(upbeat music) >> From theCube Studios in Palo Alto in Boston, bringing you data driven insights from theCube and ETR. This is Breaking Analysis with Dave Vellante. >> While by no means a safe haven, the cybersecurity sector has outpaced the broader tech market by a meaningful margin, that is up until very recently. Cybersecurity remains the number one technology priority for the C-suite, but as we've previously reported the CISO's budget has constraints just like other technology investments. Recent trends show that economic headwinds have elongated sales cycles, pushed deals into future quarters, and just like other tech initiatives, are pacing cybersecurity investments and breaking them into smaller chunks. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis we explain how cybersecurity trends are reverting to the mean and tracking more closely with other technology investments. We'll make a couple of valuation comparisons to show the magnitude of the challenge and which cyber firms are feeling the heat, which aren't. There are some exceptions. We'll then show the latest survey data from ETR to quantify the contraction in spending momentum and close with a glimpse of the landscape of emerging cybersecurity companies, the private companies that could be ripe for acquisition, consolidation, or disruptive to the broader market. First, let's take a look at the recent patterns for cyber stocks relative to the broader tech market as a benchmark, as an indicator. Here's a year to date comparison of the bug ETF, which comprises a basket of cyber security names, and we compare that with the tech heavy NASDAQ composite. Notice that on April 13th of this year the cyber ETF was actually in positive territory while the NAS was down nearly 14%. Now by August 16th, the green turned red for cyber stocks but they still meaningfully outpaced the broader tech market by more than 950 basis points as of December 2nd that Delta had contracted. As you can see, the cyber ETF is now down nearly 25%, year to date, while the NASDAQ is down 27% and change. Now take a look at just how far a few of the high profile cybersecurity names have fallen. Here are six security firms that we've been tracking closely since before the pandemic. We've been, you know, tracking dozens but let's just take a look at this data and the subset. We show for comparison the S&P 500 and the NASDAQ, again, just for reference, they're both up since right before the pandemic. They're up relative to right before the pandemic, and then during the pandemic the S&P shot up more than 40%, relative to its pre pandemic level, around February is what we're using for the pre pandemic level, and the NASDAQ peaked at around 65% higher than that February level. They're now down 85% and 71% of their previous. So they're at 85% and 71% respectively from their pandemic highs. You compare that to these six companies, Splunk, which was and still is working through a transition is well below its pre pandemic market value and 44, it's 44% of its pre pandemic high as of last Friday. Palo Alto Networks is the most interesting here, in that it had been facing challenges prior to the pandemic related to a pivot to the Cloud which we reported on at the time. But as we said at that time we believe the company would sort out its Cloud transition, and its go to market challenges, and sales compensation issues, which it did as you can see. And its valuation jumped from 24 billion prior to Covid to 56 billion, and it's holding 93% of its peak value. Its revenue run rate is now over 6 billion with a healthy growth rate of 24% expected for the next quarter. Similarly, Fortinet has done relatively well holding 71% of its peak Covid value, with a healthy 34% revenue guide for the coming quarter. Now, Okta has been the biggest disappointment, a darling of the pandemic Okta's communication snafu, with what was actually a pretty benign hack combined with difficulty absorbing its 7 billion off zero acquisition, knocked the company off track. Its valuation has dropped by 35 billion since its peak during the pandemic, and that's after a nice beat and bounce back quarter just announced by Okta. Now, in our view Okta remains a viable long-term leader in identity. However, its recent fiscal 24 revenue guide was exceedingly conservative at around 16% growth. So either the company is sandbagging, or has such poor visibility that it wants to be like super cautious or maybe it's actually seeing a dramatic slowdown in its business momentum. After all, this is a company that not long ago was putting up 50% plus revenue growth rates. So it's one that bears close watching. CrowdStrike is another big name that we've been talking about on Breaking Analysis for quite some time. It like Okta has led the industry in a key ETR performance indicator that measures customer spending momentum. Just last week, CrowdStrike announced revenue increased more than 50% but new ARR was soft and the company guided conservatively. Not surprisingly, the stock got absolutely crushed as CrowdStrike blamed tepid demand from smaller and midsize firms. Many analysts believe that competition from Microsoft was one factor along with cautious spending amongst those midsize and smaller customers. Notably, large customers remain active. So we'll see if this is a longer term trend or an anomaly. Zscaler is another company in the space that we've reported having great customer spending momentum from the ETR data. But even though the company beat expectations for its recent quarter, like other companies its Outlook was conservative. So other than Palo Alto, and to a lesser extent Fortinet, these companies and others that we're not showing here are feeling the economic pinch and it shows in the compression of value. CrowdStrike, for example, had a 70 billion valuation at one point during the pandemic Zscaler top 50 billion, Okta 45 billion. Now, having said that Palo Alto Networks, Fortinet, CrowdStrike, and Zscaler are all still trading well above their pre pandemic levels that we tracked back in February of 2020. All right, let's go now back to ETR'S January survey and take a look at how much things have changed since the beginning of the year. Remember, this is obviously pre Ukraine, and pre all the concerns about the economic headwinds but here's an X Y graph that shows a net score, or spending momentum on the y-axis, and market presence on the x-axis. The red dotted line at 40% on the vertical indicates a highly elevated net score. Anything above that we think is, you know, super elevated. Now, we filtered the data here to show only those companies with more than 50 responses in the ETR survey. Still really crowded. Note that there were around 20 companies above that red 40% mark, which is a very, you know, high number. It's a, it's a crowded market, but lots of companies with, you know, positive momentum. Now let's jump ahead to the most recent October survey and take a look at what, what's happening. Same graphic plotting, spending momentum, and market presence, and look at the number of companies above that red line and how it's been squashed. It's really compressing, it's still a crowded market, it's still, you know, plenty of green, but the number of companies above 40% that, that key mark has gone from around 20 firms down to about five or six. And it speaks to that compression and IT spending, and of course the elongated sales cycles pushing deals out, taking them in smaller chunks. I can't tell you how many conversations with customers I had, at last week at Reinvent underscoring this exact same trend. The buyers are getting pressure from their CFOs to slow things down, do more with less and, and, and prioritize projects to those that absolutely are critical to driving revenue or cutting costs. And that's rippling through all sectors, including cyber. Now, let's do a bit more playing around with the ETR data and take a look at those companies with more than a hundred citations in the survey this quarter. So N, greater than or equal to a hundred. Now remember the followers of Breaking Analysis know that each quarter we take a look at those, what we call four star security firms. That is, those are the, that are in, that hit the top 10 for both spending momentum, net score, and the N, the mentions in the survey, the presence, the pervasiveness in the survey, and that's what we show here. The left most chart is sorted by spending momentum or net score, and the right hand chart by shared N, or the number of mentions in the survey, that pervasiveness metric. that solid red line denotes the cutoff point at the top 10. And you'll note we've actually cut it off at 11 to account for Auth 0, which is now part of Okta, and is going through a go to market transition, you know, with the company, they're kind of restructuring sales so they can take advantage of that. So starting on the left with spending momentum, again, net score, Microsoft leads all vendors, typical Microsoft, very prominent, although it hadn't always done so, it, for a while, CrowdStrike and Okta were, were taking the top spot, now it's Microsoft. CrowdStrike, still always near the top, but note that CyberArk and Cloudflare have cracked the top five in Okta, which as I just said was consistently at the top, has dropped well off its previous highs. You'll notice that Palo Alto Network Palo Alto Networks with a 38% net score, just below that magic 40% number, is healthy, especially as you look over to the right hand chart. Take a look at Palo Alto with an N of 395. It is the largest of the independent pure play security firms, and has a very healthy net score, although one caution is that net score has dropped considerably since the beginning of the year, which is the case for most of the top 10 names. The only exception is Fortinet, they're the only ones that saw an increase since January in spending momentum as ETR measures it. Now this brings us to the four star security firms, that is those that hit the top 10 in both net score on the left hand side and market presence on the right hand side. So it's Microsoft, Palo Alto, CrowdStrike, Okta, still there even not accounting for a Auth 0, just Okta on its own. If you put in Auth 0, it's, it's even stronger. Adding then in Fortinet and Zscaler. So Microsoft, Palo Alto, CrowdStrike, Okta, Fortinet, and Zscaler. And as we've mentioned since January, only Fortinet has shown an increase in net score since, since that time, again, since the January survey. Now again, this talks to the compression in spending. Now one of the big themes we hear constantly in cybersecurity is the market is overcrowded. Everybody talks about that, me included. The implication there, is there's a lot of room for consolidation and that consolidation can come in the form of M&A, or it can come in the form of people consolidating onto a single platform, and retiring some other vendors, and getting rid of duplicate vendors. We're hearing that as a big theme as well. Now, as we saw in the previous, previous chart, this is a very crowded market and we've seen lots of consolidation in 2022, in the form of M&A. Literally hundreds of M&A deals, with some of the largest companies going private. SailPoint, KnowBe4, Barracuda, Mandiant, Fedora, these are multi billion dollar acquisitions, or at least billion dollars and up, and many of them multi-billion, for these companies, and hundreds more acquisitions in the cyberspace, now less you think the pond is overfished, here's a chart from ETR of emerging tech companies in the cyber security industry. This data comes from ETR's Emerging Technologies Survey, ETS, which is this diamond in a rough that I found a couple quarters ago, and it's ripe with companies that are candidates for M&A. Many would've liked, many of these companies would've liked to, gotten to the public markets during the pandemic, but they, you know, couldn't get there. They weren't ready. So the graph, you know, similar to the previous one, but different, it shows net sentiment on the vertical axis and that's a measurement of, of, of intent to adopt against a mind share on the X axis, which measures, measures the awareness of the vendor in the community. So this is specifically a survey that ETR goes out and, and, and fields only to track those emerging tech companies that are private companies. Now, some of the standouts in Mindshare, are OneTrust, BeyondTrust, Tanium and Endpoint, Net Scope, which we've talked about in previous Breaking Analysis. 1Password, which has been acquisitive on its own. In identity, the managed security service provider, Arctic Wolf Network, a company we've also covered, we've had their CEO on. We've talked about MSSPs as a real trend, particularly in small and medium sized business, we'll come back to that, Sneek, you know, kind of high flyer in both app security and containers, and you can just see the number of companies in the space this huge and it just keeps growing. Now, just to make it a bit easier on the eyes we filtered the data on these companies with with those, and isolated on those with more than a hundred responses only within the survey. And that's what we show here. Some of the names that we just mentioned are a bit easier to see, but these are the ones that really stand out in ERT, ETS, survey of private companies, OneTrust, BeyondTrust, Taniam, Netscope, which is in Cloud, 1Password, Arctic Wolf, Sneek, BitSight, SecurityScorecard, HackerOne, Code42, and Exabeam, and Sim. All of these hit the ETS survey with more than a hundred responses by, by the IT practitioners. Okay, so these firms, you know, maybe they do some M&A on their own. We've seen that with Sneek, as I said, with 1Password has been inquisitive, as have others. Now these companies with the larger footprint, these private companies, will likely be candidate for both buying companies and eventually going public when the markets settle down a bit. So again, no shortage of players to affect consolidation, both buyers and sellers. Okay, so let's finish with some key questions that we're watching. CrowdStrike in particular on its earnings calls cited softness from smaller buyers. Is that because these smaller buyers have stopped adopting? If so, are they more at risk, or are they tactically moving toward the easy button, aka, Microsoft's good enough approach. What does that mean for the market if smaller company cohorts continue to soften? How about MSSPs? Will companies continue to outsource, or pause on on that, as well as try to free up, to try to free up some budget? Adam Celiski at Reinvent last week said, "If you want to save money the Cloud's the best place to do it." Is the cloud the best place to save money in cyber? Well, it would seem that way from the standpoint of controlling budgets with lots of, lots of optionality. You could dial up and dial down services, you know, or does the Cloud add another layer of complexity that has to be understood and managed by Devs, for example? Now, consolidation should favor the likes of Palo Alto and CrowdStrike, cause they're platform players, and some of the larger players as well, like Cisco, how about IBM and of course Microsoft. Will that happen? And how will economic uncertainty impact the risk equation, a particular concern is increase of tax on vulnerable sectors of the population, like the elderly. How will companies and governments protect them from scams? And finally, how many cybersecurity companies can actually remain independent in the slingshot economy? In so many ways the market is still strong, it's just that expectations got ahead of themselves, and now as earnings forecast come, come, come down and come down to earth, it's going to basically come down to who can execute, generate cash, and keep enough runway to get through the knothole. And the one certainty is nobody really knows how tight that knothole really is. All right, let's call it a wrap. Next week we dive deeper into Palo Alto Networks, and take a look at how and why that company has held up so well and what to expect at Ignite, Palo Alto's big user conference coming up later this month in Las Vegas. We'll be there with theCube. Okay, many thanks to Alex Myerson on production and manages the podcast, Ken Schiffman as well, as our newest edition to our Boston studio. Great to have you Ken. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Silicon Angle. He does some great editing for us. Thank you to all. Remember these episodes are all available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibond.com and siliconangle.com, or you can email me directly David.vellante@siliconangle.com or DM me @DVellante, or comment on our LinkedIn posts. Please do checkout etr.ai, they got 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 next time on Breaking Analysis. (upbeat music)

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all as we know, data is changing the world and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to "theCUBE"'s presentation of democratizing analytics across the enterprise, made possible by Alteryx. An Alteryx commissioned IDC info brief entitled, "Four Ways to Unlock Transformative Business Outcomes from Analytics Investments" found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special "CUBE" presentation, Jason Klein, product marketing director of Alteryx, will join me to share key findings from the new Alteryx commissioned IDC brief and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, chief data and analytics officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then in our final segment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who is the global head of tax technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, product marketing director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research, which spoke with about 1500 leaders, what nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees, and this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics, and we're able to focus on the behaviors driving higher ROI. >> So the info brief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the info brief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack, what's driving this demand, this need for analytics across organizations? >> Sure, well first there's more data than ever before, the data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins and to improve customer experiences. And analytics along with automation and AI is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> One of the things that the study also showed was that not all analytics spending is resulting in the same ROI. What are some of the discrepancies that the info brief uncovered with respect to the changes in ROI that organizations are achieving? >> Our research with IDC revealed significant roadblocks across people, processes, and technologies. They're preventing companies from reaping greater benefits from their investments. So for example, on the people side, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% from our survey, are still not using the full breadth of data types available. Yet data's never been this prolific, it's going to continue to grow, and orgs should be using it to their advantage. And lastly organizations, they need to provide the right analytics tools to help everyone unlock the power of data. >> So they- >> They instead rely on outdated spreadsheet technology. In our survey, nine out of 10 respondents said less than half of their knowledge workers are active users of analytics software beyond spreadsheets. But true analytic transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and be driving value. >> Should we retake that, since I started talking over Jason accidentally? >> Yep, absolutely we can do so. We'll just go, yep, we'll go back to Lisa's question. Let's just, let's do the, retake the question and the answer, that'll be able to. >> It'll be not all analytics spending results in the same ROI, what are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we get that clean question and answer. >> Okay. >> Thank you for that. On your ISO, we're still speeding, Lisa, so give it a beat in your head and then on you. >> Yet not all analytics spending is resulting in the same ROI. So what are some of the discrepancies that the info brief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes, and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead they're relying on outdated spreadsheet technology. Nine of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically, then what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value from their data and analytics and achieve more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So very strategic investments. Did the survey uncover any specific areas where most companies are falling short, like any black holes that organizations need to be aware of at the outset? >> It did, it did. So organizations, they need to build a data-centric culture. And this begins with people. But what the survey told us is that the people aspect of analytics is the most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone in the organization has access to the data and analytics technology they need. And then the organizations also have to align their investments with upskilling in data literacy to enjoy that higher ROI. Companies who did so experience higher ROI than companies who underinvested in analytics literacy. So among the high ROI achievers, 78% have a good or great alignment between analytics investment and workforce upskilling compared to only 64% among those without positive ROI. And as more orgs adopt cloud data warehouses or cloud data lakes, in order to manage the massively increasing workloads- Can I start that one over. >> Sure. >> Can I redo this one? >> Yeah. >> Of course, stand by. >> Tongue tied. >> Yep, no worries. >> One second. >> If we could do the same, Lisa, just have a clean break, we'll go your question. >> Yep, yeah. >> On you Lisa. Just give that a count and whenever you're ready. Here, I'm going to give us a little break. On you Lisa. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture and this begins with people, but we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources, compared to only 67% among the ROI laggards. >> So interesting that you mentioned people, I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand, we know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right, so analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively and letting them do so cross-functionally. >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side. And it's expected to spend more on analytics than other IT. What risks does this present to the overall organization, if IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this isn't because the lines of business have recognized the value of analytics and plan to invest accordingly, but a lack of alignment between IT and business. This will negatively impact governance, which ultimately impedes democratization and hence ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up in Alteryx environment, but also to take a look at your analytics stack and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process, and technologies. Jason, thank you so much for joining me today, unpacking the IDC info brief and the great nuggets in there. Lots that organizations can learn and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you, it's been a pleasure. >> In a moment, Alan Jacobson, who's the chief data and analytics officer at Alteryx is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching "theCUBE", the leader in tech enterprise coverage. >> Somehow many have come to believe that data analytics is for the few, for the scientists, the PhDs, the MBAs. Well, it is for them, but that's not all. You don't have to have an advanced degree to do amazing things with data. You don't even have to be a numbers person. You can be just about anything. A titan of industry or a future titan of industry. You could be working to change the world, your neighborhood, or the course of your business. You can be saving lives or just looking to save a little time. The power of data analytics shouldn't be limited to certain job titles or industries or organizations because when more people are doing more things with data, more incredible things happen. Analytics makes us smarter and faster and better at what we do. It's practically a superpower. That's why we believe analytics is for everyone, and everything, and should be everywhere. That's why we believe in analytics for all. (upbeat music) >> Hey, everyone. Welcome back to "Accelerating Analytics Maturity". I'm your host, Lisa Martin. Alan Jacobson joins me next. The chief of data and analytics officer at Alteryx. Alan, it's great to have you on the program. >> Thanks, Lisa. >> So Alan, as we know, everyone knows that being data driven is very important. It's a household term these days, but 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. What's your advice, your recommendations for organizations who are just starting out with analytics? >> You're spot on, many organizations really aren't leveraging the full capability of their knowledge workers. And really the first step is probably assessing where you are on the journey, whether that's you personally, or your organization as a whole. We just launched an assessment tool on our website that we built with the International Institute of Analytics, that in a very short period of time, in about 15 minutes, you can go on and answer some questions and understand where you sit versus your peer set versus competitors and kind of where you are on the journey. >> So when people talk about data analytics, they often think, ah, this is for data science experts like people like you. So why should people in the lines of business like the finance folks, the marketing folks, why should they learn analytics? >> So domain experts are really in the best position. They know where the gold is buried in their companies. They know where the inefficiencies are. And it is so much easier and faster to teach a domain expert a bit about how to automate a process or how to use analytics than it is to take a data scientist and try to teach them to have the knowledge of a 20 year accounting professional or a logistics expert of your company. Much harder to do that. And really, if you think about it, the world has changed dramatically in a very short period of time. If you were a marketing professional 30 years ago, you likely didn't need to know anything about the internet, but today, do you know what you would call that marketing professional if they didn't know anything about the internet, probably unemployed or retired. And so knowledge workers are having to learn more and more skills to really keep up with their professions. And analytics is really no exception. Pretty much in every profession, people are needing to learn analytics to stay current and be capable for their companies. And companies need people who can do that. >> Absolutely, it seems like it's table stakes these days. Let's look at different industries now. Are there differences in how you see analytics in automation being employed in different industries? I know Alteryx is being used across a lot of different types of organizations from government to retail. I also see you're now with some of the leading sports teams. Any differences in industries? >> Yeah, there's an incredible actually commonality between the domains industry to industry. So if you look at what an HR professional is doing, maybe attrition analysis, it's probably quite similar, whether they're in oil and gas or in a high tech software company. And so really the similarities are much larger than you might think. And even on the sports front, we see many of the analytics that sports teams perform are very similar. So McLaren is one of the great partners that we work with and they use Alteryx across many areas of their business from finance to production, extreme sports, logistics, wind tunnel engineering, the marketing team analyzes social media data, all using Alteryx, and if I take as an example, the finance team, the finance team is trying to optimize the budget to make sure that they can hit the very stringent targets that F1 Sports has, and I don't see a ton of difference between the optimization that they're doing to hit their budget numbers and what I see Fortune 500 finance departments doing to optimize their budget, and so really the commonality is very high, even across industries. >> I bet every Fortune 500 or even every company would love to be compared to the same department within McLaren F1. Just to know that wow, what they're doing is so incredibly important as is what we're doing. >> So talk- >> Absolutely. >> About lessons learned, what lessons can business leaders take from those organizations like McLaren, who are the most analytically mature? >> Probably first and foremost, is that the ROI with analytics and automation is incredibly high. Companies are having a ton of success. It's becoming an existential threat to some degree, if your company isn't going on this journey and your competition is, it can be a huge problem. IDC just did a recent study about how companies are unlocking the ROI using analytics. And the data was really clear, organizations that have a higher percentage of their workforce using analytics are enjoying a much higher return from their analytic investment, and so it's not about hiring two double PhD statisticians from Oxford. It really is how widely you can bring your workforce on this journey, can they all get 10% more capable? And that's having incredible results at businesses all over the world. An another key finding that they had is that the majority of them said that when they had many folks using analytics, they were going on the journey faster than companies that didn't. And so picking technologies that'll help everyone do this and do this fast and do it easily. Having an approachable piece of software that everyone can use is really a key. >> So faster, able to move faster, higher ROI. I also imagine analytics across the organization is a big competitive advantage for organizations in any industry. >> Absolutely the IDC, or not the IDC, the International Institute of Analytics showed huge correlation between companies that were more analytically mature versus ones that were not. They showed correlation to growth of the company, they showed correlation to revenue and they showed correlation to shareholder values. So across really all of the key measures of business, the more analytically mature companies simply outperformed their competition. >> And that's key these days, is to be able to outperform your competition. You know, one of the things that we hear so often, Alan, is people talking about democratizing data and analytics. You talked about the line of business workers, but I got to ask you, is it really that easy for the line of business workers who aren't trained in data science to be able to jump in, look at data, uncover and extract business insights to make decisions? >> So in many ways, it really is that easy. I have a 14 and 16 year old kid. Both of them have learned Alteryx, they're Alteryx certified and it was quite easy. It took 'em about 20 hours and they were off to the races, but there can be some hard parts. The hard parts have more to do with change management. I mean, if you're an accountant that's been doing the best accounting work in your company for the last 20 years, and all you happen to know is a spreadsheet for those 20 years, are you ready to learn some new skills? And I would suggest you probably need to, if you want to keep up with your profession. The big four accounting firms have trained over a hundred thousand people in Alteryx. Just one firm has trained over a hundred thousand. You can't be an accountant or an auditor at some of these places without knowing Alteryx. And so the hard part, really in the end, isn't the technology and learning analytics and data science, the harder part is this change management, change is hard. I should probably eat better and exercise more, but it's hard to always do that. And so companies are finding that that's the hard part. They need to help people go on the journey, help people with the change management to help them become the digitally enabled accountant of the future, the logistics professional that is E enabled, that's the challenge. >> That's a huge challenge. Cultural shift is a challenge, as you said, change management. How do you advise customers if you might be talking with someone who might be early in their analytics journey, but really need to get up to speed and mature to be competitive, how do you guide them or give them recommendations on being able to facilitate that change management? >> Yeah, that's a great question. So people entering into the workforce today, many of them are starting to have these skills. Alteryx is used in over 800 universities around the globe to teach finance and to teach marketing and to teach logistics. And so some of this is happening naturally as new workers are entering the workforce, but for all of those who are already in the workforce, have already started their careers, learning in place becomes really important. And so we work with companies to put on programmatic approaches to help their workers do this. And so it's, again, not simply putting a box of tools in the corner and saying free, take one. We put on hackathons and analytic days, and it can be great fun. We have a great time with many of the customers that we work with, helping them do this, helping them go on the journey, and the ROI, as I said, is fantastic. And not only does it sometimes affect the bottom line, it can really make societal changes. We've seen companies have breakthroughs that have really made great impact to society as a whole. >> Isn't that so fantastic, to see the difference that that can make. It sounds like you guys are doing a great job of democratizing access to Alteryx to everybody. We talked about the line of business folks and the incredible importance of enabling them and the ROI, the speed, the competitive advantage. Can you share some specific examples that you think of Alteryx customers that really show data breakthroughs by the lines of business using the technology? >> Yeah, absolutely, so many to choose from. I'll give you two examples quickly. One is Armor Express. They manufacture life saving equipment, defensive equipments, like armor plated vests, and they were needing to optimize their supply chain, like many companies through the pandemic. We see how important the supply chain is. And so adjusting supply to match demand is really vital. And so they've used Alteryx to model some of their supply and demand signals and built a predictive model to optimize the supply chain. And it certainly helped out from a dollar standpoint. They cut over a half a million dollars of inventory in the first year, but more importantly, by matching that demand and supply signal, you're able to better meet customer demand. And so when people have orders and are looking to pick up a vest, they don't want to wait. And it becomes really important to get that right. Another great example is British Telecom. They're a company that services the public sector. They have very strict reporting regulations that they have to meet and they had, and this is crazy to think about, over 140 legacy spreadsheet models that they had to run to comply with these regulatory processes and report, and obviously running 140 legacy models that had to be done in a certain order and length, incredibly challenging. It took them over four weeks each time that they had to go through that process. And so to save time and have more efficiency in doing that, they trained 50 employees over just a two week period to start using Alteryx and learn Alteryx. And they implemented an all new reporting process that saw a 75% reduction in the number of man hours it took to run in a 60% run time performance. And so, again, a huge improvement. I can imagine it probably had better quality as well, because now that it was automated, you don't have people copying and pasting data into a spreadsheet. And that was just one project that this group of folks were able to accomplish that had huge ROI, but now those people are moving on and automating other processes and performing analytics in other areas. So you can imagine the impact by the end of the year that they will have on their business, potentially millions upon millions of dollars. And this is what we see again and again, company after company, government agency after government agency, is how analytics are really transforming the way work is being done. >> That was the word that came to mind when you were describing the all three customer examples, transformation, this is transformative. The ability to leverage Alteryx, to truly democratize data and analytics, give access to the lines of business is transformative for every organization. And also the business outcome you mentioned, those are substantial metrics based business outcomes. So the ROI in leveraging a technology like Alteryx seems to be right there, sitting in front of you. >> That's right, and to be honest, it's not only important for these businesses. It's important for the knowledge workers themselves. I mean, we hear it from people that they discover Alteryx, they automate a process, they finally get to get home for dinner with their families, which is fantastic, but it leads to new career paths. And so knowledge workers that have these added skills have so much larger opportunity. And I think it's great when the needs of businesses to become more analytic and automate processes actually matches the needs of the employees, and they too want to learn these skills and become more advanced in their capabilities. >> Huge value there for the business, for the employees themselves to expand their skillset, to really open up so many opportunities for not only the business to meet the demands of the demanding customer, but the employees to be able to really have that breadth and depth in their field of service. Great opportunities there, Alan. Is there anywhere that you want to point the audience to go to learn more about how they can get started? >> Yeah, so one of the things that we're really excited about is how fast and easy it is to learn these tools. So any of the listeners who want to experience Alteryx, they can go to the website, there's a free download on the website. You can take our analytic maturity assessment, as we talked about at the beginning, and see where you are on the journey and just reach out. We'd love to work with you and your organization to see how we can help you accelerate your journey on analytics and automation. >> Alan, it was a pleasure talking to you about democratizing data and analytics, the power in it for organizations across every industry. We appreciate your insights and your time. >> Thank you so much. >> In a moment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who's the global head of tax technology at eBay, will join me. You're watching "theCUBE", the leader in high tech enterprise coverage. >> 1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops. >> Make that 2.3. >> Sector times out the wazoo. >> Way too much of this. >> Velocities, pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into winning insights, they turn to Alteryx. Alteryx, analytics automation. (upbeat music) >> Hey, everyone, welcome back to the program. Lisa Martin here, I've got two guests joining me. Please welcome back to "theCUBE" Paula Hansen, the chief revenue officer and president at Alteryx, and Jacqui Van der Leij Greyling joins us as well, the global head of tax technology at eBay. They're going to share with you how Alteryx is helping eBay innovate with analytics. Ladies, welcome, it's great to have you both on the program. >> Thank you, Lisa, it's great to be here. >> Yeah, Paula, we're going to start with you. In this program, we've heard from Jason Klein, we've heard from Alan Jacobson. They talked about the need to democratize analytics across any organization to really drive innovation. With analytics, as they talked about, at the forefront of software investments, how's Alteryx helping its customers to develop roadmaps for success with analytics? >> Well, thank you, Lisa. It absolutely is about our customers' success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts of course with our innovative technology and platform, but ultimately we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics, through things like enablement programs, skills assessments, hackathons, setting up centers of excellence to help their organization scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practices, so they can make better business decisions and compete in their respective industries. >> Excellent, sounds like a very strategic program, we're going to unpack that. Jacqui, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How Jacqui did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >> So I think the main thing for us is when we started out was is that, our, especially in finance, they became spreadsheet professionals instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and being more effective. So ultimately we really started very, very actively embedding analytics in our people and our data and our processes. >> Starting with people is really critical. Jacqui, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >> So I think eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year, so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them to move forward. The other thing is that people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals. And there was no, we were not independent. You couldn't move forward, you would've put it on somebody else's roadmap to get the data and to get the information if you want it. So really finding something that everybody could access analytics or access data. And finally we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy, and that is not so daunting on somebody who's brand new to the field? And I would call those out as your major roadblocks, because you always have, not always, but most of the times you have support from the top, and in our case we have, but at the end of the day, it's our people that need to actually really embrace it, and making that accessible for them, I would say is definitely not per se, a roadblock, but basically a block you want to be able to move. >> It's really all about putting people first. Question for both of you, and Paula we'll start with you, and then Jacqui we'll go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone, should be for everyone. Let's talk now about how both of your organizations are empowering people, those in the organization that may not have technical expertise to be able to leverage data, so that they can actually be data driven. Paula. >> Yes, well, we leverage our platform across all of our business functions here at Alteryx. And just like Jacqui explained, at eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jacqui mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO Kevin Rubin has been a key sponsor for using our own technology. We use Alteryx for forecasting all of our key performance metrics, for business planning, across our audit function, to help with compliance and regulatory requirements, tax, and even to close our books at the end of each quarter. So it's really going to remain across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other, to problem solve and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jacqui mentioned, it's really about ensuring that people feel comfortable, that they feel supported, that they have access to the training that they need, and ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >> That confidence is key. Jacqui, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >> Yeah, I think it means to what Paula has said in terms of getting people excited about it, but it's also understanding that this is a journey and everybody is at a different place in their journey. You have folks that's already really advanced who has done this every day. And then you have really some folks that this is brand new or maybe somewhere in between. And it's about how you get everybody in their different phases to get to the initial destination. I say initial, because I believe a journey is never really complete. What we have done is that we decided to invest, and built a proof of concept, and we got our CFO to sponsor a hackathon. We opened it up to everybody in finance in the middle of the pandemic. So everybody was on Zoom and we told people, listen, we're going to teach you this tool, it's super easy, and let's just see what you can do. We ended up having 70 entries. We had only three weeks. So and these are people that do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon from the 70 entries with people that have never, ever done anything like this before. And there you have the result. And then it just went from there. People had a proof of concept. They knew that it worked and they overcame the initial barrier of change. And that's where we are seeing things really, really picking up now. >> That's fantastic. And the business outcome that you mentioned there, the business impact is massive, helping folks get that confidence to be able to overcome sometimes the cultural barriers is key here. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you're empowering the next generation of data workers? Paula, we'll start with you. >> Absolutely, and Jacqui says it so well, which is that it really is a journey that organizations are on and we as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Alteryx to help address this skillset gap on a global level is through a program that we call SparkED, which is essentially a no-cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed just to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with SparkED. We started last May, but we currently have over 850 educational institutions globally engaged across 47 countries, and we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close the gap and empower more people with the necessary analytics skills to solve all the problems that data can help solve. >> So SparkED has made a really big impact in such a short time period. It's going to be fun to watch the progress of that. Jacqui, let's go over to you now. Talk about some of the things that eBay is doing to empower the next generation of data workers. >> So we basically wanted to make sure that we kept that momentum from the hackathon, that we don't lose that excitement. So we just launched the program called eBay Masterminds. And what it basically is, is it's an inclusive innovation in each other, where we firmly believe that innovation is for upskilling for all analytics roles. So it doesn't matter your background, doesn't matter which function you are in, come and participate in in this where we really focus on innovation, introducing new technologies and upskilling our people. We are, apart from that, we also said, well, we shouldn't just keep it to inside eBay. We have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use Alteryx. And we're working with, actually, we're working with SparkED and they're helping us develop that program. And we really hope that at, say, by the end of the year, we have a pilot and then also next year, we want to roll it out in multiple locations in multiple countries and really, really focus on that whole concept of analytics for all. >> Analytics for all, sounds like Alteryx and eBay have a great synergistic relationship there that is jointly aimed at especially going down the stuff and getting people when they're younger interested, and understanding how they can be empowered with data across any industry. Paula, let's go back to you, you were recently on "theCUBE"'s Supercloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating what is by default a multi-cloud world. How does the Alteryx Analytics Cloud platform enable CIOs to democratize analytics across their organization? >> Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last I checked, there was 2 million data scientists in the world, so that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CIOs, with business leaders is that they're integrating data analysis and the skillset of data analysis into virtually every job function, and that is what we think of when we think of analytics for all. And so our mission with Alteryx Analytics Cloud is to empower all of those people in every job function, regardless of their skillset, as Jacqui pointed out from people that are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Alteryx Analytics Cloud, and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze, and report out so that we can break down data silos across the enterprise and help drive real business outcomes as a result of unlocking the potential of data. >> As well as really lessening that skill gap. As you were saying, there's only 2 million data scientists. You don't need to be a data scientist, that's the beauty of what Alteryx is enabling and eBay is a great example of that. Jacqui, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where Alteryx fits in as that analytics maturity journey continues and what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >> When we're starting up and getting excited about things when it comes to analytics, I can go on all day, but I'll keep it short and sweet for you. I do think we are on the top of the pool of data scientists. And I really feel that that is your next step, for us anyways, is that how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's something completely different. And it's something that is in everybody in a certain extent. So again, partnering with Alteryx who just released the AI ML solution, allowing folks to not have a data scientist program, but actually build models and be able to solve problems that way. So we have engaged with Alteryx and we purchased the licenses, quite a few. And right now through our Masterminds program, we're actually running a four month program for all skill levels, teaching them AI ML and machine learning and how they can build their own models. We are really excited about that. We have over 50 participants without a background from all over the organization. We have members from our customer services. We have even some of our engineers are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I want to give you a quick example of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution where there is a checkout feedback functionality on the eBay side where sellers or buyers can verbatim add information. And she built a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we as a human even step in, and now instead of us or somebody going to verbatim and try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value, and it's a beautiful tool and I was very impressed when I saw the demo and definitely developing that sort of thing. >> That sounds fantastic. And I think just the one word that keeps coming to mind, and we've said this a number of times in the program today is empowerment. What you're actually really doing to truly empower people across the organization with varying degrees of skill level, going down to the high school level, really exciting. We'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I want to thank you so much for joining me on the program today and talking about how Alteryx and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you. >> Thank you, Lisa. >> Thank you so much. (cheerful electronic music) >> As you heard over the course of our program, organizations where more people are using analytics who have deeper capabilities in each of the four Es, that's everyone, everything, everywhere, and easy analytics, those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling and empowering line of business users to use analytics, not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We want to thank you so much for watching the program today. Remember you can find all of the content on thecube.net. You can find all of the news from today on siliconangle.com and of course alteryx.com. We also want to thank Alteryx for making this program possible and for sponsoring "theCUBE". For all of my guests, I'm Lisa Martin. We want to thank you for watching and bye for now. (upbeat music)

>>Okay. Okay. We're back at Caesar's Forum. The Snowflake summit 2022. The cubes. Continuous coverage this day to wall to wall coverage. We're so excited to have the analyst panel here, some of my colleagues that we've done a number. You've probably seen some power panels that we've done. David McGregor is here. He's the senior vice president and research director at Ventana Research. To his left is Tony Blair, principal at DB Inside and my in the co host seat. Sanjeev Mohan Sanremo. Guys, thanks so much for coming on. I'm glad we can. Thank you. You're very welcome. I wasn't able to attend the analyst action because I've been doing this all all day, every day. But let me start with you, Dave. What have you seen? That's kind of interested you. Pluses, minuses. Concerns. >>Well, how about if I focus on what I think valuable to the customers of snowflakes and our research shows that the majority of organisations, the majority of people, do not have access to analytics. And so a couple of things they've announced I think address those are helped to address those issues very directly. So Snow Park and support for Python and other languages is a way for organisations to embed analytics into different business processes. And so I think that will be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most most people in the organisation or not, analysts, they're doing some line of business function. Their HR managers, their marketing people, their salespeople, their finance people right there, not sitting there mucking around in the data. They're doing a job and they need analytics in that job. So, >>Tony, I thank you. I've heard a lot of data mesh talk this week. It's kind of funny. Can't >>seem to get away from it. You >>can't see. It seems to be gathering momentum, but But what have you seen? That's been interesting. >>What I have noticed. Unfortunately, you know, because the rooms are too small, you just can't get into the data mesh sessions, so there's a lot of interest in it. Um, it's still very I don't think there's very much understanding of it, but I think the idea that you can put all the data in one place which, you know, to me, stuff like it seems to be kind of sort of in a way, it sounds like almost like the Enterprise Data warehouse, you know, Clouded Cloud Native Edition, you know, bring it all in one place again. Um, I think it's providing, sort of, You know, it's I think, for these folks that think this might be kind of like a a linchpin for that. I think there are several other things that actually that really have made a bigger impression on me. Actually, at this event, one is is basically is, um we watch their move with Eunice store. Um, and it's kind of interesting coming, you know, coming from mongo db last week. And I see it's like these two companies seem to be going converging towards the same place at different speeds. I think it's not like it's going to get there faster than Mongo for a number of different reasons, but I see like a number of common threads here. I mean, one is that Mongo was was was a company. It's always been towards developers. They need you know, start cultivating data, people, >>these guys going the other way. >>Exactly. Bingo. And the thing is that but they I think where they're converging is the idea of operational analytics and trying to serve all constituencies. The other thing, which which also in terms of serving, you know, multiple constituencies is how snowflake is laid out Snow Park and what I'm finding like. There's an interesting I economy. On one hand, you have this very ingrained integration of Anaconda, which I think is pretty ingenious. On the other hand, you speak, let's say, like, let's say the data robot folks and say, You know something our folks wanna work data signs us. We want to work in our environment and use snowflake in the background. So I see those kind of some interesting sort of cross cutting trends. >>So, Sandy, I mean, Frank Sullivan, we'll talk about there's definitely benefits into going into the walled garden. Yeah, I don't think we dispute that, but we see them making moves and adding more and more open source capabilities like Apache iceberg. Is that a Is that a move to sort of counteract the narrative that the data breaks is put out there. Is that customer driven? What's your take on that? >>Uh, primarily I think it is to contract this whole notion that once you move data into snowflake, it's a proprietary format. So I think that's how it started. But it's hugely beneficial to the customers to the users, because now, if you have large amounts of data in parquet files, you can leave it on s three. But then you using the the Apache iceberg table format. In a snowflake, you get all the benefits of snowflakes. Optimizer. So, for example, you get the, you know, the micro partitioning. You get the meta data. So, uh, in a single query, you can join. You can do select from a snowflake table union and select from iceberg table, and you can do store procedures, user defined functions. So I think they what they've done is extremely interesting. Uh, iceberg by itself still does not have multi table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache iceberg in a raw format, they don't have it. But snowflake does, >>right? There's hence the delta. And maybe that maybe that closes over time. I want to ask you as you look around this I mean the ecosystems pretty vibrant. I mean, it reminds me of, like reinvent in 2013, you know? But then I'm struck by the complexity of the last big data era and a dupe and all the different tools. And is this different, or is it the sort of same wine new new bottle? You guys have any thoughts on that? >>I think it's different and I'll tell you why. I think it's different because it's based around sequel. So if back to Tony's point, these vendors are coming at this from different angles, right? You've got data warehouse vendors and you've got data lake vendors and they're all going to meet in the middle. So in your case, you're taught operational analytical. But the same thing is true with Data Lake and Data Warehouse and Snowflake no longer wants to be known as the Data Warehouse. There a data cloud and our research again. I like to base everything off of that. >>I love what our >>research shows that organisation Two thirds of organisations have sequel skills and one third have big data skills, so >>you >>know they're going to meet in the middle. But it sure is a lot easier to bring along those people who know sequel already to that midpoint than it is to bring big data people to remember. >>Mrr Odula, one of the founders of Cloudera, said to me one time, John Kerry and the Cube, that, uh, sequel is the killer app for a Yeah, >>the difference at this, you know, with with snowflake, is that you don't have to worry about taming the zoo. Animals really have thought out the ease of use, you know? I mean, they thought about I mean, from the get go, they thought of too thin to polls. One is ease of use, and the other is scale. And they've had. And that's basically, you know, I think very much differentiates it. I mean, who do have the scale, but it didn't have the ease of use. But don't I >>still need? Like, if I have, you know, governance from this vendor or, you know, data prep from, you know, don't I still have to have expertise? That's sort of distributed in those those worlds, right? I mean, go ahead. Yeah. >>So the way I see it is snowflake is adding more and more capabilities right into the database. So, for example, they've they've gone ahead and added security and privacy so you can now create policies and do even set level masking, dynamic masking. But most organisations have more than snowflake. So what we are starting to see all around here is that there's a whole series of data catalogue companies, a bunch of companies that are doing dynamic data masking security and governance data observe ability, which is not a space snowflake has gone into. So there's a whole ecosystem of companies that that is mushrooming, although, you know so they're using the native capabilities of snowflake, but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other, like relational databases, you can run these cross platform capabilities in that layer. So so that way, you know, snowflakes done a great job of enabling that ecosystem about >>the stream lit acquisition. Did you see anything here that indicated there making strong progress there? Are you excited about that? You're sceptical. Go ahead. >>And I think it's like the last mile. Essentially. In other words, it's like, Okay, you have folks that are basically that are very, very comfortable with tableau. But you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency, um, to San James Point. I think part of it, this kind of plays into it is what makes this different from the ado Pere is the fact that this all these capabilities, you know, a lot of vendors are taking it very seriously to make put this native obviously snowflake acquired stream. Let's so we can expect that's extremely capabilities are going to be native. >>And the other thing, too, about the Hadoop ecosystem is Claudia had to help fund all those different projects and got really, really spread thin. I want to ask you guys about this super cloud we use. Super Cloud is this sort of metaphor for the next wave of cloud. You've got infrastructure aws, azure, Google. It's not multi cloud, but you've got that infrastructure you're building a layer on top of it that hides the underlying complexities of the primitives and the a p I s. And you're adding new value in this case, the data cloud or super data cloud. And now we're seeing now is that snowflake putting forth the notion that they're adding a super path layer. You can now build applications that you can monetise, which to me is kind of exciting. It makes makes this platform even less discretionary. We had a lot of talk on Wall Street about discretionary spending, and that's not discretionary. If you're monetising it, um, what do you guys think about that? Is this something that's that's real? Is it just a figment of my imagination, or do you see a different way of coming any thoughts on that? >>So, in effect, they're trying to become a data operating system, right? And I think that's wonderful. It's ambitious. I think they'll experience some success with that. As I said, applications are important. That's a great way to deliver information. You can monetise them, so you know there's there's a good economic model around it. I think they will still struggle, however, with bringing everything together onto one platform. That's always the challenge. Can you become the platform that's hard, hard to predict? You know, I think this is This is pretty exciting, right? A lot of energy, a lot of large ecosystem. There is a network effect already. Can they succeed in being the only place where data exists? You know, I think that's going to be a challenge. >>I mean, the fact is, I mean, this is a classic best of breed versus the umbrella play. The thing is, this is nothing new. I mean, this is like the you know, the old days with enterprise applications were basically oracle and ASAP vacuumed up all these. You know, all these applications in their in their ecosystem, whereas with snowflake is. And if you look at the cloud, folks, the hyper scale is still building out their own portfolios as well. Some are, You know, some hyper skills are more partner friendly than others. What? What Snowflake is saying is that we're going to give all of you folks who basically are competing against the hyper skills in various areas like data catalogue and pipelines and all that sort of wonderful stuff will make you basically, you know, all equal citizens. You know the burden is on you to basically we will leave. We will lay out the A P. I s Well, we'll allow you to basically, you know, integrate natively to us so you can provide as good experience. But the but the onus is on your back. >>Should the ecosystem be concerned, as they were back to reinvent 2014 that Amazon was going to nibble away at them or or is it different? >>I find what they're doing is different. Uh, for example, data sharing. They were the first ones out the door were data sharing at a large scale. And then everybody has jumped in and said, Oh, we also do data sharing. All the hyper scholars came in. But now what snowflake has done is they've taken it to the next level. Now they're saying it's not just data sharing. It's up sharing and not only up sharing. You can stream the thing you can build, test deploy, and then monetise it. Make it discoverable through, you know, through your marketplace >>you can monetise it. >>Yes. Yeah, so So I I think what they're doing is they are taking it a step further than what hyper scale as they are doing. And because it's like what they said is becoming like the data operating system You log in and you have all of these different functionalities you can do in machine learning. Now you can do data quality. You can do data preparation and you can do Monetisation. Who do you >>think is snowflakes? Biggest competitor? What do you guys think? It's a hard question, isn't it? Because you're like because we all get the we separate computer from storage. We have a cloud data and you go, Okay, that's nice, >>but there's, like, a crack. I think >>there's uniqueness. I >>mean, put it this way. In the old days, it would have been you know, how you know the prime household names. I think today is the hyper scholars and the idea what I mean again, this comes down to the best of breed versus by, you know, get it all from one source. So where is your comfort level? Um, so I think they're kind. They're their co op a Titian the hyper scale. >>Okay, so it's not data bricks, because why they're smaller. >>Well, there is some okay now within the best of breed area. Yes, there is competition. The obvious is data bricks coming in from the data engineering angle. You know, basically the snowflake coming from, you know, from the from the data analyst angle. I think what? Another potential competitor. And I think Snowflake, basically, you know, admitted as such potentially is mongo >>DB. Yeah, >>Exactly. So I mean, yes, there are two different levels of sort >>of a on a longer term collision course. >>Exactly. Exactly. >>Sort of service now and in salesforce >>thing that was that we actually get when I say that a lot of people just laughed. I was like, No, you're kidding. There's no way. I said Excuse me, >>But then you see Mongo last week. We're adding some analytics capabilities and always been developers, as you say, and >>they trashed sequel. But yet they finally have started to write their first real sequel. >>We have M c M Q. Well, now we have a sequel. So what >>were those numbers, >>Dave? Two thirds. One third. >>So the hyper scale is but the hyper scale urz are you going to trust your hyper scale is to do your cross cloud. I mean, maybe Google may be I mean, Microsoft, perhaps aws not there yet. Right? I mean, how important is cross cloud, multi cloud Super cloud Whatever you want to call it What is your data? >>Shows? Cloud is important if I remember correctly. Our research shows that three quarters of organisations are operating in the cloud and 52% are operating across more than one cloud. So, uh, two thirds of the organisations are in the cloud are doing multi cloud, so that's pretty significant. And now they may be operating across clouds for different reasons. Maybe one application runs in one cloud provider. Another application runs another cloud provider. But I do think organisations want that leverage over the hyper scholars right they want they want to be able to tell the hyper scale. I'm gonna move my workloads over here if you don't give us a better rate. Uh, >>I mean, I I think you know, from a database standpoint, I think you're right. I mean, they are competing against some really well funded and you look at big Query barely, you know, solid platform Red shift, for all its faults, has really done an amazing job of moving forward. But to David's point, you know those to me in any way. Those hyper skills aren't going to solve that cross cloud cloud problem, right? >>Right. No, I'm certainly >>not as quickly. No. >>Or with as much zeal, >>right? Yeah, right across cloud. But we're gonna operate better on our >>Exactly. Yes. >>Yes. Even when we talk about multi cloud, the many, many definitions, like, you know, you can mean anything. So the way snowflake does multi cloud and the way mongo db two are very different. So a snowflake says we run on all the hyper scalar, but you have to replicate your data. What Mongo DB is claiming is that one cluster can have notes in multiple different clouds. That is right, you know, quite something. >>Yeah, right. I mean, again, you hit that. We got to go. But, uh, last question, um, snowflake undervalued, overvalued or just about right >>in the stock market or in customers. Yeah. Yeah, well, but, you know, I'm not sure that's the right question. >>That's the question I'm asking. You know, >>I'll say the question is undervalued or overvalued for customers, right? That's really what matters. Um, there's a different audience. Who cares about the investor side? Some of those are watching, but But I believe I believe that the from the customer's perspective, it's probably valued about right, because >>the reason I I ask it, is because it has so hyped. You had $100 billion value. It's the past service now is value, which is crazy for this student Now. It's obviously come back quite a bit below its IPO price. So But you guys are at the financial analyst meeting. Scarpelli laid out 2029 projections signed up for $10 billion.25 percent free time for 20% operating profit. I mean, they better be worth more than they are today. If they do >>that. If I If I see the momentum here this week, I think they are undervalued. But before this week, I probably would have thought there at the right evaluation, >>I would say they're probably more at the right valuation employed because the IPO valuation is just such a false valuation. So hyped >>guys, I could go on for another 45 minutes. Thanks so much. David. Tony Sanjeev. Always great to have you on. We'll have you back for sure. Having us. All right. Thank you. Keep it right there. Were wrapping up Day two and the Cube. Snowflake. Summit 2022. Right back. Mm. Mhm.

[Music] in the 2010s organizations became keenly aware that data would become the key ingredient in driving competitive advantage differentiation and growth but to this day putting data to work remains a difficult challenge for many if not most organizations now as the cloud matures it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible we've also seen better tooling in the form of data workflows streaming machine intelligence ai developer tools security observability automation new databases and the like these innovations they accelerate data proficiency but at the same time they had complexity for practitioners data lakes data hubs data warehouses data marts data fabrics data meshes data catalogs data oceans are forming they're evolving and exploding onto the scene so in an effort to bring perspective to the sea of optionality we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond hello everyone my name is dave vellante with the cube and i'd like to welcome you to a special cube presentation analyst predictions 2022 the future of data management we've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade let me introduce our six power panelists sanjeev mohan is former gartner analyst and principal at sanjamo tony bear is principal at db insight carl olufsen is well-known research vice president with idc dave meninger is senior vice president and research director at ventana research brad shimon chief analyst at ai platforms analytics and data management at omnia and doug henschen vice president and principal analyst at constellation research gentlemen welcome to the program and thanks for coming on thecube today great to be here thank you all right here's the format we're going to use i as moderator are going to call on each analyst separately who then will deliver their prediction or mega trend and then in the interest of time management and pace two analysts will have the opportunity to comment if we have more time we'll elongate it but let's get started right away sanjeev mohan please kick it off you want to talk about governance go ahead sir thank you dave i i believe that data governance which we've been talking about for many years is now not only going to be mainstream it's going to be table stakes and all the things that you mentioned you know with data oceans data lakes lake houses data fabric meshes the common glue is metadata if we don't understand what data we have and we are governing it there is no way we can manage it so we saw informatica when public last year after a hiatus of six years i've i'm predicting that this year we see some more companies go public uh my bet is on colibra most likely and maybe alation we'll see go public this year we we i'm also predicting that the scope of data governance is going to expand beyond just data it's not just data and reports we are going to see more transformations like spark jaws python even airflow we're going to see more of streaming data so from kafka schema registry for example we will see ai models become part of this whole governance suite so the governance suite is going to be very comprehensive very detailed lineage impact analysis and then even expand into data quality we already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management data catalogs also data access governance so these so what we are going to see is that once the data governance platforms become the key entry point into these modern architectures i'm predicting that the usage the number of users of a data catalog is going to exceed that of a bi tool that will take time and we already seen that that trajectory right now if you look at bi tools i would say there are 100 users to a bi tool to one data catalog and i i see that evening out over a period of time and at some point data catalogs will really become you know the main way for us to access data data catalog will help us visualize data but if we want to do more in-depth analysis it'll be the jumping-off point into the bi tool the data science tool and and that is that is the journey i see for the data governance products excellent thank you some comments maybe maybe doug a lot a lot of things to weigh in on there maybe you could comment yeah sanjeev i think you're spot on a lot of the trends uh the one disagreement i think it's it's really still far from mainstream as you say we've been talking about this for years it's like god motherhood apple pie everyone agrees it's important but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking i think one thing that deserves uh mention in this context is uh esg mandates and guidelines these are environmental social and governance regs and guidelines we've seen the environmental rags and guidelines imposed in industries particularly the carbon intensive industries we've seen the social mandates particularly diversity imposed on suppliers by companies that are leading on this topic we've seen governance guidelines now being imposed by banks and investors so these esgs are presenting new carrots and sticks and it's going to demand more solid data it's going to demand more detailed reporting and solid reporting tighter governance but we're still far from mainstream adoption we have a lot of uh you know best of breed niche players in the space i think the signs that it's going to be more mainstream are starting with things like azure purview google dataplex the big cloud platform uh players seem to be uh upping the ante and and addressing starting to address governance excellent thank you doug brad i wonder if you could chime in as well yeah i would love to be a believer in data catalogs um but uh to doug's point i think that it's going to take some more pressure for for that to happen i recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the 90s and that didn't happen quite the way we we anticipated and and uh to sanjeev's point it's because it is really complex and really difficult to do my hope is that you know we won't sort of uh how do we put this fade out into this nebulous nebula of uh domain catalogs that are specific to individual use cases like purview for getting data quality right or like data governance and cyber security and instead we have some tooling that can actually be adaptive to gather metadata to create something i know is important to you sanjeev and that is this idea of observability if you can get enough metadata without moving your data around but understanding the entirety of a system that's running on this data you can do a lot to help with with the governance that doug is talking about so so i just want to add that you know data governance like many other initiatives did not succeed even ai went into an ai window but that's a different topic but a lot of these things did not succeed because to your point the incentives were not there i i remember when starbucks oxley had come into the scene if if a bank did not do service obviously they were very happy to a million dollar fine that was like you know pocket change for them instead of doing the right thing but i think the stakes are much higher now with gdpr uh the floodgates open now you know california you know has ccpa but even ccpa is being outdated with cpra which is much more gdpr like so we are very rapidly entering a space where every pretty much every major country in the world is coming up with its own uh compliance regulatory requirements data residence is becoming really important and and i i think we are going to reach a stage where uh it won't be optional anymore so whether we like it or not and i think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption we were focused on features and these features were disconnected very hard for business to stop these are built by it people for it departments to to take a look at technical metadata not business metadata today the tables have turned cdo's are driving this uh initiative uh regulatory compliances are beating down hard so i think the time might be right yeah so guys we have to move on here and uh but there's some some real meat on the bone here sanjeev i like the fact that you late you called out calibra and alation so we can look back a year from now and say okay he made the call he stuck it and then the ratio of bi tools the data catalogs that's another sort of measurement that we can we can take even though some skepticism there that's something that we can watch and i wonder if someday if we'll have more metadata than data but i want to move to tony baer you want to talk about data mesh and speaking you know coming off of governance i mean wow you know the whole concept of data mesh is decentralized data and then governance becomes you know a nightmare there but take it away tony we'll put it this way um data mesh you know the the idea at least is proposed by thoughtworks um you know basically was unleashed a couple years ago and the press has been almost uniformly almost uncritical um a good reason for that is for all the problems that basically that sanjeev and doug and brad were just you know we're just speaking about which is that we have all this data out there and we don't know what to do about it um now that's not a new problem that was a problem we had enterprise data warehouses it was a problem when we had our hadoop data clusters it's even more of a problem now the data's out in the cloud where the data is not only your data like is not only s3 it's all over the place and it's also including streaming which i know we'll be talking about later so the data mesh was a response to that the idea of that we need to debate you know who are the folks that really know best about governance is the domain experts so it was basically data mesh was an architectural pattern and a process my prediction for this year is that data mesh is going to hit cold hard reality because if you if you do a google search um basically the the published work the articles and databases have been largely you know pretty uncritical um so far you know that you know basically learning is basically being a very revolutionary new idea i don't think it's that revolutionary because we've talked about ideas like this brad and i you and i met years ago when we were talking about so and decentralizing all of us was at the application level now we're talking about at the data level and now we have microservices so there's this thought of oh if we manage if we're apps in cloud native through microservices why don't we think of data in the same way um my sense this year is that you know this and this has been a very active search if you look at google search trends is that now companies are going to you know enterprises are going to look at this seriously and as they look at seriously it's going to attract its first real hard scrutiny it's going to attract its first backlash that's not necessarily a bad thing it means that it's being taken seriously um the reason why i think that that uh that it will you'll start to see basically the cold hard light of day shine on data mesh is that it's still a work in progress you know this idea is basically a couple years old and there's still some pretty major gaps um the biggest gap is in is in the area of federated governance now federated governance itself is not a new issue uh federated governance position we're trying to figure out like how can we basically strike the balance between getting let's say you know between basically consistent enterprise policy consistent enterprise governance but yet the groups that understand the data know how to basically you know that you know how do we basically sort of balance the two there's a huge there's a huge gap there in practice and knowledge um also to a lesser extent there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data you know basically through the full life cycle from developed from selecting the data from you know building the other pipelines from determining your access control determining looking at quality looking at basically whether data is fresh or whether or not it's trending of course so my predictions is that it will really receive the first harsh scrutiny this year you are going to see some organization enterprises declare premature victory when they've uh when they build some federated query implementations you're going to see vendors start to data mesh wash their products anybody in the data management space they're going to say that whether it's basically a pipelining tool whether it's basically elt whether it's a catalog um or confederated query tool they're all going to be like you know basically promoting the fact of how they support this hopefully nobody is going to call themselves a data mesh tool because data mesh is not a technology we're going to see one other thing come out of this and this harks back to the metadata that sanji was talking about and the catalogs that he was talking about which is that there's going to be a new focus on every renewed focus on metadata and i think that's going to spur interest in data fabrics now data fabrics are pretty vaguely defined but if we just take the most elemental definition which is a common metadata back plane i think that if anybody is going to get serious about data mesh they need to look at a data fabric because we all at the end of the day need to speak you know need to read from the same sheet of music so thank you tony dave dave meninger i mean one of the things that people like about data mesh is it pretty crisply articulates some of the flaws in today's organizational approaches to data what are your thoughts on this well i think we have to start by defining data mesh right the the term is already getting corrupted right tony said it's going to see the cold hard uh light of day and there's a problem right now that there are a number of overlapping terms that are similar but not identical so we've got data virtualization data fabric excuse me for a second sorry about that data virtualization data fabric uh uh data federation right uh so i i think that it's not really clear what each vendor means by these terms i see data mesh and data fabric becoming quite popular i've i've interpreted data mesh as referring primarily to the governance aspects as originally you know intended and specified but that's not the way i see vendors using i see vendors using it much more to mean data fabric and data virtualization so i'm going to comment on the group of those things i think the group of those things is going to happen they're going to happen they're going to become more robust our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access again whether you define it as mesh or fabric or virtualization isn't really the point here but this notion that there are different elements of data metadata and governance within an organization that all need to be managed collectively the interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not it's almost double 68 of organizations i'm i'm sorry um 79 of organizations that were using virtualized access express satisfaction with their access to the data lake only 39 expressed satisfaction if they weren't using virtualized access so thank you uh dave uh sanjeev we just got about a couple minutes on this topic but i know you're speaking or maybe you've spoken already on a panel with jamal dagani who sort of invented the concept governance obviously is a big sticking point but what are your thoughts on this you are mute so my message to your mark and uh and to the community is uh as opposed to what dave said let's not define it we spent the whole year defining it there are four principles domain product data infrastructure and governance let's take it to the next level i get a lot of questions on what is the difference between data fabric and data mesh and i'm like i can compare the two because data mesh is a business concept data fabric is a data integration pattern how do you define how do you compare the two you have to bring data mesh level down so to tony's point i'm on a warp path in 2022 to take it down to what does a data product look like how do we handle shared data across domains and govern it and i think we are going to see more of that in 2022 is operationalization of data mesh i think we could have a whole hour on this topic couldn't we uh maybe we should do that uh but let's go to let's move to carl said carl your database guy you've been around that that block for a while now you want to talk about graph databases bring it on oh yeah okay thanks so i regard graph database as basically the next truly revolutionary database management technology i'm looking forward to for the graph database market which of course we haven't defined yet so obviously i have a little wiggle room in what i'm about to say but that this market will grow by about 600 percent over the next 10 years now 10 years is a long time but over the next five years we expect to see gradual growth as people start to learn how to use it problem isn't that it's used the problem is not that it's not useful is that people don't know how to use it so let me explain before i go any further what a graph database is because some of the folks on the call may not may not know what it is a graph database organizes data according to a mathematical structure called a graph a graph has elements called nodes and edges so a data element drops into a node the nodes are connected by edges the edges connect one node to another node combinations of edges create structures that you can analyze to determine how things are related in some cases the nodes and edges can have properties attached to them which add additional informative material that makes it richer that's called a property graph okay there are two principal use cases for graph databases there's there's semantic proper graphs which are used to break down human language text uh into the semantic structures then you can search it organize it and and and answer complicated questions a lot of ai is aimed at semantic graphs another kind is the property graph that i just mentioned which has a dazzling number of use cases i want to just point out is as i talk about this people are probably wondering well we have relational databases isn't that good enough okay so a relational database defines it uses um it supports what i call definitional relationships that means you define the relationships in a fixed structure the database drops into that structure there's a value foreign key value that relates one table to another and that value is fixed you don't change it if you change it the database becomes unstable it's not clear what you're looking at in a graph database the system is designed to handle change so that it can reflect the true state of the things that it's being used to track so um let me just give you some examples of use cases for this um they include uh entity resolution data lineage uh um social media analysis customer 360 fraud prevention there's cyber security there's strong supply chain is a big one actually there's explainable ai and this is going to become important too because a lot of people are adopting ai but they want a system after the fact to say how did the ai system come to that conclusion how did it make that recommendation right now we don't have really good ways of tracking that okay machine machine learning in general um social network i already mentioned that and then we've got oh gosh we've got data governance data compliance risk management we've got recommendation we've got personalization anti-money money laundering that's another big one identity and access management network and i.t operations is already becoming a key one where you actually have mapped out your operation your your you know whatever it is your data center and you you can track what's going on as things happen there root cause analysis fraud detection is a huge one a number of major credit card companies use graph databases for fraud detection risk analysis tracking and tracing churn analysis next best action what-if analysis impact analysis entity resolution and i would add one other thing or just a few other things to this list metadata management so sanjay here you go this is your engine okay because i was in metadata management for quite a while in my past life and one of the things i found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it but grass can okay grafts can do things like say this term in this context means this but in that context it means that okay things like that and in fact uh logistics management supply chain it also because it handles recursive relationships by recursive relationships i mean objects that own other objects that are of the same type you can do things like bill materials you know so like parts explosion you can do an hr analysis who reports to whom how many levels up the chain and that kind of thing you can do that with relational databases but yes it takes a lot of programming in fact you can do almost any of these things with relational databases but the problem is you have to program it it's not it's not supported in the database and whenever you have to program something that means you can't trace it you can't define it you can't publish it in terms of its functionality and it's really really hard to maintain over time so carl thank you i wonder if we could bring brad in i mean brad i'm sitting there wondering okay is this incremental to the market is it disruptive and replaceable what are your thoughts on this space it's already disrupted the market i mean like carl said go to any bank and ask them are you using graph databases to do to get fraud detection under control and they'll say absolutely that's the only way to solve this problem and it is frankly um and it's the only way to solve a lot of the problems that carl mentioned and that is i think it's it's achilles heel in some ways because you know it's like finding the best way to cross the seven bridges of konigsberg you know it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique uh it it still unfortunately kind of stands apart from the rest of the community that's building let's say ai outcomes as the great great example here the graph databases and ai as carl mentioned are like chocolate and peanut butter but technologically they don't know how to talk to one another they're completely different um and you know it's you can't just stand up sql and query them you've got to to learn um yeah what is that carlos specter or uh special uh uh yeah thank you uh to actually get to the data in there and if you're gonna scale that data that graph database especially a property graph if you're gonna do something really complex like try to understand uh you know all of the metadata in your organization you might just end up with you know a graph database winter like we had the ai winter simply because you run out of performance to make the thing happen so i i think it's already disrupted but we we need to like treat it like a first-class citizen in in the data analytics and ai community we need to bring it into the fold we need to equip it with the tools it needs to do that the magic it does and to do it not just for specialized use cases but for everything because i i'm with carl i i think it's absolutely revolutionary so i had also identified the principal achilles heel of the technology which is scaling now when these when these things get large and complex enough that they spill over what a single server can handle you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down so that's still a problem to be solved sanjeev any quick thoughts on this i mean i think metadata on the on the on the word cloud is going to be the the largest font uh but what are your thoughts here i want to like step away so people don't you know associate me with only meta data so i want to talk about something a little bit slightly different uh dbengines.com has done an amazing job i think almost everyone knows that they chronicle all the major databases that are in use today in january of 2022 there are 381 databases on its list of ranked list of databases the largest category is rdbms the second largest category is actually divided into two property graphs and rdf graphs these two together make up the second largest number of data databases so talking about accolades here this is a problem the problem is that there's so many graph databases to choose from they come in different shapes and forms uh to bright's point there's so many query languages in rdbms is sql end of the story here we've got sci-fi we've got gremlin we've got gql and then your proprietary languages so i think there's a lot of disparity in this space but excellent all excellent points sanji i must say and that is a problem the languages need to be sorted and standardized and it needs people need to have a road map as to what they can do with it because as you say you can do so many things and so many of those things are unrelated that you sort of say well what do we use this for i'm reminded of the saying i learned a bunch of years ago when somebody said that the digital computer is the only tool man has ever devised that has no particular purpose all right guys we gotta we gotta move on to dave uh meninger uh we've heard about streaming uh your prediction is in that realm so please take it away sure so i like to say that historical databases are to become a thing of the past but i don't mean that they're going to go away that's not my point i mean we need historical databases but streaming data is going to become the default way in which we operate with data so in the next say three to five years i would expect the data platforms and and we're using the term data platforms to represent the evolution of databases and data lakes that the data platforms will incorporate these streaming capabilities we're going to process data as it streams into an organization and then it's going to roll off into historical databases so historical databases don't go away but they become a thing of the past they store the data that occurred previously and as data is occurring we're going to be processing it we're going to be analyzing we're going to be acting on it i mean we we only ever ended up with historical databases because we were limited by the technology that was available to us data doesn't occur in batches but we processed it in batches because that was the best we could do and it wasn't bad and we've continued to improve and we've improved and we've improved but streaming data today is still the exception it's not the rule right there's there are projects within organizations that deal with streaming data but it's not the default way in which we deal with data yet and so that that's my prediction is that this is going to change we're going to have um streaming data be the default way in which we deal with data and and how you label it what you call it you know maybe these databases and data platforms just evolve to be able to handle it but we're going to deal with data in a different way and our research shows that already about half of the participants in our analytics and data benchmark research are using streaming data you know another third are planning to use streaming technologies so that gets us to about eight out of ten organizations need to use this technology that doesn't mean they have to use it throughout the whole organization but but it's pretty widespread in its use today and has continued to grow if you think about the consumerization of i.t we've all been conditioned to expect immediate access to information immediate responsiveness you know we want to know if an uh item is on the shelf at our local retail store and we can go in and pick it up right now you know that's the world we live in and that's spilling over into the enterprise i.t world where we have to provide those same types of capabilities um so that's my prediction historical database has become a thing of the past streaming data becomes the default way in which we we operate with data all right thank you david well so what what say you uh carl a guy who's followed historical databases for a long time well one thing actually every database is historical because as soon as you put data in it it's now history it's no longer it no longer reflects the present state of things but even if that history is only a millisecond old it's still history but um i would say i mean i know you're trying to be a little bit provocative in saying this dave because you know as well as i do that people still need to do their taxes they still need to do accounting they still need to run general ledger programs and things like that that all involves historical data that's not going to go away unless you want to go to jail so you're going to have to deal with that but as far as the leading edge functionality i'm totally with you on that and i'm just you know i'm just kind of wondering um if this chain if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way m applications work um saying that uh an application should respond instantly as soon as the state of things changes what do you say about that i i think that's true i think we do have to think about things differently that's you know it's not the way we design systems in the past uh we're seeing more and more systems designed that way but again it's not the default and and agree 100 with you that we do need historical databases you know that that's clear and even some of those historical databases will be used in conjunction with the streaming data right so absolutely i mean you know let's take the data warehouse example where you're using the data warehouse as context and the streaming data as the present you're saying here's a sequence of things that's happening right now have we seen that sequence before and where what what does that pattern look like in past situations and can we learn from that so tony bear i wonder if you could comment i mean if you when you think about you know real-time inferencing at the edge for instance which is something that a lot of people talk about um a lot of what we're discussing here in this segment looks like it's got great potential what are your thoughts yeah well i mean i think you nailed it right you know you hit it right on the head there which is that i think a key what i'm seeing is that essentially and basically i'm going to split this one down the middle is i don't see that basically streaming is the default what i see is streaming and basically and transaction databases um and analytics data you know data warehouses data lakes whatever are converging and what allows us technically to converge is cloud native architecture where you can basically distribute things so you could have you can have a note here that's doing the real-time processing that's also doing it and this is what your leads in we're maybe doing some of that real-time predictive analytics to take a look at well look we're looking at this customer journey what's happening with you know you know with with what the customer is doing right now and this is correlated with what other customers are doing so what i so the thing is that in the cloud you can basically partition this and because of basically you know the speed of the infrastructure um that you can basically bring these together and or and so and kind of orchestrate them sort of loosely coupled manner the other part is that the use cases are demanding and this is part that goes back to what dave is saying is that you know when you look at customer 360 when you look at let's say smart you know smart utility grids when you look at any type of operational problem it has a real-time component and it has a historical component and having predictives and so like you know you know my sense here is that there that technically we can bring this together through the cloud and i think the use case is that is that we we can apply some some real-time sort of you know predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction we have this real time you know input sanjeev did you have a comment yeah i was just going to say that to this point you know we have to think of streaming very different because in the historical databases we used to bring the data and store the data and then we used to run rules on top uh aggregations and all but in case of streaming the mindset changes because the rules normally the inference all of that is fixed but the data is constantly changing so it's a completely reverse way of thinking of uh and building applications on top of that so dave menninger there seemed to be some disagreement about the default or now what kind of time frame are you are you thinking about is this end of decade it becomes the default what would you pin i i think around you know between between five to ten years i think this becomes the reality um i think you know it'll be more and more common between now and then but it becomes the default and i also want sanjeev at some point maybe in one of our subsequent conversations we need to talk about governing streaming data because that's a whole other set of challenges we've also talked about it rather in a two dimensions historical and streaming and there's lots of low latency micro batch sub second that's not quite streaming but in many cases it's fast enough and we're seeing a lot of adoption of near real time not quite real time as uh good enough for most for many applications because nobody's really taking the hardware dimension of this information like how do we that'll just happen carl so near real time maybe before you lose the customer however you define that right okay um let's move on to brad brad you want to talk about automation ai uh the the the pipeline people feel like hey we can just automate everything what's your prediction yeah uh i'm i'm an ai fiction auto so apologies in advance for that but uh you know um i i think that um we've been seeing automation at play within ai for some time now and it's helped us do do a lot of things for especially for practitioners that are building ai outcomes in the enterprise uh it's it's helped them to fill skills gaps it's helped them to speed development and it's helped them to to actually make ai better uh because it you know in some ways provides some swim lanes and and for example with technologies like ottawa milk and can auto document and create that sort of transparency that that we talked about a little bit earlier um but i i think it's there's an interesting kind of conversion happening with this idea of automation um and and that is that uh we've had the automation that started happening for practitioners it's it's trying to move outside of the traditional bounds of things like i'm just trying to get my features i'm just trying to pick the right algorithm i'm just trying to build the right model uh and it's expanding across that full life cycle of building an ai outcome to start at the very beginning of data and to then continue on to the end which is this continuous delivery and continuous uh automation of of that outcome to make sure it's right and it hasn't drifted and stuff like that and because of that because it's become kind of powerful we're starting to to actually see this weird thing happen where the practitioners are starting to converge with the users and that is to say that okay if i'm in tableau right now i can stand up salesforce einstein discovery and it will automatically create a nice predictive algorithm for me um given the data that i that i pull in um but what's starting to happen and we're seeing this from the the the companies that create business software so salesforce oracle sap and others is that they're starting to actually use these same ideals and a lot of deep learning to to basically stand up these out of the box flip a switch and you've got an ai outcome at the ready for business users and um i i'm very much you know i think that that's that's the way that it's going to go and what it means is that ai is is slowly disappearing uh and i don't think that's a bad thing i think if anything what we're going to see in 2022 and maybe into 2023 is this sort of rush to to put this idea of disappearing ai into practice and have as many of these solutions in the enterprise as possible you can see like for example sap is going to roll out this quarter this thing called adaptive recommendation services which which basically is a cold start ai outcome that can work across a whole bunch of different vertical markets and use cases it's just a recommendation engine for whatever you need it to do in the line of business so basically you're you're an sap user you look up to turn on your software one day and you're a sales professional let's say and suddenly you have a recommendation for customer churn it's going that's great well i i don't know i i think that's terrifying in some ways i think it is the future that ai is going to disappear like that but i am absolutely terrified of it because um i i think that what it what it really does is it calls attention to a lot of the issues that we already see around ai um specific to this idea of what what we like to call it omdia responsible ai which is you know how do you build an ai outcome that is free of bias that is inclusive that is fair that is safe that is secure that it's audible etc etc etc etc that takes some a lot of work to do and so if you imagine a customer that that's just a sales force customer let's say and they're turning on einstein discovery within their sales software you need some guidance to make sure that when you flip that switch that the outcome you're going to get is correct and that's that's going to take some work and so i think we're going to see this let's roll this out and suddenly there's going to be a lot of a lot of problems a lot of pushback uh that we're going to see and some of that's going to come from gdpr and others that sam jeeve was mentioning earlier a lot of it's going to come from internal csr requirements within companies that are saying hey hey whoa hold up we can't do this all at once let's take the slow route let's make ai automated in a smart way and that's going to take time yeah so a couple predictions there that i heard i mean ai essentially you disappear it becomes invisible maybe if i can restate that and then if if i understand it correctly brad you're saying there's a backlash in the near term people can say oh slow down let's automate what we can those attributes that you talked about are non trivial to achieve is that why you're a bit of a skeptic yeah i think that we don't have any sort of standards that companies can look to and understand and we certainly within these companies especially those that haven't already stood up in internal data science team they don't have the knowledge to understand what that when they flip that switch for an automated ai outcome that it's it's gonna do what they think it's gonna do and so we need some sort of standard standard methodology and practice best practices that every company that's going to consume this invisible ai can make use of and one of the things that you know is sort of started that google kicked off a few years back that's picking up some momentum and the companies i just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing you know so like for the sap example we know for example that it's convolutional neural network with a long short-term memory model that it's using we know that it only works on roman english uh and therefore me as a consumer can say oh well i know that i need to do this internationally so i should not just turn this on today great thank you carl can you add anything any context here yeah we've talked about some of the things brad mentioned here at idc in the our future of intelligence group regarding in particular the moral and legal implications of having a fully automated you know ai uh driven system uh because we already know and we've seen that ai systems are biased by the data that they get right so if if they get data that pushes them in a certain direction i think there was a story last week about an hr system that was uh that was recommending promotions for white people over black people because in the past um you know white people were promoted and and more productive than black people but not it had no context as to why which is you know because they were being historically discriminated black people being historically discriminated against but the system doesn't know that so you know you have to be aware of that and i think that at the very least there should be controls when a decision has either a moral or a legal implication when when you want when you really need a human judgment it could lay out the options for you but a person actually needs to authorize that that action and i also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases and to some extent they always will so we'll always be chasing after them that's that's absolutely carl yeah i think that what you have to bear in mind as a as a consumer of ai is that it is a reflection of us and we are a very flawed species uh and so if you look at all the really fantastic magical looking supermodels we see like gpt three and four that's coming out z they're xenophobic and hateful uh because the people the data that's built upon them and the algorithms and the people that build them are us so ai is a reflection of us we need to keep that in mind yeah we're the ai's by us because humans are biased all right great okay let's move on doug henson you know a lot of people that said that data lake that term's not not going to not going to live on but it appears to be have some legs here uh you want to talk about lake house bring it on yes i do my prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering i say offering that doesn't mean it's going to be the dominant thing that organizations have out there but it's going to be the predominant vendor offering in 2022. now heading into 2021 we already had cloudera data bricks microsoft snowflake as proponents in 2021 sap oracle and several of these fabric virtualization mesh vendors join the bandwagon the promise is that you have one platform that manages your structured unstructured and semi-structured information and it addresses both the beyond analytics needs and the data science needs the real promise there is simplicity and lower cost but i think end users have to answer a few questions the first is does your organization really have a center of data gravity or is it is the data highly distributed multiple data warehouses multiple data lakes on-premises cloud if it if it's very distributed and you you know you have difficulty consolidating and that's not really a goal for you then maybe that single platform is unrealistic and not likely to add value to you um you know also the fabric and virtualization vendors the the mesh idea that's where if you have this highly distributed situation that might be a better path forward the second question if you are looking at one of these lake house offerings you are looking at consolidating simplifying bringing together to a single platform you have to make sure that it meets both the warehouse need and the data lake need so you have vendors like data bricks microsoft with azure synapse new really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements can meet the user and query concurrency requirements meet those tight slas and then on the other hand you have the or the oracle sap snowflake the data warehouse uh folks coming into the data science world and they have to prove that they can manage the unstructured information and meet the needs of the data scientists i'm seeing a lot of the lake house offerings from the warehouse crowd managing that unstructured information in columns and rows and some of these vendors snowflake in particular is really relying on partners for the data science needs so you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement well thank you doug well tony if those two worlds are going to come together as doug was saying the analytics and the data science world does it need to be some kind of semantic layer in between i don't know weigh in on this topic if you would oh didn't we talk about data fabrics before common metadata layer um actually i'm almost tempted to say let's declare victory and go home in that this is actually been going on for a while i actually agree with uh you know much what doug is saying there which is that i mean we i remembered as far back as i think it was like 2014 i was doing a a study you know it was still at ovum predecessor omnia um looking at all these specialized databases that were coming up and seeing that you know there's overlap with the edges but yet there was still going to be a reason at the time that you would have let's say a document database for json you'd have a relational database for tran you know for transactions and for data warehouse and you had you know and you had basically something at that time that that resembles to do for what we're considering a day of life fast fo and the thing is what i was saying at the time is that you're seeing basically blur you know sort of blending at the edges that i was saying like about five or six years ago um that's all and the the lake house is essentially you know the amount of the the current manifestation of that idea there is a dichotomy in terms of you know it's the old argument do we centralize this all you know you know in in in in in a single place or do we or do we virtualize and i think it's always going to be a yin and yang there's never going to be a single single silver silver bullet i do see um that they're also going to be questions and these are things that points that doug raised they're you know what your what do you need of of of your of you know for your performance there or for your you know pre-performance characteristics do you need for instance hiking currency you need the ability to do some very sophisticated joins or is your requirement more to be able to distribute and you know distribute our processing is you know as far as possible to get you know to essentially do a kind of brute force approach all these approaches are valid based on you know based on the used case um i just see that essentially that the lake house is the culmination of it's nothing it's just it's a relatively new term introduced by databricks a couple years ago this is the culmination of basically what's been a long time trend and what we see in the cloud is that as we start seeing data warehouses as a checkbox item say hey we can basically source data in cloud and cloud storage and s3 azure blob store you know whatever um as long as it's in certain formats like you know like you know parquet or csv or something like that you know i see that as becoming kind of you know a check box item so to that extent i think that the lake house depending on how you define it is already reality um and in some in some cases maybe new terminology but not a whole heck of a lot new under the sun yeah and dave menger i mean a lot of this thank you tony but a lot of this is going to come down to you know vendor marketing right some people try to co-opt the term we talked about data mesh washing what are your thoughts on this yeah so um i used the term data platform earlier and and part of the reason i use that term is that it's more vendor neutral uh we've we've tried to uh sort of stay out of the the vendor uh terminology patenting world right whether whether the term lake house is what sticks or not the concept is certainly going to stick and we have some data to back it up about a quarter of organizations that are using data lakes today already incorporate data warehouse functionality into it so they consider their data lake house and data warehouse one in the same about a quarter of organizations a little less but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake so it's pretty obvious that three quarters of organizations need to bring this stuff together right the need is there the need is apparent the technology is going to continue to verge converge i i like to talk about you know you've got data lakes over here at one end and i'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a in a server and you ignore it right that's not what a data lake is so you've got data lake people over here and you've got database people over here data warehouse people over here database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities so it's obvious that they're going to meet in the middle i mean i think it's like tony says i think we should there declare victory and go home and so so i it's just a follow-up on that so are you saying these the specialized lake and the specialized warehouse do they go away i mean johnny tony data mesh practitioners would say or or advocates would say well they could all live as just a node on the on the mesh but based on what dave just said are we going to see those all morph together well number one as i was saying before there's always going to be this sort of you know kind of you know centrifugal force or this tug of war between do we centralize the data do we do it virtualize and the fact is i don't think that work there's ever going to be any single answer i think in terms of data mesh data mesh has nothing to do with how you physically implement the data you could have a data mesh on a basically uh on a data warehouse it's just that you know the difference being is that if we use the same you know physical data store but everybody's logically manual basically governing it differently you know um a data mission is basically it's not a technology it's a process it's a governance process um so essentially um you know you know i basically see that you know as as i was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring but there are going to be cases where for instance if i need let's say like observe i need like high concurrency or something like that there are certain things that i'm not going to be able to get efficiently get out of a data lake um and you know we're basically i'm doing a system where i'm just doing really brute forcing very fast file scanning and that type of thing so i think there always will be some delineations but i would agree with dave and with doug that we are seeing basically a a confluence of requirements that we need to essentially have basically the element you know the ability of a data lake and a data laid out their warehouse we these need to come together so i think what we're likely to see is organizations look for a converged platform that can handle both sides for their center of data gravity the mesh and the fabric vendors the the fabric virtualization vendors they're all on board with the idea of this converged platform and they're saying hey we'll handle all the edge cases of the stuff that isn't in that center of data gradient that is off distributed in a cloud or at a remote location so you can have that single platform for the center of of your your data and then bring in virtualization mesh what have you for reaching out to the distributed data bingo as they basically said people are happy when they virtualize data i i think yes at this point but to this uh dave meningas point you know they have convert they are converging snowflake has introduced support for unstructured data so now we are literally splitting here now what uh databricks is saying is that aha but it's easy to go from data lake to data warehouse than it is from data warehouse to data lake so i think we're getting into semantics but we've already seen these two converge so is that so it takes something like aws who's got what 15 data stores are they're going to have 15 converged data stores that's going to be interesting to watch all right guys i'm going to go down the list and do like a one i'm going to one word each and you guys each of the analysts if you wouldn't just add a very brief sort of course correction for me so sanjeev i mean governance is going to be the maybe it's the dog that wags the tail now i mean it's coming to the fore all this ransomware stuff which really didn't talk much about security but but but what's the one word in your prediction that you would leave us with on governance it's uh it's going to be mainstream mainstream okay tony bear mesh washing is what i wrote down that's that's what we're going to see in uh in in 2022 a little reality check you you want to add to that reality check is i hope that no vendor you know jumps the shark and calls their offering a data mesh project yeah yeah let's hope that doesn't happen if they do we're going to call them out uh carl i mean graph databases thank you for sharing some some you know high growth metrics i know it's early days but magic is what i took away from that it's the magic database yeah i would actually i've said this to people too i i kind of look at it as a swiss army knife of data because you can pretty much do anything you want with it it doesn't mean you should i mean that's definitely the case that if you're you know managing things that are in a fixed schematic relationship probably a relational database is a better choice there are you know times when the document database is a better choice it can handle those things but maybe not it may not be the best choice for that use case but for a great many especially the new emerging use cases i listed it's the best choice thank you and dave meninger thank you by the way for bringing the data in i like how you supported all your comments with with some some data points but streaming data becomes the sort of default uh paradigm if you will what would you add yeah um i would say think fast right that's the world we live in you got to think fast fast love it uh and brad shimon uh i love it i mean on the one hand i was saying okay great i'm afraid i might get disrupted by one of these internet giants who are ai experts so i'm gonna be able to buy instead of build ai but then again you know i've got some real issues there's a potential backlash there so give us the there's your bumper sticker yeah i i would say um going with dave think fast and also think slow uh to to talk about the book that everyone talks about i would say really that this is all about trust trust in the idea of automation and of a transparent invisible ai across the enterprise but verify verify before you do anything and then doug henson i mean i i look i think the the trend is your friend here on this prediction with lake house is uh really becoming dominant i liked the way you set up that notion of you know the the the data warehouse folks coming at it from the analytics perspective but then you got the data science worlds coming together i still feel as though there's this piece in the middle that we're missing but your your final thoughts we'll give you the last well i think the idea of consolidation and simplification uh always prevails that's why the appeal of a single platform is going to be there um we've already seen that with uh you know hadoop platforms moving toward cloud moving toward object storage and object storage becoming really the common storage point for whether it's a lake or a warehouse uh and that second point uh i think esg mandates are uh are gonna come in alongside uh gdpr and things like that to uh up the ante for uh good governance yeah thank you for calling that out okay folks hey that's all the time that that we have here your your experience and depth of understanding on these key issues and in data and data management really on point and they were on display today i want to thank you for your your contributions really appreciate your time enjoyed it thank you now in addition to this video we're going to be making available transcripts of the discussion we're going to do clips of this as well we're going to put them out on social media i'll write this up and publish the discussion on wikibon.com and siliconangle.com no doubt several of the analysts on the panel will take the opportunity to publish written content social commentary or both i want to thank the power panelist and thanks for watching this special cube presentation this is dave vellante be well and we'll see you next time [Music] you

(bright music) >> In the 2010s, organizations became keenly aware that data would become the key ingredient to driving competitive advantage, differentiation, and growth. But to this day, putting data to work remains a difficult challenge for many, if not most organizations. Now, as the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We've also seen better tooling in the form of data workflows, streaming, machine intelligence, AI, developer tools, security, observability, automation, new databases and the like. These innovations they accelerate data proficiency, but at the same time, they add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs, data oceans are forming, they're evolving and exploding onto the scene. So in an effort to bring perspective to the sea of optionality, we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond. Hello everyone, my name is Dave Velannte with theCUBE, and I'd like to welcome you to a special Cube presentation, analysts predictions 2022: the future of data management. We've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade. Let me introduce our six power panelists. Sanjeev Mohan is former Gartner Analyst and Principal at SanjMo. Tony Baer, principal at dbInsight, Carl Olofson is well-known Research Vice President with IDC, Dave Menninger is Senior Vice President and Research Director at Ventana Research, Brad Shimmin, Chief Analyst, AI Platforms, Analytics and Data Management at Omdia and Doug Henschen, Vice President and Principal Analyst at Constellation Research. Gentlemen, welcome to the program and thanks for coming on theCUBE today. >> Great to be here. >> Thank you. >> All right, here's the format we're going to use. I as moderator, I'm going to call on each analyst separately who then will deliver their prediction or mega trend, and then in the interest of time management and pace, two analysts will have the opportunity to comment. If we have more time, we'll elongate it, but let's get started right away. Sanjeev Mohan, please kick it off. You want to talk about governance, go ahead sir. >> Thank you Dave. I believe that data governance which we've been talking about for many years is now not only going to be mainstream, it's going to be table stakes. And all the things that you mentioned, you know, the data, ocean data lake, lake houses, data fabric, meshes, the common glue is metadata. If we don't understand what data we have and we are governing it, there is no way we can manage it. So we saw Informatica went public last year after a hiatus of six. I'm predicting that this year we see some more companies go public. My bet is on Culebra, most likely and maybe Alation we'll see go public this year. I'm also predicting that the scope of data governance is going to expand beyond just data. It's not just data and reports. We are going to see more transformations like spark jawsxxxxx, Python even Air Flow. We're going to see more of a streaming data. So from Kafka Schema Registry, for example. We will see AI models become part of this whole governance suite. So the governance suite is going to be very comprehensive, very detailed lineage, impact analysis, and then even expand into data quality. We already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management, data catalogs, also data access governance. So what we are going to see is that once the data governance platforms become the key entry point into these modern architectures, I'm predicting that the usage, the number of users of a data catalog is going to exceed that of a BI tool. That will take time and we already seen that trajectory. Right now if you look at BI tools, I would say there a hundred users to BI tool to one data catalog. And I see that evening out over a period of time and at some point data catalogs will really become the main way for us to access data. Data catalog will help us visualize data, but if we want to do more in-depth analysis, it'll be the jumping off point into the BI tool, the data science tool and that is the journey I see for the data governance products. >> Excellent, thank you. Some comments. Maybe Doug, a lot of things to weigh in on there, maybe you can comment. >> Yeah, Sanjeev I think you're spot on, a lot of the trends the one disagreement, I think it's really still far from mainstream. As you say, we've been talking about this for years, it's like God, motherhood, apple pie, everyone agrees it's important, but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking. I think one thing that deserves mention in this context is ESG mandates and guidelines, these are environmental, social and governance, regs and guidelines. We've seen the environmental regs and guidelines and posts in industries, particularly the carbon-intensive industries. We've seen the social mandates, particularly diversity imposed on suppliers by companies that are leading on this topic. We've seen governance guidelines now being imposed by banks on investors. So these ESGs are presenting new carrots and sticks, and it's going to demand more solid data. It's going to demand more detailed reporting and solid reporting, tighter governance. But we're still far from mainstream adoption. We have a lot of, you know, best of breed niche players in the space. I think the signs that it's going to be more mainstream are starting with things like Azure Purview, Google Dataplex, the big cloud platform players seem to be upping the ante and starting to address governance. >> Excellent, thank you Doug. Brad, I wonder if you could chime in as well. >> Yeah, I would love to be a believer in data catalogs. But to Doug's point, I think that it's going to take some more pressure for that to happen. I recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the nineties and that didn't happen quite the way we anticipated. And so to Sanjeev's point it's because it is really complex and really difficult to do. My hope is that, you know, we won't sort of, how do I put this? Fade out into this nebula of domain catalogs that are specific to individual use cases like Purview for getting data quality right or like data governance and cybersecurity. And instead we have some tooling that can actually be adaptive to gather metadata to create something. And I know its important to you, Sanjeev and that is this idea of observability. If you can get enough metadata without moving your data around, but understanding the entirety of a system that's running on this data, you can do a lot. So to help with the governance that Doug is talking about. >> So I just want to add that, data governance, like any other initiatives did not succeed even AI went into an AI window, but that's a different topic. But a lot of these things did not succeed because to your point, the incentives were not there. I remember when Sarbanes Oxley had come into the scene, if a bank did not do Sarbanes Oxley, they were very happy to a million dollar fine. That was like, you know, pocket change for them instead of doing the right thing. But I think the stakes are much higher now. With GDPR, the flood gates opened. Now, you know, California, you know, has CCPA but even CCPA is being outdated with CPRA, which is much more GDPR like. So we are very rapidly entering a space where pretty much every major country in the world is coming up with its own compliance regulatory requirements, data residents is becoming really important. And I think we are going to reach a stage where it won't be optional anymore. So whether we like it or not, and I think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption. We were focused on features and these features were disconnected, very hard for business to adopt. These are built by IT people for IT departments to take a look at technical metadata, not business metadata. Today the tables have turned. CDOs are driving this initiative, regulatory compliances are beating down hard, so I think the time might be right. >> Yeah so guys, we have to move on here. But there's some real meat on the bone here, Sanjeev. I like the fact that you called out Culebra and Alation, so we can look back a year from now and say, okay, he made the call, he stuck it. And then the ratio of BI tools to data catalogs that's another sort of measurement that we can take even though with some skepticism there, that's something that we can watch. And I wonder if someday, if we'll have more metadata than data. But I want to move to Tony Baer, you want to talk about data mesh and speaking, you know, coming off of governance. I mean, wow, you know the whole concept of data mesh is, decentralized data, and then governance becomes, you know, a nightmare there, but take it away, Tony. >> We'll put this way, data mesh, you know, the idea at least as proposed by ThoughtWorks. You know, basically it was at least a couple of years ago and the press has been almost uniformly almost uncritical. A good reason for that is for all the problems that basically Sanjeev and Doug and Brad we're just speaking about, which is that we have all this data out there and we don't know what to do about it. Now, that's not a new problem. That was a problem we had in enterprise data warehouses, it was a problem when we had over DoOP data clusters, it's even more of a problem now that data is out in the cloud where the data is not only your data lake, is not only us three, it's all over the place. And it's also including streaming, which I know we'll be talking about later. So the data mesh was a response to that, the idea of that we need to bait, you know, who are the folks that really know best about governance? It's the domain experts. So it was basically data mesh was an architectural pattern and a process. My prediction for this year is that data mesh is going to hit cold heart reality. Because if you do a Google search, basically the published work, the articles on data mesh have been largely, you know, pretty uncritical so far. Basically loading and is basically being a very revolutionary new idea. I don't think it's that revolutionary because we've talked about ideas like this. Brad now you and I met years ago when we were talking about so and decentralizing all of us, but it was at the application level. Now we're talking about it at the data level. And now we have microservices. So there's this thought of have we managed if we're deconstructing apps in cloud native to microservices, why don't we think of data in the same way? My sense this year is that, you know, this has been a very active search if you look at Google search trends, is that now companies, like enterprise are going to look at this seriously. And as they look at it seriously, it's going to attract its first real hard scrutiny, it's going to attract its first backlash. That's not necessarily a bad thing. It means that it's being taken seriously. The reason why I think that you'll start to see basically the cold hearted light of day shine on data mesh is that it's still a work in progress. You know, this idea is basically a couple of years old and there's still some pretty major gaps. The biggest gap is in the area of federated governance. Now federated governance itself is not a new issue. Federated governance decision, we started figuring out like, how can we basically strike the balance between getting let's say between basically consistent enterprise policy, consistent enterprise governance, but yet the groups that understand the data and know how to basically, you know, that, you know, how do we basically sort of balance the two? There's a huge gap there in practice and knowledge. Also to a lesser extent, there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data. You know, basically through the full life cycle, from develop, from selecting the data from, you know, building the pipelines from, you know, determining your access control, looking at quality, looking at basically whether the data is fresh or whether it's trending off course. So my prediction is that it will receive the first harsh scrutiny this year. You are going to see some organization and enterprises declare premature victory when they build some federated query implementations. You going to see vendors start with data mesh wash their products anybody in the data management space that they are going to say that where this basically a pipelining tool, whether it's basically ELT, whether it's a catalog or federated query tool, they will all going to get like, you know, basically promoting the fact of how they support this. Hopefully nobody's going to call themselves a data mesh tool because data mesh is not a technology. We're going to see one other thing come out of this. And this harks back to the metadata that Sanjeev was talking about and of the catalog just as he was talking about. Which is that there's going to be a new focus, every renewed focus on metadata. And I think that's going to spur interest in data fabrics. Now data fabrics are pretty vaguely defined, but if we just take the most elemental definition, which is a common metadata back plane, I think that if anybody is going to get serious about data mesh, they need to look at the data fabric because we all at the end of the day, need to speak, you know, need to read from the same sheet of music. >> So thank you Tony. Dave Menninger, I mean, one of the things that people like about data mesh is it pretty crisply articulate some of the flaws in today's organizational approaches to data. What are your thoughts on this? >> Well, I think we have to start by defining data mesh, right? The term is already getting corrupted, right? Tony said it's going to see the cold hard light of day. And there's a problem right now that there are a number of overlapping terms that are similar but not identical. So we've got data virtualization, data fabric, excuse me for a second. (clears throat) Sorry about that. Data virtualization, data fabric, data federation, right? So I think that it's not really clear what each vendor means by these terms. I see data mesh and data fabric becoming quite popular. I've interpreted data mesh as referring primarily to the governance aspects as originally intended and specified. But that's not the way I see vendors using it. I see vendors using it much more to mean data fabric and data virtualization. So I'm going to comment on the group of those things. I think the group of those things is going to happen. They're going to happen, they're going to become more robust. Our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half, so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access. Again, whether you define it as mesh or fabric or virtualization isn't really the point here. But this notion that there are different elements of data, metadata and governance within an organization that all need to be managed collectively. The interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not, it's almost double, 68% of organizations, I'm sorry, 79% of organizations that were using virtualized access express satisfaction with their access to the data lake. Only 39% express satisfaction if they weren't using virtualized access. >> Oh thank you Dave. Sanjeev we just got about a couple of minutes on this topic, but I know you're speaking or maybe you've always spoken already on a panel with (indistinct) who sort of invented the concept. Governance obviously is a big sticking point, but what are your thoughts on this? You're on mute. (panelist chuckling) >> So my message to (indistinct) and to the community is as opposed to what they said, let's not define it. We spent a whole year defining it, there are four principles, domain, product, data infrastructure, and governance. Let's take it to the next level. I get a lot of questions on what is the difference between data fabric and data mesh? And I'm like I can't compare the two because data mesh is a business concept, data fabric is a data integration pattern. How do you compare the two? You have to bring data mesh a level down. So to Tony's point, I'm on a warpath in 2022 to take it down to what does a data product look like? How do we handle shared data across domains and governance? And I think we are going to see more of that in 2022, or is "operationalization" of data mesh. >> I think we could have a whole hour on this topic, couldn't we? Maybe we should do that. But let's corner. Let's move to Carl. So Carl, you're a database guy, you've been around that block for a while now, you want to talk about graph databases, bring it on. >> Oh yeah. Okay thanks. So I regard graph database as basically the next truly revolutionary database management technology. I'm looking forward for the graph database market, which of course we haven't defined yet. So obviously I have a little wiggle room in what I'm about to say. But this market will grow by about 600% over the next 10 years. Now, 10 years is a long time. But over the next five years, we expect to see gradual growth as people start to learn how to use it. The problem is not that it's not useful, its that people don't know how to use it. So let me explain before I go any further what a graph database is because some of the folks on the call may not know what it is. A graph database organizes data according to a mathematical structure called a graph. The graph has elements called nodes and edges. So a data element drops into a node, the nodes are connected by edges, the edges connect one node to another node. Combinations of edges create structures that you can analyze to determine how things are related. In some cases, the nodes and edges can have properties attached to them which add additional informative material that makes it richer, that's called a property graph. There are two principle use cases for graph databases. There's semantic property graphs, which are use to break down human language texts into the semantic structures. Then you can search it, organize it and answer complicated questions. A lot of AI is aimed at semantic graphs. Another kind is the property graph that I just mentioned, which has a dazzling number of use cases. I want to just point out as I talk about this, people are probably wondering, well, we have relation databases, isn't that good enough? So a relational database defines... It supports what I call definitional relationships. That means you define the relationships in a fixed structure. The database drops into that structure, there's a value, foreign key value, that relates one table to another and that value is fixed. You don't change it. If you change it, the database becomes unstable, it's not clear what you're looking at. In a graph database, the system is designed to handle change so that it can reflect the true state of the things that it's being used to track. So let me just give you some examples of use cases for this. They include entity resolution, data lineage, social media analysis, Customer 360, fraud prevention. There's cybersecurity, there's strong supply chain is a big one actually. There is explainable AI and this is going to become important too because a lot of people are adopting AI. But they want a system after the fact to say, how do the AI system come to that conclusion? How did it make that recommendation? Right now we don't have really good ways of tracking that. Machine learning in general, social network, I already mentioned that. And then we've got, oh gosh, we've got data governance, data compliance, risk management. We've got recommendation, we've got personalization, anti money laundering, that's another big one, identity and access management, network and IT operations is already becoming a key one where you actually have mapped out your operation, you know, whatever it is, your data center and you can track what's going on as things happen there, root cause analysis, fraud detection is a huge one. A number of major credit card companies use graph databases for fraud detection, risk analysis, tracking and tracing turn analysis, next best action, what if analysis, impact analysis, entity resolution and I would add one other thing or just a few other things to this list, metadata management. So Sanjeev, here you go, this is your engine. Because I was in metadata management for quite a while in my past life. And one of the things I found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it, but graphs can, okay? Graphs can do things like say, this term in this context means this, but in that context, it means that, okay? Things like that. And in fact, logistics management, supply chain. And also because it handles recursive relationships, by recursive relationships I mean objects that own other objects that are of the same type. You can do things like build materials, you know, so like parts explosion. Or you can do an HR analysis, who reports to whom, how many levels up the chain and that kind of thing. You can do that with relational databases, but yet it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It's not supported in the database. And whenever you have to program something, that means you can't trace it, you can't define it. You can't publish it in terms of its functionality and it's really, really hard to maintain over time. >> Carl, thank you. I wonder if we could bring Brad in, I mean. Brad, I'm sitting here wondering, okay, is this incremental to the market? Is it disruptive and replacement? What are your thoughts on this phase? >> It's already disrupted the market. I mean, like Carl said, go to any bank and ask them are you using graph databases to get fraud detection under control? And they'll say, absolutely, that's the only way to solve this problem. And it is frankly. And it's the only way to solve a lot of the problems that Carl mentioned. And that is, I think it's Achilles heel in some ways. Because, you know, it's like finding the best way to cross the seven bridges of Koenigsberg. You know, it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique, it's still unfortunately kind of stands apart from the rest of the community that's building, let's say AI outcomes, as a great example here. Graph databases and AI, as Carl mentioned, are like chocolate and peanut butter. But technologically, you think don't know how to talk to one another, they're completely different. And you know, you can't just stand up SQL and query them. You've got to learn, know what is the Carl? Specter special. Yeah, thank you to, to actually get to the data in there. And if you're going to scale that data, that graph database, especially a property graph, if you're going to do something really complex, like try to understand you know, all of the metadata in your organization, you might just end up with, you know, a graph database winter like we had the AI winter simply because you run out of performance to make the thing happen. So, I think it's already disrupted, but we need to like treat it like a first-class citizen in the data analytics and AI community. We need to bring it into the fold. We need to equip it with the tools it needs to do the magic it does and to do it not just for specialized use cases, but for everything. 'Cause I'm with Carl. I think it's absolutely revolutionary. >> Brad identified the principal, Achilles' heel of the technology which is scaling. When these things get large and complex enough that they spill over what a single server can handle, you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down. So that's still a problem to be solved. >> Sanjeev, any quick thoughts on this? I mean, I think metadata on the word cloud is going to be the largest font, but what are your thoughts here? >> I want to (indistinct) So people don't associate me with only metadata, so I want to talk about something slightly different. dbengines.com has done an amazing job. I think almost everyone knows that they chronicle all the major databases that are in use today. In January of 2022, there are 381 databases on a ranked list of databases. The largest category is RDBMS. The second largest category is actually divided into two property graphs and IDF graphs. These two together make up the second largest number databases. So talking about Achilles heel, this is a problem. The problem is that there's so many graph databases to choose from. They come in different shapes and forms. To Brad's point, there's so many query languages in RDBMS, in SQL. I know the story, but here We've got cipher, we've got gremlin, we've got GQL and then we're proprietary languages. So I think there's a lot of disparity in this space. >> Well, excellent. All excellent points, Sanjeev, if I must say. And that is a problem that the languages need to be sorted and standardized. People need to have a roadmap as to what they can do with it. Because as you say, you can do so many things. And so many of those things are unrelated that you sort of say, well, what do we use this for? And I'm reminded of the saying I learned a bunch of years ago. And somebody said that the digital computer is the only tool man has ever device that has no particular purpose. (panelists chuckle) >> All right guys, we got to move on to Dave Menninger. We've heard about streaming. Your prediction is in that realm, so please take it away. >> Sure. So I like to say that historical databases are going to become a thing of the past. By that I don't mean that they're going to go away, that's not my point. I mean, we need historical databases, but streaming data is going to become the default way in which we operate with data. So in the next say three to five years, I would expect that data platforms and we're using the term data platforms to represent the evolution of databases and data lakes, that the data platforms will incorporate these streaming capabilities. We're going to process data as it streams into an organization and then it's going to roll off into historical database. So historical databases don't go away, but they become a thing of the past. They store the data that occurred previously. And as data is occurring, we're going to be processing it, we're going to be analyzing it, we're going to be acting on it. I mean we only ever ended up with historical databases because we were limited by the technology that was available to us. Data doesn't occur in patches. But we processed it in patches because that was the best we could do. And it wasn't bad and we've continued to improve and we've improved and we've improved. But streaming data today is still the exception. It's not the rule, right? There are projects within organizations that deal with streaming data. But it's not the default way in which we deal with data yet. And so that's my prediction is that this is going to change, we're going to have streaming data be the default way in which we deal with data and how you label it and what you call it. You know, maybe these databases and data platforms just evolved to be able to handle it. But we're going to deal with data in a different way. And our research shows that already, about half of the participants in our analytics and data benchmark research, are using streaming data. You know, another third are planning to use streaming technologies. So that gets us to about eight out of 10 organizations need to use this technology. And that doesn't mean they have to use it throughout the whole organization, but it's pretty widespread in its use today and has continued to grow. If you think about the consumerization of IT, we've all been conditioned to expect immediate access to information, immediate responsiveness. You know, we want to know if an item is on the shelf at our local retail store and we can go in and pick it up right now. You know, that's the world we live in and that's spilling over into the enterprise IT world We have to provide those same types of capabilities. So that's my prediction, historical databases become a thing of the past, streaming data becomes the default way in which we operate with data. >> All right thank you David. Well, so what say you, Carl, the guy who has followed historical databases for a long time? >> Well, one thing actually, every database is historical because as soon as you put data in it, it's now history. They'll no longer reflect the present state of things. But even if that history is only a millisecond old, it's still history. But I would say, I mean, I know you're trying to be a little bit provocative in saying this Dave 'cause you know, as well as I do that people still need to do their taxes, they still need to do accounting, they still need to run general ledger programs and things like that. That all involves historical data. That's not going to go away unless you want to go to jail. So you're going to have to deal with that. But as far as the leading edge functionality, I'm totally with you on that. And I'm just, you know, I'm just kind of wondering if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way applications work. Saying that an application should respond instantly, as soon as the state of things changes. What do you say about that? >> I think that's true. I think we do have to think about things differently. It's not the way we designed systems in the past. We're seeing more and more systems designed that way. But again, it's not the default. And I agree 100% with you that we do need historical databases you know, that's clear. And even some of those historical databases will be used in conjunction with the streaming data, right? >> Absolutely. I mean, you know, let's take the data warehouse example where you're using the data warehouse as its context and the streaming data as the present and you're saying, here's the sequence of things that's happening right now. Have we seen that sequence before? And where? What does that pattern look like in past situations? And can we learn from that? >> So Tony Baer, I wonder if you could comment? I mean, when you think about, you know, real time inferencing at the edge, for instance, which is something that a lot of people talk about, a lot of what we're discussing here in this segment, it looks like it's got a great potential. What are your thoughts? >> Yeah, I mean, I think you nailed it right. You know, you hit it right on the head there. Which is that, what I'm seeing is that essentially. Then based on I'm going to split this one down the middle is that I don't see that basically streaming is the default. What I see is streaming and basically and transaction databases and analytics data, you know, data warehouses, data lakes whatever are converging. And what allows us technically to converge is cloud native architecture, where you can basically distribute things. So you can have a node here that's doing the real-time processing, that's also doing... And this is where it leads in or maybe doing some of that real time predictive analytics to take a look at, well look, we're looking at this customer journey what's happening with what the customer is doing right now and this is correlated with what other customers are doing. So the thing is that in the cloud, you can basically partition this and because of basically the speed of the infrastructure then you can basically bring these together and kind of orchestrate them sort of a loosely coupled manner. The other parts that the use cases are demanding, and this is part of it goes back to what Dave is saying. Is that, you know, when you look at Customer 360, when you look at let's say Smart Utility products, when you look at any type of operational problem, it has a real time component and it has an historical component. And having predictive and so like, you know, my sense here is that technically we can bring this together through the cloud. And I think the use case is that we can apply some real time sort of predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction, we have this real-time input. >> Sanjeev, did you have a comment? >> Yeah, I was just going to say that to Dave's point, you know, we have to think of streaming very different because in the historical databases, we used to bring the data and store the data and then we used to run rules on top, aggregations and all. But in case of streaming, the mindset changes because the rules are normally the inference, all of that is fixed, but the data is constantly changing. So it's a completely reversed way of thinking and building applications on top of that. >> So Dave Menninger, there seem to be some disagreement about the default. What kind of timeframe are you thinking about? Is this end of decade it becomes the default? What would you pin? >> I think around, you know, between five to 10 years, I think this becomes the reality. >> I think its... >> It'll be more and more common between now and then, but it becomes the default. And I also want Sanjeev at some point, maybe in one of our subsequent conversations, we need to talk about governing streaming data. 'Cause that's a whole nother set of challenges. >> We've also talked about it rather in two dimensions, historical and streaming, and there's lots of low latency, micro batch, sub-second, that's not quite streaming, but in many cases its fast enough and we're seeing a lot of adoption of near real time, not quite real-time as good enough for many applications. (indistinct cross talk from panelists) >> Because nobody's really taking the hardware dimension (mumbles). >> That'll just happened, Carl. (panelists laughing) >> So near real time. But maybe before you lose the customer, however we define that, right? Okay, let's move on to Brad. Brad, you want to talk about automation, AI, the pipeline people feel like, hey, we can just automate everything. What's your prediction? >> Yeah I'm an AI aficionados so apologies in advance for that. But, you know, I think that we've been seeing automation play within AI for some time now. And it's helped us do a lot of things especially for practitioners that are building AI outcomes in the enterprise. It's helped them to fill skills gaps, it's helped them to speed development and it's helped them to actually make AI better. 'Cause it, you know, in some ways provide some swim lanes and for example, with technologies like AutoML can auto document and create that sort of transparency that we talked about a little bit earlier. But I think there's an interesting kind of conversion happening with this idea of automation. And that is that we've had the automation that started happening for practitioners, it's trying to move out side of the traditional bounds of things like I'm just trying to get my features, I'm just trying to pick the right algorithm, I'm just trying to build the right model and it's expanding across that full life cycle, building an AI outcome, to start at the very beginning of data and to then continue on to the end, which is this continuous delivery and continuous automation of that outcome to make sure it's right and it hasn't drifted and stuff like that. And because of that, because it's become kind of powerful, we're starting to actually see this weird thing happen where the practitioners are starting to converge with the users. And that is to say that, okay, if I'm in Tableau right now, I can stand up Salesforce Einstein Discovery, and it will automatically create a nice predictive algorithm for me given the data that I pull in. But what's starting to happen and we're seeing this from the companies that create business software, so Salesforce, Oracle, SAP, and others is that they're starting to actually use these same ideals and a lot of deep learning (chuckles) to basically stand up these out of the box flip-a-switch, and you've got an AI outcome at the ready for business users. And I am very much, you know, I think that's the way that it's going to go and what it means is that AI is slowly disappearing. And I don't think that's a bad thing. I think if anything, what we're going to see in 2022 and maybe into 2023 is this sort of rush to put this idea of disappearing AI into practice and have as many of these solutions in the enterprise as possible. You can see, like for example, SAP is going to roll out this quarter, this thing called adaptive recommendation services, which basically is a cold start AI outcome that can work across a whole bunch of different vertical markets and use cases. It's just a recommendation engine for whatever you needed to do in the line of business. So basically, you're an SAP user, you look up to turn on your software one day, you're a sales professional let's say, and suddenly you have a recommendation for customer churn. Boom! It's going, that's great. Well, I don't know, I think that's terrifying. In some ways I think it is the future that AI is going to disappear like that, but I'm absolutely terrified of it because I think that what it really does is it calls attention to a lot of the issues that we already see around AI, specific to this idea of what we like to call at Omdia, responsible AI. Which is, you know, how do you build an AI outcome that is free of bias, that is inclusive, that is fair, that is safe, that is secure, that its audible, et cetera, et cetera, et cetera, et cetera. I'd take a lot of work to do. And so if you imagine a customer that's just a Salesforce customer let's say, and they're turning on Einstein Discovery within their sales software, you need some guidance to make sure that when you flip that switch, that the outcome you're going to get is correct. And that's going to take some work. And so, I think we're going to see this move, let's roll this out and suddenly there's going to be a lot of problems, a lot of pushback that we're going to see. And some of that's going to come from GDPR and others that Sanjeev was mentioning earlier. A lot of it is going to come from internal CSR requirements within companies that are saying, "Hey, hey, whoa, hold up, we can't do this all at once. "Let's take the slow route, "let's make AI automated in a smart way." And that's going to take time. >> Yeah, so a couple of predictions there that I heard. AI simply disappear, it becomes invisible. Maybe if I can restate that. And then if I understand it correctly, Brad you're saying there's a backlash in the near term. You'd be able to say, oh, slow down. Let's automate what we can. Those attributes that you talked about are non trivial to achieve, is that why you're a bit of a skeptic? >> Yeah. I think that we don't have any sort of standards that companies can look to and understand. And we certainly, within these companies, especially those that haven't already stood up an internal data science team, they don't have the knowledge to understand when they flip that switch for an automated AI outcome that it's going to do what they think it's going to do. And so we need some sort of standard methodology and practice, best practices that every company that's going to consume this invisible AI can make use of them. And one of the things that you know, is sort of started that Google kicked off a few years back that's picking up some momentum and the companies I just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing. You know, so like for the SAP example, we know, for example, if it's convolutional neural network with a long, short term memory model that it's using, we know that it only works on Roman English and therefore me as a consumer can say, "Oh, well I know that I need to do this internationally. "So I should not just turn this on today." >> Thank you. Carl could you add anything, any context here? >> Yeah, we've talked about some of the things Brad mentioned here at IDC and our future of intelligence group regarding in particular, the moral and legal implications of having a fully automated, you know, AI driven system. Because we already know, and we've seen that AI systems are biased by the data that they get, right? So if they get data that pushes them in a certain direction, I think there was a story last week about an HR system that was recommending promotions for White people over Black people, because in the past, you know, White people were promoted and more productive than Black people, but it had no context as to why which is, you know, because they were being historically discriminated, Black people were being historically discriminated against, but the system doesn't know that. So, you know, you have to be aware of that. And I think that at the very least, there should be controls when a decision has either a moral or legal implication. When you really need a human judgment, it could lay out the options for you. But a person actually needs to authorize that action. And I also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases. In some extent, they always will. So we'll always be chasing after them. But that's (indistinct). >> Absolutely Carl, yeah. I think that what you have to bear in mind as a consumer of AI is that it is a reflection of us and we are a very flawed species. And so if you look at all of the really fantastic, magical looking supermodels we see like GPT-3 and four, that's coming out, they're xenophobic and hateful because the people that the data that's built upon them and the algorithms and the people that build them are us. So AI is a reflection of us. We need to keep that in mind. >> Yeah, where the AI is biased 'cause humans are biased. All right, great. All right let's move on. Doug you mentioned mentioned, you know, lot of people that said that data lake, that term is not going to live on but here's to be, have some lakes here. You want to talk about lake house, bring it on. >> Yes, I do. My prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering. I say offering that doesn't mean it's going to be the dominant thing that organizations have out there, but it's going to be the pro dominant vendor offering in 2022. Now heading into 2021, we already had Cloudera, Databricks, Microsoft, Snowflake as proponents, in 2021, SAP, Oracle, and several of all of these fabric virtualization/mesh vendors joined the bandwagon. The promise is that you have one platform that manages your structured, unstructured and semi-structured information. And it addresses both the BI analytics needs and the data science needs. The real promise there is simplicity and lower cost. But I think end users have to answer a few questions. The first is, does your organization really have a center of data gravity or is the data highly distributed? Multiple data warehouses, multiple data lakes, on premises, cloud. If it's very distributed and you'd have difficulty consolidating and that's not really a goal for you, then maybe that single platform is unrealistic and not likely to add value to you. You know, also the fabric and virtualization vendors, the mesh idea, that's where if you have this highly distributed situation, that might be a better path forward. The second question, if you are looking at one of these lake house offerings, you are looking at consolidating, simplifying, bringing together to a single platform. You have to make sure that it meets both the warehouse need and the data lake need. So you have vendors like Databricks, Microsoft with Azure Synapse. New really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements, can meet the user and query concurrency requirements. Meet those tight SLS. And then on the other hand, you have the Oracle, SAP, Snowflake, the data warehouse folks coming into the data science world, and they have to prove that they can manage the unstructured information and meet the needs of the data scientists. I'm seeing a lot of the lake house offerings from the warehouse crowd, managing that unstructured information in columns and rows. And some of these vendors, Snowflake a particular is really relying on partners for the data science needs. So you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement. >> Thank you Doug. Well Tony, if those two worlds are going to come together, as Doug was saying, the analytics and the data science world, does it need to be some kind of semantic layer in between? I don't know. Where are you in on this topic? >> (chuckles) Oh, didn't we talk about data fabrics before? Common metadata layer (chuckles). Actually, I'm almost tempted to say let's declare victory and go home. And that this has actually been going on for a while. I actually agree with, you know, much of what Doug is saying there. Which is that, I mean I remember as far back as I think it was like 2014, I was doing a study. I was still at Ovum, (indistinct) Omdia, looking at all these specialized databases that were coming up and seeing that, you know, there's overlap at the edges. But yet, there was still going to be a reason at the time that you would have, let's say a document database for JSON, you'd have a relational database for transactions and for data warehouse and you had basically something at that time that resembles a dupe for what we consider your data life. Fast forward and the thing is what I was seeing at the time is that you were saying they sort of blending at the edges. That was saying like about five to six years ago. And the lake house is essentially on the current manifestation of that idea. There is a dichotomy in terms of, you know, it's the old argument, do we centralize this all you know in a single place or do we virtualize? And I think it's always going to be a union yeah and there's never going to be a single silver bullet. I do see that there are also going to be questions and these are points that Doug raised. That you know, what do you need for your performance there, or for your free performance characteristics? Do you need for instance high concurrency? You need the ability to do some very sophisticated joins, or is your requirement more to be able to distribute and distribute our processing is, you know, as far as possible to get, you know, to essentially do a kind of a brute force approach. All these approaches are valid based on the use case. I just see that essentially that the lake house is the culmination of it's nothing. It's a relatively new term introduced by Databricks a couple of years ago. This is the culmination of basically what's been a long time trend. And what we see in the cloud is that as we start seeing data warehouses as a check box items say, "Hey, we can basically source data in cloud storage, in S3, "Azure Blob Store, you know, whatever, "as long as it's in certain formats, "like, you know parquet or CSP or something like that." I see that as becoming kind of a checkbox item. So to that extent, I think that the lake house, depending on how you define is already reality. And in some cases, maybe new terminology, but not a whole heck of a lot new under the sun. >> Yeah. And Dave Menninger, I mean a lot of these, thank you Tony, but a lot of this is going to come down to, you know, vendor marketing, right? Some people just kind of co-op the term, we talked about you know, data mesh washing, what are your thoughts on this? (laughing) >> Yeah, so I used the term data platform earlier. And part of the reason I use that term is that it's more vendor neutral. We've tried to sort of stay out of the vendor terminology patenting world, right? Whether the term lake houses, what sticks or not, the concept is certainly going to stick. And we have some data to back it up. About a quarter of organizations that are using data lakes today, already incorporate data warehouse functionality into it. So they consider their data lake house and data warehouse one in the same, about a quarter of organizations, a little less, but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake. So it's pretty obvious that three quarters of organizations need to bring this stuff together, right? The need is there, the need is apparent. The technology is going to continue to converge. I like to talk about it, you know, you've got data lakes over here at one end, and I'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a server and you ignore it, right? That's not what a data lake is. So you've got data lake people over here and you've got database people over here, data warehouse people over here, database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities. So it's obvious that they're going to meet in the middle. I mean, I think it's like Tony says, I think we should declare victory and go home. >> As hell. So just a follow-up on that, so are you saying the specialized lake and the specialized warehouse, do they go away? I mean, Tony data mesh practitioners would say or advocates would say, well, they could all live. It's just a node on the mesh. But based on what Dave just said, are we gona see those all morphed together? >> Well, number one, as I was saying before, there's always going to be this sort of, you know, centrifugal force or this tug of war between do we centralize the data, do we virtualize? And the fact is I don't think that there's ever going to be any single answer. I think in terms of data mesh, data mesh has nothing to do with how you're physically implement the data. You could have a data mesh basically on a data warehouse. It's just that, you know, the difference being is that if we use the same physical data store, but everybody's logically you know, basically governing it differently, you know? Data mesh in space, it's not a technology, it's processes, it's governance process. So essentially, you know, I basically see that, you know, as I was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring, but there are going to be cases where, for instance, if I need, let's say like, Upserve, I need like high concurrency or something like that. There are certain things that I'm not going to be able to get efficiently get out of a data lake. And, you know, I'm doing a system where I'm just doing really brute forcing very fast file scanning and that type of thing. So I think there always will be some delineations, but I would agree with Dave and with Doug, that we are seeing basically a confluence of requirements that we need to essentially have basically either the element, you know, the ability of a data lake and the data warehouse, these need to come together, so I think. >> I think what we're likely to see is organizations look for a converge platform that can handle both sides for their center of data gravity, the mesh and the fabric virtualization vendors, they're all on board with the idea of this converged platform and they're saying, "Hey, we'll handle all the edge cases "of the stuff that isn't in that center of data gravity "but that is off distributed in a cloud "or at a remote location." So you can have that single platform for the center of your data and then bring in virtualization, mesh, what have you, for reaching out to the distributed data. >> As Dave basically said, people are happy when they virtualized data. >> I think we have at this point, but to Dave Menninger's point, they are converging, Snowflake has introduced support for unstructured data. So obviously literally splitting here. Now what Databricks is saying is that "aha, but it's easy to go from data lake to data warehouse "than it is from databases to data lake." So I think we're getting into semantics, but we're already seeing these two converge. >> So take somebody like AWS has got what? 15 data stores. Are they're going to 15 converge data stores? This is going to be interesting to watch. All right, guys, I'm going to go down and list do like a one, I'm going to one word each and you guys, each of the analyst, if you would just add a very brief sort of course correction for me. So Sanjeev, I mean, governance is going to to be... Maybe it's the dog that wags the tail now. I mean, it's coming to the fore, all this ransomware stuff, which you really didn't talk much about security, but what's the one word in your prediction that you would leave us with on governance? >> It's going to be mainstream. >> Mainstream. Okay. Tony Baer, mesh washing is what I wrote down. That's what we're going to see in 2022, a little reality check, you want to add to that? >> Reality check, 'cause I hope that no vendor jumps the shark and close they're offering a data niche product. >> Yeah, let's hope that doesn't happen. If they do, we're going to call them out. Carl, I mean, graph databases, thank you for sharing some high growth metrics. I know it's early days, but magic is what I took away from that, so magic database. >> Yeah, I would actually, I've said this to people too. I kind of look at it as a Swiss Army knife of data because you can pretty much do anything you want with it. That doesn't mean you should. I mean, there's definitely the case that if you're managing things that are in fixed schematic relationship, probably a relation database is a better choice. There are times when the document database is a better choice. It can handle those things, but maybe not. It may not be the best choice for that use case. But for a great many, especially with the new emerging use cases I listed, it's the best choice. >> Thank you. And Dave Menninger, thank you by the way, for bringing the data in, I like how you supported all your comments with some data points. But streaming data becomes the sort of default paradigm, if you will, what would you add? >> Yeah, I would say think fast, right? That's the world we live in, you got to think fast. >> Think fast, love it. And Brad Shimmin, love it. I mean, on the one hand I was saying, okay, great. I'm afraid I might get disrupted by one of these internet giants who are AI experts. I'm going to be able to buy instead of build AI. But then again, you know, I've got some real issues. There's a potential backlash there. So give us your bumper sticker. >> I'm would say, going with Dave, think fast and also think slow to talk about the book that everyone talks about. I would say really that this is all about trust, trust in the idea of automation and a transparent and visible AI across the enterprise. And verify, verify before you do anything. >> And then Doug Henschen, I mean, I think the trend is your friend here on this prediction with lake house is really becoming dominant. I liked the way you set up that notion of, you know, the data warehouse folks coming at it from the analytics perspective and then you get the data science worlds coming together. I still feel as though there's this piece in the middle that we're missing, but your, your final thoughts will give you the (indistinct). >> I think the idea of consolidation and simplification always prevails. That's why the appeal of a single platform is going to be there. We've already seen that with, you know, DoOP platforms and moving toward cloud, moving toward object storage and object storage, becoming really the common storage point for whether it's a lake or a warehouse. And that second point, I think ESG mandates are going to come in alongside GDPR and things like that to up the ante for good governance. >> Yeah, thank you for calling that out. Okay folks, hey that's all the time that we have here, your experience and depth of understanding on these key issues on data and data management really on point and they were on display today. I want to thank you for your contributions. Really appreciate your time. >> Enjoyed it. >> Thank you. >> Thanks for having me. >> In addition to this video, we're going to be making available transcripts of the discussion. We're going to do clips of this as well we're going to put them out on social media. I'll write this up and publish the discussion on wikibon.com and siliconangle.com. No doubt, several of the analysts on the panel will take the opportunity to publish written content, social commentary or both. I want to thank the power panelists and thanks for watching this special CUBE presentation. This is Dave Vellante, be well and we'll see you next time. (bright music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from the cube in ETR. This is Breaking Analysis with Dave Vellante. >> On Friday, December 10th, Oracle announced a strong earnings beat and raise, on the strength of its licensed business, and slightly better than expected cloud performance. The stock was up sharply on the day and closed up nearly 16% surpassing 280 billion in market value. Oracle's success is due largely to its execution, of a highly differentiated strategy, that has really evolved over the past decade or more, deeply integrating its hardware and software, heavily investing in next generation cloud, creating a homogeneous experience across its application portfolio, and becoming the number one platform. Number one for the world's most mission critical applications. Now, while investors piled into the stock, skeptics will point to the beat being weighed toward licensed revenue and likely keep one finger on the sell button until they're convinced Oracle's cloud momentum, is more consistent and predictable. Hello and welcome to this week's Wikibond CUBE insights powered by ETR. In this breaking analysis, we'll review Oracle's most recent quarter, and pull in some ETR survey data, to frame the company's cloud business, the momentum of fusion ERP, where the company is winning and some gaps and opportunities that we see. The numbers this quarter was strong, particularly top line growth. Here are a few highlights. Oracle's revenues that grew 6% year on year that's in constant currency, surpassed $10 billion for the quarter. Oracle's non-gap operating margins, were an impressive 47%. Safra Catz has always said cloud is more profitable business and it's really starting to show in the income statement. Operating cash and free cash flow were 10.3 billion and 7.1 billion respectively, for the past four quarters, and would have been higher, if not for charges largely related to litigation expenses tied to the hiring of Mark Hurd, which the company said would not repeat in the future quarters. And you can see in this chart how Oracle breaks down its business, which is kind of a mishmash of items they lump into so-called the cloud. The largest piece of the revenue pie is cloud services, and licensed support, which in reading 10Ks, you'll find statements like the following; licensed support revenues are our largest revenue stream and include product upgrades, and maintenance releases and patches, as well as technical support assistance and statements like the following; cloud and licensed revenue, include the sale of cloud services, cloud licenses and on-premises licenses, which typically represent perpetual software licenses purchased by customers, for use in both cloud, and on-premises, IT environments. And cloud license and on-prem license revenues primarily represent amounts earned from granting customers perpetual licenses to use our database middleware application in industry specific products, which our customers use for cloud-based, on-premise and other IT environments. So you tell me, "is that cloud? I don't know." In the early days of Oracle cloud, the company used to break out, IaaS, PaaS and SaaS revenue separately, but it changed its mind, which really makes it difficult to determine what's happening in true cloud. Look I have no problem including same same hardware software control plane, et cetera. The hybrid if it's on-prem in a true hybrid environment like exadata cloud@customer or AWS outposts. But you have to question what's really cloud in these numbers. And Larry in the earnings call mentioned that Salesforce licenses the Oracle database, to run its cloud and Oracle doesn't count that in its cloud number, rather it counts it in license revenue, but as you can see it varies that into a line item that starts with the word cloud. So I guess I would say that Oracle's reporting is maybe somewhat better than IBM's cloud reporting, which is the worst, but I can't really say what is and isn't cloud, in these numbers. Nonetheless, Oracle is getting it done for investors. Here's a chart comparing the five-year performance of Oracle to some of its legacy peers. We excluded Microsoft because it skews the numbers. Microsoft would really crush all these names including Oracle. But look at Oracle. It's wedged in between the performance of the NASDAQ and the S&P 500, it's up over 160% in that five-year timeframe, well ahead of SAP which is up 59% in that time, and way ahead of the dismal -22% performance of IBM. Well, it's a shame. The tech tide is rising, it's lifting all boats but, IBM has unfortunately not been able to capitalize. That's a story for another day. As a market watcher, you can't help but love Larry Ellison. I only met him once at an IDC conference in Paris where I got to interview Scott McNealy, CEO at the time. Ellison is great for analysts because, he's not afraid to talk about the competition. He'll brag, he'll insult, he'll explain, and he'll pitch his stories. Now on the earnings call last night, he went off. Educating the analyst community, on the upside in the fusion ERP business, making the case that because only a thousand of the 7,500 legacy on-prem ERP customers from Oracle, JD Edwards and PeopleSoft have moved Oracle's fusion cloud ERP, and he predicted that Oracle's cloud ERP business will surpass 20 billion in five years. In fact, he said it's going to bigger than that. He slammed the hybrid cloud washing. You can see one of the quotes here in this chart, that's going on when companies have customers running in the cloud and they claim whatever they have on premise hybrid, he called that ridiculous. I would agree. And then he took an opportunity to slam the hyperscale cloud vendors, citing a telco customer that said Oracle's cloud never goes down, and of course, he chose the same week, that AWS had a major outage. And so to these points, I would say that Oracle really was the first tech company, to announce a true hybrid cloud strategy, where you have an entirely identical experience on prem and in the cloud. This was announced with cloud@customer, two years, before AWS announced outposts. Now it probably took Oracle two years to get it working as advertised, but they were first. And to the second point, this is where Oracle differentiates itself. Oracle is number one for mission critical applications. No other vendor really can come close to Oracle in this regard. And I would say that Oracle is recent quarterly performance to a large extent, is due to this differentiated approach. Over the past 10 years, we've talked to hundreds literally. Hundreds and hundreds of Oracle customers. And while they may not always like the tactics and licensing policies of Oracle in their contracting, they will tell you, that business case for investing and staying with Oracle are very strong. And yes, a big part of that is lock-in but R&D investments innovation and a keen sense of market direction, are just as important to these customers. When you're chairman and founder is a technologist and also the CTO, and has the cash on hand to invest, the results are a highly competitive story. Now that's not to say Oracle is not without its challenges. That's not to say Oracle is without its challenges. Those who follow this program know that when it comes to ETR survey data, the story is not always pretty for Oracle. So let's take a look. This chart shows the breakdown of ETR is net score methodology, Net score measures spending momentum and works ETR. Each quarter asks customers, are you adding in the platform, That's the lime green. Increasing spend by 6% or more, that's the fourth green. Is you're spending E+ or minus 5%, that's the gray. You're spending climbing by 6%, that's the pinkish. Or are you leaving the platform, that's the bright red retiring. You subtract the reds from the greens, and that yields a net score, which an Oracle's overall case, is an uninspiring -4%. This is one of the anomalies in the ETR dataset. The net score doesn't track absolute actual levels, of spending the dollars. Remember, as the leader in mission critical workloads, Oracle commands a premium price. And so what happens here is the gray, is still spending a large amount of money, enough to offset the declines, and the greens are spending more than they would on other platforms because Oracle could command higher prices. And so that's how Oracle is able to grow its overall revenue by 6% for example, whereas the ETR methodology, doesn't capture that trend. So you have to dig into the data a bit deeper. We're not going to go too deep today, but let's take a look at how some of Oracle's businesses are performing relative to its competitors. This is a popular view that we like to share. It shows net score or spending momentum on the vertical axis, and market share. Market share is a measure of pervasiveness in the survey. Think of it as mentioned share. That's on the x-axis. And we've broken down and circled Oracle overall, Oracle on prem, which is declining on the vertical axis, Oracle fusion and NetSuite, which are much higher than Oracle overall. And in the case of fusion, much closer to that 40% magic red horizontal line, remember anything above that line, we consider to be elevated. Now we've added SAP overall which has, momentum comparable to fusion in the survey, using this methodology and IBM, which is in between fusion and Oracle, overall on the y-axis. Oracle as you can see on the horizontal axis, has a larger presence than any of these firms that are below the 40% line. Now, above that 40% line, you see companies with a smaller presence in the survey like Workday, salesforce.com, pretty big presence still, Google cloud also, and Snowflake. Smaller presence but much much higher net score than anybody else on this chart. And AWS and Microsoft overall with both a strong presence, and impressive momentum, especially for their respective sizes. Now that view that we just showed you excluded on purpose Oracle specific cloud offering. So let's now take a look at that relative to other cloud providers. This chart shows the same XY view, but it cuts the data by cloud only. And you can see Oracle while still well below the 40% line, has a net score of +15 compared to a -4 overall that we showed you earlier. So here we see two key points. One, despite the convoluted reporting that we talked about earlier, the ETR data supports that Oracle's cloud business has significantly more momentum than Oracle's overall average momentum. And two, while Oracle is smaller and doesn't have the growth of the hyperscale giants, it's cloud is performing noticeably better than IBM's within the ETR survey data. Now a key point Ellison emphasized on the earnings call, was the importance of ERP, and the work that Oracle has done in this space. It lives by this notion of a cloud first mentality. It builds stuff for the cloud and then, would bring it on-prem. And it's been attracting new customers according to the company. He said Oracle has 8,500 fusion ERP customers, and 28,000 NetSuite customers in the cloud. And unlike Microsoft, it hasn't migrated its on-prem install base, to the cloud yet. Meaning these are largely new customers. Now this chart isolates fusion and NetSuite, within a sector ETR calls GPP. The very giant, public and private companies. And this is a bellwether of spending in the ETR dataset. They've gone back and it correlates to performance. So think large public companies, the biggest ones, and also privates big privates like Mars or Cargo or Fidelity. The chart shows the net score breakdown over time for fusion and NetSuite going back to 2019. And you can see, a big uptick as shown in the blue line from the October, 2020 survey. So Oracle has done a good job building and now marketing its cloud ERP to these important customers. Now, the last thing we want to show you is Oracle's performance within industry sectors. On the earnings call, Oracle said that it had a very strong momentum for fusion in financial services and healthcare. And this chart shows the net score for fusion, across each industry sector that ETR tracks, for three survey points. October, 2020, that's the gray bars, July 21, that's the blue bars and October, 2021, the yellow bars. So look it confirms Oracles assertions across the board that they're seeing fusion perform very well including the two verticals that are called out healthcare and banking slash financial services. Now the big question is where does Oracle go from here? Oracle has had a history of looking like it's going to break out, only to hit some bumps in the road. And so investors are likely going to remain a bit cautious and take profits off the table along the way. But since the Barron's article came out, we reported on that earlier this year in February, declaring Oracle a cloud giant, the stock is up more than 50% of course. 16 of those points were from Friday's move upward, but still, Oracle's highly differentiated strategy of integrating hardware and software together, investing in a modern cloud platform and selectively offering services that cater to the hardcore mission critical buyer, these have served the company, its customers and investors as well. From a cloud standpoint, we'd like to see Oracle be more inclusive, and aggressively expand its marketplace and its ecosystem. This would provide both greater optionality for customers, and further establish Oracle as a major cloud player. Indeed, one of the hallmarks of both AWS and Azure is the momentum being created, by their respective ecosystems. As well, we'd like to see more clear confirmation that Oracle's performance is being driven by its investments in technology IE cloud, same same hybrid, and industry features these modern investments, versus a legacy licensed cycles. We are generally encouraged and are reminded, of years ago when Sam Palmisano, he was retiring and leaving as the CEO of IBM. At the time, HP under the direction ironically of Mark Hurd, was the now company, Palmisano was asked, "do you worry about HP?" And he said in fact, "I don't worry about HP. I worry about Oracle because Oracle invests in R&D." And that statement has proven present. What do you think? Has Oracle hit the next inflection point? Let me know. Don't forget these episodes they're all available as podcasts wherever you listen, all you do is search it. Breaking Analysis podcast, check out ETR website at etr.plus. We also publish a full report every week on wikibon.com and siliconANGLE.com. You can get in touch with me on email David.vellante@siliconangle.com, you can DM me @dvellante on Twitter or, comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights. Powered by ETR. Have a great week everybody. Stay safe, be well, and we'll see you next time. (upbeat music)

welcome back to la we are live in los angeles at kubecon cloudnativecon 21 lisa martin and dave nicholson we've been talking to folks all day great to be here in person about 2 700 folks are here the kubernetes the community the cncf community is huge 138 000 folks great to see some of them in person back collaborating once again dave and i are pleased to welcome our next guest we have suresh ragaram co-founder and ceo of platform 9. sarish welcome to the program thank you for having me it's a pleasure to be here give our audience an overview of platform 9 who are you guys what do you do when were you founded all that good stuff so we are about seven years old we were founded with a mission to make it easy to run private hybrid and edge clouds my co-founders and i were early engineers at vmware and what we realized is that it's really easy to go use the public cloud because the public clouds have this innovation which is they have a control plane which serves as a it serves as a foundation for them to launch a lot of services and make that really simple and easy to use but if you need to get that experience in a private cloud or a hybrid cloud or in the edge nobody gives you that cloud control plane you get it from amazon in amazon get it from azure in azure google and google who gives you a sas cloud control plane to run private clouds or edge clouds or hybrid clouds nobody and this is uh this is what we do so this is we make it easy to run these clouds using technologies like kubernetes with our our sas control plane now is it limited to kubernetes because when you you you mentioned your background at vmware uh is this a control plane for what people would think of as private clouds using vmware style abstraction or is this primarily cloud native so when we first started actually docker did not exist like okay so at the time our first product to market was actually an infrastructure service product and at the time we looked at what is what is out there we knew vmware vsphere was out there it's a vmware technology there was apache cloud stack and openstack and we had look the open ecosystem around vms and infrastructure as a service is openstack so we chose open source as the lingua franca for the service endpoint so our control plane we deliver openstack as a service that was our first product when kubernetes when the announcement of communities came out from google we knew at that time we're going to go launch because we'd already been studying lxc and and docker we knew at the time we're going to standardize on kubernetes because we believe that an open ecosystem was forming around that that was a big bet for us you know this this this foundation and this this community is proof that that was a good bet and today that's actually a flap flagship product it's our you know the biggest biggest share of revenue biggest share of install base uh but we do have more than one product we have openstack as a service we have bare metal as a service we have containers as a service with kubernetes i want to ask you some of the the i'm looking at your website here platform9.com some of the three marketing messages i want you to break these down for me simplify day two ops multi-cloud ready on day one and we know so many businesses are multi-cloud and percentage is only going up and faster time to market talk to me about this let's start with simplified day two ops how do you enable that so you know one of the biggest if you talk to anyone who runs like a large vmware environment and you ask them when was the last time you did an upgrade or for that matter somebody who's running like a large-scale kubernetes environment or an openstack environment uh probably in a private cloud deployment awesome when was the last time you did an upgrade how did that go when was the last time you had an outage who did you call how did that go right and you'll hear an outpouring of emotion okay same thing you go ask people when you use kubernetes in the public cloud how do these things work and they'll say it's pretty easy it's not that hard and so the question the idea of platform 9 is why is there such a divide there's this you know we talk about digital divide there is a cloud divide the public clouds have figured out something that the rest of the industry has not and people suffer with private clouds there's a lot of demand for private clouds very few people can make it work because they try to do it with a lot of like handheld tools and you know limited automation skills and scripting what you need is you need the automation that makes sure that ongoing troubleshooting 24x7 alerting upgrades to new versions are all fully managed when amazon doesn't upgrade to a new version people don't have to worry about it they don't have to stay up at night they don't deal with outages you shouldn't have to deal with that in your private cloud so those are the kinds of problems right the troubleshooting the upgrades the the remediation when things go wrong that are taken for granted in the public cloud that we bring to the customers who want to run them in private or hybrid or edge cloud environments how do you help customers and what does future proofing mean like how do you help customers future proof their cloud native journey what does that mean to platform 9 and what does that mean to your customers i'll give you one of my favorite stories is actually one of our early customers is snapfish it's a photo sharing company it's a consumer company right when they got started with us they were coming off of vmware they wanted to run an openstack environment they started nearly four years ago and they started using us with openstack and vms and infrastructure as a service fast forward to today 85 percent of the usage on us is containers and they didn't have to hire openstack experts nor do they have to hire kubernetes experts but their application development teams got went from moving from a somewhat legacy vmware style id environment to a modern self-service developer experience with openstack and then to containers and kubernetes and we're gonna we're gonna work on the next generation of innovation with serverless technologies simplifying you know building modern more elastic applications and so our control plane the beauty of our model is our control plane adds value it added value with openstack it added value with kubernetes it'll add value with what's next around the evolution of serverless technologies right it's evergreen and our customers get the benefit of all of that so when you talk about managing environments that are on premises and in clouds i assume you're talking hyperscale clouds like aws azure gcp um what kind of infrastructure needs to be deployed and when i say infrastructure that's can be software what needs to be deployed in say aws for this to work what does it look like so some 30 of our users use us on in the public cloud and the majority of that actually happens in aws uh because they're the number one cloud and we really give people three choices right so they can choose to use and consume aws the way they want to so we have a small minority of customers that actually provisions bare metal servers in aws that's a small minority because the specific use cases they're trying to do and they try to deploy like kubernetes on bare metal but the bare metal happens to be running on aws okay that's a small minority a larger majority of our users in aws or some hyperscale cloud brings their vpc under management so they come in get started sign up with platform 9 in their platform 9 control plane they go and say i want to plug in this vpc and i want to give you this much authorization to this vpc and in that vpc we essentially can impersonate them and on their behalf provision nodes and provision clusters using our communities open source kubernetes upstream cncf kubernetes but we also have customers that said hey i already have some clusters with eks i really like what the rest of your platform allows me to do and i think it's a better platform for me to use for a variety of reasons can you bring my eks clusters under management and then help me provision new new clusters on top and the answer is you can so you can choose to bring your bare metal you can choose to bring your vpc and just provision like virtual machine and treat them as nodes for communities clusters or you can bring pre-built kubernetes clusters and manage them using our management uh product what are your routes to market so we have three routes to market um we have a completely self-serve completely free forever uh experience where people can just go sign up log in get access to the control plane and be up and running within minutes right they can plug in their server hardware on premises at the edge in the cloud their vpcs and they can be up and running from there they can choose to upgrade upsell into a grow into an uh growth tier or you know choose to request for more support and a higher touch experience and work with our sales team and get into an enterprise tier and our that is our second go to market which is a direct go to market uh companies in the retail space companies tech companies uh companies in fintech companies that are investing in digital transformation a big way have lots of software developers and are adopting these technologies in a big way but want private or hybrid or edge clouds that's the second go to market the third and and in the last two years this is new to us really exciting go to market to us is a partner partner let go to market where partners like rackspace have oem platform line so we have a partnership d partnership with rackspace all of rackspace's customers and they install base essentially including customers who are consuming public cloud services wire rackspace get access to platform 9 and rackspace working together with rackspace's ability to kind of service the whole mile uh and also uh we have a very important partnership with maveneer in the 5g space so 5g we think is a large opportunity and there's a there's a joint product there called maven webscape platform to run 5g networks on our community stack so platform nine why what does that mean harry potter harry potter so it's platform nine and three quarters okay we had this realization my cofounders and i were at vmware for 10 for 10 15 years and we were struggling with this problem of why is the public cloud so easy to use why is it so hard to run a private cloud and even today i think not many people realize uh and that's the analogy to platform nine and three quarters it's like it's right in the middle of king's cross station you go through it and you enter the whole new world of magic that that secret door that platform nine and three quarters is a sas control plane that is a secret sauce that amazon has and azure has and google has and we're bringing that for anybody who wants to use it on any infrastructure of their choice where can customers go to learn more about platform nine so platform nine dot com uh follow us on twitter platform line says or on linkedin you know and if any of our viewers are here at kubecon they can stop by your booth what are some of the things that you're featuring there we are at the booth we have our product managers we have our support engineers we have the people that are actually doing the real work behind the product right there we're talking about our roadmap we're talking about the product demos we're doing like specific show talks on specific deep dives in our product and we're also talking about some some really cool things that are coming up in the garage uh in the in the next six months can you leave us with any teasers about what some of the cool things are that are coming up in the garage yeah one one one thing that is a really big deal is um uh is the ability to manage kubernetes clusters as as as cattle right kubernetes makes node management and app management lets you treat them as cattle instead of pets but kubernetes clusters themselves our customers tell us like even in amazon eks and others these clusters themselves become pets and they become hard to manage so we have a really really interesting capability to manage these as more as you know from infrastructure code with githubs uh as cattle we actually have an announcement that i'm not able to share at this point which is coming out in two weeks uh in the ed space so you'll have to stay tuned for that so folks can go to platformnine.com.com check out that announcement two weeks two weeks from now by the end of october that's right awesome sharers thank you so much for joining us i love the fact that you asked that question because i kept thinking platform nine where do i know that from and i just googled harry potter that's right from nine and five dying because i didn't automatically make the correlation because my son and i are the most unbelievable potterheads ever yeah well so we have that in common that's fantastic awesome thank you for joining us sharing what platform mine is some of the exciting stuff coming out and two weeks learn to hear some great news about the edge absolutely awesome thank you for joining us my pleasure thank you for having me uh our pleasure as well for dave nicholson i'm lisa martin live in los angeles thecube is covering kubecon cloudnativecon21 stick around we'll be right back with our next guest

>> From the CUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. Welcome to this CUBE conversation. I'm Lisa Martin, and today I'm talking with Ivanti again, Nayaki Nayyar, their Chief Product Officer EVP is back with us, as is another Cube alumni, Sumedh Thakar, the President and Chief Product Officer of Qualys. Nayaki, sweet, great to have you guys both back on the program. >> Great to be back here, Lisa. I think it's becoming a habit for me to be here, talking to you almost... >> I like it. >> every week. >> Good to be here, thank you for inviting me. >> So, let's go right into some exciting news here, so Ivanti has had a lot of momentum in the last week or so, Nayaki with launch announcements, talk to us about what you're announcing today in terms of an expansion with the Ivanti-Qualys partnership. >> So Lisa, as you remember, this week we had a great week this week with the launch of our Ivanti neurons platform, that really helps our customers address end-to-end management of their endpoints and security of those endpoints. How we can help them, would be called self fuel, self secure and self service the endpoints. And one of the key strengths Ivanti has, in our portfolio, is our ability to manage all the patches. Today, with our Ivanti patch management solution, we patch approximately 1.2 billion patches on an annual basis. So that's a pretty big volume, and we are extremely excited as a part of this launch announcement, to also share the partnership we have with Qualys and how we are extending and helping Qualys with their overall vision for VMDR. >> So Sumedh, let's go right into that, talk to us about the VMDR, vulnerability management has been around for a while, what is VMDR and Qualys perspective? And what are you looking to do with your partnership with Ivanti? >> I should know about vulnerability management being around for a while, I've been 18 years at Qualys, so we've been doing for a long time, and, what's happened is with the hybrid infrastructure exploding and a lot more devices being added and focus shifting from just servers to endpoint, I think that is just a need to be able to do vulnerability management, in addition, also have the ability to do assessment of your devices in terms of inventory, etcetera, so, discovering your devices, being able to do vulnerability assessment, configuration assessment, but also be able to prioritize those vulnerabilities on which one do you really need to patch because you just have way too many vulnerabilities. And then at the end, all of this vulnerability management is not useful if we can't do something about it, and that's where, you need the ability to patch and fix those issues, and this is where VMDR really brings that workflow in a single platform end-to-end, So instead of just throwing a big report of CVEs, we provide the ability to go from detection of the device, to the patching, and this is where Ivanti partnership has been something that has really helped our customers because they bring in that patching piece, and this is one of the most complicated things you do, and because taking a vulnerability and mapping it to a particular patch is very complex to do and that's where the Ivanti partnership is helping us. >> And so, this is an expansion Sumedh, you guys have been doing this for Windows and Linux, and now this is adding Mac support and others. Tell me a little bit more about the additional capabilities that you're enabling. >> What's interesting is that, when we started working on this, this was before the pandemic hit, and COVID has certainly added a very interesting twist to the patching challenge, and the ability for the system admins to suddenly patch 100,000 to 200,000 devices, which are not in your office with a high speed internet anymore, they are sitting in little apartments all over the world with low bandwidth, WiFi connections, etcetera, how do you patch those endpoints? And so when, while the focus of the beginning was a lot more on Windows and Linux, which are more on the server side, with the pandemic hitting, there is a big need now for people also to be able to do their Macs and other endpoints that are now remote and at people's homes, and so obviously, with the success of the patch management capabilities on Windows that we got with Ivanti, they are a natural partner for us to also expand that into being able to do it for the Macs as well, and so, now we're working together to get this done for the Macs. >> So Nayaki, in terms of the announcements from Ivanti that they've been coming out the last week or so, we talked with Jeff Abbott last week about the partnerships and the GTM, talk to me about from a strategic perspective, how does the expansion of the Qualys partnership dial up Ivanti's vision? >> Lisa, when you take a look at what's really happening across every enterprise, every large company, especially during COVID, and post COVID, is what we call this explosive growth of remote workers, as everyone is trying to manage what the transformation to remote working means, the explosive growth of devices that now have to be managed by every IT organization, not to mention how to secure those devices, which is where this partnership with Qualys becomes extremely strategic for us. Now we can extend that overall vision that we have with our Ivanti neurons to discover every device we have, the customers' have, sense any security vulnerabilities, anomalies that are on those devices, prioritize those based on risk-based priority of it and going through priority as we embed more and more AI Amal into it, and get into what we call this auto remediation, remediating all those vulnerabilities, which nicely fits into Qualys's, or our VMDR vision and strategy. So, this truly helps our customers, go beyond just managing the endpoints to now what we call sub securing those endpoints, being able to automatically detect all security vulnerabilities and issues and get closer and closer to the self remediation of those vulnerabilities, and that's why this partnership makes, a great strategic benefit for all of our customers and large enterprise. >> So Sumedh, talk to us about the VMDR lifecycle, give us a picture of where your customers are and that how does this really going to help them deal with the new normal of even more devices going to be remote for a long period of time? >> what's happening now is that, this is being extended to home devices, customers in the past were only looking at enterprise devices that were owned by the organization, and we continuously now see, we can't get a new laptop to the user, or they're using their home device, home desktop, because it's bigger screen, more powerful, whatever it is, so people are starting to do that, and you can't really stop them from doing that if you want to get work done, and so, essentially VMDR is four things, which is, continuous asset inventory discovery, Second is, detection of all security issues, including vulnerabilities and misconfigurations. Third is the prioritization based on the knowledge of the device, and what's running on the device just because you have a severity, five vulnerability or highly exploitable vulnerability does not mean that you need to prioritize that as the first one to patch, and then you need to be able to patch it, and so that's the four elements that make up the VMDR lifecycle, and as customers have no good way to detect what devices are there, what is connecting to the VPN, because now they don't actually, physically see the devices, the traditional network devices that were... office firewalls that are sitting in the office, that were detecting devices are now not useful because everybody's outside the firewall. And so that entire life cycle, is something that customers want to do, because at the end, you want to reduce your risk quickly. And having a single platform that does all of that, is the key benefit that we get from there. >> Talk to me a little about the go-to market, in terms of how are your customers, joint customers buying the solution? >> I think what we've really worked on is typically what happens today is the customers'... different vendors are providing individual pieces, you have to go buy a different inventory solution, a different vulnerability solution, a different prioritization, a different patch solution, so, working with Ivanti, we've really worked on creating a single platform, and this took us a quite a bit of time to really make that engineering integration work, to be able to have Ivanti patch management directly embedded into the Qualys' agent. So that way, customers don't have to deploy another agent, and they don't have to buy different solutions for different consoles, so, from a go-to market perspective, we keep it very simple for our customers, they essentially have a one price for the entire asset and then if they choose to do the patch management, this is something that we sell as a capability that is directly available through Qualys and Ivanti has done a huge amount of work to integrate seamlessly in the back end to help the customer so that they don't have to, buy from one, buy from another and try to integrate it themselves. >> And Lisa if you look at it, it's really a way for customers to handle heterogeneous landscape, patching of heterogeneous landscape that they have, in their environment all the way from the data centers to those endpoints, the Windows devices, Mac devices, Linux devices, and in future, we'll also be supporting multiple other devices and platforms through Qualys VMDR, absolutely. >> Let's talk about the target audience and really understanding, from a security perspective, it's top of mind for the C-suite all the way up to the board, now with COVID and the increase in ransomware, and some of the things, the device spread, that's probably only going to spread even more, Nayaki, starting with you, how are you seeing the customer conversations change? Are you now not just talking to ITs elevated up the stack? Is this a CEO, board level concern that you're helping them to remediate? >> Absolutely, Lisa, this conversation about cyber security challenges, especially as organizations are trying to figure out what this transformation to remote working means, this is really not just limited to an IT organization or a CIO level conversation, this is a C-suite conversation at the CEO level, and in most cases, I'm also seeing this becoming a board conversation and I'm on a couple of boards myself, and this is truly a board conversation where discussing how we help enterprises transform to remote working and cyber security challenges as more and more workers are working from home, securing those devices is top of mind, for pretty much CEOs and the boards, and helping them through the transition is a number one priority. So, this is between the partnership with Qualys and Ivanti, for us to offer this joint solution, and really make it available where they can address the security concerns that they have, in their environment. >> And Sumedh, in terms of target market, we talked with Nayaki and Jeff last week about, from a vertical perspective, they've got a lot of strengths in healthcare and retail, for example, are you looking at any leading edge markets right now, verticals that really are at most risk? Or are you attacking us from a GTM perspective, or in a horizontal way? >> It's not even our choice anymore, because what's happened with remote working in no matter what industry you are in, everybody's workers are working from home essentially, and using laptops and the number of attacks have significantly multiplied because now that this endpoint is outside of your traditional defenses that you have in an office environment, these endpoints are a lot more vulnerable, and they are in a home network, I have devices in my home network for my kids that are running all kinds of fortnight and things like that, that now actually could have access to my work laptop, so that is becoming a big concern and the other realization that you cannot really use enterprise solutions as you have in the past, for patching and securing your endpoint that's not inside the enterprise, because if a single SMB goes vulnerability patches 350 Megs for one device, if you have that patch 1000 devices trying to download that over VPN, it's just not going to work, and it kills the VPN, so that is this big push towards moving into a cloud based method of deploying these patches, So you going to actually get these patches deployed without hitting your VPN environments, and this is really the big thing, and the other day I read something that that asked like, what is accelerating the digital transformation to the cloud for your enterprise? And, there was a CEO and the Sea So and then COVID, so unfortunately, the pandemic has been bad in many ways, but in other ways, it has really helped organizations move more quickly, to get approvals from the board and the management because the other option is just not a choice anymore, which is trying to use on-prem solution so that resistance to cloud based solutions is significantly decreasing because, today, we're all sitting in different locations and meeting every day on video, etcetera and that's really powered by that cloud-based platforms that we have today. >> I call it the COVID catalysts, there are a lot of interesting things that are positive, that are being catalyzed as a result of this massive change. One more question Sumedh for you, in terms of, this enabling VMDR to become a category, a target market for endpoint security, how does this help? >> I think, the more we can provide the customer ability to reduce the number of different steps that they have to go through and the different tools that they have to purchase and multiple agents and multiple consoles that they have to put together, then it just becomes a category in itself because you kind of have that ability to do detection, prioritization and response in a single solution, which is something that nobody else offers today because everybody is focused on just one aspect of it, and so, today the response from our customers has been absolutely tremendous, they are extremely happy to have this ability to very quickly figure out what's wrong, one of the things we didn't talk a lot about, but I would say in patch management process, the biggest challenge and where most time is spent is mapping a CVE to a specific patch that needs to be deployed on a specific machine, because of 64-bit architecture, 32-bit architecture, so, the Ivanti catalog helps us tremendously to help bring the knowledge that we have on the CVEs to that catalog, and then give our customers a way to be able to get those patches deployed in a very, very quick way, and so that essentially is just created this new category, when you have this end-to-end ability on a single platform. So whether it comes from Qualys or somebody else, I think the need is there to say, when I'm looking at patch management, I want the discovery of vulnerability and patching all of that to be done together. >> And that speed is absolutely critical. So in terms of the general availability, Sumedh, is this available now, when do customers get access? >> So with the partnership with Ivanti, VMDR in general has been available now for our customers for a couple of months, but now with the enhanced partnership, it was available for Windows or is currently available for Windows and now we are working with Ivanti for the next few months to get the Mac version out, so, we would think about in the next couple of quarters, we will have that available through Qualys VMDR, the ability to patch the Macs as well. >> Excellent. Nayaki let's go ahead and take this home with you, in terms of give me kind of an overall, round this out, the expansion of the partnership, the importance of helping customers in these disparate environments, and the momentum that this gives Ivanti for the rest of the year and going into 2021? >> This really rounds our entire Ivanti's vision and strategy, reservoir, our ability to discover every asset customers have on their endpoints and point assets as devices, being able to manage those devices holistically and to secure those devices, and also do service management of those devices and I had mentioned this, we are the only vendor in the market, that can do all of this end-to-end all the way from discovery, to security, to service managing the devices which... and the partnership with Qualys really helps as round it off across the board is full lifecycle of endpoint management, device management, and also enables us to extend to the natural adjacencies of IoT with Ivanti neurons, vision and strategy and truly get into a world of what we call self healing and self securing, the autonomous edge that we really strive to in the longer term. >> Congratulations both of you on this expansion of the partnership, we thank you for taking the time to explain to us the value in it, the challenges that this going to solve for your customers, Nayaki it's always great to have you on the program, thank you for joining me. >> Thank you, thank you Lisa and Sumedh, absolutely a great pleasure talking to all of you. >> Thank you for inviting me and good seeing both of you and I look forward to seeing you guys again. Have a good day >> Yeah, Sumedh. Great to meet you as well. For my guests, I'm Lisa Martin, you're watching this CUBE conversation. (upbeat music)

>>From the cube studios in Palo Alto in Boston. It's the cube covering IBM thing brought to you by IBM. >>Hi everybody. We're back. This is Dave Volante for the cube and you watching our continuous coverage of the IBM thing, 2020 digital event experience. And ed Walsh is here as the general manager. So the IBM storage division and software defined infrastructure. Ed, last time you were about to four feet to my left. I wish you were face to face but this'll, this'll have to do. Thanks for coming on the new normal. I like to call this maybe the new abnormal as some of us are still in lockdown but is the new normal. So we'll see more of this. So welcome it. I embrace it. No. So had you, you've obviously seen a number of, of downturns. You've run a lot, a lot of businesses, you've been on rocket ship businesses, you've been at IBM for a couple of stints. Obviously we've never seen anything like this. >>When did you first start getting visibility, uh, that this was going to be an issue? Obviously you guys have presence in China, okay. In AP. Uh, but when did you start to see it and what was your first move for the team? Yeah, sure. And so, uh, yeah, I've had the opportunity to lead a couple businesses and that was it. Okay. One, 2008. Ah, and this is, it is very different. But as far as our visibility on this, um, we have a worldwide and I'll say awesome. Right. Okay. So we saw this as far as a supply chain issue, um, and we came into it hot from Q4. We had a very good Q4 so I came into it hot or something. Why? So we are tracking it early and then we started to see the issues in China in late January. Then of course they shut down, came back to open after the Chinese new year in to be honest, they weren't quite back. >>So we were watching it almost as a support. Right. Main challenge. Yes. We do a lot of business in China, so we were also watching that, but it was light chain. But every single day managing that supply chain, I get out and give a compliment to my team. Uh, I don't think anyone has a better supply chain, but then of course quickly moved and everyone says, well, you should have seen it. This happened really fast. So it's a, it's different than other crises because it actually has to do with humans in life. Okay. All the other crisis were financial crisis. These, and we largely just manage the business through it and you're worried about your employees from the stress level, but you don't worry about the employees by the health level. So, uh, so we did see it early with supply chain that quickly gotten demand. And to be honest, when Italy went down, well, when Italy had the challenges that it happened so fast, when it shut down, uh, that was kind of a big wake up call for us. >>Mmm. You saw IBM respond very quickly. Um, everyone was at home almost immediately, even in countries weren't set up for it really took care of our people. But then we immediately, you saw the IBM was going to work really helping our clients. So we saw it kind of early, but it went from a hundred percent supply chain to a demand issue. And then we did have different real uh, interesting is a bad word, but interesting supply chain challenges as well. What it went on different countries stopping shipping's coming in, had to get a government approvals to get things. Mmm. So it was a good partnership with some of our um, get things where they need be in the right time. Ah. But it was probably a, I'll remember this quarter for a lot of different reasons. Um, and it worked out good for us. But uh, to be honest, it was, it is different from the other crisis's because it wasn't just a financial issue, which I think were just getting into actually, um, it was human and you saw different, two of our best regions were Italy and Spain that you think, Whoa, why? >>You know, you think the thing about other than going on in the quarter and but it was a relationship. It was, you know, we got our, the IB members got safe real quick, but then we quickly got them to engage with the clients, but we didn't Bush and was natural. Next thing you know that trust, I think there was a flight back to quality. You saw these different companies and that was the things they had to get done. Um, but it was, it was pretty amazing quarter to me. It was more seeing the team, you see your teams reacted. Crisis is in challenges in different ways and sometimes they paralyzed and we didn't see that at all in the team, which was pretty intelligent. Um, but we it coming from the beginning, call it before this we saw supply chain did, we came into Q1 hot on supply. So we kind of saw our early and we're already doing drills. So we saw it kind of right when it was hitting. Okay. >>But it was interesting you used the term interesting the challenging because it was sort of not only day to day for you, it was probably like minute by minute, hour by hour, country by country, region by region. How did you change the way in which you communicated to your teams or did you >>well so quickly? Um, so one I think culture, so I've been in a couple different companies, big and small. Mmm. I've seen different cultures react and the IBM culture is one that I've, I kind of look back and on this last quarter just because it's very customer intimacy. You don't have to, if the customer's in trouble, you can't stop them from running to help the clients. So we saw a natural, you know, we, IBM made sure they oil is refined to have one at home. Well we saw them quickly go after it. So most of it, any indication you do see it if these crisises um, you see some groups kind of freeze and, and you have to kind of walk them through it and make sure one, they're okay. This, this one was different yet to make sure your team was okay. Um, both mentally, physically, and their families. >>And it was a different stress level, was very personal and effected all of them. Where are the financial crises? In fact, it didn't affect everyone as much. It was more sterile. Uh, this one was wow, really different from a leadership. Um, but it's all the same. You have to get the team together and make sure they're healthy, happy, a healthy and mentally healthy too. And then you have to get people to kind of how do you go drive and help clients out. In this case it was helping you make sure your clients are okay, they're healthy, and then what can we do to help them? And I think that became more natural. And then of course it's Viber, Katelyn's drive, the business supply chain, which is I would say with any of the different um, challenges. But it's all communication. Well on this one, it was really had to check with the team often. >>We also had this new normal, I call this the new abnormal, which, you know, all of a sudden you can't meet with people so you couldn't get people physically together. So I call abnormal cause we're still, we'll get to the new normal, we'll use a lot more remote type of communication. But it was, I've never been so busy and I'm on video calls with all my teams every day. You see people using different tools to communicate like Slack, but also a lot more video. Uh, so it's communication, communication, which is the same thing. It's all the same thing with teams getting together, getting your direction. Well in this one it was mixture. They're safe first and then move on. Same thing with clients. Make sure let's say. Yeah. And that was what was fundamentally different about this. Um, Hey, what's up? Yeah. You know, and we were both grinders. >>I always joke, I work a half day every day. It doesn't matter which 12 hours the same way I have it twice. I'd take 12 hour days in a heartbeat these days. I mean, it's just really been crazy and I have to agree that the teams around the world at our, at our client space, of course the cube teams have barely really stepped up. But I want to ask you about the quarter. You're right. You came in hot in December, meaning you had a really good Q4. I, you know, I reached out to Tom Rosamilia last week, members said, Hey, nice announcement. And he said, did you cover it? I said, I did. And I sent them my breaking analysis. I, I really dug into the life cycles of the Z and how it affects, you know, IBM's overall business. And I predicted this is going to go on for several quarters where IBM has done a real chill tailwind, not only in, in systems hardware, but also, you know, the storage piece of the system's hardware business. >>We saw that last 40 accrued 19% and storage 60. Yeah. In, in, in Z hardware. Pretty amazing what's going on. Unpack. Okay. The quarter for us a little bit. Yeah. So if it wasn't for the crisis, I think all that would be plate. We had some announcements okay. Across the entire source portfolio. So what we do for storage for Z big announcements in Q3, uh, directly aligned with what we do with the new store. You know, the new Z, uh, you get a lot of value. One-on-one is three. So a lot of senators, I think it's different platform. So hit the demand and what clients are trying to do. Mmm. Bring a new, you know, uh, cloud development platforms, you know, native cloud development, but also using cloud. So there's a whole bunch of different things we brought to that platform. But we also launched new AI platforms, so stores for AI and big data. >>Uh, and then it the one we launched our new distributor. So we're kind of coming in from an offering set in fact water, uh, you know, 19% growth. Um, I think it's like speaks volumes no on the offering. Yes. But more how are we reacting to their clients more than anything else? I think it was a, Ashley's I talked about earlier, it was an interesting quarter. I think it's clients were responding to the flight equality, but also who's engaging with them the right way. So we do have a company absolutely refresh offerings across. In fact, this quarter, every single one of our offerings, every single new offerings group. Yeah. It's more of a, if you have the right offerings meet in the market, helping them with it, it correct after two, right. Your own journey. The cloud, moving, modernizing your environment. We need to free up our teams. >>We did a dramatic simplification on but what we do with storage or Z, but also distributed storage and what we do for storage AI and a big focus on cyber resiliency. Those are hitting what I'll say the market was in Q4 but they happen to also be hitting the market for what's going on now the noodles. So a lot of the simplification was that, how do you remote manage, how do you do things? One of the biggest things we do to our clients is, and we have all these tools, we give you a lot of things for free baseline, but we also have these increase the pro versions. We're just said, take them, I use them because it allows you to monitor and manage your environment better remotely. It was all web based. Uh, and that was one of the biggest things to do. But that is hidden the market. >>That's, that's the new normal. And we did that across those Z distributed storage. Mmm. But also what we did in a cyber resiliency in AI. I want to hit on a couple of those points. I mean, I'm going to start with the cyber resiliency because we were one of the first to report with our, with our partner ETR, our data partner that the work from home offset it was somewhat cushioning the downturn. I mean it's ugly, but chill worked from home pivot and that included, uh, uh, solutions around ransomware, data protection, cyber resiliency. So yep. Investment, actually 20% of the CIO is that we surveyed actually by not spending more in 2020, because of there wasn't zoom and WebEx, it was, there was other infrastructure around it, VDI, et cetera. So you're seeing that, uh, it sounds like, well, maybe talk a little bit about, so the cyber resiliency, and I'm especially interested in the context of going forward, feels like this is going to be one of those permanent things. >>You know, clients might sacrifice some near term profitability to have more flexibility and resiliency in their business and not rely so much on just narrow dr but more business continuance. No, I think you agree. In fact, um, we've always been, you know, a leader in business continuance. We still are. But cyber resiliency is yes. What a million different factories hovering from a ransomware or uh, um, you know, malware incident is different fundamentally different tool sets than what you're doing. You need to have a copy of your data of course, but very different than when if you were dr single server come up and running. Okay. You see us and mostly I think we're ahead of it because as IBM, we're the largest outsource firm in the world. So we actually live with these incidents as IBM. So in normal storage you hear about them and typically it's a storage issue. >>That issue that came back running. We are living with what we do or how to, our storage or outsourcing or strategic outsourcing group. And so we're putting into all of our products a lot of unique things from cyber resiliency. So what we did for storage for Z, it literally is a safe card. Copies an offering that little gifty 500 recover points. Yeah. Separated administratively and physically. So you're really able to literally, internal and external threats, protect yourself best in class. No one else has a solution set. We did the same thing and distributed. So, but in distributed, what we're trying to do is help people, not only, I used the term, left the boom and right up, boom, left the boom is before incident. How do you prepare? How do you have the right backup recovery? How do you have the right tool sets? Recover points? >>How do you protect yourself? How do you make sure you're um, you know, monitoring for ransomware? Every single night we'll get back power tools. Okay? The right of boom is once you do get hit, you go into this incident response situation where eyes drawn, your lights are on you. How do you give the humans, uh, the right cool. So they can react the right way and be quick. So also storage plays a huge role with ransomware and malware. Also. You get into, all right, the boom hits, you get the call, it's from the CEO. You got to fix it. You need new tools. Right? What recover point do you go back to? Um, it's iterative in nature. Uh, well yeah, it hit on, I got a call on Friday, but I don't know when the malware got and it was a Wednesday or Tuesday. It might be different per system. >>It's an internet process. You need the right tools, you use all your copies, primary storage, secondary storage for sure. He copies VR copies and find out what's your best recover point. And it's imperative you have to Lily bring up environments, you have to have fence network capabilities and all your tools to allow you to literally bring them up quickly in succession or altogether find them. That's recover point you get to as soon as you can. So those are the things I think we're leading. And we launched all this before this issue. Well we also saw an increase in malware in our client set. So to be honest, you know, even with all this crisis that we're seeing an increase and in malware, ransomware is where the storage infrastructure layer really matters in the incident response capability where if you have an incident, someone stole your data sets and typically storage guys that they call now IBM has great solution sets around their AI, direct driven. >>The ability is to allow you to protect yourself there. But this is on ransomware. It's something that storage plays a huge role. We do undistributed we do on mainframe with specialized solution sets. No one else in the industry is doing that. And of course back, uh, and recovery. Yeah. Quick recovery and orchestrated fashion. That's what we do around spectrum protect all day long. Right. Okay. Yeah. Last time we met. Oh, okay. You shared with us your, your consolidation strategy, your big, you know, announcement, uh, last fall, uh, and obviously, you know, great board or 90% growth. Well, a lot of that was drafting off the Z and the, you know, the hundred, but, but I'm wondering how that, how that consolidation work. We talked about the challenges of doing that know yep. The importance of that, how others are going to have to respond. And we're seeing that in the industry for a lot of the large portfolio players. >>But how did that, you know, how's that going? Can you give us, what, can you tell us about the progress there terms of its uptake and adoption? Sure, sure. So really what we did is we kind of looked at the industry and said everyone's adding too much complexity. You know, the whole industry is based on having a high end mid range and low end storage environment and the high end did everything custom and silk concrete performance, but you had to pay a price for it. And then the whole industry is based upon just get each of the next gen. So if you're a high end about problem is every client has high end, mid range and low in storage. So you have dual vendor strategy, but what you do is you have to, the whole industry is just getting to the next high end. Uh, you see EMC, Dell hashtag next generation, midbrain storage, the whole industry, including in the past, IBM was structure and getting you there. >>So we basically announced no more of that. Doesn't make sense. It used to, it no longer makes sense. We drive a lot of innovation what we're doing with Silicon, but software and we need to one platform, one platform that allow you at different price points down the stack from low end, mid range and high end, well without compromise. What's a dramatic simplification, right? Uh, that was a well-respected, you know, I would say we got an unbelievable response from that. And you saw a dramatic growth. So you kind of hit upon, we grew across all of our segments. Yes. We had a good growth on what we do for stores for Z. Well, we had an equally good growth at, as we did on distribute storage. So if you have physical environments, virtual environments, VMware, hyper V containers, public cloud, hybrid cloud, our distributed storage portfolio. >>So one of the biggest increases. Mmm. And we, again, we grew in every one of these segments. So one the simplification. Okay. Chapter two, how do you free up your team? How do you modernize your applications so you can innovate? Mmm. critical. You're free of your team. So that one thing that we also did a lot of, you know, Billy do remote management. I made it very simple to use Mmm. And simple to support, which also helps them the new normal, but it hit the right tone with it, our partners, but also our clients. And you saw a pretty massive uptick after the February announcement. So it was only half a quarter. We saw quite a large lift. I want to ask you about the storage for big data and AI as well. There seems to be a new emerging workload. You got all this data out there collected and Hadoop and analytics over the last 10 years. >>Now you're applying, we've talked about this, the new innovation cocktail. You got data AI and okay, well it gives you the scale whether it's on grammar in the public cloud, uh, but there seems to be a new workload where you get up what kind of a data store. You've got the analytic workloads that are in there. You've got some data science tooling, uh, and other, you know, AI that, that seems to be an emerging workload beyond, um, just kind of infrastructure as a service. But okay, really new way to get insights out of data, data, wonderful insights or not yet. So talk about that workload and how that is, is powering your business. And what are you seeing there? Well, I think this is where I see IBM, uh, really I'm helping clients with this journey to building smarter businesses cause AI is going to be in every workload. >>You're bringing up very specific workloads around machine learning, learning, bring customer on Silicon, like GPS into it, on these big data Lake. Uh, how do you take a data swamp and make a data Lake? Um, okay. Uh, what I'll say is IBM's doing this and we use the term ladder, the AI, and there's no AI without IAA information architecture. You have to have the right infrastructure to do it. We also see different groups having random acts of AI, a data scientist and the visionary does something is kind of interesting. Another group does something interesting and maybe a third. It's like the early days of data warehousing, but they're not able to take it together and bring it to, they can infuse AI across all the processes in a company and have one single view of the truth. Do we see people going through this natural progression, some start independently, a fight technology then bring it together. >>So everything we're doing from, I'll talk about what we're doing is storage infrastructure servers, but also across what we're doing, you know, are um, Mmm cloud pack for data offering and make it very simple for you to pull and get the use case out of it. But for storage is about when you want to bring it together, you need the right performance. But we bar none have the best source for AI. And data. It's based upon our, you know, Lily, um, award-winning. Yeah. Scale up a file system called GPFS or spectrum scale. It runs the largest AI supercomputers in the world. The same as X software, but you can buy it to your device that we launched it in December, which is feller. ESS, um, 3000 is a single all flash array. It's a cluster, but you can no compromise. You go from that device and the largest AI supercomputer in the world configuration, exact same technology, hardware and software that we do. >>Floyd. So now you can start small and grow and then we're helping along. How do you get the value out of it? So that's typically where storage ends. I gave you the best platform you can possibly have, cost effective, small, and you can scale to the biggest thing you want to do. The next thing we're doing, which people say, well that's not storage and why are you doing that? We're doing things called spectrum discover. It's managing your metadata and making your data scientists the most productive possible. They spent any 80% of the time literally just understanding the data, tagging the data, organizing it so they know what they're doing with, cause if you don't have the right AI data sets, you really can't get the outcome. Okay. But we have what's called spectrum discover works across a whole bunch of other products, but also all of our portfolio, both object storage file system block allows you to look at an environment, organize it, and save dramatic amount of time for data scientists. >>And of course that's easy feed into all the things we do around cloud pack for data, which is where IBM has really put a lot of these open source and our own tools together so you can move forward pretty quickly. The key thing is how does IBM help you not technology. We know what you want to accomplish, let's help you but not limit you by we're letting you use all the different open source. Yes. I just want allow you to move forward and help you in that journey. And it is a journey and we're meeting clients where they are because everyone's on it different. Yeah. I guess segment of the journey and how do we help you go through it and from a storage, uh, you're seeing that environment really double every quarter. Mmm. For the people that are looking for it, no one really touches us. >>Mmm. In fact, our number two and three customers, Mmm. Competitors in the space use the same software that we OEM so we're in a very good position when it comes to stores for AI, big data. So they say it's better to be lucky than good. I say it's, it's better to be good and lucky. And so, you know, we're not going back it's not happening. we've got this new abnormal, as you call it, and you've done a lot of the hard work in terms of rationalizing the port folio. You've done the R and D and you started this years ago and it took a long time. Mmm. But I wonder if you could just talk about why you feel like you're in a good position coming out of this thing and who knows how we're going to come out of it, but what are the critical components that you feel you have in your arsenal that will make you stronger and more competitive or you know, relative to, you know, the, uh, the landscape out there, your thoughts? >>Yeah. So now this is going to sound, uh, well good. So all these different issues we've been through all these Bryce disease we've been through in our careers. Um, there's an old adage, if you can last room and you get resourced, you can come out stronger. And it's very true. So you can grow, you can do the right things, but you have to have the right offerings. Sometimes that's low, lucky you entered, right? I think we perfectly with the right innovation that did take us years ago, but we're hitting the current market. But also what I'll say is the new normal market. Mmm. And I think that's an opportunity. And I've always said, listen, the world doesn't need another storage. Right. Well, they're looking for solutions around the source challenges and I think what we've done around product portfolio with, we use the term offerings was the offerings around it with a different software allows you to actually, we're really free, you know, if it's really chapter two now we're trying to do monetize your core infrastructure, you need to free up your team so they can innovate. >>We're going to do that dramatically in what we're doing. Storage, they help you with that journey to cloud either OnPrem or into the public cloud or really what we see is a hybrid multicloud fabric happening, but also we do cyber resiliency as we built it from the or. So I think we're good hitting it, right? Mmm. Now the new normal is all the things that it has to be simple, it has to be rope managed and those are all the things we made massive investments across every one of our portfolio items. They just got launched a launch in the last two quarters. So I think we're in good stead. But to be honest, in these times, as we talked earlier, you work harder. You've got to really embrace the client feedback. Mmm. I think IBM is a good position to do that. Also with the greater IBM, we see vigor, Mmm. Opportunity set to find out how to help clients. >>Okay. We're the number one AI company in the world. So we're seeing what clients really want to do with AI and how they. There's actually holding it back. Number one outsourcer. We're seeing how people are really dealing with cyber resiliency and especially now where ransomware, where storage really impacts you. We're seeing exactly how to do it and what tools push forward and that's where you're seeing very unique opportunities in these times. If you can have the right product, the right go to market and do very well and more importantly you'd do it by helping clients. If you can help clients through this, do you come out stronger? I think some other people's storage, it becomes more challenging. I don't think people just want you know, the next flash array. I think they're looking for solution sets a companies to help them get through and get to the really the new, I think we're going to get to the new normal. I think this is a new abnormal, I can't call it normal. When we're all locked away, the new normal is going to be much faster. You're gonna have to go faster. So I think IBM and the IBM storage is aligned with let's help you with the cloud journey. Let's help you build our businesses. We'll make sure cyber resiliency built in there. Well, we're going to, you're seeing it across every division of IBM, step up and help you in that. Mmm. In that direction. That's what I think is differentiated. Why I'm excited about >>what we're doing. IBM in general, but also, yeah, again, storage is perfectly aligned with that overall mission and it's, it's kind of exciting to see it kind of play out in front of class. Well, I think you're right. I think the last decade was a lot of, it was about the all flash data center and, and the future is about powering innovation infrastructure for machine intelligence. Uh, and, and really getting insights out of data scaling. Uh, ed ed Walsh. Always great to have you on the, uh, hopefully we can do this, you know, a little closer face to face, maybe six feet apart. Um, and then eventually we could shake hands or high five or whatever it works. Thanks so much for coming to the Cuba. It's great to see you looking good and stay safe. Hey, thank you. Stay safe. All right. And thank you for watching everybody. This is Dave Volante for the cube and our continuous coverage of the IBM, that 20, 20 digital events experience. I'll be right back. Sorry for the short break.

>> live from Las Vegas. It's the queue covering Del Technologies. World twenty nineteen. Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Del Technologies, World twenty nineteen here in Las Vegas. I'm Stew Minutemen with my co host, Dave Volonte, talking multi cloud talking about Del Technologies and all the pieces of the environment. And we're gonna drill in some to some of the storage environment. Happy to welcome back to the program. Ah, Sudhir Vossen, Who's the senior vice president and CEO of the storage division of Delhi? Emcee, Sit here. Thanks so much for joining us. >> Thanks. Thanks for having me, Stew. >> All right, So, as I said, day one lot of the vision Digital transformation multi cloud with such an Adele up on stage. Got a little bit about today. Got back into the products, everything, you know, such a broad portfolio, everything from the latter tattooed, you know, business devices through Of course, many updates on the storage world Been digging in with the number your team gives little flavor as Teo, You know what you've been working on? You know, I know. As a CEO, you can't have a favorite family but in the family. But some of the things you and the team were really proud of to unveil >> Absolutely thanks. It's been a big day as well, and I would say a big year for us. So we, uh, we've shown incredible growth in our business in the last four quarters, taking share every four for every one of those four quarters. Just a phenomenal year. A lot of that has to do with just the strength of the portfolio. Have been investing a lot in innovation in the portfolio. So, uh, I think the biggest one today that I'm really proud of is the unity launch. Think it's, uh, it's a long time coming. We've been working on it for quite a while. The the amount of performance that is going to deliver while also delivering incredible storage efficiency data reduction. That's a huge, a huge boost. But what way haven't spent a whole lot of time talking about from a technology point of view as a Ziggy. What's cool about unity? X TV that you may not have heard a lot about is that it actually is using machine >> learning inside. So last year we lost the power Max that had machine learning inside for making all these real time decisions were taking that across the family and unity >> x t uses. Was she learning in order to actually do deliver that data reduction that we just talked about? The five to one data reduction. And what's why that school is Because, you know, we've had products that do data reduction with brute force where they use a lot of memory. You can't do that in a mid range part because that kicks you out of the cost profile. So we use machine learning, tio take advantage of a little amount of memory, but they still not compromise on the data reduction. >> Yeah, actually, I had to cover they should day talking about power, Max. We made a big deal about what was happening internally as well as what does that mean for the customers and the decisions that they don't have to make you know, in our industry, we've talked about intelligence and, you know, automation in storage for decades. So yeah, and then the mid range. What does that mean? What? What will be different from customers for as they roll out thie X t product line. So >> I think it's simplicity. It's just he's a fuse. We talk about zero touch in this case, this this fewer knobs and dials. You actually don't have to do a lot of tuning at all out of the box. It'LL will serve the majority of the use cases and the requirements. You still have the option if you want to go in. If you're sort of the black, no type and you want to do, uh, customize it to your own needs. You could do that. But that sort of this journey we're on is way. Call this the autonomous or self driving story, so a lot of people are talking about it. We're actually doing it across the portfolio, and it's actually coupled with two parts are coupled with another part. There's intelligence in Unity, Eckstine and Power Max. But there's also intelligence and cloud I. Q, which is our global Blake brain in the Cloud way, saw that on stage today as well, where it's doing long term analytics deeper, learning across longer time rises to help you manage the system without really much effort. >> So couple follow ups, if I may, on the on the data reduction front. Sounds like that's a new innovation. You guys develop come from scratch. Yeah. Um, you bringing it across the portfolio, or is it sort of obviously unity extra? Today it is. The technology apply to other potentially >> absolutely does. And in fact, that's Ah, that's something we're doing across the board from last year to this year. You you've seen with become one storage team, and there's a lot of technology views going on now inside the inside the portfolio. Things that we're doing in unstructured, for example, are we're looking at applying it into other parts of the portfolio. Data reduction is obviously one of the key ones. It's it's the first example that people think off, so we're definitely looking at that. But I'LL also say is from a technology point of view, we're changing the way software is built. We're not building it as monolithic within micro code anymore. It's containerized assets that we can embed in different products >> and then, in terms of the autonomous storage piece, you know, go roll back five, ten years ago, cheering, you know you had and you had a lot of knobs to turn and and that was always featured as an advantage because people wanted to play with it. What you're talking about today is a Zen environment that's much more complex and talk about Maur. What autonomous storages is it? Hands off on great >> questions. So we have this, this internal Carter almost of most. Joke, we call it. You know, we're talking my self driving cars. Surely we can build a self driving storage >> system. Why now, Right? It's it's It's kind of a shame that we're not doing that, but I would say it's four steps just like you have four levels of autonomy and self driving cars. If you follow that level five, I think, is the is the ultimate polio zero fully autonomous way. We'LL never get there, but similarly in storage, I break it up into four parts. One is it's got to be application aware you're not dealing with lungs and file systems and raid groups anymore you're dealing with. This is my application. That's how the human or the user interacts with it. That's easy. Relatively easy. Second element >> really took fifty years. Okay, good >> second, second element is is sort of self awareness are actually actually before. That is policy based. So if you're driving a car, you're not telling the car which which route you want to take. You want to say, I want to take the fastest route or I want to take the scenic route. That's it. And the car needs to figure out what that is. So that's policy based. I want to optimize for Leighton. See performance level. Third element is self awareness, which is story. System needs to know where it's operating in its comfort zone is that close to the edge is going to drive off the cliff. Is it gonna exit the lane to use the car analogies, right? He's You know how far away it is from the car ahead. That's also that's the stuff that we're now releasing with Bara Max and what we're doing. Immunity. That's where we using learning to figure out how close to the operating edge system itself. It's once you have that, then you can start optimizing self healing. >> That's a level four, and that self awareness. So you've got you've got decades of data. Were you able to leverage that data? Or is that is that not a cz much you. So you have >> absolutely the case. Okay, that's that's the key differentiator. Actually, thanks for bringing it up because there's a lot of washing going on. Right is everybody says that about you, but the eyes, one thing you can't just deliver develop over way have used all of the decades of dial home data we've been working on with she learning technologies for the last five years. I would say, at least so were those models are being trained with the dial home data and cloud, like you is doing that on a daily basis. Now, >> why now in two thousand nineteen? Severe is that we at the point where this has become reality is a compute power. Is that the amount of data? Just better algorithms. It's Do you >> think you nailed it? Those two things, it's It's first and foremost compute power. But also I think, uh, algorithms they they're they're much more sophisticated now. And they were well understood what algorithms to use for what types of problems. I think there was initial thirty years ago. There was like, uber intelligence. That was a very ambitious goal, I would say, even today, that's not reality. while we're succeeding is we're applying it to very focused problems, just like in the rest of the industry. Were playing through focus problems that we can't solve and then broadening our effort >> had to be clear. This is this is meta data. It's not customer data utilizing obviously across the portfolio. >> No way. We're looking at things like how much CPU it's using. How much memories? Using what? How's the Leighton Sea varying over time, how far it is away, this from its service level. Things like >> you're still just another advantage of being old. Yeah, so you talked >> about that's metadata. But what one of things we talk about is when you talk about digital transformation, it's customers become data driven, right? So wave covered this year, this the tenth year we've been at this show. In the early days it was storage and oh, my gosh, my growth of data and I can't take care of it. Big data was the bit flip of turned that from a challenge to I should be able to turn that into an opportunity city. And the next wave of a I is I should be able to monetize that run my business and the data is one of the most valuable things we have bring us inside. You know how that shift in thinking in data is impacting storage architectures and how you work with customers. >> That's awesome. Great questions. O Data Capital is the big thing around. You've heard that today as well. Wear definitely sort of growing. Going beyond thinking of ourselves as a storage division to a data division. And I'm locking the data capital. I'd say there's several elements wonders building the best storage fore fore data applications, especially I and M L. So I think our unstructured products clearly are leading the charge of this. We've got the machine learning solution with Isil on. It's a perfect fit for that kind of application that's here and now already using a GPU Technologies in conjunction with our scale, our architectures critical. But going beyond we're looking at doesn't make sense for some of these data crunching applications to be closer to the storage layer, you know, thinking meet similar to what hyper converse is done for general computer. Is that a thing that would that would really unlock the data capital? We think that's a lot of potentials. So >> and I'm glad you brought that up because you know, when the storage geeks, you know, talk about envy me, envy me over fabric and storage class memory. Explain how that fits into what you were talking about, and not just the next, you know, major wave of, you know, a tool inside the infrastructure >> train. So I think so. Storage. Envy me. Envy me over fabric was part one off a two part story, as is your You know that that allowed us to get that super Lolita C high speed connection from application to storage with the data. But the data devices themselves were still very flash is great prepared TV, but they're talking single microsecond type of sub microsecond applications that need that kind of leniency. And that's where storage last memory comes in. Right? So we're finally getting to that point where the storage devices are in operating in that ten microsecond range, which will start to really get us to back if we can get those things go located close by unlocks a lot of things. And the beauty of envy me over fabric is that it can give you the sense of being closed by without actually physically being close by. So you could still be disaggregated, and that opens up a whole lot of architectural options >> can fall. Question on storage class memory The skeptics would say. It's just way too expensive and you're not going to get the volume of flash that you get with these. Uh, what do you What do you think? >> That's what they said about Flash dude in there, >> last one in tow. Consumer devices, not you're on this scale. Bring the price down. >> Maybe maybe before iPhones. They said that, but iPhone was the catalyst. Eyes. They're a consumer analog for storage, club consumer >> and long. I think that's fair, but I think there will be volume to drive it down. However, I will say it's a fair point. I think that with actual magic lies and combining superfast, perhaps expensive storage last memory with cheaper flash storage, and so you almost have a hybrid solution again. So the old hybrid becomes you hybrids back in such >> fashion, even with solid state, >> the storage pyramid lives Exactly way. >> Think that's going to be the killer combination? >> All right, so sit here. Can't let you go without. Give us a little bit of a look, for we talked about where we are. Talk about some of the journeys that were there. So it's our tenth year here at the show. Come back for your eleven, you know, How do you foresee the industry maturing and moving forward? >> I think for your eleven, the big things we're going to see is Cloud Two things I would say one is CL Cloud and the other is software to find. I think those are the two that are going to be big news next year. >> We're seeing some sneak previews of that this year with the cloud announcements we made you'LL see a lot more of that next year from from the storage side, both in be part of the Delta Clock Technologies Cloud Platform but also cloud enabling our storage arrays across all the all public clouds. And then the second part is software defined. I think that's really the next way. So, as I said, we are a long journey internally. We've already been on it where were transforming our internal storage assets to be more software centric, and you'LL start to see some of that All right, well, >> sit here. Really appreciate you helping us geek out on, dig into, You know, a lot of the pieces here at Del Technology World twenty nineteen. Thank you. Alright. For David Dante, I'm stew minimum, and this is the end of two days of water wall coverage. We're coming back for one more. And as always, check out the cute dot net for all the videos. Silicon angle dot com For all the articles. Wiki bond dot com For all of the in depth analysis Hit up, Dave myself, John furry in the whole team were available on social media channels and, as always, thank you for watching the cue.

>> Welcome to the Special Cube conversation. We're here in Palo Alto, California Cube headquarters. I'm John for a host of the Cube were Mohit parents, founder and CEO of Cohesive Serial entrepreneur. Successful Distribute computing, phD, computer science. Welcome to the Cube. Thanks for having me here. So thanks for coming. You guys been very successful. You found the couple in twenty thirteen. Great traction. Great success, Great technology. What's the vision of Cohee City? >> Let me first start by describing the problem. And then I'll go under describing the vision. The problem in a nutshell, is ah what we call mass data fragmentation. It refers to the fact that everything sets in silos, whether it's the sender or whether it's the cloud All our data sets in silos in appliances. Just expect all across the whole universe. And our vision is to basically consolidate that onto one platform I The easiest way to understand our vision is to look at it. What a smartphone that in the consumer space before the smartphone came the all used to carry multiple devices, right? Phone, music, player, camera, and so on. So forth. Uh, the smartphone came. It put all of those on one platform gave us a single U I to manage it all, um, gave us the notion ofthe marketplace from where we could download maps and run on this platform and gave us machine learning. Our vision is something very similar for the world of leader in the world. That data is the most valuable resource today in the world, much more so than oil. And he had the infrastructure. Where we put that data is very fragmented. Let's look at the ladies under backups is one silo probably bought from different renders test. And there was another side of analytics is another one five chairs and object storage is another one. Our vision is is to put all of that on one platform, make it very simple. Make that platform span the data center and the cloud manager using one us bring machine learning concepts to it and at a market place from where people >> could, you know, the smart phones. A good analogy. I like that because you had a market where they made devices to make phone calls and then text messaging. Beak was like killer half of the time. But having the computer enable the whole new class of services functionality, usability and capability and that that iPhone was a seminal moment There. You see, the same thing in Tech right now with Cloud Cloud has changed again. Seeing cloud be successful. Scale is a huge thing. So functionality, new kinds of functionality and large scales with cloud computing has proven. And APS have come around that. So I gotta ask you, you know, backup has been in category that has been dominated. Public offerings dated domain, but the list is endless of great companies built great backup solutions or a K phones. And I think that's what you're getting at the phones is the backup. You guys are building new functions. I want to explain the reels, um, capabilities that's going to come out of the data because if you have data being backed up, you're touching the data. So if you built a platform for scale, it seems that you guys have talked about that product. What is the unique thinking behind it? How did you come to it? And what are some of the examples? >> Yeah, so let's start one step at a time. So, uh, even though it's a platform that can do multiple things just like the smartphone had to be a great phone to begin with. This is a great backup product to begin with. And once we've solved the back a problem for the customer, then we encouraged them to do more on this may be to file shares, baby to object storage. Maybe start using the clouds and sunset. The next thing you'LL say that. Imagine you will work on that data. So you've ingested some data using backups and you want to get some insights from that data today? What you're forced to do is you probably have to copy that data out into another side of creating one more fragment. One more copy of the data. Why not move APS to the data? But other than dated adapts. So our whole concept is that take this platform and take whatever happened. You wanted to run outside off this just running on this platform and thereby you're moving APS to the data. Not the dinner, perhaps moving their eyes. Heart. It is. Ah, is big moving abscess. Easy. So and that's what the hell is this about On That's the platform. That's the capability of the platform. It's a distributed platform. Let's you're on APS close to where that it is. That's the underlying a lot of >> people say I remember we're going back a couple years now talking about Cloud or once I want to be like Google. I want to be like Amazon because they were offering at large scale using open source software. You can. You were lead engineer on Google file system, so you know a lot about scale. But a lot of people wanted the scale and functionality of Google, but they wanted the ease of use of Apple. And I've heard you mentioned that when were before we came on. So this is actually an interesting dynamic. But not everyone's like, Oh, but they have now data scaling similar challenges that Google has one song or another's large scale. Talk about that dynamic because you're changing the game on backup did since you touching the data, you're going to make that more valuable beyond just backing up. And this the concept of moving absolute data talkabout this dynamic of scale, functionality and ease of use because if you're doing all the work with the data, why not extend that out? This is essentially what you're doing. Can you explain that? >> Yeah. I think about the problems that Google would have if they were dealing with lots and lots of fragments of data. If everything was studying in a different appliance, Uh, with the volume of data that day deal that they'LL just be going knots pulling their hair all day long, right? So they built a web scale system that was sort of like a single platform. I was fortunate to be part ofthe some of those technologies, like the Google file system. So they built that Web scale file system to make it look like make all of that look like one platform. And now that it was one platform, they could move the APP store. And we're basically trying tow do something similar to the realm ofthe second reader naps. Because we have lots and lots of data here today. It sets and silos be the backups or passed on diver filers, Object storage. We're gonna build one big platform that scales out in a Google like fashion which can be managed very simply, using one you Iike an apple like manageability. And with this concept, we become very similar to those hyper skill er's, and we bring some of the same innovations to people out there. I >> want to share a common e we were talking about before we came on camera. You were just preferred something. You said I'd like to solve one problem at a time and then move on. But what's interesting here? Competitive strategy wise, you're solving the backup problem. But why you got your hands on the data? You're actually going to re imagine the usability of that data. So you're essentially adding value to a basic function back up, putting a platform around and extending that out, perhaps to come to it. And it's kind >> of a >> land grab that's working. This is a unique It's a different way to think about, Is that right? >> So I like to say that we like the master's off one trade at a time, nor Jack of all trades, uh, and that first trade for us that we would be masters off his backups once we're happy there. Then we can go on and focus on, you know, maybe filers or object storage. And this is how we build the platform right eye. I always say that when you architect a system, you have to think about all this from day one. You can't incrementally at patches and expect the system to grow right. I sometimes draw an analogy between why Google won the war against Yahoo. Google, Tara, Phil are all as a platform there. Thought about all the use cases they'd be, you know, putting on the platform. Yeah, who just build something that was good for search. Didn't think beyond that. That's why they you know about a bunch of naps. And >> that's where they saw it and thought of >> the Google file system and then YouTube on top and Gmail on top and blah, blah, blah. No. So I was the same approach. We've talked about the problem and the problem off. The problem We want to address mastered a recommendation up front, and our system has bean architected to solve that. Even if we start by being masters of backups first, the system has been architected tto do way more than that. >> So it be safe to say that cohesive from a software core competency standpoint is distributed computing core competence or disputed systems large scale from a computer science, you know standpoint and then data. So expertise are those two is intact. >> Yes. Oh, distributed computing and distributed file systems. Those would be there to core competencies. But then again, depending on like whether it's backups or its testing, that their competences of within those domains. >> So I want to get into the private tech. First of all, thanks for saying you have responded to that. The product text. Phenomenal. You have platform can do multiple things. I want to talk about span F S on Spann Os. You have some news. You've got something share on overview of what that is and what the new news is. >> So when you're trying to control on manage of lots and lots of data, you better have a distributed file system. So we built one, and we call it Spanish Fast. The name comes from the fact that it's supposed to span nodes in the very center that's supposed to span multiple kinds of storage in the data center. It's supposed to span the data center and your multi cloud environment, their hands the names pan a fast, But since we were building it like a platform, that's not just there for your data. It also runs apse on top off this platform. Uh, the span of fast is not enough. It becomes full scale us, if you may want to call it. What? So where's it has a file system and it has the ability to run laps on the file system, and the same ability was built here. And the name's patter well, so we can store data, but we can also naps close to that >> and with multi cloud on the horizon are actually president today. A lot of people use multiple clouds, and certainly Salesforce's considered cloud you got Amazon. So especially this moment clouds of existing today in the Enterprise, the coordinated all but hybrid and and these things they're going on. Premise. It's cloud operations. This becomes an important part of the distributed environments that need to be managed. Talk about the impact of multi cloud in today's world because it's a systems thinking. You gotta think about it from day one, which is kind of today. I got on premise. I got multiple clouds out there, and some clouds or great, depending on the workload, picked the cloud for the workload. I'm a big believer in that. Your thoughts, though, on as people tried to get their arms around this and make it, you know, one environment with a lot of decoupled elements that are highly cohesive. Talk about that dynamic. >> Yeah. So Cloud is a very, um, nice entrant into the infrastructure world. It provides a lot ofthe functionality, but it doesn't quite solve that problem off massive fragmentation. When you put your dinner in the cloud, it's still fragmented. And when you're dealing with, often our customers are big. Customers are dealing with multiple clouds and the data centers, and they have dedicated people trying to move data and applications between them. That's the problem that Cohee City can actually solve very well, because we're building a platform that spans all this. Um, all of that becomes underlying infrastructure that we use. And now through us, they can easily move APS. They could easily move data. They can access the data anywhere. That's the value we been to them. We have a customer here in California, and that was spending, uh, hundred twenty thousand dollars per month. It's a new company, uh, one hundred one hundred twenty thousand dollars per month on the eight of us both after they consolidated that stuff threw us in the cloud, their ability used to seventeen thousand dollars per month. That's the kind of value we can bring. The customers >> well, the Amazon Dana. It's interesting cause you got storage and you got E C two of the compute you need compute to manage towards so against. Not just storage. That's the cost. It's it's data is driving the economics. That's where you're getting it. >> Yeah, So I think data and storage and compute go together as I'm a big fan off hyper convergence, which me, along with the rest of my team Edna tonics. And Monday it's gonna doing multiple things on the side I'm back from. And you can't do that without storage and compute both working in tandem >> so consolidating with cohesive because I'll be using cohesive, he allows the better management lower costs on Amazon. >> That's right. That's right, because we store the data efficiently on Amazon, cutting the costs, and then you can run your raps on top. You don't have to copy out the data toe, run your wraps, you can actually land on the platform and all that saves costs. >> That's a great tidbit. Notes no to the audience out there. Great to tip their pro tip. Talk about the announcement you have now have APS coming out. You got three native cohesively absence. My word. I don't know. You guys call it Think Caps is going to the Alps and then for third party application developers. So again, this kind of teases out there beyond backup story, which is platform. What of the apse, Where this come from? What? Some of the reasons why they're being built. Can you share specifics on that news? >> This goes back to our analogy to a smartphone on one of the innovations the smartphone, brother. The world was the notion of a marketplace. You could go to the marketplace and down wrap. Some of the gaps are from the vendor who built the smartphone. Some of them are from third parties. So we are. And when the first iphone came out that I had basically five straight and then now there are millions of them. So what we have seeded the system with is we have, ah, a couple ofthe third party apse for in particular one a splunk that runs on the platform with in a container. One is from a company called Menace. One is actually two laps are anti virus absent. One vendor is scented. One when is clam? Maybe, um though that third party APS But then we've built some, um, APs from cohesively itself when his app called spotlight on the security app. One is an app called Insight searches through the data when his app called Easy Scripts allows our customers to upload scraps on drawing them from Go easy. So these are the apse that I'd be exceeded the system where were also announcing an SD came in just like your smartphone has a nasty cave. The world out there can go and use that and build ups on top if he would like people out there in the world. Third parties are partners to build ups and run on this bathroom >> so moment, what's their motivation behind the app system or functionality? As the demand grows, functionalities needed. So I'll see platforms should be enabling, so I get why APS could build on platforms. But what was the motivation that around the apse now just l of evolution capabilities? What's the thoughts >> It goes back to our philosophy that if you need to do something, you shouldn't buy one more silo to do it. You should be able to extend your existing platform and then do stuff. That's what your smartphone does. Uh, basically, even you, by your smartphone, it can be a phone, and I'm number for the things. But then you extended the functionality of that by downloading maps. It's the same motivation, you know, extend the abilities of this platform. Just download maps and then extended right. >> Give the value proposition pitch for the developers out there. Why would they want to develop on? Complicity is it is a certain kind of developer. What's the makeup of the target audience? Who would build on obesity? >> So all kinds of people we expect to build on this platform. So the value for our customers, for instance, now rather than, uh, copying the data out of this platform onto one more silo and that's very expensive, they can actually build a nap that runs on this platform so that they don't have to move the data around, and it's very, very simple. That's the value for our customers. For the developers out there. Uh, it's the same value that they get when they build an app on a smart phone. Uh, they building up some cash, but out there can download that app and the APP and then pay that developer some money so they don't have to build the whole company or the whole thing. Now they can build a nap that runs on cohesive. It's really simple for them. They get a cut of whatever the customer pays, so there's value all around. It's a ven ven for everyone >> it's not. And it's good business model, too good community going to get an ecosystem developing its a classic growth growth opportunity for you guys. Congratulate. So what a business you guys have talked about a couple quarters ago Publicly, about two million to million dollars run rate. Give us the update on the business in terms of growth. Employee headcount. Key milestones. Can you share? Seok was empty, >> so you know the momentum is phenomenal. We're very flattered by the fact that despite the fact that we're a young company we've been selling for more than three years, of seventy percent of our customers are enterprise customers. The big guys with lots and lots of data. Uh, some of the biggest banks in the world now use us. Some of the biggest credit card companies in the world use us. Uh, a lot of the secret of federal agencies. You, us? Um, uh, some of the public customers I convention Hyatt uses us. Ah, big financial. Northern Trust uses us the famous. Uh uh, you know, food chain. Wendy's uses us. So those are the names I can I can mention that are actually using and benefiting from cohesive. Um, so lots of lots of great stuff. Um, we had three hundred percent year over year growth in revenue. Our head count, actually, er this week crossed one thousand people. So we spoke to our chief people. Officer. We should mention our one thousand employees in a special way. So all that great stuff is happening. >> It's like walking through the door. All the bills go office because you guys were two hundred last year. About this time >> when you get back, we are about to enter. People's a factor of five growth and about one years phenomenal had come growth. >> Well, that's massive growth. How big is this guy's a real state growing and buy more office space. >> Yeah, well, uh, they're headquartered in a building and son who's a downtown. We start, but we got it. That building about when you're back, we only had two floors were really expanded toe like five floors now and looking toe, you know, rent more. We've also expanded to other locations. Geographically, we now have an office and rally. We have ah, uh in office and cork in Ireland. We already had an office in Bangalore. We setting one up in pony. We're setting one up in Toronto, So lots and lots of expansion worldwide. Not >> really looking good as well. I mean, let's think about the economics. >> So this is the time they're being in mustard and growth. That's looking phenomenal on DH. There's a path to profitability. Um uh, it all depends on you know, our economics and what the board decides on how and when we wanna charge towards profitability, we can get there. It's looks easy, but I think it's our productive ity off our sales reps looks phenomenal. On average, productively is very high, which basically means that you know, we can get to profitability fairly quickly. If you want. >> We're going to say, very impressed with the growth and impressed that you go out on the road, talk to customers closing business. That's sign of a great CEO. Always make sure the customers are happy. >> Um, eventually, that sort of companies about a happy employees and be happy customers. Uh, and my job is to see you is to make sure what happened >> before we get in Some of the questions I have from the community. I prepare because people want knew you were coming on. I want to ask you about entrepreneurship in your journey. You've had quite the career Google image in that nutanix. And now here, >> Look at look at >> today's environment. I mean, it was a lot of talk about how entrepreneurship changed and starting a company, you know, you got a rocket ship, so you had a lot people coming on Now from the your journey you're on now. But a lot of other offers out there right now, kind of like looking transition. People say tech is bad, not good for society. Seen bad, negative press in their entrepreneurship is a great opportunity right now in tech. What's your thoughts on the current landscape and opportunities for, you know, folks out there building new things and going in solving a problem from old market and reimagining it for the new. Because a lot of new going on seeing a new sea change with cloud. And on premise, >> I would say, Um, this is probably the best time to do a company then ever in the past because technology is there to help people. Young entrepreneurs. Uh, there's plenty of money to be raised from the sea. Species are very happy to be helping. End of news a couple of pieces of caution that I wantto give to would be entrepreneurs. Uh, number one. Don't be in a hurry. Learn their hopes of doing a company first. Ah, before jumping and doing it because often I find that they burn their fingers and then they don't want to do a company again. First, go to a good company, learn the ropes of playing a company, and then do a company. That's number one number two. Uh, I would like to incorrigible and avenues to think about their ideas in the context. Off the following two thoughts one is, uh, the company needs to have a great entry point. That's how the company takes off. But then it also needs to have a bigger vision to look up to. And I often find that company's lack one or the other of these, Uh, and that's why they eventually fail or they never take off the ground. In our case, the entry point was backups, and the big vision is the consolidation off seconded and haps that I spoke about, Ah, one or the other if they're missing, it's not >> an extensive abilities key there, too. You get the beachheads real specific seconds, and then you see you point >> out of a vision. That's >> what broader beachhead without trying to take it all too fast or not knowing where to lay. That's gonna much the analogy. >> That's what I say. I beat master of one traitor, go ahead in the beachhead and then expanded the bigger >> and by the way, that's a classic proven way to do it. So, you know, just stay with what works, All right, let's get to the questions from the community. A lot of people wanted to ask your first question moment. You've a great perspective on the difference between hyper scale on enterprise worlds Is the enterprise still ten plus years behind the Giants in Tech? And how have you helped bring hyper scale thinking to the enterprise architecture? >> Um, the enterprise is, actually, surprisingly is getting closer and closer. Uh, with all the great technologies available, hyper convergence has bean. One of those technologies that has made hyper convergence combined with upscale, uh, is one of those technologies that has brought the enterprise were very close to the hyper scholars. Now they can buy products that are hyper energy that scale out in a group like fashion, and they can get some of the same benefits that the hyper scholars have enjoyed over the years, eh? So I won't say they have that far behind anymore. They're catching up, and they're catching up. Eyes >> used to be a few years ago, you could look at saying old relic, you know, modern cloud >> the and and the companies that I have found it have. I'm very flattered to say that have gonna, uh, hasten that journey. Uh, happy convergence. And he's even solving this problem of massive fragmentation. The hyper skills have kind of, you know, already solved that problem. They have massive, upscale systems that don't deliver data fragmentation. It's one platform, and you're gonna bring that value to the world through cohesive, >> great, great success. Okay, second question. There's a ton of money pouring into the data protection space again, a category that's there's a card in magic water for that. But again, you start Cummings that don't have magic watches because it's new. Why is this money pouring into space? Why now? >> Number one dealer is exploding. There's lots of lots of data. Ah, bulk off the data sets in what we call second story. It comes to it through back up straight. Your your production stuff has some production data, but eventually that data. Nobody wants to believe that they would keep it in there for at least six seven years, maybe forever. All dated, it comes to backups. The opportunity that people have seen is that they can actually now doom or with that data. It's not just dumb waiter sitting there, so it's not just data protection. It becomes more of a data management and you do data management through APS. That's what cohesion is exploding. We get the data onto a platform through backups, but then we expand into arrest of the vision and Kendra naps to extract value from the dealer right? That's why the money is coming. >> Well, you just answer the next question, which is, you know, why cohesively wind now the space is crowded, a lot of competition, So I'll just move on Ransomware, what's going on there and what's unique about Kohi City and what do you bring to the table with respect to Ransomware. >> So Ransomware is, uh, uh, something that we now live in. Its every enterprise is at risk, uh, being affected by ransomware. So what we have announced recently eating a month back, we announced our ransomware support. Uh, we can offer not just the detection, but also a number for the things we can detect Ransom where we can allow our customers toe apply fixes. When When that happens, we really allow things to be recovered once ransomware happened. So it's built into our data protection environment, right? That's how customers like it. So it adds value to the data that they already have. It's not just a dumb backup. >> And with all the third party and S t k stuff happening potential extensive bility on that core, >> that's right. Now we can have apse that can detect more round somewhere by virtue of the fact that we can support running absolutes to data. Some of those APs could be Andy dancing, perhaps help protect the data, do some custom stuff. Once said handsome, it is detected. All that becomes possible >> last question from the crowd here, the community multi cloud. Everyone's going up to the space. What is multi cloud data protection really about? And why cohesive? Isn't this just really a multi cloud vendor? Khun, do it all mean a lot of people saying they're multi cloud vendors. Y you what is multi cloud data protection all about? >> So, you know, big enterprise customers probably have a foot in every cloud, and they call it a multi cloud infrastructure. And if they want to protect the data and forced me, the data is very fragmented. So they need a backup solution for one for every cloud that's roughly multi cloudy. The production. Uh, we're cool. Here's the adds value. It's building one platform that spans your multicolored environment. So one platform can now take care ofall that those backups eso it really simplifies the job off doing backups or data protection in a multicolored environment. And that's where the Queen's devalue comes in. >> Well, congratulations. Final question for this interview. How would you summarize the state of cohesive the right now? Thousand employees growth on the customer traction side and revenue business funding. Males look good economic with a platform, certainly software margins looking very good growth. What's it all about right now? Culture value, proposition don't. >> It's kind of like a rocket ship, and we're just hanging on. But it's Ah, I think that focus is, um, when you grow this fast, uh, the challenge becomes, uh, keeping your culture intact and we tryto put a lot of effort on our culture. Our core values are cultural guidelines were fanatics about that. So we want everyone to feel that they're coming in and this is home away from home, and they treat others to make them feel it's home away from home. We're trying to build a family here, so there's a lot of emphasis on that. But at the same time, you know, we all work hard and let the company >> and the new ecosystem opportunity for you is looking really good because if he zaps takeoff, certainly the cohesively APS. And now you got third party with an S t. K. This is potentially a game changer for you as a company to a CZ Wells, you have product company. Software company makes a lot of scared, but now you're gonna be bringing developers and impact there. >> The impact, the talk, leadership impact. Uh, you know, I'm personally very fun off er you know I do these companies because I want to change the world. I won't change the way the world thinks this is the way I think. And if I can help the world think in this fashion contributed something to the world. And so that's the excitement that sort of mission is. Team is excited about that. It's just >> we got a great mind phD in computer science and two ships systems entrepreneur that thinks up new things that disrupt the status quo. And the old guard certainly track record their congratulations. Know what? Thanks for coming on The Cube. This's the Cube conversation here. Palo Alto. I'm John every year. Thanks for watching. What?

(electronic dance music) (Intel Jingle) (ethereal electronic dance music) ♪ Okay ♪ (upbeat techno dance music) ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Yeah everybody get loose yeah ♪ ♪ Yeah ♪ ♪ Ye-yeah yeah ♪ ♪ Yeah yeah ♪ ♪ Everybody everybody yeah ♪ ♪ Whoo whoo ♪ ♪ Whoo whoo ♪ ♪ Whoo yeah ♪ ♪ Everybody get loose whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ >> As a courtesy to the presenters and those around you, please silence all mobile devices, thank you. (electronic dance music) ♪ Everybody get loose ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ (upbeat salsa music) ♪ Ha ha ha ♪ ♪ Ah ♪ ♪ Ha ha ha ♪ ♪ So happy ♪ ♪ Whoo whoo ♪ (female singer scatting) >> Ladies and gentlemen, please take your seats. Our program will begin momentarily. ♪ Hey ♪ (female singer scatting) (male singer scatting) ♪ Hey ♪ ♪ Whoo ♪ (female singer scatting) (electronic dance music) ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ Red don't go ♪ ♪ All hands are in don't go ♪ ♪ In don't go ♪ ♪ Oh red go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are red don't go ♪ ♪ All hands are in red red red red ♪ ♪ All hands are in don't go ♪ ♪ All hands are in red go ♪ >> Ladies and gentlemen, there are available seats. Towards house left, house left there are available seats. If you are please standing, we ask that you please take an available seat. We will begin momentarily, thank you. ♪ Let go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ (upbeat electronic dance music) ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ I live ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Hey ♪ ♪ Yeah ♪ ♪ Oh ♪ ♪ Ah ♪ ♪ Ah ah ah ah ah ah ♪ ♪ Just make me ♪ ♪ Just make me ♪ (bouncy techno music) >> Ladies and gentlemen, once again we ask that you please take the available seats to your left, house left, there are many available seats. If you are standing, please make your way there. The program will begin momentarily, thank you. Good morning! This is Lenovo Transform 2.0! (keyboard clicks) >> Progress. Why do we always talk about it in the future? When will it finally get here? We don't progress when it's ready for us. We need it when we're ready, and we're ready now. Our hospitals and their patients need it now, our businesses and their customers need it now, our cities and their citizens need it now. To deliver intelligent transformation, we need to build it into the products and solutions we make every day. At Lenovo, we're designing the systems to fight disease, power businesses, and help you reach more customers, end-to-end security solutions to protect your data and your companies reputation. We're making IT departments more agile and cost efficient. We're revolutionizing how kids learn with VR. We're designing smart devices and software that transform the way you collaborate, because technology shouldn't just power industries, it should power people. While everybody else is talking about tomorrow, we'll keep building today, because the progress we need can't wait for the future. >> Please welcome to the stage Lenovo's Rod Lappen! (electronic dance music) (audience applauding) >> Alright. Good morning everyone! >> Good morning. >> Ooh, that was pretty good actually, I'll give it one more shot. Good morning everyone! >> Good morning! >> Oh, that's much better! Hope everyone's had a great morning. Welcome very much to the second Lenovo Transform event here in New York. I think when I got up just now on the steps I realized there's probably one thing in common all of us have in this room including myself which is, absolutely no one has a clue what I'm going to say today. So, I'm hoping very much that we get through this thing very quickly and crisply. I love this town, love New York, and you're going to hear us talk a little bit about New York as we get through here, but just before we get started I'm going to ask anyone who's standing up the back, there are plenty of seats down here, and down here on the right hand side, I think he called it house left is the professional way of calling it, but these steps to my right, your left, get up here, let's get you all seated down so that you can actually sit down during the keynote session for us. Last year we had our very first Lenovo Transform. We had about 400 people. It was here in New York, fantastic event, today, over 1,000 people. We have over 62 different technology demonstrations and about 15 breakout sessions, which I'll talk you through a little bit later on as well, so it's a much bigger event. Next year we're definitely going to be shooting for over 2,000 people as Lenovo really transforms and starts to address a lot of the technology that our commercial customers are really looking for. We were however hampered last year by a storm, I don't know if those of you who were with us last year will remember, we had a storm on the evening before Transform last year in New York, and obviously the day that it actually occurred, and we had lots of logistics. Our media people from AMIA were coming in. They took the, the plane was circling around New York for a long time, and Kamran Amini, our General Manager of our Data Center Infrastructure Group, probably one of our largest groups in the Lenovo DCG business, took 17 hours to get from Raleigh, North Carolina to New York, 17 hours, I think it takes seven or eight hours to drive. Took him 17 hours by plane to get here. And then of course this year, we have Florence. And so, obviously the hurricane Florence down there in the Carolinas right now, we tried to help, but still Kamran has made it today. Unfortunately, very tragically, we were hoping he wouldn't, but he's here today to do a big presentation a little bit later on as well. However, I do want to say, obviously, Florence is a very serious tragedy and we have to take it very serious. We got, our headquarters is in Raleigh, North Carolina. While it looks like the hurricane is just missing it's heading a little bit southeast, all of our thoughts and prayers and well wishes are obviously with everyone in the Carolinas on behalf of Lenovo, everyone at our headquarters, everyone throughout the Carolinas, we want to make sure everyone stays safe and out of harm's way. We have a great mixture today in the crowd of all customers, partners, industry analysts, media, as well as our financial analysts from all around the world. There's over 30 countries represented here and people who are here to listen to both YY, Kirk, and Christian Teismann speak today. And so, it's going to be a really really exciting day, and I really appreciate everyone coming in from all around the world. So, a big round of applause for everyone whose come in. (audience applauding) We have a great agenda for you today, and it starts obviously a very consistent format which worked very successful for us last year, and that's obviously our keynote. You'll hear from YY, our CEO, talk a little bit about the vision he has in the industry and how he sees Lenovo's turned the corner and really driving some great strategy to address our customer's needs. Kirk Skaugen, our Executive Vice President of DCG, will be up talking about how we've transformed the DCG business and once again are hitting record growth ratios for our DCG business. And then you'll hear from Christian Teismann, our SVP and General Manager for our commercial business, get up and talk about everything that's going on in our IDG business. There's really exciting stuff going on there and obviously ThinkPad being the cornerstone of that I'm sure he's going to talk to us about a couple surprises in that space as well. Then we've got some great breakout sessions, I mentioned before, 15 breakout sessions, so while this keynote section goes until about 11:30, once we get through that, please go over and explore, and have a look at all of the breakout sessions. We have all of our subject matter experts from both our PC, NBG, and our DCG businesses out to showcase what we're doing as an organization to better address your needs. And then obviously we have the technology pieces that I've also spoken about, 62 different technology displays there arranged from everything IoT, 5G, NFV, everything that's really cool and hot in the industry right now is going to be on display up there, and I really encourage all of you to get up there. So, I'm going to have a quick video to show you from some of the setup yesterday on a couple of the 62 technology displays we've got on up on stage. Okay let's go, so we've got a demonstrations to show you today, one of the greats one here is the one we've done with NC State, a high-performance computing artificial intelligence demonstration of fresh produce. It's about modeling the population growth of the planet, and how we're going to supply water and food as we go forward. Whoo. Oh, that is not an apple. Okay. (woman laughs) Second one over here is really, hey Jonas, how are you? Is really around virtual reality, and how we look at one of the most amazing sites we've got, as an install on our high-performance computing practice here globally. And you can see, obviously, that this is the Barcelona supercomputer, and, where else in New York can you get access to being able to see something like that so easily? Only here at Lenovo Transform. Whoo, okay. (audience applauding) So there's two examples of some of the technology. We're really encouraging everyone in the room after the keynote to flow into that space and really get engaged, and interact with a lot of the technology we've got up there. It seems I need to also do something about my fashion, I've just realized I've worn a vest two days in a row, so I've got to work on that as well. Alright so listen, the last thing on the agenda, we've gone through the breakout sessions and the demo, tonight at four o'clock, there's about 400 of you registered to be on the cruise boat with us, the doors will open behind me. the boat is literally at the pier right behind us. You need to make sure you're on the boat for 4:00 p.m. this evening. Outside of that, I want everyone to have a great time today, really enjoy the experience, make it as experiential as you possibly can, get out there and really get in and touch the technology. There's some really cool AI displays up there for us all to get involved in as well. So ladies and gentlemen, without further adieu, it gives me great pleasure to introduce to you a lover of tennis, as some of you would've heard last year at Lenovo Transform, as well as a lover of technology, Lenovo, and of course, New York City. I am obviously very pleasured to introduce to you Yang Yuanqing, our CEO, as we like to call him, YY. (audience applauding) (upbeat funky music) >> Good morning, everyone. >> Good morning. >> Thank you Rod for that introduction. Welcome to New York City. So, this is the second year in a row we host our Transform event here, because New York is indeed one of the most transformative cities in the world. Last year on this stage, I spoke about the Fourth Industrial Revolution, and our vision around the intelligent transformation, how it would fundamentally change the nature of business and the customer relationships. And why preparing for this transformation is the key for the future of our company. And in the last year I can assure you, we were being very busy doing just that, from searching and bringing global talents around the world to the way we think about every product and every investment we make. I was here in New York just a month ago to announce our fiscal year Q1 earnings, which was a good day for us. I think now the world believes it when we say Lenovo has truly turned the corner to a new phase of growth and a new phase of acceleration in executing the transformation strategy. That's clear to me is that the last few years of a purposeful disruption at Lenovo have led us to a point where we can now claim leadership of the coming intelligent transformation. People often asked me, what is the intelligent transformation? I was saying this way. This is the unlimited potential of the Fourth Industrial Revolution driven by artificial intelligence being realized, ordering a pizza through our speaker, and locking the door with a look, letting your car drive itself back to your home. This indeed reflect the power of AI, but it just the surface of it. The true impact of AI will not only make our homes smarter and offices more efficient, but we are also completely transformed every value chip in every industry. However, to realize these amazing possibilities, we will need a structure built around the key components, and one that touches every part of all our lives. First of all, explosions in new technology always lead to new structures. This has happened many times before. In the early 20th century, thousands of companies provided a telephone service. City streets across the US looked like this, and now bundles of a microscopic fiber running from city to city bring the world closer together. Here's what a driving was like in the US, up until 1950s. Good luck finding your way. (audience laughs) And today, millions of vehicles are organized and routed daily, making the world more efficient. Structure is vital, from fiber cables and the interstate highways, to our cells bounded together to create humans. Thankfully the structure for intelligent transformation has emerged, and it is just as revolutionary. What does this new structure look like? We believe there are three key building blocks, data, computing power, and algorithms. Ever wondered what is it behind intelligent transformation? What is fueling this miracle of human possibility? Data. As the Internet becomes ubiquitous, not only PCs, mobile phones, have come online and been generating data. Today it is the cameras in this room, the climate controls in our offices, or the smart displays in our kitchens at home. The number of smart devices worldwide will reach over 20 billion in 2020, more than double the number in 2017. These devices and the sensors are connected and generating massive amount of data. By 2020, the amount of data generated will be 57 times more than all the grains of sand on Earth. This data will not only make devices smarter, but will also fuel the intelligence of our homes, offices, and entire industries. Then we need engines to turn the fuel into power, and the engine is actually the computing power. Last but not least the advanced algorithms combined with Big Data technology and industry know how will form vertical industrial intelligence and produce valuable insights for every value chain in every industry. When these three building blocks all come together, it will change the world. At Lenovo, we have each of these elements of intelligent transformations in a single place. We have built our business around the new structure of intelligent transformation, especially with mobile and the data center now firmly part of our business. I'm often asked why did you acquire these businesses? Why has a Lenovo gone into so many fields? People ask the same questions of the companies that become the leaders of the information technology revolution, or the third industrial transformation. They were the companies that saw the future and what the future required, and I believe Lenovo is the company today. From largest portfolio of devices in the world, leadership in the data center field, to the algorithm-powered intelligent vertical solutions, and not to mention the strong partnership Lenovo has built over decades. We are the only company that can unify all these essential assets and deliver end to end solutions. Let's look at each part. We now understand the important importance data plays as fuel in intelligent transformation. Hundreds of billions of devices and smart IoTs in the world are generating better and powering the intelligence. Who makes these devices in large volume and variety? Who puts these devices into people's home, offices, manufacturing lines, and in their hands? Lenovo definitely has the front row seats here. We are number one in PCs and tablets. We also produces smart phones, smart speakers, smart displays. AR/VR headsets, as well as commercial IoTs. All of these smart devices, or smart IoTs are linked to each other and to the cloud. In fact, we have more than 20 manufacturing facilities in China, US, Brazil, Japan, India, Mexico, Germany, and more, producing various devices around the clock. We actually make four devices every second, and 37 motherboards every minute. So, this factory located in my hometown, Hu-fi, China, is actually the largest laptop factory in the world, with more than three million square feet. So, this is as big as 42 soccer fields. Our scale and the larger portfolio of devices gives us access to massive amount of data, which very few companies can say. So, why is the ability to scale so critical? Let's look again at our example from before. The early days of telephone, dozens of service providers but only a few companies could survive consolidation and become the leader. The same was true for the third Industrial Revolution. Only a few companies could scale, only a few could survive to lead. Now the building blocks of the next revolution are locking into place. The (mumbles) will go to those who can operate at the scale. So, who could foresee the total integration of cloud, network, and the device, need to deliver intelligent transformation. Lenovo is that company. We are ready to scale. Next, our computing power. Computing power is provided in two ways. On one hand, the modern supercomputers are providing the brute force to quickly analyze the massive data like never before. On the other hand the cloud computing data centers with the server storage networking capabilities, and any computing IoT's, gateways, and miniservers are making computing available everywhere. Did you know, Lenovo is number one provider of super computers worldwide? 170 of the top 500 supercomputers, run on Lenovo. We hold 89 World Records in key workloads. We are number one in x86 server reliability for five years running, according to ITIC. a respected provider of industry research. We are also the fastest growing provider of hyperscale public cloud, hyper-converged and aggressively growing in edge computing. cur-ges target, we are expand on this point soon. And finally to run these individual nodes into our symphony, we must transform the data and utilize the computing power with advanced algorithms. Manufactured, industry maintenance, healthcare, education, retail, and more, so many industries are on the edge of intelligent transformation to improve efficiency and provide the better products and services. We are creating advanced algorithms and the big data tools combined with industry know-how to provide intelligent vertical solutions for several industries. In fact, we studied at Lenovo first. Our IT and research teams partnered with our global supply chain to develop an AI that improved our demand forecasting accuracy. Beyond managing our own supply chain we have offered our deep learning supply focused solution to other manufacturing companies to improve their efficiency. In the best case, we have improved the demand, focused the accuracy by 30 points to nearly 90 percent, for Baosteel, the largest of steel manufacturer in China, covering the world as well. Led by Lenovo research, we launched the industry-leading commercial ready AR headset, DaystAR, partnering with companies like the ones in this room. This technology is being used to revolutionize the way companies service utility, and even our jet engines. Using our workstations, servers, and award-winning imaging processing algorithms, we have partnered with hospitals to process complex CT scan data in minutes. So, this enable the doctors to more successfully detect the tumors, and it increases the success rate of cancer diagnosis all around the world. We are also piloting our smart IoT driven warehouse solution with one of the world's largest retail companies to greatly improve the efficiency. So, the opportunities are endless. This is where Lenovo will truly shine. When we combine the industry know-how of our customers with our end-to-end technology offerings, our intelligent vertical solutions like this are growing, which Kirk and Christian will share more. Now, what will drive this transformation even faster? The speed at which our networks operate, specifically 5G. You may know that Lenovo just launched the first-ever 5G smartphone, our Moto Z3, with the new 5G Moto model. We are partnering with multiple major network providers like Verizon, China Mobile. With the 5G model scheduled to ship early next year, we will be the first company to provide a 5G mobile experience to any users, customers. This is amazing innovation. You don't have to buy a new phone, just the 5G clip on. What can I say, except wow. (audience laughs) 5G is 10 times the fast faster than 4G. Its download speed will transform how people engage with the world, driverless car, new types of smart wearables, gaming, home security, industrial intelligence, all will be transformed. Finally, accelerating with partners, as ready as we are at Lenovo, we need partners to unlock our full potential, partners here to create with us the edge of the intelligent transformation. The opportunities of intelligent transformation are too profound, the scale is too vast. No company can drive it alone fully. We are eager to collaborate with all partners that can help bring our vision to life. We are dedicated to open partnerships, dedicated to cross-border collaboration, unify the standards, share the advantage, and market the synergies. We partner with the biggest names in the industry, Intel, Microsoft, AMD, Qualcomm, Google, Amazon, and Disney. We also find and partner with the smaller innovators as well. We're building the ultimate partner experience, open, shared, collaborative, diverse. So, everything is in place for intelligent transformation on a global scale. Smart devices are everywhere, the infrastructure is in place, networks are accelerating, and the industries demand to be more intelligent, and Lenovo is at the center of it all. We are helping to drive change with the hundreds of companies, companies just like yours, every day. We are your partner for intelligent transformation. Transformation never stops. This is what you will hear from Kirk, including details about Lenovo NetApp global partnership we just announced this morning. We've made the investments in every single aspect of the technology. We have the end-to-end resources to meet your end-to-end needs. As you attend the breakout session this afternoon, I hope you see for yourself how much Lenovo has transformed as a company this past year, and how we truly are delivering a future of intelligent transformation. Now, let me invite to the stage Kirk Skaugen, our president of Data Center growth to tell you about the exciting transformation happening in the global Data C enter market. Thank you. (audience applauding) (upbeat music) >> Well, good morning. >> Good morning. >> Good morning! >> Good morning! >> Excellent, well, I'm pleased to be here this morning to talk about how we're transforming the Data Center and taking you as our customers through your own intelligent transformation journey. Last year I stood up here at Transform 1.0, and we were proud to announce the largest Data Center portfolio in Lenovo's history, so I thought I'd start today and talk about the portfolio and the progress that we've made over the last year, and the strategies that we have going forward in phase 2.0 of Lenovo's transformation to be one of the largest data center companies in the world. We had an audacious vision that we talked about last year, and that is to be the most trusted data center provider in the world, empowering customers through the new IT, intelligent transformation. And now as the world's largest supercomputer provider, giving something back to humanity, is very important this week with the hurricanes now hitting North Carolina's coast, but we take this most trusted aspect very seriously, whether it's delivering the highest quality products on time to you as customers with the highest levels of security, or whether it's how we partner with our channel partners and our suppliers each and every day. You know we're in a unique world where we're going from hundreds of millions of PCs, and then over the next 25 years to hundred billions of connected devices, so each and every one of you is going through this intelligent transformation journey, and in many aspects were very early in that cycle. And we're going to talk today about our role as the largest supercomputer provider, and how we're solving humanity's greatest challenges. Last year we talked about two special milestones, the 25th anniversary of ThinkPad, but also the 25th anniversary of Lenovo with our IBM heritage in x86 computing. I joined the workforce in 1992 out of college, and the IBM first personal server was launching at the same time with an OS2 operating system and a free mouse when you bought the server as a marketing campaign. (audience laughing) But what I want to be very clear today, is that the innovation engine is alive and well at Lenovo, and it's really built on the culture that we're building as a company. All of these awards at the bottom are things that we earned over the last year at Lenovo. As a Fortune now 240 company, larger than companies like Nike, or AMEX, or Coca-Cola. The one I'm probably most proud of is Forbes first list of the top 2,000 globally regarded companies. This was something where 15,000 respondents in 60 countries voted based on ethics, trustworthiness, social conduct, company as an employer, and the overall company performance, and Lenovo was ranked number 27 of 2000 companies by our peer group, but we also now one of-- (audience applauding) But we also got a perfect score in the LGBTQ Equality Index, exemplifying the diversity internally. We're number 82 in the top working companies for mothers, top working companies for fathers, top 100 companies for sustainability. If you saw that factory, it's filled with solar panels on the top of that. And now again, one of the top global brands in the world. So, innovation is built on a customer foundation of trust. We also said last year that we'd be crossing an amazing milestone. So we did, over the last 12 months ship our 20 millionth x86 server. So, thank you very much to our customers for this milestone. (audience applauding) So, let me recap some of the transformation elements that have happened over the last year. Last year I talked about a lot of brand confusion, because we had the ThinkServer brand from the legacy Lenovo, the System x, from IBM, we had acquired a number of networking companies, like BLADE Network Technologies, et cetera, et cetera. Over the last year we've been ramping based on two brand structures, ThinkAgile for next generation IT, and all of our software-defined infrastructure products and ThinkSystem as the world's highest performance, highest reliable x86 server brand, but for servers, for storage, and for networking. We have transformed every single aspect of the customer experience. A year and a half ago, we had four different global channel programs around the world. Typically we're about twice the mix to our channel partners of any of our competitors, so this was really important to fix. We now have a single global Channel program, and have technically certified over 11,000 partners to be technical experts on our product line to deliver better solutions to our customer base. Gardner recently recognized Lenovo as the 26th ranked supply chain in the world. And, that's a pretty big honor, when you're up there with Amazon and Walmart and others, but in tech, we now are in the top five supply chains. You saw the factory network from YY, and today we'll be talking about product shipping in more than 160 countries, and I know there's people here that I've met already this morning, from India, from South Africa, from Brazil and China. We announced new Premier Support services, enabling you to go directly to local language support in nine languages in 49 countries in the world, going directly to a native speaker level three support engineer. And today we have more than 10,000 support specialists supporting our products in over 160 countries. We've delivered three times the number of engineered solutions to deliver a solutions orientation, whether it's on HANA, or SQL Server, or Oracle, et cetera, and we've completely reengaged our system integrator channel. Last year we had the CIO of DXE on stage, and here we're talking about more than 175 percent growth through our system integrator channel in the last year alone as we've brought that back and really built strong relationships there. So, thank you very much for amazing work here on the customer experience. (audience applauding) We also transformed our leadership. We thought it was extremely important with a focus on diversity, to have diverse talent from the legacy IBM, the legacy Lenovo, but also outside the industry. We made about 19 executive changes in the DCG group. This is the most senior leadership team within DCG, all which are newly on board, either from our outside competitors mainly over the last year. About 50 percent of our executives were now hired internally, 50 percent externally, and 31 percent of those new executives are diverse, representing the diversity of our global customer base and gender. So welcome, and most of them you're going to be able to meet over here in the breakout sessions later today. (audience applauding) But some things haven't changed, they're just keeping getting better within Lenovo. So, last year I got up and said we were committed with the new ThinkSystem brand to be a world performance leader. You're going to see that we're sponsoring Ducati for MotoGP. You saw the Ferrari out there with Formula One. That's not a surprise. We want the Lenovo ThinkSystem and ThinkAgile brands to be synonymous with world record performance. So in the last year we've gone from 39 to 89 world records, and partners like Intel would tell you, we now have four times the number of world record workloads on Lenovo hardware than any other server company on the planet today, with more than 89 world records across HPC, Java, database, transaction processing, et cetera. And we're proud to have just brought on Doug Fisher from Intel Corporation who had about 10-17,000 people on any given year working for him in workload optimizations across all of our software. It's just another testament to the leadership team we're bringing in to keep focusing on world-class performance software and solutions. We also per ITIC, are the number one now in x86 server reliability five years running. So, this is a survey where CIOs are in a blind survey asked to submit their reliability of their uptime on their x86 server equipment over the last 365 days. And you can see from 2016 to 2017 the downtime, there was over four hours as noted by the 750 CXOs in more than 20 countries is about one percent for the Lenovo products, and is getting worse generation from generation as we went from Broadwell to Pearlie. So we're taking our reliability, which was really paramount in the IBM System X heritage, and ensuring that we don't just recognize high performance but we recognize the highest level of reliability for mission-critical workloads. And what that translates into is that we at once again have been ranked number one in customer satisfaction from you our customers in 19 of 22 attributes, in North America in 18 of 22. This is a survey by TVR across hundreds of customers of us and our top competitors. This is the ninth consecutive study that we've been ranked number one in customer satisfaction, so we're taking this extremely seriously, and in fact YY now has increased the compensation of every single Lenovo employee. Up to 40 percent of their compensation bonus this year is going to be based on customer metrics like quality, order to ship, and things of this nature. So, we're really putting every employee focused on customer centricity this year. So, the summary on Transform 1.0 is that every aspect of what you knew about Lenovo's data center group has transformed, from the culture to the branding to dedicated sales and marketing, supply chain and quality groups, to a worldwide channel program and certifications, to new system integrator relationships, and to the new leadership team. So, rather than me just talk about it, I thought I'd share a quick video about what we've done over the last year, if you could run the video please. Turn around for a second. (epic music) (audience applauds) Okay. So, thank you to all our customers that allowed us to publicly display their logos in that video. So, what that means for you as investors, and for the investor community out there is, that our customers have responded, that this year Gardner just published that we are the fastest growing server company in the top 10, with 39 percent growth quarter-on-quarter, and 49 percent growth year-on-year. If you look at the progress we've made since the transformation the last three quarters publicly, we've grown 17 percent, then 44 percent, then 68 percent year on year in revenue, and I can tell you this quarter I'm as confident as ever in the financials around the DCG group, and it hasn't been in one area. You're going to see breakout sessions from hyperscale, software-defined, and flash, which are all growing more than a 100 percent year-on-year, supercomputing which we'll talk about shortly, now number one, and then ultimately from profitability, delivering five consecutive quarters of pre-tax profit increase, so I think, thank you very much to the customer base who's been working with us through this transformation journey. So, you're here to really hear what's next on 2.0, and that's what I'm excited to talk about today. Last year I came up with an audacious goal that we would become the largest supercomputer company on the planet by 2020, and this graph represents since the acquisition of the IBM System x business how far we were behind being the number one supercomputer. When we started we were 182 positions behind, even with the acquisition for example of SGI from HP, we've now accomplished our goal actually two years ahead of time. We're now the largest supercomputer company in the world. About one in every four supercomputers, 117 on the list, are now Lenovo computers, and you saw in the video where the universities are said, but I think what I'm most proud of is when your customers rank you as the best. So the awards at the bottom here, are actually Readers Choice from the last International Supercomputing Show where the scientific researchers on these computers ranked their vendors, and we were actually rated the number one server technology in supercomputing with our ThinkSystem SD530, and the number one storage technology with our ThinkSystem DSS-G, but more importantly what we're doing with the technology. You're going to see we won best in life sciences, best in data analytics, and best in collaboration as well, so you're going to see all of that in our breakout sessions. As you saw in the video now, 17 of the top 25 research institutions in the world are now running Lenovo supercomputers. And again coming from Raleigh and watching that hurricane come across the Atlantic, there are eight supercomputers crunching all of those models you see from Germany to Malaysia to Canada, and we're happy to have a SciNet from University of Toronto here with us in our breakout session to talk about what they're doing on climate modeling as well. But we're not stopping there. We just announced our new Neptune warm water cooling technology, which won the International Supercomputing Vendor Showdown, the first time we've won that best of show in 25 years, and we've now installed this. We're building out LRZ in Germany, the first ever warm water cooling in Peking University, at the India Space Propulsion Laboratory, at the Malaysian Weather and Meteorological Society, at Uninett, at the largest supercomputer in Norway, T-Systems, University of Birmingham. This is truly amazing technology where we're actually using water to cool the machine to deliver a significantly more energy-efficient computer. Super important, when we're looking at global warming and some of the electric bills can be millions of dollars just for one computer, and could actually power a small city just with the technology from the computer. We've built AI centers now in Morrisville, Stuttgart, Taipei, and Beijing, where customers can bring their AI workloads in with experts from Intel, from Nvidia, from our FPGA partners, to work on their workloads, and how they can best implement artificial intelligence. And we also this year launched LICO which is Lenovo Intelligent Compute Orchestrator software, and it's a software solution that simplifies the management and use of distributed clusters in both HPC and AI model development. So, what it enables you to do is take a single cluster, and run both HPC and AI workloads on it simultaneously, delivering better TCO for your environment, so check out LICO as well. A lot of the customers here and Wall Street are very excited and using it already. And we talked about solving humanity's greatest challenges. In the breakout session, you're going to have a virtual reality experience where you're going to be able to walk through what as was just ranked the world's most beautiful data center, the Barcelona Supercomputer. So, you can actually walk through one of the largest supercomputers in the world from Barcelona. You can see the work we're doing with NC State where we're going to have to grow the food supply of the world by 50 percent, and there's not enough fresh water in the world in the right places to actually make all those crops grow between now and 2055, so you're going to see the progression of how they're mapping the entire globe and the water around the world, how to build out the crop population over time using AI. You're going to see our work with Vestas is this largest supercomputer provider in the wind turbine areas, how they're working on wind energy, and then with University College London, how they're working on some of the toughest particle physics calculations in the world. So again, lots of opportunity here. Take advantage of it in the breakout sessions. Okay, let me transition to hyperscale. So in hyperscale now, we have completely transformed our business model. We are now powering six of the top 10 hyperscalers in the world, which is a significant difference from where we were two years ago. And the reason we're doing that, is we've coined a term called ODM+. We believe that hyperscalers want more procurement power than an ODM, and Lenovo is doing about $18 billion of procurement a year. They want a broader global supply chain that they can get from a local system integrator. We're more than 160 countries around the world, but they want the same world-class quality and reliability like they get from an MNC. So, what we're doing now is instead of just taking off the shelf motherboards from somewhere, we're starting with a blank sheet of paper, we're working with the customer base on customized SKUs and you can see we already are developing 33 custom solutions for the largest hyperscalers in the world. And then we're not just running notebooks through this factory where YY said, we're running 37 notebook boards a minute, we're now putting in tens and tens and tens of thousands of server board capacity per month into this same factory, so absolutely we can compete with the most aggressive ODM's in the world, but it's not just putting these things in in the motherboard side, we're also building out these systems all around the world, India, Brazil, Hungary, Mexico, China. This is an example of a new hyperscale customer we've had this last year, 34,000 servers we delivered in the first six months. The next 34,000 servers we delivered in 68 days. The next 34,000 servers we delivered in 35 days, with more than 99 percent on-time delivery to 35 data centers in 14 countries as diverse as South Africa, India, China, Brazil, et cetera. And I'm really ashamed to say it was 99.3, because we did have a forklift driver who rammed their forklift right through the middle of the one of the server racks. (audience laughing) At JFK Airport that we had to respond to, but I think this gives you a perspective of what it is to be a top five global supply chain and technology. So last year, I said we would invest significantly in IP, in joint ventures, and M and A to compete in software defined, in networking, and in storage, so I wanted to give you an update on that as well. Our newest software-defined partnership is with Cloudistics, enabling a fully composable cloud infrastructure. It's an exclusive agreement, you can see them here. I think Nag, our founder, is going to be here today, with a significant Lenovo investment in the company. So, this new ThinkAgile CP series delivers the simplicity of the public cloud, on-premise with exceptional support and a marketplace of essential enterprise applications all with a single click deployment. So simply put, we're delivering a private cloud with a premium experience. It's simple in that you need no specialists to deploy it. An IT generalist can set it up and manage it. It's agile in that you can provision dozens of workloads in minutes, and it's transformative in that you get all of the goodness of public cloud on-prem in a private cloud to unlock opportunity for use. So, we're extremely excited about the ThinkAgile CP series that's now shipping into the marketplace. Beyond that we're aggressively ramping, and we're either doubling, tripling, or quadrupling our market share as customers move from traditional server technology to software-defined technology. With Nutanix we've been public, growing about more than 150 percent year-on-year, with Nutanix as their fastest growing Nutanix partner, but today I want to set another audacious goal. I believe we cannot just be Nutanix's fastest growing partner but we can become their largest partner within two years. On Microsoft, we are already four times our market share on Azure stack of our traditional business. We were the first to launch our ThinkAgile on Broadwell and on Skylake with the Azure Stack Infrastructure. And on VMware we're about twice our market segment share. We were the first to deliver an Intel-optimized Optane-certified VSAN node. And with Optane technology, we're delivering 50 percent more VM density than any competitive SSD system in the marketplace, about 10 times lower latency, four times the performance of any SSD system out there, and Lenovo's first to market on that. And at VMworld you saw CEO Pat Gelsinger of VMware talked about project dimension, which is Edge as a service, and we're the only OEM beyond the Dell family that is participating today in project dimension. Beyond that you're going to see a number of other partnerships we have. I'm excited that we have the city of Bogota Columbia here, an eight million person city, where we announced a 3,000 camera video surveillance solution last month. With pivot three you're going to see city of Bogota in our breakout sessions. You're going to see a new partnership with Veeam around backup that's launching today. You're going to see partnerships with scale computing in IoT and hyper-converged infrastructure working on some of the largest retailers in the world. So again, everything out in the breakout session. Transitioning to storage and data management, it's been a great year for Lenovo, more than a 100 percent growth year-on-year, 2X market growth in flash arrays. IDC just reported 30 percent growth in storage, number one in price performance in the world and the best HPC storage product in the top 500 with our ThinkSystem DSS G, so strong coverage, but I'm excited today to announce for Transform 2.0 that Lenovo is launching the largest data management and storage portfolio in our 25-year data center history. (audience applauding) So a year ago, the largest server portfolio, becoming the largest fastest growing server OEM, today the largest storage portfolio, but as you saw this morning we're not doing it alone. Today Lenovo and NetApp, two global powerhouses are joining forces to deliver a multi-billion dollar global alliance in data management and storage to help customers through their intelligent transformation. As the fastest growing worldwide server leader and one of the fastest growing flash array and data management companies in the world, we're going to deliver more choice to customers than ever before, global scale that's never been seen, supply chain efficiencies, and rapidly accelerating innovation and solutions. So, let me unwrap this a little bit for you and talk about what we're announcing today. First, it's the largest portfolio in our history. You're going to see not just storage solutions launching today but a set of solution recipes from NetApp that are going to make Lenovo server and NetApp or Lenovo storage work better together. The announcement enables Lenovo to go from covering 15 percent of the global storage market to more than 90 percent of the global storage market and distribute these products in more than 160 countries around the world. So we're launching today, 10 new storage platforms, the ThinkSystem DE and ThinkSystem DM platforms. They're going to be centrally managed, so the same XClarity management that you've been using for server, you can now use across all of your storage platforms as well, and it'll be supported by the same 10,000 plus service personnel that are giving outstanding customer support to you today on the server side. And we didn't come up with this in the last month or the last quarter. We're announcing availability in ordering today and shipments tomorrow of the first products in this portfolio, so we're excited today that it's not just a future announcement but something you as customers can take advantage of immediately. (audience applauding) The second part of the announcement is we are announcing a joint venture in China. Not only will this be a multi-billion dollar global partnership, but Lenovo will be a 51 percent owner, NetApp a 49 percent owner of a new joint venture in China with the goal of becoming in the top three storage companies in the largest data and storage market in the world. We will deliver our R and D in China for China, pooling our IP and resources together, and delivering a single route to market through a complementary channel, not just in China but worldwide. And in the future I just want to tell everyone this is phase one. There is so much exciting stuff. We're going to be on the stage over the next year talking to you about around integrated solutions, next-generation technologies, and further synergies and collaborations. So, rather than just have me talk about it, I'd like to welcome to the stage our new partner NetApp and Brad Anderson who's the senior vice president and general manager of NetApp Cloud Infrastructure. (upbeat music) (audience applauding) >> Thank You Kirk. >> So Brad, we've known each other a long time. It's an exciting day. I'm going to give you the stage and allow you to say NetApp's perspective on this announcement. >> Very good, thank you very much, Kirk. Kirk and I go back to I think 1994, so hey good morning and welcome. My name is Brad Anderson. I manage the Cloud Infrastructure Group at NetApp, and I am honored and privileged to be here at Lenovo Transform, particularly today on today's announcement. Now, you've heard a lot about digital transformation about how companies have to transform their IT to compete in today's global environment. And today's announcement with the partnership between NetApp and Lenovo is what that's all about. This is the joining of two global leaders bringing innovative technology in a simplified solution to help customers modernize their IT and accelerate their global digital transformations. Drawing on the strengths of both companies, Lenovo's high performance compute world-class supply chain, and NetApp's hybrid cloud data management, hybrid flash and all flash storage solutions and products. And both companies providing our customers with the global scale for them to be able to meet their transformation goals. At NetApp, we're very excited. This is a quote from George Kurian our CEO. George spent all day yesterday with YY and Kirk, and would have been here today if it hadn't been also our shareholders meeting in California, but I want to just convey how excited we are for all across NetApp with this partnership. This is a partnership between two companies with tremendous market momentum. Kirk took you through all the amazing results that Lenovo has accomplished, number one in supercomputing, number one in performance, number one in x86 reliability, number one in x86 customers sat, number five in supply chain, really impressive and congratulations. Like Lenovo, NetApp is also on a transformation journey, from a storage company to the data authority in hybrid cloud, and we've seen some pretty impressive momentum as well. Just last week we became number one in all flash arrays worldwide, catching EMC and Dell, and we plan to keep on going by them, as we help customers modernize their their data centers with cloud connected flash. We have strategic partnerships with the largest hyperscalers to provide cloud native data services around the globe and we are having success helping our customers build their own private clouds with just, with a new disruptive hyper-converged technology that allows them to operate just like hyperscalers. These three initiatives has fueled NetApp's transformation, and has enabled our customers to change the world with data. And oh by the way, it has also fueled us to have meet or have beaten Wall Street's expectations for nine quarters in a row. These are two companies with tremendous market momentum. We are also building this partnership for long term success. We think about this as phase one and there are two important components to phase one. Kirk took you through them but let me just review them. Part one, the establishment of a multi-year commitment and a collaboration agreement to offer Lenovo branded flash products globally, and as Kurt said in 160 countries. Part two, the formation of a joint venture in PRC, People's Republic of China, that will provide long term commitment, joint product development, and increase go-to-market investment to meet the unique needs to China. Both companies will put in storage technologies and storage expertise to form an independent JV that establishes a data management company in China for China. And while we can dream about what phase two looks like, our entire focus is on making phase one incredibly successful and I'm pleased to repeat what Kirk, is that the first products are orderable and shippable this week in 160 different countries, and you will see our two companies focusing on the here and now. On our joint go to market strategy, you'll see us working together to drive strategic alignment, focused execution, strong governance, and realistic expectations and milestones. And it starts with the success of our customers and our channel partners is job one. Enabling customers to modernize their legacy IT with complete data center solutions, ensuring that our customers get the best from both companies, new offerings the fuel business success, efficiencies to reinvest in game-changing initiatives, and new solutions for new mission-critical applications like data analytics, IoT, artificial intelligence, and machine learning. Channel partners are also top of mind for both our two companies. We are committed to the success of our existing and our future channel partners. For NetApp channel partners, it is new pathways to new segments and to new customers. For Lenovo's channel partners, it is the competitive weapons that now allows you to compete and more importantly win against Dell, EMC, and HP. And the good news for both companies is that our channel partner ecosystem is highly complementary with minimal overlap. Today is the first day of a very exciting partnership, of a partnership that will better serve our customers today and will provide new opportunities to both our companies and to our partners, new products to our customers globally and in China. I am personally very excited. I will be on the board of the JV. And so, I look forward to working with you, partnering with you and serving you as we go forward, and with that, I'd like to invite Kirk back up. (audience applauding) >> Thank you. >> Thank you. >> Well, thank you, Brad. I think it's an exciting overview, and these products will be manufactured in China, in Mexico, in Hungary, and around the world, enabling this amazing supply chain we talked about to deliver in over 160 countries. So thank you Brad, thank you George, for the amazing partnership. So again, that's not all. In Transform 2.0, last year, we talked about the joint ventures that were coming. I want to give you a sneak peek at what you should expect at future Lenovo events around the world. We have this Transform in Beijing in a couple weeks. We'll then be repeating this in 20 different locations roughly around the world over the next year, and I'm excited probably more than ever about what else is coming. Let's talk about Telco 5G and network function virtualization. Today, Motorola phones are certified on 46 global networks. We launched the world's first 5G upgradable phone here in the United States with Verizon. Lenovo DCG sells to 58 telecommunication providers around the world. At Mobile World Congress in Barcelona and Shanghai, you saw China Telecom and China Mobile in the Lenovo booth, China Telecom showing a video broadband remote access server, a VBRAS, with video streaming demonstrations with 2x less jitter than they had seen before. You saw China Mobile with a virtual remote access network, a VRAN, with greater than 10 times the throughput and 10x lower latency running on Lenovo. And this year, we'll be launching a new NFV company, a software company in China for China to drive the entire NFV stack, delivering not just hardware solutions, but software solutions, and we've recently hired a new CEO. You're going to hear more about that over the next several quarters. Very exciting as we try to drive new economics into the networks to deliver these 20 billion devices. We're going to need new economics that I think Lenovo can uniquely deliver. The second on IoT and edge, we've integrated on the device side into our intelligent devices group. With everything that's going to consume electricity computes and communicates, Lenovo is in a unique position on the device side to take advantage of the communications from Motorola and being one of the largest device companies in the world. But this year, we're also going to roll out a comprehensive set of edge gateways and ruggedized industrial servers and edge servers and ISP appliances for the edge and for IoT. So look for that as well. And then lastly, as a service, you're going to see Lenovo delivering hardware as a service, device as a service, infrastructure as a service, software as a service, and hardware as a service, not just as a glorified leasing contract, but with IP, we've developed true flexible metering capability that enables you to scale up and scale down freely and paying strictly based on usage, and we'll be having those announcements within this fiscal year. So Transform 2.0, lots to talk about, NetApp the big news of the day, but a lot more to come over the next year from the Data Center group. So in summary, I'm excited that we have a lot of customers that are going to be on stage with us that you saw in the video. Lots of testimonials so that you can talk to colleagues of yourself. Alamos Gold from Canada, a Canadian gold producer, Caligo for data optimization and privacy, SciNet, the largest supercomputer we've ever put into North America, and the largest in Canada at the University of Toronto will be here talking about climate change. City of Bogota again with our hyper-converged solutions around smart city putting in 3,000 cameras for criminal detection, license plate detection, et cetera, and then more from a channel mid market perspective, Jerry's Foods, which is from my home state of Wisconsin, and Minnesota which has about 57 stores in the specialty foods market, and how they're leveraging our IoT solutions as well. So again, about five times the number of demos that we had last year. So in summary, first and foremost to the customers, thank you for your business. It's been a great journey and I think we're on a tremendous role. You saw from last year, we're trying to build credibility with you. After the largest server portfolio, we're now the fastest-growing server OEM per Gardner, number one in performance, number one in reliability, number one in customer satisfaction, number one in supercomputing. Today, the largest storage portfolio in our history, with the goal of becoming the fastest growing storage company in the world, top three in China, multibillion-dollar collaboration with NetApp. And the transformation is going to continue with new edge gateways, edge servers, NFV solutions, telecommunications infrastructure, and hardware as a service with dynamic metering. So thank you for your time. I've looked forward to meeting many of you over the next day. We appreciate your business, and with that, I'd like to bring up Rod Lappen to introduce our next speaker. Rod? (audience applauding) >> Thanks, boss, well done. Alright ladies and gentlemen. No real secret there. I think we've heard why I might talk about the fourth Industrial Revolution in data and exactly what's going on with that. You've heard Kirk with some amazing announcements, obviously now with our NetApp partnership, talk about 5G, NFV, cloud, artificial intelligence, I think we've hit just about all the key hot topics. It's with great pleasure that I now bring up on stage Mr. Christian Teismann, our senior vice president and general manager of commercial business for both our PCs and our IoT business, so Christian Teismann. (techno music) Here, take that. >> Thank you. I think I'll need that. >> Okay, Christian, so obviously just before we get down, you and I last year, we had a bit of a chat about being in New York. >> Exports. >> You were an expat in New York for a long time. >> That's true. >> And now, you've moved from New York. You're in Munich? >> Yep. >> How does that feel? >> Well Munich is a wonderful city, and it's a great place to live and raise kids, but you know there's no place in the world like New York. >> Right. >> And I miss it a lot, quite frankly. >> So what exactly do you miss in New York? >> Well there's a lot of things in New York that are unique, but I know you spent some time in Japan, but I still believe the best sushi in the world is still in New York City. (all laughing) >> I will beg to differ. I will beg to differ. I think Mr. Guchi-san from Softbank is here somewhere. He will get up an argue very quickly that Japan definitely has better sushi than New York. But obviously you know, it's a very very special place, and I have had sushi here, it's been fantastic. What about Munich? Anything else that you like in Munich? >> Well I mean in Munich, we have pork knuckles. >> Pork knuckles. (Christian laughing) Very similar sushi. >> What is also very fantastic, but we have the real, the real Oktoberfest in Munich, and it starts next week, mid-September, and I think it's unique in the world. So it's very special as well. >> Oktoberfest. >> Yes. >> Unfortunately, I'm not going this year, 'cause you didn't invite me, but-- (audience chuckling) How about, I think you've got a bit of a secret in relation to Oktoberfest, probably not in Munich, however. >> It's a secret, yes, but-- >> Are you going to share? >> Well I mean-- >> See how I'm putting you on the spot? >> In the 10 years, while living here in New York, I was a regular visitor of the Oktoberfest at the Lower East Side in Avenue C at Zum Schneider, where I actually met my wife, and she's German. >> Very good. So, how about a big round of applause? (audience applauding) Not so much for Christian, but more I think, obviously for his wife, who obviously had been drinking and consequently ended up with you. (all laughing) See you later, mate. >> That's the beauty about Oktoberfest, but yes. So first of all, good morning to everybody, and great to be back here in New York for a second Transform event. New York clearly is the melting pot of the world in terms of culture, nations, but also business professionals from all kind of different industries, and having this event here in New York City I believe is manifesting what we are trying to do here at Lenovo, is transform every aspect of our business and helping our customers on the journey of intelligent transformation. Last year, in our transformation on the device business, I talked about how the PC is transforming to personalized computing, and we've made a lot of progress in that journey over the last 12 months. One major change that we have made is we combined all our device business under one roof. So basically PCs, smart devices, and smart phones are now under the roof and under the intelligent device group. But from my perspective makes a lot of sense, because at the end of the day, all devices connect in the modern world into the cloud and are operating in a seamless way. But we are also moving from a device business what is mainly a hardware focus historically, more and more also into a solutions business, and I will give you during my speech a little bit of a sense of what we are trying to do, as we are trying to bring all these components closer together, and specifically also with our strengths on the data center side really build end-to-end customer solution. Ultimately, what we want to do is make our business, our customer's businesses faster, safer, and ultimately smarter as well. So I want to look a little bit back, because I really believe it's important to understand what's going on today on the device side. Many of us have still grown up with phones with terminals, ultimately getting their first desktop, their first laptop, their first mobile phone, and ultimately smartphone. Emails and internet improved our speed, how we could operate together, but still we were defined by linear technology advances. Today, the world has changed completely. Technology itself is not a limiting factor anymore. It is how we use technology going forward. The Internet is pervasive, and we are not yet there that we are always connected, but we are nearly always connected, and we are moving to the stage, that everything is getting connected all the time. Sharing experiences is the most driving force in our behavior. In our private life, sharing pictures, videos constantly, real-time around the world, with our friends and with our family, and you see the same behavior actually happening in the business life as well. Collaboration is the number-one topic if it comes down to workplace, and video and instant messaging, things that are coming from the consumer side are dominating the way we are operating in the commercial business as well. Most important beside technology, that a new generation of workforce has completely changed the way we are working. As the famous workforce the first generation of Millennials that have now fully entered in the global workforce, and the next generation, it's called Generation Z, is already starting to enter the global workforce. By 2025, 75 percent of the world's workforce will be composed out of two of these generations. Why is this so important? These two generations have been growing up using state-of-the-art IT technology during their private life, during their education, school and study, and are taking these learnings and taking these behaviors in the commercial workspace. And this is the number one force of change that we are seeing in the moment. Diverse workforces are driving this change in the IT spectrum, and for years in many of our customers' focus was their customer focus. Customer experience also in Lenovo is the most important thing, but we've realized that our own human capital is equally valuable in our customer relationships, and employee experience is becoming a very important thing for many of our customers, and equally for Lenovo as well. As you have heard YY, as we heard from YY, Lenovo is focused on intelligent transformation. What that means for us in the intelligent device business is ultimately starting with putting intelligence in all of our devices, smartify every single one of our devices, adding value to our customers, traditionally IT departments, but also focusing on their end users and building products that make their end users more productive. And as a world leader in commercial devices with more than 33 percent market share, we can solve problems been even better than any other company in the world. So, let's talk about transformation of productivity first. We are in a device-led world. Everything we do is connected. There's more interaction with devices than ever, but also with spaces who are increasingly becoming smart and intelligent. YY said it, by 2020 we have more than 20 billion connected devices in the world, and it will grow exponentially from there on. And users have unique personal choices for technology, and that's very important to recognize, and we call this concept a digital wardrobe. And it means that every single end-user in the commercial business is composing his personal wardrobe on an ongoing basis and is reconfiguring it based on the work he's doing and based where he's going and based what task he is doing. I would ask all of you to put out all the devices you're carrying in your pockets and in your bags. You will see a lot of you are using phones, tablets, laptops, but also cameras and even smartwatches. They're all different, but they have one underlying technology that is bringing it all together. Recognizing digital wardrobe dynamics is a core factor for us to put all the devices under one roof in IDG, one business group that is dedicated to end-user solutions across mobile, PC, but also software services and imaging, to emerging technologies like AR, VR, IoT, and ultimately a AI as well. A couple of years back there was a big debate around bring-your-own-device, what was called consumerization. Today consumerization does not exist anymore, because consumerization has happened into every single device we build in our commercial business. End users and commercial customers today do expect superior display performance, superior audio, microphone, voice, and touch quality, and have it all connected and working seamlessly together in an ease of use space. We are already deep in the journey of personalized computing today. But the center point of it has been for the last 25 years, the mobile PC, that we have perfected over the last 25 years, and has been the undisputed leader in mobility computing. We believe in the commercial business, the ThinkPad is still the core device of a digital wardrobe, and we continue to drive the success of the ThinkPad in the marketplace. We've sold more than 140 million over the last 26 years, and even last year we exceeded nearly 11 million units. That is about 21 ThinkPads per minute, or one Thinkpad every three seconds that we are shipping out in the market. It's the number one commercial PC in the world. It has gotten countless awards but we felt last year after Transform we need to build a step further, in really tailoring the ThinkPad towards the need of the future. So, we announced a new line of X1 Carbon and Yoga at CES the Consumer Electronics Show. And the reason is not we want to sell to consumer, but that we do recognize that a lot of CIOs and IT decision makers need to understand what consumers are really doing in terms of technology to make them successful. So, let's take a look at the video. (suspenseful music) >> When you're the number one business laptop of all time, your only competition is yourself. (wall shattering) And, that's different. Different, like resisting heat, ice, dust, and spills. Different, like sharper, brighter OLA display. The trackpoint that reinvented controls, and a carbon fiber roll cage to protect what's inside, built by an engineering and design team, doing the impossible for the last 25 years. This is the number one business laptop of all time, but it's not a laptop. It's a ThinkPad. (audience applauding) >> Thank you very much. And we are very proud that Lenovo ThinkPad has been selected as the best laptop in the world in the second year in a row. I think it's a wonderful tribute to what our engineers have been done on this one. And users do want awesome displays. They want the best possible audio, voice, and touch control, but some users they want more. What they want is super power, and I'm really proud to announce our newest member of the X1 family, and that's the X1 extreme. It's exceptionally featured. It has six core I9 intel chipset, the highest performance you get in the commercial space. It has Nvidia XTX graphic, it is a 4K UHD display with HDR with Dolby vision and Dolby Atmos Audio, two terabyte in SSD, so it is really the absolute Ferrari in terms of building high performance commercial computer. Of course it has touch and voice, but it is one thing. It has so much performance that it serves also a purpose that is not typical for commercial, and I know there's a lot of secret gamers also here in this room. So you see, by really bringing technology together in the commercial space, you're creating productivity solutions of one of a kind. But there's another category of products from a productivity perspective that is incredibly important in our commercial business, and that is the workstation business . Clearly workstations are very specifically designed computers for very advanced high-performance workloads, serving designers, architects, researchers, developers, or data analysts. And power and performance is not just about the performance itself. It has to be tailored towards the specific use case, and traditionally these products have a similar size, like a server. They are running on Intel Xeon technology, and they are equally complex to manufacture. We have now created a new category as the ultra mobile workstation, and I'm very proud that we can announce here the lightest mobile workstation in the industry. It is so powerful that it really can run AI and big data analysis. And with this performance you can go really close where you need this power, to the sensors, into the cars, or into the manufacturing places where you not only wannna read the sensors but get real-time analytics out of these sensors. To build a machine like this one you need customers who are really challenging you to the limit. and we're very happy that we had a customer who went on this journey with us, and ultimately jointly with us created this product. So, let's take a look at the video. (suspenseful music) >> My world involves pathfinding both the hardware needs to the various work sites throughout the company, and then finding an appropriate model of desktop, laptop, or workstation to match those needs. My first impressions when I first seen the ThinkPad P1 was I didn't actually believe that we could get everything that I was asked for inside something as small and light in comparison to other mobile workstations. That was one of the I can't believe this is real sort of moments for me. (engine roars) >> Well, it's better than general when you're going around in the wind tunnel, which isn't alway easy, and going on a track is not necessarily the best bet, so having a lightweight very powerful laptop is extremely useful. It can take a Xeon processor, which can support ECC from when we try to load a full car, and when we're analyzing live simulation results. through and RCFT post processor or example. It needs a pretty powerful machine. >> It's come a long way to be able to deliver this. I hate to use the word game changer, but it is that for us. >> Aston Martin has got a lot of different projects going. There's some pretty exciting projects and a pretty versatile range coming out. Having Lenovo as a partner is certainly going to ensure that future. (engine roars) (audience applauds) >> So, don't you think the Aston Martin design and the ThinkPad design fit very well together? (audience laughs) So if Q, would get a new laptop, I think you would get a ThinkPad X P1. So, I want to switch gears a little bit, and go into something in terms of productivity that is not necessarily on top of the mind or every end user but I believe it's on top of the mind of every C-level executive and of every CEO. Security is the number one threat in terms of potential risk in your business and the cost of cybersecurity is estimated by 2020 around six trillion dollars. That's more than the GDP of Japan and we've seen a significant amount of data breach incidents already this years. Now, they're threatening to take companies out of business and that are threatening companies to lose a huge amount of sensitive customer data or internal data. At Lenovo, we are taking security very, very seriously, and we run a very deep analysis, around our own security capabilities in the products that we are building. And we are announcing today a new brand under the Think umbrella that is called ThinkShield. Our goal is to build the world's most secure PC, and ultimately the most secure devices in the industry. And when we looked at this end-to-end, there is no silver bullet around security. You have to go through every aspect where security breaches can potentially happen. That is why we have changed the whole organization, how we look at security in our device business, and really have it grouped under one complete ecosystem of solutions, Security is always something where you constantly are getting challenged with the next potential breach the next potential technology flaw. As we keep innovating and as we keep integrating, a lot of our partners' software and hardware components into our products. So for us, it's really very important that we partner with companies like Intel, Microsoft, Coronet, Absolute, and many others to really as an example to drive full encryption on all the data seamlessly, to have multi-factor authentication to protect your users' identity, to protect you in unsecured Wi-Fi locations, or even simple things like innovation on the device itself, to and an example protect the camera, against usage with a little thing like a thinkShutter that you can shut off the camera. SO what I want to show you here, is this is the full portfolio of ThinkShield that we are announcing today. This is clearly not something I can even read to you today, but I believe it shows you the breadth of security management that we are announcing today. There are four key pillars in managing security end-to-end. The first one is your data, and this has a lot of aspects around the hardware and the software itself. The second is identity. The third is the security around online, and ultimately the device itself. So, there is a breakout on security and ThinkShield today, available in the afternoon, and encourage you to really take a deeper look at this one. The first pillar around productivity was the device, and around the device. The second major pillar that we are seeing in terms of intelligent transformation is the workspace itself. Employees of a new generation have a very different habit how they work. They split their time between travel, working remotely but if they do come in the office, they expect a very different office environment than what they've seen in the past in cubicles or small offices. They come into the office to collaborate, and they want to create ideas, and they really work in cross-functional teams, and they want to do it instantly. And what we've seen is there is a huge amount of investment that companies are doing today in reconfiguring real estate reconfiguring offices. And most of these kind of things are moving to a digital platform. And what we are doing, is we want to build an entire set of solutions that are just focused on making the workspace more productive for remote workforce, and to create technology that allow people to work anywhere and connect instantly. And the core of this is that we need to be, the productivity of the employee as high as possible, and make it for him as easy as possible to use these kind of technologies. Last year in Transform, I announced that we will enter the smart office space. By the end of last year, we brought the first product into the market. It's called the Hub 500. It's already deployed in thousands of our customers, and it's uniquely focused on Microsoft Skype for Business, and making meeting instantly happen. And the product is very successful in the market. What we are announcing today is the next generation of this product, what is the Hub 700, what has a fantastic audio quality. It has far few microphones, and it is usable in small office environment, as well as in major conference rooms, but the most important part of this new announcement is that we are also announcing a software platform, and this software platform allows you to run multiple video conferencing software solutions on the same platform. Many of you may have standardized for one software solution or for another one, but as you are moving in a world of collaborating instantly with partners, customers, suppliers, you always will face multiple software standards in your company, and Lenovo is uniquely positioned but providing a middleware platform for the device to really enable multiple of these UX interfaces. And there's more to come and we will add additional UX interfaces on an ongoing base, based on our customer requirements. But this software does not only help to create a better experience and a higher productivity in the conference room or the huddle room itself. It really will allow you ultimately to manage all your conference rooms in the company in one instance. And you can run AI technologies around how to increase productivity utilization of your entire conference room ecosystem in your company. You will see a lot more devices coming from the node in this space, around intelligent screens, cameras, and so on, and so on. The idea is really that Lenovo will become a core provider in the whole movement into the smart office space. But it's great if you have hardware and software that is really supporting the approach of modern IT, but one component that Kirk also mentioned is absolutely critical, that we are providing this to you in an as a service approach. Get it what you want, when you need it, and pay it in the amount that you're really using it. And within UIT there is also I think a new philosophy around IT management, where you're much more focused on the value that you are consuming instead of investing into technology. We are launched as a service two years back and we already have a significant number of customers running PC as a service, but we believe as a service will stretch far more than just the PC device. It will go into categories like smart office. It might go even into categories like phone, and it will definitely go also in categories like storage and server in terms of capacity management. I want to highlight three offerings that we are also displaying today that are sort of building blocks in terms of how we really run as a service. The first one is that we collaborated intensively over the last year with Microsoft to be the launch pilot for their Autopilot offering, basically deploying images easily in the same approach like you would deploy a new phone on the network. The purpose really is to make new imaging and enabling new PC as seamless as it's used to be in the phone industry, and we have a complete set of offerings, and already a significant number customers have deployed Autopilot with Lenovo. The second major offering is Premier Support, like in the in the server business, where Premier Support is absolutely critical to run critical infrastructure, we see a lot of our customers do want to have Premier Support for their end users, so they can be back into work basically instantly, and that you have the highest possible instant repair on every single device. And then finally we have a significant amount of time invested into understanding how the software as a service really can get into one philosophy. And many of you already are consuming software as a service in many different contracts from many different vendors, but what we've created is one platform that really can manage this all together. All these things are the foundation for a device as a service offering that really can manage this end-to-end. So, implementing an intelligent workplace can be really a daunting prospect depending on where you're starting from, and how big your company ultimately is. But how do you manage the transformation of technology workspace if you're present in 50 or more countries and you run an infrastructure for more than 100,000 people? Michelin, famous for their tires, infamous for their Michelin star restaurant rating, especially in New York, and instantly recognizable by the Michelin Man, has just doing that. Please welcome with me Damon McIntyre from Michelin to talk to us about the challenges and transforming collaboration and productivity. (audience applauding) (electronic dance music) Thank you, David. >> Thank you, thank you very much. >> We on? >> So, how do you feel here? >> Well good, I want to thank you first of all for your partnership and the devices you create that helped us design, manufacture, and distribute the best tire in the world, okay? I just had to say it and put out there, alright. And I was wondering, were those Michelin tires on that Aston Martin? >> I'm pretty sure there is no other tire that would fit to that. >> Yeah, no, thank you, thank you again, and thank you for the introduction. >> So, when we talk about the transformation happening really in the workplace, the most tangible transformation that you actually see is the drastic change that companies are doing physically. They're breaking down walls. They're removing cubes, and they're moving to flexible layouts, new desks, new huddle rooms, open spaces, but the underlying technology for that is clearly not so visible very often. So, tell us about Michelin's strategy, and the technology you are deploying to really enable this corporation. >> So we, so let me give a little bit a history about the company to understand the daunting tasks that we had before us. So we have over 114,000 people in the company under 170 nationalities, okay? If you go to the corporate office in France, it's Clermont. It's about 3,000 executives and directors, and what have you in the marketing, sales, all the way up to the chain of the global CIO, right? Inside of the Americas, we merged in Americas about three years ago. Now we have the Americas zone. There's about 28,000 employees across the Americas, so it's really, it's really hard in a lot of cases. You start looking at the different areas that you lose time, and you lose you know, your productivity and what have you, so there, it's when we looked at different aspects of how we were going to manage the meeting rooms, right? because we have opened up our areas of workspace, our CIO, CEOs in our zones will no longer have an office. They'll sit out in front of everybody else and mingle with the crowd. So, how do you take those spaces that were originally used by an individual but now turn them into like meeting rooms? So, we went through a large process, and looked at the Hub 500, and that really met our needs, because at the end of the day what we noticed was, it was it was just it just worked, okay? We've just added it to the catalog, so we're going to be deploying it very soon, and I just want to again point that I know everybody struggles with this, and if you look at all the minutes that you lose in starting up a meeting, and we know you know what I'm talking about when I say this, it equates to many many many dollars, okay? And so at the end the day, this product helps us to be more efficient in starting up the meeting, and more productive during the meeting. >> Okay, it's very good to hear. Another major trend we are seeing in IT departments is taking a more hands-off approach to hardware. We're seeing new technologies enable IT to create a more efficient model, how IT gets hardware in the hands of end-users, and how they are ultimately supporting themselves. So what's your strategy around the lifecycle management of the devices? >> So yeah you mentioned, again, we'll go back to the 114,000 employees in the company, right? You imagine looking at all the devices we use. I'm not going to get into the number of devices we have, but we have a set number that we use, and we have to go through a process of deploying these devices, which we right now service our own image. We build our images, we service them through our help desk and all that process, and we go through it. If you imagine deploying 25,000 PCs in a year, okay? The time and the daunting task that's behind all that, you can probably add up to 20 or 30 people just full-time doing that, okay? So, with partnering with Lenovo and their excellent technology, their technical teams, and putting together the whole process of how we do imaging, it now lifts that burden off of our folks, and it shifts it into a more automated process through the cloud, okay? And, it's with the Autopilot on the end of the project, we'll have Autopilot fully engaged, but what I really appreciate is how Lenovo really, really kind of got with us, and partnered with us for the whole process. I mean it wasn't just a partner between Michelin and Lenovo. Microsoft was also partnered during that whole process, and it really was a good project that we put together, and we hope to have something in a full production mode next year for sure. >> So, David thank you very, very much to be here with us on stage. What I really want to say, customers like you, who are always challenging us on every single aspect of our capabilities really do make the big difference for us to get better every single day and we really appreciate the partnership. >> Yeah, and I would like to say this is that I am, I'm doing what he's exactly said he just said. I am challenging Lenovo to show us how we can innovate in our work space with your devices, right? That's a challenge, and it's going to be starting up next year for sure. We've done some in the past, but I'm really going to challenge you, and my whole aspect about how to do that is bring you into our workspace. Show you how we make how we go through the process of making tires and all that process, and how we distribute those tires, so you can brainstorm, come back to the table and say, here's a device that can do exactly what you're doing right now, better, more efficient, and save money, so thank you. >> Thank you very much, David. (audience applauding) Well it's sometimes really refreshing to get a very challenging customers feedback. And you know, we will continue to grow this business together, and I'm very confident that your challenge will ultimately help to make our products even more seamless together. So, as we now covered productivity and how we are really improving our devices itself, and the transformation around the workplace, there is one pillar left I want to talk about, and that's really, how do we make businesses smarter than ever? What that really means is, that we are on a journey on trying to understand our customer's business, deeper than ever, understanding our customer's processes even better than ever, and trying to understand how we can help our customers to become more competitive by injecting state-of-the-art technology in this intelligent transformation process, into core processes. But this cannot be done without talking about a fundamental and that is the journey towards 5G. I really believe that 5G is changing everything the way we are operating devices today, because they will be connected in a way like it has never done before. YY talked about you know, 20 times 10 times the amount of performance. There are other studies that talk about even 200 times the performance, how you can use these devices. What it will lead to ultimately is that we will build devices that will be always connected to the cloud. And, we are preparing for this, and Kirk already talked about, and how many operators in the world we already present with our Moto phones, with how many Telcos we are working already on the backend, and we are working on the device side on integrating 5G basically into every single one of our product in the future. One of the areas that will benefit hugely from always connected is the world of virtual reality and augmented reality. And I'm going to pick here one example, and that is that we have created a commercial VR solution for classrooms and education, and basically using consumer type of product like our Mirage Solo with Daydream and put a solution around this one that enables teachers and schools to use these products in the classroom experience. So, students now can have immersive learning. They can studying sciences. They can look at environmental issues. They can exploring their careers, or they can even taking a tour in the next college they're going to go after this one. And no matter what grade level, this is how people will continue to learn in the future. It's quite a departure from the old world of textbooks. In our area that we are looking is IoT, And as YY already elaborated, we are clearly learning from our own processes around how we improve our supply chain and manufacturing and how we improve also retail experience and warehousing, and we are working with some of the largest companies in the world on pilots, on deploying IoT solutions to make their businesses, their processes, and their businesses, you know, more competitive, and some of them you can see in the demo environment. Lenovo itself already is managing 55 million devices in an IoT fashion connecting to our own cloud, and constantly improving the experience by learning from the behavior of these devices in an IoT way, and we are collecting significant amount of data to really improve the performance of these systems and our future generations of products on a ongoing base. We have a very strong partnership with a company called ADLINK from Taiwan that is one of the leading manufacturers of manufacturing PC and hardened devices to create solutions on the IoT platform. The next area that we are very actively investing in is commercial augmented reality. I believe augmented reality has by far more opportunity in commercial than virtual reality, because it has the potential to ultimately improve every single business process of commercial customers. Imagine in the future how complex surgeries can be simplified by basically having real-time augmented reality information about the surgery, by having people connecting into a virtual surgery, and supporting the surgery around the world. Visit a furniture store in the future and see how this furniture looks in your home instantly. Doing some maintenance on some devices yourself by just calling the company and getting an online manual into an augmented reality device. Lenovo is exploring all kinds of possibilities, and you will see a solution very soon from Lenovo. Early when we talked about smart office, I talked about the importance of creating a software platform that really run all these use cases for a smart office. We are creating a similar platform for augmented reality where companies can develop and run all their argumented reality use cases. So you will see that early in 2019 we will announce an augmented reality device, as well as an augmented reality platform. So, I know you're very interested on what exactly we are rolling out, so we will have a first prototype view available there. It's still a codename project on the horizon, and we will announce it ultimately in 2019, but I think it's good for you to take a look what we are doing here. So, I just wanted to give you a peek on what we are working beyond smart office and the device productivity in terms of really how we make businesses smarter. It's really about increasing productivity, providing you the most secure solutions, increase workplace collaboration, increase IT efficiency, using new computing devices and software and services to make business smarter in the future. There's no other company that will enable to offer what we do in commercial. No company has the breadth of commercial devices, software solutions, and the same data center capabilities, and no other company can do more for your intelligent transformation than Lenovo. Thank you very much. (audience applauding) >> Thanks mate, give me that. I need that. Alright, ladies and gentlemen, we are done. So firstly, I've got a couple of little housekeeping pieces at the end of this and then we can go straight into going and experiencing some of the technology we've got on the left-hand side of the room here. So, I want to thank Christian obviously. Christian, awesome as always, some great announcements there. I love the P1. I actually like the Aston Martin a little bit better, but I'll take either if you want to give me one for free. I'll take it. We heard from YY obviously about the industry and how the the fourth Industrial Revolution is impacting us all from a digital transformation perspective, and obviously Kirk on DCG, the great NetApp announcement, which is going to be really exciting, actually that Twitter and some of the social media panels are absolutely going crazy, so it's good to see that the industry is really taking some impact. Some of the publications are really great, so thank you for the media who are obviously in the room publishing right no. But now, I really want to say it's all of your turn. So, all of you up the back there who are having coffee, it's your turn now. I want everyone who's sitting down here after this event move into there, and really take advantage of the 15 breakouts that we've got set there. There are four breakout sessions from a time perspective. I want to try and get you all out there at least to use up three of them and use your fourth one to get out and actually experience some of the technology. So, you've got four breakout sessions. A lot of the breakout sessions are actually done twice. If you have not downloaded the app, please download the app so you can actually see what time things are going on and make sure you're registering correctly. There's a lot of great experience of stuff out there for you to go do. I've got one quick video to show you on some of the technology we've got and then we're about to close. Alright, here we are acting crazy. Now, you can see obviously, artificial intelligence machine learning in the browser. God, I hate that dance, I'm not a Millenial at all. It's effectively going to be implemented by healthcare. I want you to come around and test that out. Look at these two guys. This looks like a Lenovo management meeting to be honest with you. These two guys are actually concentrating, using their brain power to race each others in cars. You got to come past and give that a try. Give that a try obviously. Fantastic event here, lots of technology for you to experience, and great partners that have been involved as well. And so, from a Lenovo perspective, we've had some great alliance partners contribute, including obviously our number one partner, Intel, who's been a really big loyal contributor to us, and been a real part of our success here at Transform. Excellent, so please, you've just seen a little bit of tech out there that you can go and play with. I really want you, I mean go put on those black things, like Scott Hawkins our chief marketing officer from Lenovo's DCG business was doing and racing around this little car with his concentration not using his hands. He said it's really good actually, but as soon as someone comes up to speak to him, his car stops, so you got to try and do better. You got to try and prove if you can multitask or not. Get up there and concentrate and talk at the same time. 62 different breakouts up there. I'm not going to go into too much detai, but you can see we've got a very, very unusual numbering system, 18 to 18.8. I think over here we've got a 4849. There's a 4114. And then up here we've got a 46.1 and a 46.2. So, you need the decoder ring to be able to understand it. Get over there have a lot of fun. Remember the boat leaves today at 4:00 o'clock, right behind us at the pier right behind us here. There's 400 of us registered. Go onto the app and let us know if there's more people coming. It's going to be a great event out there on the Hudson River. Ladies and gentlemen that is the end of your keynote. I want to thank you all for being patient and thank all of our speakers today. Have a great have a great day, thank you very much. (audience applauding) (upbeat music) ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ba do ♪

>> From Boston, Massachusetts, it's The Cube, covering WTG Transform 2018. Brought to you by Winslow Technology Group. >> Welcome back to The Cube here at WTG Transform 2018, I'm Stu Miniman and happy to welcome to the program, a first time guest, Claude Lavigne, who's the director of product management and servers at Dell EMC. Claude, thanks so much for joining us. >> Thank you, Stu. >> All right, so I had David Singer on this morning, who is from the EMC legacy side of things >> Yes. >> talking about storage, so you're from the legacy Dell side, if we will. You know we're working on servers. So tell us a little bit about your background and what you work on these days. >> So yes, so I'm doing planning for several powers. I've been on the server team for about 18 years. So, all the way from the beginning to now I'm #1 in the world, so it feels very, very good after 18 years. But yeah, that's why we do it. So I'm based in Austin with the product group over there and our role is to optimize and plan the best possible road map and portfolio of servers for rack towers and module. >> Yeah, it's funny, I remember back, you go back 5-10 years ago >> Yes >> and some people were like: wait, Dell does servers? You know, aren't they, you know, here's a Dell laptop sitting in front of me, and it was like Dell's done a lot of servers. I mean, I remember when Dell's Blade server first came out. A lot of pieces, but you know, so get us at a high level. You know, what's Dell's position in the marketplace these days? >> I mean, #1, but also to me, at the profile level #1, but at the platform level, we have one rack, the rack, the R740, our mainstream rack. It's the #1 server sold in the world. So, we're #1 profile, even at the platform level, and the 740 is used across, it's kind of a bedrock, it's used for as a server, but also in all of the EMC solution. It's used for virtualization, VDI, so that platform is kind of the, it's doing very, very well these days. >> Yeah, it's one of those things: see, people sometimes forget that a server isn't just something that does the compute for, you know, when an operating system sits on it, there's servers in lots of devices. Every year when I go to Dell World, I would see that giant OEM rack, >> Yes. >> and it was servers, OEM storage, OEM, HCI of course all has servers inside of it. And it was one of those things when the acquisition was initially announced of EMC, it was like, well, look, EMC both in their products and bundled with their solutions really pushes a lot of servers >> Yes. >> that didn't get talked about, you know, in the discussion. >> So now it's great for us. We have the full portfolio. I mean, every day you're going to see new announcements. We're going to have the best VFC solution, the best VxRail. We have best in class, you know, performance, and all that, I mean, the big part I mean, it's the research of years and investments and the system management we have at the server level. Because we have a great automation and system management, we're able to kind of reimagine or create profile on our servers, so it can be a VDI server again, but the best VSUN server or the best VxRail server. And all that because we have a great system management engine called the iTrack 9 inside our servers. And that's years of engineering and6 development, but now finally, now you have the hardware on the system management. We get the reconnection from the customers, and I think that's what made us #1. I mean, we see a lot of acceptance in 14G, a lot of demand for our security and system management capabilities. So I think that's the overall solution that help us, I mean, get to the point. >> All right, so Claude actually as an industry the server business is doing pretty well. >> Yeah. >> Especially for the last few quarters. What's driving growth? You're working on the product strategy, you know, what are some of the interesting nuggets in the portfolio? >> All, I mean, so how much we have to call. So the, like I just mentioned, the one year and two year servers but there is a couple pockets on innovations, so that's what we did in the last few months. So, I mean, the first one is, you see different key or card architecture that something to be very interesting not on the AMD, so we're trying to balance the portfolio to really showcase each of the goodness of this architecture. So that's why we launched the full AMD portfolio not a long time ago. Then, the other one is in the full circuit. What's really interesting, something I've never seen in 15 years, more and more people are doing, not on the database, machine learning, AI on full server, they're requiring more chip use and all flash solution within the server to get the best possible performance. So that's what we did, we announced that a month ago at the award and now in the future, it's the, we're moving to kinetic architectures. So we had a preview at the award of our Annex platform that we're going to launch later this year. And that one is really going to take the IT infrastructure to the next level. So, it's designed for the next 10 years of, again, modular, flexible, kinetic architecture that not only can optimize the right balance between compute and storage, but future technology like all flash and in-memory compute with the right fabric. So that's what you're going to see from us. It's going from the traditional rack servers to this advanced, modular architecture for the big design for the next 10 years. >> Claude, maybe give us a little compare and contrast. How is the module architecture different from what we saw on traditional racks or even blade servers? >> Yeah, so the traditional blade servers, I mean the, we don't blade for, I don't know, 10-15 years. And in the past it was, compute and then outer storage will external to a send. It was very traditional, but now with scale out storage, #1, scale out storage and all this HCI solution, customers asking us have very flexible computer and storage architecture. So that's kind of the first step and most of the blade architecture today are out there. It's like storage and compute, but moving forward we're going to go way beyond that. It's, we're going to have blocks of memory and GPU and flash storage, you know, and different cares of, you know, architecture ahead of the traditional storage. And if you look at the modular architecture, nobody else can deal with that today. Annex is going to be the first platform that can handle all that. It's not going to happen in the next three months, but the chassis is designed to be ready for this architecture of the future. Because we have very unique design in the bag. You know, we're getting rid of the traditional midplane that we have in the past and our competition is still using today. So it's going to be much more open to future, flexible, connectivity in the back of the chassis. So, that's why they never asked us to be ready for these next 10 years of IT innovation. >> Alright, so, Claude we're here at the Winslow Technology user event. What kind of feedback are you hearing from Winslow and their customers and what kind of things are you talking to the channel about these days? >> I mean, we've been in this conference for the last few years and we still think it's a great, great poniverse and they are part of what we call the technical council back in Austin. So, they are CTO sits with us on the PG side to help us plan future warmups, so they are very, very close now. And that's what we're doing, I mean, this, we're trying to optimize the portfolio with these guys. But right now, it's mostly the trying to improve the server, I mean, around storage and compute. So what you're going to see us launching toward the end of the year on the storage side. We're going to have a, remember the #1 server mentioned the alt 740, #1 in the world. Again, talking to the Winslow team and their customers, that server is great, but not good enough, so you're going to see us later this year, based on that feedback launching a new, improved version that will still base, use the 740 base, but be even more optimized for HCI and this several story solution. And that's the perfect example of the feedback we're getting from the Winslow team that we're integrating into the roadmap. So yeah, we are here every year because, I mean these guys are very sharp, they are good. >> Claude, last thing I wanted to ask you is, you know, for a very long time when you thought about the server market, it was, let's watch the intel, the tick-tock, the roadmap. So every few months you could expect something happened. That was what drove the innovation. >> Yes. >> What's the cadence today and what's driving innovation going forward? >> I mean, the cadence is getting faster, so for serving planning it makes it a little bit difficult. Also, now you have again you have these two vendors. Before it's really the innovation and I think that's what made us #1 is beyond the server, beyond the two processors and the storage solution. It's how we manage, how we make sure we have the best security end-to-end. So, our system management, the way we can provision servers, make sure we can do the update in the most secure way. The premise, that's kind of what makes a difference for PowerEdge and I think that's why, I mean, it took years of investment from, you know, the pitching team but I think that's what is making a difference, and the differentiation right now. It's like, you know, our servers are not just like any other servers. They're much easier to, much more secure, much easier to manage, you know, through the entire life cycle. So, I think that's the key differentiation is how we manage the server, you know, so not just at the server level, but we have deployments of thousands of servers. So we have a new console called OpenManage Enterprise. We just launched that, I think that was, what, six months ago, and that's the latest and greatest of one too many managements of servers and it's free. I mean, it's, that's another good think about PowerEdge. You can get OpenImage Enterprise for free. >> Alright, well Claude Lavigne really appreciate the update on everything happening in the server world. Lots more coverage. Check out theCUBE.net for everything we're doing, as well as, we actually take some of the key analysis from the shows that we go to, put that in our podcast that's called theCUBE Insights. Find that on iTunes, GooglePlay, Spotify, your favorite podcast player. I'm Stu Miniman. Thanks so much for watching theCUBE. (techno music)

>> From Orlando, Florida, it's theCUBE, covering SAP Sapphire Now 2018. (upbeat electronic music) Brought to you by NetApp. >> Welcome to theCUBE, I'm Lisa Martin, in the NetApp booth, at Sapphire Now 2018. We are in Orlando, this is an enormous event, there's more than 20,000 people here, and there's about a million people that SAP is expecting to engage online, that's a lot. We're excited to welcome to theCUBE for the first time, Klara Young, the director of Strategic Alliances from AppBuddy and Steven Cox, the head of Global Sales Tools at NetApp, hi, guys. >> Howdy. >> Hello. >> Hi, Lisa. >> Thanks for having us. >> Absolutely, so Klara tell me about AppBuddy. Who are you guys and what do you do? >> So AppBuddy is a provider of a user experience layer that can sit on top of core systems like SAP Sales Cloud or SAP Service Cloud and that really allows the administrators to configure a dream workspace where you can get all the data that you need to work with in one place, and then, the users can interact with that very easily. And so, it's all very user friendly and it allows us to enable sales processes, I want to manage my pipelines, or my accounts, my contacts, all with a very easy to use interface right in the middle of the core system. >> So your target audience would be customers that are already using SAP or customers that are maybe in the transition from, say Oracle to SAP, or something like that? >> So any users that are planning to use SAP or are already using SAP and then want to enhance that user experience, want to give them a faster way to interact with the data, more intuitive, more functionality, right in the same core interface. So those would be good clients for us to enhance that experience, absolutely. >> And what about customers by industry know SAP really kind of being very, very strong in a lot of industries but manufacturing, digital supply chain, but if you look at their customers that are here at Sapphire and there's a million of them, they span so many industries. >> Yeah. >> I think yesterday they were saying HANA is installed in 23,000 customers across 60 industries. Does AppBuddy have a particular suite of industries where you really add even more value, or is it fairly horizontal? >> Oh, that's a real good question. Actually what's the beauty, I think, of AppBuddy's product, is that it is completely agnostic of which process or which industry that you're deploying it in. So you decide what objects, what information I want to put on that. It's not a purpose-built application specifically for one process or one industry. So we serve clients in all sorts of industries. We have a lot in high tech, or in the health care industry, manufacturing, as well but we're not specific to one industry. So really welcoming any use case and we'd love to hear from customers, hey, can I do this? With AppBuddy, could I put this object and that object together and build a process basically, almost in your own app. And we're very looking forward to those feedback from customers and wanting to build those use cases with them. >> And that's been such a huge theme or really an undertone at SAP Sapphire the last few days is how much SAP listens to their customers and really involves them and especially strategic accounts like in a collaborative way and yesterday, Steven, we spoke with your CIO Bill Miller. We talked to him about NetApp and SAP have been partners for 17 years. NetApp is 26 years young now and has undergone a big transformation. Bill talked about some of that yesterday, but you guys also did a big transformation that you were leading within your sales processes and your CRM move into SAP, talk to us about that. What were some of the reasons for that transformation? >> Yeah, it's working with Bill and his team I'm represent the business side and we're looking as NetApp is transforming from a traditional storage company to more a cloud. It's a change in the way we go to market. In the past we shipped boxes to people and they install them or we install them. And in the future, we're looking to more services and cloud-oriented things. And so the kind of infrastructure that we built up to support our large sales force doesn't work as well in the new world. And so we about two years ago, started a pretty big transformation journey to move from this more old-school hardware to more new cloud and through that process, we needed to change our systems. Changing out our CRM became an important component of that 'cause we need more flexibility and we needed to sort of be more contemporary and we worked with AppBuddy and our old system, we used to have Salesforce, and the field was pretty used to using that kind of interface. And when you build stuff like this, you don't always know how important it is to the field. You know, you have guesses at it, and as we looked at things that we had to do to prepare to move this was always something on our list that we felt like was important but we weren't able to do it immediately. It took us an extra release to get it out, so an extra few months. And through those few months, we learned the hard way that the field really wanted it. It was really impacting them. And we had guessed that we thought it was somewhere around 25% improvement in their overall productivity. And what we found was that it's at least that, if not more. >> Wow. >> Because they came back and said, "We can't do our jobs "without this, you guys gotta get it for us." >> So they said either AppBuddy or the highway? >> Yeah, pretty much. (laughs) Pretty much, AppBuddy or they're not happy. They're not happy all the time anyway but I feel like they-- >> Salespeople. >> That by getting that to 'em we were enabling them to go faster in a few things. And it's simple, it's hard to understand, I think, for everybody, it's a simple layer. Whenever you build a CRM or any kinda system, your job is to collect information and then display it back, make it easy to change. And the way CRMs typically work today is, you have a list for you of stuff, opportunities, or new registrations, quotes and you just have to look at that list and then pick one you wanna edit and then go to this details screen and look at it and then go to the edit screen and then edit it and then go back, back, back. And what AppBuddy provides, is it takes all that noise and makes it into one screen so that you can just simply make and change the data, the way you would expect to on a spreadsheet, in a simple experience. And once you give it to the reps, they sorta expect that as the tablestakes, and it's a gap if you look at most CRMs they don't have this kind of in-line edit capability out of the box. And so this is a great, SAP is really excited about this 'cause it gives them a way to solve this problem without having to build it themselves and that's the beauty of these kind of infrastructures where you can add capabilities by just plugging something in. >> Right. >> And it speaks using the APIs to the tool. And so all the rules that we build around the data about who should access it, what should happen when they change stuff, should we protect data. All that is followed, because AppBuddy works right through our APIs, through the SAP provides. And so it doesn't require a lot extra coding or anything. In fact. >> That's right. >> IT guys are standing over there somewhere. They don't like it 'cause I do it myself. I'll actually build experiences for the field really quickly 'cause that I can make a quick custom business process to support something that's needed. >> So, on the AppBuddy website, Klara, I saw, I love stats, and you guys said, we can save time and improve enterprise productivity by 5X to 10X. >> That's right. >> Those are big numbers. >> That's right. >> And you were saying there's been a massive improvement in employment productivity and I imagine in terms of the speed is essential. You know, we were talking, one of the underlying themes here at Sapphire, this year, is the intelligent enterprise, which demands the integration and the embedding of advanced emerging technologies, AI, for example, to make these enterprises truly intelligent, connecting supply chain and demand chain and it's essential, its table stakes these days. >> Yep. >> To be able to drive things faster, right? So that you guys can get what your customers need faster. >> Yep. >> So, you mentioned that huge productivity boost there but also that you were familiar with AppBuddy before your sales guys and gals were like, hey we need to have something that we're familiar with to be able to make our jobs better, so you're also doing, it sounds like a pretty good job of listening to your customers. >> Yeah, I try >> Who are probably very vocal. >> I try, I try, I mean, it's a hard job because you're sort of channeling the sales guys and in our world they're very different. In Europe, they sell very different than they sell in the US and APAC is different. And even within different sections of Europe or in the US, they act differently, and our goal is to try to streamline that so that they can act as much the same as they can across that and we can deploy sort of one experience without having to customize it totally. But tools like AppBuddy give us the ability to be much more targeted and flexible. A simple example I've been given pretty commonly is we have our sales kick-off this week also in Las Vegas and all of our sales guys are going there to learn about how to sell better, how to sell our new products and solutions and leverage some of our improved selling processes and before they go there, we wanted to have them identify a few key opportunities they're working on to say hey, these are the one's that I'm gonna use as my work case as I'm learning these new things, and in theory as we go through and finish our sales kick-off they go back and start the selling process those opportunities should sell at a higher rate then the other opportunities. And so to make that work, I configured a grid, or an AppBuddy list view, and all I put on it was the list of opportunities in one field that says, this is appropriate for our kick-off and so, instead of putting it in the middle of a very complex world, I sent 'em an email, they had a list and they just had to say this guy, this guy, and that guy, and that's all they had to do. And so our response rate on something, which if you sent a list of things to do for the field, they're not gonna respond. They're busy, they're makin' money. But in this case, because it was tied to the new learning and they felt value in it, 80% of 'em responded within 10 days. >> Yeah, wow. >> And you know, you just don't see that kind of response. But it works because it's a simple experience, right? The only thing they could do with that, they get an email that says, do this, they open it, they see the list, they click, yes, yes, yes, and it's done. And that's a whole business process that in the old days could take months to prepare for and create fields and deploy new code and do all the things you have to do. And in this case, I can create the fields in a day, create the grid in five minutes, and then I put it in an email, and done, you know? So this is where you take things to the next level and make it easier for the sales reps to do the things they need to do help us all be successful. >> Did it also sort of abstract, I can imagine, the fundamental challenges that go along with replacing an entire new CRM, going from Salesforce to SAP. >> Yeah. >> Has that been able to help kind of abstract some of the inner machinations of that so that the sales people can just focus on we know this same interface? >> It totally does, because the list views that we create are only the things they have to have. In any system like this you have a bunch of other fields that are specialized for, say, we have a professional services group and they really want to know blah blah but most sales reps, they don't deal with that at all. But you need it on the page, I need to build that. In these views, I can build it for a sales rep view that is perfect for them, right? Meaning there's no extra fields on that list. It's what you need to get your job done. And so it's like a laser focus, and then I can build a separate one for a different kind of role and give that one to them. So without changing the tool, I'm just creating a focused experience. It all uses the same things. You need sorting, you need filtering, you need a simple edit and that's all available and once they learn that core capability then the rest just kind of falls in. >> And then from your perspective it's probably business outcomes that, George, your CEO, is going to be really excited about, cost savings, employee productivity. >> Yep. >> I'm wondering though, we're talking about it in the context of what you're doing within your sales processes and your CRM. Klara, so obviously working with SAP, are there other businesses processes that AppBuddy can sit on top of and help to streamline the interface with? >> Yeah, great question, and actually thank you for asking 'cause I was gonna say, we talked a lot about sales but we could be enabling any other processes as well and services, for example, is a big one. I've got a list, a queue of cases, I want to make quick updates to that. I want to change things or I'm doing some forecasting, some account planning, but our vision, ultimately is to be able to bring from lead to cache all processes and again tailor it for each user, role specifically for them and we're not giving the solution, the customers are defining what do they need for each one of those processes and that's the power, I think, of this configurability and agility that you get. It's not built and hard coded. It's really you who puts it together. But again, we really have that vision of not only linking the CRM data but ultimately we would love to be able to get more use cases of, hey the CRM data together maybe with your ERP data, I want to see my opportunities but I also want to see the orders and I want to see the invoices so get really this 360 view of your customers that I think we've talked a lot about, even Bill McDermott was talking about it. It's so essential and critical to be customer focused is to have that visibility and with this application where you can basically pull data from wherever you need it for that specific view, you give your users that full visibility and therefore much faster answer questions, be in contexts, not lose critical information of a customer. >> Right, you're right, Bill McDermott did mention yesterday in the keynote about really what, SAP's been pretty vocal about for a while, they want to be one of the top 10 global brands. >> Mm-hmm. >> Right. >> Most valuable brands, and they want to be up there with Apple and Google. >> Right. >> And Coca-Cola, and that's for a software company that sells invisible technology, they're on their way. They're now ranked number 17, but he talked about this. >> Yeah. >> Kind of unique position that SAP's in to link and synchronize >> That's right. >> The demand chain with the supply chain >> That's right. >> Which is pretty revolutionary but ultimately, it's not about just having a 360 view of sales automation, it's of the entire customer process. >> Correct, yeah. >> So Steven, sounds like you are a rockstar in that app, with your sales guys going, hey, we need this AppBuddy technology to make our lives easier, our jobs easier. Do you foresee rolling the AppBuddy technology out to include other business processes? >> All the time, yeah, it's all about the data. And change management or getting the field to act in the same way is really hard and it doesn't sound like it should be but, (Lisa laughs) it's like having 1,000 cats on the table and getting them all to look one direction, it just doesn't happen, right? So my job is to make that and if I can have it with a single user experience, right, without having different flavors of screens and extra fields and narrow it down to what they need, bringing whatever data they need to flow from end to end it makes life easier and I've got 'em all trained. You know, we had very high usage in our previous platform and we're building now from that but they all know how to use it now so I don't have to train the cats to look in the same direction, they all know where to go. All I gotta do is add the data, right? And if you look at NetApp's transformation, from a storage company to a data company my job is really data, it's not about the tools as much. It's about how do we facilitate the salespeople to do more with what they have, right? How do I do a cross-sell, up-sell, how do I get them enabled so they can move faster so that's innate and built into what they do? >> Yeah. >> And in that you have to build, and we were just at another panel talking with SAP about, you have to give back to the sales reps and to the people doing the data 'cause CRM's not fun, I mean, it's not like, hey, I'm gonna go play my CRM tonight. (laughs) It's a different deal. CRM requires work and so you need to give them stuff back. Do machine learning, do things that provide scoring, show the probability of close, help them be more successful at their job and bring the data together in one spot. >> You know, I think yesterday one of the themes also was data and trust, the new currency, right? If you can't access it and extract valuable insights immediately and act on them then you risk being usurped by your competition. So being able to enable the data to be accessible, insights gleaned as quickly as possible, you must be the king. >> Well, I don't know about that. >> The data king. (laughs) >> Yeah, it's definitely our job. >> But as we wrap here in the last few seconds, digital transformation and every company has to go through it or you're not relevant but that requires a cultural transformation as well. >> It does. >> And it sounds like what you guys are doing together is helping that at least from the sales force's perspective of where change has to happen. >> Yep. >> Not only is it improving the efficiency of your SAP environment, your CRM environment, but it's also helping, sounds like, from a cultural perspective, as, hey, we've got to go through this transformation, let's make it where we can simplify, let's do that. >> Very much so. Just like I was talking about the cat problem. You've got the reps that are used to doing something the way and you're saying hey, we're gonna evolve and do something different and that change is rough and people don't feel like it's the right thing at times. The great news with this change and the timing of it is that when you're moving from one platform to the other, it's the one time in the life cycle of these products where you can make significant change, drop whole business process and they won't even notice it. I dropped three quarters of the stuff that we had before and just didn't build it. And I don't have people coming to me going, hey, I really miss doing that, and that's good news, we're helping drive the change. >> Yeah. >> Well, thank so much you guys for stopping by theCUBE and Klara telling us about AppBuddy, what you guys do, how you're working together with NetApp and SAP. We appreciate your time. >> Thank you so much. >> Thank you for the opportunity, Lisa, thank you. >> We want to thank you for watching theCUBE. I'm Lisa Martin at SAP Sapphire 2018. Thanks for watching. (upbeat electronic music)

(techy music) >> Announcer: Live from Las Vegas, it's theCUBE, covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back, everyone, to theCUBE's live coverage of ServiceNow Knowledge 18 here in Las Vegas, Nevada. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We're joined by CJ Desai. He is the Chief Product Officer for ServiceNow. Thanks so much for coming on theCUBE again, CJ. >> Thank you, it's great to be here. First time I came was last Knowledge, which was my first Knowledge, so I'm a lot more educated and equipped this time as compared to firing round of questions from Dave last time. >> We will pick your brain, exactly. So you were up on the stage this morning, a great keynote, and you said, "Welcome to the era of great experiences." Unpack that a little bit. What do you mean by that? >> First of all, thank you for remembering that. That was supposed to be the idea. But on a serious note, we feel, if you think about even our company name is ServiceNow, so you provide service, and when you provide service, that's not a technology you provide, you provide an experience, whether it's IT service, customer service, employee, whatever the case might be. And, if you are not delivering experiences, then you are not that relevant. So we are trying to truly, and we are in the beginning of this journey, truly internalize that, that if people are using us, they call themselves service desk, insider organization, IT service desk, customer service desk, whatever the terms you want to use, there is about experiences. Rather than focusing on bits and bytes, we want to focus on experiences, deliver those experiences via our platform. It's not software as a service, it's software as an experience. It's software as an experience, that's the idea, correct. Thank you for-- >> You also talked about the eras. You know, we went back to the industrial era and then went through the ages of computing. Yeah, I was not sure if that was going to work or not, but the point I was trying to make, Dave, was just around the quality of work and how work has evolved. That's it, that was the idea. >> But I think my takeaway was even more than that, because we are entering, in my view, anyway, a new era, and I'd love to get your comments. We're moving from what is real tailwind for you, which is the Cloud era, and obviously, Cloud is an important part of the new era where you have a remote set of services to one where you have this ubiquitous set of digital services that do things like sense, hear, read, act, respond. That's a different world, and it's all about the experience, and I don't know how to define that yet. Digital, I guess, is how we define it. But what are your thoughts? >> The one thing, even simple things, and these are not simple things to understand. When I look at things like even genomic sequencing, that's so different. They are using technology to figure out how to sequence the human genome so that it can help you with your health, live longer, even things like knowing that somebody rings a doorbell at my home and I can see on my phone. Everything is connected, humans are connected, when mobile came and computer came and internet came. But things being connected is pretty exciting for me. That just transforms our lives and how we work, and I really like that it is all about us, and other than us being focusing on the technology itself. So that's the point. It's that we're humans, and let's focus on humans and experience, rather than worry about, oh, this runs two times faster than the other thing, or this thing is smaller than other thing. That's interesting, but not that interesting. >> At this conference, this is really the message that you're getting across. It's the new tag line, we are making the world of work work better for people. How does the Now platform really deliver on that promise? How does it make the employees life easier? I would say we have a bunch of use cases, but as you know, we started out early on with IT service management, and the whole idea was can we provide, as long as computers are there, as long as software is there, password reset is going to be there for a very, very long time. So, my point is that that's when it started. Okay, I need to do password reset, I want to upgrade my laptop. Every year there is a new laptop, every year there is a new phone, and that cycle will continue, and as long as we are using technology for our knowledge workers, IT help desk will be there, right? And where we are evolving is enterprise service management, because you don't, as an employee, you may deal with IT, you may deal with HR, you may have a contractual issue with legal, you may need something related to your payroll from finance. People think payroll is HR, but payroll is finance. And as you try to go across in a day in a life of an employee, you need to make it as easy as possible. So that's what we are focused on, deliver better experiences. You know, artificial intelligence that listen today, I believe, is more about optimization, rather than intelligence. Yeah, we want to use your data to be able to predict, like if you see in Gmail, I don't know if you use Gmail, but if you have Gmail, you get an email, it'll suggest auto-responses. Those auto-responses are almost positive. Have you noticed that? They are never negative. >> Yeah. >> Oh, of course. >> They're like, no, I don't want to come to your meeting. (laughing) It's kind of like trying to predict most likely what you would want to say, and I think if we can use intelligence to make people more productive, that's what we want. >> I mean, I use that function. I actually like it. >> CJ: Yeah, exactly. >> You know, it gives you three choices, and one of 'em is pretty close to what I would normally, and if I'm busy, I'm done. >> Yeah, right, exactly. >> I like that. This is the other thing we've talked about. We've talked about this with Farrel this morning. Try to anticipate my needs, right? So that means you've got to infuse AI into the application and identify specific use cases. You guys have done some M&A there, you talked to the financial analysts meeting, obviously, not disclosing anything, but watch for us to do some more M&A. You got to believe that that machine intelligence space is really ripe for innovation. >> And what we believe is if I look at the big Cloud providers, like Google, are investing a lot in deep learning and many, many other technologies, so whenever they expose it, and some of them do a really good job, we will just leverage their libraries. But there are things specific to enterprise, because there are things specific to enterprise, like if you use the word network at a hardware company, that's always in context of compute network and storage. If you use the word network at a healthcare company, that's a network of physicians, networks of hospitals, networks of whatever. And if you use the word network at a Telco company, that is a whole different network. My point is we want to understand those pieces, and if we can make it easier based on your data, so if all your cases, which are, Oh, part of your network is down. Ah, that's what you mean from the context end point, so we want to use wherever folks like Google are investing, we will leverage that, but if we need to leverage, we'll do that too. >> It's interesting, we were talking to a customer today, it might have been Worldpay, and they took the CMDV language and transformed it into the language of the business. What a rare and powerful concept for somebody from IT to do that, because if the lingua franca is business, then the adoption's going to go through the roof. >> So does that make sense? >> Yeah, it makes a lot of sense. Well, I appreciate you talking about the value and the customer experience versus the technology. Certainly, it speeds and feeds you right. Boring. But the platform is important. Many products, one platform, that's unique for an enterprise software company, and you guys aspire to be the next great enterprise software company. Talk about how the platform enables you to get there. >> So I will tell you simple. You know our founder, Fred Luddy, started with the platform in 2004, so that was 14 years ago now, and his idea was you should be able to route work through the enterprise using our platform, and then we started with the IT service management and use case. The biggest advantage we have is that we are a very customer-driven organization. Many companies say that, but you see it here. Dave, you have been coming to Knowledge for a long time, I don't know about you. >> This is my first rodeo, but it's cool. >> It's the first thing you see. >> These are 80-plus person sessions, are customer sessions. They're not our sessions, where they are sharing best practices with them. So we get all these requests, CJ, we have built emergency response system using ServiceNow, CJ, we have built financial close using ServiceNow. Can you productize it? And we say, okay, thank you for the idea, which is great, thank you for the idea. How do I prioritize all of that? And, Dave, where platform comes in, because all the services I talked about today, service intelligence, service experience, user experience, they're all built in the platform, and I'm trying to be cautious, but if I want to create a brand new product on our platform, a brand new product on our platform, 40-use case, a 1.0 product where I feel comfortable the customers can use it, I would say 12 to 18 engineers. That's it. >> Rebecca: Wow. >> If I want to create one product, it's 12 to 18 engineers. So the R&D leverage, and that's the point I was trying to get across, that whether it's my own team creating product or whether our customer building apps on our product, because on platform, because we provide all the common services integration, the incremental cost to create something, now sales marketing, with my close friend, Dave Schneider, is much harder, because he has to scale it, build specialty in it and all that, but to create the product is not an issue for us on the platform. >> But this is where Cloud economics are so important, because at volume, your marginal costs go to practically zero. >> CJ: That's exactly right. >> But people may say, oh, 12 to 18, that sounds like a lot, but we're talking about an enterprise class software product here, and Fred Luddy, in the 2004 time frame, I mean, the state of enterprise software then, frankly, and now, was terrible. The guys at 37signals, I don't know if you know Jason, they made valid attempts, but it wasn't enterprise class software, it wasn't a platform. I've said, a number of times this week, the reference model for enterprise software is painfully mediocre, so you guys have done a great job, and now you've really got to take the next step and stay ahead on innovation. >> Correct on innovation card, that's what I said, innovation should be my top priority. You heard me at the Financial Analysts Day. Customer Service Management, brand new product, we actually launched it at Knowledge 16. Okay, that's when we launched it. It was engineers and teens who created that product, so many teens, the 1.0, now we have evolved quite a bit, 500 customers two weeks ago, 500 enterprise customers. You guys know that we don't go to the small line of the business. 500 in two years, eight quarters. >> And I found out last night, I think it was 75, or it might even be higher, reference customers. >> CJ: Yeah, already, using CSM. >> That's the difference. I do, we do, a lot of these shows. >> That's the platform impact. >> And you're talking about the customer focus. You do a lot of these shows. The customers talk about the impact on their business. They don't talk about how they installed some box, or like you say, runs faster. It's the business impact that really makes a difference, and that's why we're excited to be here. >> You saw today when I talked about Flow Designer and Integration Hub. IT wants to provide software so that business analysts can model business processes in a Cloud way with whoever you need to integrate with, so we are really keeping that as the north star for our customers, and how can we make their life easier, whatever they want to automate, some manual processes, all of manual processes. I remember speaking to Fred when I joined initially, and I said, "Fred, how did you think about TAM?" He said, "What do you mean, TAM?" You know, he's a funny guy, and he was serious. His point was there are so many manual workflows, how do you put a TAM around it? Every business is unique, their processes are complex, so don't box yourself and say, Oh, this is a $4 billion TAM and I'm going to get 20% of it. Every enterprise, as long as they exist, they will have manual workflows, you go and give it our platform so they can automate however they want. >> Well, I'm going to make you laugh about TAM. I'm a former industry analyst, so when you guys did the IPO way back when, well before your time-- >> CJ: 2012. >> when Frank was here, there was a research company saying this is small market, maybe it's a billion dollars and it's shrinking, so I, with some of my colleagues, developed a TAM analysis, and it was more than 30 billion. I published 30 billion, you can go on our old Wiki and see that, and the guy said to me, "Dave, you can't publish more than 30 billion. You'll look like a fool." The TAM is much, much bigger than 30 billion. You can't even quantify it, it's so large when you start looking at it. >> And now, because people are recognizing that we automate all the manual workflows in a enterprise on a Cloud platform, last week somebody published a report and I just saw the headlines, I didn't go through the details, 126 billion. So from in 2012 to that small number, and we don't know what the number is. >> Could it be bigger? >> I would have no idea. I would be completely disingenuous if I told you I know what my TAM is, but I don't think that way. I say what customer problems can I solve? >> Well, that's what I wanted to ask you. So you're here with so many different customers. Just on the show, we've had ones in payments, in insurance, in health care. What are you hearing from customers, and what are sort of your favorite applications of what you're doing? What makes you the proudest? >> Yeah, so I would say the proudest moments for me are when I'm like, wow, you do that with ServiceNow? I would have never thought that. So when I didn't expect, when I expect something, Oh, I had this routine email, text collaboration, and I switched it to ServiceNow, get it, like not a big aha moment. I had this one customer who said he has a big distribution network, all these partners, and those guys have ServiceNow, he has ServiceNow, and when they have problem with the product, their product, my customer's product, they all communicate via ServiceNow to each other. So they have created a whole ServiceNow network, truly a B2B kind of exchange, kind of, using ServiceNow. One of our median and entertainment customers who owns a bunch of parks, they refill the popcorn machine using ServiceNow. When the popcorn levels dip, they have those people who carry around the cart, Oh! The popcorn level dip, it marks the sensor, it routines the workflow, goes to the corporate, Ah, we need to fill up popcorn on by this particular ride. For me-- >> And even at my house, I love it. >> Yeah, so that's exciting to me. >> We talked to Siemens today. >> Yes, great customer. >> Awesome, and I want to run a line by you. We talk about AI a lot, machine intelligence. I wrote down during, you know, data is the fuel for AI. Well, you know we love data here at theCUBE, and he was describing that, he said, you know, even though CJ was not prescribing taking the data out, we could leave it in so it learns, right now, we take some of the data out. Well, you described that. Well, we put it to SAP HANA, we throw a little Watson in there, we do some Azure, machine learning, we use Tableau for visualization, he's probably got some Hadoop and Kafka in there, a very complicated, big data pipeline. And I said to him, Okay, in two years, do you want to do that inside of ServiceNow? He goes, "Absolutely. That would be my dream come true." So, I guess I'm laying down the gauntlet. Do you see that as a reality? >> So, we are talk to Siemens, great customer, they keep us honest, so I love that and I did actually meet the team who was in charge of their BI and reporting and they did share the same story a few months ago when I met them. And we are trying to figure out, Dave, if I knew the answer, I would have told you, but you know my style. I don't know the answer. We are seriously trying to figure out, Do we become an analytics hub? We are really good with ServiceNow data, we can build connectors with other data, but do I want to be in the BI and reporting market? Absolutely not. Do I want to help customers as their processes span across and provide them more visual credit tools than others, text-based searches, whatever they need, the answer is yes. Performance analytics, as you know, we have been moving along really at a good pace, and now we have what every single product, but this is something that Eric Miller, who runs that business, we talk about it all the time, because currently our analytics is building the platform, and now you know that data has a Cloud issue, so if you have data here, you have data there, you have data there, we are in our own Cloud. Can we build a connector, potentially, to OnPrem? Don't know the answer, but this is something, it's a fair gauntlet having to solve. >> Humbly, I'd like to give you my input, if I may. >> Yes. >> We see innovation, as I said before, it's data, applying machine learning to that data, and then leveraging Cloud economics. The project with big data projects, as you well know, is the complexity has killed them. Now you see the Cloud guys, whether it's Amazon or Microsoft, and that's where the data pipelines are being simplified and built. Now, I don't know if it's the right business decision for you guys, but wow, wouldn't that be powerful if you guys could do that, certainly, for your customers. >> And, truly, that is, as you heard me on Financial Analysts Day, I'm a huge fan of Geoffrey Moore's work, and he defines system of record, ERP CRM, system of action where we fall in, and then he has System of Intelligence, which is all the things around data and how do you harness the power of data. And that's something that I really, in our product teams, we talk about all the time, if I can solve Siemens problem with everything in ServiceNow, that'd be awesome, but is that something I want to prioritize right now, or is there something, we should give them the flexibility. I don't know. >> Well, you're one of the top product guys in our industry. It's why they found you. No, seriously, I put you up there with the greats. >> You're kind, thank you. >> It's true. You've got an incredible future ahead of you. But as a lead product person, you have to make those decisions, and you have to be very circumspect about where you put your resources. You can't just run to every customer requirement, right? >> And I tell, coincidentally, my wife asks me What's your job, by the way? I said, that's a good question. >> I'm married to a product officer, too, I feel the same way. What do you do all day? You do a lot of meetings. >> Yeah, exactly. So I said that I do a lot of meetings, and she said why do you do a lot of meetings? And I said I'm making a some decision or help my team make a decision because they already analyze a bunch of things. And I said, my hope is, as long as I can make more good decisions than bad decisions, specifically about product strategy, because you never know unless you make the chess pieces move and think of two or three steps ahead, and some things could be right and some things could be wrong. I have a simple framework on my whiteboard for every meeting. No jokes, right? So, my framework is very simple. Question number one, What customer problems we are trying to solve. If you cannot articulate that, for any new product idea you have, I don't go past that question, What customer problem we are trying to solve? Second is Why now? Why do we need to solve this problem now? Like you said, there are many problems, which one are you prioritize? And then, third, Why us? Why should we solve that problem? So, if you can articulate the problem, which always is a challenge because you kind of know what problems you have, but unless you really, really understand the customer pain point, you cannot articulate it. Then you say, why now? Like why is the time right now for us to invest in this, say, analytics, as a service? Why right now? And, third, why you, as in why us? Why is ServiceNow should solve it? That, at least, gives me a guiding compass to say because I have many products, as you know, I am very protective of our platform, and all these use cases come in, every product line wants to go deeper, rightfully so, because they are trying to solve for customers, and the new products want to be built on this platform. Sometimes I say maybe a partner should build it, so we made a decision, facilities product, Should our ISB partner build it? And that's the right place because we feel they are more suited, they have the skill set, all of that. But that's it, what problem, why now, why you? >> Rebecca: Really, I love it. >> Well, the Why you? it's a great framework. The why you is unclear for the Siemens problem, and I can understand that. You take the DemOps announcement that Pat stole from you today-- >> I know, that's not cool, man. >> But that's a problem that you guys solved internally, clear problem. >> He did a nice job of articulating it, very nice job. >> Yeah, definitely. >> But we feel that there always is a process when you need a workflow across, because in planning there are a bunch of companies, as the patch, or in build there are a bunch of companies in develop there are a bunch of companies. That's fine. They could be the system of records for those chevrons and we are the workflow that cuts across. So we feel loved. We showed our value to our customers by doing that. >> Rebecca: That's great. >> I know we've got to go, but lastly, it's roadmap. Last year, you talked about how you guys do releases by alphabet, twice a year. You were really transparent today, laid out the room and talked a lot about Madrid, you laid out well into the future what you guys are doing so, as an analyst, I love that. I'm sure you're customers love it, so-- >> A lot of people to picture, so that's nice. And Twitter, a lot of people posted on social media as well, so clearly there was a customer pain point, as we call it, that they needed a roadmap. In speaking to customers last one year, number one thing, if you tell us what you're building, then we don't have to build it. If you tell us when you're shipping, then we can plan around it, and then we will set aside resources to do testing. Any Cloud software company, whether it's us, CRM software or HR software, people still test, because you cannot mess up your employee experience or customer experience, and they just said give us a predictable schedule, please, so that we know. We did say two times a year, but we were not prescriptive which quarter. It could be four months and eight months, it could be six and six, it could be seven and five. I'm currently going with the quarterly-level fidelity, and eventually, I want to get to a month-level fidelity, where I say March and September, once our internal processes are organized. >> So the other subtlety there, and I know we got to go, is the ecosystem, because you're giving visibility, they have to make bets. They're making a bet on service, but then where's the white space? They're betting on white space. If you're exposing that to them, they can say, Oh, not going to solve that problem. ServiceNow's going to solve it in two quarters. >> I agree. >> Huge difference for them. >> You guys are wonderful. Thank you so much for inviting me. >> Rebecca: Thank you for coming on the show. We appreciate it. >> No, that's awesome, thank you, thank you. >> Dave: Great to have you. >> Rebecca: Great to have you. I'm Rebecca Knight, for Dave Vellante. We'll have more from ServiceNow Knowledge 18 just after this. (techy music)