you're making a lot of progress and I could see the practitioners eating this stuff up and that's got to feel good. I mean you have an impact obviously. >> It absolutely feels very good and I'm always in fact I kind of believe this coming into IBM that this is a company that has not only a number of products that are pertinent to this space but actually the framework to create an AI enterprise. These are not like disparate products. These are all going towards creating an AI enterprise and I think if you look across our portfolio of products and then you kind of map that back to our showcases, you'll see that come to life but in a very tangible way that yes if you truly want to create an AI enterprise, IBM is the place to be because they've got the answers across all the dimensions of the problem as opposed to just one specific dimension like let's say a data mining algorithm or something machine learning and that's basically it. When we cover the full gamut and you have to otherwise you can't really create an AI enterprise. >> In the portfolio obviously coming together IBM huge ambitions with with Watson and everybody's familiar with the ads and so you've done a great job of getting that you know top of mind but you're really starting to work with clients to implement this stuff. I know we got to end here but you had thrown out of stat 85% of executive CAI as a competitive advantage but only 20% can use it at scale so there's still that big gap, you're obviously trying to close that gap. >> Yeah so in a way I would correct it only 20% of them are using it at scale. I think Dave it's a part of how do they get started and I think that comes back to the fact that it shouldn't be a large transformational, scary multi-year project. It is about taking small things, starting with two or three or five use cases and growing and scaling that way and that's what our successful customers are doing. We give it to them in a way that they can use it directly or we leverage it within a number of solutions, like cyber security, like risk and compliance for financial services like health care that they can use it in those solution areas as well. >> Guys thanks so much for coming to theCUBE and sharing your story. We love coming to these events you see guys I used to say you see the practitioners, it's a board level discussion and these guys are right in it so good to see you guys, thank you. >> You too, thank you. >> You're welcome, all right keep it right to everybody, we'll be back with our next guest you're watching theCUBE live from the IBM Chief Data Officer Strategy Summit in San Francisco, we'll be right back.

>> Narrator: Live from Las Vegas, it's theCUBE! Covering IBM Think 2018. Brought to you by IBM. >> Welcome back to IBM Think 2018, you're watching theCUBE, the leader in live tech coverage, my name is Dave Vellante, I'm here with my co-host Peter Burris. Gene Chao is here as the Global VP of IBM Automation and Jason Kelley, Cube Alum, is the GM of Blockchain Services. Gentlemen, welcome back to theCUBE. >> Thank you much. >> Great to see you. >> You guys, I call you heat-seeking inefficiency missiles, so, Jason's... Just a shout-out, take it from there. What are you guys up to, what are you doing? How are you helping businesses? >> Well, we're driving trust into transactions. The elusive things that we've been trying to-- >> Gene: Whoops, there goes heat-seeking. (laughing) >> Exactly. Or we're seeking the heat. It's coming after us, as soon as we say trust, someone wants to attack you. And so what we're bringing into business is that thought that, if I can add trust into transactions, I don't need a third-party to validate it. I can now say, look, you are who you are. We both know each other. All that we do, we go way back. We know each other, and what we're about to exchange is known as well. So if I can keep that validation from happening, I'm going to remove cost, labor, time, out of it. And I'm also going to then maybe avail new market opportunities of those who could not enter the system before because we didn't trust their identities. Or we didn't trust that their goods were their goods, and they were trying to exchange it. So think of that heat-seeking missile, we're trying to bring that capability and that heat is the energy in the system now going bigger, better, faster because there's trust. >> And your role is to bring those Blockchain services to market, is that right? >> That's correct, bringing the services as a whole, because see, Blockchain isn't a product. Blockchain, you know, I don't have under the table a bucket of Blockchain. >> Dave: Let me see your Blockchain. >> Sorry, no Blockchains here. So, if in fact, we're bringing this capability to the market, there's all types of services from what's the business value design? First, what's your outcome? Why say Blockchain? Believe it or not, it says it on my chest, so it means I get paid to do it, but maybe you don't need this? And so, quite simply, maybe you need to do something else. So the first thing is, let's understand the outcome that your business is running toward, and then let's understand if it's a Blockchain, and then can we bring some automation with Gene and team? >> Okay, that's the set-up for you Gene, so you're the automation piece of the puzzle. Explain. >> So, I love the commentary around the better, faster, but we're also bringing more scale. So automation has scale. What does that mean? We're really focused on two things, guys, the first thing is around taking advantage of the new technologies to enable what I'll call software-based labor. So there's a new concept of the digital workforce model that enables how transactions or how work gets done. Coupled with that is how that workflow or process, business process, IT process, whatever it is, how does that workflow fundamentally change through these technologies. Why that's important is as we look at Blockchain, as an example, as a pivot point for trusted transactions, I need to build trusted automation around it. Trusted ways to leverage these technologies in that workflow so those transactions are easily scalable, works at machine time, and runs through very quickly. >> This is fascinating stuff, 'cause look. The way that we like to characterize the big change in the industry is we say, for the first 50 years of computing, there was no process, accounting, HR, et cetera, on known technology. How do we implement? What technology do you choose to implement? The implementation choices are becoming clear. Cloud, et cetera. What's less known is the process. The unknown process, unknown technology. Now it's unknown process, known technology. And what you guys are talking about is one of the challenges when you think about processes. Who does what? Can we verify that we've done it? Did they do it right? Did they meet to do what they said they were doing? Et cetera, the whole range of issues. And the contracting process is extremely complex, but if you set it up in a Blockchain form, you've got a simple contract, a simple definition of who is trusted, simple definitions of roles, and now we can dramatically accelerate new process creation and then automate it. Have I got that right? >> I think you got it, when you think about dramatically, dramatically accelerated, you say that it means something different to everyone. But let's think about my friend Frank Yiannas at Wal-Mart, for example, where they're working on food trust. They're trying to make sure that from farm to fork, we know where that food came from. One-third of all food that's processed goes to waste. Because we lack food trust. Food is guilty until proven innocent, right? To keep that from being-- >> Spoiled. >> Spoiled, I'm... The humor is killing me. (laughing) So, no pun intended, food trust, right? So, Frank and team wanted to understand how fast they could move this thought of tracking, tracing, with transparency, this food through the system. Just as you said, there's certain contrast, think of the handshakes from getting, in their case, a mango from a farm all the way to your home, Well, it used to take them seven days. Actually, six days, twenty-some hours, in order to figure out that process. Put it on the Blockchain? 12 seconds. And then once they cured the lag and the technology, 2.2 seconds. So think of that. Now you're shrinking this to seconds versus days, what does that do to the process? What do you do when you say, now my system can go that fast. My people can go that fast. What do you do? Think of the automation that you're bringing in now, and things that you will now have to automate, out of not just necessity, but things you will say, wow, we've opened up a whole new ecosystem of possibilities in order to do business in a different way. >> Well, so let me build on that for a second. 'Cause one of the things that potentially means is that because you can handle more complex, newly designed, process, better, faster, more automated, that you can start to expand the scope of participants in a transaction? The range of characteristics of the transaction, or the type of work? That's how you build up to new businesses and new business models, right? >> Sure. >> Right, right. >> If I can jump in on that one. There's a concept in this one, and this is where Jason and I are connected at the hip. You know, we think in terms of a smarter product, we think in terms of a smarter contract, or transaction, that the guiding principle that we're using is the old way of thinking, and I carry this narrative all over with me is, the old way of thinking is you have people following your creating process, supported by that technology. So the things that you talked about, unknown technology, unknown process, continuously sourced by people? Fundamentally changed. We're now working in a world where the process is run by the technology and supported by the people. It's not that the people are going away, it's a fundamental retooling of the skills and understanding of how to support it, but that scalability, the ability to get to that exponential growth, is because the process is the king. At the top of the food chain, now. And that technology lets it expand. >> But we could do levels of complexity in that process and the number of participants in that process, unheard of! It's scale and scope. >> Yes. >> But doesn't that force... Look, we've had some conversations, Dave and I have had some conversations, with a number of big user organizations about this stuff and we keep coming back to the issue of that they can't just look at the technology, they have to focus on the design. That one of the most crucial features of this process is the design of the Blockchain. We got that right? >> You heard me use the phrase at the very beginning, if you didn't, I'll say it again, I said, business value design. Because in fact, that design is not just a UI or UX, but let's make sure that the business and technology are doing the right thing to get to the outcome. As we say, design doesn't stop until the problem is solved. And guess what, the problem's never solved. So design happens... Many people say, "Oh we're going to do some "design thinking at the beginning. "We did that," check the block, and then they run off and do something else. For us, design's like an infinity loop. You continue to do it. From the beginning all the way to the end, and then, what you're able to do, and hint-hint, this is something that we do in our services, we start with our clients, we get them started so they understand, then we help them accelerate, and then innovate. Three steps: start, accelerate, innovate. And that's a design process in and of itself. So if you start at, you know, the days of Blockchain tourism were a couple years ago, everybody wanted to kick the tires, and then last year was PoC PoV, this year's the year of production. And people are quick in saying, "How do I quickly start "production and keep moving?" >> So let's talk about some other examples. You mentioned Wal-Mart, we heard Plastic Mag this morning, I introduced somebody, I think Evercorp was the name of the company, Diamond Providence. Others that you're excited about, that have made a business impact. >> Well, I'd be remiss if I didn't mention Mike White and others at our JV with Maersk. And you know, you think of that, where you have the classic thought of a supply chain, this linear steps in the process, you know, these handshakes that have to happen. Now what we have is we have this process of thinking how we can bring transparency into all of that, and it's not just a supply chain, but a value chain. So you have where 80% of whatever you all are touching or have owned right now, with the shipping line. But not only through a shipping line, but then there was also ground and air, and ultimately to a retail location. Then you consumed it. Well, think of all of those processes now having the transparency where you can see from point of consumption all the way back to origin. Think of the supply chain visibility, that elusive thing called supply chain optimization. Now you can do that, but not only the supply chain, but the value chain. Someone's paying invoices under that big thing called a value chain. Someone's doing trade promotion management in that value chain. Now, if you have that visibility, what do you enable? How many more packages can go through the system? How much more shipping? And the estimate is 5% increase in GDP if we're able to get all of this shipping into the Blockchain. You start talking GDP? It opens eyes. >> Right now you're talking growth, right? >> Yes. >> Real growth. >> So, it's 20% of the four trillion associated with shipping? Is bound up in paperwork? >> Yes. >> So we're talking about 800 billion dollar change. >> And returning capital into the system. Returning capital. You think of this thought of opening up new opportunity, And I'll throw another example, another client, so we're not just talking, but you think of what's happening with We.Trade. Nine banks in Europe who compete. You think of Santander Bank and a Deustche Bank and those are now, they're all coming together, saying "How do we now share data and information "so that we can let small to medium size enterprises "into the system?" So now you're getting not just savings of cost and time, but now you're opening up markets. Getting greater throughput. High waters raise all boats. And that's what we're seeing in a lot of these examples with, it's not just taking out those old things, you're thinking of new processes running the business a different way. >> And Jason's a great lead guy. You asked for an example, our friends at DBS Bank. They are fundamentally looking at changing the business models within the bank across all different divisions of the bank, whether it's credit transactions, mortgages, personal wealth, and the way they approached it was, we know these new technologies are going to allow us to fundamentally look at the workflow and change it. But here's the question: Who will be looking at changing these things? What's going to enable these model changes, the workflow changes may not be human capital. It may be working alongside this sort of man plus machine element or formula-- >> Peter: Patterns. >> Right, to allow the technology to tell you where your efficiencies could be gained. Allow the technologies to make the correlations in those disparate business models, to fundamentally change how you do business. So that's happening today. >> So, phase one is what is this, phase two, POC, now you're sort of in real production, but you obviously doing a lot more POCs, you're scaling out. Where do you see this going over the next three or four years? >> Well, I think last year was a year of the PoC PoV. I think this year's a year of production. And when you think of some of the examples that we've given, we've talked about consumer trade with Wal-Mart, we talk about shipping trade with Maersk, we talk about trade finance with We.Trade. Each of those individual networks, where do we see it going? We see these networks becoming a network of networks. Where each one of them have their own ecosystems and they come together. And they come together with trusted data, with trusted information, access that's unparalleled. So that's where we see it heading. And you have to say then, okay, it sounds really simple in the way you've just described it, so where's the challenge? The challenge is going to be doing this from a business and technology perspective. There's a lot of things that have to be figured out here. How are you going to make those processes work at that speed? What do you rightfully automate and what things don't you automate? That's more than just a technology. You can't plug a technology in and solve this. It takes an end to end capability. And that's what we're seeing, becoming more of a differentiating capability for our teams, where they can say, "Gene, Jason, "can your teams talk to us together?" 'Cause, of course, they work together. That's a differentiating effect of moving at scale and at speed, and that's where we see it going. Scale and speed. >> So what Jason and the Blockchain frame does for us, is it's an accelerant. Okay, we talk about knowledge worker, automation, we talk about different areas of software-based labor, but that accelerant is doing one big thing, is it's forcing us into what I'll call vertically integrated processes or workflow. Gone are the days of segmentation of, "Oh, that's back office," or "That's front office." We now have to take that workflow and pivot that to vertical integration. Why? That accelerant is moving at the speed of light for trusted transactions, I have to make the systems supporting that. The process, the people, I have to keep up with that pace of change. If I don't vertically integrate those processes inter and intracompany? This doesn't work. It falls down. So that's our marriage. >> Tough to go to market. How do you go to market? >> How do we go to market? We go to market as fast as we can, and we go joined at the hip, with clear and simple understanding. >> Where's the Blockchain for going to market? >> Yeah, right? >> And is there partner ecosystem that... >> Absolutely. So we talk about a Blockchain, Blockchain's a team sport. And it is a true demonstration of Metcalfe's Law, you know, the network drives the value. And so we do. We go to market with this thought of, who's going to play in that network? And we have networks where its obvious value may have a founder network, like Wal-Mart, where you say look, we see the ecosystem, we have the ecosystem, we're the founding partner, or you have a consortium such as We.Trade, where they come in and they say, "Look, let's pull all this together "'cause we see the value." So we go to market with that ecosystem, knowing that they have to partner, they have to work together. >> Outstanding. >> There's three distinct chapters in our go to market strategy. One is the services architecture, the second one is software ecosystem, and the third is around platforms, like a Blockchain. So when we start-- >> No design? >> Sorry, say again? >> No design? >> No, there is absolutely design. Absolutely design. So at a service architecture's perspective, there is fundamental workflow design happening. At a platform level, that's an even further advancement of design, because of the frameworks and blueprints happening inside a Blockchain, inside the different next-gen technologies happening. So I have to be two things, I have to be an automation-led environment where I'm providing the way to do these things, differences in RPA versus other technologies, but I also have to be an automation-attached. I have to be attached into the Blockchain framework to make sure we're coupled in the different elements of that framework. So that's how we jointly go to market. >> Peter: RPAs, I'm sorry? >> I'm sorry, Robotic Process Automation companies, so these are the relatively new technologies that enable software-based labor components. They're replicating human activity. >> Software robots? >> Software robots. >> You have a path to automation anyway. >> Exactly right. Exactly right. >> And it's funny when you ask, you know, no design. Design's in there. And this is the way we work at IBM, I mean, we're past that calling it out. So if someone's calling it out, it's like you're going to buy a phone and say, "Oh yeah, we included the battery." Like, it's there now, right? So that's how we run. So is it in there? You mention IBM, anything that you're going to consume from us? Includes IBM design. By practice. >> Wow, you guys, today was Blockchain day. I mean, you must have been thrilled to see all the main tech-- >> You mean every day's not Blockchain day? >> Dave: Well, at IBM, thinks every day... >> Okay, alright, I was just checking. >> You guys sucked all of the air out of the morning. And we heard-- >> And by the way, I certainly hope not. (laughing) >> You hope not what? >> That every day is Blockchain day. >> I hope so. Jason here. >> Makes me not have to buy a new wardrobe. >> If every day's Blockchain day, it ain't working. This is going to be one of those technologies, the less we know about it, the more successful it's been. >> I agree, I agree. >> Well, gentlemen, thanks very much for coming on theCUBE. Always a pleasure. >> Thank you guys. >> Thanks very much. >> Appreciate it. >> Alright, keep it right there, buddy. We'll be back with our next guest right after this short break. You're watching theCUBE live from IBM Think 2018. Be right back.

>> Announcer: Live from Las Vegas, it's the CUBE. Covering IMB Think 2018. Brought to you by IBM. >> Hello everyone, welcome to the Cube here at IBM Think 2018. It's our flagship program where we extract the signal noise live entertainment and technology coverage here. Of course we're going to get all the data as well. Inderpal Bhandari, Global Chief Data Officer for IBM is here in the CUBE, CUBE alumni. The chief of the data for the entire company your job is pretty secure right now. Jean Merriman was talking about how data's the center of the value proposition, blockchain and A.I. Dave and I call it the innovation sandwich. You've got job security right now. >> (laughs) I guess you could put it that way. >> (laughs) So, obviously the data, all kidding aside, we've talked before in the CUBE, the importance of data and, you know, we're data driven, we're data geeks. This is a wonderful time to be in this world because the disruptive enabling that's going on with data is really been, I think, underplayed. It's been more of a tech conversation but the business benefits that this enables, I mean, just blockchain alone, what that could do for efficiencies in rewiring the value chains in a decentralized environment. And then what A.I. promises with the use of data to automate value creation, this is pretty spectacular. >> No, I would completely agree with you. I think it's a very exciting time to be in our industry. And, John, I think the challenge though, is what does it mean for the enterprise? If you put yourselves in the shoes of our customers, they're trying to understand, what does this really mean for the enterprise? What's an A.I. enterprise? What are the use cases for blockchain that play in the enterprise? And that's one of the major foci that I have within my organization, you know. And my role within IBM and the Global Chief Data Officer is to create an A.I. enterprise within IBM itself and then use that as a showcase for our customers so they're able to understand, clearly, what the use cases are that make a lot of sense. Because, frankly, IBM looks a lot like some of our customers. You know we are a large enterprise, we've been around for a while and the fits the profile for the large customers that we serve. >> Well, IBM is the perfect melting pot and Petri dish, if you will, to look at the future, 'cause you have legacy, you know, hundreds of years of being in business, so you've been around but you're also pushing the latest technologies. How has IBM been using the tech? Can you give an example, because this is the digital transformation challenge that most existing leaders have. You know, you don't need to be only five years old just to be, kind of, an old relic compared to what's on the table right now, the speed of innovation. So there has to be a constant energy on understanding how to create sustainable tech and business models and have that regenerate self-healing. I mean, this is a new normal that is just hitting us. How do you guys do it? Can you give some examples? >> Yes, no, absolutely. So we've taken the view that we want to transform our key processes within the company. And examples of these processes, they're not typical to us, they're typical of any large enterprise, you know, these could be procurement, supply chain, marketing, research, data. So we've got these end-to-end processes, which we are now transforming through the use of A.I. and blockchain, these kinds of technologies so that we are able to then re-use those as showcases. So in terms of examples of how we are making use of these today, they.. I'll give you some examples that are more, you know, just at a whole process level, for instance, supply chain. Trying to understand what are the risks to our supply chain based on emerging weather conditions, based on emerging political events. Trying to unravel all that and then essentially use that intelligent system to guide us to make the best decisions with regard to supply chain. That's kind of what I would call a process level example. I'll give you one example within data that seems to some extent quite trivial but actually there are literally thousands and thousands of such decision that are made everyday in a large enterprise. So one of the things that we do in my organization is try to understand if a client that we're dealing with is a government owned entity. And since we operate globally and there are rules that regulate how one deals with government owned entities, very important for us to get it right so that we do business ethically. And it's, you know, you might think, 'well that's a simple decision' it's actually quite complicated and a lot of different parties have a stake in the ground on this. You know, the legal department, the sales area. But now, the way the process is transforming is all that input is fed into an intelligence system that has an understanding of what we've done in the past. It can look at the external data, the news feeds that are available about that organization as well as what are the different points of view and then come to an understanding and then finally be able to explain back to us its rationale as to why it considers something a government owned entity or not. So every subject matter expert in the company should be able to make use of this technology. That's what an A.I. enterprise is and there are literally thousands and thousands such people within an enterprise. >> I mean, you're putting real complex data at their fingertips almost as easy as putting numbers on a spreadsheet. >> Inderpal: Yes. >> That's the kind of work that you guys are thinking. >> Yes, the way I would put it to you, it's more in the sense of engaging the subject matter expert in a dialog. So it's like you've got this intelligent system, Watson, that's working with this subject matter expert, taking them through the whole scenario. They come in with a use case in mind, I used the example of government owned entity or a risk insight for supply chain, they're coming in with a use case in mind, the system is guiding them through. Here's the internal data that's relevant. >> Yeah. >> Here's the external data that's relevant. Here's how you can link them. Here are the insights that you can draw from. So it's kind of a two-way street but it just ends being a much more accurate decision made much more quickly. >> Jean's talk on speech and the theme here at Think 2018 is, putting smart to work. I'll edit that for you in our conversation, putting smart data to work, 'cause that's what you're getting at here. How do you make data intelligent? I know, you know, I mean if you look at it, we can kind of go in the high levels in the clouds and look down and say, 'yeah, you know, that's a great mission.' You know it's hard as heck! >> It's it's very hard. >> So you've got an intelligent data, is it the right data, is it conceptually relevant, is it in the right place at the right time, does the application have the ability to ingest and use the data? >> How reliable it is? All that stuff comes into play and that's where, I think, you know, we've thought of IBM as having a very large portfolio of products that span from, you know, data management, data quality, those kinds of things, all the way to A.I. and Watson and so forth. Think of it more now as bringing together that portfolio into a cohesive data and cognitive framework or data and cognitive backbone for the enterprise. And that's really essentially what we're putting together. >> Inderpal I want to get your thoughts on something. I'm going to kind of go on a tangent since it just popped in my head. I wrote blog posted in 2007, way back in the day, 10 years ago, that said data's the new developer kit. And it's kind of a riff on that data's going to be the software. So we're seeing that now. I interviewed Rob Thomas earlier where he was talking about data containers. We're starting to get to that level with these Kubernetes and these cloud technologies, you now have new models emerging around data where people want to act on data, whether as a subject matter expert or developer. They are essentially develop users. So data's got to be programmable, it's got to be accessible. How do we get to a world where it's being developed on in a seamless way? Just like software's developed on. 'Cause most of the software, 90% of most software is open source, only 10%, put in a Linux foundation, is actually raw intellectual property. So you can almost think of data the same way. >> Inderpal: Yes, no no question. >> How does using data in a development context? What's your vision on that? >> So, you know, we have a blueprint to make an enterprise into am A.I. enterprise or a cognitive enterprise and it has four elements to it. One of the elements is actually data for precisely the reasons that you just annunciated. You know, developers, if they have to go off and search for data and try to find it then it's not a productive use of their time. So to some extent you have to bring the data eco system to them and that needs to be part of an A.I. enterprise. That that data is readily available for developers so that they're able to harness that. And so, now you get into all the hard questions, right? How to do you find it? What is the lineage of the data? So you need to have a super catalog enterprise-wide that enables all that and.. >> Hey, we're making up a new category as we speak it's called data ops. Data as code. We have DevOps as infrastructure's code. You know, I've been kind of, I was talking about this a year ago, didn't get any traction with the idea but what was circling in my head was if infrastructure as code, which was DevOps, which is now serverless when we look at the cloud computing as a set of programmable resources, you can almost make the stretch that data as code is a similar nirvana. >> Inderpal: Yes. >> Okay, it's available, I'm not searching for it but I don't need to reconstruct it, I don't need to essentially ingest it, yeah I'm ingesting it as a function, but, in a free-flowing world, what's your thoughts on that? What's your reaction to that? >> Well the way, you know, that's why setting up the central backbone for data and cognition is extremely important. And I think the right way to think about it is as a continuum. So you've got data and then you've got, essentially, API's on top of the data, that may, may be representing certain functions that you're running on the data. You think about that as a continuum because those functions end up with data as a result. Right? So you've got derived data. So, what the backbone needs to be able to do is give developers very quick access to all the raw data, the source data, as well as the derived data in terms that they can understand and it's easy for them to fathom what that is so that they're able to make judgments in conjunction with an intelligent system that guides them. >> Yeah, that's the key thing and that why Jean brought up Moore's law and Metcalfe's law in her speech because she's intimating at two things, faster smaller cheaper, performance improvements. Metcalfe's law is a network effect. Okay, so you know where I'm going with this, right?. So now we're in a network effect gamification world. We see blockchain, we see crypto currency, we see decentralized application developers coming on on board very quickly. So you have a world with token economics is becoming front and center and where I see innovation, certainly ICOs, initial point offerings are scaring me right now, but it is highlighting the innovation and arbitrage of an inefficient capital market, so, I just use that as a use case. But blockchain and crypto currency is an opportunity to create new business models from the enabling blockchain capability. How do you view that? Because we're still talking about data now. If you're freeing up more people to have more time to actually do their job, they're going to create new things maybe new business models and enter interstate token economics combined with blockchain, this is where we really see a lot of great innovation. Your thoughts in this area of token economics. >> Sure, yeah absolutely. So, I think there are two ways to think about it, one is in the transaction of business itself. What you're doing is you're bringing in a stakeholders for a particular business transaction and you're giving them a way to, a distributed way, a distributed way to arrive at the decision, right? As to whether or not to move forward. So, distributed consensus. You're making that very easy and simple of them so that they can rapidly reach a decision and make their decision, whether they're going to put in money, take out money et cetera. That's one aspect of it, and we literally have.. >> And by the way, consensus is now a new data source? >> Yes. >> And active real time.. >> Yes. >> Data set? >> Absolutely, it is creating, it is creating a data set, in and of its own right. So, but that's kind of one aspect of it, which is in the transaction of business, making it much more efficient, much faster and so forth. But I think it's also instructive to look at blockchain and apply it in terms of a second reuse to the process of managing data itself. So to the extent you're able to establish identities, to the extent you're able to establish permissions and roles. It's going to make governance of data much easier and much faster and much more efficient. These are typically very hard problems for enterprises to solve but I would say that as you go forward, maybe in this year or next year, you're going to see examples. >> And the opportunity too, is to actually break down some structural barriers. >> Yes. >> With this new technology. >> Absolutely. >> It's the bulldozer of innovation. It's not easy but there is a path. You guys have what, close to a hundred customers in blockchain? >> Yes. >> And it's a data story. Supply chain, blockchain, value chain, chain activities, interesting. >> It's going to just lead to a lot a lot more efficiency and accuracy as we move forward. >> Awesome! Inderpal Bhandari Global Chief Data Officer here on the CUBE, sharing his insights on data. We didn't even get to the good part around social data and graphs and all that great stuff that we love talking about. But more CUBE coverage is going to continue here. Day two coverage of IBM Think. I'm John Furrier, thanks for watching. (electronic music)

>> Narrator: Live from Las Vegas, it's The Cube. Covering IBM Think 2018. Brought to you by IBM. >> Hello everyone and welcome to The Cube here at IBM Think in Las Vegas, the Mandalay Bay. I'm John Furrier, the host of The Cube. We're here in this Cube studio as a set for IBM Think. My next guest is Jennifer Shiin who's the founder of 8 Path Solutions. Twitter handle Jenn, J-S-H-I-N. Great to see you. Thanks for joining me. >> Yeah, happy to be here. >> I'm glad you stopped by. I wanted to get your thoughts. You're thought leader in the industry. You've been on multiple Cube panels. Thank you very much. And also Cube alumni. You know, IBM with the data center of the value proposition. The CEO's up on the stage today saying you got data, you got blockchain and you got AI, which is such the infrastructure of the future. And AI is the software of the future, data's at the middle. Dave and I were talking about that as the innovation sandwich. The data is being sandwiched between blockchain and AI, two super important things. And she also mentioned Moore's law. Faster, smaller, cheaper. Every 6 months doubling in speed and performance. And then Metcalfe's law, which is more of a network effect. Kind of teasing out token economics. You see kind of where the world's going. This is an interesting position from IBM. I like it. Is it real? >> Well it sounds very data sciency, right? You have the economics part, you have the networking. You have all these things in your plane. So I think it's very much in line with what you would expect if data science actually sustains (mumbles), which thankfully it has. >> Yeah. >> And I think the reality is you know, we like to boil things down into nice, simple concepts but in the real world when you're actually figuring it all out its going to be multiple effects. It's going to be, you know a lot of different things that interact. >> And they kind of really tease out their cloud strategy in a very elegant way. I mean they essentially said, 'Look we're into the cloud and we're not going to try to.' They didn't say it directly, but they basically said it. We're not going to compete with Amazon head-to-head. We're going to let our offerings to do the talking. We're going to use data and give customers choice with multi cloud. How does that jive for you? How does that work because at the end of the day I got to have business logics. I need applications. >> Yes. >> You know whether its blockchains, cryptocurrency or apps. The killer app's now money. >> Yep. >> If no one's making any money. >> Sure. >> No commerce is being done. >> Right. I mean I think it makes sense. You know, Amazon has such a strong hold in the infrastructure part, right? Being able to store your data elsewhere and have it be cloud. I don't think that was really IBM's core business. You know, a lot of I think their business model was built around business and business relationships and these days, one of the great things about all these data technologies is that one company doesn't have to do all of it, right? You have partnerships and actually partners so that you know, one company does AI. You partner with another company that has data. And that way you can actually both make money, right? There's more than enough work to go around and that much you can say having worked in data science teams right? If I can offload some of my work to different divisions, fantastic. That'd be great. Saves us time. You get to market faster. You can build things quicker. So I think that's one of the great things about what's happening with data these days, right? There's enough work to get around. >> And it's beautiful too because if you think about the concept that made cloud great is DevOps. Blockchain is an opportunity to use desensualization to take away a lot of inefficiencies. AI is also an automation opportunity to create value. So you got inefficiencies on block chains side and AI to create value, your thoughts and reaction to where that's going to go. You know, in light of the first death on a Uber self-driving car. Again, historic yesterday right? And so you know, the reality is right there. We're not perfect. >> Yeah. >> But there's a path. >> Well so most of its inefficiency out there. It's not the technology. It's all the people using technology, right? You broke the logic by putting in something you shouldn't have put in that data set, you know? The data's now dirty because you put in things that you know, the developer didn't think you'd put in there. So the reality is we're going to keep making mistakes and there will be more and more opportunities for new technologies to help you know, cheer that up. >> So I was talking to Rob Thomas, GM of the analytics team. You know Rob, great guy. He's smart. He's also an executive but he knows the tech. He and I were talking about this notion of data containers. So with Kubernetes now front and center as an orchestration layer for cloud and application workloads, IBM has an interesting announcement with this cloud private approach. Where data is the central thing in this. Because you've got things like GDPR out there and the regulatory environment not going to get any easier. You got blockchain crypto. That's a regulatory nightmare. We know a GDBR. That's a total nightmare. So this is happening, right? So what should customers be doing, in your experience? Customers are scratching their head. They don't want to make a wrong bet, but they need good data, good strategy. They need to do things differently. How do they get the best out of their data architecture knowing that there's hurdles and potential blockers in front of them? >> Well so I think you want to be careful of what you select. and how much are you going to be indebted to that one service that you selected, right? So if you're not sure yet maybe you don't want to invest all of your budget into this one thing you're not sure is going to be what you really want to be paying for a year or two, right? So I think being really open to how you're going to plan for things long term and thinking about where you can have some flexibility, whereas certain things you can't. For instance, if you're going to be in an industry that is going to be you know, strict on regulatory requirements right? Then you have less wiggle room than let's say an industry where that's not going to be an absolute necessary part of your technology. >> Let me ask you a question and being kind of a historian you know, what say one year is seven dog years or whatever the expression is in the data space. It just seems like yesterday that Hadoop was going to save the world. So that as kind of context, what is some technologies that just didn't pan out? Is the data link working? You know, what didn't work and what replaced it if you can make an observation? >> Well, so I think that's hard because I think the way I understood technology is probably not the way everyone else did right? I mean, you know at the end of the day it just is being a way to store data right? And just being able to use you know, more information store faster, but I'll tell you what I think is hilarious. I've seen people using Hadoop and then writing sequel queries the same way we did like ten plus years ago, same inefficiencies and they're not leveling the fact that it's Hadoop. Right? They're treating it like I want to create eight million tables and then use joins. So they're not really using the technology. I think that's probably the biggest disappointment is that without that knowledge sharing, without education you have people making the same mistakes you made when technology wasn't as efficient. >> I mean if you're a hammer, everything else is like a nail I guess if that's the expression. >> Right. >> On the exciting side, what are you excited about in technology right now? What are you looking at that's a you know, next 20 mile stare of potential goodness that could be coming out of the industry? >> So I think anytime you have better science, better measurements. So measurement's huge, right? If you think about media industry, right? Everyone's trying to measure. I think there was an article that came out about some of YouTube's failure about measurement, right? And I think in general like Facebook is you know, very well known for measurement. That's going to be really interesting to see, right? What methodologies come out in terms of how well can we measure? I think another one will be say, target advertising right? That's another huge market that you know, a lot of companies are going after. I think what's really going to be cool in the next few years is to see what people come up with, right? It's really the human ingenuity of it, right? We have the technology now. We have data engineers. What can we actually build? And how are we going to be able to partner to be able to do that? >> And there's new stacks that are developing. You think about the ecommerce stack. It's a 30 year old stack. AdTech and DNS and cookiing, now you've got social and network effects going on. You mentioned you know, the Metcalfe's law. So with all that, I want to get just your personal thoughts on blockchain. Beyond blockchain, token economics because there are a lot people who are doing stuff with crypto. But what's really kind of pointing as a mega trands standpoint is a new class of desensualized application developers are coming in. >> Right. >> Okay. They're dealing with data now on a desensualized basis. At the heart of that is the token economics, which is changing some of the business model dynamics. Have you seen anything? Your thoughts on token economics? >> So I haven't seen it from the economics standpoint. I've seen it from more of the algorithms and that standpoint. I actually have a good friend of mine, she's at Yale. And she actually runs the, she's executive director of their corporate law center. So I hear some from her on the legal side. I think what's really interesting is there's all these different arenas. Legal being a very important component in blockchain. As well as, from the mathematical standpoint. You know when I was in school way back when, we studied things like hash keys and you know, RSA keys and so from a math standpoint that's also a really cool aspect of it. So I think it's probably too early to say for sure what the economics part is going to actually look like. I think that's going to be a little more longterm. But what is exciting about this, is you actually see different parts of businesses, right? Not just the financial sector but also the legal sector and then you know say, the math and algorithms and you know. Having that integration of being able to build cooler things for that reason. >> Yeah the math's certainly exciting. Machine learning, obviously that's well documented. The growth and success of what, and certainly the interests are there. You seeing Amazon celebrating all the time. I just saw Werner Vogels, the CTO. Talking about another SageMaker, a success. They're looking at machine learning that way. You got Google with TensorFlow. You've got this goodness in these libraries now that are in the community. It's kind of a perfect storm of innovation. What's new in the ML world that developers are getting excited about that companies are harnessing for value? You seeing anything there? Can you share some commentary on the current machine learning trends? >> So I think a lot of companies have gotten a little more adjusted to the idea of ML. At the beginning everyone was like, 'Oh this is all new.' They loved the idea of it but they didn't really know what they were doing, right? Right now they know a little bit more. I think in general everyone thinks deep learning is really cool, neural networks. I think what's interesting though is everyone's trying to figure out where's the line. What's the different between AI versus machine learning versus deep learning versus neural networks. I think it's a little bit fun for me just to see everyone kind of struggle a little bit and actually even know the terminology so we can have a conversation. So I think all of that, right? Just anything related to that you know, when do you TensorFlow? What do you use it for? And then also say, from Google right? Which parts do you actually send through an API? I mean that's some of the conversations I've been having with people in the business industry, like which parts do you send through an API. Which parts do you actually have in house versus you know, having to outsource out? >> And that's really kind of your thinking there is what, around core competencies where people need to kind of own it and really build a core competency and then outsource where its more a femoral invalue. Is there a formula, I guess to know when to bring it in house and build around? >> Right. >> What's your thoughts there? >> Well part of it, I think is scalability. If you don't have the resources or the time, right? Sometimes time. If you don't have the time to build it in house, it does make sense actually to outsource it out. Also if you don't think that's part of your core business, developing that within house do you're spending all that money and resources to hire the best data scientists, may not be worth it because in fact the majority of your actual sales is with the sale department. I mean they're the ones that actually bring in that revenue. So I think it's finding a balance of what investment's actually worth it. >> And sometimes personnel could leave and you could be a big problem, you know. Someone walks about the door, gets another job because its a hot commodity to be. >> That's actually one of the big complaints I've heard is that we spend all this time investing in certain young people and then they leave. I think part of this is actually that human factor. How do you encourage them to stay? >> Let's talk about you. How did you get here? School? Interests? Did you go off the path? Did you come in from another vector? How did you get into what you're doing now and share a little bit about who you are? >> Yeah so I studied economics, mathematics, creative writing as an undergrad and statistics as a grad student. So you know, kind of perfect storm. >> Natural math, bring it all together. >> Yeah but you know its funny because I actually wrote about and talked about how data is going to be this big thing. This is like 2009, 2010 and people didn't think it was that important, you know? I was like next three to five years mathematicians are going to be a hot hire. No one believed me. So I ended up going, 'Okay well, the economy crashed.' I was in management consulting in finance, private equity hedge funds. Everyone swore like, if you do this you're going to be set for life, right? You're on the path. You'll make money and then the economy crashed. All the jobs went away. And I went, 'Maybe not the best career choice for me.' So I did what I did at companies. I looked at the market and I went, 'Where's their growth?' I saw tech had growth and decided I'm going to pick up some skills I've never had before, learn to develop more. I mean in the beginning I had no idea what an application development process was, right? I'm like, 'What does that mean to actually develop an application?' So the last few years I've really just been spending, just learning these things. What's really cool though is last year when my patents went through and I was able to actually able to launch something with Box at their keynote. That was really awesome. >> Awesome. >> So I became a long way from I think, have the academic knowledge to being able to apply it and then learn the technologies and then developing the technologies, which is a cool thing. >> Yeah and that's a good path because you came in with a clean sheet of paper. You didn't have any dogma of waterfall and all the technologies. So you kind of jumped in. Did you use like a cloud to build on? Was it Amazon? Was it? >> Oh that's funny too. Actually I do know Legacy's technology quite well because I was in corporate America before. Yeah, so like Sequel. For instance like when I started working data science, funny enough we didn't call it data science. We just called it like whatever you call it, you know. There was no data science term at that point. You know we didn't have that idea of whether to use R or Python. I mean I've used R over ten years, but it was for statistics. It was never for like actual data science work. And then we used Sequel in corporate America. When I was taking data it was like in 2012. Around then, everyone swore that no, no. They're going to programmers. Got to know programming. To which, I'm like really? In corporate America, we're going to have programmers? I mean think about how long it's going to take to get someone to learn any language and of course, now everyone's learning. It's on Sequel again right? So. >> Isn't it fun to like, when you see someone on Facebook or Linkdin, 'Oh man data's a new oil.' And then you say, 'Yeah here's a blog post I wrote in 2009.' >> Right. Yeah, exactly. Well so funny enough Ginni Rometty today was saying about exponential versus linear and that's one of the things I've been saying over the last year about because you know, you want exponential growth. Because linear anyone can do. That's a tweet. That's not really growth. >> Well we value your opinion. You've been great on The Cube. Great to help us out on those panels, got a great view. What's going on with your company? What are you working on now? What's exciting you these days? >> Yeah so one of the cool things we worked on, it's very much in line with what the IBM announcement was, so being smarter, right? So I developed some technology in the photo industry, digital assent management as well as being able to automate the renaming of files, right? So you think you probably a picture on your digital camera you never moved over because you, I remember the process. You open it, you rename it, you saved it. You open the next one. Takes forever. >> Sometimes its the same number. I got same version files. It's a nightmare. >> Exactly. So I basically automated that process of having all of that automatically renamed. So the demo that I did I had 120 photos renamed in less than two minutes, right? Just making it faster and smarter. So really developing technologies that you can actually use every day and leverage for things like photography and some cooler stuff with OCR, which is the long term goal. To be able to allow photographers to never touch the computer and have all of their clients photos automatically uploaded, renamed and sent to the right locations instantly. >> How did you get to start that app? Are you into photography or? >> No >> More of, I got a picture problem and I got to fix it? >> Well actually its funny. I had a photographer taking my picture and she showed me what she does, the process. And I went, 'This is not okay. You can do better than this.' So I can code so I basically went to Python and went, 'Alright I think this could work,' built a proof of concept and then decided to patent it. >> Awesome. Well congratulations on the patent. Final thoughts here about IBM Think? Overall sentiment of the show? Ginni's keynote. Did you get a chance to check anything out? What's the hallway conversations like? What are some of the things that you're hearing? >> So I think there's a general excitement about what might be coming, right? So a lot of the people who are here are actually here to, I think share notes. They want to know what everyone else is doing, so that's actually great. You get to see more people here who are actually interested in this technology. I think there's probably some questions about alignment, about where does everything fit. That seems to be a lot of the conversation here. It's much bigger this year as I'm sure you've noticed, right? It's a lot bigger so that's probably the biggest thing I've heard like there's so many more people than we expected there to be so. >> I like the big tent events. I'm a big fan of it. I think if I was going to be critical I would say, they should do a business event and do a technical one under the same kind of theme and bring more alpha geeks to the technical one and make this much more of a business conversation because the business transformation seems to be the hottest thing here but I want to get down in the weeds, you know? Get down and dirty so I would like to see two. That's my take. >> I think its really hard to cater to both. Like whenever I give a talk, I don't give a really nerdy talk to say a business crowd. I don't give a really business talk to a nerdy crowd, you know? >> It's hard. >> You just have to know, right? I think they both have a very different sensibility, so really if you want to have a successful talk. Generally you want both. >> Jennifer thanks so much for coming by and spending some time with The Cube. Great to see you. Thanks for sharing your insights. Jennifer Shin here inside The Cube at IBM Think 2018. I'm John Furrier, host of The Cube. We'll be back with more coverage after this short break.

>> Narrator: Live from Las Vegas it's theCUBE. Covering IBM Think 2018. Brought to you by IBM. >> Hello everyone, welcome to theCUBE here at IBM Think 2018. I'm John Furrier. We're on the ground with theCUBE. In theCUBE studio today we have a live audience on break but I had a chance to meet with the Chief Digital Officer of Hybrid Cloud, Janine Snead, who's just appointed. She's here in set on theCUBE. Great to see you at IBM Think. >> Hi, great to see you. Thanks for having me. >> Thanks for coming on. I'm super excited. When I interviewed Bob Lord last year, Chief Digital Officer, you know we love digital on theCUBE so we get really excited. We're like great, that's awesome. Now IBM's got more Chief Digital Officers being appointed >> Janine: That's right. >> You're the first Chief Digital Officer in a business unit. That's awesome, congratulations. >> Thank you. Yeah we're excited about it. We know and we believe that the future is really in the hands of the web. And we know that customers are engaging with us differently. They want much more of a self service. They want to experience the products without always I'll say a person interacting with them. And we know that from a product perspective there's things that we need to do to make our offerings much more digitally consumable. So we're taking this very seriously. And we put an organization in place Digital within Hybrid Cloud, that truly focuses on the time from a customer goes out and actually does a search, all the way through the buyer journey to the time they get to the product. >> John: You know I've been a student of IBM I actually worked at IBM as a co-op back in my early days. IBM has always been on the leading edge of marketing. And you guys are looking at socially you looked at social in an early way, digital in an early way, but now with the cloud you can actually engage customers digitally. So I've got to ask you, you know, how are you going to do that? >> Janine: Yeah >> John: Because you've got to remember websites are now the fabric of all this that's 30 year old tech stack. You've got cloud now, you've got APIs with the synchronous software packages. You've got blockchain. All these new things. So what's the vision as you guys go out and start putting stakes in the ground for a digital strategy. How are you guys doing it, can you share the vision? >> Yeah, I think it starts with using our own technology. So within the Hybrid Cloud organization, we have a lot of software and we're putting that software out on the cloud. We want customers to engage with us digitally through a technical experience. So we're taking our products, putting product demos, we're putting POTs, we're putting even proof of concept secure in the cloud, guided demos where they can come and experience these offerings without ever engaging with us. Now of course once they're ready they can engage with us but this is truly about a low touch, self service way for customers to engage with our products. >> Now a lot of people, and we talk about this all the time, but the general sentiment online now is you have the kind of crazies out there you've seen that on Reddit, fake news, weaponizing content. Then you have the other side of the spectrum where people are like, I don't want to be sold to. I'm discovering, I want to learn. >> Janine: Yes. >> John: I'm in communities. I know you guys address that. I want you to just clarify, because there's a model now where people just want to be ingratiated in. You know, kick the tires. Which by the way, kicking tires right now is much different than it was years ago because you have APIs. You have SARS source code. You have credits for cloud. >> Janine: That's right. >> What is the digital motion there? I mean obviously it's a light touch. >> Yeah >> But is it still an IBM.com? >> It is. So we're still on IBM.com properties. And we're nurturing with the ecosystem and the communities to also go where they are, but bring them back to the IBM.com properties and engage with them when they're ready. You know, we've done the research. We know that 70% of b2b buyers learn about your products and your services without ever talking to you. So we want to be where those users are and eventually that will be back on our property but we also want to find them where they are. >> You know, one of the things we were talking about before you came on camera here, We've been doing theCUBE for seven years or so plus six shows now to one show. But the thought leadership on theCUBE has always been powerful. And that's seemed to be a great way to get into communities. And IBM's got a lot of thought leaders. So I'm sure you have a plan for thought leaders. You have IBM Fellows. You've got R&D. You've got a lot of content opportunities. >> We do. We've got a lot of partners. So here at this conference we've been talking to a lot of our partners who want to be a part of this experience. We've got great solutions and all of our solutions a lot of them are delivered with partners. And so it's working the community. It's working the ecosystem. And it's doing this together with partners to allow them to contribute and allow customers to come and consume solutions. In much of a use case way, of course you can have product by product by products, but how do you essentially deliver solutions based on use cases. >> So I'll ask you a personal question. How did you get here? Was it like hey, I want to do the digital job, was it an itch that you were scratching, did Bob Lord lure you into the job? (Janine laughs) Did he recruit you? I mean -- >> No, it's -- >> How did you get it? >> It's a great question >> Because this is a great opportunity. >> It is. I'm a product person by training. And I spent the last 18 months in sales. And I enjoyed every minute of that and listening and understanding how our sellers want to consume. Short, snackable type of learning and training and watching what was going on with the digital ecosystem I thought it was a great way to really mix my skills that I have within product with what I just learned from my sales role. And I did nine months in marketing. So I felt like it was kind of a mixture. And we have a huge opportunity here. So the opportunity presented itself. >> Sales always has a my favorite sales expression is people love to buy from people that they like. How are you going to make IBM likable digitally? Is there a strategy there? >> Oh, it's simple. (John laughs) It is so dead simple. It's about the user experience. When users come, you have to give them the best experience possible because you never get a second chance to make a good first impression. So I want to basically set the bar. And we're an MVP right now with a lot of the stuff that we're doing out. >> You mean software and tools and stuff? >> Yeah, no, well, our experience right now so when you come and you experience our tools I'm sorry, our demos and our proof of technologies and our tutorials out on our site it's MVP. We're 45 days old. But it's about the user experience. And so we've been serving users here that are coming to try our stuff. >> So the Digital Technical Engagement, that's the DTE? >> Janine: DTE, yep. >> That's the one that's 45 days? >> That's the one that's 45 days old. >> The IBM site's not 45 days old. >> Yeah, yeah. >> But this new program. So take a minute to explain what the DTE, the Digital Technical Engagement program is. What was the guiding principles behind it >> Yeah >> What's some of the deign objectives is there any new cool tech under the covers? Share a little bit of color on that. >> Sure, sure. Happy to. So back in the fourth quarter of last year we took a look and we said, how are customers consuming? How are we engaging? How are we showing up? And what do we need to do to shift to become more agile and lighten the way that we showed up. And so we really gathered a few smart creatives from the CIO's office, from IBM design, from product and from marketing and we said guys, we're going to run an experiment. We want to set up a site off of IBM.com a page off of IBM.com and it's very simple. Keep it so clean. Keep the user experience clean. Take something like IBM Cloud Private. Give me three product demos. Give me one guided demo where in 10 minutes a client can get through IBM Cloud Private without getting stuck and then give them a way to try it for two weeks. Just experiment. Well, in 90 days we've had 10,500 users try that guided demo and our NPS is 56. >> What does NPS mean? - Net Promoter Score >> That's what I figured, okay. >> So it's about experimentation. And so in this world that we're going into we want to experiment. And so from there, what happened, that proved to be successful. We now have an organization of about 60 people within digital technical engagement deep product experts, but we also have a platform team to drive that experience. >> So there's some real value there. I mean, a lot of people look at website and digital technologies as ad tech, you know, and there's a lot of bad press out there now with Facebook where a lot of people are looking at Facebook as content that got weaponized for fake news and the ad tech has a bad track record of fill out a form, they're going to sell me something. How are you going to change that perception? >> That's a great question. So a lot of the folks that we're working with right now say you have to capture user information capture user information. And for me, I don't want to be bothered. So I'm kind of looking at this maybe a little bit too selfishly saying I want to demo without giving you my information. We have our product demos and our guided demos, we don't collect any information from the user. When you are going to reserve our software for two weeks, up to a month, we do collect some information about you. >> John: You got to. >> We have to. >> At some point. >> So we're keeping it very low touch because we know that's how users want to engage. >> You don't want to gate the hell out of it. >> No, we don't want to gate the hell out of it. We want to keep it just, let them explore without being all over them. Right? >> Talk about the new IBM. You know, one of the things that's transforming right now that I'm impressed with is IBM's constantly reinventing themself. I was impressed with Ginni's keynote. The way she talks about data in the middle, blockchain on one side and AI on the other. I call it the innovation sandwich. >> Janine: Yeah >> How are you applying that vision to digital? I mean not yet obviously, you're only at the beginning. >> Right But that vision is pretty solid. And she brought up Moore's Law and Metcalfe's Law. >> That's right. >> Moore's Law is making things faster, smaller, cheaper. >> Right >> Component wise and speed. >> Yes >> Metcalfe's Law is about network effect and the future of digital is either going to be token economics or blockchain with programatic tooling that gives users great experiences. So how do you tie that together? Maybe it's too early to ask, but-- >> No, no. It's simple. I'm a consumer of this stuff. I'm using the cloud. I'm using the IBM Design Thinking because I brought in three designers from Phil Gilbert's group. Right? I'm embedded in the digital organization basically, regardless of where I sit. So we are adopting best practices that come from IBM's big chief digital office. >> So you get to use your own tools, that's one of the things she said. >> Yeah and we'll embed, we'll get there. Right? >> Yeah >> Well actually, we already are doing, we embedded chat. So we've got Watson Chat running on our SPSS statistics page So it's about the cloud, it's about user experience. It's about applying digital practices from Bob Lord's organization and then it's about Watson. >> I was having a great Twitter thread with a bunch of people that were on Twitter just ranting on the weekend a couple weekends ago about digital transformation. Tom Peters actually jumped in, the famous Tom Peters who wrote the books there, a management consultant, about digital transformation. I love digital transformation, it's overused, but it's legit. People are transforming. So the question was, how do you do it successfully? And all the canned answers came out. Well, you need commitment from the top. You've got to have this and that. And I said look, bottom line, if people don't have the expertise, and if they don't know what they're doing, they can't transform. So it begs the question for skills gap. A lot of people are learning, so there's a learning environment. It's not just sales. Proficiency, getting the product buying. There's a community thirst for learning. How is that incorporated in, if any? >> I think I have a little bit of a different hurdle. The people that we're working with are learning. They're out in the communities they're engaging. I think one of the things that we have to continue to do is continue to show the value of digital transformation. Remember, IBM is a big company. I'm not a ten person startup. Right? We're a bigger organization so what we have to do is show why digital is important back in with our product teams. I think for the most part our marketing teams get it. Because you have to make trade offs. Am I going to invest in this feature in the product or am I going to put in something like eCommerce so you can subscribe and buy. >> Priorities. But you're a product person, so it's all about the trade offs. >> Yeah, it's all about the trade offs, right? So the skills are part of it but some of it is just education on why this is so critical. And then the last thing is passion. You have to bring the skills, the education and then that passionate team that really believes that they can get this done. >> Okay so given that, let's go back to some of the comments I made about the people who we were talking about on Twitter >> Janine: Sure >> Commitment from the top. IBM commitment at the top is there? What are they saying, what's the marching orders? >> The marching orders is we got to go and we're not moving fast enough. Speed, speed, speed, right? So we got to move fast. >> So in an interview with Bob Lord, one of the things we talked about was interesting. He's like I like to just get stuff done. I think he might have used another word. Maybe it was off camera he said that. IBM's got a lot of process. How do you take the old IBM process and make it work for you rather than having digital work for the process? >> Yeah >> It's a lot of internal things but no need to give away too much but it's a management challenge. How do you cut through it? >> I think from a process perspective, these are conversations and you have to explain why. If you could go in and explain why you need to do something differently, then people will listen. I'd like to give an example, okay? I had 26 days to get five products out the door. I formed a team January 2nd. By January 26th, I had to be live. Now I worked with my marketing team and I said I will get into your buyer journey, but I have to launch my Digital Technical Engagement site and my products. They understood. So I went live. Now, will I back back into the process? Sure I will. >> John: But you had good alignment. >> But yeah, we have to move fast, right? So it's explaining why and having mature conversations and then people that really believe in digital they'll support you. >> Great conversation. I'm looking forward to chatting more with you. We're at theCUBE. But I want to ask you one final question before we break. What's your objective? What's the roadmap for you, what's your top priorities? Are you hiring? Who're you looking for? What kind of product priorities, what's the sales priorities? What's your to-do list? >> I think let's start with the customer. So the customer priority is to deliver the best experience possible as they engage with IBM digitally. And that's all about the user experience. From a talent perspective, it's all about diversity, inclusion, and people that come with different skills from technology, to growth hacking, to marketing, and to engineering. And some people that think differently. We want people that, no idea is a bad idea, just come and bring great ideas. >> Well, diversity and inclusion, first of all, half of the users are women. And you also have to have an understanding of the use cases. >> Yeah >> It's not just men using software. >> Yeah, that's right. >> It's a huge deal. >> That's right, that's right. >> Alright well, Janine, great to have you on theCUBE. Thanks for spending the time. >> Thank you. >> Congratulations on the new role. Janine Sneed, Chief Digital Officer from IBM Hybrid Cloud. First IBM Chief Digital Officer in a business unit. I also today have Bob Lord and a lot of other folks doing digital but great to see the digital momentum. >> Thank you. >> It's not just a selling apparatus. It's all about value for users. It's theCUBE bringing you the value here at IBM Think 2018. I'm John Furrier, back with more after this short break. (upbeat music)

>> Narrator: Live, from Las Vegas, it's The Cube, covering IBM Think 2018. Brought to you by IBM. >> Hello, everyone and welcome back to our day two of coverage here in Las Vegas, where IBM Think 2018's The Cube's three days of wall-to-wall coverage day two. Yesterday, we had kick-off, kind of partner day. Today's really the kick-off of the event. CEO of IBM up on stage for the keynote. I'm John Furrier with Dave Vellante. Dave, we're doing seven years or so plus all these six shows coming down to one for IBM Think. It's a packed house; you can't even get through the hallways. Looks like they need to go to Sands Convention Center. >> Dave: (laughs) or Moscone. >> Or Moscone, or somewhere bigger, they need a bigger boat, but the keynote kicked off, Ginni Rometty was up there. Interesting, putting smart to work, quantam, blockchain, AI data and she kind of laid out the cloud strategy, you know, using data in public cloud and private. It's clear where they're going with the cloud. Your analysis of the keynote, what's your thoughts? >> Well, first of all, John, as viewers know, I mean, I'm a big fan if Ginni Rometty. I think she's been overly criticized, but I think she's a great presenter. When I compare Ginni's presentation skills with some of the other CEOs in the industry, I think she's far superior. She connects with the audience, she looks great, she's really cogent, she's well prepared, so, I really like her as a presenter and as an executive, and, you know, another women in tech, you know we love that. Yes, you're right, putting smarter to work was her theme. She's talkin' about 30 to 40,000 people at the event. There's too many people to count I guess. You can't really figure that out, and, so, it's big, it's packed. She also did a theater in the round which was different. I noticed last year ServiceNow did that. I really like that style, so that was kind of an interesting thing. Ginni talked about three exponential growth areas. So, I'll lay 'em out and then, we can talk about it. She said they come every 25 years. The first was Moore's law, and we all know what that is, and the second was Metcalfe's law, the value of the network increases exponentially if the nodes in network increase, and then, the third, which is upon us now, is data plus AI. Her supposition was that is going to usher in a next era of incremental growth, because you're going to out-learn the competition, and she used this term of incumbent disruptors, and I heard that and went okay, hold on, (Dave laughs) 'cause I don't see it that way. >> Yeah. >> I don't see the incumbents as the disruptors. So, that was my first reaction, and then, she brought up three customers, Verizon, and I'm like, "Verizon? "A big telco is a disruptor, come on! "They're gettin' a disruptor by over the top.", but the CEO came on, Lowell McAdam, talkin' about 5G, so we'll talk about that, and then, Maersk, IBM has a joint venture with Maersk, so, Michael White came up, he's the CEO of that. Now, Maersk is using blockchain, and Maersk we all know is the container company and they're attacking inefficiencies with blockchain, so I thought that was actually a really good example, and then, Royal Bank of Canada, RBC, came up. You know, banking, to me, is an industry that has not been disrupted yet, and, so, I, again, was initially negative toward this idea of incumbent disruptors, 'cause I don't think the incumbents are disruptors, and we'll talk about why I think that, but I thought IBM did a pretty good job of showing how incumbents can actually take AI and blockchain and, at least, defend against the disruptors. >> I mean, it's clear to me that she's obviously playing to the crowd with the digital debt transformation. I mean, we talk about these traditional companies, they need to transform, and she brings up Moore's law and Metcalfe's law kind of to take a view of the past, but to look forward, she's kind of saying, "Lookit, Moore's law make things smaller, faster, "cheaper, doubling every six months." That's just on the, I mean, this applies to IoT, quantum makes everything else. Metcalfe's law I think is very relevant, 'cause if you look at blockchains about decentralized internet, you're talkin' about decentralized applications, that's where blockchain will play the major enablement there, that's about network effects, so you bring network effects in with Metcalfe's law, Moore's law on the equipments on the hardware side, I like that, so, that worked for me. The disruptors, I think it's more of overplaying her hand on that, because I just haven't seen any evidence of any incumbents truly disrupting themselves. So, maybe you can talk with Microsoft, IBM's trying to transform, but at the end of the day, they got to look back and learn from the internet era. If you don't jump on these next waves, you could be driftwood, right? So, you got to surf the new waves, and I think that's what I heard her say is IBM is putting data at the center of the value proposition using AI as a front end for that, make it smarter, and then, using blockchain as an infrastructure and protocol level opportunity to take the IBM software and data plane and wrap 'em together. So, if you look at it, you got data at the center, blockchain on one side, and AI on the other, it's the innovation sandwich. That, for me, works for me, now, let's unpack that. How real is it, and that's going to be what we're going to talk about, and I think that's a good strategy. All the elements are in play. >> Well, I think the other piece of that sandwich, maybe it's the dressing on top, is the cloud, 'cause you have to have scale and network effects in order to achieve that innovation. I just want to mention, she talked about three other things that you are going to do as a customer. You're going to, one, leverage digital platforms, you're going to, two, embed learning in, virtually, every process that you do, and, three, you're going to empower humans. So, she put forth this idea of augmented intelligence, and, as I predicted yesterday, she, unlike Larry Olsen, she doesn't come right out and slam her competition, she does it in a classy way. She said, quote, "IBM is not "in conflict with your business." In other words, we're not taking your data and then, remonetizing it at the back end. That's a big deal, IBM makes a lot of noise about that. So, it's really augmenting humans, not in conflict with your business, and bringing advanced security to things like blockchain, >> Yeah. >> and cloud, and AI. >> I like her term security to the core, I like that, but that kind of gives the impression that's core to all things, but if you look at the megatrends that are impacting the incumbents and the people trying to do digital transformation, as well as the new startups, Dave, that are trying to get a new position in the landscape is clear. You got blockchain, you got decentralized apps, you got AI, but the data's critical, and she mentioned some cool things I like with the cloud which was she's saying, "Lookit, we'll make "the data a really big thing for you. "If you want it in public cloud, "you can have it in private cloud." So, she's looking at cloud as much more of a hybrid approach on private, kind of hinting at the GDPR problem that we know's out there. So, if you want to move your data around, that's a critical asset. Also, if you look at what's going on in the news today, these days, is Facebook is getting slammed because how they were hacked with the election, and other weaponization of data, this is a big deal for companies, and I think if IBM can play that card to leverage the data and have the confidence of the companies that they serve to say, "Lookit, data's got to be owned by you, "but has to be managed in a way that's dynamic, "whether it's a GDPR or some other regulatory issue.", and, believe me, blockchain's going to have some. So, you know, they could come out and get in the front of this new wave, and I think that's a good play. So, it wasn't just a recycled cloud show, it wasn't just AI Watson, I like how she put it together. >> So, just touching on a thing, you mentioned Facebook. So she talked about Moore's law ushering in this era of back office productivity. She didn't mention Wintel; I think it's still, probably, too painful for IBM to think about that. Metcalfe's law, she said ushered in, sort of, the Facebook era. I think that's fair, the network effect of Facebook, and then, she said, "Hopefully, you know, "they'll call this Watson's law." I don't know if that's going to happen, but that notion of, >> Wishful thinking. >> hey, hey, you got to be power of positive thinking, but that notion of exponential learning. I want to talk about cloud for a minute. You and I had some interesting debates yesterday in our open about cloud. Oracle announced its earnings yesterday, cloud growth 30%. I see Oracle and IBM as very similar in their cloud strategies; both companies would vehemently disagree with that, >> Yeah. >> but I think they are very similar in that sense. The street didn't like it, because Oracle cloud only grew at 30%, stock's down, okay, great, but, to me, IBM and Oracle are similar in that they're basically cloudifying their business. They're allowing their clients to onboard customers to the cloud, putting their applications portfolios, their SAS products, their middleware into the cloud, IBM putting mainframe class stuff in the cloud, they're putting power into the cloud, storage into the cloud, pretty much everything into the cloud if you want it. Now, that's not easy to do >> Yeah. >> if you've got, you know, legacy businesses, obviously, AWS has a blank sheet of paper, that was kind of your point yesterday, >> Yeah, yeah. >> but I like the differentiation that I see from the companies like IBM and Oracle, and there really aren't many others like that. >> Yeah. I mean, my point yesterday was the definition of cloud has been totally mangled, right? Like, it's different, if you're Amazon, they have a slew of services, they have more services than anyone else on the planet, and they have more people using those services, so, by that standard, Amazon is clearly kicking everyone's butt, but that's just their perspective. If you look at IBM, their services are applications, same with Oracle. So, if you look at what IBM's doing is they're taking the same approach. Services and applications are going to be IBM's view of the cloud, but IBM's taking a multicloud approach, and I think that's different, and, when you put the data as the central component of the architecture, you're basically saying, "I'm going to look "at the cloud as more of a commodity layer. "I'll let the customers decide which cloud to use.", and that's a better strategy, now, it's hard to do multicloud, so maybe they're buying some time, but I think that's a good, solid strategy to take if they're not going to be trying to push their own cloud as 100%, because not all customers will sole source cloud unless there's functionality that that cloud does. For instance, Amazon is winning the public sector business like it's nobody's business, because they have the only cloud that has the ability to do classified and non-classified cloud. Nobody else has it, so, from a log speck standpoint, they're winning everything and from the DOD, CIA, and government. What IBM has to do is go into customer requirement saying, "We're the only company that can provide this." That's a unique opportunity for IBM. I think that's a winning approach rather than going on a frontal arms race of services with Amazon, and that's what all the big guys are doing. Microsoft, Oracle, IBM are not taking on Amazon directly, because they're going to have to match feature for feature, and then, Amazon wins that game every time. >> So, I want to go back to something Sam Palmisano said when he was CEO of IBM in 2012 on his way out. HP was the hot company, Hurd was running the company, and he was asked, "Do you worry about HP?" He said, "I don't worry about HP, "'cause they don't invest in R&D. "I worry about Oracle, 'cause they invest in R&D.", and, again, what I like about Oracle and IBM, they both invest in R&D, IBM even, you know, core stuff around blockchain, certainly quantum computing and the like. So, I think that is a very positive dynamic for both of those companies. >> Well, I mean, IBM's R&D is a secret weapon, I think, for them; they don't overplay that much. They do talk about it, but we look at what blockchain potentially could be, and I think, you know, IBM's certainly doing the messaging on blockchain. It still has a bunch of ads on T.V., and they're trying to make that a kind of a global brand, but blockchain speaks to a new infrastructure, right? It's not just distributed computing, it's decentralized computing, and we were saying on the Cube and we've been reporting there is a new wave of software developers coming on the market that are going to be writing decentralized applications for token economics. The notion of tokens isn't about ICOs and those scams, although there's a lot of those going on. The notion of token economics fit with a mobile cloud decentralized architecture whether it's IoT, or end users, or applications, token economics is going to change the impact in efficiencies up and down the stat. So, to me, the developer community that's rushing into the market on the decentralized applications will be a major opportunity, but you got to nail the blockchain and that tech is just a moving train from a protocol standpoint to an infrastructure. So, to me, I like what IBM's doing with blockchain. I think that's going to be an opportunity to move the ball down the field. >> So, the exponential innovation formula, in my view of the next ten years, is going to, and you nailed it, going to combine data with artificial intelligence, or machine intelligence, and cloud economics, and there is a set of digital services emerging. >> Well, cloud and token economics, both, it's two. >> But, so, yes, but, so, and that's part of it, but there's a set of digital services emerging in this fabric, and they're not bespoke services, they're part of this integrated fabric. The extent to which people leverage those services, those digital services, to create new business models is going to determine success or failure. Data, at the core, is critical. >> Yeah, yeah. >> I think you're right on on that, but what I like is that IBM is trying to solve some hard problems with AI. >> I mean, lookit, I was tweeting yesterday all day on some highlights from my Puerto Rico trip on the cryptocurrency events we've been covering, and one thing that we reported was the killer app for blockchain and cryptocurrency and decentralized apps is money. Money is the killer app, and we see that with the hype cycle with the ICOs, but, if you look at what IBM's doing with the supply chain side of their business, perfect storm for supply chain innovation. Blockchain is about money, marketplaces, and nailing inefficient incumbents. So, if the incumbents want to be disruptive, they're going to have to disrupt themselves by removing inefficiencies out of the system. >> Well, and the Maersk example was a good one where there's inefficiencies, you know, 20% of the cost of moving containers is admin stuff. Sometimes the admin costs exceed the shipping costs. So, that was a good example, but, again, I see blockchain as one component in this fabric, in this puzzle. >> Day two, Cube here, kicking off wall-to-wall coverage. Three days of live broadcast talking to the thought leaders. Extracting the signal from the noise, the Cube, the number one leader in live tech coverage. Go to cube.net to check out all the footage and siliconangle.com to check out all of our articles. We're reporting and the team reporting all week, and that analysis of Ginni's keynote, well done, Dave. More coverage after this short break. (techno beat) >> Narrator: Robert Herjavec.

>> Announcer: From the Silicon Angle Media Office in Boston, Massachusetts. It's the theCUBE. Now here's your host Stu Miniman. >> Hi, I'm Stu Miniman and welcome to a special theCUBE conversation here in our Boston area studio. Happy to welcome back to the program, Danny Cobb, who's with Dell EMC in the CTO office. >> Thanks Stu, great to see you here today. >> Great to see you too. So Danny, we're going to talk about a topic that like many things in the industry. It seems like it's something that happen overnight, but there's been a lot of hard work going on for quite a lot of years, even going back to heck when you and I worked together. >> Danny: That's right. >> A company use to be called EMC. NVMe, so first of all just bring everybody up to speed as to what you work on inside the Dell family. >> Danny: Sure, so my responsibility at now Dell EMC has been this whole notion of emergence systems. New technologies, new capabilities that are just coming into broad market adoption, broad readiness, technological feasibility, and those kinds of things. And then making sure that as a company we're prepared for their adoption and inclusion in our product portfolio. So it's a great set of capabilities a great set of work to be doing especially if you have a short attention span like I do. >> Danny, I spend a lot of time these days in the open source world. You talk about people are moving faster, people are trying lots of technologies. You've been doing some really hard work. The company and the industry in the standards world. What's the importance of standards these days, and bring us back to how this NVMe stuff started. >> So a great way to get everybody up to speed as you mentioned when you kicked off. NVMe, an overnight success, almost 11 years in the making now. The very first NVMe standard was about 2007. EMC joined the NVMe consortium in 2008 along with an Austin, Texas computer company called Dell. So Dell and EMC were both in the front row of defining the NVMe standard, and essentially putting in place a set of standards, a set of architectures, a set of protocols, product adoption capabilities, compatibility capabilities for the entire industry to follow, starting in 2008. Now you know from our work together that the storage industry likes to make sure that everything's mature, everything works reliably. Everything has broad interoperability standards and things like that. So since 2008, we've largely been about how do we continue to build momentum and generate support for a new storage technology that's based on broadly accepted industry standards, in order to allow the entire industry to move forward. Not just to achieve the most out of the flash revolution, but prepare the industry for coming enhancements to storage class memory. >> Yeah, so storage class memory you mentioned things like flash. One thing we've looked at for a long time is when flash rolled out. There's a lot of adoption on the consumer side first, and then that drove the enterprise piece, but flash today is still done through Ikusi interface with SaaS or Sata. And believe we're finally getting rid of when we go to NVMe. What some in the industry have called the horrible Ikusi stack. >> Danny: That's right. >> So explain to us a little bit about first, the consumer piece of where this fits first, and how it gets the enterprise. Where are we in the industry today with that? >> Yeah so as you pointed out a number of the new media technologies have actually gained a broad acceptance and a grounds full of support starting in the consumer space. The rapid adoption of mobile devices whether initially iPods and iPhones and things like that. Tablets where the more memory you have the more songs you carry, the more pictures you can take. A lot of very virtuous cycle type things occurred in the consumer space to allow flash to go from a fairly expensive perhaps niche technology to broad high volume manufacturing. And with high volume manufacturing comes much lower costs and so we always knew that flash was fast when we first started working on it at EMC in 2005. It became fast and robust when we shipped in 2008. It went from flash to robust to affordable with technologies like the move from SLC to MLC, and now TLC flash and the continuing advances of Moore's law. And so flash has been the beneficiary of high volume consumer economics along with our friend Moore's law over a number of years. >> Okay, so on the NVMe piece, your friends down in Round Rock in Dell. They've got not only the storage portfolio, but on the consumer side. There's pieces like my understanding NVMe already in the market for some part of this today, correct. >> That's right, I think one of the very first adoption scenarios for NVMe was in Lightweight laptop device. The storage deck could be more efficient. The fundamental number of gates in Silicon required to implement the stack was more efficient. Power was more efficient, so a whole bunch of things that were beneficial to a mobile high volume client device like an ultra light, ultra portable laptop made it a great place to launch the technology. >> Okay, and so bring us to what does that mean then for storage? Is that available in the enterprise storage today? >> Danny: Yeah. >> And where is that today and where is that today, and where are we going to see in the next years though? >> So here's the progression that the industry has more or less followed. If we went from that high volume, ultra light laptop device to very inexpensive M.2 devices that could be used in laptops and desktops more broadly, also gained a fair amount of traction with certain used cases and hyperscalers. And then as the spec matured and as the enterprise ecosystem around it, broader data integrity type solutions in the sili-case itself. A number of other things that are bread and butter for enterprise class devices. As those began to emerge, we've now seen NVMe move forward from laptop and client devices to high volume M.2 devices to full function, full capability dual ported enterprise NVMe devices really crossing over this year. >> Okay, so that means we're going to see not only in the customer pieces but should be seeing really enterprise roll out in I'm assuming things like storage arrays, maybe hyper converged. All the different flavors in the not too distant future. >> Absolutely right, the people who get paid to forecast these things when they look into their crystal balls. They've talked about when does NVMe get close enough to its predecessor SaaS to make the switch over be a no brainer. And often times, you get a performance factor where there's more value or you get a cost factor where suddenly that becomes the way the game is won. In the case of NVMe versus SaaS, both of those situations value and cost are more or less a wash right now across the industry. And so there are very few impediments to adoption. Much like a few years ago, there were very few impediment to adoption of enterprise SSDs versus high performance HDDs. The 15Ks and the 10K HDDs. Once we got to close enough in terms of cost parity. The entire industry went all flash over night. >> Yeah, it's a little bit different than say the original adoption of flash versus HDD. >> Danny: That's right. >> HDD versus SSD. Remember back, you had to have the algebra sheet. And you said okay, how many devices did I have.? What's the power savings that I could get out of that? Plus the performance that I had and then does this makes sense. It seems like this is a much more broadly applicable type of solution that we'll see. >> Danny: Right. >> For much faster adoption. >> Do you remember those days of a little goes a long way? >> Stu: Yeah. >> And then more is better? And then almost be really good, and so that's where we've come over what seems like a very few years. >> Okay, so we've only been talking about NVMe, the thing I know David Foyer's been look a lot from an architectural standpoint. Where we see benefit obviously from NVMe but NVMe over Fabrics is the thing that has him really excited if you talk about the architectures, maybe just explain a little bit about what I get with NVMe and what I'll get added on top with the over fabric piece of that. >> Danny: Sure. >> And what's that roll out look like? >> Can I tell you a little story about what I think of as the birth of NVMe over Fabrics? >> Stu: Please. >> Some of your viewers might remember a project at EMC called Thunder. And Thunder was PCI flash with an RDMA over ethernet front end on it. We took that system to Intel developers forum as a proof of concept. Around the corner from me was an engineer named Dave Min-turn, who's an Intel engineer. Who had almost exactly the same software stack up and running except it was an Intel RDMA capability nick and an Intel flash drive, and of course some changes to the Intel processor stack to support the used case that he had in mind. And we started talking and we realized that we were both counting the number of instructions from packet arriving across the network to bytes being read or written on the vis-tory fast PCI E device. And we realized that there has to be a better way, and so from that day, I think it was September 2013, maybe it was August. We actually started working together on how can we take the benefits of the NVMe standard that exists mapped onto PCI E. And then map those same parameters as cleanly as we possibly can onto, at that time ethernet but also InfiniBand, Fiber channel, and perhaps some other transports as a way to get the benefits of the NVMe software stack, and build on top of the new high performance capabilities of these RDMA capable interconnects. So it goes way back to 2013, we moved it into the NVMe standard as a proposal in 2014. And again three, four years later now, we're starting to see solutions roll out that begin to show the promise that we saw way back then. >> Yeah and the challenge with networking obviously is sounds like you've got a few different transport layers that I can use there. Probably a number of different providers. How baked is the standard? Where do things like hits the interoperability fit into the mix? When do customers get their hands on it, and what can they expect the roll out to be? >> We're clearly at the beginning of what's about to be a very, I think long and healthy future for NVMe over Fabrics. I don't know about you. I was at Flash Memory Summit back in August in Santa Clara and there were a number of vendors there starting to talk about NVMe over Fabrics basics. FPGA implementation, system on chip implementations, software implementations across a variety of stacks. The great thing was NVMe over Fabrics was a phrase of the entire show. The challenging thing was probably no two of those solutions interoperated with each other yet. We were still at the running water through the pipes phase, not really checking for leaks and getting to broad adoption. Broad adoption I think comes when we've got a number of vendors broad interoperability, multi-supplier, component availability and those things, that let a number of implementations exists and interoperate because our customers live in a diverse multi-vendor environment. So that's what it will take to go from interesting proof of concept technology which I think is what we're seeing in terms of early customers engagement today to broad base deployment in both existing fiber channel implementations, and also in some next generation data center implementations, probably beginning next year. >> Okay, so Danny, I talked to a lot of companies out there. Everyone that's involved in this (mumbles) has been talking about NVMe over Fabric for a couple of years now. From a user standpoint, how are they going to help sort this out? What will differentiate the check box. Yes, I have something that follows this to, oh wait this will actually help performance so much better. What works with my environment? Where are the pitfalls and where are the things that are going to help companies? What's going to differentiate the marketplace? >> As an engineer, we always get into the speeds and the feeds and the weeds on performance and things like that, and while those are all true. We can talk about fewer and fewer instructions in the networks stack. Fewer and fewer instructions in the storage stack. We can talk about more efficient Silicon implementations. More affinity for multi-processor, multi-core processing environments, more efficient operating system implementations and things like that. But that's just the performance side. The broader benefits come to beginning to move to more cost effective data center fabric implementation. Where I'm not managing an orange wire and a blue wire unless that's really what I want. There's still a number of people who want to manage their fiber channel and will run NVMe over that. They get the compatibility that they want. They get the policies that they want and the switch behavior that they want, and the provisioning model that they want and all of those things. They'll get that in an NVMe over Fabrics implementation. A new data center however will be able to go, you know what, I'm all in day one on 25, 5000 bit gigabit ethernet as my fundamental connection of choice. I'm going 400 gigabit ethernet ports as soon as Andy Beck-tels shine or somebody gives them to me and things like that. And so if that's the data center architecture model that I'm in, that's a fundamental implementation decision that I get to make knowing that I can run an enterprise grade, storage protocol over the top of that, and the industry is ready. My external storage is ready, my servers are ready and my workloads can get the benefit of that. >> Okay, so if I just step back for a second, NVMe sounds like a lot of it is what we would consider the backend in proving that NVMe over Fabrics helps with some of the front end. From a customer stand point, what about their application standpoint? Can they work with everything that they have today? Are there things that they're going to want to do to optimize for that? So the storage industry just take care of it for them. What do they think about today and future planning from an application standpoint? >> I think it's a matter of that readiness and what is it going to take. The good news and this has analogs to the industry change from HDD to SSDs in the first place. The good new is you can make that switch over today and your data management application, your database application, your warehouse, you're analytics or whatever. Not one line of software changes. NVMe device shows up in the block stack of your favorite operating system, and you get lower latency, more IOs in parallel. More CPU back for your application to run because you don't need it in the storage stack anymore. So you get the benefits of that just by changing over to this new protocol. For applications who then want to optimize for this new environment, you can start thinking about having more IOs in flight in parallel. You could start thinking about what happens when those IOs are satisfied more rapidly without as much overhead in and interrupt processing and a number of things like that. You could start thinking about what happens when your application goes from hundred micro-second latencies and IOs like the flash devices to 10 microsecond or one microsecond IOs. Would perhaps with some of these new storage class memory devices that are out there. Those are the benefits that people are going to see when they start thinking about an all NVMe stack. Not just being beneficial for existing flash implementations but being fundamentally required and mandatory to get the benefits of storage class memory implementations. So this whole notion of future ready was one of the things that was fundamental in how NVMe was initially designed over 10 years ago. And we're starting to see that long term view pay benefits in the marketplace. >> Any insight from the customer standpoint? Is it certain applications or verticals where this is really going to help? I think back to the move to SSDs. It was David Foyer who just wet around the entire news feed. He was like, database, database, database is where we can have the biggest impact. What's NVMe going to impact? >> I think what we always see with these things. First of all, NVMe is probably going to have a very rapid advancement and impact across the industry much more quickly than the transition from HDD to SSD, so we don't have to go through that phase of a little goes a long way. You can largely make the switch and as your ecosystem supports it as your vendor of choice supports it. You can make that switch and to a large extent have the application be agnostic from that. So that's a really good way to start. The other place is you and I have had this conversation before. If you take out a cocktail napkin and you draw an equation that says time equals money. That's an obvious place where NVMe and NVMe over Fabrics benefit someone initially. High speed analytics, real time, high frequency trading, a number of things where more efficiency. My ability to do more work per unit time than yours gives me a competitive advantage. Makes my algorithms better, exposes my IP in a more advantageous way. Those are wonderful places for these types of emerging technologies to get adopted because the value proposition is just slam dunk simple. >> Yeah, so running through my head are all the latest buzz words. Is everything at Wikibon when we did our predictions for this year, data is at the center of all of it. But machine learning, AI, heck blockchain, Edge computing all of these things can definitely be affected by that. Is NVMe going to help all of them? >> Oh machine learning. Incredible high bandwidth application. Wonderful thing stream data in, compute on it, get your answers and things like that. Wonderful benefits for a new squeaky clean storage stack to run into. Edge where often times, real time is required. The ability to react to a stimulus and provide a response because of human safety issue or a risk management issue or what have you. Any place that performance let's you get close, get you outer close to real time is a win. And the efficiency of NVMe has a significant advantage in those environments. So NVMe is largely able to help the industry be ready just at the time that new processing models are coming in such as machine learning, artificial intelligence. New data center deployment architectures like the Edge come in and the new types of telemetry and algorithms that they maybe running there. It's really a technology that's arriving just at the time that the industry needs it. >> Yeah, was reading up on some of the blogs on the Dell sites. Jeff Brew-dough said, "We should expect "to see things from 2018." Not expecting you to pre-announce anything but what should we be looking for from Dell and the Dell family in 2018 when it comes to this space? >> We're very bullish on NVMe. We've been pushing very, very hard in the standards community. Obviously, we have already shipped NVMe for a series of internal use cases in our storage platforms. So we have confidence in the technology, its readiness, the ability of our software stacks to do what they need to do. We have a robust, multi-supplier supply chain ready to go so that we can service our customers, and provide them the choice in capacities and capabilities and things like that that are required to bet your business, and long term supply assurance for and things like that. So we're seeing the next year or so be the full transition to NVMe and we're ready for it. We've been getting ready for a long time. Now, the ecosystem is there and we're predicting very big things in the future. >> Okay, so Danny, you've been working on this for 11 years. Give us just a little bit of insight. What you learned, what this group has learned from previous transitions? What's excited you the most? Give us a little bit of sausage making? >> What's been funny about this is we talk about the initial transition to flash, and just getting to the point where a little goes a long way. That was a three year journey. We started in 2005, we shipped in 2008. We moved from there. We flash in a raise as a tier, as a cache, as the places where a little latency, high performance media adds value and those things. Then we saw the industry begin to develop into some server centric storage solutions. You guys have been at the front of forecasting what that market looks like with software defined storage. We see that in technologies like ScaleIO and VSAN where their abilities to start using the media when it's resident in a server became important. And suddenly that began to grow as a peer to the external storage market. Another market San alternative came along with them. Now we're moving even further out where it seems like we use to ask why flash? And it will get asked that. Now it's why not flash? Why don't we move there? So what we've seen is a combination of things. As we get more and more efficient low latency storage protocols. The bottle neck stops being about the network and start being about something else. As we get more multi-core compute capabilities and Moore's law continues to tickle along. We suddenly have enough compute and enough bandwidth and the next thing to target is the media. As we get faster and faster more capable media such as the move to flash and now the move to storage class memory. Again the bottle neck moves away from the media, maybe back to something else in the stack. As I advance compute in media and interconnect, suddenly it becomes beneficial for me to rewrite my application or re-platform it, and create an entire new set of applications that exploit the current capabilities or the technologies. And so we are in that rinse, lather repeat cycle right now in the technology. And for guys like you and me who've been doing this for awhile, we've seen this movie before. We know how it hands. It actually doesn't end. There are just new technologies and new bottlenecks and new manifestations of Moore's law and Holmes law and Metcalfe's law that come into play here. >> Alright so Danny, any final predictions from you on what we should be seeing? What's the next thing you work on that you call victory soon right? >> Yes, so I'm starting to lift my eyes a little bit and we think we see some really good capabilities coming at us from the device physicists in the white coats with the pocket protectors back in the fabs. We're seeing a couple of storage class memories begin to come to market now. You're led by Intel and microns, 3D XPoint but a number of other candidates on the horizon that will take us from this 100 microsecond world to a 10 microsecond world maybe to a 100 nanosecond world. And you and I we back here talking about that fairly soon I predict. >> Excellent, well Danny Cobb always a pleasure to catch up with you. Thanks so much for walking us through all of the pieces. We'll have lots more coverage of this technology and lots more more. Check out theCUBE.net. You can see Dell Technology World and lots of the other shows will be back. Thank you so much for watching theCUBE. (uptempo techno music)