>> From theCUBE Studios in Palo Alto and Boston bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> After a two year hiatus, AWS re:Inforce is back on as an in-person event in Boston next week. Like the All-Star break in baseball, re:Inforce gives us an opportunity to evaluate the cyber security market overall, the state of cloud security and cross cloud security and more specifically what AWS is up to in the sector. Welcome to this week's Wikibon cube insights powered by ETR. In this Breaking Analysis we'll share our view of what's changed since our last cyber update in May. We'll look at the macro environment, how it's impacting cyber security plays in the market, what the ETR data tells us and what to expect at next week's AWS re:Inforce. We start this week with a checkpoint from Breaking Analysis contributor and stock trader Chip Simonton. We asked for his assessment of the market generally in cyber stocks specifically. So we'll summarize right here. We've kind of moved on from a narrative of the sky is falling to one where the glass is half empty you know, and before today's big selloff it was looking more and more like glass half full. The SNAP miss has dragged down many of the big names that comprise the major indices. You know, earning season as always brings heightened interest and this time we're seeing many cross currents. It starts as usual with the banks and the money centers. With the exception of JP Morgan the numbers were pretty good according to Simonton. Investment banks were not so great with Morgan and Goldman missing estimates but in general, pretty positive outlooks. But the market also shrugged off IBM's growth. And of course, social media because of SNAP is getting hammered today. The question is no longer recession or not but rather how deep the recession will be. And today's PMI data was the weakest since the start of the pandemic. Bond yields continue to weaken and there's a growing consensus that Fed tightening may be over after September as commodity prices weaken. Now gas prices of course are still high but they've come down. Tesla, Nokia and AT&T all indicated that supply issues were getting better which is also going to help with inflation. So it's no shock that the NASDAQ has done pretty well as beaten down as tech stocks started to look oversold you know, despite today's sell off. But AT&T and Verizon, they blamed their misses in part on people not paying their bills on time. SNAP's huge miss even after guiding lower and then refusing to offer future guidance took that stock down nearly 40% today and other social media stocks are off on sympathy. Meta and Google were off, you know, over 7% at midday. I think at one point hit 14% down and Google, Meta and Twitter have all said they're freezing new hires. So we're starting to see according to Simonton for the first time in a long time, the lower income, younger generation really feeling the pinch of inflation. Along of course with struggling families that have to choose food and shelter over discretionary spend. Now back to the NASDAQ for a moment. As we've been reporting back in mid-June and NASDAQ was off nearly 33% year to date and has since rallied. It's now down about 25% year to date as of midday today. But as I say, it had been, you know much deeper back in early June. But it's broken that downward trend that we talked about where the highs are actually lower and the lows are lower. That's started to change for now anyway. We'll see if it holds. But chip stocks, software stocks, and of course the cyber names have broken those down trends and have been trading above their 50 day moving averages for the first time in around four months. And again, according to Simonton, we'll see if that holds. If it does, that's a positive sign. Now remember on June 24th, we recorded a Breaking Analysis and talked about Qualcomm trading at a 12 X multiple with an implied 15% growth rate. On that day the stock was 124 and it surpassed 155 earlier this month. That was a really good call by Simonton. So looking at some of the cyber players here SailPoint is of course the anomaly with the Thoma Bravo 7 billion acquisition of the company holding that stock up. But the Bug ETF of basket of cyber stocks has definitely improved. When we last reported on cyber in May, CrowdStrike was off 23% year to date. It's now off 4%. Palo Alto has held steadily. Okta is still underperforming its peers as it works through the fallout from the breach and the ingestion of its Auth0 acquisition. Meanwhile, Zscaler and SentinelOne, those high flyers are still well off year to date, with Ping Identity and CyberArk not getting hit as hard as their valuations hadn't run up as much. But virtually all these tech stocks generally in cyber issues specifically, they've been breaking their down trend. So it will now come down to earnings guidance in the coming months. But the SNAP reaction is quite stunning. I mean, the environment is slowing, we know that. Ad spending gets cut in that type of market, we know that too. So it shouldn't be a huge surprise to anyone but as Chip Simonton says, this shows that sellers are still in control here. So it's going to take a little while to work through that despite the positive signs that we're seeing. Okay. We also turned to our friend Eric Bradley from ETR who follows these markets quite closely. He frequently interviews CISOs on his program, on his round tables. So we asked to get his take and here's what ETR is saying. Again, as we've reported while CIOs and IT buyers have tempered spending expectations since December and early January when they called for an 8% plus spending growth, they're still expecting a six to seven percent uptick in spend this year. So that's pretty good. Security remains the number one priority and also is the highest ranked sector in the ETR data set when you measure in terms of pervasiveness in the study. Within security endpoint detection and extended detection and response along with identity and privileged account management are the sub-sectors with the most spending velocity. And when you exclude Microsoft which is just dominant across the board in so many sectors, CrowdStrike has taken over the number one spot in terms of spending momentum in ETR surveys with CyberArk and Tanium showing very strong as well. Okta has seen a big dropoff in net score from 54% last survey to 45% in July as customers maybe put a pause on new Okta adoptions. That clearly shows in the survey. We'll talk about that in a moment. Look Okta still elevated in terms of spending momentum, but it doesn't have the dominant leadership position it once held in spend velocity. Year on year, according to ETR, Tenable and Elastic are seeing the biggest jumps in spending momentum, with SailPoint, Tanium, Veronis, CrowdStrike and Zscaler seeing the biggest jump in new adoptions since the last survey. Now on the downside, SonicWall, Symantec, Trellic which is McAfee, Barracuda and TrendMicro are seeing the highest percentage of defections and replacements. Let's take a deeper look at what the ETR data tells us about the cybersecurity space. This is a popular view that we like to share with net score or spending momentum on the Y axis and overlap or pervasiveness in the data on the X axis. It's a measure of presence in the data set we used to call it market share. With the data, the dot positions, you see that little inserted table, that's how the dots are plotted. And it's important to note that this data is filtered for firms with at least 100 Ns in the survey. That's why some of the other ones that we mentioned might have dropped off. The red dotted line at 40% that indicates highly elevated spending momentum and there are several firms above that mark including of course, Microsoft, which is literally off the charts in both dimensions in the upper right. It's quite incredible actually. But for the rest of the pack, CrowdStrike has now taken back its number one net score position in the ETR survey. And CyberArk and Okta and Zscaler, CloudFlare and Auth0 now Okta through the acquisition, are all above the 40% mark. You can stare at the data at your leisure but I'll just point out, make three quick points. First Palo Alto continues to impress and as steady as she goes. Two, it's a very crowded market still and it's complicated space. And three there's lots of spending in different pockets. This market has too many tools and will continue to consolidate. Now I'd like to drill into a couple of firms net scores and pick out some of the pure plays that are leading the way. This series of charts shows the net score or spending velocity or granularity for Okta, CrowdStrike, Zscaler and CyberArk. Four of the top pure plays in the ETR survey that also have over a hundred responses. Now the colors represent the following. Bright red is defections. We're leaving the platform. The pink is we're spending less, meaning we're spending 6% or worse. The gray is flat spend plus or minus 5%. The forest green is spending more, i.e, 6% or more and the lime green is we're adding the platform new. That red dotted line at the 40% net score mark is the same elevated level that we like to talk about. All four are above that target. Now that blue line you see there is net score. The yellow line is pervasiveness in the data. The data shown in each bar goes back 10 surveys all the way back to January 2020. First I want to call out that all four again are seeing down trends in spending momentum with the whole market. That's that blue line. They're seeing that this quarter, again, the market is off overall. Everybody is kind of seeing that down trend for the most part. Very few exceptions. Okta is being hurt by fewer new additions which is why we highlighted in red, that red dotted area, that square that we put there in the upper right of that Okta bar. That lime green, new ads are off as well. And the gray for Okta, flat spending is noticeably up. So it feels like people are pausing a bit and taking a breather for Okta. And as we said earlier, perhaps with the breach earlier this year and the ingestion of Auth0 acquisition the company is seeing some friction in its business. Now, having said that, you can see Okta's yellow line or presence in the data set, continues to grow. So it's a good proxy from market presence. So Okta remains a leader in identity. So again, I'll let you stare at the data if you want at your leisure, but despite some concerns on declining momentum, notice this very little red at these companies when it comes to the ETR survey data. Now one more data slide which brings us to our four star cyber firms. We started a tradition a few years ago where we sorted the ETR data by net score. That's the left hand side of this graphic. And we sorted by shared end or presence in the data set. That's the right hand side. And again, we filtered by companies with at least 100 N and oh, by the way we've excluded Microsoft just to level the playing field. The red dotted line signifies the top 10. If a company cracks the top 10 in both spending momentum and presence, we give them four stars. So Palo Alto, CrowdStrike, Okta, Fortinet and Zscaler all made the cut this time. Now, as we pointed out in May if you combined Auth0 with Okta, they jumped to the number two on the right hand chart in terms of presence. And they would lead the pure plays there although it would bring down Okta's net score somewhat, as you can see, Auth0's net score is lower than Okta's. So when you combine them it would drag that down a little bit but it would give them bigger presence in the data set. Now, the other point we'll make is that Proofpoint and Splunk both dropped off the four star list this time as they both saw marked declines in net score or spending velocity. They both got four stars last quarter. Okay. We're going to close on what to expect at re:Inforce this coming week. Re:Inforce, if you don't know, is AWS's security event. They first held it in Boston back in 2019. It's dedicated to cloud security. The past two years has been virtual and they announced that reinvent that it would take place in Houston in June, which everybody said, that's crazy. Who wants to go to Houston in June and turns out nobody did so they postponed the event, thankfully. And so now they're back in Boston, starting on Monday. Not that it's going to be much cooler in Boston. Anyway, Steven Schmidt had been the face of AWS security at all these previous events as the Chief Information Security Officer. Now he's dropped the I from his title and is now the Chief Security Officer at Amazon. So he went with Jesse to the mothership. Presumably he dropped the I because he deals with physical security now too, like at the warehouses. Not that he didn't have to worry about physical security at the AWS data centers. I don't know. Anyway, he and CJ Moses who is now the new CISO at AWS will be keynoting along with some others including MongoDB's Chief Information Security Officer. So that should be interesting. Now, if you've been following AWS you'll know they like to break things down into, you know, a couple of security categories. Identity, detection and response, data protection slash privacy slash GRC which is governance, risk and compliance, and we would expect a lot more talk this year on container security. So you're going to hear also product updates and they like to talk about how they're adding value to services and try to help, they try to help customers understand how to apply services. Things like GuardDuty, which is their threat detection that has machine learning in it. They'll talk about Security Hub, which centralizes views and alerts and automates security checks. They have a service called Detective which does root cause analysis, and they have tools to mitigate denial of service attacks. And they'll talk about security in Nitro which isolates a lot of the hardware resources. This whole idea of, you know, confidential computing which is, you know, AWS will point out it's kind of become a buzzword. They take it really seriously. I think others do as well, like Arm. We've talked about that on previous Breaking Analysis. And again, you're going to hear something on container security because it's the hottest thing going right now and because AWS really still serves developers and really that's what they're trying to do. They're trying to enable developers to design security in but you're also going to hear a lot of best practice advice from AWS i.e, they'll share the AWS dogfooding playbooks with you for their own security practices. AWS like all good security practitioners, understand that the keys to a successful security strategy and implementation don't start with the technology, rather they're about the methods and practices that you apply to solve security threats and a top to bottom cultural approach to security awareness, designing security into systems, that's really where the developers come in, and training for continuous improvements. So you're going to get heavy doses of really strong best practices and guidance and you know, some good preaching. You're also going to hear and see a lot of partners. They'll be very visible at re:Inforce. AWS is all about ecosystem enablement and AWS is going to host close to a hundred security partners at the event. This is key because AWS doesn't do it all. Interestingly, they don't even show up in the ETR security taxonomy, right? They just sort of imply that it's built in there even though they have a lot of security tooling. So they have to apply the shared responsibility model not only with customers but partners as well. They need an ecosystem to fill gaps and provide deeper problem solving with more mature and deeper security tooling. And you're going to hear a lot of positivity around how great cloud security is and how it can be done well. But the truth is this stuff is still incredibly complicated and challenging for CISOs and practitioners who are understaffed when it comes to top talent. Now, finally, theCUBE will be at re:Inforce in force. John Furry and I will be hosting two days of broadcast so please do stop by if you're in Boston and say hello. We'll have a little chat, we'll share some data and we'll share our overall impressions of the event, the market, what we're seeing, what we're learning, what we're worried about in this dynamic space. Okay. That's it for today. Thanks for watching. Thanks to Alex Myerson, who is on production and manages the podcast. Kristin Martin and Cheryl Knight, they helped get the word out on social and in our newsletters and Rob Hoff is our Editor in Chief over at siliconangle.com. You did some great editing. Thank you all. Remember all these episodes they're available, this podcast. Wherever you listen, all you do is search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. You can get in touch with me by emailing avid.vellante@siliconangle.com or DM me @dvellante, or comment on my LinkedIn post and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching and we'll see you in Boston next week if you're there or next time on Breaking Analysis (soft music)

>> Announcer: theCube presents HPE Discover 2022 brought to you by HPE. >> Hello and welcome back to theCube's continuous coverage HPE Discover 2022 and from Las Vegas the formerly Sands Convention Center now Venetian, John Furrier and Dave Vellante here were excited to welcome in Jen Huffstetler. Who's the Chief product Sustainability Officer at Intel Jen, welcome to theCube thanks for coming on. >> Thank you very much for having me. >> You're really welcome. So you dial back I don't know, the last decade and nobody really cared about it but some people gave it lip service but corporations generally weren't as in tune, what's changed? Why has it become so top of mind? >> I think in the last year we've noticed as we all were working from home that we had a greater appreciation for the balance in our lives and the impact that climate change was having on the world. So I think across the globe there's regulations industry and even personally, everyone is really starting to think about this a little more and corporations specifically are trying to figure out how are they going to continue to do business in these new regulated environments. >> And IT leaders generally weren't in tune cause they weren't paying the power bill for years it was the facilities people, but then they started to come together. How should leaders in technology, business tech leaders, IT leaders, CIOs, how should they be thinking about their sustainability goals? >> Yeah, I think for IT leaders specifically they really want to be looking at the footprint of their overall infrastructure. So whether that is their on-prem data center, their cloud instances, what can they do to maximize the resources and lower the footprint that they contribute to their company's overall footprint. So IT really has a critical role to play I think because as you'll find in IT, the carbon footprint of the data center of those products in use is actually it's fairly significant. So having a focus there will be key. >> You know compute has always been one of those things where, you know Intel's been makes chips so that, you know heat is important in compute. What is Intel's current goals? Give us an update on where you guys are at. What's the ideal goal in the long term? Where are you now? You guys always had a focus on this for a long, long time. Where are we now? Cause I won't say the goalpost of changed, they're changing the definitions of what this means. What's the current state of Intel's carbon footprint and overall goals? >> Yeah, no thanks for asking. As you mentioned, we've been invested in lowering our environmental footprint for decades in fact, without action otherwise, you know we've already lowered our carbon footprint by 75%. So we're really in that last mile. And that is why when we recently announced a very ambitious goal Net-Zero 2040 for our scope one and two for manufacturing operations, this is really an industry leading goal. And partly because the technology doesn't even exist, right? For the chemistries and for making the silicon into the sand into, you know, computer chips yet. And so by taking this bold goal, we're going to be able to lead the industry, partner with academia, partner with consortia, and that drive is going to have ripple effects across the industry and all of the components in semiconductors. >> Is there a changing definition of Net-Zero? What that means, cause some people say they're Net-Zero and maybe in one area they might be but maybe holistically across the company as it becomes more of a broader mandate society, employees, partners, Wall Street are all putting pressure on companies. Is the Net-Zero conversation changed a little bit or what's your view on that? >> I think we definitely see it changing with changing regulations like those coming forth from the SEC here in the US and in Europe. Net-Zero can't just be lip service anymore right? It really has to be real reductions on your footprint. And we say then otherwise and even including in our supply chain goals what we've taken new goals to reduce, but our operations are growing. So I think everybody is going through this realization that you know, with the growth, how do we keep it lower than it would've been otherwise, keep focusing on those reductions and have not just renewable credits that could have been bought in one location and applied to a different geographical location but real credible offsets for where the the products manufactured or the computes deployed. >> Jen, when you talk about you've reduced already by 75% you're on that last mile. We listened to Pat Gelsinger very closely up until recently he was the number one most frequently had on theCube guest. He's been busy I guess. But as you apply that discipline to where you've been, your existing business and now Pat's laid out this plan to increase the Foundry business how does that affect your... Are you able to carry through that reduction to, you know, the new foundries? Do you have to rethink that? How does that play in? >> Certainly, well, the Foundry expansion of our business with IBM 2.0 is going to include the existing factories that already have the benefit of those decades of investment and focus. And then, you know we have clear goals for our new factories in Ohio, in Europe to achieve goals as well. That's part of the overall plan for Net-Zero 2040. It's inclusive of our expansion into Foundry which means that many, many many more customers are going to be able to benefit from the leadership that Intel has here. And then as we onboard acquisitions as any company does we need to look at the footprint of the acquisition and see what we can do to align it with our overall goals. >> Yeah so sustainable IT I don't know for some reason was always an area of interest to me. And when we first started, even before I met you, John we worked with PG&E to help companies get rebates for installing technologies that would reduce their carbon footprint. >> Jen: Very forward thinking. >> And it was a hard thing to get, you know, but compute was the big deal. And there were technologies and I remember virtualization at the time was one and we would go in and explain to the PG&E engineers how that all worked. Cause they had metrics and that they wanted to see, but anyway, so virtualization was clearly one factor. What are the technologies today that people should be paying, flash storage was another one. >> John: AI's going to have a big impact. >> Reduce the spinning disk, but what are the ones today that are going to have an impact? >> Yeah, no, that's a great question. We like to think of the built in acceleration that we have including some of the early acceleration for virtualization technologies as foundational. So built in accelerated compute is green compute and it allows you to maximize the utilization of the transistors that you already have deployed in your data center. This compute is sitting there and it is ready to be used. What matters most is what you were talking about, John that real world workload performance. And it's not just you know, a lot of specsmanship around synthetic benchmarks, but AI performance with the built in acceleration that we have in Xeon processors with the Intel DL Boost, we're able to achieve four X, the AI performance per Watts without you know, doing that otherwise. You think about the consolidation you were talking about that happened with virtualization. You're basically effectively doing the same thing with these built in accelerators that we have continued to add over time and have even more coming in our Sapphire Generation. >> And you call that green compute? Or what does that mean, green compute? >> Well, you are greening your compute. >> John: Okay got it. >> By increasing utilization of your resources. If you're able to deploy AI, utilize the telemetry within the CPU that already exists. We have customers KDDI in Japan has a great Proofpoint that they already announced on their 5G data center, lowered their data center power by 20%. That is real bottom line impact as well as carbon footprint impact by utilizing all of those built in capabilities. So, yeah. >> We've heard some stories earlier in the event here at Discover where there was some cooling innovations that was powering moving the heat to power towns and cities. So you start to see, and you guys have been following this data center and been part of the whole, okay and hot climates, you have cold climates, but there's new ways to recycle energy where's that cause that sounds very Sci-Fi to me that oh yeah, the whole town runs on the data center exhaust. So there's now systems thinking around compute. What's your reaction to that? What's the current view on re-engineering a system to take advantage of that energy or recycling? >> I think when we look at our vision of sustainable compute over this horizon it's going to be required, right? We know that compute helps to solve society's challenges and the demand for it is not going away. So how do we take new innovations looking at a systems level as compute gets further deployed at the edge, how do we make it efficient? How do we ensure that that compute can be deployed where there is air pollution, right? So some of these technologies that you have they not only enable reuse but they also enable some you know, closing in of the solution to make it more robust for edge deployments. It'll allow you to place your data center wherever you need it. It no longer needs to reside in one place. And then that's going to allow you to have those energy reuse benefits either into district heating if you're in, you know Northern Europe or there's examples with folks putting greenhouses right next to a data center to start growing food in what we're previously food deserts. So I don't think it's science fiction. It is how we need to rethink as a society. To utilize everything we have, the tools at our hand. >> There's a commercial on the radio, on the East Coast anyway, I don't know if you guys have heard of it, it's like, "What's your one thing?" And the gentleman comes on, he talks about things that you can do to help the environment. And he says, "What's your one thing?" So what's the one thing or maybe it's not just one that IT managers should be doing to affect carbon footprint? >> The one thing to affect their carbon footprint, there are so many things. >> Dave: Two, three, tell me. >> I think if I was going to pick the one most impactful thing that they could do in their infrastructure is it's back to John's comment. It's imagine if the world deployed AI, all the benefits not only in business outcomes, you know the revenue, lowering the TCO, but also lowering the footprint. So I think that's the one thing they could do. If I could throw in a baby second, it would be really consider how you get renewable energy into your computing ecosystem. And then you know, at Intel, when we're 80% renewable power, our processors are inherently low carbon because of all the work that we've done others have less than 10% renewable energy. So you want to look for products that have low carbon by design, any Intel based system and where you can get renewables from your grid to ask for it, run your workload there. And even the next step to get to sustainable computing it's going to take everyone, including every enterprise to think differently and really you know, consider what would it look like to bring renewables onto my site? If I don't have access through my local utility and many customers are really starting to evaluate that. >> Well Jen its great to have you on theCube. Great insight into the current state of the art of sustainability and carbon footprint. My final question for you is more about the talent out there. The younger generation coming in I'll say the pressure, people want to work for a company that's mission driven we know that, the Wall Street impact is going to be financial business model and then save the planet kind of pressure. So there's a lot of talent coming in. Is there awareness at the university level? Is there a course where can, do people get degrees in sustainability? There's a lot of people who want to come into this field what are some of the talent backgrounds of people learning or who might want to be in this field? What would you recommend? How would you describe how to onboard into the career if they want to contribute? What are some of those factors? Cause it's not new, new, but it's going to be globally aware. >> Yeah well there certainly are degrees with focuses on sustainability maybe to look at holistically at the enterprise, but where I think the globe is really going to benefit, we didn't really talk about the software inefficiency. And as we delivered more and more compute over the last few decades, basically the programming languages got more inefficient. So there's at least 35% inefficiency in the software. So being a software engineer, even if you're not an AI engineer. So AI would probably be the highest impact being a software engineer to focus on building new applications that are going to be efficient applications that they're well utilizing the transistor that they're not leaving zombie you know, services running that aren't being utilized. So I actually think-- >> So we got a program in assembly? (all laughing) >> (indistinct), would get really offended. >> Get machine language. I have to throw that in sorry. >> Maybe not that bad. (all laughing) >> That's funny, just a joke. But the question is what's my career path. What's a hot career in this area? Sustainability, AI totally see that. Anything else, any other career opportunities you see or hot jobs or hot areas to work on? >> Yeah, I mean, just really, I think it takes every architect, every engineer to think differently about their design, whether it's the design of a building or the design of a processor or a motherboard we have a whole low carbon architecture, you know, set of actions that are we're underway that will take to the ecosystem. So it could really span from any engineering discipline I think. But it's a mindset with which you approach that customer problem. >> John: That system thinking, yeah. >> Yeah sustainability designed in. Jen thanks so much for coming back in theCube, coming on theCube. It's great to have you. >> Thank you. >> All right. Dave Vellante for John Furrier, we're sustaining theCube. We're winding down day three, HPE Discover 2022. We'll be right back. (upbeat music)

(inspiring music) >> Announcer: theCUBE presents HPE Discover 2022. Brought to you by HPE. >> Welcome back to theCUBE's coverage of HPE Discover 2022, from the Venetian in Las Vegas, the first Discover since 2019. I really think this is my 14th Discover, when you include HP, when you include Europe. And I got to say this Discover, I think has more energy than any one that I've ever seen, about 8,000 people here. Really excited to have one of HPE's longstanding partners, Veeam CTO, Danny Allen is here, joined by David Harvey, Vice President of Strategic Alliances at Veeam. Guys, good to see you again. It was just earlier, let's see, last month, we were together out here. >> Yeah, just a few weeks ago. It's fantastic to be back and what it's telling us, technology industry is coming back. >> And the events business, of course, is coming back, which we love. I think the expectations were cautious. You saw it at VeeamON, a little more than you expected, a lot of great energy. A lot of people, 'cause it was last month, it was their first time out, >> Yes. >> in two years. Here, I think people have started to go out more, but still, an energy that's palpable. >> You can definitely feel it. Last night, I think I went to four consecutive events and everyone's out having those discussions and having conversations, it's good to be back. >> You guys hosted the Storage party last night, which is epic. I left at midnight, I took a picture, it was still packed. I said, okay, time to go, nothing good happens after midnight kids. David, talk about the alliance with HPE, how it's evolved, and where you see it going? >> I appreciate it, and certainly this, as you said, has been a big alliance for us. Over 10 years or so, fantastic integrations across the board. And you touched on 2019 Discover. We launched with GreenLake at that event, we were one of the launch partners, and we've seen fantastic growth. Overall, what we're excited about, is that continuation of the movement of the customer's buying patterns in line with HPE's portfolio and in line with Veeam. We continue to be with all their primary, secondary storage, we continue to be a spearhead position with GreenLake, which we're really excited about. And we're also really excited to hear from HPE, unfortunately under NDA, some of their future stuff they're investing in, which is a really nice invigoration for what they're doing for their portfolio. And we see that being a big deal for us over the next 24 months. >> Your relationship with HPE predates the HP, HPE split. >> Mmm. >> Yes. >> But it was weird, because they had Data Protector, and that was a quasi-competitor, or really not, but it was a competitor, a legacy competitor, of what you guys have, kind of modern data protection I think is the tagline, if I got it right. Post the split, that was an S-curve moment, wasn't it, in terms of the partnership? >> It really was. If you go back 10 years, we did our first integration sending data to StoreOnce and we had some blueprints around that. But now, if you look what we have, we have integrations on the primary side, so, 3PAR, Primera, Nimble, all their top-tier storage, we can manage the snapshots. We have integration on the target side. We integrate with Catalyst in the movement of data and the management of data. And, as David alluded to, we integrate with GreenLake. So, customers who want to take this as a consumption model, we integrate with that. And so it's been, like you said, the strongest relationship that we have on the technology alliance side. >> So, V12, you announced at VeeamON. What does that mean for HPE customers, the relationship? Maybe you guys could both talk about that. >> Technology side, to touch on a few things that we're doing with them, ransomware has been a huge issue. Security's been a big theme, obviously, at the conference, >> Dave: Yeah, you bet. and one of the things we're doing in V12 is adding immutability for both StoreOnce and StoreEver. So, we take the features that our partners have, immutability being big in the security space, and we integrate that fully into the product. So a customer checks a box and says, hey, I want to make sure that the data is secure. >> Yeah, and also, it's another signification about the relationship. Every single release we've done has had HPE at the heart of it, and the same thing is being said with V12. And it shows to our customers, the continual commitment. Relationships come and go. They're hard, and the great news is, 10 years has proven that we get through good times and tricky situations, and we both continue to invest, et cetera. And I think there's a lot of peace of mind and the revenue figures prove that, which is what we're really excited about. >> Yeah I want to come back to that, but just to follow up, Danny, on that immutability, that's a feature that you check? It's service within GreenLake, or within Veeam? How does that all work? >> We have immutability now depending on the target. We introduced the ability to send data, for example, into S3 two years ago, and make it immutable when you send it to an S3 or S3 compatible environment. We added, in Version 11, the ability to take a Linux repository and make it, and harden it, essentially make it immutable. But what we're doing now is taking our partner systems like StoreOnce, like StoreEver, and when we send data there, we take advantage of an API flag or whatever it happens to be, that it makes the data, when it's written to that system, can't be deleted, can't be encrypted. Now, what does that mean for a customer? Well, we do all the hard work in the back end, it's just a check box. They say, I want to make it immutable, and we manage how long it's immutable. Because if you made everything immutable forever, that's hugely expensive, right? So, it's all about, how long is that immutable before you age it out and make sure the new data coming in is immutable. >> Dave: It's like an insurance policy, you have that overlap. >> Yes. >> Right, okay. And then David, you mentioned the revenue, Lou bears that out. I got the IDC guys comin' on later on today. I'll ask 'em about that, if that's their swim lane. But you guys are basically a statistical tie, with Dell for number one? Am I getting that right? And you're growing at a faster rate, I believe, it's hard to tell 'cause I don't think Dell reports on the pace of its growth within data protection. You guys obviously do, but is that right? It's a statistical tie, is it? >> Yeah, hundred percent. >> Yeah, statistical tie for first place, which we're super excited about. When I joined Veeam, I think we were in fifth place, but we've been in the leader's quadrant of the Gartner Magic- >> Cause and effect there or? (panelists laughing) >> No, I don't think so. >> Dave: Ha, I think maybe. >> We've been on a great trajectory. But statistical tie for first place, greatest growth sequentially, and year-over-year, of all of the data protection vendors. And that's a testament not just to the technology that we're doing, but partnerships with HPE, because you never do this, the value of a technology is not that technology alone, it's the value of that technology within the ecosystem. And so that's why we're here at HPE Discover. It's our joint technology solutions that we're delivering. >> What are your thoughts or what are you seeing in the field on As-a-service? Because of course, the messaging is all about As-a-service, you'd think, oh, a hundred percent of everything is going to be As-a-service. A lot of customers, they don't mind CapEx, they got good, balance sheet, and they're like, hey, we'll take care of this, and, we're going to build our own little internal cloud. But, what are you seeing in the market in terms of As-a-service, versus, just traditional licensing models? >> Certainly, there's a mix between the two. What I'd say, is that sources that are already As-a-service, think Microsoft 365, think AWS, Azure, GCP, the cloud providers. There's a natural tendency for the customer to want the data protection As-a-service, as well for those. But if you talk about what's on premises, customers who have big data centers deployed, they're not yet, the pendulum has not shifted for that to be data protection As-a-service. But we were early to this game ourselves. We have 10,000, what we call, Veeam Cloud Service Providers, that are offering data protection As-a-service, whether it be on premises, so they're remotely managing it, or cloud hosted, doing data protection for that. >> So, you don't care. You're providing the technology, and then your customers are actually choosing the delivery model. Is that correct? >> A hundred percent, and if you think about what GreenLake is doing for example, that started off as being a financial model, but now they're getting into that services delivery. And what we want to do is enable them to deliver it, As-a-service, not just the financial model, but the outcome for the customer. And so our technology, it's not just do backup, it's do backup for a multi-tenant, multi-customer environment that does all of the multi-tenancy and billing and charge back as part of that service. >> Okay, so you guys don't report on this, but I'm going to ask the question anyway. You're number one now, let's call you, let's declare number one, 'cause we're well past that last reporting and you're growin' faster. So go another quarter, you're now number one, so you're the largest. Do you spend more on R&D in data protection than any other company? >> Yes, I'm quite certain that we do. Now, we have an unfair advantage because we have 450,000 customers. I don't think there's any other data protection company out there, the size and scope and scale, that we have. But we've been expanding, our largest R&D operation center's in Prague, it's in Czech Republic, but we've been expanding that. Last year it grew 40% year on year in R&D, so big investment in that space. You can see this just through our product space. Five years ago, we did data protection of VMware only, and now we do all the virtual environments, all the physical environments, all the major cloud environments, Kubernetes, Microsoft 365, we're launching Salesforce. We announced that at VeeamON last month and it will be coming out in Q3. All of that is coming from our R&D investments. >> A lot of people expect that when a company like Insight, a PE company, purchases a company like Veeam, that one of the things they'll dial down is R&D. That did not happen in this case. >> No, they very much treat us as a growth company. We had 22% year-over-year growth in 2020, and 25% year-over-year last year. The growth has been tremendous, they continue to give us the freedom. Now, I expect they'll want returns like that continuously, but we have been delivering, they have been investing. >> One of my favorite conversations of the year was our supercloud conversation, which was awesome, thank you for doing that with me. But that's clearly an area of focus, what we call supercloud, and you don't use that term, I know, you do sometimes, but it's not your marketing, I get that. But that is an R&D intensive effort, is it not? To create that common experience. And you see HPE, attempting to do that as well, across all these different estates. >> A hundred percent. We focus on three things, I always say, our differentiators, simplicity, flexibility, and reliability. Making it simple for the customers is not an easy thing to do. Making that checkbox for immutability? We have to do a lot behind the scenes to make it simple. Same thing on flexibility. We don't care if they're using 3PAR, Primera, Nimble, whatever you want to choose as the primary storage, we will take that out of your hands and make it really easy. You mentioned supercloud. We don't care what the cloud infrastructure, it can be on GreenLake, it can be on AWS, can be on Azure, it can be on GCP, it can be on IBM cloud. It is a lot of effort on our part to abstract the cloud infrastructure, but we do that on behalf of our customers to take away that complexity, it's part of our platform. >> Quick follow-up, and then I want to ask a question of David. I like talking to you guys because you don't care where it is, right? You're truly agnostic to it all. I'm trying to figure out this repatriation thing, cause I hear a lot of hey, Dave, you should look into repatriation that's happened all over the place, and I see pockets of it. What are you seeing in terms of repatriation? Have customers over-rotated to the cloud and now they're pullin' back a little bit? Or is it, as I'm claiming, in pockets? What's your visibility on that? >> Three things I see happening. There's the customers who lifted up their data center, moved it into the cloud and they get the first bill. >> (chuckling) Okay. >> And they will repatriate, there's no question. If I talk to those customers who simply lifted up and moved it over because the CIO told them to, they're moving it back on premises. But a second thing that we see is people moving it over, with tweaks. So they'll take their SQL server database and they'll move it into RDS, they'll change some things. And then you have people who are building cloud-native, they're never coming back on premises, they are building it for the cloud environment. So, we see all three of those. We only really see repatriation on that first scenario, when they get that first bill. >> And when you look at the numbers, I think it gets lost, 'cause you see the cloud is growing so fast. So David, what are the conversations like? You had several events last night, The Veeam party, slash Storage party, from HPE. What are you hearing from your alliance partners and the customers at the event. >> I think Danny touched on that point, it's about philosophy of evolution. And I think at the end of the day, whether we're seeing it with our GSI alliances we've got out there, or with the big enterprise conversations we're having with HPE, it's about understanding which workloads they want to move. In our mind, the customers are getting much smarter in making that decision, rather than experimenting. They're really taking a really solid look. And the work we're doing with the GSIs on workplace modernization, data center transformation, they're really having that investment work up front on the workloads, to be able to say, this works for me, for my personality and my company. And so, to the point about movement, it's more about decisive decision at the start, and not feeling like the remit is, I have to do one thing or another, it's about looking at that workflow position. And that's what we've seen with the revenue part as well. We've seen our movement to GreenLake tremendously grow in the last 18 months to two years. And from our GSI work as well, we're seeing the types of conversations really focus on that workload, compared to, hey, I just need a backup solution, and that's really exciting. >> Are you having specific conversations about security, or is it a data protection conversation still, (David chuckles) that's an adjacency to security? >> That's a great question. And I think it's a complex one, because if you come to a company like Veeam, we are there, and you touched on it before, we provide a solution when something has happened with security. We're not doing intrusion detection, we're not doing that barrier position at the end of it, but it's part of an end-to-end assumption. And I don't think that at this particular point, I started in security with RSA and Check Point, it was about layers of protection. Now it's layers of protection, and the inevitability that at some point something will happen, so about the recovery. So the exciting conversations we're having, especially with the big enterprises, is not about the fear factor, it's about, at some point something's going to occur. Speed of recovery is the conversation. And so for us, and your question is, are they talking to us about security, or more, the continuity position? And that's where the synergy's getting a lot simpler, rather than a hard demark between security and backup. >> Yeah, when you look at the stock market, everything's been hit, but security, with the exception of Okta, 'cause it got that weird benign hack, but security, generally, is an area that CIOs have said, hey, we can't really dial that back. We can maybe, some other discretionary stuff, we'll steal and prioritize. But security seems to be, and I think data protection is now part of that discussion. You're not a security company. We've seen some of your competitors actually pivot to become security companies. You're not doing that, but it's very clearly an adjacency, don't you think? >> It's an adjacency, and it's a new conversation that we're having with the Chief Information Security Officer. I had a meeting an hour ago with a customer who was hit by ransomware, and they got the call at 2:00 AM in the morning, after the ransomware they recovered their entire portfolio within 36 hours, from backups. Didn't even contact Veeam, I found out during this meeting. But that is clearly something that the Chief Information Security Officer wants to know about. It's part of his purview, is the recovery of that data. >> And they didn't pay the ransom? >> And they did not pay the ransom, not a penny. >> Ahh, we love those stories. Guys, thanks so much for coming on theCUBE. Congratulations on all the success. Love when you guys come on, and it was such a fun event at VeeamON. Great event here, and your presence is, was seen. The Veeam green is everywhere, so appreciate your time. >> Thank you. >> Thanks, Dave. >> Okay, and thank you for watching. This is Dave Vellante for John Furrier and Lisa Martin. We'll be back right after this short break. You're watching theCUBE's coverage of HPE Discover 2022, from Las Vegas. (inspiring music)

(upbeat music) >> Hello, and welcome back to AWS Startup Showcase, I'm John Furrier, your host. This is the Hero panel, the AWS Heroes. These are folks that have a lot of experience in Open Source, having fun building great projects and commercializing the value and best practices of Open Source innovation. We've got some great guests here. Liz Rice, Chief Open Source Officer, Isovalent. CUBE alumni, great to see you. Brian LeRoux, who is the Co-founder and CTO of begin.com. Erica Windisch who's an Architect for Developer Experience. AWS Hero, also CUBE alumni. Casey Lee, CTO Gaggle. Doing some great stuff in ed tech. Great collection of experts and experienced folks doing some fun stuff, welcome to this conversation this CUBE panel. >> Hi. >> Thanks for having us. >> Hello. >> Let's go down the line. >> I don't normally do this, but since we're remote and we have such great guests, go down the line and talk about why Open Source is important to you guys. What projects are you currently working on? And what's the coolest thing going on there? Liz we'll start with you. >> Okay, so I am very involved in the world of Cloud Native. I'm the chair of the technical oversight committee for the Cloud Native Computing Foundation. So that means I get to see a lot of what's going on across a very broad range of Cloud Native projects. More specifically, Isovalent. I focus on Cilium, which is it's based on a technology called EBPF. That is to me, probably the most exciting technology right now. And then finally, I'm also involved in an organization called OpenUK, which is really pushing for more use of open technologies here in the United Kingdom. So spread around lots of different projects. And I'm in a really fortunate position, I think, to see what's happening with lots of projects and also the commercialization of lots of projects. >> Awesome, Brian what project are you working on? >> Working project these days called Architect. It's a Open Source project built on top of AWSM. It adds a lot of sugar and terseness to the SM experience and just makes it a lot easier to work with and get started. AWS can be a little bit intimidating to people at times. And the Open Source community is stepping up to make some of that bond ramp a little bit easier. And I'm also an Apache member. And so I keep a hairy eyeball on what's going on in that reality all the time. And I've been doing this open-source thing for quite a while, and yeah, I love it. It's a great thing. It's real science. We get to verify each other's work and we get to expand and build on human knowledge. So that's a huge honor to just even be able to do that and I feel stoked to be here so thanks for having me. >> Awesome, yeah, and totally great. Erica, what's your current situation going on here? What's happening? >> Sure, so I am currently working on developer experience of a number of Open Source STKS and CLI components from my current employer. And previously, recently I left New Relic where I was working on integrating with OpenTelemetry, as well as a number of other things. Before that I was a maintainer of Docker and of OpenStack. So I've been in this game for a while as well. And I tend to just put my fingers in a lot of little pies anywhere from DVD players 20 years ago to a lot of this open telemetry and monitoring and various STKs and developer tools is where like Docker and OpenStack and the STKs that I work on now, all very much focusing on developer as the user. >> Yeah, you're always on the wave, Erica great stuff. Casey, what's going on? Do you got some great ed techs happening? What's happening with you? >> Yeah, sure. The primary Open Source project that I'm contributing to right now is ACT. This is a tool I created a couple of years back when GitHub Actions first came out, and my motivation there was I'm just impatient. And that whole commit, push, wait time where you're testing out your pipelines is painful. And so I wanted to build a tool that allowed developers to test out their GitHub Actions workflows locally. And so this tool uses Docker containers to emulate, to get up action environment and gives you fast feedback on those workflows that you're building. Lot of innovation happening at GitHub. And so we're just trying to keep up and continue to replicate those new features functionalities in the local runner. And the biggest challenge I've had with this project is just keeping up with the community. We just passed 20,000 stars, and it'd be it's a normal week to get like 10 PRs. So super excited to announce just yesterday, actually I invited four of the most active contributors to help me with maintaining the project. And so this is like a big deal for me, letting the project go and bringing other people in to help lead it. So, yeah, huge shout out to those folks that have been helping with driving that project. So looking forward to what's next for it. >> Great, we'll make sure the SiliconANGLE riders catch that quote there. Great call out. Let's start, Brian, you made me realize when you mentioned Apache and then you've been watching all the stuff going on, it brings up the question of the evolution of Open Source, and the commercialization trends have been very interesting these days. You're seeing CloudScale really impact also with the growth of code. And Liz, if you remember, the Linux Foundation keeps making projections and they keep blowing past them every year on more and more code and more and more entrance coming in, not just individuals, corporations. So you starting to see Netflix donates something, you got Lyft donate some stuff, becomes a project company forms around it. There's a lot of entrepreneurial activity that's creating this new abstraction layers, new platforms, not just tools. So you start to see a new kickup trajectory with Open Source. You guys want to comment on this because this is going to impact how fast the enterprise will see value here. >> I think a really great example of that is a project called Backstage that's just come out of Spotify. And it's going through the incubation process at the CNCF. And that's why it's front of mind for me right now, 'cause I've been working on the due diligence for that. And the reason why I thought it was interesting in relation to your question is it's spun out of Spotify. It's fully Open Source. They have a ton of different enterprises using it as this developer portal, but they're starting to see some startups emerging offering like a hosted managed version of Backstage or offering services around Backstage or offering commercial plugins into Backstage. And I think it's really fascinating to see those ecosystems building up around a project and different ways that people can. I'm a big believer. You cannot sell the Open Source code, but you can sell other things that create value around Open Source projects. So that's really exciting to see. >> Great point. Anyone else want to weigh in and react to that? Because it's the new model. It's not the old way. I mean, I remember when I was in college, we had the Pirate software. Open Source wasn't around. So you had to deal under the table. Now it's free. But I mean the old way was you had to convince the enterprise, like you've got a hard knit, it builds the community and the community manage the quality of the code. And then you had to build the company to make sure they could support it. Now the companies are actually involved in it, right? And then new startups are forming faster. And the proof points are shorter and highly accelerated for that. I mean, it's a whole new- >> It's a Cambrian explosion, and it's great. It's one of those things that it's challenging for the new developers because they come in and they're like, "Whoa, what is all this stuff that I'm supposed to figure out?" And there's no right answer and there's no wrong answer. There's just tons of it. And I think that there's a desire for us to have one sort of well-known trot and happy path, that audience we're a lot better with a more diverse community, with lots of options, with lots of ways to approach these problems. And I think it's just great. A challenge that we have with all these options and all these Cambrian explosion of projects and all these competing ideas, right now, the sustainability, it's a bit of a tricky question to answer. We know that there's a commercialization aspect that helps us fund these projects, but how we compose the open versus the commercial source is still a bit of a tricky question and a tough one for a lot of folks. >> Erica, would you chime in on that for a second. I want to get your angle on that, this experience and all this code, and I'm a new person, I'm an existing person. Do I get like a blue check mark and verify? I mean, these are questions like, well, how do you navigate? >> Yeah, I think this has been something happening for a while. I mean, back in the early OpenStack days, 2010, for instance, Rackspace Open Sourcing, OpenStack and ANSU Labs and so forth, and then trying, having all these companies forming in creating startups around this. I started at a company called Cloudccaling back in late 2010, and we had some competitors such as Piston and so forth where a lot of the ANSUL Labs people went. But then, the real winners, I think from OpenStack ended up being the enterprises that jumped in. We had Red Hat in particular, as well as HP and IBM jumping in and investing in OpenStack, and really proving out a lot of... not that it was the first time, but this is when we started seeing billions of dollars pouring into Open Source projects and Open Source Foundations, such as the OpenStack Foundation, which proceeded a lot of the things that we now see with the Linux Foundation, which was then created a little bit later. And at the same time, I'm also reflecting a little bit what Brian said because there are projects that don't get funded, that don't get the same attention, but they're also getting used quite significantly. Things like Log4j really bringing this to the spotlight in terms of projects that are used everywhere by everything with significant outsized impacts on the industry that are not getting funded, that aren't flashy enough, that aren't exciting enough because it's just logging, but a vulnerability in it brings every everything and everybody down and has possibly billions of dollars of impact to our industry because nobody wanted to fund this project. >> I think that brings up the commercialization point about maybe bringing a venture capital model in saying, "Hey, that boring little logging thing could be a key ingredient for say solving some observability problems so I think let's put some cash." Again then we'd never seen that before. Now you're starting to see that kind of a real smart investment thesis going into Open Source projects. I mean, Promethease, Crafter, these are projects that turned off companies. This is turning up companies. >> A decade ago, there was no money in Dev tools that I think that's been fully debunked now. They used to be a concept that the venture community believed, but there's just too much evidence to the contrary, the companies like Cash Court, Datadog, the list goes on and on. I think the challenge for the Open Source (indistinct) comes back to foundations and working (indistinct) these developers make this code safe and secure. >> Casey, what's your reaction to all of this? You've got, so a project has gained some traction, got some momentum. There's a lot of mission critical. I won't say white spaces, but the opportunities in the big cloud game happening. And there's a lot of, I won't say too many entrepreneurial, but there's a lot of community action happening that's precommercialization that's getting traction. How does this all develop naturally and then vector in quickly when it hits? >> Yeah, I want to go back to the Log4j topic real quick. I think that it's a great example of an area that we need to do better at. And there was a cool article that Rob Pike wrote describing how to quantify the criticality. I think that's sort of quantifying criticality was the article he wrote on how to use metrics, to determine how valuable, how important a piece of Open Source is to the community. And we really need to highlight that more. We need a way to make it more clear how important this software is, how many people depend on it and how many people are contributing to it. And because right now we all do that. Like if I'm going to evaluate an Open Source software, sure, I'll look at how many stars it has and how many contributors it has. But I got to go through and do all that work myself and come up with. It would be really great if we had an agreed upon method for ranking the criticality of software, but then also the risk, hey, that this is used by a ton of people, but nobody's contributing to it anymore. That's a concern. And that would be great to potential users of that to signal whether or not it makes sense. The Open Source Security Foundation, just getting off the ground, they're doing some work in this space, and I'm really excited to see where they go with that looking at ways to stop score critically. >> Well, this brings up a good point while we've got everyone here, let's take a plug and plug a project you think that's not getting the visibility it needs. Let's go through each of you, point out a project that you think people should be looking at and talking about that might get some free visibility here. Anyone want to highlight projects they think should be focused more on, or that needs a little bit of love? >> I think, I mean, particularly if we're talking about these sort of vulnerability issues, there's a ton of work going on, like in the Secure Software Foundation, other foundations, I think there's work going on in Apache somewhere as well around the bill of material, the software bill of materials, the Secure Software supply chain security, even enumerating your dependencies is not trivial today. So I think there's going to be a ton of people doing really good work on that, as well as the criticality aspect. It's all like that. There's a really great xkcd cartoon with your software project and some really big monolithic lumps. And then, this tiny little piece in a very important point that's maintained by somebody in his bedroom in Montana or something and if you called it out. >> Yeah, you just opened where the next lightening and a bottle comes from. And this is I think the beauty of Open Source is that you get a little collaboration, you get three feet in a cloud of dust going and you get some momentum, and if it's relevant, it rises to the top. I think that's the collective intelligence of Open Source. The question I want to ask that the panel here is when you go into an enterprise, and now that the game is changing with a much more collaborative and involved, what's the story if they say, hey, what's in it for me, how do I manage the Open Source? What's the current best practice? Because there's no doubt I can't ignore it. It's in everything we do. How do I organize around it? How do I build around it to be more efficient and more productive and reduce the risk on vulnerabilities to managing staff, making sure the right teams in place, the right agility and all those things? >> You called it, they got to get skin in the game. They need to be active and involved and donating to a sustainable Open Source project is a great way to start. But if you really want to be active, then you should be committing. You should have a goal for your organization to be contributing back to that project. Maybe not committing code, it could be committing resources into the darks or in the tests, or even tweeting about an Open Source project is contributing to it. And I think a lot of these enterprises could benefit a lot from getting more active with the Open Source Foundations that are out there. >> Liz, you've been actively involved. I know we've talked personally when the CNCF started, which had a great commercial uptake from companies. What do you think the current state-of-the-art kind of equation is has it changed a little bit? Or is it the game still the same? >> Yeah, and in the early days of the CNCF, it was very much dominated by vendors behind the project. And now we're seeing more and more membership from end-user companies, the kind of enterprises that are building their businesses on Cloud Native, but their business is not in itself. That's not there. The infrastructure is not their business. And I think seeing those companies, putting money in, putting time in, as Brian says contributing resources quite often, there's enough money, but finding the talent to do the work and finding people who are prepared to actually chop the wood and carry the water, >> Exactly. >> that it's hard. >> And if enterprises can find peoples to spend time on Open Source projects, help with those chores, it's hugely valuable. And it's one of those the rising tide floats all the boats. We can raise security, we can reduce the amount of dependency on maintain projects collectively. >> I think the business models there, I think one of the things I'll react to and then get your guys' comments is remember which CubeCon it was, it was one of the early ones. And I remember seeing Apple having a booth, but nobody was manning. It was just an Apple booth. They weren't doing anything, but they were recruiting. And I think you saw the transition of a business model where the worry about a big vendor taking over a project and having undue influence over it goes away because I think this idea of participation is also talent, but also committing that talent back into the communities as a model, as a business model, like, okay, hire some great people, but listen, don't screw up the Open Source piece of it 'cause that's a critical. >> Also hire a channel, right? They can use those contributions to source that talent and build the reputation in the communities that they depend on. And so there's really a lot of benefit to the larger organizations that can do this. They'll have a huge pipeline of really qualified engineers right out the gate without having to resort to cheesy whiteboard interviews, which is pretty great. >> Yeah, I agree with a lot of this. One of my concerns is that a lot of these corporations tend to focus very narrowly on certain projects, which they feel that they depend greatly, they'll invest in OpenStack, they'll invest in Docker, they'll invest in some of the CNCF projects. And then these other projects get ignored. Something that I've been a proponent of for a little bit for a while is observability of your dependencies. And I don't think there's quite enough projects and solutions to this. And it sounds maybe from lists, there are some projects that I don't know about, but I also know that there's some startups like Snyk and so forth that help with a little bit of this problem, but I think we need more focus on some of these edges. And I think companies need to do better, both in providing, having some sort of solution for observability of the dependencies, as well as understanding those dependencies and managing them. I've seen companies for instance, depending on software that they actively don't want to use based on a certain criteria that they already set projects, like they'll set a requirement that any project that they use has a code of conduct, but they'll then use projects that don't have codes of conduct. And if they don't have a code of conduct, then employees are prohibited from working on those projects. So you've locked yourself into a place where you're depending on software that you have instructed, your employees are not allowed to contribute to, for certain legal and other reasons. So you need to draw a line in the sand and then recognize that those projects are ones that you don't want to consume, and then not use them, and have observability around these things. >> That's a great point. I think we have 10 minutes left. I want to just shift to a topic that I think is relevant. And that is as Open Source software, software, people develop software, you see under the hood kind of software, SREs developing very quickly in the CloudScale, but also you've got your classic software developers who were writing code. So you have supply chain, software supply chain challenges. You mentioned developer experience around how to code. You have now automation in place. So you've got the development of all these things that are happening. Like I just want to write software. Some people want to get and do infrastructure as code so DevSecOps is here. So how does that look like going forward? How has the future of Open Source going to make the developers just want to code quickly? And the folks who want to tweak the infrastructure a bit more efficient, any views on that? >> At Gaggle, we're using AWS' CDK, exclusively for our infrastructure as code. And it's a great transition for developers instead of writing Yammel or Jason, or even HCL for their infrastructure code, now they're writing code in the language that they're used to Python or JavaScript, and what that's providing is an easier transition for developers into that Infrastructure as code at Gaggle here, but it's also providing an opportunity to provide reusable constructs that some Devs can build on. So if we've got a very opinionated way to deploy a serverless app in a database and do auto-scaling behind and all stuff, we can present that to a developer as a library, and they can just consume it as it is. Maybe that's as deep as they want to go and they're happy with that. But then they want to go deeper into it, they can either use some of the lower level constructs or create PRs to the platform team to have those constructs changed to fit their needs. So it provides a nice on-ramp developers to use the tools and languages they're used to, and then also go deeper as they need. >> That's awesome. Does that mean they're not full stack developers anymore that they're half stack developers they're taking care of for them? >> I don't know either. >> We'll in. >> No, only kidding. Anyway, any other reactions to this whole? I just want to code, make it easy for me, and some people want to get down and dirty under the hood. >> So I think that for me, Docker was always a key part of this. I don't know when DevSecOps was coined exactly, but I was talking with people about it back in 2012. And when I joined Docker, it was a part of that vision for me, was that Docker was applying these security principles by default for your application. It wasn't, I mean, yes, everybody adopted because of the portability and the acceleration of development, but it was for me, the fact that it was limiting what you could do from a security angle by default, and then giving you these tuna balls that you can control it further. You asked about a project that may not get enough recognition is something called DockerSlim, which is designed to optimize your containers and will make them smaller, but it also constraints the security footprint, and we'll remove capabilities from the container. It will help you build security profiles for app armor and the Red Hat one. SELinux. >> SELinux. >> Yeah, and this is something that I think a lot of developers, it's kind of outside of the realm of things that they're really thinking about. So the more that we can automate those processes and make it easier out of the box for users or for... when I say users, I mean, developers, so that it's straightforward and automatic and also giving them the capability of refining it and tuning it as needed, or simply choosing platforms like serverless offerings, which have these security constraints built in out of the box and sometimes maybe less tuneable, but very strong by default. And I think that's a good place for us to be is where we just enforced these things and make you do things in a secure way. >> Yeah, I'm a huge fan of Kubernetes, but it's not the right hammer for every nail. And there are absolutely tons of applications that are better served by something like Lambda where a lot more of that security surface is taken care of for the developer. And I think we will see better tooling around security profiling and making it easier to shrink wrap your applications that there are plenty of products out there that can help you with this in a cloud native environment. But I think for the smaller developer let's say, or an earlier stage company, yeah, it needs to be so much more straightforward. Really does. >> Really an interesting time, 10 years ago, when I was working at Adobe, we used to requisition all these analysts to tell us how many developers there were for the market. And we thought there was about 20 million developers. If GitHub's to be believed, we think there is now around 80 million developers. So both these groups are probably wrong in their numbers, but the takeaway here for me is that we've got a lot of new developers and a lot of these new developers are really struck by a paradox of choice. And they're typically starting on the front end. And so there's a lot of movement in the stack moved towards the front end. We saw that at re:Invent when Amazon was really pushing Amplify 'cause they're seeing this too. It's interesting because this is where folks start. And so a lot of the obstructions are moving in that direction, but maybe not always necessarily totally appropriate. And so finding the right balance for folks is still a work in progress. Like Lambda is a great example. It lets me focus totally on just business logic. I don't have to think about infrastructure pretty much at all. And if I'm newer to the industry, that makes a lot of sense to me. As use cases expand, all of a sudden, reality intervenes, and it might not be appropriate for everything. And so figuring out what those edges are, is still the challenge, I think. >> All right, thank you very much for coming on the CUBE here panel. AWS Heroes, thanks everyone for coming. I really appreciate it, thank you. >> Thank you. >> Thank you. >> Okay. >> Thanks for having me. >> Okay, that's a wrap here back to the program and the awesome startups. Thanks for watching. (upbeat music)

>>from around the globe. It's the cube with digital >>coverage of IBM >>Think 2021 brought to >>you by IBM. Welcome back everyone to the cube coverage of IBM Think 2021 virtual um john for your host of the cube. Our next guest taylor Holloway. Chief technology officer at advent one. Tyler welcome to the cube from down under in Australia and we're in Palo alto California. How are you? >>Well thanks john thanks very much. Glad to be glad to be on here. >>Love love the virtual cube of the virtual events. We can get to talk to people really quickly with click um great conversation here around hybrid cloud, multi cloud and all things software enterprise before we get started. I wanna take a minute to explain what you guys do at advent one. What's the main focus? >>Yeah. So look we have a lot of customers in different verticals. Um so you know generally what we provide depends on the particular industry the customers in. But generally speaking we see a lot of demand for operational efficiency, helping our clients tackle cyber security risks, adopt cloud and set them up to modernize the applications. >>And this is this has been a big wave coming in for sure with, you know, cloud and scale. So I gotta ask you, what are the main challenges that you guys are solvent for your customers um and how are you helping them overcome come that way and transformative innovative way? >>Yeah, look, I think helping our clients um improve their security posture is a big one. We're finding as well that our customers are gaining a lot of operational efficiency by adopting sort of open source technology. Red Hearts, an important partner of ours is IBM um and we're seeing them sort of move away from some more proprietary solutions. Automation is a big focus for us as well. We've had some great outcomes with our clients or helping them automate um and you know deliver um, you know, the stand up and data operations of environments a lot quickly, a lot more easily. And uh and to be able to sort of apply some standards across multiple sort of areas of their estate. >>What are some of the solutions that you guys are doing with IBM's portfolio in the I. T. Infrastructure side? You got red hat, you got a lot of open source stuff to meet the needs of clients. What do you mean? What's that mean? >>Um Yeah, I think on the storage side will probably help our clients sort of tackle the expanding data in structured and particularly unstructured data they're trying to take control of so, you know, looking at spectrum scale and those type of products from an audio perspective for unstructured data is a good example. And so they're flash systems for more block storage and more run of the mill sort of sort of environments. We have helped our clients consolidate and modernize on IBM Power systems. Having Red Hat is both a UNIX operating system and having I can shift as a container platform really helps there. And Red Hat also provides management overlay, which has been great on what we do with IBM Power systems. We've been working on a few different sort of use cases on power in particular, sort of more recently. Um SAP Hana is a big one where we've had some success with our clients migrating Muhanna on to onto IBM power systems and we've also helped our customers, you know, improve some um some environments on the other end of the side, such as IBM I, we still have a large number of customers with with IBM I and and you know how do we help them? You know some of them are moving to cloud in one way or another others are consuming some kind of IRS and we can sort of wrap around a managed service to to help them through. >>So I gotta ask you the question, you know U. C. T. Oh you played a lot of technology actually kubernetes just become this lingua franca for this kind of like I'll call a middleware kind of orchestration layer uh containers. Obviously you're awesome but I gotta ask you when you walk into a client's environment you have to name names but you know usually you see kind of two pictures man, they need some serious help or they got their act together. So either way they're both opportunities for Hybrid cloud. How do you how do you how do you evaluate the environment when you go in, when you walk into those two scenarios? What goes through your mind? What some of the conversations that you guys have with those clients? Can you take me through a kind of day in the life of both scenarios? The ones that are like I can't get the job done, I'm so close in on the right team and the other ones, like we're grooving, we're kicking butt. >>Yeah. So look, let's start, well, I supposed to start off with you try and take somewhat of a technology agnostic view and just sort of sit down and listen to what they're trying to achieve, how they're going for customers who have got it. You know, as you say, all nailed down things are going really well. Um it's just really understanding what what can we do to help. Is there an opportunity for us to help at all like there? Um, you know, generally speaking, there's always going to be something and it may be, you know, we don't try and if someone is going really well, they might just want someone to help with a bespoke use case or something very specific where they need help. On the other end of the scale where a customer is sort of pretty early on and starting to struggle. We generally try and help them not boil the ocean at once. Just try and get some winds, pick some key use cases, you know, deliver some value back and then sort of growing from there rather than trying to go into a customer and trying to do everything at once tends to be a challenge. Just understand what the priorities are and help them get going. >>What's the impact been for red hat? Um, in your customer base, a lot of overlap. Some overlap, no overlap coming together. What's the general trend that you're seeing? What's the reaction been? >>Yeah I think it's been really good. Obviously IBM have a lot of focus on cloud packs where they're bringing their software on red hat open shift that will run on multiple clouds. So I think that's one that we'll see a lot more of overtime. Um Also helping customers automate their I. T. Operations with answerable is one we do quite a lot of um and there's some really bespoke use cases we've done with that as well as some standardized one. So helping with day two operations and all that sort of thing. But there's also some really sort of out there things customers have needed to automate. That's been a challenge for them and being able to use open source tools to do it has worked really well. We've had some good wins there, >>you know, I want to ask you about the architecture and I'm just some simplify it real just for the sake of devops, um you know, segmentation, you got hybrid clouds, take a programmable infrastructure and then you've got modern applications that need to have a I some have said, I've even said on the cube and other broadcasts that if you don't have a I you're gonna be at a handicap some machine learning, some data has to be in there. You can probably see aI and mostly everything as you go in and try to architect that out for customers um and help them get to a hybrid cloud infrastructure with real modern application front end with using data. What's what's the playbook, do you have any best practices or examples you can share or scenarios or visions that you see uh playing >>out? I think the yeah, the first one is obviously making sure customers data is in the right place. So if they might be wanting to use um some machine learning in one particular cloud provider and they've got a lot of their applications and data in another, you know, how do we help them make it mobile and able to move data from one cloud to another or back into court data center? So there's a lot of that. I think that we spend a lot of time with customers to try and get a right architecture and also how do we make sure it's secure from end to end. So if they're moving things from into multiple one or more public clouds as well as maybe in their own data center, making sure connectivity is all set up properly. All the security requirements are met. So I think we sort of look at it from a from a high level design point of view, we look at obviously what the target state is going to be versus the current state that really take into account security, performance, connectivity or those sort of things to make sure that they're going to have a good result. >>You know, one of the things you mentioned and this comes up a lot of my interviews with partners of IBM is they always comment about their credibility and all the other than the normal stuff. But one of the things that comes out a lot pretty much consistently is their experience in verticals. Uh just have such a track record in verticals and this is where AI and machine learning data has to be very much scoped in on the vertical. You can't generalize and have a general purpose data plane inside of vertically specialized kind of focus. How how do you see that evolving, how does IBM play there with this kind of the horizontally scalable mindset of a hybrid model, both on premise in the cloud, but that's still saying provide that that intimacy with the data to fuel the machine learning or NLP or power that AI, which seems to be critical. >>Yeah, I think there's a lot of services where, you know, public cloud providers are bringing out new services all the time and some of it is pre can and easy to consume. I think what IBM from what I've observed being really good at is handling some of those really bespoke use cases. So if you have a particular vertical with a challenge, um you know, there's going to be sort of things that are pre can that you can go and consume. But if you need to do something custom that could be quite challenging. How do they sort of build something that could be quite specific for a particular industry and then obviously being able to repeat that afterwards for us, that's obviously something we're very interested in. >>Yeah, taylor love chatting, whether you love getting the low down, also, people might not know your co author of a book performance guy with IBM Power Systems, so I gotta ask you, since I got you here and I don't mean to put you on the spot, but if you can just share your vision or any kind of anecdotal observation as people start to put together their architecture and again, you know, Beauty's in the eye of the beholder, every environment is different. But still, hybrid, distributed concept is distributed computing, Is there a KPI is there a best practice on as a manager or systems architect to kind of keep an eye on what what good is and how how good becomes better because the day to operations becomes a super important concept. We're seeing some called Ai ops where Okay, I'm provisioning stuff out on a hybrid Cloud operational environment. But now day two hits are things happen as more stuff entered into the equation. What's your vision on KPs and management? What to keep >>tracking? Yeah, I think obviously attention to detail is really important to be able to build things properly. A good KPI particularly managed service area that I'm curious that understanding is how often do you actually have to log into the systems that you're managing? So if you're logging in and recitation into servers and all this sort of stuff all the time, all of your automation and configuration management is not set up properly. So, really a good KPI an interesting one is how often do you log into things all the time if something went wrong, would you sooner go and build another one and shoot the one that failed or go and restore from backup? So thinking about how well things are automated. If things are immutable using infrastructure as code, those are things that I think are really important when you look at, how is something going to be scalable and easy to manage going forward. What I hate to see is where, you know, someone build something and automated all in the first place and they're too scared to run it again afterwards in case it breaks something. >>It's funny the next generation of leaders probably won't even know like, hey, yeah, taylor and john they had to log into systems back in the day. You know, I mean, I could be like a story they tell their kids. Uh but no, that's a good metric. This is this automation. So it's on the next level. Let's go the next level automation. Um what's the low hanging fruit for automation? Because you're getting at really the kind of the killer app there which is, you know, self healing systems, good networks that are programmable but automation will define more value. >>What's your take? I think the main thing is where you start to move from a model of being able to start small and automate individual things which could be patching or system provisioning or anything like that. But what you really want to get to is to be able to drive everything through. Get So instead of having a written up paper, change request, I'm going to change your system and all the rest of it. It really should be driven through a pull request and have things through it and and build pipelines to go and go and make a change running in development, make sure it's successful and then it goes and gets pushed into production. That's really where I think you want to get to and you can start to have a lot of people collaborating really well on this particular project or a customer that also have some sort of guard rails around what happens in some level of governance rather than being a free for >>all. Okay, final question. Where do you see event one headed? What's your future plans to continue to be a leader? I. T. Service by leader for this guy? BMS infrastructure portfolio? >>I think it comes down to people in the end, so really making sure that we partner with our clients and to be well positioned to understand what they want to achieve and and have the expertise in our team to bring to the table to help them do it. I think open source is a key enabler to help our clients adopt a hybrid cloud model to sort of touched on earlier as well as be able to make use of multiple clouds where it makes sense From a managed service perspective. I think everyone is really considering themselves next year managed service provider, but what that means for us is to provide a different, differentiated managed service and also have the strong technical expertise to back it up. >>Taylor Holloway, chief technology officer advent one remote videoing in from down under in Australia. I'm john ferrier and Palo alto with cube coverage of IBM thing. Taylor, thanks for joining me today from the cube. >>Thank you very much. >>Okay, cube coverage. Thanks for watching ever. Mhm

>> Announcer: From around the globe, it's theCUBE with digital coverage of MIT Chief Data Officer and Information Quality symposium brought to you by SiliconANGLE Media. >> Hi everybody. This is Dave Vellante and welcome back to theCUBE's coverage of the MIT CDOIQ 2020 event. Of course, it's gone virtual. We wish we were all together in Cambridge. They were going to move into a new building this year for years they've done this event at the Tang Center, moving into a new facility, but unfortunately going to have to wait at least a year, we'll see, But we've got a great guest. Nonetheless, Doug Laney is here. He's a Business Value Strategist, the bestselling author, an analyst, consultant then a long time CUBE friend. Doug, great to see you again. Thanks so much for coming on. >> Dave, great to be with you again as well. So can I ask you? You have been an advocate for obviously measuring the value of data, the CDO role. I don't take this the wrong way, but I feel like the last 150 days have done more to accelerate people's attention on the importance of data and the value of data than all the great work that you've done. What do you think? (laughing) >> It's always great when organizations, actually take advantage of some of these concepts of data value. You may be speaking specifically about the situation with United Airlines and American Airlines, where they have basically collateralized their customer loyalty data, their customer loyalty programs to the tunes of several billion dollars each. And one of the things that's very interesting about that is that the third party valuations of their customer loyalty data, resulted in numbers that were larger than the companies themselves. So basically the value of their data, which is as we've discussed previously off balance sheet is more valuable than the market cap of those companies themselves, which is just incredibly fascinating. >> Well, and of course, all you have to do is look to the Trillionaire's Club. And now of course, Apple pushing two trillion to really see the value that the market places on data. But the other thing is of course, COVID, everybody talks about the COVID acceleration. How have you seen it impact the awareness of the importance of data, whether it applies to business resiliency or even new monetization models? If you're not digital, you can't do business. And digital is all about data. >> I think the major challenge that most organizations are seeing from a data and analytics perspective due to COVID is that their traditional trend based forecast models are broken. If you're a company that's only forecasting based on your own historical data and not taking into consideration, or even identifying what are the leading indicators of your business, then COVID and the economic shutdown have entirely broken those models. So it's raised the awareness of companies to say, "Hey, how can we predict our business now? We can't do it based on our own historical data. We need to look externally at what are those external, maybe global indicators or other kinds of markets that proceed our own forecasts or our own activity." And so the conversion from trend based forecast models to what we call driver based forecast models, isn't easy for a lot of organizations to do. And one of the more difficult parts is identifying what are those external data factors from suppliers, from customers, from partners, from competitors, from complimentary products and services that are leading indicators of your business. And then recasting those models and executing on them. >> And that's a great point. If you think about COVID and how it's changed things, everything's changed, right? The ideal customer profile has changed, your value proposition to those customers has completely changed. You got to rethink that. And of course, it's very hard to predict even when this thing eventually comes back, some kind of hybrid mode, you used to be selling to people in an office environment. That's obviously changed. There's a lot that's permanent there. And data is potentially at least the forward indicator, the canary in the coal mine. >> Right. It also is the product and service. So not only can it help you and improve your forecasting models, but it can become a product or service that you're offering. Look at us right now, we would generally be face to face and person to person, but we're using video technology to transfer this content. And then one of the things that I... It took me awhile to realize, but a couple of months after the COVID shutdown, it occurred to me that even as a consulting organization, Caserta focuses on North America. But the reality is that every consultancy is now a global consultancy because we're all doing business remotely. There are no particular or real strong localization issues for doing consulting today. >> So we talked a lot over the years about the role of the CDO, how it's evolved, how it's changed the course of the early... The pre-title days it was coming out of a data quality world. And it's still vital. Of course, as we heard today from the Keynote, it's much more public, much more exposed, different public data sources, but the role has certainly evolved initially into regulated industries like financial, healthcare and government, but now, many, many more organizations have a CDO. My understanding is that you're giving a talk in the business case for the CDO. Help us understand that. >> Yeah. So one of the things that we've been doing here for the last couple of years is a running an ongoing study of how organizations are impacted by the role of the CDO. And really it's more of a correlation and looking at what are some of the qualities of organizations that have a CDO or don't have a CDO. So some of the things we found is that organizations with a CDO nearly twice as often, mention the importance of data and analytics in their annual report organizations with a C level CDO, meaning a true executive are four times more often likely to be using data, to transform the business. And when we're talking about using data and advanced analytics, we found that organizations with a CIO, not a CDO responsible for their data assets are only half as likely to be doing advanced analytics in any way. So there are a number of interesting things that we found about companies that have a CDO and how they operate a bit differently. >> I want to ask you about that. You mentioned the CIO and we're increasingly seeing lines of reporting and peer reporting alter shift. The sands are shifting a little bit. In the early days the CDO and still predominantly I think is an independent organization. We've seen a few cases and increasingly number where they're reporting into the CIO, we've seen the same thing by the way with the chief Information Security Officer, which used to be considered the fox watching the hen house. So we're seeing those shifts. We've also seen the CDO become more aligned with a technical role and sometimes even emerging out of that technical role. >> Yeah. I think the... I don't know, what I've seen more is that the CDOs are emerging from the business, companies are realizing that data is a business asset. It's not an IT asset. There was a time when data was tightly coupled with applications of technologies, but today data is very easily decoupled from those applications and usable in a wider variety of contexts. And for that reason, as data gets recognized as a business, not an IT asset, you want somebody from the business responsible for overseeing that asset. Yes, a lot of CDOs still report to the CIO, but increasingly more CDOs you're seeing and I think you'll see some other surveys from other organizations this week where the CDOs are more frequently reporting up to the CEO level, meaning they're true executives. Along I advocated for the bifurcation of the IT organization into separate I and T organizations. Again, there's no reason other than for historical purposes to keep the data and technology sides of the organizations so intertwined. >> Well, it makes sense that the Chief Data Officer would have an affinity with the lines of business. And you're seeing a lot of organizations, really trying to streamline their data pipeline, their data life cycles, bringing that together, infuse intelligence into that, but also take a systems view and really have the business be intimately involved, if not even owned into the data. You see a lot of emphasis on self-serve, what are you seeing in terms of that data pipeline or the data life cycle, if you will, that used to be wonky, hard core techies, but now it really involving a lot more constituent. >> Yeah. Well, the data life cycle used to be somewhat short. The data life cycles, they're longer and they're more a data networks than a life cycle and or a supply chain. And the reason is that companies are finding alternative uses for their data, not just using it for a single operational purpose or perhaps reporting purpose, but finding that there are new value streams that can be generated from data. There are value streams that can be generated internally. There are a variety of value streams that can be generated externally. So we work with companies to identify what are those variety of value streams? And then test their feasibility, are they ethically feasible? Are they legally feasible? Are they economically feasible? Can they scale? Do you have the technology capabilities? And so we'll run through a process of assessing the ideas that are generated. But the bottom line is that companies are realizing that data is an asset. It needs to be not just measured as one and managed as one, but also monetized as an asset. And as we've talked about previously, data has these unique qualities that it can be used over and over again, and it generate more data when you use it. And it can be used simultaneously for multiple purposes. So companies like, you mentioned, Apple and others have built business models, based on these unique qualities of data. But I think it's really incumbent upon any organization today to do so as well. >> But when you observed those companies that we talk about all the time, data is at the center of their organization. They maybe put people around that data. That's got to be one of the challenge for many of the incumbents is if we talked about the data silos, the different standards, different data quality, that's got to be fairly major blocker for people becoming a "Data-driven organization." >> It is because some organizations were developed as people driven product, driven brand driven, or other things to try to convert. To becoming data-driven, takes a high degree of data literacy or fluency. And I think there'll be a lot of talk about that this week. I'll certainly mention it as well. And so getting the organization to become data fluent and appreciate data as an asset and understand its possibilities and the art of the possible with data, it's a long road. So the culture change that goes along with it is really difficult. And so we're working with 150 year old consumer brand right now that wants to become more data-driven and they're very product driven. And we hear the CIO say, "We want people to understand that we're a data company that just happens to produce this product. We're not a product company that generates data." And once we realized that and started behaving in that fashion, then we'll be able to really win and thrive in our marketplace. >> So one of the key roles of a Chief Data Officers to understand how data affects the monetization of an organization. Obviously there are four profit companies of your healthcare organization saving lives, obviously being profitable as well, or at least staying within the budget, depending upon the structure of the organization. But a lot of people I think oftentimes misunderstand that it's like, "Okay, do I have to become a data broker? Am I selling data directly?" But I think, you pointed out many times and you just did that unlike oil, that's why we don't like that data as a new oil analogy, because it's so much more valuable and can be use, it doesn't fall because of its scarcity. But what are you finding just in terms of people's application of that notion of monetization? Cutting costs, increasing revenue, what are you seeing in the field? What's that spectrum look like? >> So one of the things I've done over the years is compile a library of hundreds and hundreds of examples of how organizations are using data and analytics in innovative ways. And I have a book in process that hopefully will be out this fall. I'm sharing a number of those inspirational examples. So that's the thing that organizations need to understand is that there are a variety of great examples out there, and they shouldn't just necessarily look to their own industry. There are inspirational examples from other industries as well, many clients come to me and they ask, "What are others in my industry doing?" And my flippant response to that is, "Why do you want to be in second place or third place? Why not take an idea from another industry, perhaps a digital product company and apply that to your own business." But like you mentioned, there are a variety of ways to monetize data. It doesn't involve necessarily selling it. You can deliver analytics, you can report on it, you can use it internally to generate improved business process performance. And as long as you're measuring how data's being applied and what its impact is, then you're in a position to claim that you're monetizing it. But if you're not measuring the impact of data on business processes or on customer relationships or partner supplier relationships or anything else, then it's difficult to claim that you're monetizing it. But one of the more interesting ways that we've been working with organizations to monetize their data, certainly in light of GDPR and the California consumer privacy act where I can't sell you my data anymore, but we've identified ways to monetize your customer data in a couple of ways. One is to synthesize the data, create synthetic data sets that retain the original statistical anomalies in the data or features of the data, but don't share actually any PII. But another interesting way that we've been working with organizations to monetize their data is what I call, Inverted data monetization, where again, I can't share my customer data with you, but I can share information about your products and services with my customers. And take a referral fee or a commission, based on that. So let's say I'm a hospital and I can't sell you my patient data, of course, due to variety of regulations, but I know who my diabetes patients are, and I can introduce them to your healthy meal plans, to your gym memberships, to your at home glucose monitoring kits. And again, take a referral fee or a cut of that action. So we're working with customers and the financial services firm industry and in the healthcare industry on just those kinds of examples. So we've identified hundreds of millions of dollars of incremental value for organizations that from their data that we're just sitting on. >> Interesting. Doug because you're a business value strategist at the top, where in the S curve do you see you're able to have the biggest impact. I doubt that you enter organizations where you say, "Oh, they've got it all figured out. They can't use my advice." But as well, sometimes in the early stages, you may not be able to have as big of an impact because there's not top down support or whatever, there's too much technical data, et cetera, where are you finding you can have the biggest impact, Doug? >> Generally we don't come in and run those kinds of data monetization or information innovation exercises, unless there's some degree of executive support. I've never done that at a lower level, but certainly there are lower level more immediate and vocational opportunities for data to deliver value through, to simply analytics. One of the simple examples I give is, I sold a home recently and when you put your house on the market, everybody comes out of the woodwork, the fly by night, mortgage companies, the moving companies, the box companies, the painters, the landscapers, all know you're moving because your data is in the U.S. and the MLS directory. And it was interesting. The only company that didn't reach out to me was my own bank, and so they lost the opportunity to introduce me to a Mortgage they'd retain me as a client, introduce me to my new branch, print me new checks, move the stuff in my safe deposit box, all of that. They missed a simple opportunity. And I'm thinking, this doesn't require rocket science to figure out which of your customers are moving, the MLS database or you can harvest it from Zillow or other sites is basically public domain data. And I was just thinking, how stupid simple would it have been for them to hire a high school programmer, give him a can of red bull and say, "Listen match our customer database to the MLS database to let us know who's moving on a daily or weekly basis." Some of these solutions are pretty simple. >> So is that part of what you do, come in with just hardcore tactical ideas like that? Are you also doing strategy? Tell me more about how you're spending your time. >> I trying to think more of a broader approach where we look at the data itself and again, people have said, "If you tortured enough, what would you tell us? We're just take that angle." We look at examples of how other organizations have monetized data and think about how to apply those and adapt those ideas to the company's own business. We look at key business drivers, internally and externally. We look at edge cases for their customers' businesses. We run through hypothesis generating activities. There are a variety of different kinds of activities that we do to generate ideas. And most of the time when we run these workshops, which last a week or two, we'll end up generating anywhere from 35 to 50 pretty solid ideas for generating new value streams from data. So when we talk about monetizing data, that's what we mean, generating new value streams. But like I said, then the next step is to go through that feasibility assessment and determining which of these ideas you actually want to pursue. >> So you're of course the longtime industry watcher as well, as a former Gartner Analyst, you have to be. My question is, if I think back... I've been around a while. If I think back at the peak of Microsoft's prominence in the PC era, it was like windows 95 and you felt like, "Wow, Microsoft is just so strong." And then of course the Linux comes along and a lot of open source changes and low and behold, a whole new set of leaders emerges. And you see the same thing today with the Trillionaire's Club and you feel like, "Wow, even COVID has been a tailwind for them." But you think about, "Okay, where could the disruption come to these large players that own huge clouds, they have all the data." Is data potentially a disruptor for what appear to be insurmountable odds against the newbies" >> There's always people coming up with new ways to leverage data or new sources of data to capture. So yeah, there's certainly not going to be around for forever, but it's been really fascinating to see the transformation of some companies I think nobody really exemplifies it more than IBM where they emerged from originally selling meat slicers. The Dayton Meat Slicer was their original product. And then they evolved into Manual Business Machines and then Electronic Business Machines. And then they dominated that. Then they dominated the mainframe software industry. Then they dominated the PC industry. Then they dominated the services industry to some degree. And so they're starting to get into data. And I think following that trajectory is something that really any organization should be looking at. When do you actually become a data company? Not just a product company or a service company or top. >> We have Inderpal Bhandari is one of our huge guests here. He's a Chief-- >> Sure. >> Data Officer of IBM, you know him well. And he talks about the journey that he's undertaken to transform the company into a data company. I think a lot of people don't really realize what's actually going on behind the scenes, whether it's financially oriented or revenue opportunities. But one of the things he stressed to me in our interview was that they're on average, they're reducing the end to end cycle time from raw data to insights by 70%, that's on average. And that's just an enormous, for a company that size, it's just enormous cost savings or revenue generating opportunity. >> There's no doubt that the technology behind data pipelines is improving and the process from moving data from those pipelines directly into predictive or diagnostic or prescriptive output is a lot more accelerated than the early days of data warehousing. >> Is the skills barrier is acute? It seems like it's lessened somewhat, the early Hadoop days you needed... Even data scientist... Is it still just a massive skill shortage, or we're starting to attack that. >> Well, I think companies are figuring out a way around the skill shortage by doing things like self service analytics and focusing on more easy to use mainstream type AI or advanced analytics technologies. But there's still very much a need for data scientists and organizations and the difficulty in finding people that are true data scientists. There's no real certification. And so really anybody can call themselves a data scientist but I think companies are getting good at interviewing and determining whether somebody's got the goods or not. But there are other types of skills that we don't really focus on, like the data engineering skills, there's still a huge need for data engineering. Data doesn't self-organize. There are some augmented analytics technologies that will automatically generate analytic output, but there really aren't technologies that automatically self-organize data. And so there's a huge need for data engineers. And then as we talked about, there's a large interest in external data and harvesting that and then ingesting it and even identifying what external data is out there. So one of the emerging roles that we're seeing, if not the sexiest role of the 21st century is the role of the Data Curator, somebody who acts as a librarian, identifying external data assets that are potentially valuable, testing them, evaluating them, negotiating and then figuring out how to ingest that data. So I think that's a really important role for an organization to have. Most companies have an entire department that procures office supplies, but they don't have anybody who's procuring data supplies. And when you think about which is more valuable to an organization? How do you not have somebody who's dedicated to identifying the world of external data assets that are out there? There are 10 million data sets published by government, organizations and NGOs. There are thousands and thousands of data brokers aggregating and sharing data. There's a web content that can be harvested, there's data from your partners and suppliers, there's data from social media. So to not have somebody who's on top of all that it demonstrates gross negligence by the organization. >> That is such an enlightening point, Doug. My last question is, I wonder how... If you can share with us how the pandemic has effected your business personally. As a consultant, you're on the road a lot, obviously not on the road so much, you're doing a lot of chalk talks, et cetera. How have you managed through this and how have you been able to maintain your efficacy with your clients? >> Most of our clients, given that they're in the digital world a bit already, made the switch pretty quick. Some of them took a month or two, some things went on hold but we're still seeing the same level of enthusiasm for data and doing things with data. In fact some companies have taken our (mumbles) that data to be their best defense in a crisis like this. It's affected our business and it's enabled us to do much more international work more easily than we used to. And I probably spend a lot less time on planes. So it gives me more time for writing and speaking and actually doing consulting. So that's been nice as well. >> Yeah, there's that bonus. Obviously theCUBE yes, we're not doing physical events anymore, but hey, we've got two studios operating. And Doug Laney, really appreciate you coming on. (Dough mumbles) Always a great guest and sharing your insights and have a great MIT CDOIQ. >> Thanks, you too, Dave, take care. (mumbles) >> Thanks Doug. All right. And thank you everybody for watching. This is Dave Vellante for theCUBE, our continuous coverage of the MIT Chief Data Officer conference, MIT CDOIQ, will be right back, right after this short break. (bright music)

(upbeat music) >> Announcer: From the cube Studios in Palo Alto and Boston, it's the cube! Covering IBM Think. Brought to you by IBM. >> Hi everybody, this is Dave Vellante of theCUBE, and you're watching our coverage of the IBM digital event experience. A multi-day program, tons of content, and it's our pleasure to be able to bring in experts, practitioners, customers, and partners. Sriram Raghavan is here. He's the Vice President of IBM Research in AI. Sriram, thanks so much for coming on thecUBE. >> Thank you, pleasure to be here. >> I love this title, I love the role. It's great work if you're qualified for it.(laughs) So, tell us a little bit about your role and your background. You came out of Stanford, you had the pleasure, I'm sure, of hanging out in South San Jose at the Almaden labs. Beautiful place to create. But give us a little background. >> Absolutely, yeah. So, let me start, maybe go backwards in time. What do I do now? My role's responsible for AI strategy, planning, and execution in IBM Research across our global footprint, all our labs worldwide and their working area. I also work closely with the commercial parts. The parts of IBM, our Software and Services business that take the innovation, AI innovation, from IBM Research to market. That's the second part of what I do. And where did I begin life in IBM? As you said, I began life at our Almaden Research Center up in San Jose, up in the hills. Beautiful, I had in a view. I still think it's the best view I had. I spent many years there doing work at the intersection of AI and large-scale data management, NLP. Went back to India, I was running the India lab there for a few years, and now I'm back here in New York running AI strategy. >> That's awesome. Let's talk a little bit about AI, the landscape of AI. IBM has always made it clear that you're not doing consumer AI. You're really tying to help businesses. But how do you look at the landscape? >> So, it's a great question. It's one of those things that, you know, we constantly measure ourselves and our partners tell us. I think we, you've probably heard us talk about the cloud journey . But look barely 20% of the workloads are in the cloud, 80% still waiting. AI, at that number is even less. But, of course, it varies. Depending on who you ask, you would say AI adoption is anywhere from 4% to 30% depending on who you ask in this case. But I think it's more important to look at where is this, directionally? And it's very, very clear. Adoption is rising. The value is more, it's getting better appreciated. But I think more important, I think is, there is broader recognition, awareness and investment, knowing that to get value out of AI, you start with where AI begins, which is data. So, the story around having a solid enterprise information architecture as the base on which to drive AI, is starting to happen. So, as the investments in data platform, becoming making your data ready for AI, starts to come through. We're definitely seeing that adoption. And I think, you know, the second imperative that businesses look for obviously is the skills. The tools and the skills to scale AI. It can't take me months and months and hours to go build an AI model, I got to accelerate it, and then comes operationalizing. But this is happening, and the upward trajectory is very, very clear. >> We've been talking a lot on theCUBE over the last couple of years, it's not the innovation engine of our industry is no longer Moore's Law, it's a combination of data. You just talked about data. Applying machine technology to that data, being able to scale it, across clouds, on-prem, wherever the data lives. So. >> Right. >> Having said that, you know, you've had a journey. You know, you started out kind of playing "Jeopardy!", if you will. It was a very narrow use case, and you're expanding that use case. I wonder if you could talk about that journey, specifically in the context of your vision. >> Yeah. So, let me step back and say for IBM Research AI, when I think about how we, what's our strategy and vision, we think of it as in two parts. One part is the evolution of the science and techniques behind AI. And you said it, right? From narrow, bespoke AI that all it can do is this one thing that it's really trained for, it takes a large amount of data, a lot of computing power. Two, how do you have the techniques and the innovation for AI to learn from one use case to the other? Be less data hungry, less resource hungry. Be more trustworthy and explainable. So, we call that the journey from narrow to broad AI. And one part of our strategy, as scientists and technologists, is the innovation to make that happen. So that's sort of one part. But, as you said, as people involved in making AI work in the enterprise, and IBM Research AI vision would be incomplete without the second part, which is, what are the challenges in scaling and operationalizing AI? It isn't sufficient that I can tell you AI can do this, how do I make AI do this so that you get the right ROI, the investment relative to the return makes sense and you can scale and operationalize. So, we took both of these imperatives. The AI narrow-to-broad journey, and the need to scale and operationalize. And what of the things that are making it hard? The things that make scaling and operationalizing harder: data challenges, we talked about that, skills challenges, and the fact that in enterprises, you have to govern and manage AI. And we took that together and we think of our AI agenda in three pieces: Advancing, trusting, and scaling AI. Advancing is the piece of pushing the boundary, making AI narrow to broad. Trusting is building AI which is trustworthy, is explainable, you can control and understand its behavior, make sense of it and all of the technology that goes with it. And scaling AI is when we address the problem of, how do I, you know, reduce the time and cost for data prep? How do I reduce the time for model tweaking and engineering? How do I make sure that a model that you build today, when something changes in the data, I can quickly allow for you to close the loop and improve the model? All of the things, think of day-two operations of AI. All of that is part of our scaling AI strategy. So advancing, trusting, scaling is sort of the three big mantras around which the way we think about our AI. >> Yeah, so I've been doing a little work in this around this notion of DataOps. Essentially, you know, DevOps applied to the data and the data pipeline, and I had a great conversation recently with Inderpal Bhandari, IBM's Global Chief Data Officer, and he explained to me how, first of all, customers will tell you, it's very hard to operationalize AIs. He and his team took that challenge on themselves and have had some great success. And, you know, we all know the problem. It's that, you know AI has to wait for the data. It has to wait for the data to be cleansed and wrangled. Can AI actually help with that part of the problem, compressing that? >> 100%. In fact, the way we think of the automation and scaling story is what we call the "AI For AI" story. So, AI in service of helping you build the AI that helps you make this with speed, right? So, and I think of it really in three parts. It's AI for data automation, our DataOps. AI used in better discovery, better cleansing, better configuration, faster linking, quality assessment, all of that. Using AI to do all of those data problems that you had to do. And I called it AI for data automation. The second part is using AI to automatically figure out the best model. And that's AI for data science automation, which is, feature engineering, hyperparameter optimization, having them all do work, why should a data scientist take weeks and months experimenting? If the AI can accelerate that from weeks to a matter of hours? That's data science automation. And then comes the important part, also, which is operations automation. Okay, I've put a data model into an application. How do I monitor its behavior? If the data that it's seeing is different from the data it was trained on, how do I quickly detect it? And a lot of the work from Research that was part of that Watson OpenScale offering is really addressing the operational side. So AI for data, AI for data science automation, and AI to help automate production of AI, is the way we break that problem up. >> So, I always like to ask folks that are deep into R&D, how they are ultimately are translating into commercial products and offerings? Because ultimately, you got to make money to fund more R&D. So, can you talk a little bit about how you do that, what your focus is there? >> Yeah, so that's a great question, and I'm going to use a few examples as well. But let me say at the outset, this is a very, very closed partnership. So when we, the Research part of AI and our portfolio, it's a closed partnership where we're constantly both drawing problem as well as building technology that goes into the offering. So, a lot of our work, much of our work in AI automation that we were talking about, is part of our Watson Studio, Watson Machine Learning, Watson OpenScale. In fact, OpenScale came out of Research working Trusted AI, and is now a centerpiece of our Watson project. Let me give a very different example. We have a very, very strong portfolio and focus in NLP, Natural Language Processing. And this directly goes into capabilities out of Watson Assistant, which is our system for conversational support and customer support, and Watson Discovery, which is about making enterprise understand unstructurally. And a great example of that is the Working Project Debater that you might have heard, which is a grand challenge in Research about building a machine that can do debate. Now, look, we weren't looking to go sell you a debating machine. But what did we build as part of doing that, is advances in NLP that are all making their way into assistant and discovery. And we actually just talked about earlier this year, announced a set of capabilities around better clustering, advanced summarization, deeper sentiment analysis. These made their way into Assistant and Discovery but are born out of research innovation and solving a grand problem like building a debating machine. That's just an example of how that journey from research to product happens. >> Yeah, the Debater documentary, I've seen some of that. It's actually quite astounding. I don't know what you're doing there. It sounds like you're taking natural language and turning it into complex queries with data science and AI, but it's quite amazing. >> Yes, and I would encourage you, you will see that documentary, by the way, on Channel 7, in the Think Event. And I would encourage you, actually the documentary around how Debater happened, sort of featuring back of the you know, backdoor interviews with the scientist who created it was actually featured last minute at Copenhagen International Documentary Festival. I'll invite viewers to go to Channel 7 and Data and AI Tech On-Demand to go take a look at that documentary. >> Yeah, you should take a look at it. It's actually quite astounding and amazing. Sriram, what are you working on these days? What kind of exciting projects or what's your focus area today? >> Look, I think there are three imperatives that we're really focused on, and one is very, you know, just really the project you're talking about, NLP. NLP in the enterprise, look, text is a language of business, right? Text is the way business is communicated. Within each other, with their partners, with the entire world. So, helping machines understand language, but in an enterprise context, recognizing that data and the enterprises live in complex documents, unstructured documents, in e-mail, they live in conversations with the customers. So, really pushing the boundary on how all our customers and clients can make sense of this vast volume of unstructured data by pushing the advances of NLP, that's one focus area. Second focus area, we talked about trust and how important that is. And we've done amazing work in monitoring and explainability. And we're really focused now on this emerging area of causality. Using causality to explain, right? The model makes this because the model believes this is what it wants, it's a beautiful way. And the third big focus continues to be on automation. So, NLP, trust, automation. Those are, like, three big focus areas for us. >> sriram, how far do you think we can take AI? I know it's a topic of conversation, but from your perspective, deep into the research, how far can it go? And maybe how far should it go? >> Look, I think we are, let me answer it this way. I think the arc of the possible is enormous. But I think we are at this inflection point in which I think the next wave of AI, the AI that's going to help us this narrow-to-broad journey we talked about, look, the narrow-to-broad journey's not like a one-week, one-year. We're talking about a decade of innovation. But I think we are at a point where we're going to see a wave of AI that we like to call "neuro-symbolic AI," which is AI that brings together two sort of fundamentally different approaches to building intelligence systems. One approach of building intelligence system is what we call "knowledge driven." Understand data, understand concept, logically, reasonable. We human beings do that. That was really the way AI was born. The more recent last couple of decades of AI was data driven, Machine learning. Give me vast volumes of data, I'll use neural techniques, deep learning, to to get value. We're at a point where we're going to bring both of them together. Cause you can't build trustworthy, explainable systems using only one, you can't get away from not using all of the data that you have to make them. So, neuro-symbolic AI is, I think, going to be the linchpin of how we advance AI and make it more powerful and trustworthy. >> So, are you, like, living your childhood dream here or what? >> Look, for me I'm fascinated. I've always been fascinated. And any time you can't find a technology person who hasn't dreamt of building an intelligent machine. To have a job where I can work across our worldwide set of 3,000 plus researchers and think and brainstorm on strategy with AI. And then, most importantly, not to forget, right? That you talked about being able to move it into our portfolios so it actually makes a difference for our clients. I think it's a dream job and a whole lot of fun. >> Well, Sriram, it was great having you on theCUBE. A lot of fun, interviewing folks like you. I feel a little bit smarter just talking to you. So thanks so much for coming on. >> Fantastic. It's been a pleasure to be here. >> And thank you for watching, everybody. You're watching theCUBE's coverage of IBM Think 2020. This is Dave Vellante. We'll be right back right after this short break. (upbeat music)

from around the globe it's the cube with digital coverage of Red Hat summit 2020 brought to you by Red Hat welcome back this is the cubes coverage of Red Hat summit 2020 I'm Stu minimun and it's our seventh year doing the cube this year of course it is a digital event which means we are reaching all the community members where they are around the globe really excited to bring program first time guests and a first time to redhead summit Vachon Vora he's the chief technology officer of Vodafone idea joining me from Mumbai India bhishan nice to see you take so much for joining us it's a pleasure to be here as >> I'm looking forward to this interaction all right so as I said I've been at Red Hat show for many years the the telecommunications space you know service providers are some of the you know more interesting in the technology space you talk about scale you talk about change you talk about you know software eating the world all of those discussions are ones I've had for many years but you know I think many people know Vodafone may be a vote a fun idea escape for us you know the organisation and of course you've got the CTO at so you know what that means inside your organization sure so what a fun idea is a company that came came to acclaim as a result of a merger about 18 months ago so the number two and number three operators in India which was Vodafone an idea came together to create a telco serving over 300 million subscribers and we've been integrating the the networks over the last 18 months and consolidating and doing one of the largest integration in the world of two networks comprising over 200 thousand sites and carrying you know more than 50 billion MB of traffic per hour per day serving more than 40 million voltaic customers and we have been duplicating the network very very busy with her and we taken down so far almost a hundred thousand base stations which is equivalent to the size of a large operator in us so that's about the carnahan that is about the scale of the the operator that word of an idea is and what we've been busy with for the last day yeah well well Besant of course the reason we're doing this event online is because right now with the global pandemic the vast majority of the population they're at home so you know healthcare of course you know one of the major concerns I actually have done interviews with some of the power and energy companies critically important at this time but you know telecommunications you know what one of the top of the list you know in normal times for what people need but today it's the the only way that we can all connect it so tell us a little bit about do you know what the current situation you know the impact and importance this really highlights of your business yeah so just as the rest of the world India is also in a lockdown and India actually has one of the largest the largest lockdown in the world putting all 1.3 billion people in a lockdown yeah across the entire country so within that context the telecom network is crucial to make sure that the life goes on the essential services are delivered the industry continues to still operate as the best it can and all of that is made possible because of a stable and reliable network that we offer so a huge huge impact on the society always has been but in in this current context it is even more more critical and crucial so what we do is we make sure that we are the invisible layer you talked about health healthcare workers and emergency services well we are the invisible essential service that probably many people don't see but we are the ones who are really helping this country survive this this crisis and so far we have seen 25 30% increase in traffic in a single day in one week we experienced the same amount of traffic growth we would have experienced in the entire year so we we scalability is very very critical in our network and we've been able to keep up with that kind of a growth and continue to serve the communities and in this crucial juncture and all this dude large extent has been made possible because of a large-scale deployment of cloud technologies that we have done over the last 18 months which has really helped us scale up a large lot of our capabilities in the back yeah I'd love if you could explain a little bit more on that it you know challenging times you know I'm curious the amount of people that are using your services probably haven't changed but the demand and how much they're using it as change a lot so cloud obviously gives you scalability but you know are there concerns about what this does the profitability how you maintain things how much of this is a temporary change and how much will this be you know I know in the United States there's a lot of talk about how much work from home will become more of a standard than it had been before this pandemic so you know short term what's the impact on your business and what are you and other telecommunication companies thinking about what long-term impact this will have >> I think that's a very very interesting question I think even for me and my organization what we have been able to do working from home is amazing I never would have thought that it was possible to do as much as we've been able to do just staying young with most of the work for staying at home and that has really I think happened across industries across the entire country I think many organizations have now realized that work from home or work from anywhere which is the other term he's gonna become quite possible and prevalent going forward because people have realized that you can just get you can get just as much productivity out you can get so many things done working from home and it gives so much more personal flexibility to the individuals so I see when I look back at our organizational experience I see our productivity has been actually quite good actually better then haha where probably even in the office days so I think that is definitely one thing that is gonna come out as a global change across all industries I think the second thing that is gonna happen is data analytics I think there is going to be far more analysis of data to understand patterns and understand trends and how to take advantage of that I think of course the immediate application is in the healthcare and the spread of the pandemic but I think this will spur a lot of other analytics I think the third thing is going to do is the adoption of digital as the primary mode digital was already something that most companies are working on as is a top priority but I think going outward is gonna become very evident to people that it is actually essential just talking about my business I can tell you today you know all the stores all the shops every place that we used to check our cell or recharge vouchers are closed so the only place we are able to get any revenue from is our digital channel and on end only place where customers have been able to recharge their prepaid subscriptions etc has all been through digital I think digital we will also become a massive massive requirement so in that context I think telecom will be seen as a critical critical backbone I think to a large extent it has been seen by many in the past is more of an essential commodity but I think many organizations will realise that this is actually a value creator so I think it's a great exciting opportunity for us to take advantage of those new business opportunities that will come and at the same time be a very very important player in the digital economy that every nation around the world is gonna press you know for Sean said it really appreciates some really good commentary there you know we've been talking for years about customers going through their digital transformation it's really about the data and how they leverage that and if you're data-driven then you really have gone through that transformation and you kind of described what we call the new innovation cocktail you're leveraging cloud that there's data you put those all together as to how you drive your company and you can drive innovation oftentimes when we think about what results we're going to get from deploying cloud and using these types of new technologies we think we know what we're gonna get but the reality of how your company is dealing with things today of course you know proves what you were hoping that build for here help us understand you know what we're talking here is part of red hat summit this week you know what's red hats role in this piece and you know how did the reality of rolling this out and then how it has helped you in the current global situation impacted your business sure oh so I would say actually the three words that I used digital cloud and analytics to me they're actually inseparable cuz I do not believe that you can have a digital business that is not based on cloud or that is not good at data analytics I think if you want to really have a successful cloud offering it implies that automatically that you are a digital business and you're gonna do extensive modern data and analytics and build those capabilities I think those are three inseparable terms now speaking specifically about a red head I would say that red head has been a very very critical partner for us right from the beginning 18 months ago when the two companies too came together to create this network we knew that we had to do several things number one was actually to have a completely rationalized structure which was around extracting the synergies from the from the merger but beyond that we needed to build a telco of the future technology company of the future which will let us transform the business and create capabilities that will give us a step ahead a leapfrog ahead of our competition and cloud was a very very essential part of the journey and we knew we needed to build a cloud based on open systems because we did not want to get into a proprietary logins with anybody and we are a very large business we have suffered a sufficiently large scale to really be able to build a very large cloud so we started working with Red Hat about two years ago and it in the last two years we have deployed 80 plus cloud locations distributed cloud locations across the country and these all of these clouds our vision is to orchestrate them as a single cloud our vision is to build a cloud there is a universal cloud actually that is the word there is a word we use when we talk about cloud it's a universal cloud what does that mean that means that cloud will carry not only the traditional telco workloads but it will also carry IT workloads it will also carry lot of the enterprise offerings we have so - for the end-user for our enterprise clients and all of those capabilities out to be accommodated with a platform that is versatile that is scalable and that is gonna give me in enormous amount of flexibility and control as a organization so Red Hat has been a very important part of the journey and on the red head OpenStack cloud today I have a Daffy's working from any major supplier you can think of I have any enemies working from Nokia Ericsson Huawei ziti even some smaller players like Marvin here so we have demonstrated that this is possible we've been able to break the lock in that the traditional naps have had on their cloud offerings which were really more of a virtualized offerings rather than a cloud computer is a truly universal cloud on the back of the technology provided by a red well that's that's fantastic congratulations on that I love the the result of what you're calling Universal Cloud is the promise that we talked about for a number of years you know is that nitty gritty networking piece it was like you know network functions virtualization and if be sitting an open stack and everybody's like well OpenStack am I trying to build a cloud to compete against the public cloud providers it was like no what you said exactly there's services that you want to be able to deliver and it's not just about oh we're getting away from hardware appliances it's you know just like most people today they're used to whatever smart device they're doing I want to be able to turn on channels and access new things that's your now you know reducing that barrier to Vodafone idea to deliver that to your users have I captured that properly that is correct as a matter of fact I'll just give you one proof point my water phone app is the app that we we have for our consumers and that app is currently running on my telco clock what used to be called the telco cloud so on that platform we are running my packet or actually there are about 40 and FB is for virtualized traditional calculations running alongside with an IT application a digital application okay so one of the things I you know I would like to understand there that what you've deployed there over the last couple of years sounds like a significant shift so you know you're talking about apps you're talking more of a developer type of environment bring us inside a little organizationally you know what new skills have new people had to learn has there been new people added to the organization have there been in a restructures what what is this this this whole initiative to get to universal cloud meant for your organization sure so I look after both the network and IT pieces of the part of parts of the company and you know we traditionally were in the past legacy we have had a IT cloud and we have heard indigo cloud what we are now creating is a single universal cloud what either of the two workloads are gonna be facilitated so for that actually the two organization the two parts of the organization need to come together and start to really work as one now it is very important that the telco guys understand the scale and the 99-year the five nines required in a running a network but at the same time IT guys also understand very much what all of the the flexibility that the business requires and the responsiveness required for the for the enterprise so bringing those two talents together I think in infusing that to create a single organization is one of the biggest challenges I think any telco has we also face it that is one aspect of it the second aspect of it is that there just aren't too many cloud experts in the world and we have been struggling with that I think skill shortage is a clear challenge for us now we try to address it using variety of means we of course try to upscale rescale lot of the traditional network core engineers that we have had we also try to use talent available or from consultants and then we also try to use our vendors so one of the concepts we've been working with our vendors is a concept of a resident engineer so we try to actually get them to second some of their engineers to work with us and at the same time we've been now working with both IBM and redhead to create a program to really go out and create a community around us of developers who can really work with this cloud and therefore we will have enough of skills available to leverage all of the potential benefits there are then the platform but can only be unleashed if I have the right skills and right people you touched on a very important issue it is a challenge but we are working our way through it and so far we've been a bit we make good all right well if it's shot I can't let a CTO go without looking a little bit into the future so want to help understand we talked about some of the technologies talked about transformation of what's happening your business what's happening your organization and there's some big waves coming week you know cloud is still in early days 5g of course you know is expected to have massive impact on on everyone's environment for this so what is the winning formula for the the telecoms going forward well I think Phi G is an exciting world we are a 4G network today the Phi G spectrum hasn't been auctioned in India but what we are building today is what I call a 4G plus network which means the lot of the architectural principles of PI G we have already applied in my core networks today and in my transport network in that world I think IOT is gonna play a very very big role and if you want to do things like IOT and if you want to do things like blockchain now I think telco cloud has a huge role to play because we are the telcos are traditionally the only ones in a country anywhere in the world who have experiences experience in operating in very far front powerful places dealing with lot of the infrastructure challenges especially if you're in a developing country you know that you have to work with a poor power availability poor transport etc I do not see any of the big guys the the big cloud players really having those capabilities today I think telcos are gonna play a very big role in enabling that pi g io t work and it is going to be an exciting journey for telcos I think telcos will very soon be called tech companies that is one thing that I strongly believe in I think also many of the things that depend on blockchain will require the kind of cloud that telcos will create because a telco cloud is far more demanding than a traditional IT application in many ways for example latency or for example throughputs now all those things aren't very important in blockchain apple type of applications I think that's another exciting opportunity for telcos really is to get into that and of course there are discussions about smart cities smart government government and because of Kovach kharkova crisis I think many governments are gonna explore new ways of organizing Society's new ways of governing economic activities and the backbone for a lot of those things is gonna be our telecom networks and the cloud distributed clouds to the edge that we create so I think it'll create many many exciting business opportunities as a consequence of some of those technological innovation yeah Shanta I can't remember who said it as they said don't waste a crisis but Vasant Bora CTO of Vodafone idea pleasure talking with you thank you so much for joining us hope you enjoy the Red Hat event as it is distributed this year and definitely look to be able to meet you sometime at a future physical event back when we have those in the future Thank You Stu it's been a pleasure meeting you virtually and look forward to these all right lots more coverage from the cubes Red Hat summit at 20/20 activity I'm Stu minimun and thanks as always for watching [Music]

>>from the Cube Studios in >>Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. Everybody welcome this special digital presentation where we're covering the topic of data ops and specifically how IBM is really operationalize ing and automating the data pipeline with data office. And with me is Interpol Bhandari, who is the global chief data officer at IBM and Paul. It's always great to see you. Thanks for coming on. >>My pleasure. >>So, you know the standard throwaway question from guys like me And you know what keeps the chief data officer up at night? Well, I know what's keeping you up that night. It's coverted 19. How you >>doing? It's keeping keeping all of us. >>Yeah, for sure. Um, So how are you guys making out as a leader I'm interested in, You know, how you have responded would whether it's communications. Obviously you're doing much more stuff you remotely You're not on airplanes. Certainly like you used to be. But But what was your first move when you actually realized this was going to require a shift? >>Well, I think one of the first things that I did wants to test the ability of my organization, You work remotely. This was well before the the recommendations came in from the government just so that we wanted to be sure that this is something that we could pull off if there were extreme circumstances where even everybody was. And so that was one of the first things we did along with that. I think another major activity that's being boxed off is even that we have created this Central Data and AI platform for idea using our hybrid, multi cloud approach. How could that the adaptive very, very quickly help them look over the city? But those were the two big items that my team and my embarked on and again, like I said, this is before there was any recommendations from the government or even internally, within IBM. Have any recommendations be? We decided that we wanted to run ahead and make sure that we were ready to ready to operate in that fashion. And I believe a lot of my colleagues did the same. Yeah, >>there's a there's a conversation going on right now just around productivity hits that people may be taking because they really weren't prepared it sounds like you're pretty comfortable with the productivity impact that you're achieving. >>Oh, I'm totally comfortable with the politics. I mean, in fact, I will tell you that while we've gone down this spot, we've realized that in some cases the productivity is actually going to be better when people are working from home and they're able to focus a lot more on the work, you know, And this could. This one's the gamut from the nature of the jaw, where you know somebody who basically needs to be in the front of the computer and is remotely taking care of operations. You know, if they don't have to come in, their productivity is going to go up Somebody like myself who had a long drive into work, you know, which I would use a phone calls, but that that entire time it can be used a lot more productivity, locked in a lot more productive manner. So there is. We realized that there's going to be some aspect of productivity that will actually be helped by the situation. Why did you are able to deliver the services that you deliver with the same level of quality and satisfaction that you want Now there were certain other aspect where you know the whole activity is going to be effective. So you know my team. There's a lot off white boarding that gets done there lots off informal conversations that spot creativity. But those things are much harder to replicate in a remote and large. So we've got a sense off. You know where we have to do some work? Well, things together. This is where we're actually going to be mobile. But all in all, they're very comfortable that we can pull this off. >>That's great. I want to stay on Cove it for a moment and in the context of just data and data ops, and you know why Now, obviously, with a crisis like this, it increases the imperative to really have your data act together. But I want to ask you both specifically as it relates to covert, why Data office is so important. And then just generally, why at this this point in time, >>So, I mean, you know, the journey we've been on. Thank you. You know, when I joined our data strategy centered around cloud data and ai, mainly because IBM business strategy was around that, and because there wasn't the notion off AI and Enterprise, right, there was everybody understood what AI means for the consumer. But for the enterprise, people don't really understand. Well, what a man. So our data strategy became one off, actually making IBM itself into an AI and and then using that as a showcase for our clients and customers who look a lot like us, you make them into AI. And in a nutshell, what that translated to was that one had two in few ai into the workflow off the key business processes off enterprise. So if you think about that workflow is very demanding, right, you have to be able to deliver. They did not insights on time just when it's needed. Otherwise, you can essentially slow down the whole workflow off a major process within an end. But to be able to pull all that off you need to have your own data works very, very streamlined so that a lot of it is automated and you're able to deliver those insights as the people who are involved in the work floor needed. So we've spent a lot of time while we were making IBM into any I enterprise and infusing AI into our key business processes into essentially a data ops pipeline that was very, very streamlined, which then allowed us to do very quickly adapt do the over 19 situation and I'll give you one specific example that will go to you know how one would someone would essentially leverage that capability that I just talked about to do this. So one of the key business processes that we have taken a map, it was our supply chain. You know, if you're a global company and our supply chain is critical, you have lots of suppliers, and they are all over the globe. And we have different types of products so that, you know, has a multiplication factors for each of those, you have additional suppliers and you have events. You have other events, you have calamities, you have political events. So we have to be able to very quickly understand the risks associated with any of those events with regard to our supply chain and make appropriate adjustments on the fly. So that was one off the key applications that we built on our central data. And as Paul about data ops pipeline. That meant we ingest the ingestion off those several 100 sources of data not to be blazingly fast and also refresh very, very quickly. Also, we have to then aggregate data from the outside from external sources that had to do with weather related events that had to do with political events. Social media feeds a separate I'm overly that on top off our map of interest with regard to our supply chain sites and also where they were supposed to deliver. We also leave them our capabilities here, track of those shipments as they flowed and have that data flow back as well so that we would know exactly where where things were. This is only possible because we had a streamline data ops capability and we have built this Central Data and AI platform for IBM. Now you flip over to the Coleman 19 situation when Corbyn 19 merged and we began to realize that this was going to be a significant significant pandemic. What we were able to do very quickly wants to overlay the over 19 incidents on top of our sites of interest, as well as pick up what was being reported about those sites of interests and provide that over to our business continuity. So this became an immediate exercise that we embark. But it wouldn't have been possible if you didn't have the foundation off the data office pipeline as well as that Central Data and AI platform even plays to help you do that very, very quickly and adapt. >>So what I really like about this story and something that I want to drill into is it Essentially, a lot of organizations have a really tough time operational izing ai, infusing it to use your word and the fact that you're doing it, um is really a good proof point that I want to explore a little bit. So you're essentially there was a number of aspects of what you just described. There was the data quality piece with your data quality in theory, anyway, is going to go up with more data if you can handle it and the other was speed time to insight, so you can respond more quickly if it's talk about this Covic situation. If you're days behind for weeks behind, which is not uncommon, sometimes even worse, you just can't respond. I mean, the things change daily? Um, sometimes, Certainly within the day. Um, so is that right? That's kind of the the business outcome. An objective that you guys were after. >>Yes, you know, So Rama Common infuse ai into your business processes right over our chain. Um, don't come metric. That one focuses on is end to end cycle time. So you take that process the end to end process and you're trying to reduce the end to end cycle time by several factors, several orders of magnitude. And you know, there are some examples off things that we did. For instance, in my organ organization that has to do with the generation of metadata is data about data. And that's usually a very time consuming process. And we've reduced that by over 95%. By using AI, you actually help in the metadata generation itself. And that's applied now across the board for many different business processes that, you know IBM has. That's the same kind of principle that was you. You'll be able to do that so that foundation essentially enables you to go after that cycle time reduction right off the bat. So when you get to a situation like over 19 situation which demands urgent action. Your foundation is already geared to deliver on that. >>So I think actually, we might have a graphic. And then the second graphic, guys, if you bring up a 2nd 1 I think this is Interpol. What you're talking about here, that sort of 95% reduction. Ah, guys, if you could bring that up, would take a look at it. So, um, this is maybe not a cove. It use case? Yeah. Here it is. So that 95% reduction in the cycle time improvement in data quality. What we talked about this actually some productivity metrics, right? This is what you're talking about here in this metadata example. Correct? >>Yeah. Yes, the metadata. Right. It's so central to everything that one does with. I mean, it's basically data about data, and this is really the business metadata that you're talking about, which is once you have data in your data lake. If you don't have business metadata describing what that data is, then it's very hard for people who are trying to do things to determine whether they can, even whether they even have access to the right data. And typically this process is being done manually because somebody looks at the data that looks at the fields and describe it. And it could easily take months. And what we did was we essentially use a deep learning and natural language processing of road. Look at all the data that we've had historically over an idea, and we've automated metadata generation. So whether it was, you know, you were talking about the data relevant for 19 or for supply chain or far receivable process any one of our business processes. This is one of those fundamental steps that one must go through. You'll be able to get your data ready for action. And if you were able to take that cycle time for that step and reduce it by 95% you can imagine the acceleration. >>Yeah, and I like you were saying before you talk about the end to end concept, you're applying system thinking here, which is very, very important because, you know, a lot of a lot of clients that I talk to, they're so focused on one metric maybe optimizing one component of that end to end, but it's really the overall outcome that you're trying to achieve. You may sometimes, you know, be optimizing one piece, but not the whole. So that systems thinking is very, very important, isn't it? >>The systems thinking is extremely important overall, no matter you know where you're involved in the process off designing the system. But if you're the data guy, it's incredibly important because not only does that give you an insight into the cycle time reduction, but it also give clues U N into what standardization is necessary in the data so that you're able to support an eventual out. You know, a lot of people will go down the part of data governance and the creation of data standards, and you can easily boil the ocean trying to do that. But if you actually start with an end to end, view off your key processes and that by extension the outcomes associated with those processes as well as the user experience at the end of those processes and kind of then work backwards as one of the standards that you need for the data that's going to feed into all that, that's how you arrive at, you know, a viable practical data standards effort that you can essentially push forward so that there are multiple aspect when you take that end to end system view that helps the chief legal. >>One of the other tenants of data ops is really the ability across the organization for everybody to have visibility. Communications is very key. We've got another graphic that I want to show around the organizational, you know, in the right regime, and it's a complicated situation for a lot of people. But it's imperative, guys, if you bring up the first graphic, it's a heritage that organizations, you know, find bringing the right stakeholders and actually identify those individuals that are going to participate so that this full visibility everybody understands what their roles are. They're not in silos. So, guys, if you could show us that first graphic, that would be great. But talk about the organization and the right regime there. Interpol? >>Yes, yes, I believe you're going to know what you're going to show up is actually my organization, but I think it's yes, it's very, very illustrative what one has to set up. You'll be able to pull off the kind of impact that I thought So let's say we talked about that Central Data and AI platform that's driving the entire enterprise, and you're infusing AI into key business processes like the supply chain. Then create applications like the operational risk in size that we talked about that extended over. Do a fast emerging and changing situation like the over 19. You need an organization that obviously reflects the technical aspects of the right, so you have to have the data engineering on and AI on. You know, in my case, there's a lot of emphasis around deep learning because that's one of those skill set areas that's really quite rare, and it also very, very powerful. So uh huh you know, the major technology arms off that. There's also the governance on that I talked about. You have to produce the set off standards and implement them and enforce them so that you're able to make this into an impact. But then there's also there's a there's an adoption there. There's a There's a group that reports into me very, very, you know, Empowered Group, which essentially has to convince the rest of the organization to adopt. Yeah, yeah, but the key to their success has been in power in the sense that they're on power. You find like minded individuals in our key business processes. We're also empowered. And if they agree that just move forward and go and do it because you know, we've already provided the central capabilities by Central. I don't mean they're all in one location. You're completely global and you know it's it's It's a hybrid multi cloud set up, but it's a central in the sense that it's one source to come for for trusted data as well as the the expertise that you need from an AI standpoint to be able to move forward and deliver the business out. So when these business teams come together, be an option, that's where the magic happens. So that's another another aspect of the organization that's critical. And then we've also got, ah, Data Officer Council that I chair, and that has to do with no people who are the chief data officers off the individual business units that we have. And they're kind of my extended teams into the rest of the organization, and we levers that bolt from a adoption off the platform standpoint. But also in terms of defining and enforcing standards. It helps them stupid. >>I want to come back over and talk a little bit about business resiliency people. I think it probably seen the news that IBM providing supercomputer resource is that the government to fight Corona virus. You've also just announced that that some some RTP folks, um, are helping first responders and non profits and providing capabilities for no charge, which is awesome. I mean, it's the kind of thing. Look, I'm sensitive companies like IBM. You know, you don't want to appear to be ambulance chasing in these times. However, IBM and other big tech companies you're in a position to help, and that's what you're doing here. So maybe you could talk a little bit about what you're doing in this regard. Um, and then we'll tie it up with just business resiliency and importance of data. >>Right? Right. So, you know, I explained that the operational risk insights application that we had, which we were using internally, we call that 19 even we're using. We're using it primarily to assess the risks to our supply chain from various events and then essentially react very, very quickly. Do those doodles events so you could manage the situation. Well, we realize that this is something that you know, several non government NGOs that they could essentially use. There's a stability because they have to manage many of these situations like natural disaster. And so we've given that same capability, do the NGOs to you and, uh, to help that, to help them streamline their planning. And there's thinking, by the same token, But you talked about over 19 that same capability with the moment 19 data over layed on double, essentially becomes a business continuity, planning and resilience. Because let's say I'm a supply chain offers right now. I can look at incidents off over night, and I can I know what my suppliers are and I can see the incidents and I can say, Oh, yes, no, this supplier and I can see that the incidences going up this is likely to be affected. Let me move ahead and stop making plans backup plans, just in case it reaches a crisis level. On the other hand, if you're somebody in revenue planning, you know, on the finance side and you know where you keep clients and customers are located again by having that information over laid that those sites, you can make your own judgments and you can make your own assessment to do that. So that's how it translates over into business continuity and resolute resilience planning. True, we are internally. No doing that now to every department. You know, that's something that we're actually providing them this capability because we build rapidly on what we have already done to be able to do that as we get inside into what each of those departments do with that data. Because, you know, once they see that data, once they overlay it with their sights of interest. And this is, you know, anybody and everybody in IBM, because no matter what department they're in, there are going to decide the interests that are going to be affected. And they haven't understanding what those sites of interest mean in the context off the planning that they're doing and so they'll be able to make judgments. But as we get a better understanding of that, we will automate those capabilities more and more for each of those specific areas. And now you're talking about the comprehensive approach and AI approach to business continuity and resilience planning in the context of a large IT organization like IBM, which obviously will be of great interest to our enterprise, clients and customers. >>Right? One of the things that we're researching now is trying to understand. You know, what about this? Prices is going to be permanent. Some things won't be, but we think many things will be. There's a lot of learnings. Do you think that organizations will rethink business resiliency in this context that they might sub optimize profitability, for example, to be more prepared crises like this with better business resiliency? And what role would data play in that? >>So, you know, it's a very good question and timely fashion, Dave. So I mean, clearly, people have understood that with regard to that's such a pandemic. Um, the first line of defense, right is is not going to be so much on the medicine side because the vaccine is not even available and will be available for a period of time. It has to go through. So the first line of defense is actually think part of being like approach, like we've seen play out across the world and then that in effect results in an impact on the business, right in the economic climate and on the business is there's an impact. I think people have realized this now they will honestly factor this in and do that in to how they do become. One of those things from this is that I'm talking about how this becomes a permanent. I think it's going to become one of those things that if you go responsible enterprise, you are going to be landing forward. You're going to know how to implement this, the on the second go round. So obviously you put those frameworks and structures in place and there will be a certain costs associated with them, and one could argue that that would eat into the profitability. On the other hand, what I would say is because these two points really that these are fast emerging fluid situations. You have to respond very, very quickly. You will end up laying out a foundation pretty much like we did, which enables you to really accelerate your pipeline, right? So the data ops pipelines we talked about, there's a lot of automation so that you can react very quickly, you know, data injection very, very rapidly that you're able to do that kind of thing, that meta data generation. That's the entire pipeline that you're talking about, that you're able to respond very quickly, bring in new data and then aggregated at the right levels, infuse it into the work flows on the delivery, do the right people at the right time. Well, you know that will become a must. But once you do that, you could argue that there's a cost associated with doing that. But we know that the cycle time reductions on things like that they can run, you know? I mean, I gave you the example of 95% 0 you know, on average, we see, like a 70% end to end cycle time where we've implemented the approach, and that's been pretty pervasive within IBM across the business. So that, in essence, then actually becomes a driver for profitability. So yes, it might. You know this might back people into doing that, but I would argue that that's probably something that's going to be very good long term for the enterprises and world, and they'll be able to leverage that in their in their business and I think that just the competitive director off having to do that will force everybody down that path. But I think it'll be eventually ago >>that end and cycle time. Compression is huge, and I like what you're saying because it's it's not just a reduction in the expected loss during of prices. There's other residual benefits to the organization. Interpol. Thanks so much for coming on the Cube and sharing this really interesting and deep case study. I know there's a lot more information out there, so really appreciate your done. >>My pleasure. >>Alright, take everybody. Thanks for watching. And this is Dave Volante for the Cube. And we will see you next time. Yeah, yeah, yeah.

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi and welcome to the cube Studios for another cube conversation where we go in-depth with thought leaders driving innovation across the tech industry I'm your host Peter Burris one of the dominant considerations that every business faces today is how do they work through the complex outcomes associated with cybersecurity as they find new ways to use their data and apply it to new classes of customer and market problems this is not a small problem especially given that so many bad actors out there are now also seeing a company's data as a potential enormous source of value now to see what businesses are doing to try to achieve those complex outcomes while at the same time lowering their overall security risk we've got a great conversation first off welcoming back Eric Herzog who's the chief marketing officer and vice president of worldwide storage channels from IBM storage Eric welcome back to the queue yeah thank you love to come and Eric you bought with you a really distinguished individual kaeleen Sanchez as vice president of IBM worldwide Systems lab service and some technical universities Colleen welcome back to the cube thank you so let's get the quick update where are we in this world of the outcomes of businesses seeking let's start with you what what our business is trying to do with cybersecurity today protect data and ensure that we provide a certain level of security levels to enable the overall end-to-end protection holistically so it's really important that we enable a full stack hence the strong partnership with Eric and I too and also the global team to pull together solutions then enable data protection so let's talk about the reasons why it's becomes that much more acute or that needs to become that much more acute because we've got we've got the reality that everybody's going to get penetrated in the next year of any size that it takes a long time often to figure out that you have been penetrated you've got new types of attacks the old ones of just kind of know phishing and whatnot while still prevalent so a problem now we've got ransomware and we've got a lot of new types of actions that bad people are taking what are some of the things that we're trying to protect ourselves from these days so the B's thing is you mentioned ransomware it's like this idea that we want to protect and also act as a worm so to speak to provide an abstraction layer to enable protection holistically of any given solution because data can be everywhere nowhere so yeah there's discussions about it's the new oil it's not the new oil necessarily it's pervasive it's everywhere so our data from our perspective can be in any device any media type and we need to figure out how to protect it at its core and so you see it as a full stack that just means we have to go lower in layers in order to protect the overall data so kind of what you're saying is that the more the security is closer to the data the more the data itself is secure the less reliant we are on policy which can lead to human error or human mistakes which could allow folks come in I got that right you're correct it's smart data it's this idea that it's multiple pieces and multiple owners of a maker checker policy that keeps the overall solution accountable that doesn't diminish the need for policy but Eric it certainly raises the specter or the spectrum of the fact that increasingly the smart folks within a business that are insuring or trying to diminish risk and ensuring assurance of the data need to start looking at how storage or the role that storage plays in this overall security framework I got that right yeah if you think about a traditional company their approach is we need to get security software to keep the bad guys out and they mean it's the Chiricahua for when we are breached to track them down talking to several CIOs at even midsize companies let alone the fortune 500 is sometimes it takes them days even weeks as you said to know they've even been training penetrated yet track it down while they're doing that imagine someone coming into your house and the police don't show up for 10 minutes even though your alarm went off and by the time the police go your house is totally empty and IBM stores you make sure that that doesn't happen it's as if everything is bolted down everything is locked and if they do steal something for example it's say write once read many technology they can't really use it right because it's wormed they can't change it so it's almost as if your TV required a fingerprint and even if they stole it they couldn't use your TV and that's the kind of thing I want to do is be pervasive and get enterprises as well as even small and mean courts to realize an overall cyber resiliency and security strategy involves keeping the bad guys out email will track them down but when they are in the house making sure everything is secure and essentially nothing can be stolen or utilized of your incredibly valuable data so using your metaphor of making sure the TV is bolted down or whatever is is bolted down that's however doesn't diminish the business's ability to move the TV if they want to if they have the rights and privileges to do so so let's talk about how the new tooling of storage is being bought together with some of the new services approaches to achieve these complex outcomes how is IBM looking at storage and storage related technologies as a as a foundation for achieving the new outcomes that the businesses want so for my services perspective we go in and partner with our core technologies within the storage portfolio to enable like something like bare metal to enable the armor around the overall solution we work to with the client to understand their pain points etc and how we optimize the solution to substantiate that we provide highly resilient flexible access to data but at the same time it's protected now this is a fast changing world and it's there's there's an enormous expertise both on the good side and the bad side obviously you've got you've got a development background talk a little bit about how IBM is relying on customers relying on universities other sources of deep knowledge about security issues and then translating that into IP that then finds itself into places like Eric storage portfolio so so we have processes like for instance the technical universities so we have discussions with an extended set of worldwide engineers and scientists to talk about specific important pain points related to cyber security so when we obtain that data we provide the training we collect information and then we provide or funnel that back into Eric's portfolio from an IBM storage perspective so Eric look you've you're an old man for an act as amaya as am i and so that is one area where security has not been an afterthought it's not been that separate how to what degree has that relationship between security and data and storage of permeated the way that you think about solutions solution directions and engaging your customers with your value propositions so one of the big things we've done is make sure that our security is across the entire portfolio primary data flash disk secondary data disk or tape and in fact as you know IBM is known for its hybrid multi cloud storage technology capable of easily and transparently tearing out to multiple public cloud providers when that data is in flight sure site better be encrypted so we've made sure that where this ransom where malware protection data encryption rest across the entire portfolio right once read many technology things like FIPS 140 - - which is a very important federal specification around security malware and ransomware protection with air gapping both to tape but also to cloud so we've made sure that the security aspect of storage is pervasive primary storage secondary storage cloud storage whatever you're doing your storage will always be secure so when they do breach the wall and they track the bad guy down as they're rooting around your file your block your object storage it's secure and they can't get anybody from the data you still can but they can't steal that data from you and that's a critical capability of spreading it beyond just the mainframe we have great technology with our new safeguard copy product we brought out last year that does incredible things to secure data but in fact we make sure that all sorts of security and resiliency technologies from an IBM perspective are spread even into our lowest end product our store wise 50 10 e has full data rest encryption encryption and flight so all those technologies everyone from the very entry products all the way up to our high-end product the DES family and everything in between yeah well one of the things about digital business is we're discovering new ways of leveraging data and unanticipated avenues to try to generate additional business and one of the things we've seen as we talk to customers is that increasingly that means that the weakest link in your security chain is going to be it's going to be instrumental at defining your overall security policy so treating security is an option is because you can secure something over here is increasingly difficult as you find new ways of integrating data so how is IBM helping to get customers to see that so I would say two points from lab services perspective as well as our business partners we we take on a consultative discussion or partnership so we learn from our clients and partners and users as much as they learn from us and we provide offerings to really explore that full stack to make that data smarter as we discussed before so digital business is happening it's transforming very rapidly IBM talks about the rise of the incumbents as they bring some of these digital native capabilities into their business I'm going to ask each of you for the one thing that you think is going to be most important for customers to think about this relationship between data storage and security Eric I'll start with you what do you think is the one thing that people need to start thinking more about over the course next year storage is not an afterthought in your secure strategy killing how about you co-create with our end users to enable the full and and prediction as you mentioned before and as you co-create don't forget that storage is intrinsic to whether or not it's secure or not great conversation thank you both for being here Eric Herzog's the chief marketing officer and vice president worldwide storage channels at IBM storage Colleen Sanchez is the vice president IBM worldwide Systems lab services and technical universities once again thank you both for being here and talking about this crucially important area thank you for having us thank you alright and once again I'm Peter Burroughs and until next time this has been a cube conversation [Music] you

>> Narrator: From Seattle, Washington it's the CUBE covering AWS Imagine non-profit. Brought to you by Amazon web services. >> Hey welcome back everybody, Jeff Frick here with the CUBE. We're on the waterfront in Seattle, it's an absolutely gorgeous couple of days here at the AWS Imagine Nonprofit Conference. We went to the AWS Imagine Education Conference, this is really all about nonprofits and we're hearing all kinds of interesting stories about how these people are using AWS to help conquer really big problems. We're going to shift gears a little bit from the two footed problems to the four footed problems and that's animals and everybody likes animals but nobody likes animal shelters and nobody likes the ultimate solution that many animal shelters used to use to take care of problems. But thank you to our next guest, that is not quite the case so much anymore. So we're really happy to have Angie Embree on. She is the CIO of Best Friends Animal Society, Angie great to see you. >> It's great to see you as well and thank you for having me. >> Oh absolutely! So before we got on I just heard this crazy, crazy statistic that when your organization started in 1984 approximately 17 million animals were killed in US shelters per year. That number is now down to 700 thousand, that is a giant, giant reduction. And yet you, with big audacious goals really are looking to get that to zero. So, that's a giant goal, give us a little bit of background on the organization and how you decided to go after a goal like that and some of the ways you are actually going to achieve it. >> Well, the organization started in 1984 and it started with a group of friends in Southern Utah who decided that, you know the killing in America's shelters just had to go. So really the Best Friends founders started the no-kill movement along with a gentleman in San Francisco by the name of Rich Avanzino. And as you said, they took you know the killing down from 17 million in 1984 to approximately 733 thousand now. The organization started as just the sanctuary, we have the largest no-kill companion animal sanctuary in the country where we hold about 17 hundred animals every day. And we also have, you know, knowing that we needed to help out the rest of the country we have built life saving centers in Houston, Texas. Or we're working on Houston, Texas but Los Angels, California, New York City, Salt Lake City, Atlanta, Georgia, it seems like I've left somebody out but, >> Probably, but that's okay. >> We have life saving centers all over the country. So it was really, you know, when they realized what was going on in America's shelters it was really the idea that we should not be killing animals for space. So, just recently in fact, I will say recently but in the last few years, Julie Castle our CEO put kind of, did our moon shot, put that stake in the ground and said we're going to take this country no-kill by the year 2025. >> Right. >> So it's super exciting. >> So it's really interesting because you guys are trying to execute your vision, and it's easy to execute your own vision, but it's a whole different thing when you're trying to execute your vision through this huge infrastructure of shelters that have been around forever. So, I wonder if you can explain kind of what's your relationship with shelters that you don't own. I guess, I think you said before we turned on the cameras there are affiliates, so how does that relationship work? How do you help them achieve your goal which is no-kill. >> Yeah, so we have over 27 hundred network partners around the country. And what we do is we help to educate them on, you know we understand their problems, we have creative programs to solve those problems. So we help to educate them on, you know, how they can implement these programs within their shelters. We provide them grant funding, we have an annual conference every year where they can come and learn. But they're really our partners and you know we know we can't do it alone. It's going to take us, it's going to take them and it's going to take everybody in every community to really step up and help solve the problem. >> Right, and what was the biggest thing that changed in terms of kind of attitude in terms of the way they operate the shelter because I think you said before that a lot of the killing was done to make room. >> Right, killing is done usually for space. >> So what do they do know? Clearly the space demands probably haven't changed so what are they doing alternatively where before they would put the animal down? >> Well alternatively we're doing transport programs. So there are areas in the country that actually have a demand for animals. So instead of killing the animals, we put them on some sort of transport vehicle and we take them to the areas that are in demand. We also do what's called a trap-neuter-return program. So one of the biggest problems across the country are community cats so those, a lot of people call them feral cats but they're community cats and usually have a caretaker. But what we do is we trap those cats, we take them into the shelter, we neuter them and vaccinate them and then return them to their home. That keeps them from making a lot of other little cats. >> Making babies (laughs) >> So yeah, cat's are one of the biggest problems in shelters today because of the community cats, they're feral cats and they're not adoptable. So if we can, we don't have to kill them. We can, you know, we can keep them from reproducing as I said and then we can put them back in their habitat where they live a long healthy life, happy life. >> Right, so you said you've joined the organization 5 years ago, 5 and 1/2 years ago and you're the CIO, first ever CIO. >> I am (laughs) >> What brought you here and then now that you're here with kind of a CIO hat, what are some of the new perspective that you can bring to the organization that didn't necessarily, that they had had before from kind of a technical perspective? >> Well, what brought me here was, I never expected to be here, if you would have told me I would be the CIO at Best Friends Animal Society you know 10 years ago I would have said you're kidding because I didn't really realize that there were professional positions in organizations like Best Friends. But I, you know, my journey begins the same as, began the same as a lot of peoples did. I was that little kid always bringing home animals and you know my mother hated it. You know it was always something showing up at our doorstep with me, you know. And I just loved animals all my life and as I went through college and got my degree and started my professional career, then I thought well I'm going to of course have animals because I can have as many as I want now, right! (laughs) So I started adopting, and I didn't even realize until I was in my 30s that they were killing in shelters and I learned that in Houston, Texas when I lived there. I was working for IBM at the time, and one day a lady came on the television and she said they were doing a new segment and she said we're a no-kill shelter and I thought oh my god if there are no-kill shelters then there are kill shelters, right? >> There must be the other. >> Yeah so, to make a long story short then I started not working in animal welfare but doing more to support the movement and donating. Adopting from shelters and fostering animals and then one day I had been to Best Friends as a visitor vacationing in this beautiful part of Utah. But I saw the CIO ... >> Position. >> position open and I said I'm going for it. >> Good for you. >> Yeah. >> Good for you, so now you're there so what are some of the things you've implemented from kind of a techy, you know kind of data perspective that they didn't have before? >> Well, they didn't have a lot. >> They probably didn't have a lot, besides email and the obvious things. >> Being the first CIO I don't know that I knew what I was walking into at the time because I got to Kanab, and Kanab Utah where the sanctuary is, is the headquarters. And Kanab is very infrastructure challenged. >> (laughs) Infrastructure challenged, I like that. >> There is one ISP in Kanab and there is no redundancy in networks so we really don't have, you know, you come from the city and you think, you take these things for granted and you find out oh my god, what am I going to do? And Kanab is you know the hub of our network, so if Kanab goes down, you know the whole organization is down so one of the first decisions I made was that we were going to the cloud. >> Right, right. >> Because we had to get Kanab out of that position and that was one of our, one of the first major decisions I made and we chose AWS as our partner to do that so that was very very exciting. We knew that they had infrastructure we couldn't dream of providing. >> Right, right. >> And, you know we could really make our whole network more robust, our applications would be available and we could really do some great things. >> You're not worried about the one ISP provider in Kanab because of an accident that knocks a phone pole down. >> Yeah, yeah. >> All right but then you're talking about some new things that you're working on and a new thing you talked about before we turned the cameras on community lifesaving dashboards, what is that all about? >> Okay, so a couple of years ago the community lifesaving dashboard is the culmination of two years of work. From all across the Best Friends organization not just the IT department, in fact it was the brainchild of our Chief Mission Officer Holly Sizemore. But it's really, in animal welfare there's never been a national picture of what the problem really is regarding killing animals in shelters. So we did this big. >> Because they're all regional right? They're all regional shelters, very local. >> They're all local community shelters, yes. And transparency isn't forced, so you know some states force transparency, they reinforce in the report numbers but a lot of states don't. >> At the state level. >> Yeah, a lot of states don't, so. You know when you're killing animals in shelters you really don't want people to know that. >> Yeah, yeah it's not something you want to advertise. >> Because the American public doesn't believe in it. So anyway we worked really hard to collect all this data from across the country and we put it all into this dashboard and it is now a tool where anybody in the public, it's on our website, can look at it and they can see that where we're at from a national level. They can see where they're at from a state level, they can drill down into their community and they can drill down to an individual shelter. >> Wow. >> And the idea behind the dashboard is to really, is to get communities behind helping their shelters. Because as I said earlier, it's going to take us all. >> Right. >> And not only Best Friends and our partners but the public plays a big part of this. >> Right, and so when did that roll out? Do you have any kind of feedback, how's it working? >> It's working wonderfully, we rolled it out at our conference in July. >> So recently, so it's a pretty new initiative. >> Yeah it's just a few weeks old. >> Okay. >> We rolled it out at our national conference and we were all a bit nervous about it, you know especially from a technology perspective. >> Right, right. >> We knew that being the first of it's kind ever in animal welfare that you know it was going to get a lot of publicity both inside and outside the movement. >> (laughs) How you want to say both pro and con. >> Yeah, and it's sitting on our website, well really pro and con. >> Right, right. >> But it's sitting on our website and we're like okay, we don't know what kind of traffic we're going to get, you know what are we going to do about this? So we spent a lot of time with Amazon prior to the launch, you know having them look at our environment and getting advice, discussing it with them. >> Not going to bring down that ISP in Utah. >> No, thank god! (laughs) >> (laughs) >> No it wasn't, thank god we were in the cloud. So Amazon really helped us prepare and then the day of the launch, we knew the time of the launch. So we actually had a war room set up, a virtual war room and we had Amazon employees participating in our war room. We watched the traffic and we did get huge spikes in traffic at all times through the day when certain things were happening. And I'm happy to say from a technology perspective it was a non-event because we did not crash we stayed up, we handled all the traffic, we scaled when we needed to, and we did it you know, virtually at the press of a button. >> Awesome. >> Or the flick of a switch, whatever you want to say. >> That's what you want right? >> Yeah, exactly. >> You just don't want anyone to know, I was like give a good ref, nobody's talking about you you probably did a good job. >> Yeah, exactly yeah. >> Good, so before I let you go so what are some of your initiatives now looking forward. You've got this great partner in AWS, you have basically as much horsepower as you need to get done what you need to get done. What are some of the things that you see, you know kind of next for your roadmap? >> Well, we have a lot. >> Don't give me the whole list (laughs) >> No I'm just going to hit on a few key points. I think, you know we used Amazon initially as our cloud infrastructure but I think the biggest thing we're looking at is platform as a service. There is so much capability out there with predictive analytics, machine learning, artificial intelligence, ARVR, you name it facial recognitions, so we're really investigating those technologies because we think they have you know they could have a huge impact on our movement and really help us achieve life saving. >> Right, right. >> And, I think that, you know we're starting we have our fledgling data science program. We're using the Amazon data lake technology, Athena, Glue, they were just telling me about data lake formation which I just a few minutes ago emailed my data guy and said start looking at data lake formation. >> Right, right. >> So, I mean we're really investing in the platform as a service. The other thing I see is that we're, animal welfare is sort of broken from a technology perspective and a data perspective. In that we have no interoperability and you know we don't have the data available. So lets say you want to adopt a 5-year old animal. Well, you go to a shelter you can't get 5 years of history on a 5 year old animal. So it's really starting to fix the foundation for the movement as a whole, not just Best Friends. So, making sure that you know the veterinary data is there, all the data from the pet ecosystem is there. So we're investigating with AWS they're actually coming to our sanctuary in a couple of months, we're going to do a workshop to figure out how we do this, how we really fix it so that we have interoperability between every shelter when an animal moves from shelter to rescue or whatever so that their data follows them wherever they go. So adopters are fully informed when adopting an animal. >> Because you're in a pretty interesting position, because you're not with any one particular shelter you kind of cross many many boundaries. So you're in a good position to be that aggregator of that data. >> Yeah, I don't know that we want to be the aggregator but we want to lead the movement towards doing that. Just getting the technology players, the shelter management systems, the other people who play a role in technology for animal welfare, getting them in a room and talking and figuring out this problem is huge. >> Right. >> And with a partner like Amazon we feel it can be solved. >> Right. Well Angie thank you for taking a few minutes and sharing your story, really really enjoyed hearing it. >> All right thank you so much. >> All right, she's Angie, I'm Jeff you're watching the CUBE we're at AWS Imagine in Seattle, thanks for watching we'll see you next time. (upbeat music)

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape all right buddy welcome to this cute conversation my name is Dave Ville on time or the co-host of the cube and we're gonna have a conversation to really try to explore does infrastructure matter you hear a lot today I've ever since I've been in this business I've heard Oh infrastructure is dead hardware is dead but we're gonna explore that premise and with me is Randy Arsenault and Steve Kenaston they're both global market development execs at IBM guys thanks for coming in and let's riff thanks for having us Dave so here's one do I want to start with the data we were just recently at the MIT chief data officer event 10 years ago that role didn't even exist now data is everything so I want to start off with you here this bro my data is the new oil and we've said you know what data actually is more valuable than oil oil I can put in my car I can put in my house but I can't put it in both data is it doesn't follow the laws of scarcity I can use the same data multiple times and I can copy it and I can find new value I can cut cost I can raise revenue so data in some respects is more valuable what do you think right yeah I would agree and I think it's also to your point kind of a renewable resource right so so data has the ability to be reused regularly to be repurposed so I would take it even further we've been talking a lot lately about this whole concept that data is really evolving into its own tier so if you think about a traditional infrastructure model where you've got sort of compute and network and applications and workloads and on the edge you've got various consumers and producers of that data the data itself has those pieces have evolved the data has been evolving as well it's becoming more complicated it's becoming certainly larger and more voluminous it's better instrumented it carries much more metadata it's typically more proximal with code and compute so the data itself is evolving into its own tier in a sense so we we believe that we want to treat data as a tier we want to manage it to wrap the services around it that enable it to reach its maximum potential in a sense so guys let's we want to make this interactive in a way and I'd love to give you my opinions as well as links are okay with that but but so I want to make an observation Steve if you take a look at the top five companies in terms of market cap in the US of Apple Google Facebook Amazon and of course Microsoft which is now over a trillion dollars they're all data companies they've surpassed the bank's the insurance companies the the Exxon Mobil's of the world as the most valuable companies in the world what are your thoughts on that why is that I think it's interesting but I think it goes back to your original statement about data being the new oil the and unlike oil Ray's said you can you can put it in house what you can't put it in your car you also when it's burnt it's gone right but with data you you have it around you generate more of it you keep using it and the more you use it and the more value you get out of it the more value the company gets out of it and so as those the reason why they continue to grow in value is because they continue to collect data they continue to leverage that data for intelligent purposes to make user experiences better their business better to be able to go faster to be able to new new things faster it's all part of part of this growth so data is one of the superpowers the other superpower of course is machine intelligence or what everybody talks about as AI you know it used to be that processing power doubling every 18 months was what drove innovation in the industry today it's a combination of data which we have a lot of it's AI and cloud for scaling we're going to talk about cloud but I want to spend a minute talking about AI when I first came into this business AI was all the rage but we didn't have the amount of data that we had today we don't we didn't have the processing power it was too expensive to store all this data that's all changed so now we have this emerging machine intelligence layer being used for a lot of different inks but it's sort of sitting on top of all these workloads that's being injected into databases and applications it's being used to detect fraud to sell us more stuff you know in real time to save lives and I'm going to talk about that but it's one of these superpowers that really needs new hardware architectures so I want to explore machine intelligence a little bit it really is a game changers it really is and and and tying back to the first point about sort of the the evolution of data and the importance of data things like machine learning and adaptive infrastructure and cognitive infrastructure have driven to your point are a hard requirement to adapt and improve the infrastructure upon which that lives and runs and operates and moves and breathes so we always had Hardware evolution or development or improvements and networks and the basic you know components of the infrastructure being driven again by advances in material science and silicon etc well now what's happening is the growth and importance and and Dynamis city of data is far outpacing the ability of the physical sciences to keep pace right that's a reality that we live in so therefore things like you know cognitive computing machine learning AI are kind of bridging the gap almost between the limitations we're bumping up against in physical infrastructure and the immense unlocked potential of data so that intermediary is really where this phenomenon of AI and machine learning and deep learning is happening and you're also correct in pointing out that it's it's everywhere I mean it's imbuing every single workload it's transforming every industry and a fairly blistering pace IBM's been front and center around artificial intelligence in cognitive computing since the beginning we have a really interesting perspective on it and I think we bring that to a lot of the solutions that we offer as well Ginni Rometty a couple years ago actually use the term incumbent disruptors and when I think of that I think about artificial intelligence and I think about companies like the ones I mentioned before that are very valuable they have data at their core most incumbents don't they have data all over the place you know they might have a bottling plant at the core of the manufacturing plant or some human process at the core so to close that gap artificial intelligence from the incumbents the appointees they're gonna buy that from companies like IBM they're gonna you know procure Watson or other AI tools and you know or maybe you know use open-source AI tools but they're gonna then figure out how to apply those to their business to do whatever fraud detection or recommendation engines or maybe even improve security and we're going to talk about this in detail but Steve this there's got to be new infrastructure behind that we can't run these new workloads on infrastructure that was designed 30 40 years ago exactly I mean I think I am truly fascinated by with this growth of data it's now getting more exponential and why we think about why is it getting more exponential it's getting more exponential because the ease at which you can actually now take advantage of that data it's going beyond the big financial services companies the big healthcare companies right we're moving further and further and further towards the edge where people like you and I and Randi and I have talked about the maker economy right I want to be able to go in and build something on my own and then deliver it to either as a service as a person a new application or as a service to my infrastructure team to go then turn it on and make something out of that that infrastructure it's got to come down in cost but all the things that you said before performance reliability speed to get there intelligence about data movement how do we get smarter about those things all of the underlying ways we used to think about how we managed protect secure that it all has evolved and it's continuing to evolve everybody talks about the journey the journey to cloud why does that matter it's not just the cloud it's also the the componentry underneath and it's gonna go much broader much bigger much faster well and I would just add just amplify what Steve said about this whole maker movement one of the other pressures that that's putting on corporate IT is it's driving essentially driving product development and innovation out to the end to the very edge to the end user level so you have all these very smart people who are developing these amazing new services and applications and workloads when it gets to the point where they believe it can add value to the business they then hand it off to IT who is tasked with figuring out how to implement it scale it protect it secured debt cetera that's really where I believe I um plays a key role or where we can play a key role add a lot of values we understand that process of taking that from inception to scale and implementation in a secure enterprise way and I want to come back to that so we talked about data as one of the superpowers an AI and the third one is cloud so again it used to be processor speed now it's data plus AI and cloud why is cloud important because cloud enables scale there's so much innovation going on in cloud but I want to talk about you know cloud one dot o versus cloud two dot o IBM talks about you know the new era of cloud so what was cloud one dot o it was largely lift and shift it was taking a lot of crap locations and putting him in the public cloud it was a lot of tests in dev a lot of startups who said hey I don't need to you know have IT I guess like the cube we have no ID so it's great for small companies a great way to experiment and fail fast and pay for you know buy the drink that was one dot o cloud to dot all to datos is emerging is different it's hybrid it's multi cloud it's massively distributed systems distributed data on Prem in many many clouds and it's a whole new way of looking at infrastructure and systems design so as Steve as you and I have talked about it's programmable so it's the API economy very low latency we're gonna talk more about what that means but that concept of shipping code to data wherever it lives and making that cloud experience across the entire infrastructure no matter whether it's on Prem or in cloud a B or C it's a complicated problem it really is and when you think about the fact that you know the big the big challenge we started to run into when we were talking about cloud one always shadow IT right so folks really wanted to be able to move faster and they were taking data and they were actually copying it to these different locations to be able to use it for them simply and easily well once you broke that mold you started getting away from the security and the corporate furnance that was required to make sure that the business was safe right it but it but it but following the rules slowed business down so this is why they continued to do it in cloud 2.0 I like the way you position this right is the fact that I no longer want to move data around moving data it within the infrastructure is the most expensive thing to do in the data center so if I can move code to where I need to be able to work on it to get my answers to do my AI to do my intelligent learning that all of a sudden brings a lot more value and a lot more speed and speed as time as money rate if I can get it done faster I get more valuable and then just you know people often talk about moving data but you're right on you the last thing you want to do is move data in just think about how long it takes to back up the first time you ever backed up your iPhone how long it took well and that's relatively small compared to all the data in a data center there's another subtext here from a standpoint of cloud 2.0 and it involves the edge the edge is a new thing and we have a belief inside of wiki bond and the cube that we talk about all the time that a lot of the inference is going to be done at the edge what does that mean it means you're going to have factory devices autonomous vehicles a medical device equipment that's going to have intelligence in there with new types of processors and we'll talk about that but a lot of the the inference is that conclusions were made real-time and and by the way these machines will be able to talk to each other so you'll have a machine to machine communication no humans need to be involved to actually make a decision as to where should I turn or you know what should be the next move on the factory floor so again a lot of the data is gonna stay in place now what does that mean for IBM you still have an opportunity to have data hubs that collect that data analyze it maybe push it up to the cloud develop models iterate and push it back down but the edge is a fundamentally new type of approach that we've really not seen before and it brings in a whole ton of new data yeah that's a great point and it's a market phenomenon that has moved and is very rapidly moving from smartphones to the enterprise right so right so your point is well-taken if you look in the fact is we talked earlier that compute is now proximal to the data as opposed to the other way around and the emergence of things like mesh networking and you know high bandwidth local communications peer-to-peer communications it's it's not only changing the physical infrastructure model and the and the best practices around how to implement that infrastructure it's also fundamentally changing the way you buy them the way you consume them the way you charge for them so it's it's that shift is changing and having a ripple effect across our industry in every sense whether it's from the financial perspective the operational perspective the time to market perspective it's also and we talked a lot about industry transformation and disruptors that show up you know in an industry who work being the most obvious example and just got an industry from the from the bare metal and recreate it they are able to do that because they've mastered this new environment where the data is king how you exploit that data cost-effectively repeatably efficiently is what differentiates you from the pack and allows you to create a brand new business model that that didn't exist prior so that's really where every other industry is going you talking about those those those big five companies in North America that are that are the top top companies now because of data I often think about rewind you know 25 years do you think Amazon when they built Amazon really thought they were going to be in the food service business that the video surveillance business the drone business all these other book business right maybe the book business right but but their architecture had to scale and change and evolve with where that's going all around the data because then they can use these data components and all these other places to get smarter bigger and grow faster and that's that's why they're one of the top five this is a really important point especially for the young people in the audience so it used to be that if you were in an industry if you were in health care or you were in financial services or you were in manufacturing you were in that business for life every industry had its own stack the sales the marketing the R&D everything was wired to that industry and that industry domain expertise was really not portable across businesses because of data and because of digital transformations companies like Amazon can get into content they can get into music they can get it to financial services they can get into healthcare they can get into grocery it's all about that data model being portable across those industries it's a very powerful concept that you and I mean IBM owns the weather company right so I mean there's a million examples of traditional businesses that have developed ways to either enter new markets or expand their footprint in existing markets by leveraging new sources of data so you think about a retailer or a wholesale distributor they have to very accurately or as accurately as possible forecast demand for goods and make sure logistically the goods are in the right place at the right time well there are million factors that go into that there's whether there's population density there's local cultural phenomena there's all sorts of things that have to be taken into consideration previously that would be near impossible to do now you can sit down again as an individual maker I can sit down at my desk and I can craft a model that consumes data from five readily available public api's or data sets to enhance my forecast and I can then create that model execute it and give it to two of my IT guy to go scale-out okay so I want to start getting into the infrastructure conversation again remember the premise of this conversation it doesn't read for structure matter we want to we want to explore that oh I start at the high level with with with cloud multi-cloud specifically we said cloud 2.0 is about hybrid multi cloud I'm gonna make a statements of you guys chime in my my assertion is that multi cloud has largely been a symptom of multi-vendor shadow IT different developers different workloads different lines of business saying hey we want to we want to do stuff in the cloud this happened so many times in the IT business all and then I was gonna govern it how is this gonna be secure who's got access control on and on and on what about compliance what about security then they throw it over to IT and they say hey help us fix this and so itea said look we need a strategy around multi cloud it's horses for courses maybe we go for cloud a for our collaboration software cloud B for the cognitive stuff cloud C for the you know cheap and deep storage different workloads for different clouds but there's got to be a strategy around that so I think that's kind of point number one and I T is being asked to kind of clean up this stuff but the future today the clouds are loosely coupled there may be a network that connects them but there's there's not a really good way to take data or rather to take code ship it to data wherever it lives and have it be a consistent well you were talking about an enterprise data plane that's emerging and that's kind of really where the opportunity is and then you maybe move into the control plane and the management piece of it and then bring in the edge but envision this mesh of clouds if you will whether it's on pram or in the public cloud or some kind of hybrid where you can take metadata and code ship it to wherever the data is leave it there much smaller you know ship five megabytes of code to a petabyte of data as opposed to waiting three months to try to ship you know petabytes to over the network it's not going to work so that's kind of the the spectrum of multi cloud loosely coupled today going to this you know tightly coupled mesh your guys thoughts on that yeah that's that's a great point and and I would add to it or expand that even further to say that it's also driving behavioral fundamental behavioral and organizational challenges within a lot of organizations and large enterprises cloud and this multi cloud proliferation that you spoke about one of the other things that's done that we talked about but probably not enough is it's almost created this inversion situation where in the past you'd have the business saying to IT I need this I need this supply chain application I need this vendor relationship database I need this order processing system now with the emergence of this cloud and and how easy it is to consume and how cost-effective it is now you've got the IT guys and the engineers and the designers and the architects and the data scientists pushing ideas to the business hey we can expand our footprint and our reach dramatically if we do this so you've get this much more bi-directional conversation happening now which frankly a lot of traditional companies are still working their way through which is why you don't see you know 100% cloud adoption but it drives those very productive full-duplex conversations at a level that we've never seen before I mean we encounter clients every day who are having these discussions are sitting down across the table and IT is not just doesn't just have a seat at the table they are often driving the go-to-market strategy so that's a really interesting transformation that we see as well in addition to the technology so there are some amazing things happening Steve underneath the covers and the plumbing and infrastructure and look at we think infrastructure matters that's kind of why we're here we're infrastructure guys but I want to make a point so for decades this industry is marked to the cadence of Moore's law the idea that you can double processing speeds every 18 months disk drive processors disk drives you know they followed that curve you could plot it out the last ten years that started to attenuate so what happened is chip companies would start putting more cores on to the real estate well they're running out of real estate now so now what's happening is we've seen this emergence of alternative processors largely came from mobile now you have arm doing a lot of offload processing a lot of the storage processing that's getting offloaded those are ARM processors in video with GPUs powering a lot of a lot of a is yours even seeing FPGAs they're simple they're easy them to spin up Asics you know making a big comeback so you've seen these alternative processes processors powering things underneath where the x86 is and and of course they're still running applications on x86 so that's one sort of big thing big change in infrastructure to support this distributed systems the other is flash we saw flash basically take out spinning disk for all high-speed applications we're seeing the elimination of scuzzy which is a protocol that sits in between the the the disk you know and the rest of the network that's that's going away you're hearing things like nvme and rocky and PCIe basically allowing stores to directly talk to the so now a vision envision this multi-cloud system where you want to ship metadata and code anywhere these high speed capabilities interconnects low latency protocols are what sets that up so there's technology underneath this and obviously IBM is you know an inventor of a lot of this stuff that is really gonna power this next generation of workloads your comments yeah I think I think all that 100% true and I think the one component that we're fading a little bit about it even in the infrastructure is the infrastructure software right there's hardware we talked a lot talked about a lot of hardware component that are definitely evolving to get us better stronger faster more secure more reliable and that sort of thing and then there's also infrastructure software so not just the application databases or that sort of thing but but software to manage all this and I think in a hybrid multi cloud world you know you've got these multiple clauses for all practical purposes there's no way around it right marketing gets more value out of the Google analytic tools and Google's cloud and developers get more value out of using the tools in AWS they're gonna continue to use that at the end of the day I as a business though need to be able to extract the value from all of those things in order to make different business decisions to be able to move faster and surface my clients better there's hardware that's gonna help me accomplish that and then there are software things about managing that whole consetta component tree so that I can maximize the value across that entire stack and that stack is multiple clouds plus the internal clouds external clouds everything yeah so it's great point and you're seeing clear examples of companies investing in custom hardware you see you know Google has its own ship Amazon its own ship IBM's got you know power 9 on and on but none of this stuff works if you can't manage it so we talked before about programmable infrastructure we talked about the data plane and the control plane that software that's going to allow us to actually manage these multiple clouds as at least a quasi single entity you know something like a logical entity certainly within workload classes and in Nirvana across the entire you know network well and and the principal or the principle drivers of that evolution of course is containerization right so the containerization phenomenon and and you know obviously with our acquisition of red hat we're now very keenly aware and acutely plugged into the whole containerization phenomenon which is great we're you're seeing that becoming almost the I can't think of us a good metaphor but you're seeing containerization become the vernacular that's being spoken in multiple different types of reference architectures and use case environments that are vastly different in their characteristics whether they're high throughput low latency whether they're large volume whether they're edge specific whether they're more you know consolidated or hub-and-spoke models containerization is becoming the standard by which those architectures are being developed and with which they're being deployed so we think we're very well-positioned working with that emerging trend and that rapidly developing trend to instrument it in a way that makes it easier to deploy easier to instrument easier to develop so that's key and I want to sort of focus now on the relevance of IBM one side one thing that we understand because that that whole container is Asian think back to your original point Dave about moving data being very expensive and the fact that the fact that you want to move code out to the data now with containers microservices all of that stuff gets a lot easier development becomes a lot faster and you're actually pushing the speed of business faster well and the other key point is we talked about moving code you know to the data as you move the code to the data and run applications anywhere wherever the data is using containers the kubernetes etc you don't have to test it it's gonna run you know assuming you have the standard infrastructure in place to do that and the software to manage it that's huge because that means business agility it means better quality and speed alright let's talk about IBM the world is complex this stuff is not trivial the the more clouds we have the more edge we have the more data we have the more complex against IBM happens to be very good at complex three components of the innovation cocktail data AI and cloud IBM your customers have a lot of data you guys are good with data it's very strong analytics business artificial intelligence machine intelligence you've invested a lot in Watson that's a key component business and cloud you have a cloud it's not designed to compete not knock heads and the race to zero with with the cheap and deep you know storage clouds it's designed to really run workloads and applications but you've got all three ingredients as well you're going hard after the multi cloud world for you guys you've got infrastructure underneath you got hardware and software to manage that infrastructure all the modern stuff that we've talked about that's what's going to power the customers digital transformations and we'll talk about that in a moment but maybe you could expand on that in terms of IBM's relevance sure so so again using the kind of maker the maker economy metaphor bridging from that you know individual level of innovation and creativity and development to a broadly distributed you know globally available work loader or information source of some kind the process of that bridge is about scale and reach how do you scale it so that it runs effectively optimally is easily managed Hall looks and feels the same falls under the common umbrella of services and then how do you get it to as many endpoints as possible whether it's individuals or entities or agencies or whatever scale and reach iBM is all about scale and reach I mean that's kind of our stock and trade we we are able to take solutions from small kind of departmental level or kind of skunkworks level and make them large secure repeatable easily managed services and and make them as turnkey as possible our services organizations been doing it for decades exceptionally well our product portfolio supports that you talk about Watson and kind of the cognitive computing story we've been a thought leader in this space for decades I mean we didn't just arrive on the scene two years ago when machine learning and deep learning and IO ste started to become prominent and say this sounds interesting we're gonna plant our flag here we've been there we've been there for a long time so you know I kind of from an infrastructure perspective I kind of like to use the analogy that you know we are technology ethos is built on AI it's built on cognitive computing and and sort of adaptive computing every one of our portfolio products is imbued with that same capability so we use it internally we're kind of built from AI for AI so maybe that's the answer to this question of it so what do you say that somebody says well you know I want to buy you know my flash storage from pure AI one of my bi database from Oracle I want to buy my you know Intel servers from Dell you know whatever I want to I want to I want control and and and I gotta go build it myself why should I work with IBM do you do you get that a lot and how do you respond to that Steve I think I think this whole new data economy has opened up a lot of places for data to be stored anywhere I think at the end of the day it really comes down to management and one of the things that I was thinking about as you guys were we're conversing is the enterprise class or Enterprise need for things like security and protection that sort of thing that rounds out the software stack in our portfolio one of the things we can bring to the table is sure you can go by piece parts and component reform from different people that you want right and in that whole notion around fail-fast sure you can get some new things that might be a little bit faster that might be might be here first but one of the things that IBM takes a lot of pride was a lot of qual a lot of pride into is is the quality of their their delivery of both hardware and software right so so to me even though the infrastructure does matter quite a bit the question is is is how much into what degree so when you look at our core clients the global 2,000 right they want to fail fast they want to fail fast securely they want to fail fast and make sure they're protected they want to fail fast and make sure they're not accidentally giving away the keys to the kingdom at the end of the day a lot of the large vendor a lot of the large clients that we have need to be able to protect their are their IP their brain trust there but also need the flexibility to be creative and create new applications that gain new customer bases so the way I the way I look at it and when I talk to clients and when I talk to folks is is we want to give you them that while also making sure they're they're protected you know that said I would just add that that and 100% accurate depiction the data economy is really changing the way not only infrastructure is deployed and designed but the way it can be I mean it's opening up possibilities that didn't exist and there's new ones cropping up every day to your point if you want to go kind of best to breed or you want to have a solution that includes multi vendor solutions that's okay I mean the whole idea of using again for instance containerization thinking about kubernetes and docker for instance as a as a protocol standard or a platform standard across heterogeneous hardware that's fine like like we will still support that environment we believe there are significant additive advantages to to looking at IBM as a full solution or a full stack solution provider and our largest you know most mission critical application clients are doing that so we think we can tell a pretty compelling story and I would just finally add that we also often see situations where in the journey from the kind of maker to the largely deployed enterprise class workload there's a lot of pitfalls along the way and there's companies that will occasionally you know bump into one of them and come back six months later and say ok we encountered some scalability issues some security issues let's talk about how we can develop a new architecture that solves those problems without sacrificing any of our advanced capabilities all right let's talk about what this means for customers so everybody talks about digital transformation and digital business so what's the difference in a business in the digital business it's how they use data in order to leverage data to become one of those incumbent disruptors using Ginny's term you've got to have a modern infrastructure if you want to build this multi cloud you know connection point enterprise data pipeline to use your term Randy you've got to have modern infrastructure to do that that's low latency that allows me to ship data to code that allows me to run applet anywhere leave the data in place including the edge and really close that gap between those top five data you know value companies and yourselves now the other piece of that is you don't want to waste a lot of time and money managing infrastructure you've got to have intelligence infrastructure you've got to use modern infrastructure and you've got to redeploy those labor assets toward a higher value more productive for the company activities so we all know IT labor is a chop point and we spend more on IT labor managing Leung's provisioning servers tuning databases all that stuff that's gotta change in order for you to fund digital transformations so that to me is the big takeaway as to what it means for customer and we talked about that sorry what we talked about that all the time and specifically in the context of the enterprise data pipeline and within that pipeline kind of the newer generation machine learning deep learning cognitive workload phases the data scientists who are involved at various stages along the process are obviously kind of scarce resources they're very expensive so you can't afford for them to be burning cycles and managing environments you know spinning up VMs and moving data around and creating working sets and enriching metadata that they that's not the best use of their time so we've developed a portfolio of solutions specifically designed to optimize them as a resource as a very valuable resource so I would vehemently agree with your premise we talked about the rise of the infrastructure developer right so at the end of the day I'm glad you brought this topic up because it's not just customers it's personas Pete IBM talks to different personas within our client base or our prospect base about why is this infrastructure important to to them and one of the core components is skill if you have when we talk about this rise of the infrastructure developer what we mean is I need to be able to build composable intelligent programmatic infrastructure that I as IT can set up not have to worry about a lot of risk about it break have to do in a lot of troubleshooting but turn the keys over to the users now let them use the infrastructure in such a way that helps them get their job done better faster stronger but still keeps the business protected so don't make copies into production and screw stuff up there but if I want to make a copy of the data feel free go ahead and put it in a place that's safe and secure and it won't it won't get stolen and it also won't bring down the enterprise's is trying to do its business very key key components - we talked about I infused data protection and I infused storage at the end of the day it's what is an AI infused data center right it needs to be an intelligent data center and I don't have to spend a lot of time doing it the new IT person doesn't want to be troubleshooting all day long they want to be in looking at things like arm and vme what's that going to do for my business to make me more competitive that's where IT wants to be focused yeah and it's also we just to kind of again build on this this whole idea we haven't talked a lot about it but there's obviously a cost element to all this right I mean you know the enterprise's are still very cost-conscious and they're still trying to manage budgets and and they don't have an unlimited amount of capital resources so things like the ability to do fractional consumption so by you know pay paper drink right buy small bits of infrastructure and deploy them as you need and also to Steve's point and this is really Steve's kind of area of expertise and where he's a leader is kind of data efficiency you you also can't afford to have copy sprawl excessive data movement poor production schemes slow recovery times and recall times you've got a as especially as data volumes are ramping you know geometrically the efficiency piece and the cost piece is absolutely relevant and that's another one of the things that often gets lost in translation between kind of the maker level and the deployment level so I wanted to do a little thought exercise for those of you think that this is all you know bromide and des cloud 2.0 is also about we're moving from a world of cloud services to one where you have this mesh which is ubiquitous of of digital services you talked about intelligence Steve you know the intelligent data center so all these all these digital services what am I talking about AI blockchain 3d printing autonomous vehicles edge computing quantum RPA and all the other things on the Gartner hype cycle you'll be able to procure these as services they're part of this mesh so here's the thought exercise when do you think that owning and driving your own vehicle is no longer gonna be the norm right interesting thesis question like why do you ask the question well because these are some of the disruptions so the questions are designed to get you thinking about the potential disruptions you know is it possible that our children's children aren't gonna be driving their own car it's because it's a it's a cultural change when I was 16 year olds like I couldn't wait but you started to see a shifted quasi autonomous vehicles it's all sort of the rage personally I don't think they're quite ready yet but it's on the horizon okay I'll give you another one when will machines be able to make better diagnosis than doctors actually both of those are so so let's let's hit on autonomous and self-driving vehicles first I agree they're not there yet I will say that we have a pretty thriving business practice and competency around working with a das providers and and there's an interesting perception that a das autonomous driving projects are like there's okay there's ten of them around the world right maybe there's ten metal level hey das projects around the world what people often don't see is there is a gigantic ecosystem building around a das all the data sourcing all the telemetry all the hardware all the network support all the services I mean building around this is phenomenal it's growing at a had a ridiculous rate so we're very hooked into that we see tremendous growth opportunities there if I had to guess I would say within 10 to 12 years there will be functionally capable viable autonomous vehicles not everywhere but they will be you will be able as a consumer to purchase one yeah that's good okay and so that's good I agree that's a the time line is not you know within the next three to five years all right how about retail stores will well retail stores largely disappeared we're we're rainy I was just someplace the other day and I said there used to be a brick-and-mortar there and we were walking through the Cambridge Tseng Galleria and now the third floor there's no more stores right there's gonna be all offices they've shrunken down to just two floors of stores and I highly believe that it's because you know the brick you know the the retailers online are doing so well I mean think about it used to be tricky and how do you get in and and and I need the Walmart minute I go cuz I go get with Amazon and that became very difficult look at places like bombas or Casper or all the luggage plate all this little individual boutique selling online selling quickly never having to have to open up a store speed of deployment speed of product I mean it's it's it's phenomenal yeah and and frankly if if Amazon could and and they're investing billions of dollars and they're trying to solve the last mile problem if Amazon could figure out a way to deliver ninety five percent of their product catalog Prime within four to six hours brick-and-mortar stores would literally disappear within a month and I think that's a factual statement okay give me another one will banks lose control traditional banks lose control of the payment systems you can Moselle you see that banks are smart they're buying up you know fin tech companies but right these are entrenched yeah that's another one that's another one with an interesting philosophical element to it because people and some of its generational right like our parents generation would be horrified by the thought of taking a picture of a check or using blockchain or some kind of a FinTech coin or any kind of yeah exactly so Bitcoin might I do my dad ask you're not according I do I don't bit going to so we're gonna we're waiting it out though it's fine by the way I just wanted to mention that we don't hang out in the mall that's actually right across from our office I want to just add that to the previous comment so there is a philosophical piece of it they're like our generation we're fairly comfortable now because we've grown up in a sense with these technologies being adopted our children the concept of going to a bank for them will be foreign I mean it will make it all have no context for the content for the the the process of going to speak face to face to another human it just say it won't exist well will will automation whether its robotic process automation and other automation 3d printing will that begin to swing the pendulum back to onshore manufacturing maybe tariffs will help to but again the idea that machine intelligence increasingly will disrupt businesses there's no industry that's safe from disruption because of the data context that we talked about before Randy and I put together a you know IBM loves to use were big words of transformation agile and as a sales rep you're in the field and you're trying to think about okay what does that mean what does that mean for me to explain to my customer so he put together this whole thing about what his transformation mean to one of them was the taxi service right in the another one was retail so and not almost was fencers I mean you're hitting on on all the core things right but this transformation I mean it goes so deep and so wide when you think about exactly what Randy said before about uber just transforming just the taxi business retailers and taxis now and hotel chains and that's where the thing that know your customer they're getting all of that from data data that I'm putting it not that they're doing work to extract out of me that I'm putting in so that autonomous vehicle comes to pick up Steve Kenaston it knows that Steve likes iced coffee on his way to work gives me a coupon on a screen I hit the button it automatically stops at Starbucks for me and it pre-ordered it for me you're talking about that whole ecosystem wrapped around just autonomous vehicles and data now it's it's unbeliev we're not far off from the Minority Report era of like Anthem fuck advertising targeted at an individual in real time I mean that's gonna happen it's almost there now I mean you just use point you will get if I walk into Starbucks my phone says hey why don't you use some points while you're here Randy you know so so that's happening at facial recognition I mean that's all it's all coming together so and again underneath all this is infrastructure so infrastructure clearly matters if you don't have the infrastructure to power these new workloads you're drugged yeah and I would just add and I think we're all in agreement on that and and from from my perspective from an IBM perspective through my eyes I would say we're increasingly being viewed as kind of an arms dealer and that's a probably a horrible analogy but we're being used we're being viewed as a supplier to the providers of those services right so we provide the raw materials and the machinery and the tooling that enables those innovators to create those new services and do it quickly securely reliably repeatably at a at a reasonable cost right so it's it's a step back from direct engagement with consumer with with customers and clients and and architects but that's where our whole industry is going right we are increasingly more abstracted from the end consumer we're dealing with the sort of assembly we're dealing with the assemblers you know they take the pieces and assemble them and deliver the services so we're not as often doing the assembly as we are providing the raw materials guys great conversation I think we set a record tends to be like that so thank you very much for no problem yeah this is great thank you so much for watching everybody we'll see you next time you're watching the cube

>> From Cambridge, Massachusetts it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by, SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts, everybody. You're watching theCUBE, the leader in live tech coverage. I'm Dave Vellante with my co-host, Paul Gillan. Chris Lynch, good friend is here CEO, newly minted CEO and AtScale and legend. Good to see you. >> In my own mind. >> In mine too. >> It's great to be here. >> It's awesome, thank you for taking time. I know how busy you are, you're running around like crazy your next big thing. I was excited to hear that you got back into it. I predicted it a while ago you were a very successful venture capitalists but at heart, you're startup guy, aren't ya? >> Yeah 100%, 100%. I couldn't be more thrilled, I feel invigorated. I think I've told you many times, when you've interviewed me and asked me about the transition from being an entrepreneur to being a VC and since it's a PG show, I've got a different analog than the one I usually give you. I used to be a movie star and now I'm an executive producer of movies. Now am back to being a movie star, hopefully. >> yeah well, so you told me when you first became a VC you said, I look for startups that have a 10X impact either 10X value, 10X cost reduction. What was it that attracted you to AtScale? What's the 10X? >> AtScale, addresses $150 billion market problem which is basically bringing traditional BI to the cloud. >> That's the other thing you told me, big markets. >> Yeah, so that's the first thing massive market opportunity. The second is, the innovation component and where the 10X comes we're uniquely qualified to virtualize data into the pipeline and out. So I like to say that we're the bridge between BI and AI and back. We make every BI user, a citizen data scientist and that's a game changer. And that's sort of the new futuristic component of what we do. So one part is steeped in, that $150 billion BI marketplace in a traditional analytics platforms and then the second piece is into you delivering the data, into these BI excuse me, these AI machine learning platforms. >> Do you see that ultimately getting integrated into some kind of larger, data pipeline framework. I mean, maybe it lives in the cloud or maybe on prem, how do you see that evolving over time? >> So I believe that, with AtScale as one single pane of glass, we basically are providing an API, to the data and to the user, one single API. The reason that today we haven't seen the delivery of the promise of big data is because we don't have big data. Fortunate 2000 companies don't have big data. They have lots of data but to me big data means you can have one logical view of that data and get the best data pumped into these models in these tools, and today that's not the case. They're constricted by location they're constricted by vendor they're constricted by whether it's in the cloud or on prem. We eliminate those restrictions. >> The single API, I think is important actually. Because when you look at some of these guys what they're doing with their data pipeline they might have 10 or 15 unique API's that they're trying to manage. So there's a simplification aspect to, I suppose. >> One of the knocks on traditional BI has always been the need for extract databases and all the ETL that goes that's involved in that. Do you guys avoid that stage? You go to the production data directly or what's the-- >> It's a great question. The way I put it is, we bring Moses to the mountain the mountain being the data, Moses being the user. Traditionally, what people have been trying to do is bring the mountain to Moses, doesn't scale. At AtScale, we provide an abstraction a logical distraction between the data and the BI user. >> You don't touch, you don't move the data. >> We don't move the data. Which is what's unique and that's what's delivering I think, way more than a 10X delivery in value. >> Because you leave the data in place you bring that value to wherever the data is. Which is the original concept of Hadoop, by the way. That was what was profound about Hadoop everybody craps on it now, but that was the game changer and if you could take advantage of that that's how you tap your 10X. >> To the difference is, we're not, to your point we're not moving the data. Hadoop, in my humble opinion why it plateaued is because to get the value, you had to ask the user to bring and put data in yet another platform. And the reason that we're not delivering on big data as an industry, I believe is because we've too many data sources, too many platforms too many consumers of data and too many producers. As we build all these islands of data, with no connectivity. The idea is, we'll create this big data lake and we're going to physically put everything in there. Guess what? Someday turned out to be never. Because people aren't going to deal with the business disruption. We move thousands of users from a platform like Teradata to a platform like Snowflake or Google BigQuery, we don't care. We're a multi-cloud and we're a hybrid cloud. But we do it without any disruption. You're using Excel, you just continue and use it. You just see the results are faster. You use Tableau, same difference. >> So we had all the vertical rock stars in here. So we had Colin in yesterday, we had Stonebraker around earlier. Andy Palmer just came on and Chris here with the CEO who ultimately sold the company to HP. That really didn't do anything with it and then spun it off and now it's back. Aaron was, he had a spring in his step yesterday. So when you think about, Vertica. The technology behind Vertica go back 10 years and where we come now give us a little journey of, your data journey. >> So I think it plays into the, the original assertion is that, vertical is a best-in-class platform for analytics but it was yet another platform. The analog I give now, is now we have Snowflake and six months, 12 months from now we're going to have another one. And that creates a set of problems if you have to live in the physical world. Because you've all these islands of data and I believe, it's about the data not about the models, it's about the data. You can't get optimal results if you don't have an optimal access to the pertinent data. I believe that having that Universal API is going to make the next platform that more valuable. You're not going to be making the trade-off is, okay we have this platform that has some neat capability but the trade-off is from an enterprise architecture perspective we're never going to be able to connect all this stuff. That's how all of these things proliferated. My view is, in a world where you have that single pane of glass, that abstraction layer between the user and the data. Then innovation can be spawned quicker and you can use these tools effectively 'cause you're not compromising being able to get a logical view of the data and get access to it as a user. >> What's your issue with Snowflake you mentioned them, Mugli's company-- >> No issue, they're a great partner of ours. We eliminate the friction between the user going from an on-prem solution to the cloud. >> Slootman just took over there. So you know where that's going. >> Yep (laughing) >> Frank's got the magic touch. Okay good, you say they're a partner yours how are you guys partnering? >> They refer us into customers that, if you want to buy Snowflake now the next issue is, how do i migrate? You don't. You put our virtualization layer in and then we allow you access to Snowflake in a non-disruptive way, versus having to move data into their system or into a particular cloud which creates sales friction. >> Moving data is just, you want to avoid it at all cost. >> I do want to ask you because I met with your predecessors, Dave Mariani last year and I know he was kind of a reluctant CEO he didn't really want to be CEO but wanted to be CTO, which is what he is now. How did that come about, that they found you that you connected with them and decided this was the right opportunity. >> That's a great question. I actually looked at the company at the seed stage when I was in venture, but I had this thing as you know that, I wanted to move companies to Boston and they're about my vintage age-wise and he's married with four kids so that wasn't in the cards. I said look, it doesn't make sense for me to seed this company 'cause I can't give you the time you're out in California everything I'm instrumenting is around Boston. We parted friends. And I was skeptical whether he could build this 'cause people have been talking about building a heterogeneous universal semantic layer, for years and it's never come to fruition. And then he read in Fortune or Forbes that I was leaving Accomplice and that I was looking for one more company to operate. He reached out and he told me what they were doing that hey, we really built it but we need help and I don't want to run this. It's not right for the company and the opportunity So he said, "I'll come and I'll consult to you." I put together a plan and I had my Vertica and data robot. NekTony guys do the technical diligence to make sure that the architecture wasn't wedded to the dupe, like all the other ones were and when I saw it wasn't then I knew the market opportunity was to take that, rifle and point it at that legacy $150 billion BI market not at the billion dollar market of Hadoop. And when we did that, we've been growing at 162% quarter-over-quarter. We've built development centers in Bulgaria. We've moved all operations, non-technical to Boston here down in our South Station. We've been on fire and we are the partner of choice of every cloud manner, because we eliminate the sales friction, for customers being able to take advantage of movement to the cloud and we're able through our intelligent pipeline and capability. We're able to reduce the cost significantly of queries because we understand and we were able to intelligently cash those queries. >> Sales ops is here, all-- >> Sales marketing, customer support, customer success and we're building a machine learning team here at Dev team here. >> Where are you in that sort of Boston build-out? >> We have an office on 711 Atlantic that we opened in the fall. We're actually moving from 4,000 square feet to 10,000 this month. In less than six months and we'll house by the first year, 100 employees in Boston 100 in Bulgaria and about that same hundred in San Mateo. >> Are you going after net new business mainly? Or there's a lot of legacy BI out there are you more displacing those products? >> A couple of things. What we find is that, customers want to evolve into the cloud, they don't want a revolution they want a evolution. So we allow them, because we support hybrid cloud to keep some data behind the firewall and then experiment with moving other data to the cloud platform of choice but we're still providing that one logical view. I would say most of our customers are looking to reap platform, off of Teradata or something onto a, another platform like Snowflake. And then we have a set of customers that see that as part of the solution but not the whole solution. They're more true hybrids but I would say that 80% of our customers are traditional BI customers that are trying to contemporize their environments and be able to take advantage of tabular support and multidimensional, the things that we do in addition to the cube world. >> They can keep whatever they're using. >> Correct, that's the key. >> Did you do the series D, you did, right? >> Yes, Morgan Stanely led. >> So you're not actively but you're good for now, It was like $50 million >> Yeah we raised $50 million. >> You're good for a bit. Who's in the Chris Lynch target? (laughs) Who's the enemy? Vertica, I could say it was the traditional database guys. Who's the? >> We're in a unique position, we're almost Switzerland so we could be friend to foe, of anybody in that ecosystem because we can, non-disruptively re-platform customers between legacy platforms or from legacy platforms to the cloud. We're an interesting position. >> So similar to the file sharing. File virtualization company >> The Copier. >> Copier yeah. >> It puts us in an interesting position. They need to be friends with us and at the same time I'm sure that they're concerned about the capabilities we have but we have a number of retail customers for instance that have asked us to move down from Amazon to Google BigQuery, which we accommodate and because we can do that non-disruptively. The cost and the ability to move is eliminated. It gives customers true freedom of choice. >> How worried are you, that AWS tries to replicate what you guys do. You're in their sights. >> I think there are technical, legal and structural barriers to them doing that. The technical is, this team has been at it for six and a half years. So to do what we do, they'll have to do what we've done. Structurally from a business perspective if they could, I'm not sure they want to. The way to think about Amazon is, they're no different than Teradata, except for they want the same vendor lock-in except they want it to be the Amazon Cloud when Teradata wanted it to be, their data warehouse. >> They don't promote multi-cloud versus-- >> Yeah, they don't want multi-cloud they don't want >> On Prem >> Customers to have a freedom of choice. Would they really enable a heterogeneous abstraction layer, I don't think they would nor do I think any of the big guys would. They all claim to have this capability for their system. It's like the old IBM adage I'm in prison but the food's going to get three squares a day, I get cable TV but I'm in prison. (laughing) >> Awesome, all right, parting thoughts. >> Parting thoughts, oh geez you got to give me a question I'm not that creative. >> What's next, for you guys? What should we be paying attention to? >> I think you're going to see some significant announcements in September regarding the company and relationships that I think will validate the impact we're having in the market. >> Give you some leverage >> Yeah, will give us, better channel leverage. We have a major technical announcement that I think will be significant to the marketplace and what will be highly disruptive to some of the people you just mentioned. In terms of really raising the bar for customers to be able to have the freedom of choice without any sort of vendor lock-in. And I think that that will create some counter strike which we'll be ready for. (laughing) >> If you've never heard of AtScale before trust me you're going to in the next 18 months. Chris Lynch, thanks so much for coming on theCUBE. >> It's my pleasure. >> Great to see you. All right, keep it right there everybody we're back with our next guest, right after this short break you're watching theCUBE from MIT, right back. (upbeat music)

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, you're watching The Cube, the leader in tech coverage. My name is Dave Vellante here with my cohost Paul Gillin. This is day one of our two day coverage of the MIT CDOIQ conferences. CDO, Chief Data Officer, IQ, information quality. Colin Mahoney is here, he's a good friend and long time CUBE alum. I haven't seen you in awhile, >> I know >> But thank you so much for taking some time, you're like a special guest here >> Thank you, yeah it's great to be here, thank you. >> Yeah, so, this is not, you know, something that you would normally attend. I caught up with you, invited you in. This conference has started as, like back office governance, information quality, kind of wonky stuff, hidden. And then when the big data meme took off, kind of around the time we met. The Chief Data Officer role emerged, the whole Hadoop thing exploded, and then this conference kind of got bigger and bigger and bigger. Still intimate, but very high level, very senior. It's kind of come full circle as we've been saying, you know, information quality still matters. You have been in this data business forever, so I wanted to invite you in just to get your perspectives, we'll talk about what's new with what's going on in your company, but let's go back a little bit. When we first met and even before, you saw it coming, you kind of invested your whole career into data. So, take us back 10 years, I mean it was so different, remember it was Batch, it was Hadoop, but it was cool. There was a lot of cool >> It's still cool. (laughs) projects going on, and it's still cool. But, take a look back. >> Yeah, so it's changed a lot, look, I got into it a while ago, I've always loved data, I had no idea, the explosion and the three V's of data that we've seen over the last decade. But, data's really important, and it's just going to get more and more important. But as I look back I think what's really changed, and even if you just go back a decade I mean, there's an insatiable appetite for data. And that is not slowing down, it hasn't slowed down at all, and I think everybody wants that perfect solution that they can ask any question and get an immediate answers to. We went through the Hadoop boom, I'd argue that we're going through the Hadoop bust, but what people actually want is still the same. You know, they want real answers, accurate answers, they want them quickly, and they want it against all their information and all their data. And I think that Hadoop evolved a lot as well, you know, it started as one thing 10 years ago, with MapReduce and I think in the end what it's really been about is disrupting the storage market. But if you really look at what's disrupting storage right now, public clouds, S3, right? That's the new data league. So there's always a lot of hype cycles, everybody talks about you know, now it's Cloud, everything, for maybe the last 10 years it was a lot of Hadoop, but at the end of the day I think what people want to do with data is still very much the same. And a lot of companies are still struggling with it, hence the role for Chief Data Officers to really figure out how do I monetize data on the one hand and how to I protect that asset on the other hand. >> Well so, and the cool this is, so this conference is not a tech conference, really. And we love tech, we love talking about this, this is why I love having you on. We kind of have a little Vertica thread that I've created here, so Colin essentially, is the current CEO of Vertica, I know that's not your title, you're GM and Senior Vice President, but you're running Vertica. So, Michael Stonebreaker's coming on tomorrow, >> Yeah, excellent. >> Chris Lynch is coming on tomorrow, >> Oh, great, yeah. >> we've got Andy Palmer >> Awesome, yeah. >> coming up as well. >> Pretty cool. (laughs) >> So we have this connection, why is that important? It's because, you know, Vertica is a very cool company and is all about data, and it was all about disrupting, sort of the traditional relational database. It's kind of doing more with data, and if you go back to the roots of Vertica, it was like how do you do things faster? How do you really take advantage of data to really drive new business? And that's kind of what it's all about. And the tech behind it is really cool, we did your conference for many, many years. >> It's coming back by the way. >> Is it? >> Yeah, this March, so March 30th. >> Oh, wow, mark that down. >> At Boston, at the new Encore Hotel. >> Well we better have theCUBE there, bro. (laughs) >> Yeah, that's great. And yeah, you've done that conference >> Yep. >> haven't you before? So very cool customers, kind of leading edge, so I want to get to some of that, but let's talk the disruption for a minute. So you guys started with the whole architecture, MPP and so forth. And you talked about Cloud, Cloud really disrupted Hadoop. What are some of the other technology disruptions that you're seeing in the market space? >> I think, I mean, you know, it's hard not to talk about AI machine learning, and what one means versus the other, who knows right? But I think one thing that is definitely happening is people are leveraging the volumes of data and they're trying to use all the processing power and storage power that we have to do things that humans either are too expensive to do or simply can't do at the same speed and scale. And so, I think we're going through a renaissance where a lot more is being automated, certainly on the Vertica roadmap, and our path has always been initially to get the data in and then we want the platform to do a lot more for our customers, lots more analytics, lots more machine-learning in the platform. So that's definitely been a lot of the buzz around, but what's really funny is when you talk to a lot of customers they're still struggling with just some basic stuff. Forget about the predictive thing, first you've got to get to what happened in the past. Let's give accurate reporting on what's actually happening. The other big thing I think as a disruption is, I think IOT, for all the hype that it's getting it's very real. And every device is kicking off lots of information, the feedback loop of AB testing or quality testing for predictive maintenance, it's happening almost instantly. And so you're getting massive amounts of new data coming in, it's all this machine sensor type data, you got to figure out what it means really quick, and then you actually have to do something and act on it within seconds. And that's a whole new area for so many people. It's not their traditional enterprise data network warehouse and you know, back to you comment on Stonebreaker, he got a lot of this right from the beginning, you know, and I think he looked at the architectures, he took a lot of the best in class designs, we didn't necessarily invent everything, but we put a lot of that together. And then I think the other you've got to do is constantly re-invent your platform. We came out with our Eon Mode to run cloud native, we just got rated the best cloud data warehouse from a net promoter score rating perspective, so, but we got to keep going you know, we got to keep re-inventing ourselves, but leverage everything that we've done in the past as well. >> So one of the things that you said, which is kind of relevant for here, Paul, is you're still seeing a real data quality issue that customers are wrestling with, and that's a big theme here, isn't it? >> Absolutely, and the, what goes around comes around, as Dave said earlier, we're still talking about information quality 13 years after this conference began. Have the tools to improve quality improved all that much? >> I think the tools have improved, I think that's another area where machine learning, if you look at Tamr, and I know you're going to have Andy here tomorrow, they're leveraging a lot of the augmented things you can do with the processing to make it better. But I think one thing that makes the problem worse now, is it's gotten really easy to pour data in. It's gotten really easy to store data without having to have the right structure, the right quality, you know, 10 years ago, 20 years ago, everything was perfect before it got into the platform. Right, everything was, there was quality, everything was there. What's been happening over the last decade is you're pumping data into these systems, nobody knows if it's redundant data, nobody knows if the quality's any good, and the amount of data is massive. >> And it's cheap to store >> Very cheap to store. >> So people keep pumping it in. >> But I think that creates a lot of issues when it comes to data quality. So, I do think the technology's gotten better, I think there's a lot of companies that are doing a great job with it, but I think the challenge has definitely upped. >> So, go ahead. >> I'm sorry. You mentioned earlier that we're seeing the death of Hadoop, but I'd like you to elaborate on that becuase (Dave laughs) Hadoop actually came up this morning in the keynote, it's part of what GlaxoSmithKline did. Came up in a conversation I had with the CEO of Experian last week, I mean, it's still out there, why do you think it's in decline? >> I think, I mean first of all if you look at the Hadoop vendors that are out there, they've all been struggling. I mean some of them are shutting down, two of them have merged and they've got killed lately. I think there are some very successful implementations of Hadoop. I think Hadoop as a storage environment is wonderful, I think you can process a lot of data on Hadoop, but the problem with Hadoop is it became the panacea that was going to solve all things data. It was going to be the database, it was going to be the data warehouse, it was going to do everything. >> That's usually the kiss of death, isn't it? >> It's the kiss of death. And it, you know, the killer app on Hadoop, ironically, became SQL. I mean, SQL's the killer app on Hadoop. If you want to SQL engine, you don't need Hadoop. But what we did was, in the beginning Mike sort of made fun of it, Stonebreaker, and joked a lot about he's heard of MapReduce, it's called Group By, (Dave laughs) and that created a lot of tension between the early Vertica and Hadoop. I think, in the end, we embraced it. We sit next to Hadoop, we sit on top of Hadoop, we sit behind it, we sit in front of it, it's there. But I think what the reality check of the industry has been, certainly by the business folks in these companies is it has not fulfilled all the promises, it has not fulfilled a fraction on the promises that they bet on, and so they need to figure those things out. So I don't think it's going to go away completely, but I think its best success has been disrupting the storage market, and I think there's some much larger disruptions of technologies that frankly are better than HTFS to do that. >> And the Cloud was a gamechanger >> And a lot of them are in the cloud. >> Which is ironic, 'cause you know, cloud era, (Colin laughs) they didn't really have a cloud strategy, neither did Hortonworks, neither did MapR and, it just so happened Amazon had one, Google had one, and Microsoft has one, so, it's just convenient to-- >> Well, how is that affecting your business? We've seen this massive migration to the cloud (mumbles) >> It's actually been great for us, so one of the things about Vertica is we run everywhere, and we made a decision a while ago, we had our own data warehouse as a service offering. It might have been ahead of its time, never really took off, what we did instead is we pivoted and we say "you know what? "We're going to invest in that experience "so it's a SaaS-like experience, "but we're going to let our customers "have full control over the cloud. "And if they want to go to Amazon they can, "if they want to go to Google they can, "if they want to go to Azure they can." And we really invested in that and that experience. We're up on the Amazon marketplace, we have lots of customers running up on Amazon Cloud as well as Google and Azure now, and then about two years ago we went down and did this endeavor to completely re-architect our product so that we could separate compute and storage so that our customers could actually take advantage of the cloud economics as well. That's been huge for us, >> So you scale independent-- >> Scale independently, cloud native, add compute, take away compute, and for our existing customers, they're loving the hybrid aspect, they love that they can still run on Premise, they love that they can run up on a public cloud, they love that they can run in both places. So we will continue to invest a lot in that. And it is really, really important, and frankly, I think cloud has helped Vertica a lot, because being able to provision hardware quickly, being able to tie in to these public clouds, into our customers' accounts, give them control, has been great and we're going to continue on that path. >> Because Vertica's an ISV, I mean you're a software company. >> We're a software company. >> I know you were a part of HP for a while, and HP wanted to mash that in and run it on it's hardware, but software runs great in the cloud. And then to you it's another hardware platform. >> It's another hardware platform, exactly. >> So give us the update on Micro Focus, Micro Focus acquired Vertica as part of the HPE software business, how many years ago now? Two years ago? >> Less than two years ago. >> Okay, so how's that going, >> It's going great. >> Give us the update there. >> Yeah, so first of all it is great, HPE and HP were wonderful to Vertica, but it's great being part of a software company. Micro Focus is a software company. And more than just a software company it's a company that has a lot of experience bridging the old and the new. Leveraging all of the investments that you've made but also thinking about cloud and all these other things that are coming down the pike. I think for Vertica it's been really great because, as you've seen Vertica has gotten its identity back again. And that's something that Micro Focus is very good at. You can look at what Micro Focus did with SUSE, the Linux company, which actually you know, now just recently spun out of Micro Focus but, letting organizations like Vertica that have this culture, have this product, have this passion, really focus on our market and our customers and doing the right thing by them has been just really great for us and operating as a software company. The other nice thing is that we do integrate with a lot of other products, some of which came from the HPE side, some of which came from Micro Focus, security products is an example. The other really nice thing is we've been doing this insource thing at Micro Focus where we open up our source code to some of the other teams in Micro Focus and they've been contributing now in amazing ways to the product. In ways that we would just never be able to scale, but with 4,000 engineers strong in Micro Focus, we've got a much larger development organization that can actually contribute to the things that Vertica needs to do. And as we go into the cloud and as we do a lot more operational aspects, the experience that these teams have has been incredible, and security's another great example there. So overall it's been great, we've had four different owners of Vertica, our job is to continue what we do on the innovation side in the culture, but so far Micro Focus has been terrific. >> Well, I'd like to say, you're kind of getting that mojo back, because you guys as an independent company were doing your own thing, and then you did for a while inside of HP, >> We did. >> And that obviously changed, 'cause they wanted more integration, but, and Micro Focus, they know what they're doing, they know how to do acquisitions, they've been very successful. >> It's a very well run company, operationally. >> The SUSE piece was really interesting, spinning that out, because now RHEL is part of IBM, so now you've got SUSE as the lone independent. >> Yeah. >> Yeah. >> But I want to ask you, go back to a technology question, is NoSQL the next Hadoop? Are these databases, it seems to be that the hot fad now is NoSQL, it can do anything. Is the promise overblown? >> I think, I mean NoSQL has been out almost as long as Hadoop, and I, we always say not only SQL, right? Mike's said this from day one, best tool for the job. Nothing is going to do every job well, so I think that there are, whether it's key value stores or other types of NoSQL engines, document DB's, now you have some of these DB's that are running on different chips, >> Graph, yeah. >> there's always, yeah, graph DBs, there's always going to be specialty things. I think one of the things about our analytic platform is we can do, time series is a great example. Vertica's a great time series database. We can compete with specialized time series databases. But we also offer a lot of, the other things that you can do with Vertica that you wouldn't be able to do on a database like that. So, I always think there's going to be specialty products, I also think some of these can do a lot more workloads than you might think, but I don't see as much around the NoSQL movement as say I did a few years ago. >> But so, and you mentioned the cloud before as kind of, your position on it I think is a tailwind, not to put words in your mouth, >> Yeah, yeah, it's a great tailwind. >> You're in the Amazon marketplace, I mean they have products that are competitive, right? >> They do, they do. >> But, so how are you differentiating there? >> I think the way we differentiate, whether it's Redshift from Amazon, or BigQuery from Google, or even what Azure DB does is, first of all, Vertica, I think from, feature functionality and performance standpoint is ahead. Number one, I think the second thing, and we hear this from a lot of customers, especially at the C-level is they don't want to be locked into these full stacks of the clouds. Having the ability to take a product and run it across multiple clouds is a big thing, because the stack lock-in now, the full stack lock-in of these clouds is scary. It's really easy to develop in their ecosystems but you get very locked into them, and I think a lot of people are concerned about that. So that works really well for Vertica, but I think at the end of the day it's just, it's the robustness of the product, we continue to innovate, when you look at separating compute and storage, believe it or not, a lot of these cloud-native databases don't do that. And so we can actually leverage a lot of the cloud hardware better than the native cloud databases do themselves. So, like I said, we have to keep going, those guys aren't going to stop, and we actually have great relationships with those companies, we work really well with the clouds, they seem to care just as much about their cloud ecosystem as their own database products, and so I think that's going to continue as well. >> Well, Colin, congratulations on all the success >> Yeah, thank you, yeah. >> It's awesome to see you again and really appreciate you coming to >> Oh thank you, it's great, I appreciate the invite, >> MIT. >> it's great to be here. >> All right, keep it right there everybody, Paul and I will be back with our next guest from MIT, you're watching theCUBE. (electronic jingle)

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Hi, everybody, welcome back to Cambridge, Massachusetts. This is theCUBE, the leader in tech coverage. I'm Dave Vellante with my cohost, Paul Gillin, and we're here covering the MIT Chief Data Officer Information Quality Conference, #MITCDOIQ. Lisa Ehrlinger is here, she's the Senior Researcher at the Johannes Kepler University in Linz, Austria, and the Software Competence Center in Hagenberg. Lisa, thanks for coming in theCUBE, great to see you. >> Thanks for having me, it's great to be here. >> You're welcome. So Friday you're going to lay out the results of the study, and it's a study of Data Quality Tools. Kind of the long tail of tools, some of those ones that may not have made the Gartner Magic Quadrant and maybe other studies, but talk about the study and why it was initiated. >> Okay, so the main motivation for this study was actually a very practical one, because we have many company projects with companies from different domains, like steel industry, financial sector, and also focus on automotive industry at our department at Johannes Kepler University in Linz. We have experience with these companies for more than 20 years, actually, in this department, and what reoccurred was the fact that we spent the majority of time in such big data projects on data quality measurement and improvement tasks. So at some point we thought, okay, what possibilities are there to automate these tasks and what tools are out there on the market to automate these data quality tasks. So this was actually the motivation why we thought, okay, we'll look at those tools. Also, companies ask us, "Do you have any suggestions? "Which tool performs best in this-and-this domain?" And I think this study answers some questions that have not been answered so far in this particular detail, in these details. For example, Gartner Magic Quadrant of Data Quality Tools, it's pretty interesting but it's very high-level and focusing on some global windows, but it does not look on the specific measurement functionalities. >> Yeah, you have to have some certain number of whatever, customers or revenue to get into the Magic Quadrant. So there's a long tail that they don't cover. But talk a little bit more about the methodology, was it sort of you got hands-on or was it more just kind of investigating what the capabilities of the tools were, talking to customers? How did you come to the conclusions? >> We actually approached this from a very scientific side. We conducted a systematic search, which tools are out there on the market, not only industrial tools, but also open-sourced tools were included. And I think this gives a really nice digest of the market from different perspectives, because we also include some tools that have not been investigated by Gartner, for example, like more BTQ, Data Quality, or Apache Griffin, which has really nice monitoring capabilities, but lacks some other features from these comprehensive tools, of course. >> So was the goal of the methodology largely to capture a feature function analysis of being able to compare that in terms of binary, did it have it or not, how robust is it? And try to develop a common taxonomy across all these tools, is that what you did? >> So we came up with a very detailed requirements catalog, which is divided into three fields, like the focuses on data profiling to get a first insight into data quality. The second is data quality management in terms of dimensions, metrics, and rules. And the third part is dedicated to data quality monitoring over time, and for all those three categories, we came up with different case studies on a database, on a test database. And so we conducted, we looked, okay, does this tool, yes, support this feature, no, or partially? And when partially, to which extent? So I think, especially on the partial assessment, we got a lot into detail in our survey, which is available on Archive online already. So the preliminary results are already online. >> How do you find it? Where is it available? >> On Archive. >> Archive? >> Yes. >> What's the URL, sorry. Archive.com, or .org, or-- >> Archive.org, yeah. >> Archive.org. >> But actually there is a ID I have not with me currently, but I can send you afterwards, yeah. >> Yeah, maybe you can post that with the show notes. >> We can post it afterwards. >> I was amazed, you tested 667 tools. Now, I would've expected that there would be 30 or 40. Where are all of these, what do all of these long tail tools do? Are they specialized by industry or by function? >> Oh, sorry, I think we got some confusion here, because we identified 667 tools out there on the market, but we narrowed this down. Because, as you said, it's quite impossible to observe all those tools. >> But the question still stands, what is the difference, what are these very small, niche tools? What do they do? >> So most of them are domain-specific, and I think this really highlights also these very basic early definition about data quality, of like data qualities defined as fitness for use, and we can pretty much see it here that we excluded the majority of these tools just because they assess some specific kind of data, and we just really wanted to find tools that are generally applicable for different kinds of data, for structured data, unstructured data, and so on. And most of these tools, okay, someone came up with, we want to assess the quality of our, I don't know, like geological data or something like that, yeah. >> To what extent did you consider other sort of non-technical factors? Did you do that at all? I mean, was there pricing or complexity of downloading or, you know, is there a free version available? Did you ignore those and just focus on the feature function, or did those play a role? >> So basically the focus was on the feature function, but of course we had to contact the customer support. Especially with the commercial tools, we had to ask them to provide us with some trial licenses, and there we perceived different feedback from those companies, and I think the best comprehensive study here is definitely Gartner Magic Quadrant for Data Quality Tools, because they give a broad assessment here, but what we also highlight in our study are companies that have a very open support and they are very willing to support you. For example, Informatica Data Quality, we perceived a really close interaction with them in terms of support, trial licenses, and also like specific functionality. Also Experian, our contact from Experian from France was really helpful here. And other companies, like IBM, they focus on big vendors, and here, it was not able to assess these tools, for example, yeah. >> Okay, but the other differences of the Magic Quadrant is you guys actually used the tools, played with them, experienced firsthand the customer experience. >> Exactly, yeah. >> Did you talk to customers as well, or, because you were the customer, you had that experience. >> Yes, I were the customer, but I was also happy to attend some data quality event in Vienna, and there I met some other customers who had experience with single tools. Not of course this wide range we observed, but it was interesting to get feedback on single tools and verify our results, and it matched pretty good. >> How large was the team that ran the study? >> Five people. >> Five people, and how long did it take you from start to finish? >> Actually, we performed it for one year, roughly. The assessment. And I think it's a pretty long time, especially when you see how quick the market responds, especially in the open source field. But nevertheless, you need to make some cut, and I think it's a very recent study now, and there is also the idea to publish it now, the preliminary results, and we are happy with that. >> Were there any surprises in the results? >> I think the main results, or one of the surprises was that we think that there is definitely more potential for automation, but not only for automation. I really enjoyed the keynote this morning that we need more automation, but at the same time, we think that there is also the demand for more declaration. We observed some tools that say, yeah, we apply machine learning, and then you look into their documentation and find no information, which algorithm, which parameters, which thresholds. So I think this is definitely, especially if you want to assess the data quality, you really need to know what algorithm and how it's attuned and give the user, which in most case will be a technical person with technical background, like some chief data officer. And he or she really needs to have the possibility to tune these algorithms to get reliable results and to know what's going on and why, which records are selected, for example. >> So now what? You're presenting the results, right? You're obviously here at this conference and other conferences, and so it's been what, a year, right? >> Yes. >> And so what's the next wave? What's next for you? >> The next wave, we're currently working on a project which is called some Knowledge Graph for Data Quality Assessment, which should tackle two problems in ones. The first is to come up with a semantic representation of your data landscape in your company, but not only the data landscape itself in terms of gathering meta data, but also to automatically improve or annotate this data schema with data profiles. And I think what we've seen in the tools, we have a lot of capabilities for data profiling, but this is usually left to the user ad hoc, and here, we store it centrally and allow the user to continuously verify newly incoming data if this adheres to this standard data profile. And I think this is definitely one step into the way into more automation, and also I think it's the most... The best thing here with this approach would be to overcome this very arduous way of coming up with all the single rules within a team, but present the data profile to a group of data, within your data quality project to those peoples involved in the projects, and then they can verify the project and only update it and refine it, but they have some automated basis that is presented to them. >> Oh, great, same team or new team? >> Same team, yeah. >> Oh, great. >> We're continuing with it. >> Well, Lisa, thanks so much for coming to theCUBE and sharing the results of your study. Good luck with your talk on Friday. >> Thank you very much, thank you. >> All right, and thank you for watching. Keep it right there, everybody. We'll be back with our next guest right after this short break. From MIT CDOIQ, you're watching theCUBE. (upbeat music)