>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> This past week we saw two of the Big 3 cloud providers present the latest update on their respective cloud visions, their business progress, their announcements and innovations. The content at these events had many overlapping themes, including modern cloud infrastructure at global scale, applying advanced machine intelligence, AKA AI, end-to-end data platforms, collaboration software. They talked a lot about the future of work automation. And they gave us a little taste, each company of the Metaverse Web 3.0 and much more. Despite these striking similarities, the differences between these two cloud platforms and that of AWS remains significant. With Microsoft leveraging its massive application software footprint to dominate virtually all markets and Google doing everything in its power to keep up with the frenetic pace of today's cloud innovation, which was set into motion a decade and a half ago by AWS. Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this Breaking Analysis, we unpack the immense amount of content presented by the CEOs of Microsoft and Google Cloud at Microsoft Ignite and Google Cloud Next. We'll also quantify with ETR survey data the relative position of these two cloud giants in four key sectors: cloud IaaS, BI analytics, data platforms and collaboration software. Now one thing was clear this past week, hybrid events are the thing. Google Cloud Next took place live over a 24-hour period in six cities around the world, with the main gathering in New York City. Microsoft Ignite, which normally is attended by 30,000 people, had a smaller event in Seattle, in person with a virtual audience around the world. AWS re:Invent, of course, is much different. Yes, there's a virtual component at re:Invent, but it's all about a big live audience gathering the week after Thanksgiving, in the first week of December in Las Vegas. Regardless, Satya Nadella keynote address was prerecorded. It was highly produced and substantive. It was visionary, energetic with a strong message that Azure was a platform to allow customers to build their digital businesses. Doing more with less, which was a key theme of his. Nadella covered a lot of ground, starting with infrastructure from the compute, highlighting a collaboration with Arm-based, Ampere processors. New block storage, 60 regions, 175,000 miles of fiber cables around the world. He presented a meaningful multi-cloud message with Azure Arc to support on-prem and edge workloads, as well as of course the public cloud. And talked about confidential computing at the infrastructure level, a theme we hear from all cloud vendors. He then went deeper into the end-to-end data platform that Microsoft is building from the core data stores to analytics, to governance and the myriad tooling Microsoft offers. AI was next with a big focus on automation, AI, training models. He showed demos of machines coding and fixing code and machines automatically creating designs for creative workers and how Power Automate, Microsoft's RPA tooling, would combine with Microsoft Syntex to understand documents and provide standard ways for organizations to communicate with those documents. There was of course a big focus on Azure as developer cloud platform with GitHub Copilot as a linchpin using AI to assist coders in low-code and no-code innovations that are coming down the pipe. And another giant theme was a workforce transformation and how Microsoft is using its heritage and collaboration and productivity software to move beyond what Nadella called productivity paranoia, i.e., are remote workers doing their jobs? In a world where collaboration is built into intelligent workflows, and he even showed a glimpse of the future with AI-powered avatars and partnerships with Meta and Cisco with Teams of all firms. And finally, security with a bevy of tools from identity, endpoint, governance, et cetera, stressing a suite of tools from a single provider, i.e., Microsoft. So a couple points here. One, Microsoft is following in the footsteps of AWS with silicon advancements and didn't really emphasize that trend much except for the Ampere announcement. But it's building out cloud infrastructure at a massive scale, there is no debate about that. Its plan on data is to try and provide a somewhat more abstracted and simplified solutions, which differs a little bit from AWS's approach of the right database tool, for example, for the right job. Microsoft's automation play appears to provide simple individual productivity tools, kind of a ground up approach and make it really easy for users to drive these bottoms up initiatives. We heard from UiPath that forward five last month, a little bit of a different approach of horizontal automation, end-to-end across platforms. So quite a different play there. Microsoft's angle on workforce transformation is visionary and will continue to solidify in our view its dominant position with Teams and Microsoft 365, and it will drive cloud infrastructure consumption by default. On security as well as a cloud player, it has to have world-class security, and Azure does. There's not a lot of debate about that, but the knock on Microsoft is Patch Tuesday becomes Hack Wednesday because Microsoft releases so many patches, it's got so much Swiss cheese in its legacy estate and patching frequently, it becomes a roadmap and a trigger for hackers. Hey, patch Tuesday, these are all the exploits that you can go after so you can act before the patches are implemented. And so it's really become a problem for users. As well Microsoft is competing with many of the best-of-breed platforms like CrowdStrike and Okta, which have market momentum and appear to be more attractive horizontal plays for customers outside of just the Microsoft cloud. But again, it's Microsoft. They make it easy and very inexpensive to adopt. Now, despite the outstanding presentation by Satya Nadella, there are a couple of statements that should raise eyebrows. Here are two of them. First, as he said, Azure is the only cloud that supports all organizations and all workloads from enterprises to startups, to highly regulated industries. I had a conversation with Sarbjeet Johal about this, to make sure I wasn't just missing something and we were both surprised, somewhat, by this claim. I mean most certainly AWS supports more certifications for example, and we would think it has a reasonable case to dispute that claim. And the other statement, Nadella made, Azure is the only cloud provider enabling highly regulated industries to bring their most sensitive applications to the cloud. Now, reasonable people can debate whether AWS is there yet, but very clearly Oracle and IBM would have something to say about that statement. Now maybe it's not just, would say, "Oh, they're not real clouds, you know, they're just going to hosting in the cloud if you will." But still, when it comes to mission-critical applications, you would think Oracle is really the the leader there. Oh, and Satya also mentioned the claim that the Edge browser, the Microsoft Edge browser, no questions asked, he said, is the best browser for business. And we could see some people having some questions about that. Like isn't Edge based on Chrome? Anyway, so we just had to question these statements and challenge Microsoft to defend them because to us it's a little bit of BS and makes one wonder what else in such as awesome keynote and it was awesome, it was hyperbole. Okay, moving on to Google Cloud Next. The keynote started with Sundar Pichai doing a virtual session, he was remote, stressing the importance of Google Cloud. He mentioned that Google Cloud from its Q2 earnings was on a $25-billion annual run rate. What he didn't mention is that it's also on a 3.6 billion annual operating loss run rate based on its first half performance. Just saying. And we'll dig into that issue a little bit more later in this episode. He also stressed that the investments that Google has made to support its core business and search, like its global network of 22 subsea cables to support things like, YouTube video, great performance obviously that we all rely on, those innovations there. Innovations in BigQuery to support its search business and its threat analysis that it's always had and its AI, it's always been an AI-first company, he's stressed, that they're all leveraged by the Google Cloud Platform, GCP. This is all true by the way. Google has absolutely awesome tech and the talk, as well as his talk, Pichai, but also Kurian's was forward thinking and laid out a vision of the future. But it didn't address in our view, and I talked to Sarbjeet Johal about this as well, today's challenges to the degree that Microsoft did and we expect AWS will at re:Invent this year, it was more out there, more forward thinking, what's possible in the future, somewhat less about today's problem, so I think it's resonates less with today's enterprise players. Thomas Kurian then took over from Sundar Pichai and did a really good job of highlighting customers, and I think he has to, right? He has to say, "Look, we are in this game. We have customers, 9 out of the top 10 media firms use Google Cloud. 8 out of the top 10 manufacturers. 9 out of the top 10 retailers. Same for telecom, same for healthcare. 8 out of the top 10 retail banks." He and Sundar specifically referenced a number of companies, customers, including Avery Dennison, Groupe Renault, H&M, John Hopkins, Prudential, Minna Bank out of Japan, ANZ bank and many, many others during the session. So you know, they had some proof points and you got to give 'em props for that. Now like Microsoft, Google talked about infrastructure, they referenced training processors and regions and compute optionality and storage and how new workloads were emerging, particularly data-driven workloads in AI that required new infrastructure. He explicitly highlighted partnerships within Nvidia and Intel. I didn't see anything on Arm, which somewhat surprised me 'cause I believe Google's working on that or at least has come following in AWS's suit if you will, but maybe that's why they're not mentioning it or maybe I got to do more research there, but let's park that for a minute. But again, as we've extensively discussed in Breaking Analysis in our view when it comes to compute, AWS via its Annapurna acquisition is well ahead of the pack in this area. Arm is making its way into the enterprise, but all three companies are heavily investing in infrastructure, which is great news for customers and the ecosystem. We'll come back to that. Data and AI go hand in hand, and there was no shortage of data talk. Google didn't mention Snowflake or Databricks specifically, but it did mention, by the way, it mentioned Mongo a couple of times, but it did mention Google's, quote, Open Data cloud. Now maybe Google has used that term before, but Snowflake has been marketing the data cloud concept for a couple of years now. So that struck as a shot across the bow to one of its partners and obviously competitor, Snowflake. At BigQuery is a main centerpiece of Google's data strategy. Kurian talked about how they can take any data from any source in any format from any cloud provider with BigQuery Omni and aggregate and understand it. And with the support of Apache Iceberg and Delta and Hudi coming in the future and its open Data Cloud Alliance, they talked a lot about that. So without specifically mentioning Snowflake or Databricks, Kurian co-opted a lot of messaging from these two players, such as life and tech. Kurian also talked about Google Workspace and how it's now at 8 million users up from 6 million just two years ago. There's a lot of discussion on developer optionality and several details on tools supported and the open mantra of Google. And finally on security, Google brought out Kevin Mandian, he's a CUBE alum, extremely impressive individual who's CEO of Mandiant, a leading security service provider and consultancy that Google recently acquired for around 5.3 billion. They talked about moving from a shared responsibility model to a shared fate model, which is again, it's kind of a shot across AWS's bow, kind of shared responsibility model. It's unclear that Google will pay the same penalty if a customer doesn't live up to its portion of the shared responsibility, but we can probably assume that the customer is still going to bear the brunt of the pain, nonetheless. Mandiant is really interesting because it's a services play and Google has stated that it is not a services company, it's going to give partners in the channel plenty of room to play. So we'll see what it does with Mandiant. But Mandiant is a very strong enterprise capability and in the single most important area security. So interesting acquisition by Google. Now as well, unlike Microsoft, Google is not competing with security leaders like Okta and CrowdStrike. Rather, it's partnering aggressively with those firms and prominently putting them forth. All right. Let's get into the ETR survey data and see how Microsoft and Google are positioned in four key markets that we've mentioned before, IaaS, BI analytics, database data platforms and collaboration software. First, let's look at the IaaS cloud. ETR is just about to release its October survey, so I cannot share the that data yet. I can only show July data, but we're going to give you some directional hints throughout this conversation. This chart shows net score or spending momentum on the vertical axis and overlap or presence in the data, i.e., how pervasive the platform is. That's on the horizontal axis. And we've inserted the Wikibon estimates of IaaS revenue for the companies, the Big 3. Actually the Big 4, we included Alibaba. So a couple of points in this somewhat busy data chart. First, Microsoft and AWS as always are dominant on both axes. The red dotted line there at 40% on the vertical axis. That represents a highly elevated spending velocity and all of the Big 3 are above the line. Now at the same time, GCP is well behind the two leaders on the horizontal axis and you can see that in the table insert as well in our revenue estimates. Now why is Azure bigger in the ETR survey when AWS is larger according to the Wikibon revenue estimates? And the answer is because Microsoft with products like 365 and Teams will often be considered by respondents in the survey as cloud by customers, so they fit into that ETR category. But in the insert data we're stripping out applications and SaaS from Microsoft and Google and we're only isolating on IaaS. The other point is when you take a look at the early October returns, you see downward pressure as signified by those dotted arrows on every name. The only exception was Dell, or Dell and IBM, which showing slightly improved momentum. So the survey data generally confirms what we know that AWS and Azure have a massive lead and strong momentum in the marketplace. But the real story is below the line. Unlike Google Cloud, which is on pace to lose well over 3 billion on an operating basis this year, AWS's operating profit is around $20 billion annually. Microsoft's Intelligent Cloud generated more than $30 billion in operating income last fiscal year. Let that sink in for a moment. Now again, that's not to say Google doesn't have traction, it does and Kurian gave some nice proof points and customer examples in his keynote presentation, but the data underscores the lead that Microsoft and AWS have on Google in cloud. And here's a breakdown of ETR's proprietary net score methodology, that vertical axis that we showed you in the previous chart. It asks customers, are you adopting the platform new? That's that lime green. Are you spending 6% or more? That's the forest green. Is you're spending flat? That's the gray. Is you're spending down 6% or worse? That's the pinkest color. Or are you replacing the platform, defecting? That's the bright red. You subtract the reds from the greens and you get a net score. Now one caveat here, which actually is really favorable from Microsoft, the Microsoft data that we're showing here is across the entire Microsoft portfolio. The other point is, this is July data, we'll have an update for you once ETR releases its October results. But we're talking about meaningful samples here, the ends. 620 for AWS over a thousand from Microsoft in more than 450 respondents in the survey for Google. So the real tell is replacements, that bright red. There is virtually no churn for AWS and Microsoft, but Google's churn is 5x, those two in the survey. Now 5% churn is not high, but you'd like to see three things for Google given it's smaller size. One is less churn, two is much, much higher adoption rates in the lime green. Three is a higher percentage of those spending more, the forest green. And four is a lower percentage of those spending less. And none of these conditions really applies here for Google. GCP is still not growing fast enough in our opinion, and doesn't have nearly the traction of the two leaders and that shows up in the survey data. All right, let's look at the next sector, BI analytics. Here we have that same XY dimension. Again, Microsoft dominating the picture. AWS very strong also in both axes. Tableau, very popular and respectable of course acquired by Salesforce on the vertical axis, still looking pretty good there. And again on the horizontal axis, big presence there for Tableau. And Google with Looker and its other platforms is also respectable, but it again, has some work to do. Now notice Streamlit, that's a recent Snowflake acquisition. It's strong in the vertical axis and because of Snowflake's go-to-market (indistinct), it's likely going to move to the right overtime. Grafana is also prominent in the Y axis, but a glimpse at the most recent survey data shows them slightly declining while Looker actually improves a bit. As does Cloudera, which we'll move up slightly. Again, Microsoft just blows you away, doesn't it? All right, now let's get into database and data platform. Same X Y dimensions, but now database and data warehouse. Snowflake as usual takes the top spot on the vertical axis and it is actually keeps moving to the right as well with again, Microsoft and AWS is dominant in the market, as is Oracle on the X axis, albeit it's got less spending velocity, but of course it's the database king. Google is well behind on the X axis but solidly above the 40% line on the vertical axis. Note that virtually all platforms will see pressure in the next survey due to the macro environment. Microsoft might even dip below the 40% line for the first time in a while. Lastly, let's look at the collaboration and productivity software market. This is such an important area for both Microsoft and Google. And just look at Microsoft with 365 and Teams up into the right. I mean just so impressive in ubiquitous. And we've highlighted Google. It's in the pack. It certainly is a nice base with 174 N, which I can tell you that N will rise in the next survey, which is an indication that more people are adopting. But given the investment and the tech behind it and all the AI and Google's resources, you'd really like to see Google in this space above the 40% line, given the importance of this market, of this collaboration area to Google's success and the degree to which they emphasize it in their pitch. And look, this brings up something that we've talked about before on Breaking Analysis. Google doesn't have a tech problem. This is a go-to-market and marketing challenge that Google faces and it's up against two go-to-market champs and Microsoft and AWS. And Google doesn't have the enterprise sales culture. It's trying, it's making progress, but it's like that racehorse that has all the potential in the world, but it's just missing some kind of key ingredient to put it over at the top. It's always coming in third, (chuckles) but we're watching and Google's obviously, making some investments as we shared with earlier. All right. Some final thoughts on what we learned this week and in this research: customers and partners should be thrilled that both Microsoft and Google along with AWS are spending so much money on innovation and building out global platforms. This is a gift to the industry and we should be thankful frankly because it's good for business, it's good for competitiveness and future innovation as a platform that can be built upon. Now we didn't talk much about multi-cloud, we haven't even mentioned supercloud, but both Microsoft and Google have a story that resonates with customers in cross cloud capabilities, unlike AWS at this time. But we never say never when it comes to AWS. They sometimes and oftentimes surprise you. One of the other things that Sarbjeet Johal and John Furrier and I have discussed is that each of the Big 3 is positioning to their respective strengths. AWS is the best IaaS. Microsoft is building out the kind of, quote, we-make-it-easy-for-you cloud, and Google is trying to be the open data cloud with its open-source chops and excellent tech. And that puts added pressure on Snowflake, doesn't it? You know, Thomas Kurian made some comments according to CRN, something to the effect that, we are the only company that can do the data cloud thing across clouds, which again, if I'm being honest is not really accurate. Now I haven't clarified these statements with Google and often things get misquoted, but there's little question that, as AWS has done in the past with Redshift, Google is taking a page out of Snowflake, Databricks as well. A big difference in the Big 3 is that AWS doesn't have this big emphasis on the up-the-stack collaboration software that both Microsoft and Google have, and that for Microsoft and Google will drive captive IaaS consumption. AWS obviously does some of that in database, a lot of that in database, but ISVs that compete with Microsoft and Google should have a greater affinity, one would think, to AWS for competitive reasons. and the same thing could be said in security, we would think because, as I mentioned before, Microsoft competes very directly with CrowdStrike and Okta and others. One of the big thing that Sarbjeet mentioned that I want to call out here, I'd love to have your opinion. AWS specifically, but also Microsoft with Azure have successfully created what Sarbjeet calls brand distance. AWS from the Amazon Retail, and even though AWS all the time talks about Amazon X and Amazon Y is in their product portfolio, but you don't really consider it part of the retail organization 'cause it's not. Azure, same thing, has created its own identity. And it seems that Google still struggles to do that. It's still very highly linked to the sort of core of Google. Now, maybe that's by design, but for enterprise customers, there's still some potential confusion with Google, what's its intentions? How long will they continue to lose money and invest? Are they going to pull the plug like they do on so many other tools? So you know, maybe some rethinking of the marketing there and the positioning. Now we didn't talk much about ecosystem, but it's vital for any cloud player, and Google again has some work to do relative to the leaders. Which brings us to supercloud. The ecosystem and end customers are now in a position this decade to digitally transform. And we're talking here about building out their own clouds, not by putting in and building data centers and installing racks of servers and storage devices, no. Rather to build value on top of the hyperscaler gift that has been presented. And that is a mega trend that we're watching closely in theCUBE community. While there's debate about the supercloud name and so forth, there little question in our minds that the next decade of cloud will not be like the last. All right, we're going to leave it there today. Many thanks to Sarbjeet Johal, and my business partner, John Furrier, for their input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast and Ken Schiffman as well. Kristen Martin and Cheryl Knight helped get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does some wonderful editing. And check out SiliconANGLE, a lot of coverage on Google Cloud Next and Microsoft Ignite. Remember, all these episodes are available as podcast wherever you listen. Just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can always get in touch with me via email, david.vellante@siliconangle.com or you can DM me at dvellante or comment on my LinkedIn posts. And please do check out etr.ai, the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (gentle music)

>> Announcer: From the Cube's studios in Palo Alto in Boston, bringing you data-driven insights from The Cube and ETR. This is "Breaking Analysis" with Dave Vellante. >> Exactly one week after Pat Gelsinger's announcement of his plans to reinvent Intel. Arm announced version nine of its architecture and laid out its vision for the next decade. We believe this vision is extremely strong as it combines an end-to-end capability from Edge to Cloud, to the data center, to the home and everything in between. Arms aspirations are ambitious and powerful. Leveraging its business model, ecosystem and software compatibility with previous generations. Hello every one and welcome to this week's Wikibon Cube Insights powered by ETR. And this breaking analysis will explain why we think this announcement is so important and what it means for Intel and the broader technology landscape. We'll also share with you some feedback that we received from the Cube Community on last week's episode and a little inside baseball on how Intel, IBM, Samsung, TSMC and the U.S. government might be thinking about the shifting landscape of semiconductor technology. Now, there were two notable announcements this week that were directly related to Intel's announcement of March 23rd. The Armv9 news and TSMC's plans to invest a $100 billion in chip manufacturing and development over the next three years. That is a big number. It appears to tramp Intel's plan $20 billion investment to launch two new fabs in the U.S. starting in 2024. You may remember back in 2019, Samsung pledged to invest a $116 billion to diversify its production beyond memory trip, memory chips. Why are all these companies getting so aggressive? And won't this cause a glut in chips? Well, first, China looms large and aims to dominate its local markets, which in turn is going to confer advantages globally. The second, there's a huge chip shortage right now. And the belief is that it's going to continue through the decade and possibly beyond. We are seeing a new inflection point in the demand as we discussed last week. Stemming from digital, IOT, cloud, autos in new use cases in the home as so well presented by Sarjeet Johal in our community. As to the glut, these manufacturers believe that demand will outstrip supply indefinitely. And I understand that a lack of manufacturing capacity is actually more deadly than an oversupply. Look, if there's a glut, manufacturers can cut production and take the financial hit. Whereas capacity constraints mean you can miss entire cycles of growth and really miss out on the demand and the cost reductions. So, all these manufacturers are going for it. Now let's talk about Arm, its approach and the announcements that it made this week. Now last week, we talked about how Pat Gelsinger his vision of a system on package was an attempt to leapfrog system on chip SOC, while Arm is taking a similar system approach. But in our view, it's even broader than the vision laid out by Pat at Intel. Arm is targeting a wide variety of use cases that are shown here. Arm's fundamental philosophy is that the future will require highly specialized chips and Intel as you recall from Pat's announcement, would agree. But Arm historically takes an ecosystem approach that is different from Intel's model. Arm is all about enabling the production of specialized chips to really fit a specific application. For example, think about the amount of AI going on iPhones. They move if I remember from fingerprint to face recognition. This requires specialized neural processing units, NPUs that are designed by Apple for that particular use case. Arm is facilitating the creation of these specialized chips to be designed and produced by the ecosystem. Intel on the other hand has historically taken a one size fits all approach. Built around the x86. The Intel's design has always been about improving the processor. For example, in terms of speed, density, adding vector processing to accommodate AI, et cetera. And Intel does all the design and the manufacturing in any specialization for the ecosystem is done by Intel. Much of the value, that's added from the ecosystem is frankly been bending metal or adding displays or other features at the margin. But, the advantage is that the x86 architecture is well understood. It's consistent, reliable, and let's face it. Most enterprise software runs on x86. So, but very, very different models historically, which we heard from Gelsinger last week they're going to change with a new trusted foundry strategy. Now let's go through an example that might help explain the power of Arm's model. Let's say, your AWS and you're doing graviton. Designing graviton and graviton2. Or Apple, designing the M1 chip, or Tesla designing its own chip, or any other company in in any one of these use cases that are shown here. Tesla is a really good example. In order to optimize for video processing, Tesla needed to add specialized code firmware in the NPU for it's specific use case within autos. It was happy to take off the shelf CPU or GPU or whatever, and leverage Arm's standards there. And then it added its own value in the NPU. So the advantage of this model is Tesla could go from tape out in less or, or, or or in less than a year versus get the tape out in less than a year versus what would normally take many years. Arm is, think of Arm is like customize a Lego blocks that enable unique value add by the ecosystem with a much faster time to market. So like I say, the Tesla goes from logical tape out if you will, to Samsung and then says, okay run this against your manufacturing process. And it should all work as advertised by Arm. Tesla, interestingly, just as an aside chose the 14 nanometer process to keep its costs down. It didn't need the latest and greatest density. Okay, so you can see big difference in philosophies historically between Arm and Intel. And you can see Intel vectoring toward the Arm model based on what Gelsinger said last week for its foundry business. Essentially it has to. Now, Arm announced a new Arm architecture, Armv9. v9 is backwards compatible with previous generations. Perhaps Arm learned from Intel's failed, Itanium effort for those remember that word. Had no backward compatibility and it really floundered. As well, Arm adds some additional capabilities. And today we're going to focus on the two areas that have highlighted, machine learning piece and security. I'll take note of the call out, 300 billion chips. That's Arm's vision. That's a lot. And we've said, before, Arm's way for volumes are 10X those of x86. Volume, we sound like a broken record. Volume equals cost reduction. We'll come back to that a little bit later. Now let's have a word on AI and machine learning. Arm is betting on AI and ML. Big as are many others. And this chart really shows why, it's a graphic that shows ETR data and spending momentum and pervasiveness in the dataset across all the different sectors that ETR tracks within its taxonomy. Note that ML/AI gets the top spot on the vertical axis, which represents net score. That's a measure of spending momentum or spending velocity. The horizontal axis is market share presence in the dataset. And we give this sector four stars to signify it's consistent lead in the data. So pretty reasonable bet by Arm. But the other area that we're going to talk about is security. And its vision day, Arm talked about confidential compute architecture and these things called realms. Note in the left-hand side, showing data traveling all over the different use cases and around the world and the call-out from the CISO below, it's a large public airline CISO that spoke at an ETR Venn round table. And this individual noted that the shifting end points increase the threat vectors. We all know that. Arm said something that really resonated. Specifically, they said today, there's far too much trust on the OS and the hypervisor that are running these applications. And their broad access to data is a weakness. Arm's concept of realms as shown in the right-hand side, underscores the company strategy to remove the assumption that privileged software. Like the hypervisor needs to be able to see the data. So by creating realms, in a virtualized multi-tenant environment, data can be more protected from memory leaks which of course is a major opportunity for hackers that they exploit. So it's a nice concept in a way for the system to isolate attendance data from other users. Okay, we want, we want to share some feedback that we got last week from the community on our analysis of Intel. A tech exec from city pointed out that, Intel really didn't miss a mobile, as we said, it really missed smartphones. In fact, whell, this is a kind of a minor distinction, it's important to recognize we think. Because Intel facilitated WIFI with Centrino, under the direction of Paul Alini. Who by the way, was not an engineer. I think he was the first non-engineer to be the CEO of Intel. He was a marketing person by background. Ironically, Intel's work in wifi connectivity enabled, actually enabled the smartphone revolution. And maybe that makes the smartphone missed by Intel all that more egregious, I don't know. Now the other piece of feedback we received related to our IBM scenario and our three-way joint venture prediction bringing together Intel, IBM, and Samsung in a triumvirate where Intel brings the foundry and it's process manufacturing. IBM brings its dis-aggregated memory technology and Samsung brings its its volume and its knowledge of of volume down the learning curve. Let's start with IBM. Remember we said that IBM with power 10 has the best technology in terms of this notion of dis-aggregating compute from memory and sharing memory in a pool across different processor types. So a few things in this regard, IBM when it restructured its micro electronics business under Ginni Rometty, catalyzed the partnership with global foundries and you know, this picture in the upper right it shows the global foundries facility outside of Albany, New York in Malta. And the partnership included AMD and Samsung. But we believe that global foundries is backed away from some of its contractual commitments with IBM causing a bit of a rift between the companies and leaving a hole in your original strategy. And evidently AMD hasn't really leaned in to move the needle in any way and so the New York foundry, is it a bit of a state of limbo with respect to its original vision. Now, well, Arvind Krishna was the face of the Intel announcement. It clearly has deep knowledge of IBM semiconductor strategy. Dario Gill, we think is a key player in the mix. He's the senior vice president director of IBM research. And it is in a position to affect some knowledge sharing and maybe even knowledge transfer with Intel possibly as it relates to disaggregated architecture. His questions remain as to how open IBM will be. And how protected it will be with its IP. It's got, as we said, last week, it's got to have an incentive to do so. Now why would IBM do that? Well, it wants to compete more effectively with VMware who has done a great job leveraging x86 and that's the biggest competitor in threat to open shift. So Arvind needs Intel chips to really execute on IBM's cloud strategy. Because almost all of IBM's customers are running apps on x86. So IBM's cloud and hybrid cloud. Strategy really need to leverage that Intel partnership. Now Intel for its part has great FinFET technology. FinFET is a tactic goes beyond CMOs. You all mainframes might remember when IBM burned the boat on ECL, Emitter-coupled Logic. And then moved to CMOs for its mainframes. Well, this is the next gen beyond, and it could give Intel a leg up on AMD's chiplet intellectual properties. Especially as it relates to latency. And there could be some benefits there for IBM. So maybe there's a quid pro quo going on. Now, where it really gets interesting is New York Senator, Chuck Schumer, is keen on building up an alternative to Silicon Valley in New York now it is Silicon Alley. So it's possible that Intel, who by the way has really good process technology. This is an aside, it really allowed TSMC to run the table with the whole seven nanometers versus 10 minute nanometer narrative. TSMC was at seven nanometer. Intel was at 10 nanometer. And really, we've said in the past that Intel's 10 nanometer tech is pretty close to TSMC seven. So Intel's ahead in that regard, even though in terms of, you know, the intervener thickness density, it's it's not, you know. These are sort of games that the semiconductor companies play, but you know it's possible that Intel with the U.S. government and IBM and Samsung could make a play for that New York foundry as part of Intel's trusted foundry strategy and kind of reshuffle that deck in Albany. Sounds like a "Game of Thrones," doesn't it? By the way, TSMC has been so consumed servicing Apple for five nanometer and eventually four nanometer that it's dropped the ball on some of its other's customers, namely Nvidia. And remember, a long-term competitiveness and cost reductions, they all come down to volume. And we think that Intel can't get to volume without an Arm strategy. Okay, so maybe the JV, the Joint Venture that we talked about, maybe we're out on a limb there and that's a stretch. And perhaps Samsung's not willing to play ball, given it's made huge investments in fabs and infrastructure and other resources, locally, but we think it's still viable scenario because we think Samsung definitely would covet a presence in the United States. No good to do that directly but maybe a partnership makes more sense in terms of gaining ground on TSMC. But anyway, let's say Intel can become a trusted foundry with the help of IBM and the U.S. government. Maybe then it could compete on volume. Well, how would that work? Well, let's say Nvidia, let's say they're not too happy with TSMC. Maybe with entertain Intel as a second source. Would that do it? In and of itself, no. But what about AWS and Google and Facebook? Maybe this is a way to placate the U.S. government and call off the antitrust dogs. Hey, we'll give Intel Foundry our business to secure America's semiconductor leadership and future and pay U.S. government. Why don't you chill out, back off a little bit. Microsoft even though, you know, it's not getting as much scrutiny from the U.S. government, it's anti trustee is maybe perhaps are behind it, who knows. But I think Microsoft would be happy to play ball as well. Now, would this give Intel a competitive volume posture? Yes, we think it would, for sure. If it can gain the trust of these companies and the volume we think would be there. But as we've said, currently, this is a very, very long shot because of the, the, the new strategy, the distance the difference in the Foundry business all those challenges that we laid out last week, it's going to take years to play out. But the dots are starting to connect in this scenario and the stakes are exceedingly high hence the importance of the U.S. government. Okay, that's it for now. Thanks to the community for your comments and insights. And thanks again to David Floyer whose analysis around Arm and semiconductors. And this work that he's done for the past decade is of tremendous help. Remember I publish each week on wikibon.com and siliconangle.com. And these episodes are all available as podcasts, just search for braking analysis podcast and you can always connect on Twitter. You can hit the chat right here or this live event or email me at david.vellante@siliconangle.com. Look, I always appreciate the comments on LinkedIn and Clubhouse. You can follow me so you're notified when we start a room and riff on these topics as well as others. And don't forget to check out etr.plus where all the survey data. This is Dave Vellante for the Cube Insights powered by ETR. Be well, and we'll see you next time. (cheerful music) (cheerful music)

>> From the Cube's studios in Palo Alto in Boston, bringing you data-driven insights from The Cube and ETR. This is "Breaking Analysis" with Dave Vellante. >> Exactly one week after Pat Gelsinger's announcement of his plans to reinvent Intel. Arm announced version nine of its architecture and laid out its vision for the next decade. We believe this vision is extremely strong as it combines an end-to-end capability from Edge to Cloud, to the data center, to the home and everything in between. Arms aspirations are ambitious and powerful. Leveraging its business model, ecosystem and software compatibility with previous generations. Hello every one and welcome to this week's Wikibon Cube Insights powered by ETR. And this breaking analysis will explain why we think this announcement is so important and what it means for Intel and the broader technology landscape. We'll also share with you some feedback that we received from the Cube Community on last week's episode and a little inside baseball on how Intel, IBM, Samsung, TSMC and the U.S. government might be thinking about the shifting landscape of semiconductor technology. Now, there were two notable announcements this week that were directly related to Intel's announcement of March 23rd. The Armv9 news and TSMC's plans to invest a $100 billion in chip manufacturing and development over the next three years. That is a big number. It appears to tramp Intel's plan $20 billion investment to launch two new fabs in the U.S. starting in 2024. You may remember back in 2019, Samsung pledged to invest a $116 billion to diversify its production beyond memory trip, memory chips. Why are all these companies getting so aggressive? And won't this cause a glut in chips? Well, first, China looms large and aims to dominate its local markets, which in turn is going to confer advantages globally. The second, there's a huge chip shortage right now. And the belief is that it's going to continue through the decade and possibly beyond. We are seeing a new inflection point in the demand as we discussed last week. Stemming from digital, IOT, cloud, autos in new use cases in the home as so well presented by Sarjeet Johal in our community. As to the glut, these manufacturers believe that demand will outstrip supply indefinitely. And I understand that a lack of manufacturing capacity is actually more deadly than an oversupply. Look, if there's a glut, manufacturers can cut production and take the financial hit. Whereas capacity constraints mean you can miss entire cycles of growth and really miss out on the demand and the cost reductions. So, all these manufacturers are going for it. Now let's talk about Arm, its approach and the announcements that it made this week. Now last week, we talked about how Pat Gelsinger his vision of a system on package was an attempt to leapfrog system on chip SOC, while Arm is taking a similar system approach. But in our view, it's even broader than the vision laid out by Pat at Intel. Arm is targeting a wide variety of use cases that are shown here. Arm's fundamental philosophy is that the future will require highly specialized chips and Intel as you recall from Pat's announcement, would agree. But Arm historically takes an ecosystem approach that is different from Intel's model. Arm is all about enabling the production of specialized chips to really fit a specific application. For example, think about the amount of AI going on iPhones. They move if I remember from fingerprint to face recognition. This requires specialized neural processing units, NPUs that are designed by Apple for that particular use case. Arm is facilitating the creation of these specialized chips to be designed and produced by the ecosystem. Intel on the other hand has historically taken a one size fits all approach. Built around the x86. The Intel's design has always been about improving the processor. For example, in terms of speed, density, adding vector processing to accommodate AI, et cetera. And Intel does all the design and the manufacturing in any specialization for the ecosystem is done by Intel. Much of the value, that's added from the ecosystem is frankly been bending metal or adding displays or other features at the margin. But, the advantage is that the x86 architecture is well understood. It's consistent, reliable, and let's face it. Most enterprise software runs on x86. So, but very, very different models historically, which we heard from Gelsinger last week they're going to change with a new trusted foundry strategy. Now let's go through an example that might help explain the power of Arm's model. Let's say, your AWS and you're doing graviton. Designing graviton and graviton2. Or Apple, designing the M1 chip, or Tesla designing its own chip, or any other company in in any one of these use cases that are shown here. Tesla is a really good example. In order to optimize for video processing, Tesla needed to add specialized code firmware in the NPU for it's specific use case within autos. It was happy to take off the shelf CPU or GPU or whatever, and leverage Arm's standards there. And then it added its own value in the NPU. So the advantage of this model is Tesla could go from tape out in less or, or, or or in less than a year versus get the tape out in less than a year versus what would normally take many years. Arm is, think of Arm is like customize a Lego blocks that enable unique value add by the ecosystem with a much faster time to market. So like I say, the Tesla goes from logical tape out if you will, to Samsung and then says, okay run this against your manufacturing process. And it should all work as advertised by Arm. Tesla, interestingly, just as an aside chose the 14 nanometer process to keep its costs down. It didn't need the latest and greatest density. Okay, so you can see big difference in philosophies historically between Arm and Intel. And you can see Intel vectoring toward the Arm model based on what Gelsinger said last week for its foundry business. Essentially it has to. Now, Arm announced a new Arm architecture, Armv9. v9 is backwards compatible with previous generations. Perhaps Arm learned from Intel's failed, Itanium effort for those remember that word. Had no backward compatibility and it really floundered. As well, Arm adds some additional capabilities. And today we're going to focus on the two areas that have highlighted, machine learning piece and security. I'll take note of the call out, 300 billion chips. That's Arm's vision. That's a lot. And we've said, before, Arm's way for volumes are 10X those of x86. Volume, we sound like a broken record. Volume equals cost reduction. We'll come back to that a little bit later. Now let's have a word on AI and machine learning. Arm is betting on AI and ML. Big as are many others. And this chart really shows why, it's a graphic that shows ETR data and spending momentum and pervasiveness in the dataset across all the different sectors that ETR tracks within its taxonomy. Note that ML/AI gets the top spot on the vertical axis, which represents net score. That's a measure of spending momentum or spending velocity. The horizontal axis is market share presence in the dataset. And we give this sector four stars to signify it's consistent lead in the data. So pretty reasonable bet by Arm. But the other area that we're going to talk about is security. And its vision day, Arm talked about confidential compute architecture and these things called realms. Note in the left-hand side, showing data traveling all over the different use cases and around the world and the call-out from the CISO below, it's a large public airline CISO that spoke at an ETR Venn round table. And this individual noted that the shifting end points increase the threat vectors. We all know that. Arm said something that really resonated. Specifically, they said today, there's far too much trust on the OS and the hypervisor that are running these applications. And their broad access to data is a weakness. Arm's concept of realms as shown in the right-hand side, underscores the company strategy to remove the assumption that privileged software. Like the hypervisor needs to be able to see the data. So by creating realms, in a virtualized multi-tenant environment, data can be more protected from memory leaks which of course is a major opportunity for hackers that they exploit. So it's a nice concept in a way for the system to isolate attendance data from other users. Okay, we want, we want to share some feedback that we got last week from the community on our analysis of Intel. A tech exec from city pointed out that, Intel really didn't miss a mobile, as we said, it really missed smartphones. In fact, whell, this is a kind of a minor distinction, it's important to recognize we think. Because Intel facilitated WIFI with Centrino, under the direction of Paul Alini. Who by the way, was not an engineer. I think he was the first non-engineer to be the CEO of Intel. He was a marketing person by background. Ironically, Intel's work in wifi connectivity enabled, actually enabled the smartphone revolution. And maybe that makes the smartphone missed by Intel all that more egregious, I don't know. Now the other piece of feedback we received related to our IBM scenario and our three-way joint venture prediction bringing together Intel, IBM, and Samsung in a triumvirate where Intel brings the foundry and it's process manufacturing. IBM brings its dis-aggregated memory technology and Samsung brings its its volume and its knowledge of of volume down the learning curve. Let's start with IBM. Remember we said that IBM with power 10 has the best technology in terms of this notion of dis-aggregating compute from memory and sharing memory in a pool across different processor types. So a few things in this regard, IBM when it restructured its micro electronics business under Ginni Rometty, catalyzed the partnership with global foundries and you know, this picture in the upper right it shows the global foundries facility outside of Albany, New York in Malta. And the partnership included AMD and Samsung. But we believe that global foundries is backed away from some of its contractual commitments with IBM causing a bit of a rift between the companies and leaving a hole in your original strategy. And evidently AMD hasn't really leaned in to move the needle in any way and so the New York foundry, is it a bit of a state of limbo with respect to its original vision. Now, well, Arvind Krishna was the face of the Intel announcement. It clearly has deep knowledge of IBM semiconductor strategy. Dario Gill, we think is a key player in the mix. He's the senior vice president director of IBM research. And it is in a position to affect some knowledge sharing and maybe even knowledge transfer with Intel possibly as it relates to disaggregated architecture. His questions remain as to how open IBM will be. And how protected it will be with its IP. It's got, as we said, last week, it's got to have an incentive to do so. Now why would IBM do that? Well, it wants to compete more effectively with VMware who has done a great job leveraging x86 and that's the biggest competitor in threat to open shift. So Arvind needs Intel chips to really execute on IBM's cloud strategy. Because almost all of IBM's customers are running apps on x86. So IBM's cloud and hybrid cloud. Strategy really need to leverage that Intel partnership. Now Intel for its part has great FinFET technology. FinFET is a tactic goes beyond CMOs. You all mainframes might remember when IBM burned the boat on ECL, Emitter-coupled Logic. And then moved to CMOs for its mainframes. Well, this is the next gen beyond, and it could give Intel a leg up on AMD's chiplet intellectual properties. Especially as it relates to latency. And there could be some benefits there for IBM. So maybe there's a quid pro quo going on. Now, where it really gets interesting is New York Senator, Chuck Schumer, is keen on building up an alternative to Silicon Valley in New York now it is Silicon Alley. So it's possible that Intel, who by the way has really good process technology. This is an aside, it really allowed TSMC to run the table with the whole seven nanometers versus 10 minute nanometer narrative. TSMC was at seven nanometer. Intel was at 10 nanometer. And really, we've said in the past that Intel's 10 nanometer tech is pretty close to TSMC seven. So Intel's ahead in that regard, even though in terms of, you know, the intervener thickness density, it's it's not, you know. These are sort of games that the semiconductor companies play, but you know it's possible that Intel with the U.S. government and IBM and Samsung could make a play for that New York foundry as part of Intel's trusted foundry strategy and kind of reshuffle that deck in Albany. Sounds like a "Game of Thrones," doesn't it? By the way, TSMC has been so consumed servicing Apple for five nanometer and eventually four nanometer that it's dropped the ball on some of its other's customers, namely Nvidia. And remember, a long-term competitiveness and cost reductions, they all come down to volume. And we think that Intel can't get to volume without an Arm strategy. Okay, so maybe the JV, the Joint Venture that we talked about, maybe we're out on a limb there and that's a stretch. And perhaps Samsung's not willing to play ball, given it's made huge investments in fabs and infrastructure and other resources, locally, but we think it's still viable scenario because we think Samsung definitely would covet a presence in the United States. No good to do that directly but maybe a partnership makes more sense in terms of gaining ground on TSMC. But anyway, let's say Intel can become a trusted foundry with the help of IBM and the U.S. government. Maybe then it could compete on volume. Well, how would that work? Well, let's say Nvidia, let's say they're not too happy with TSMC. Maybe with entertain Intel as a second source. Would that do it? In and of itself, no. But what about AWS and Google and Facebook? Maybe this is a way to placate the U.S. government and call off the antitrust dogs. Hey, we'll give Intel Foundry our business to secure America's semiconductor leadership and future and pay U.S. government. Why don't you chill out, back off a little bit. Microsoft even though, you know, it's not getting as much scrutiny from the U.S. government, it's anti trustee is maybe perhaps are behind it, who knows. But I think Microsoft would be happy to play ball as well. Now, would this give Intel a competitive volume posture? Yes, we think it would, for sure. If it can gain the trust of these companies and the volume we think would be there. But as we've said, currently, this is a very, very long shot because of the, the, the new strategy, the distance the difference in the Foundry business all those challenges that we laid out last week, it's going to take years to play out. But the dots are starting to connect in this scenario and the stakes are exceedingly high hence the importance of the U.S. government. Okay, that's it for now. Thanks to the community for your comments and insights. And thanks again to David Floyer whose analysis around Arm and semiconductors. And this work that he's done for the past decade is of tremendous help. Remember I publish each week on wikibon.com and siliconangle.com. And these episodes are all available as podcasts, just search for braking analysis podcast and you can always connect on Twitter. You can hit the chat right here or this live event or email me at david.vellante@siliconangle.com. Look, I always appreciate the comments on LinkedIn and Clubhouse. You can follow me so you're notified when we start a room and riff on these topics as well as others. And don't forget to check out etr.plus where all the survey data. This is Dave Vellante for the Cube Insights powered by ETR. Be well, and we'll see you next time. (cheerful music) (cheerful music)

>>From around the globe with digital coverage brought to you by Broadcom. >>So our final segment today, so we've discussed today, the value that AI ops will bring to organizations in 2021, we'll discuss that through three different perspectives. And so now we want to bring those perspectives together and see if we can get a consensus on where AI ops needs to go for folks to be successful with it in the future. So bringing back some folks Richland is back with us. Senior analysts, serving infrastructure and operations professionals at Forester with smartness here is also back global product management at Verizon and Srinivasan, Reggie Gopaul head of product and strategy at Broadcom guys. Great to have you back. So let's jump in and Richard, we're going to, we're going to start with you, but we are going to get all three of you, a chance to answer the questions. So rich, we've talked about why organizations should adopt AI ops, but what happens if they choose not to what challenges would they face? Basically what's the cost of organizations doing nothing. >>Yeah. So it's a really good question because I think in operations for a number of reviews, we've kind of stand, uh, stood Pat, where we are, where we're afraid change things sometimes. Or we just don't think about a tooling is often the last thing to change because we're spending so much time doing project work and modernization and fighting fires on a daily basis. Uh, that problem is going to get worse if we do nothing. Um, you know, we're building new architectures like containers and microservices, which means more things to mind and keep running. Um, we're building highly distributed systems where you got moving more and more into this hybrid world, the multicloud world, uh, it's become over-complicate and I'll give a short anecdote. I think, eliminate this. Um, when I go to conferences and give speeches, it's all infrastructure operations people. And I say, you know, how many people have three X, five X, you know, uh, things to monitor them. They had, you know, three years ago, two years ago, and everyone's hand goes up, say how many people have hired more staff in that time period. Zero hands go up. That's the gap we have to fill. And we have to fill that through better automation, more intelligent systems. It's the only way we're going to be able to feel back out. >>What's your perspective, uh, if organizations choose not to adopt AI ops. >>Yeah. >>That's pretty good. So I'll do that. >>Yeah. So I think it said, I say it's related to a couple of things that probably everybody tired off lately and everybody can relate to. And this would resonate that we'd have 5g, which is old set to transform the one that we know it, of communication with these smart cities, smart communities, IOT, which is going to become pivotal to the success of businesses. And as we seen with this, COVID, you know, transformation of the world that there's a, there's a much bigger cost consciousness out there. People are trying to become much more, forward-looking much more sustainable. And I think at the heart of all of this, that the necessity that you have intelligent systems, which are bastardizing more than enough information that previous equipment overlooked, because if you don't measure engagement, not going right. People love being on the same page of this using two examples for hundreds of things that play a part in things not coming together in the best possible way. So I think it has an absolute necessity to grind those cost efficiencies rather than, you know, left right and center laying off people who are like pit Mattel to your business and have a great tribal knowledge of your business. So to speak, you can drive these efficiencies through automating a lot of those tasks that previously were being very manually intensive or resource intensive. And you could allocate those resources towards doing much better things, which let's be very honest going into 20, 21, after what we've seen with 2020, it's going to be mandate >>Shaking your head there when you, his mom was sharing his thoughts. What are your thoughts about this sounds like you agree. Yeah. I mean, uh, you know, uh, to put things in perspective, right? I mean, we are firmly in the digital economy, right? Digital economy, according to the Bureau of economic analysis is 9% of the us GDP. Just, you know, think about it in, in, in, in, in the context of the GDP, right? It's only ranked lower, slightly lower than manufacturing, which is at 11.3% GDP and slightly about finance and insurance, which is about seven and a half percent GDP. So G the digital economy is firmly in our lives, right? And so someone was talking about it, you know, software eats the world and digital, operational excellence is critical for customers, uh, to, uh, you know, to, uh, to drive profitability and growth, uh, in the digital economy. >>It's almost, you know, the key is digital at scale. So when, uh, when rich talks about some of the challenges and when newsman highlights 5g, as an example, those are the things that, that, that come to mind. So to me, what is the cost or perils of doing nothing? You know, uh, it's not an option. I think, you know, more often than not, uh, you know, C-level execs are asking their head of it and they are key influencers, a single question, are you ready? Are you ready in the context of addressing spikes in networks because of, uh, the pandemic scenario, are you ready in the context of automating away toil? Are you ready to respond rapidly to the needs of the digital business? I think AI ops is critical. >>That's a great point, Roger, where gonna stick with you. So we got kind of consensus there, as you said, wrapping it up. This is basically a, not an option. This is a must to go forward for organizations to be successful. So let's talk about some quick wins. So as you talked about, you know, organizations and C-levels asking, are you ready? What are some quick wins that that organizations can achieve when they're adopting AI? >>You know, um, immediate value. I think I would start with a question. How often do your customers find problems in your digital experience before you think about that? Right. You know, if you, if you, you know, there's an interesting web, uh, website, um, uh, you know, down detector.com, right? I think, uh, in, in Europe, there is an equal amount of that as well. It ha you know, people post their digital services that are down, whether it's a bank that, uh, you know, customers are trying to move money from checking account, the savings account and the digital services are down and so on and so forth. So some and many times customers tend to find problems before it operation teams do. So. A quick win is to be proactive and immediate value is visibility. If you do not know what is happening in your complex systems that make up your digital supply chain, it's going to be hard to be responsive. So I would start there >>Vice visibility. There's some question over to you from Verizon's perspective, quick wins. >>Yeah. So I think first of all, there's a need to ingest this multi-layered monetize spectrum data, which I don't think is humanly possible. You don't have people having expertise, you know, all seven layers of the OSI model and then across network and security and at the application of it. So I think you need systems which are now able to get that data. It shouldn't just be wasted reports that you're paying for on a monthly basis. It's about time that you started making the most of those in the form of identifying what are the efficiencies within your ecosystem. First of all, what are the things, you know, which could be better utilized subsequently you have the opportunity to reduce the noise of a troubled tickets handle. It sounds pretty trivial, but as an average, you can imagine every shop is tickets has the cost in dollars, right? >>So, and there's so many tickets and there's desserts that get on a network and across an end-user application value chain, we're talking thousands, you know, across and end user application value chain could be million in a year. So, and so many of those are not really, you know, cause of concern because the problem is somewhere else. So I think that whole triage is an immediate cost saving and the bigger your network, the bigger the cost of whether you're a provider, whether you're, you know, the end customer at the end of the day, not having to deal with problems, which nobody can resolve, which are not meant to be dealt with. If so many of those situations, right, where service has just been adopted, which is coordinate quality, et cetera, et cetera. So many reasons. So those are the, those are some of the immediate cost savings. >>They are really, really significant. Secondly, I would say Raj mentioned something about, you know, the end user application value chain and an understanding of that, especially with this hybrid cloud environment, et cetera, et cetera, right? The time it takes to identify a problem in an end-user application value chain across the seven layers that I mentioned with the OSI reference model across network and security and the application environment, it's something that in its own self has a massive cost to business, right? They could be point of sale transactions that could be obstructed because of this. There could be, and I'm going to use a very interesting example. When we talk IOT, the integrity of the IOT machine is extremely pivotal in this new world that we're stepping into. You could be running commands, which are super efficient. He has, everything is being told to the machine really fast. >>We're sending everything there. What if it's hacked? And if that robotic arm starts to involve the things you don't want it to do. So there's so much of that. That becomes a part of this naturally. And I believe, yes, this is not just like from a cost saving standpoint, but anything going wrong with that code base, et cetera, et cetera. These are massive costs to the business in the form of the revenue. They have lost the perception in the market as a result, the fed, you know, all that stuff. So these are a couple of very immediate funds, but then you also have the whole player virtualized resources where you can automate the allocation, you know, the quantification of an orchestration of those virtualized resources, rather than a person having to, you know, see something and then say, Oh yeah, I need to increase capacity over here, because then it's going to have this particular application. You have systems doing this stuff to, you know, Roger's point your customer should not be identifying your problems before you, because this digital where it's all about perception. >>Absolutely. We definitely don't want the customers finding it before. So rich, let's wrap this particular question up with you from that analyst perspective, how can companies use make big impact quickly with AI? >>Yeah, I think, you know, and it has been really summed up some really great use cases there. I think with the, uh, you know, one of the biggest struggles we've always had in operations is isn't, you know, the mean time to resolve. We're pretty good at resolving the things. We just have to find the thing we have to resolve. That's always been the problem and using these advanced analytics and machine learning algorithms now across all machine and application data, our tendency as humans is to look at the console and say, what's flashing red. That must be what we have to fix, but it could be something that's yellow, somewhere else, six services away. And we have made things so complicated. And I think this is what it was. One was saying that we can't get there anymore on our own. We need help to get there in all of this stuff that the outline. >>So, so well builds up to a higher level thing of what is the customer experience about what is the customer journey? And we've struggled for years in the digital world and measuring that a day-to-day thing. We know an online retail. If you're having a bad experience at one retailer, you just want your thing. You're going to go to another retailer, brand loyalty. Isn't one of the light. It wasn't the brick and mortal world where you had a department store near you. So you were loyal to that cause it was in your neighborhood, um, online that doesn't exist anymore. So we need to be able to understand the customer from that first moment, they touch a digital service all the way from their, their journey through that digital service, the lowest layer, whether it be a database or the network, what have you, and then back to them again, and we not understand, is that a good experience? >>We gave them. How does that compare to last week's experience? What should we be doing to improve that next week? And I think companies are starting and then the pandemic, certainly you push this timeline. If you listen to the, the, the CEO of Microsoft, he's like, you know, 10 years of digital transformation written down. And the first several months of this, um, in banks and in financial institutions, I talked to insurance companies, aren't slowing. Now they're trying to speed up. In fact, what they've discovered is that there, obviously when we were on lockdown or what have you, the use of digital services spiked very high. What they've learned is they're never going to go back down. They're never going to return to pretend levels. So now they're stuck with this new reality. Well, how do we service those customers and how do we make sure we keep them loyal to our brand? >>Uh, so, you know, they're looking for modernization opportunities. A lot of that, that things have been exposed. And I think Raj touched upon this very early in the conversation is visibility gaps. Now that we're on the outside, looking in at the data center, we know we architect things in a very specific way. Uh, we better ways of making these correlations across the Sparrow technologies to understand where the problems lies. We can give better services to our customers. And I think that's really what we're going to see a lot of the, the innovation and the people really for these new ways of doing things starting, you know, w now, I mean, I think I've seen it in customers, but I think really the push through the end of this year to next year when, you know, economy and things like that, straighten out a little bit more. I think it really, people are going to take a hard look of where they are is, you know, AI ops the way forward for them. And I think they'll find it. The answer is yes, for sure. >>So we've, we've come to a consensus that, of what the parallels are of organizations, basically the cost of doing nothing. You guys have given some great advice on where some of those quick wins are. Let's talk about something Raj touched on earlier, is organizations, are they really ready for truly automated AI? Raj, I want to start with you readiness factor. What are your thoughts? >>Uh, you know, uh, I think so, you know, we place our, her lives on automated systems all the time, right? In our, in our day-to-day lives, in the, in the digital world. I think, uh, you know, our, uh, at least the customers that I talked to our customers are, uh, are, uh, you know, uh, have a sophisticated systems, like for example, advanced automation is a reality. If you look at social media, AI and ML and automation are used to automate away, uh, misinformation, right? If you look at financial institutions, AI and ML are used to automate away a fraud, right? So I want to ask our customers why can't we automate await oil in it, operation systems, right? And that's where our customers are. Then, you know, uh, I'm a glass half full, uh, clinical person, right? Uh, this pandemic has been harder on many of our customers, but I think what we have learned from our customers is they've Rose to the occasion. >>They've used digital as a key moons, right? At scale. That's what we see with, you know, when, when Huseman and his team talk about, uh, you know, network operational intelligence, right. That's what it means to us. So I think they are ready, the intersection of customer experience it and OT, operational technology is ripe for automation. Uh, and, uh, you know, I, I wanna, I wanna sort of give a shout out to three key personas in, in this mix. It's somewhat right. One is the SRE persona, you know, site, reliability engineer. The other is the information security persona. And the third one is the it operator automation engineer persona. These folks in organizations are building a system of intelligence that can respond rapidly to the needs of their digital business. We at Broadcom, we are in the business of helping them construct a system of intelligence that will create a human augmented solution for them. Right. So when I see, when I interact with large enterprise customers, I think they, they, you know, they, they want to achieve what I would call advanced automation and AI ML solutions. And that's squarely, very I ops is, you know, is going as an it, you know, when I talked to rich and what, everything that rich says, you know, that's where it's going. And that's what we want to help our customers to. >>So rich, talk to us about your perspective of organizations being ready for truly automated AI. >>I think, you know, the conversation has shifted a lot in the last, in, in pre pandemic. Uh, I'd say at the end of last year, we're, you know, two years ago, people I'd go to conferences and people come up and ask me like, this is all smoke and mirrors, right? These systems can't do this because it is such a leap forward for them, for where they are today. Right. We we've sort of, you know, in software and other systems, we iterate and we move forward slowly. So it's not a big shock. And this is for a lot of organizations that big, big leap forward in the way that they're running their operations teams today. Um, but now they've come around and say, you know what? We want to do this. We want all the automations. We want my staff not doing the low complexity, repetitive tasks over and over again. >>Um, you know, and we have a lot of those kinds of legacy systems. We're not going to rebuild. Um, but they need certain care and feeding. So why are we having operations? People do those tasks? Why aren't we automating those out? I think the other piece is, and I'll, I'll, I'll send this out to any of the operations teams that are thinking about going down this path is that you have to understand that the operations models that we're operating under in INO and have been for the last 25 years are super outdated and they're fundamentally broken for the digital age. We have to start thinking about different ways of doing things and how do we do that? Well, it's, it's people, organization, people are going to work together differently in an AI ops world, um, for the better, um, but you know, there's going to be the, the age of the 40 person bridge call thing. >>Troubleshooting is going away. It's going to be three, four, five focused engineers that need to be there for that particular incident. Um, a lot of process mailer process we have for now level one level, two engineering. What have you running of tickets, gathering of artifacts, uh, during an incident is going to be automated. That's a good thing. We shouldn't be doing those, those things by hand anymore. So I'd say that the, to people's like start thinking about what this means to your organization. Start thinking about the great things we can do by automating things away from people, having to do them over and over again. And what that means for them, getting them matched to what they want to be doing is high level engineering tasks. They want to be doing monitorization, working with new tools and technologies. Um, these are all good things that help the organization perform better as a whole >>Great advice and great kind of some of the thoughts that you shared rich for what the audience needs to be on the, for going on. I want to go over to you, give me your thoughts on what the audience should be on the lookout for, or put on your agendas in the next 12 months. >>So there's like a couple of ways to answer that question. One thing would be in the form of, you know, what are some of the things they have to be concerned about in terms of implementing this solution or harnessing its power. The other one could be, you know, what are the perhaps advantages they should look to see? So if I was to talk about the first one, let's say that, what are some of the things you have to watch out for like possible pitfalls that everybody has data, right? So yeah, that's one strategy, we'd say, okay, you've got the data, let's see what we can do with them. But then there's the exact opposite side, which has to be considered when you're doing that analysis that, Hey, what are the use cases that you're looking to drive, right? But then use cases you have to understand, are you taking a reactive use case approach? >>Are you taking quite active use cases, right? Or that that's a very, very important consideration. Then you have to be very cognizant of where does this data that you have vision, it reside, what are the systems and where does it need to go to in order for this AI function to happen and subsequently if there needs to be any, you know, backward communication with all of that data in a process better. So I think these are some of the very critical points because you can have an AI solution, which is sitting in a customer data center. It could be in a managed services provider data center, like, right, right. It could be in a cloud data center, like an AWS or something, or you could have hybrid scenarios, et cetera, all of that stuff. So you have to be very mindful of where you're going to get the data from is going to go to what are the use cases you're trying to, you have to do a bit of backward forward. >>Okay. We've got this data cases and I think it's the judgment. Nobody can come in and say, Hey, you've built this fantastic thing. It's like Terminator two. I think it's a journey where we built starting with the network. My personal focus always comes down to the network and with 5g so much, so much more right with 5g, you're talking low latency communication. That's like the two power of 5g, right? It's low latency, it's ultra high bandwidth, but what's the point of that low latency. If then subsequently the actions that need to be taken to prevent any problems in critical applications, IOT applications, remote surgeries, uh, test driving vehicles, et cetera, et cetera. What if that's where people are sitting and sipping their coffees and trying to take action that needs to be in low latency as well. Right? So these are, I think some of the fundamental things that you have to know your data, your use cases and location, where it needs to be exchanged, what are the parameters around that for extending that data? >>And I think from that point onward, it's all about realizing, you know, in terms of business outcomes, unless AI comes in as a digital labor, that shows you, I have, I have reduced your, this amount of, you know, time, and that's a result of big problems or identified problems for anything. Or I have saved you this much resource right in a month, in a year, or whatever, the timeline that people want to see it. So I think those are some of the initial starting points, and then it all starts coming together. But the key is it's not one system that can do everything. You have to have a way where, you know, you can share data once you've got all of that data into one system, maybe you can send it to another system and make more, take more advantage, right? That system might be an AI and IOT system, which is just looking at all of your streetlights and making sure that Hey, parent switched off just to be more carbon neutral and all that great stuff, et cetera, et cetera >>For the audience, you can take her Raj, take us time from here. What are some of the takeaways that you think the audience really needs to be laser focused on as we move forward into the next year? You know, one thing that, uh, I think a key takeaway is, um, uh, you know, as we embark on 2021, closing the gap between intent and outcome and outputs and outcome will become critical, is critical. Uh, you know, especially for, uh, uh, you know, uh, digital transformation at scale for organizations context in the, you know, for customer experience becomes even more critical as Swan Huseman was talking, uh, you know, being network network aware network availability is, is a necessary condition, but not sufficient condition anymore. Right? The what, what, what customers have to go towards is going from network availability to network agility with high security, uh, what we call app aware networks, right? >>How do you differentiate between a trade, a million dollar trade that's happening between, uh, you know, London and New York, uh, versus a YouTube video training that an employee is going through? Worse is a YouTube video that millions of customers are, are watching, right? Three different context, three different customer scenarios, right? That is going to be critical. And last but not least feedback loop, uh, you know, responsiveness is all about feedback loop. You cannot predict everything, but you can respond to things faster. I think these are sort of the three, uh, three things that, uh, that, uh, you know, customers are going to have to, uh, have to really think about. And that's also where I believe AI ops, by the way, AI ops and I I'm. Yeah. You know, one of the points that was smart, shout out to what he was saying was heterogeneity is key, right? There is no homogeneous tool in the world that can solve problems. So you want an open extensible system of intelligence that, that can harness data from disparate data sources provide that visualization, the actionable insight and the human augmented recommendation systems that are so needed for, uh, you know, it operators to be successful. I think that's where it's going. >>Amazing. You guys just provided so much content context recommendations for the audience. I think we accomplished our goal on this. I'll call it power panel of not only getting to a consensus of what, where AI ops needs to go in the future, but great recommendations for what businesses in any industry need to be on the lookout for rich Huisman Raj, thank you for joining me today. >>Pleasure. Thank you. Thank you. >>We want to thank you for watching. This was such a rich session. You probably want to watch it again. Thanks for your time.

>>from around the globe. It's the Cube covering >>space and cybersecurity. Symposium 2020 hosted by Cal Poly >>Over On Welcome to this Special virtual conference. The Space and Cybersecurity Symposium 2020 put on by Cal Poly with support from the Cube. I'm John for your host and master of ceremonies. Got a great topic today in this session. Really? The intersection of space and cybersecurity. This topic and this conversation is the cybersecurity workforce development through public and private partnerships. And we've got a great lineup. We have Jeff Armstrong's the president of California Polytechnic State University, also known as Cal Poly Jeffrey. Thanks for jumping on and Bang. Go ahead. The second director of C four s R Division. And he's joining us from the office of the Under Secretary of Defense for the acquisition Sustainment Department of Defense, D O D. And, of course, Steve Jake's executive director, founder, National Security Space Association and managing partner at Bello's. Gentlemen, thank you for joining me for this session. We got an hour conversation. Thanks for coming on. >>Thank you. >>So we got a virtual event here. We've got an hour, have a great conversation and love for you guys do? In opening statement on how you see the development through public and private partnerships around cybersecurity in space, Jeff will start with you. >>Well, thanks very much, John. It's great to be on with all of you. Uh, on behalf Cal Poly Welcome, everyone. Educating the workforce of tomorrow is our mission to Cal Poly. Whether that means traditional undergraduates, master students are increasingly mid career professionals looking toe up, skill or re skill. Our signature pedagogy is learn by doing, which means that our graduates arrive at employers ready Day one with practical skills and experience. We have long thought of ourselves is lucky to be on California's beautiful central Coast. But in recent years, as we have developed closer relationships with Vandenberg Air Force Base, hopefully the future permanent headquarters of the United States Space Command with Vandenberg and other regional partners, we have discovered that our location is even more advantages than we thought. We're just 50 miles away from Vandenberg, a little closer than u C. Santa Barbara, and the base represents the southern border of what we have come to think of as the central coast region. Cal Poly and Vandenberg Air force base have partner to support regional economic development to encourage the development of a commercial spaceport toe advocate for the space Command headquarters coming to Vandenberg and other ventures. These partnerships have been possible because because both parties stand to benefit Vandenberg by securing new streams of revenue, workforce and local supply chain and Cal Poly by helping to grow local jobs for graduates, internship opportunities for students, and research and entrepreneurship opportunities for faculty and staff. Crucially, what's good for Vandenberg Air Force Base and for Cal Poly is also good for the Central Coast and the US, creating new head of household jobs, infrastructure and opportunity. Our goal is that these new jobs bring more diversity and sustainability for the region. This regional economic development has taken on a life of its own, spawning a new nonprofit called Reach, which coordinates development efforts from Vandenberg Air Force Base in the South to camp to Camp Roberts in the North. Another factor that is facilitated our relationship with Vandenberg Air Force Base is that we have some of the same friends. For example, Northrop Grumman has has long been an important defense contractor, an important partner to Cal poly funding scholarships and facilities that have allowed us to stay current with technology in it to attract highly qualified students for whom Cal Poly's costs would otherwise be prohibitive. For almost 20 years north of grimness funded scholarships for Cal Poly students this year, their funding 64 scholarships, some directly in our College of Engineering and most through our Cal Poly Scholars program, Cal Poly Scholars, a support both incoming freshman is transfer students. These air especially important because it allows us to provide additional support and opportunities to a group of students who are mostly first generation, low income and underrepresented and who otherwise might not choose to attend Cal Poly. They also allow us to recruit from partner high schools with large populations of underrepresented minority students, including the Fortune High School in Elk Grove, which we developed a deep and lasting connection. We know that the best work is done by balanced teams that include multiple and diverse perspectives. These scholarships help us achieve that goal, and I'm sure you know Northrop Grumman was recently awarded a very large contract to modernized the U. S. I. C B M Armory with some of the work being done at Vandenberg Air Force Base, thus supporting the local economy and protecting protecting our efforts in space requires partnerships in the digital realm. How Polly is partnered with many private companies, such as AWS. Our partnerships with Amazon Web services has enabled us to train our students with next generation cloud engineering skills, in part through our jointly created digital transformation hub. Another partnership example is among Cal Poly's California Cybersecurity Institute, College of Engineering and the California National Guard. This partnership is focused on preparing a cyber ready workforce by providing faculty and students with a hands on research and learning environment, side by side with military, law enforcement professionals and cyber experts. We also have a long standing partnership with PG and E, most recently focused on workforce development and redevelopment. Many of our graduates do indeed go on to careers in aerospace and defense industry as a rough approximation. More than 4500 Cal Poly graduates list aerospace and defense as their employment sector on linked in, and it's not just our engineers and computer sciences. When I was speaking to our fellow Panelists not too long ago, >>are >>speaking to bang, we learned that Rachel sins, one of our liberal arts arts majors, is working in his office. So shout out to you, Rachel. And then finally, of course, some of our graduates sword extraordinary heights such as Commander Victor Glover, who will be heading to the International space station later this year as I close. All of which is to say that we're deeply committed the workforce, development and redevelopment that we understand the value of public private partnerships and that were eager to find new ways in which to benefit everyone from this further cooperation. So we're committed to the region, the state in the nation and our past efforts in space, cybersecurity and links to our partners at as I indicated, aerospace industry and governmental partners provides a unique position for us to move forward in the interface of space and cybersecurity. Thank you so much, John. >>President, I'm sure thank you very much for the comments and congratulations to Cal Poly for being on the forefront of innovation and really taking a unique progressive. You and wanna tip your hat to you guys over there. Thank you very much for those comments. Appreciate it. Bahng. Department of Defense. Exciting you gotta defend the nation spaces Global. Your opening statement. >>Yes, sir. Thanks, John. Appreciate that day. Thank you, everybody. I'm honored to be this panel along with President Armstrong, Cal Poly in my long longtime friend and colleague Steve Jakes of the National Security Space Association, to discuss a very important topic of cybersecurity workforce development, as President Armstrong alluded to, I'll tell you both of these organizations, Cal Poly and the N S. A have done and continue to do an exceptional job at finding talent, recruiting them in training current and future leaders and technical professionals that we vitally need for our nation's growing space programs. A swell Asare collective National security Earlier today, during Session three high, along with my colleague Chris Hansen discussed space, cyber Security and how the space domain is changing the landscape of future conflicts. I discussed the rapid emergence of commercial space with the proliferations of hundreds, if not thousands, of satellites providing a variety of services, including communications allowing for global Internet connectivity. S one example within the O. D. We continue to look at how we can leverage this opportunity. I'll tell you one of the enabling technologies eyes the use of small satellites, which are inherently cheaper and perhaps more flexible than the traditional bigger systems that we have historically used unemployed for the U. D. Certainly not lost on Me is the fact that Cal Poly Pioneer Cube SATs 2020 some years ago, and they set the standard for the use of these systems today. So they saw the valiant benefit gained way ahead of everybody else, it seems, and Cal Poly's focus on training and education is commendable. I especially impressed by the efforts of another of Steve's I colleague, current CEO Mr Bill Britain, with his high energy push to attract the next generation of innovators. Uh, earlier this year, I had planned on participating in this year's Cyber Innovation Challenge. In June works Cal Poly host California Mill and high school students and challenge them with situations to test their cyber knowledge. I tell you, I wish I had that kind of opportunity when I was a kid. Unfortunately, the pandemic change the plan. Why I truly look forward. Thio feature events such as these Thio participating. Now I want to recognize my good friend Steve Jakes, whom I've known for perhaps too long of a time here over two decades or so, who was in acknowledge space expert and personally, I truly applaud him for having the foresight of years back to form the National Security Space Association to help the entire space enterprise navigate through not only technology but Polly policy issues and challenges and paved the way for operational izing space. Space is our newest horrifying domain. That's not a secret anymore. Uh, and while it is a unique area, it shares a lot of common traits with the other domains such as land, air and sea, obviously all of strategically important to the defense of the United States. In conflict they will need to be. They will all be contested and therefore they all need to be defended. One domain alone will not win future conflicts in a joint operation. We must succeed. All to defending space is critical as critical is defending our other operational domains. Funny space is no longer the sanctuary available only to the government. Increasingly, as I discussed in the previous session, commercial space is taking the lead a lot of different areas, including R and D, A so called new space, so cyber security threat is even more demanding and even more challenging. Three US considers and federal access to and freedom to operate in space vital to advancing security, economic prosperity, prosperity and scientific knowledge of the country. That's making cyberspace an inseparable component. America's financial, social government and political life. We stood up US Space force ah, year ago or so as the newest military service is like the other services. Its mission is to organize, train and equip space forces in order to protect us and allied interest in space and to provide space capabilities to the joint force. Imagine combining that US space force with the U. S. Cyber Command to unify the direction of space and cyberspace operation strengthened U D capabilities and integrate and bolster d o d cyber experience. Now, of course, to enable all of this requires had trained and professional cadre of cyber security experts, combining a good mix of policy as well as high technical skill set much like we're seeing in stem, we need to attract more people to this growing field. Now the D. O. D. Is recognized the importance of the cybersecurity workforce, and we have implemented policies to encourage his growth Back in 2013 the deputy secretary of defense signed the D. O d cyberspace workforce strategy to create a comprehensive, well equipped cyber security team to respond to national security concerns. Now this strategy also created a program that encourages collaboration between the D. O. D and private sector employees. We call this the Cyber Information Technology Exchange program or site up. It's an exchange programs, which is very interesting, in which a private sector employees can naturally work for the D. O. D. In a cyber security position that spans across multiple mission critical areas are important to the d. O. D. A key responsibility of cybersecurity community is military leaders on the related threats and cyber security actions we need to have to defeat these threats. We talk about rapid that position, agile business processes and practices to speed up innovation. Likewise, cybersecurity must keep up with this challenge to cyber security. Needs to be right there with the challenges and changes, and this requires exceptional personnel. We need to attract talent investing the people now to grow a robust cybersecurity, workforce, streets, future. I look forward to the panel discussion, John. Thank you. >>Thank you so much bomb for those comments and you know, new challenges and new opportunities and new possibilities and free freedom Operating space. Critical. Thank you for those comments. Looking forward. Toa chatting further. Steve Jakes, executive director of N. S. S. A Europe opening statement. >>Thank you, John. And echoing bangs thanks to Cal Poly for pulling these this important event together and frankly, for allowing the National Security Space Association be a part of it. Likewise, we on behalf the association delighted and honored Thio be on this panel with President Armstrong along with my friend and colleague Bonneau Glue Mahad Something for you all to know about Bomb. He spent the 1st 20 years of his career in the Air Force doing space programs. He then went into industry for several years and then came back into government to serve. Very few people do that. So bang on behalf of the space community, we thank you for your long life long devotion to service to our nation. We really appreciate that and I also echo a bang shot out to that guy Bill Britain, who has been a long time co conspirator of ours for a long time and you're doing great work there in the cyber program at Cal Poly Bill, keep it up. But professor arms trying to keep a close eye on him. Uh, I would like to offer a little extra context to the great comments made by by President Armstrong and bahng. Uh, in our view, the timing of this conference really could not be any better. Um, we all recently reflected again on that tragic 9 11 surprise attack on our homeland. And it's an appropriate time, we think, to take pause while the percentage of you in the audience here weren't even born or babies then For the most of us, it still feels like yesterday. And moreover, a tragedy like 9 11 has taught us a lot to include to be more vigilant, always keep our collective eyes and ears open to include those quote eyes and ears from space, making sure nothing like this ever happens again. So this conference is a key aspect. Protecting our nation requires we work in a cybersecurity environment at all times. But, you know, the fascinating thing about space systems is we can't see him. No, sir, We see Space launches man there's nothing more invigorating than that. But after launch, they become invisible. So what are they really doing up there? What are they doing to enable our quality of life in the United States and in the world? Well, to illustrate, I'd like to paraphrase elements of an article in Forbes magazine by Bonds and my good friend Chuck Beans. Chuck. It's a space guy, actually had Bonds job a fuse in the Pentagon. He is now chairman and chief strategy officer at York Space Systems, and in his spare time he's chairman of the small satellites. Chuck speaks in words that everyone can understand. So I'd like to give you some of his words out of his article. Uh, they're afraid somewhat. So these are Chuck's words. Let's talk about average Joe and playing Jane. Before heading to the airport for a business trip to New York City, Joe checks the weather forecast informed by Noah's weather satellites to see what pack for the trip. He then calls an uber that space app. Everybody uses it matches riders with drivers via GPS to take into the airport, So Joe has lunch of the airport. Unbeknownst to him, his organic lunch is made with the help of precision farming made possible through optimized irrigation and fertilization, with remote spectral sensing coming from space and GPS on the plane, the pilot navigates around weather, aided by GPS and nose weather satellites. And Joe makes his meeting on time to join his New York colleagues in a video call with a key customer in Singapore made possible by telecommunication satellites. Around to his next meeting, Joe receives notice changing the location of the meeting to another to the other side of town. So he calmly tells Syria to adjust the destination, and his satellite guided Google maps redirects him to the new location. That evening, Joe watches the news broadcast via satellite. The report details a meeting among world leaders discussing the developing crisis in Syria. As it turns out, various forms of quote remotely sensed. Information collected from satellites indicate that yet another band, chemical weapon, may have been used on its own people. Before going to bed, Joe decides to call his parents and congratulate them for their wedding anniversary as they cruise across the Atlantic, made possible again by communications satellites and Joe's parents can enjoy the call without even wondering how it happened the next morning. Back home, Joe's wife, Jane, is involved in a car accident. Her vehicle skids off the road. She's knocked unconscious, but because of her satellite equipped on star system, the crash is detected immediately and first responders show up on the scene. In time, Joe receives the news books. An early trip home sends flowers to his wife as he orders another uber to the airport. Over that 24 hours, Joe and Jane used space system applications for nearly every part of their day. Imagine the consequences if at any point they were somehow denied these services, whether they be by natural causes or a foreign hostility. And each of these satellite applications used in this case were initially developed for military purposes and continue to be, but also have remarkable application on our way of life. Just many people just don't know that. So, ladies and gentlemen, now you know, thanks to chuck beans, well, the United States has a proud heritage being the world's leading space faring nation, dating back to the Eisenhower and Kennedy years. Today we have mature and robust systems operating from space, providing overhead reconnaissance to quote, wash and listen, provide missile warning, communications, positioning, navigation and timing from our GPS system. Much of what you heard in Lieutenant General J. T. Thompson earlier speech. These systems are not only integral to our national security, but also our also to our quality of life is Chuck told us. We simply no longer could live without these systems as a nation and for that matter, as a world. But over the years, adversary like adversaries like China, Russia and other countries have come to realize the value of space systems and are aggressively playing ketchup while also pursuing capabilities that will challenge our systems. As many of you know, in 2000 and seven, China demonstrated it's a set system by actually shooting down is one of its own satellites and has been aggressively developing counter space systems to disrupt hours. So in a heavily congested space environment, our systems are now being contested like never before and will continue to bay well as Bond mentioned, the United States has responded to these changing threats. In addition to adding ways to protect our system, the administration and in Congress recently created the United States Space Force and the operational you United States Space Command, the latter of which you heard President Armstrong and other Californians hope is going to be located. Vandenberg Air Force Base Combined with our intelligence community today, we have focused military and civilian leadership now in space. And that's a very, very good thing. Commence, really. On the industry side, we did create the National Security Space Association devoted solely to supporting the national security Space Enterprise. We're based here in the D C area, but we have arms and legs across the country, and we are loaded with extraordinary talent. In scores of Forman, former government executives, So S s a is joined at the hip with our government customers to serve and to support. We're busy with a multitude of activities underway ranging from a number of thought provoking policy. Papers are recurring space time Webcast supporting Congress's Space Power Caucus and other main serious efforts. Check us out at NSS. A space dot org's One of our strategic priorities in central to today's events is to actively promote and nurture the workforce development. Just like cow calling. We will work with our U. S. Government customers, industry leaders and academia to attract and recruit students to join the space world, whether in government or industry and two assistant mentoring and training as their careers. Progress on that point, we're delighted. Be delighted to be working with Cal Poly as we hopefully will undertake a new pilot program with him very soon. So students stay tuned something I can tell you Space is really cool. While our nation's satellite systems are technical and complex, our nation's government and industry work force is highly diverse, with a combination of engineers, physicists, method and mathematicians, but also with a large non technical expertise as well. Think about how government gets things thes systems designed, manufactured, launching into orbit and operating. They do this via contracts with our aerospace industry, requiring talents across the board from cost estimating cost analysis, budgeting, procurement, legal and many other support. Tasker Integral to the mission. Many thousands of people work in the space workforce tens of billions of dollars every year. This is really cool stuff, no matter what your education background, a great career to be part of. When summary as bang had mentioned Aziz, well, there is a great deal of exciting challenges ahead we will see a new renaissance in space in the years ahead, and in some cases it's already begun. Billionaires like Jeff Bezos, Elon Musk, Sir Richard Richard Branson are in the game, stimulating new ideas in business models, other private investors and start up companies. Space companies are now coming in from all angles. The exponential advancement of technology and microelectronics now allows the potential for a plethora of small SAT systems to possibly replace older satellites the size of a Greyhound bus. It's getting better by the day and central to this conference, cybersecurity is paramount to our nation's critical infrastructure in space. So once again, thanks very much, and I look forward to the further conversation. >>Steve, thank you very much. Space is cool. It's relevant. But it's important, as you pointed out, and you're awesome story about how it impacts our life every day. So I really appreciate that great story. I'm glad you took the time Thio share that you forgot the part about the drone coming over in the crime scene and, you know, mapping it out for you. But that would add that to the story later. Great stuff. My first question is let's get into the conversations because I think this is super important. President Armstrong like you to talk about some of the points that was teased out by Bang and Steve. One in particular is the comment around how military research was important in developing all these capabilities, which is impacting all of our lives. Through that story. It was the military research that has enabled a generation and generation of value for consumers. This is kind of this workforce conversation. There are opportunities now with with research and grants, and this is, ah, funding of innovation that it's highly accelerate. It's happening very quickly. Can you comment on how research and the partnerships to get that funding into the universities is critical? >>Yeah, I really appreciate that And appreciate the comments of my colleagues on it really boils down to me to partnerships, public private partnerships. You mentioned Northrop Grumman, but we have partnerships with Lockie Martin, Boeing, Raytheon Space six JPL, also member of organization called Business Higher Education Forum, which brings together university presidents and CEOs of companies. There's been focused on cybersecurity and data science, and I hope that we can spill into cybersecurity in space but those partnerships in the past have really brought a lot forward at Cal Poly Aziz mentioned we've been involved with Cube set. Uh, we've have some secure work and we want to plan to do more of that in the future. Uh, those partnerships are essential not only for getting the r and d done, but also the students, the faculty, whether masters or undergraduate, can be involved with that work. Uh, they get that real life experience, whether it's on campus or virtually now during Covic or at the location with the partner, whether it may be governmental or our industry. Uh, and then they're even better equipped, uh, to hit the ground running. And of course, we'd love to see even more of our students graduate with clearance so that they could do some of that a secure work as well. So these partnerships are absolutely critical, and it's also in the context of trying to bring the best and the brightest and all demographics of California and the US into this field, uh, to really be successful. So these partnerships are essential, and our goal is to grow them just like I know other colleagues and C. S u and the U C are planning to dio, >>you know, just as my age I've seen I grew up in the eighties, in college and during that systems generation and that the generation before me, they really kind of pioneered the space that spawned the computer revolution. I mean, you look at these key inflection points in our lives. They were really funded through these kinds of real deep research. Bond talk about that because, you know, we're living in an age of cloud. And Bezos was mentioned. Elon Musk. Sir Richard Branson. You got new ideas coming in from the outside. You have an accelerated clock now on terms of the innovation cycles, and so you got to react differently. You guys have programs to go outside >>of >>the Defense Department. How important is this? Because the workforce that air in schools and our folks re skilling are out there and you've been on both sides of the table. So share your thoughts. >>No, thanks, John. Thanks for the opportunity responded. And that's what you hit on the notes back in the eighties, R and D in space especially, was dominated by my government funding. Uh, contracts and so on. But things have changed. As Steve pointed out, A lot of these commercial entities funded by billionaires are coming out of the woodwork funding R and D. So they're taking the lead. So what we can do within the deal, the in government is truly take advantage of the work they've done on. Uh, since they're they're, you know, paving the way to new new approaches and new way of doing things. And I think we can We could certainly learn from that. And leverage off of that saves us money from an R and D standpoint while benefiting from from the product that they deliver, you know, within the O D Talking about workforce development Way have prioritized we have policies now to attract and retain talent. We need I I had the folks do some research and and looks like from a cybersecurity workforce standpoint. A recent study done, I think, last year in 2019 found that the cybersecurity workforce gap in the U. S. Is nearing half a million people, even though it is a growing industry. So the pipeline needs to be strengthened off getting people through, you know, starting young and through college, like assess a professor Armstrong indicated, because we're gonna need them to be in place. Uh, you know, in a period of about maybe a decade or so, Uh, on top of that, of course, is the continuing issue we have with the gap with with stamps students, we can't afford not to have expertise in place to support all the things we're doing within the with the not only deal with the but the commercial side as well. Thank you. >>How's the gap? Get? Get filled. I mean, this is the this is again. You got cybersecurity. I mean, with space. It's a whole another kind of surface area, if you will, in early surface area. But it is. It is an I o t. Device if you think about it. But it does have the same challenges. That's kind of current and and progressive with cybersecurity. Where's the gap Get filled, Steve Or President Armstrong? I mean, how do you solve the problem and address this gap in the workforce? What is some solutions and what approaches do we need to put in place? >>Steve, go ahead. I'll follow up. >>Okay. Thanks. I'll let you correct. May, uh, it's a really good question, and it's the way I would. The way I would approach it is to focus on it holistically and to acknowledge it up front. And it comes with our teaching, etcetera across the board and from from an industry perspective, I mean, we see it. We've gotta have secure systems with everything we do and promoting this and getting students at early ages and mentoring them and throwing internships at them. Eyes is so paramount to the whole the whole cycle, and and that's kind of and it really takes focused attention. And we continue to use the word focus from an NSS, a perspective. We know the challenges that are out there. There are such talented people in the workforce on the government side, but not nearly enough of them. And likewise on industry side. We could use Maura's well, but when you get down to it, you know we can connect dots. You know that the the aspect That's a Professor Armstrong talked about earlier toe where you continue to work partnerships as much as you possibly can. We hope to be a part of that. That network at that ecosystem the will of taking common objectives and working together to kind of make these things happen and to bring the power not just of one or two companies, but our our entire membership to help out >>President >>Trump. Yeah, I would. I would also add it again. It's back to partnerships that I talked about earlier. One of our partners is high schools and schools fortune Margaret Fortune, who worked in a couple of, uh, administrations in California across party lines and education. Their fifth graders all visit Cal Poly and visit our learned by doing lab and you, you've got to get students interested in stem at a early age. We also need the partnerships, the scholarships, the financial aid so the students can graduate with minimal to no debt to really hit the ground running. And that's exacerbated and really stress. Now, with this covert induced recession, California supports higher education at a higher rate than most states in the nation. But that is that has dropped this year or reasons. We all understand, uh, due to Kobe, and so our partnerships, our creativity on making sure that we help those that need the most help financially uh, that's really key, because the gaps air huge eyes. My colleagues indicated, you know, half of half a million jobs and you need to look at the the students that are in the pipeline. We've got to enhance that. Uh, it's the in the placement rates are amazing. Once the students get to a place like Cal Poly or some of our other amazing CSU and UC campuses, uh, placement rates are like 94%. >>Many of our >>engineers, they have jobs lined up a year before they graduate. So it's just gonna take key partnerships working together. Uh, and that continued partnership with government, local, of course, our state of CSU on partners like we have here today, both Stephen Bang So partnerships the thing >>e could add, you know, the collaboration with universities one that we, uh, put a lot of emphasis, and it may not be well known fact, but as an example of national security agencies, uh, National Centers of Academic Excellence in Cyber, the Fast works with over 270 colleges and universities across the United States to educate its 45 future cyber first responders as an example, so that Zatz vibrant and healthy and something that we ought Teoh Teik, banjo >>off. Well, I got the brain trust here on this topic. I want to get your thoughts on this one point. I'd like to define what is a public private partnership because the theme that's coming out of the symposium is the script has been flipped. It's a modern error. Things air accelerated get you got security. So you get all these things kind of happen is a modern approach and you're seeing a digital transformation play out all over the world in business. Andi in the public sector. So >>what is what >>is a modern public private partnership? What does it look like today? Because people are learning differently, Covert has pointed out, which was that we're seeing right now. How people the progressions of knowledge and learning truth. It's all changing. How do you guys view the modern version of public private partnership and some some examples and improve points? Can you can you guys share that? We'll start with the Professor Armstrong. >>Yeah. A zai indicated earlier. We've had on guy could give other examples, but Northup Grumman, uh, they helped us with cyber lab. Many years ago. That is maintained, uh, directly the software, the connection outside its its own unit so that students can learn the hack, they can learn to penetrate defenses, and I know that that has already had some considerations of space. But that's a benefit to both parties. So a good public private partnership has benefits to both entities. Uh, in the common factor for universities with a lot of these partnerships is the is the talent, the talent that is, that is needed, what we've been working on for years of the, you know, that undergraduate or master's or PhD programs. But now it's also spilling into Skilling and re Skilling. As you know, Jobs. Uh, you know, folks were in jobs today that didn't exist two years, three years, five years ago. But it also spills into other aspects that can expand even mawr. We're very fortunate. We have land, there's opportunities. We have one tech part project. We're expanding our tech park. I think we'll see opportunities for that, and it'll it'll be adjusted thio, due to the virtual world that we're all learning more and more about it, which we were in before Cove it. But I also think that that person to person is going to be important. Um, I wanna make sure that I'm driving across the bridge. Or or that that satellites being launched by the engineer that's had at least some in person training, uh, to do that and that experience, especially as a first time freshman coming on a campus, getting that experience expanding and as adult. And we're gonna need those public private partnerships in order to continue to fund those at a level that is at the excellence we need for these stem and engineering fields. >>It's interesting People in technology can work together in these partnerships in a new way. Bank Steve Reaction Thio the modern version of what a public, successful private partnership looks like. >>If I could jump in John, I think, you know, historically, Dodi's has have had, ah, high bar thio, uh, to overcome, if you will, in terms of getting rapid pulling in your company. This is the fault, if you will and not rely heavily in are the usual suspects of vendors and like and I think the deal is done a good job over the last couple of years off trying to reduce the burden on working with us. You know, the Air Force. I think they're pioneering this idea around pitch days where companies come in, do a two hour pitch and immediately notified of a wooden award without having to wait a long time. Thio get feedback on on the quality of the product and so on. So I think we're trying to do our best. Thio strengthen that partnership with companies outside the main group of people that we typically use. >>Steve, any reaction? Comment to add? >>Yeah, I would add a couple of these air. Very excellent thoughts. Uh, it zits about taking a little gamble by coming out of your comfort zone. You know, the world that Bond and Bond lives in and I used to live in in the past has been quite structured. It's really about we know what the threat is. We need to go fix it, will design it says we go make it happen, we'll fly it. Um, life is so much more complicated than that. And so it's it's really to me. I mean, you take you take an example of the pitch days of bond talks about I think I think taking a gamble by attempting to just do a lot of pilot programs, uh, work the trust factor between government folks and the industry folks in academia. Because we are all in this together in a lot of ways, for example. I mean, we just sent the paper to the White House of their requests about, you know, what would we do from a workforce development perspective? And we hope Thio embellish on this over time once the the initiative matures. But we have a piece of it, for example, is the thing we call clear for success getting back Thio Uh, President Armstrong's comments at the collegiate level. You know, high, high, high quality folks are in high demand. So why don't we put together a program they grabbed kids in their their underclass years identifies folks that are interested in doing something like this. Get them scholarships. Um, um, I have a job waiting for them that their contract ID for before they graduate, and when they graduate, they walk with S C I clearance. We believe that could be done so, and that's an example of ways in which the public private partnerships can happen to where you now have a talented kid ready to go on Day one. We think those kind of things can happen. It just gets back down to being focused on specific initiatives, give them giving them a chance and run as many pilot programs as you can like these days. >>That's a great point, E. President. >>I just want to jump in and echo both the bank and Steve's comments. But Steve, that you know your point of, you know, our graduates. We consider them ready Day one. Well, they need to be ready Day one and ready to go secure. We totally support that and and love to follow up offline with you on that. That's that's exciting, uh, and needed very much needed mawr of it. Some of it's happening, but way certainly have been thinking a lot about that and making some plans, >>and that's a great example of good Segway. My next question. This kind of reimagining sees work flows, eyes kind of breaking down the old the old way and bringing in kind of a new way accelerated all kind of new things. There are creative ways to address this workforce issue, and this is the next topic. How can we employ new creative solutions? Because, let's face it, you know, it's not the days of get your engineering degree and and go interview for a job and then get slotted in and get the intern. You know the programs you get you particularly through the system. This is this is multiple disciplines. Cybersecurity points at that. You could be smart and math and have, ah, degree in anthropology and even the best cyber talents on the planet. So this is a new new world. What are some creative approaches that >>you know, we're >>in the workforce >>is quite good, John. One of the things I think that za challenge to us is you know, we got somehow we got me working for with the government, sexy, right? The part of the challenge we have is attracting the right right level of skill sets and personnel. But, you know, we're competing oftentimes with the commercial side, the gaming industry as examples of a big deal. And those are the same talents. We need to support a lot of programs we have in the U. D. So somehow we have to do a better job to Steve's point off, making the work within the U. D within the government something that they would be interested early on. So I tracked him early. I kind of talked about Cal Poly's, uh, challenge program that they were gonna have in June inviting high school kid. We're excited about the whole idea of space and cyber security, and so on those air something. So I think we have to do it. Continue to do what were the course the next several years. >>Awesome. Any other creative approaches that you guys see working or might be on idea, or just a kind of stoked the ideation out their internship. So obviously internships are known, but like there's gotta be new ways. >>I think you can take what Steve was talking about earlier getting students in high school, uh, and aligning them sometimes. Uh, that intern first internship, not just between the freshman sophomore year, but before they inter cal poly per se. And they're they're involved s So I think that's, uh, absolutely key. Getting them involved many other ways. Um, we have an example of of up Skilling a redeveloped work redevelopment here in the Central Coast. PG and e Diablo nuclear plant as going to decommission in around 2020 24. And so we have a ongoing partnership toe work on reposition those employees for for the future. So that's, you know, engineering and beyond. Uh, but think about that just in the manner that you were talking about. So the up skilling and re Skilling uh, on I think that's where you know, we were talking about that Purdue University. Other California universities have been dealing with online programs before cove it and now with co vid uh, so many more faculty or were pushed into that area. There's going to be much more going and talk about workforce development and up Skilling and Re Skilling The amount of training and education of our faculty across the country, uh, in in virtual, uh, and delivery has been huge. So there's always a silver linings in the cloud. >>I want to get your guys thoughts on one final question as we in the in the segment. And we've seen on the commercial side with cloud computing on these highly accelerated environments where you know, SAS business model subscription. That's on the business side. But >>one of The >>things that's clear in this trend is technology, and people work together and technology augments the people components. So I'd love to get your thoughts as we look at the world now we're living in co vid um, Cal Poly. You guys have remote learning Right now. It's a infancy. It's a whole new disruption, if you will, but also an opportunity to enable new ways to collaborate, Right? So if you look at people and technology, can you guys share your view and vision on how communities can be developed? How these digital technologies and people can work together faster to get to the truth or make a discovery higher to build the workforce? These air opportunities? How do you guys view this new digital transformation? >>Well, I think there's there's a huge opportunities and just what we're doing with this symposium. We're filming this on one day, and it's going to stream live, and then the three of us, the four of us, can participate and chat with participants while it's going on. That's amazing. And I appreciate you, John, you bringing that to this this symposium, I think there's more and more that we can do from a Cal poly perspective with our pedagogy. So you know, linked to learn by doing in person will always be important to us. But we see virtual. We see partnerships like this can expand and enhance our ability and minimize the in person time, decrease the time to degree enhanced graduation rate, eliminate opportunity gaps or students that don't have the same advantages. S so I think the technological aspect of this is tremendous. Then on the up Skilling and Re Skilling, where employees air all over, they can be reached virtually then maybe they come to a location or really advanced technology allows them to get hands on virtually, or they come to that location and get it in a hybrid format. Eso I'm I'm very excited about the future and what we can do, and it's gonna be different with every university with every partnership. It's one. Size does not fit all. >>It's so many possibilities. Bond. I could almost imagine a social network that has a verified, you know, secure clearance. I can jump in, have a little cloak of secrecy and collaborate with the d o. D. Possibly in the future. But >>these are the >>kind of kind of crazy ideas that are needed. Are your thoughts on this whole digital transformation cross policy? >>I think technology is gonna be revolutionary here, John. You know, we're focusing lately on what we call digital engineering to quicken the pace off, delivering capability to warfighter. As an example, I think a I machine language all that's gonna have a major play and how we operate in the future. We're embracing five G technologies writing ability Thio zero latency or I o t More automation off the supply chain. That sort of thing, I think, uh, the future ahead of us is is very encouraging. Thing is gonna do a lot for for national defense on certainly the security of the country. >>Steve, your final thoughts. Space systems are systems, and they're connected to other systems that are connected to people. Your thoughts on this digital transformation opportunity >>Such a great question in such a fun, great challenge ahead of us. Um echoing are my colleague's sentiments. I would add to it. You know, a lot of this has I think we should do some focusing on campaigning so that people can feel comfortable to include the Congress to do things a little bit differently. Um, you know, we're not attuned to doing things fast. Uh, but the dramatic You know, the way technology is just going like crazy right now. I think it ties back Thio hoping Thio, convince some of our senior leaders on what I call both sides of the Potomac River that it's worth taking these gamble. We do need to take some of these things very way. And I'm very confident, confident and excited and comfortable. They're just gonna be a great time ahead and all for the better. >>You know, e talk about D. C. Because I'm not a lawyer, and I'm not a political person, but I always say less lawyers, more techies in Congress and Senate. So I was getting job when I say that. Sorry. Presidential. Go ahead. >>Yeah, I know. Just one other point. Uh, and and Steve's alluded to this in bonded as well. I mean, we've got to be less risk averse in these partnerships. That doesn't mean reckless, but we have to be less risk averse. And I would also I have a zoo. You talk about technology. I have to reflect on something that happened in, uh, you both talked a bit about Bill Britton and his impact on Cal Poly and what we're doing. But we were faced a few years ago of replacing a traditional data a data warehouse, data storage data center, and we partner with a W S. And thank goodness we had that in progress on it enhanced our bandwidth on our campus before Cove. It hit on with this partnership with the digital transformation hub. So there is a great example where, uh, we we had that going. That's not something we could have started. Oh, covitz hit. Let's flip that switch. And so we have to be proactive on. We also have thio not be risk averse and do some things differently. Eyes that that is really salvage the experience for for students. Right now, as things are flowing, well, we only have about 12% of our courses in person. Uh, those essential courses, uh, and just grateful for those partnerships that have talked about today. >>Yeah, and it's a shining example of how being agile, continuous operations, these air themes that expand into space and the next workforce needs to be built. Gentlemen, thank you. very much for sharing your insights. I know. Bang, You're gonna go into the defense side of space and your other sessions. Thank you, gentlemen, for your time for great session. Appreciate it. >>Thank you. Thank you. >>Thank you. >>Thank you. Thank you. Thank you all. >>I'm John Furry with the Cube here in Palo Alto, California Covering and hosting with Cal Poly The Space and Cybersecurity Symposium 2020. Thanks for watching.

>>Yeah, >>from the Cube Studios in Palo Alto and Boston. It's the Cube covering IBM think wrought by IBM. >>Welcome back. I'm Stew Middleman. And this is the Cube's coverage of IBM. Think 2020. The digital experience. Happy to welcome to the program. Dinesh Nirmal, Who's the chief product officer for cloud packs inside IBM Deneche. Nice to see you. Thanks so much for joining us. >>Thank you. Still really appreciate you taking the time. >>All right, So Ah, I've been to many IBM shows, and of course, I'm an analyst in the cloud space. So I'm familiar with IBM cloud packs. But maybe, you know, just refresh our audience minds here what they are. How long have they been around for? You know, what clouds do they live on? And maybe what's what's new in 2020? That if somebody had looked at this, you know, in the past that they might not know about IBM Cloud. >>Yeah, so thanks to start with, let me say that. Well, tax at 12. Agnostic. So the whole goal is that you build once and it can run anywhere. That is the basic mantra or principle that we want to build packs with, So they're looking at them as a set of micro services containerized in the form that it can run on any public or behind the firewall. So that's the whole premise of about Pass. So when you go back to cloud packs, it's an integrator set of services that solve a specific set of business problems and also accelerates building rich set of applications and solutions. That's what cloud back springs. So you know, especially in this and moments to think about it, you know, violent underprice. My goal is how can I accelerate and how can I automate? Those are the two key things you know that comes to my mind if I am a C level execs at an enterprise. So cloud practice enables that meaning you already have a set off stitched together services that accelerate the application development. It automates a lot of things for you. So you do. They have a lot of applications running on multiple clouds or behind the firewall. How do you manage those right about banks will help, so I'll let me give you one example. Since you are specifically on 12 packs, let's stay cloud back for data the set of services that is available in cloud back for data will make it easier for all the way from ingest to visualization. There's a set of services that you can use so you don't have to go build a service or a product or use a product for ingest. Then use another product for CTL. Use another product for building models, another product to manage those models. The cloud back for data will solve all the problems and to end. It's a rich set of services that will give you all the value that you need all the way from ingest to visualization and with any personas We know whether you are a data engineer, data scientist or you are, you know, business analyst. You all can cooperate through the part. So that's the you know, two minute answer your question. What about practice? >>Awesome. Thanks in. Actually, I I guess you pointed out something right at the beginning. There I hear IBM Cloud pack and I think IBM cloud. But you said specifically this is really cloud agnostic. So you know, this week is think Last week I was covering Red Hat Summit, so I heard a lot about multi cloud deployments. You know, talk to the well team, talk to the open shift team. Um, so help me understand. You know, where do cod packed bit when we're talking about, you know, these multi cloud employments, you know? And is there some connection with the partnership that, of course, IBM has with red hat >>off course. I mean, so all cloud packs are optimized for open shipped. Meaning, you know, how do we use the set of services that open ship gives that container management that open provides. So as we build containers or micro services, how do we make sure that we are optimizing or taking advantage of open ship? So, for example, the set of services like logging, monitoring, security, all those services meeting that comes from open shift is what we are using. Basketball packs of cloud packs are optimized for open shift. Um, you know, from an automation perspective, how do we use and simple Right? So all the value that red hat an open ship brings is what about back is built on. So if you look at it as a layer as a Lego, the based Lego is open shift and rail. And then on top of it sits cloud pass and applications and solutions on top of it. So if I look at layer bass, the bass Lego layer is open shift and red pepper. >>Well, great. That's that's super important because, you know, one of the things we've been looking at for a while is you talk about hybrid cloud, You talk about multi cloud, and often it's that platform that infrastructure discussion. But the biggest challenge for companies today is how do I build new applications? How do I modernize what I have? So >>it >>sounds like this is exactly, you know where you're targeting to help people, you know, through the through that transformation that they're going through. >>Yeah, exactly. Stew. Because if you look at it, you know, in the past, products for siloed I mean, you know, you build a product, you use a set of specs to build it. It was a silo, and customers becomes the software integrators, or system integrators, where they have to take the different products, put it together. So even if I am, you know, focused on the data space or AI space before I have to bring in three or four or five different products make it all work together to build a model, deploy the model, manage the model, the lifecycle of the model, the life cycle of the data. But the cloud packs bring it all in one box, were out of the box. You're ready to go, so your time to value is much more higher with cloud packs because you already get a several stitched together services that gets working right out of the box. >>So I love the idea of out of the box when I think of Cloud native modern modern application development. Simplicity is not the first thing I think of Danish. So help me understand. You know so many customers. It's, you know, the tools, the skill set. You know, they don't necessarily have the experience. How is what you know your products and your team's doing help customers deal with, You know, the ever changing landscape and the complexity that they're faced with. >>Yeah, so the that honest roots, too, is that enterprise applications are not an app that you create and put it on iPhone, right? I mean, it is much more complex because it's dealing with you know, hundreds of millions of people trying to transact with the system. You need to make sure there is a disaster recovery, backup scalability, the elasticity. I mean, all those things. Security, I mean, obviously very critical piece and multi tenancy. All those things has to come together in an enterprise application. So when people talk about, you know, simplicity, it comes at a price. So what cloud practice is done is that you know, we have really focused on the user experience and design piece. So you, as an end user, has a great Syrians using the integrated set of services. The complexity piece will still be there to some extent, because you're building a very complex, you know, multi tenant application, the price application. But how do we make it easier for a developer or a data scientist to collaborate or reuse the assets, find the data much more easier or trusted data much more easier than before? Use AI, you know, to predict a lot of the things including, you know, bias detection, all those things. So we're making a lot off the development, automation and acceleration easier. The complexity part will be there still, because You know, enterprise applications tend to be complex by nature, but we're making it much more easier for you to develop, deploy and manage and govern what you're building. >>Yeah, so? So how does cloud packs allow you to really, you know, work with customers focus on, you know, things like innovation showing them the latest in the IBM software portfolio. >>Yeah. So off the first pieces that we made it much more easier for the different personas to collaborate. So in the past, you know what is the biggest challenge? Me as a data scientist had me as a data scientist. The biggest challenge was that getting access to the data Trusted data. Now, you know, we have put some governance around it, or by average, you can get, you know, data trusted data much more easier using our back to data governments around the data. Meaning if you have a CDO, you want to see who is using the data? How clean is that data, right? I mean, a lot of times that data might not be clean, so we want to make sure we can. You can help with that. Now let me move into the you know the line of business. He's not just the data. If I am, you know, a l l o B. And I want to use order. Made a lot of the process I have in today in my enterprise and not go to the ah, the every process automation and go through your superior or supervisor to get approval. How do we use AI in the business process? Automation also. So those kind of things you will get to cloud parts now, the other piece of it. But if I'm an I t space, right, the day two operations scalability, security, delivery of the software, backup and restore how do we automate and help it at the storage layer? Those air day two operations. So we're taking it all the way from day one. The whole experience of setting it up today to where enterprises really wherever, making it seamless and easy. Using quote Thanks. I go back to what I said in the beginning, which is how do we accelerate and automate a lot of the work that enterprises today much more easier. >>Okay, Wei talked earlier in the discussion about that. This can be used across multiple cloud environments. My understanding you mentioned one of the IBM cloud packs, one for data. There's a number of different cloud tax out there. How does that >>work from >>a customer standpoint? Do I have to choose a cloud pack for a specific cloud? Does it is a license that goes across all of my environment, Help me understand on this deployment mechanism and support inmates works >>right? So we have the base. Obviously, I said, You know, look at us. A modular Lego model. The base is obviously open shift in Rome. On top of itself sits a bedroom because which is a common set of services on the logic to experience. On top of it sits lower back for data well back for security cloud. For applications, there's cloud back for multi cloud management. There's cloud platform integration, so there's total of six power packs that's available, but you can pick and choose which about back you want. So let's say you are. You're a CD or you are an enterprise. We want to focus on data and ai. You can just speak lower back for data or let's say you are a, you know, based on processes, BPM decision rules. You can go with our back for automation, which gives you the set of tools. But the biggest benefits do is that all these quarterbacks are set up in the greatest services that can all work together since optimized on top of open ship. So all of a sudden you lead bar Cloud back for data, and now you want to do data. But now you want to expand it in New York and line of business. And you want that for automation? You can bring that in. Now those two quarterbacks works together. Well, now you want to bring it back for multi cloud management because you have data or applications running on multiple clouds. So now you can will bring it back for EMC M, which is multi cloud management. And those three work together. So it's fall as set off integrated set of services that is optimized on top of open shift, which makes it much more easier for customers to bring the rich set of services together and accelerate and automate their lifecycle journey within the enterprise. >>Great last question for you, Dan Ashe. You know what? What new in 2020. What should customers be looking at today would love if you could give a little bit of guidance as to where customers should be looking at for things that might be coming a little bit down the line here. And if they want to learn more about IBM cloud backs, where should they be looking? >>Yeah, if they want to learn more, there's, you know, the VW IBM dot com slash power packs. That's a place to go there. All the details on power packs are there. You can also get in touch with me, and I can definitely much more detail. But what is coming is that look. So we have a set of cloud parts, but we want to expand on, Make it extensible. So how do we you know, already it's built on an open platform. But how do we make sure our partners and I s feeds can come and build on top of the space cloud? So that's the focus going to be as each quote back innovate and add more value in within those cloud grants. We also wanted bandit so that you know our partners and our eyes, fees and GS size and build on top of it. So this year the focus is continuously in a way across the cloud part, but also make it much more extensible for third parties to come and build more value. That's the you know, That's one area of focus in the various EMC em right multi cloud management, because there is tremendous appetite for customers to move data or applications on cloud, and not only on one cloud hybrid cloud. So how do you manage that? Right. So multi cloud management definitely helps from that perspective. So our focus this year is going to be one. Make it extensible, make it more open, but at the same time continuously innovate on every single cloud part to make that journey for customers on automation and accelerating off application element easier. >>All right, we'll do next. Thank you so much. Yeah, the things that you talked about, that absolutely, you know, top of mind for customers that we talk to multi cloud management. As you said, it was the ACM, the advanced cluster management that we heard about from the Red Hat team last week at Summit. So thank you so much for the updates. Definitely exciting to watch cloud packed. How you're helping customers, you know, deal with that. That huge. It's the opportunity. But also the challenge of building their next applications, modernizing what they're doing without, you know, still having to think about what they have from their existing. Thanks so much. Great to talk. >>Thanks to you. >>All right, lots more coverage from IBM. Think 2020 The digital experience. I'm stew minimum. And as always, Thank you for watching the Cube. >>Yeah, Yeah, yeah, yeah, yeah.

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi everybody, welcome to this CUBE conversation. This is Dave Vellante, and as part of my CEO and CXO series I've been bringing in leaders around the industry and I'm really pleased to have Sudheesh Nair, who is the CEO of ThoughtSpot Cube alum. Great to see you against Sudheesh, thanks for coming on. >> My pleasure Dave. Thank you so much for having me. I hope everything is well with you and your family. >> Yeah ditto back at you. I know you guys were in a hot spot for a while so you know we power on together, so I got to ask you. You guys are AI specialists, maybe sometimes you can see things before they happen. At what point did you realize that this COVID-19 was really going to be something that would affect businesses globally and then specifically your business. >> Yeah it's amazing, isn't it? I mean we used to think that in Silicon Valley we are sitting at the top of the world. AI and artificial intelligence, machine learning, Cloud, IOT and all of a sudden this little virus comes in and put us all in our places basically. We are all waiting for doctors and others to figure these things out so we can actually go outside. That tells you all about what is really important in life sometimes. It's been a hard journey for most people because of what a huge health event this has been. From a Silicon Valley point of view and specifically from artificial intelligence point of view, there is not a lot of history here that we can use to predict the future, however early February we had our sales kick off and we had a lot of our sellers who came from Asia and it became sort of clear to us immediately during our sales kick off in Napa Valley that this is not like any other event. The sort of things that they were going through in Asia we sort of realized immediately that us and when it gets to the shores of the US, this is going to really hurt. So we started hunkering down as a company, but as you mentioned early when we were talking, California in general had a head start, so we've been hunkered down for almost five weeks now, as a company and as the people and the results are showing. You know it is somewhat contained. Now obviously the real question is what next? How do we go out? But that's probably the next journey. >> So a lot of the executives that I've talked to, of course they start with the number one importance is the health and well-being of our employees. We set up the work from home infrastructure, et cetera. So that's I think, been fairly well played in the media and beginning to understand that pretty well. Also, you saw I talked to Frank Slootman and he's sort of joked about the Sequoia memos, that you know eliminate unnecessary expenses and practices. I've always eliminated unnecessary expenses, keep it to the essentials, but one of the things that I haven't probed with CEOs and I'd love your thoughts on this is, did you have to rethink sort of the ideal customer profile and your value proposition in the specific context of COVID? Was that something that you deliberately did? >> Yeah so it's a really important question that you asked, and I saw the Frank interview and I a 100% agree with that. Inside the company we have this saying, and our co-founder Ajeet actually coined the phrase of living like a middle-class company, and we've always lived that, even though we have, 300 plus million dollars in the bank and we raised a big round last year. It is important to know that as a growth stage company, we are not measured on what's in the bank. It's about the value that we are delivering and how much I'll be able to collect from customers to run the business. The living like a middle-class family has always been the ethos of the company and that has been a good thing. However, I've been with ThoughtSpot for a little more than 18 months. I joined as the CEO. I was an early investor in the company and there are a couple of big changes that we made in the last 18 months, and one of is moving to Cloud which we can talk. The other one has been around narrowing our focus on who we sell to, because one of the things that, as you know very well Dave, is that the world of data is extremely complex. Every company can come in and say, "We have the best solution out there" and it can just be in the world, but the reality is no single product is going to solve every problem for a customer when it comes to a data analytics issue. All we can hope for is that we become part of a package or solution that solves a very specific problem, so in that context there's a lot of services involved, a lot of understanding of customer problems involved. We are not a bi-product in the sense of Tableau or click on Microsoft, but they do. We are about a use case based outcomes, so we knew that we can't be everywhere. So the second change we made is actually a narrower focus, exclusively sell to global. That class, the middle class mentality, really paid off now because almost all the customers we sell to are very large customers and the four work verticals that we were seeing tremendous progress, one was healthcare, second was financial sector, the third was telecom and manufacturing and the last one is repair. Out of these four, I would say manufacturing is the one where we have seen a slowdown, but the other verticals have been, I would say cautiously spending. Being very responsible and thus far, I'm not here to say that everything is fine, but the impact if you take Zoom as a spectrum, on one end of the spectrum, where everything is doing amazingly well, because they are a good product market fit to hospitality industry on the other side. I would say ThoughtSpot and our approach to data analytics is closer to this than that. >> That's very interesting Sudheesh because, of course health care, I don't think they have time to do anything right now. I mean they're just so overwhelmed so that's obviously an interesting area that's going to continue to do well I would think. And they, the Financial Services guys, there's a lot of liquidity in the system and after 2009 the FinTech guys or the financial, the banks are doing quite well. They may squeeze you a little bit because they're smart negotiators, but as you say manufacturing with the supply chains, and in retail, look, if your ecommerce I mean Amazon hit, all-time highs today up whatever, 20% in the last two weeks. I mean just amazing what's happening, so it's really specific parts of those sectors will continue to do well, won't they? >> Absolutely, I think look, I saw this joke on Twitter, what's the number one cost? What is in fact (mic cuts out). Very soon people will say it is COVID and even businesses that have been tried to, sort of relatively, reluctant to really embrace the transformation that the customers have been asking for. This has become the biggest forcing function and that's actually a good thing because consumers are going to ultimately win because once you get groceries delivered to you into your front doors, it's going to be hard to sort of go back to standing in the line in Costco, when InstaCart can actually deliver it for you and you get used to it, so there are some transformation that is going to happen because of COVID. I don't think that society will go back from, but having said that, it's also not transformation for the sake of transformation. So speaking from our point of view on data analytics, I sort of believe that the last three to four years we have been sort of living in the Renaissance of enterprise data analytics and that's primarily because of three things. The first thing, every consumer is expecting, no matter how small or the big business, is to get to know them. You know, I don't want you to treat me like an average. I don't want you treat me like a number. Treat me like a person, which means understand me but personalize the services you are delivering and make sure that everything that you send me are relevant. If there's a marketing campaign or promo or customer support call, make sure it's relevant. The relevance and personalization. The second is, in return for that. customers are willing to give you all sorts of data. The privacy, be damned, so to a certain extent they are giving you location information, medical information,-- And the last part is with Cloud, the amount of data that you can collect and free plus in data warehouse like Snowflakes, like Redshift. It's been fundamentally shifted, so when you toggle them together the customers demand for better actors from the business, then amount of data that they're willing to give and collect to IOT and variables and then cloud-based technologies that allows you to process and store this means that analyzing this data and then delivering relevant actions to the consumers is no longer a nice to have and that I think is part of the reason why ThoughtSpot is finding sort of a tailwind, even with all this global headwind that we are all in. >> Well I think too, the innovation formula really has changed in our industry. I've said many times, it's not Moore's law anymore, it's the combination of data plus AI applied to that data and Cloud for scale and you guys are at the heart of that, so I want to talk about the market space a little bit. You look at BI and analytics, you look at the market. You know the Gartner Magic Quadrant and to your point, you know the companies on there are sort of chalk and cheese, to borrow a phrase from our friends across the pond. I mean, you're not power BI, you're not SaaS. I mean you're sort of search led. You're turning natural language into complex sequel queries. You're bringing in artificial intelligence and machine intelligence to really simplify and dramatically expand and put into the hands of business people analytics. So explain a little bit. First of all, do I have that sort of roughly right? And help us frame the market space how you think about it. >> Yeah I mean first of all, it is amazing that the diverse industry and technologies that you speak to and how you are able to grasp all of them and summarize them within a matter of seconds is a term to understand in itself. You and Stew, you both have that. You are absolutely right. So the way I think of this is that BI technologies have been around and it's played out really well. It played it's part. I mean if you look at it the way I think of BI, the most biggest BI tool is still Excel. People still want to use Excel and that is the number one BI tool ever. Then 10 years ago Tableau came in and made visualizations so delightful and a pic so to speak. That became the better way to consume complex data. Then Microsoft came in Power BI and then commoditized and the visualization to a point that, you know Tableau had to fight and it ended up selling to the Salesforce. We are not trying to play there because I think if you chase the idea of visualization it is going to be a long hard journey for ThoughtSpot to catch Tableau in visualization. That's not what we are trying to do. What we are trying to do is that you have a lot of data on one hand and you have a consumer sitting here and saying data doesn't mean you treated me well. What is my action that is this quote, very customized action quote. And our question is, how does beta turn into bespoke action inside a business? The insurance company is calling. You are calling an insurance company's customer support person. How do you know that the impact that you are getting from them is customized. But turning data into insight is an algorithmic process. That's what BI does, but that's like a few people in an organization can do that. Think of them like oil. They don't mix with water, that's the business people. The merchandising specialist who figures out which one should become site and what should be the price what should be ranking. That's the merchandiser. Their customer support person, that's a business user. They don't necessarily do Python or SQL, so what happens is in businesses you have the data people like water and the business people who touch the customer and interact with them every day, they're like the water. They don't mix. The idea of ThoughtSpot is very simple. We don't want this demarcation. We don't want this chasm. We want to break it so that every single person who interact with the customer should be able to have an interactive storytelling with the data, so that every decision that they make takes data into insight to knowledge to action, and that decision-making pipeline cannot be gut driven alone. It has to be enabled by data science and human experience coming together. So in our view, a well deployed data platform, decision-making platform, will enhance and augment human experience, as opposed to human experience says, this data says that, so you've got to pick one. That's an old model and that has been the approach with natural language based interactive access with the BI being done automated through AI in the backend, parts what we are able to put very complex data science in front of a 20 year experienced merchandising specialist in a large e-commerce website without learning Python, without learning people, without understanding data warehouse >> Right so, a couple of things I want to pick up on. I mean data is plentiful, insights aren't. That's really the takeaway from one of the things that you mentioned and this notion of storytelling is very, very important. I mean, all business people, they better be storytellers in some way shape or form and what better way to tell stories than with data, and so, because as you say it's no longer gut feel, it's not the answer anymore. So it seems to me Sudheesh, that you guys are transformative. The decision to focus on the global 2000 and really not, get washed up in the Excel, well I could just do it in Excel, or I'm going to go get Power BI, it's good enough. It's really, you're trying to be transformative and you've got a really disruptive model that we talked about before, search led and you're speaking to the system, or, typing in a way that's more natural, I wonder if you could comment on that and particularly that disruption of that transformation. >> Remember we are selling to global 2000. Almost all of them will have Tableau or one of these power BI or one of these solutions already, so you're not trying to go right and change that. What we have done is very clearly focus on use cases. We're transforming data into action. We will move the needle for the bit, but for example with the COVID situation going on, one of the most popular use cases for us is around working capital management. Now a CFO who's been in the business for 20 or 30 years is an expert and have the right kind of gut feeling about how her business is running when it comes to working capital. However, imagine now she can do 20 what-if scenarios in the next five seconds or next 10 minutes without going to the SPN 18, without going to the BI team. She can say what if we reduce hiring in Japan and instead we focus them on Singapore? What if we move 20% of marketing dollars from Germany to New York? What would be the impact of AR going up by 1% versus AP going down by 1%? She needs to now do complex scenarios, but without delay. It's sort of like how do I find a restaurant through Yelp versus going to the lobby to talk to a specialist who tells me the local restaurant. This interactive database storytelling for gut enhances the decision-making is very powerful. This is why, customer have, our largest customer has spent more than $26 million with ThougthSpot and this is not small. Our average is around close to 700k. This week for example, we are having a webinar where Verizon's SVP of Analytics specifically focused on finance. He's actually going to be on a webinar with our CFO. Our CFO Sophie, one of our financial specialists and Jeff Noto from Verizon are going to be on this talking about working capital management. What parts ThoughtSpot is a portion of, but they are sharing their experience of how do we manage, so that kind of varies, like extremely rigid focus on use cases, supply chain, modeling different things so that someone who knows Asia can really interact with the data to figure out if our supply chain from Bangladesh is going to be impacted because of COVID can we go to Ecuador? What will that look like? What will be the cost? What's the transportation cost, the fuel cost, Business has become so complex you don't have time to take five, six days to look at the report, no matter how pretty that report is, you have to make it efficient. You need to be able to make a lightning fast decision and something like COVID is really exposing all of that because day by day situation on the ground is changing. You know, employees are calling in sick. The virus is breaking out in one place, other place. If it's not, curves are going up and down so you cannot have any sort of delay between human experience and data signs and all of that comes down to your point telling visual stories so that the organization can rally behind the changes that they want to make. >> So these are mission-critical use cases. They are big problems that you're solving and attacking. As you said, you're not all things to all people. One of the things you're not is a data store, right? So you've got a partner, you've got to have an ecosystem, whether it's cloud databases, the cloud itself. I wonder if you could talk about some of the key partnerships that you're forming and how you're going to market and how that's affecting your business. >> Yeah, I mean one of the things that I've always believed in Silicon Valley is that companies die out of indigestion, not out of starvation. You try to do everything. That's how you end up dying and for us in the space of data, it's an extremely humbling space because there is so much to do, data prep, data warehousing, you know a mash-up of data, hosting of data, We have clearly decided that our ability is best spent on making artificial intelligence to work, interactive storytelling for business use and that's it. With that said, we needed a high velocity agility partner in the back end and Cloud based data warehouse have become a huge tailwind for us because our entire customer deployments are on Cloud, and the number one, obviously as you know from Frank's thing, the Snowflake has actually given, customers have seen Snowflakes plus ThoughtSpot is actually a good thing and we are exclusive in global 2000 and the Snowflake is climbing up there and we are able to build a good mutual partnership, but we are also seeing a really creative partnership all the way from product design to go to market and compensation alignment with Amazon on their push on Redshift as well. Google, we have announced partnership. There is a little bit of (mic cuts out) in the beginning we are getting, and just a couple of weeks ago we started working with Microsoft on their Azure Synapse algo. Now I would say that it's lagging, we still have work to do but Amazon and Snowflake are really pushing in terms of what customers want to see, and it completely aligns with our value popular, one plus one equals three. It really works well for our customers >> And Google is what, BigQuery plus Google Cloud, or what are you doing there? >> Yep so both Amazon and Google. Well, what we are doing at three different pieces. One if obviously the hosting of their cloud platforms. Second is data warehouse and enterprise data warehouse, which is Redshift and BigQuery. Third, we are also pretty good at taking machine learning algorithms that they have built for specific verticals. We're going to take those and then ingest them and deliver better. So for example if you are one of the largest supply companies in the world and you want to know what's the shipment rate from China and it shows and then the next thing you want to know is what the failure rate on this based on last behavior when you compressed a shipment rate, and that probably could use a bit of specific algorithms and you know Google and others have actually built a library of algorithms that can be injected into ThoughtSpot. We will simply answer the question of we may have gotten that algorithm from the Google library, sort of the business use is concerned. It doesn't really matter, so we have made all that invisible and we are able to deliver democratized access to Bespoke Insights to a business user, who are too sort of been afraid to deal with the sector data. >> Since you mentioned that you've got obviously several hundred million dollars in cash. You've raised over half a billion. You've talked previously about potential acquisitions, about IPO, are you considering acquisitions? M&A at this point in time? I mean there may be some deals out there. There's certainly some talent out there, but boy the market is changing so fast. I mean, it seems to, certain sectors are actually doing quite well. Will you consider M&A at this point? >> Yes, so I think IPO and M&A are two different-- IPO definitely, it will be foolish to say that this hasn't pushed our clients back a little bit because this is a huge event. I think there will be a correction across valuation and all of that. However, it is also important for us we use this opportunity to look at how we are investing our resources and investment for long-term versus the short-term and make sure that we are more focused and more tightening at the belt. We are doing that internally. Having said that, being a private company our valuation is, you know at least in theory, frozen, and then we have a pretty good cash position of close to $300 million, which means that it is absolutely an opportunity for us to seriously consider M&A. The important thing going back to my adage of, companies don't die out of starvation. It is critical to make sure that whatever we do, we do it with clarity. Are we doing it for talent? Are we doing it for tech? Or are we doing it for market? When you have a massive event like this, it is a poor idea to go after new market. It is important to go to our existing customers who are very large global 2000 firms and then identify problems that we cannot solve otherwise and then add technology to solve those problems, so technology acquisitions are absolutely something to consider, but it needs some more time to settle in because, the first two weeks were all people who were blindsided by this, then the last two weeks we have now gotten the mojo back in sales and mojo back in engineering, and now I think it is time for us to digest and prepare for these next two, three quarters of event and as part of that, companies like us who are fortunate enough to be on a good cash position, we'll absolutely look for interesting and good deals in the M&S space. >> Yeah, it makes sense, is tell and tech and, post IPO you can worry about Tam expansion. You'll be under pressure to do that as the CEO, but for now that's a very pragmatic approach. My last question is, there's some things when you think about, you say five weeks now you've been essentially on lockdown. You must, as many of us start thinking about wow, a lot of this work from home which came so fast people wouldn't even think about it earlier. You know, some companies mandated the beehive approach. Now everybody's open to that. There are certain things that are likely to remain permanent post COVID. Have you thought much about that? Generally and specifically how it might affect your business, the permanence of post COVID. Your thoughts. >> Yeah I've thought a lot about it. In fact, this morning I was speaking with our CRO Brian McCarthy about this. I think the change will happen, think of like an onion's inner most layer, I think the most, my hope is, that the biggest change will be in every one of us internally, as a what sort of a person am I and what does my position in the world means. The ego of each one of us that we carry because if this global event in one shot did not make you rethink your own sort of position in this big universe I think that's a mess. So the first thing has to be about being a better person. The second thing is, I had this two, three days of fever which was negative for COVID but I isolated myself, but that gave me sort of an idea of dipping in the dark room where I'm hoping my family won't get infected and you know my parents are in India so I sort of also realized that what is really important for you in life and how much family should mean to you, so that goes to the first, yourself second, your relationship with family, but having said that, the third thing when it comes to business building is also the importance for building with quality people, because when things go wrong it is so critical to have people who believe in the purpose of what you are trying to build. People with good faith and unshakable faith, personal faith and unshakable faith in the purpose of the company and most importantly you mentioned something which is the story telling. People, leaders who can absolutely communicate with clarity and certainty. It becomes the most important thing to lead an organization. I mean, you are a small business owner. You know we are in a small company with around 500 people. There is nothing like sitting at home waiting to see how the company is doing over email if you're a friend line engineer or a seller. Communication becomes so critical, so having the trust and the respect of organization and have the ability to clearly and transparently communicate is the most important thing for the company and over communicating due to the time of crisis. These things are so useful even after this crisis is over. Obviously from a technology point of view, you know people have been speaking a lot about working remotely and technology changes, security, those things will happen but I think if these three things were to happen in that order. Be a better person, be a better family member and be a better leader, I think the world will be better off and the last thing I'll also tell you, that you know in Silicon Valley sometimes we have this disregard for arts and literature and fight over science. I hope that goes away, because I can't imagine living without books, without movies, without Netflix and everything. Art makes yourself creative and enriches our lives. You know, sports is no longer there on TV and the fact that people are able to immerse their imagination in books and fiction and watch TV. That also reminds you how important it is to have a good balance between arts and science in this world, so I have a long list of things that I hope we as a people and as a society will get better. >> Yeah, a lot more game playing in our household and it's good to reconnect in that regard. Well Sudheesh, you've always been a very clear thinker and you're in a great spot and an awesome leader. Thanks so much for coming on theCUBE. It was really great to see you again. All the best to you, your family and the broader community in your area. >> Dave, you've been very kind with this. Thank you so much, I wish you the same and hopefully we'll get to see face-to-face in the near future. Thanks a lot. >> I hope so, thank you. All right and thank you for watching everybody. This is Dave Vellante for theCUBE and we'll see you next time. (upbeat music)

(upbeat music) >> Live, from Las Vegas, it's theCUBE, covering UiPath Forward Americas 2019. Brought to you by UiPath. >> Welcome back to Las Vegas, everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events and we extract the signal from the noise. This is day two of UiPath Forward III, the third North American conference that UiPath-- The rocket ship that is UiPath. Clemmie Malley is here. She's the Enterprise RPA Center of Excellence Lead at NextEra Energy. Welcome. Great to have you. And Mariesa Coughanour, who is the Managing Principal of Intelligent Automation and Technology at Cognizant. Nice to see you guys. >> Nice seeing you. >> Nice to see you. >> Thanks for coming on. How's the show going for you? >> It's been great so far. >> Yes. >> It's been awesome. >> Have you been to multiple... >> This is my third. >> Yep. >> Really? Okay, great. How does this compare? >> It has changed significantly in three years, so. It was very small in New York in 2017 and even last year grew, but now it's a two-year event taking over. >> Yeah, last year Miami was-- >> I don't know. >> It was nice. >> Definitely smaller than this, but it was happening. Kind of hip vibe. We're here in Vegas, everybody loves to be in Vegas. CUBE comes to Vegas a lot. So tell me more about your role at NextEra Energy. But let's start with the company. You guys are multi billion, many, many, tens of billions, probably close to $20 billion energy firm. Really dynamic industry. >> Yeah, so NextEra Energy is actually an awesome company, right? So we're the world's largest in clean renewable energy. So with wind and solar, really, and we also have Florida Power and Light, which is one of the child companies to NextEra as a parent, which is headquartered out of Florida. So it's usually the regulated side of power in the state of Florida. >> We know those guys. We've actually done some work with Florida Power and Light. Cool people down there. And we heard, one of the keynotes today, Craig LeClaire, was saying, "Yeah, the Center of Excellence, "that's actually maybe asking too much." But there are a lot of folks here that are sort of involved in a COE and that's kind of your role. But I was surprised to hear him say that. I don't know if you were in the keynote this morning, but was it a challenge to get a Center of Excellence? What is that all about? >> So I think there's a little bit of caution around doing it initially. People are very aggressive. And we actually learned from this story. So when we started, it was more about showing value, building as many automations as possible. We didn't really care about having a COE. The COE just happened to form. >> Okay. >> Because we found out we needed some level of governance and control around what we were doing. But now that I look back on it, it's really instrumental to making sure we have the success. So whether you do a hybrid development to automation, which you can have citizen development, or you're fully centralized, I think having the strong COE to have that core governance model and control and process is important. >> Mariesa, so your title is not, there's not RPA in your title, right? RPA is too narrow, right? >> Yeah. >> In your business you're trying to help transform companies, it's all about automation. But maybe explain a little bit about your practice and your role. >> Sure, so Cognizant's been on the automation journey now for three years. We started back in 2014 and right out the gate it was all about intelligent automation, just not RPA. Because we knew to be able to do end-to-end solutions you would need multiple technologies to really get the job done and get the outcomes they wanted. So we sit now, over 2,500 folks at our practice, going out, working cross-industry, cross-regions to be able to work with people like Clemmie to put in their program. And we've even added some stuff recently. A lot of it actually inspired by NextEra. And we have an advisory team now. And our whole job is to go in and help people unstuck their programs, for lack of a better way to say it. Help them think about, how do you put that foundation? Get a little bit stronger and actually enable scale, and putting in all this technology to get outcomes? Versus just focusing on just the pure play RPA, which a lot of people struggle to gain the benefits from. >> So Clemmie, what leads you to the decision to bring in an outside firm like Cognizant? What's that discussion like internally? >> So, I'll just give you a little bit of backstory, because I think that's interesting, as well. When we started playing with RPA in late 2016, early 2017, we knew that we wanted to do a lot of things in-house, but in order to have a flex model and really develop automations across the company, we needed to have a partner. And we wanted them to focus more on delivery, so developing, and then partner with us to give us some best practices, things that we could do better. When we founded the COE we knew what we wanted to do. So we actually had two other partners before we went with Cognizant, and that was a huge challenge for us. We found we were reworking a lot of the code that they gave us. They weren't there to be our partners. They wanted to come and actually do the work for us, instead of enabling us to be successful. And we actually said, "We don't want a partner." And then Cognizant came in and they actually were like, "Let's give you somebody." So we wanted somebody around delivery, because we said, "Okay, now that we centralize, "we have a good foundation, a good model, "we're going to need to focus on scale. "So how do we do that? "We need a flex model." So Cognizant came in and they said, "Well, we're going to offer you a delivery lead "to help focus on making sure "you get the automations out the door." Well, Mariesa actually showed up, which was one of the best hidden surprises that we received. And she really just came in, learned the company, learned our culture, and was able to say, "Okay, here's some guidance. "What can you instill? "What can you bring?" Tracking, and start capturing the outcomes that she's mentioned. And I know that was a little bit more, but it's been quite a journey. >> No, it's really good, back up. So Mariesa, I'm hearing from Clemmie that you were willing to teach these guys how to fish, as opposed to just perpetual, hourly, daily rate billing. >> Yep. And that's really what our belief is. We can go in, and yes, we can augment, from resourcing perspective, help them deliver, develop, support everything, which we do. And we work with Clemmie and others to do that. But what's really important to get to scale was how do we teach them how to go do this? Because if you're going to really embed this type of automation culture and mindset, you have to teach people how to do it. It's not about just leaning on me. I needed to help Clemmie. I need to help her team, and also their leadership and their employees. On how do you identify opportunities, and how then do you make these things actually work and run? >> So you really understand the organization. Clemmie was saying you learned the culture. >> Yeah. >> So you're not just a salesperson going in and hanging out in theCUBE. So you're kind of an extension, really, of the staff. So, either of you, if you can explain to me sort of, where RPA fits into this broader vision. That would really be helpful. >> Sure, so maybe I can kick a little bit off from what I'm seeing from clients like Clemmie, and also other customers. So what you'll find is RPA tends to be like this gateway. It's the stepping stone to all things automation. Because folks in the business, they really understand it. It's rule-based, right? It's a game of Simon Says, in some ways, when you first get this going. And then after that, it's enabling the other technology and looking at, "Look, if I want to go end-to-end, "what do I need to get the job done? "What do I need around data intake? "How do I have the right framework "to pick the right OCR tool, "or put analytics on, "or machine learning?" Because there's so much out there today and you need to have the stuff that's right-fit to come in. And so it's really about looking at what's that company strategy? And then looking at this as a tool set. And how to use these tools to go and get the job done. And that's what we were doing a lot with Clemmie and team when we sat down. They have a steering committee that's chaired by their CIO, Chief Accounting Officer, and senior leaders from every business unit across their enterprise. >> So you mentioned scaling. >> Yep. >> We heard today in the predictions segment that we're going to move from snowflake to snowball. And so I would think for scaling it's important to identify reusable components. And so how have you, how has that played out for you? And how's the scaling going? >> Yeah, so that's been one really cool component that we've built out in the COE. So I had my team actually vote on a name and we said, "We want to go after reusable components." They decided to call them Microbots. So it's a cool little term that we coined. >> That's cool. >> And our CIO and CAO actually talk about them frequently. "How are our Microbots? "How many do we have? "What are they doing?" So it's pretty catchy. But what it's really enabled us is to build these reusable snippets of code that are specific to how we perform as a company that we can plug and play and reduce our cycle time. So we've actually reduced our cycle time by over 50%. And reusable components is one of the major key components. >> So how do you share those components? Are they available in some kind of internal marketplace? And how do you train people to actually know what to apply where? >> Right. So because we're centralized, it's a little bit easier, right? We have a stored repository, where they're available. We document them-- >> And it's the COE-- Sorry to interrupt. It's the COE's responsibility, and-- >> Exactly. So the COE has it. We're actually working with Cognizant right now to figure out how can we document those further, right? And UiPath. There's a lot of cool tools that were introduced this week. So I think we're definitely going to be leveraging from them. But the ability to really show what they are, make them available, and we're doing all of that internally right now. Probably a little manual. So it'll be great to have that available. >> So Amazon has this cool concept they call working backwards documents. I don't know if you ever heard this. But what they do is they basically write the press release, thinking five years in advance. This is how they started AWS, they actually wrote. This is what we want, and then they work backwards from there. So my question is around engineering outcomes. Can you engineer outcomes, and is that how you were thinking about this? Or is it just too many unknown parts of the process that you can't predict? >> So I think one of the things that we did was we did think about, "What do we want to achieve with this?" So one of the big programs that Clemmie and the team have is also around accelerate. And their key initiatives to drive, whether it's improve customer experience, more efficiencies of certain processes across the company. And so we looked at that first, and said, "Okay, how do we enable that?" That's a top strategy driven by their CEO. And even when we prioritize all the work, we actually build a model for them. So that it's objective. So if any opportunities that come in align to those key outcomes that the company's striving for, they can prioritize first to be worked on. I actually also think this is where this is all going. Everyone focuses today on these automation COEs and automation teams, but what you will see, and this is happening at NextEra, and all the places we're starting to see this scale, is you end up with this outcomes management office. This is a core nucleus of a team that is automation, there's IT at the table, there's this lean quality mindset at the table, and they're actually looking at opportunities and saying, "All right, this one's yours. "This one's yours and then I'll pick up from you." And it's driving, then, the right outcomes for the organization versus just saying, "I have a hammer, I'm going to go find a nail," which sometimes happens. >> Right, oh, for sure. And it may be a fine nail to hit, but it might not be the most strategic-- >> Exactly. >> Or the most valuable. So what are some examples of areas that you're most excited about? Where you've applied automation and have given a business outcome that's been successful? >> Yeah, so we are an energy company. And we've had a lot of really awesome brainstorming sessions that we've held with UiPath and Cognizant. And a couple of key ones that have come out of it, really around storm season is big for us in the state of Florida. And making sure that our critical infrastructure is available. So our nursing homes, our hospitals, and so on. So we've actually built automations that help us to ping and make sure that they're available, so that we can stay proactive, right? There's also a cool use-case around, really, the intelligent automations space. So our linemen in their trucks are saying, "Hey, we spend a lot of time having to log on the computer, "log our tickets, "and then we have to turn our computers off, "drive to the next site, "and we're not able to restore as much power "or resolve issues as quickly as possible." So we said, "How can we enable them?" Speech recognition, where they can talk to it, it can log a ticket for them on their behalf. So it's pretty exciting. >> So that's kind of an interesting example. Where RPA, in and of itself's not going to solve that problem, right, but speech recognition-- >> It's a combination. >> So you got to bring in other technology, so using, what, some NLP capability, or? >> Yeah, so that's one we're currently working on. But yes, you would need some type of cognitive speech recognition, and. >> So you sort of playing around with that in R&D right now? The speech [Mumbles]. >> Yeah. >> Which, as you know, is not perfect, right? >> It is not. >> Talk to us. We know about it all. Because we transcribe every word that's said on theCUBE. And so, there's some good ones and there's some not so good ones. And they're getting better, though. They're getting better. And that's going to be kind of commodity shortly. You really need just good enough, right? I mean, is that true? Or do you need near perfect? >> So I think there's a happy medium. It depends on what you're trying to do. In this case we're logging tickets, so there might be some variability that you can have. But I will say, so NextEra is really focused on energy, but they're also trying to set themselves apart. So they're trying to focus on innovation, as well. So this is a lot of the areas that they're focusing on: the machine learning, and the processing, and we even have chat bots that they're coining and branding internally, so it's pretty exciting. >> So NextEra is, are you entirely new energy? Is that right? No fossil fuels, or? >> So it's all clean energy, yes. Across the enterprise. >> Awesome. How's that going? Obviously you guys are very successful, but, I mean, what's kind of happening in the energy business today? You're sort of seeing a resurgence in oil, right, but? >> Yeah, so I think we had a really good boom. A couple years ago there were a lot of tax credits that we were able to grow that side of our company. And it enabled us to really pivot to be the clean energy that we are. >> I mean, that's key, right? I mean, United States, we want to lead in clean energy. And I'm not sure we are. I mean, like you say, there was tax incentives and credits that sort of drove a lot of innovation, but am I correct? You see countries outside the U.S., really, maybe leaning in harder. I mean, obviously we got NextEra, but. >> I mean, I think there's definitely competition out there. We're focused on trying to be, maybe not the best, but compete with the best. We're also trying to focus on what's next, right? So be proactive, and grow the company in a multitude of ways. Maybe even outside the energy sector, just to make sure that we can compete. But really what we're focused on is the clean renewables, so. >> That's awesome. I mean, as a country we need this, and it's great to have organizations like yours. Mariesa, I'll give you the final word. Kind of, the landscape of automation. What inning are we in? Baseball analogy. Or how far can this thing go? And what's your sort of, as you pull out the binoculars, maybe not the telescope, but the binoculars, where do you see it going? >> I think there's a lot of runway left. So if you look at a lot of the research out there today, I heard today, 10% was quoted by one person. I heard 13% quoted from HFS around where are we at on scale from an RPA perspective? And that's just RPA. >> Yeah. >> So that means there's still so much out there to still go and look at and be able to make an impact. But if you look, there's also a lot of runway on this intelligent automation. And that's where, I think, we have to shift the focus. You're seeing it now, at these conferences. That you're starting to see people talk about, "How do I integrate? "How do I actually think about connecting the dots "to get bigger and broader outcomes for an organization?" and I think that's where we're going to shift to, is talking about how do we bring together multiple technologies to be able to go and get these end-to-end solutions for customers? And ultimately go, what we were talking a little bit about before, on outcome-focused for an organization. Not talking about just, "How do I go do AI? "How do I go put a bot in?" But, "I want to choose this outcome for my customer. "I need to grow the top line. "I'm getting this feedback." Or even internally, "I want to get more efficient so I can deliver." And focus there, and then what we'll do is find the right tools to be able to move all that forward. >> It's interesting. We're out of time, but you think about, it's somewhat surprising when people hear what you just said, Mariesa, because people think, "Wow, we've had all this technology for 50 years. "Haven't we automated everything?" Well, Daniel Dines, last night, put forth the premise that all this technology's actually creating inefficiencies and somewhat creating the problem. So technology's kind of got us into the problem. We'll see if technology can get us out. All right? Thanks, you guys, for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you for having us. >> You're welcome. >> Thanks. >> All right, keep it right there, everybody. We'll be right back with our next guest right after this short break. UiPath Forward III from Las Vegas. You're watching theCUBE. (electronic music)

>> Live from Bellevue, Washington it's theCUBE. Covering Smartsheet Engage '18. Brought to you by Smartsheet. >> Welcome back to theCUBE. We are live at Smartsheet Engage 2018 in Bellevue, Washington. I am Lisa Martin with Jeff Frick, and we've had a great day talking with Smartsheet executives, analysts, users, and we're excited to welcome to theCube for the first time, Gurmeet Mangat, the site manager Wind Power Generation at GE Renewable Energy. Gurmeet, great to have you on the program. >> Thank you Lisa, thank you Jeff. I'm really happy to be here. >> So you're a user of Smartsheet, but you're also a renegade. So before we get into your renegade status, tell us a little bit about GE Renewable Energy and your role. You got a big role as site manager. What, 75 turbines across multiple locations? So let's talk about GE Renewable Energy and your role as site manager. >> Sure, no problem. So GE Renewable Energy. One of our missions statements is to unleash limitless energy. How we do that, we harness the power of the sun, the water, and the wind. So try to produce clean efficient energy to power countries, homes, businesses, whatever needs that powered energy. As a manager I manage, like you said three wind farms, three different customers. A very complex role to have. I'm coming right from not just operations, human resources, financials. So everything's required of someone like me to manage that business end to end. It's a challenge, at the same time I seek opportunity in a lot of what's going on and leveraging Smartsheet as one of the tools. It's something I've been using over the past year to optimize the business and run those turbines. >> So it's so funny because I would say GE turbine farms and GE engines are the most quoted, often referenced IOT devices in this next gen conversation about IOT and data and how much data they throw off of any other kind of product out there, and you're sitting right in the middle of it actually managing the real machines and managing the real data. >> Yeah, exactly. So I mean the, the machines themselves are highly automated. They're spinning out a lot of data and we've got great systems in place to manage that information. Make it transferrable, viewable to a lot of the people that need it. The opportunity is not necessarily in the equipment that GE manufactures but the back-end business that drives that manufacturing, that drives those services. That's where, again we leveraged Smartsheet over the last year to close a lot of data quality issues. We're ruling out and canceling a lot of the human error of the process steps that we're seeing in a lot of businesses today. We're really taking the initiative of managing our data, bringing us, making us actually competitive in the fourth industrial revolution. I mean I've had a strong believe that if you're not managing your data correctly today, you'll market yourself out of the business, you won't stay ahead of the game. So I think, like I was saying before the biggest opportunity right now is the back-end of the business. Smart GE does a great job at manufacturing and producing high quality products. I think there's huge opportunity in saving the back end and optimizing the process that runs that. >> When you say the back end, there's always a lot of conversation about you know going from reactive to predictive to prescriptive. Analytics, again everybody likes to talk about keeping the turbine up. Are you talking about those types of processes or is it more, you know how that energy is fed into the grid and more kind of the connection to the broader ecosystem, when you say back end? >> Let's talk about the proactive and reactive situation, 'cause that's really what we're trying to drive. >> Okay. >> There can be particular cases where a turbine could fail in the middle of winter, a high-wind season and the visibility's not great. So what we've done is we've taken Smartsheet. We've given our technicians a mobile application tool to collect data as they visit turbines. We're taking information within Smartsheet, we aggregate it, we quantify it, and now we're able to predict turbine behavior based on this information. A little bit faster than some of the tools that GE provides today. A perfect example is about a month ago we determined that a turbine needed a quarter of a million dollar repair before any GE tool told us that. That was simply because of giving our technicians a tool, which is a Smartsheet webform and telling us what happens everyday you visit that turbine. That goes into the background. We take the information, aggregate it into a dashboard viewings. That gives us a great visual control and visual aid of our business. >> That visibility-- >> I was going to say, is he collecting different data, or are you processing it in a different way with the tooling that you set up with Smartsheet that gives you that visibility? >> They are, so we are collecting different data. So GE gives us a lot of data on our turbine health efficiency, how it's operating. It might quantify the number of faults per megawatt hour and per (mumbles) it for us for example. But what we're creating with Smartsheet is we're creating our own organic KPIs I'll call them, some metrics that we are creating ourselves to try to drive different behavior. So when the techs go in, we talk about parts consumptions, for example. So if this part's been consumed 20 times over the last month, you've got to ask why. You know, why do you keep visiting this turbine to do that. So that visibility drives a different discussion now, so now we can engage with engineers with different type, different information. They might be able to say, "Okay, "you know what, you guys got some good data here. "We think you're right. "We should execute this repair." >> So, that example that you gave and give me the number again that working with Smartsheets your team was able to find a, what did you say, a $250 million? >> $250,000 repair. >> Thousand dollar repair. >> That's the cost of the repair, but it's a proactive repair versus reactive so now we're not facing a long wait time, finding a crane, bringing a crane on site, getting the paperwork in place to get the job done 'cause it's not an easy repair. >> But there's a very impressive snowball effect of the benefits back to the business. You've found it faster. You were able to get, you know the parts needed faster, repair it faster. Clearly that goes all the way back up the chain from a revenue perspective. >> Absolutely. >> But you, when I alluded to you earlier, this renegade status, you brought Smartsheet in from your previous job and you've said, "This has enabled us "to find something faster than "our brand of technology's product would have been "able to do." Talk to us about this conviction that you brought in and is it kind of becoming viral within GE Renewable Energy yet? >> Good question. It's becoming viral, a lot of people are listening now. So we've talked to GE digital VPs. I've talked to the ERP providers in Europe, what they're doing with GE. So we've essentially, I call it a success story. They're not going to adopt Smartsheet. They want to build their own enterprise solution but, the reason why I call it a success story is because I've changed the way that they are thinking today. >> That's huge. Cultural change? >> I've presented a solution to them. I've essentially told them, you need to give us something that works for us faster. If you do this, it gives managers capacity to improve your business, really develop people that are working underneath you, engage them, empower them, and move the business forward not on a typical five year plan that most businesses have in place. But it's a step change. >> Right, right. >> It takes you year over year and you're stepping every year to something new, and I think in today's day and age with how fast things are moving, you need that. >> And I'm curious to unpack a little bit on this example where you said you know, it's this failing part that was giving you a leading indicator that there was a bigger problem. So that was just kind of a different way to look at the solution, right? You're identifying kind of a stupid consumption pattern on a spare part that shouldn't happen as opposed to the core data that's coming off that machine and that's what gave you kind of the unique insights. Does that come from you? Does that come techs who are in the field and have kind of a sense of, "Maybe we should be looking at this, "maybe we should be looking at that." How do you start to empower people or where do some of these different kind of points of view that then can be backed up with data in the Smartsheet process come from? >> So, it's all techs. (clears throat) Coming into the job last year, I asked one of the techs, I said, "Why are you going to this turbine?" And the question why is such a powerful question to ask. They said, "We're going to fix this." So what happened last time? They had no idea. So I said, "There's no "information to support your visit today? "And you don't understand why you're going today." They said, "As a result of something that was not "done correctly before." So we fixed that part first. We started giving them the information upfront. We gave them a tool to collect the data. So now they are empowered to provide very direct feedback to myself as a manager and even to an engineering team, like in New York for example. Something technicians never felt empowered to do before. They are the driving factors for those data collection, the decision making. I definitely appreciate that by giving them feedback on a daily basis, that what you guys are doing is changing the way that we manage the business. It's a very driven culture change by the front line. It's not something that I'm pushing down. I'm asking them to help me push it upwards to the senior level. >> And they've got to love it. They've got to love thinking that they've actually got input as opposed to just being called to go out and fix things when it breaks. >> Exactly. They're driving their day. They can go to work in the morning. They can look at the whole personality of a turbine, what's outstanding, what was done last time and the conversations are very quick in the morning. It used to be a 7 o'clock startup. They're not driving out 'til eight, 8:30, nine o'clock by the time they get their stuff together. I mean we're averaging a seven am to about 7:30 departure now. >> So each person is saving 60 to 90 minutes everyday. >> Every day now departure. >> That's a big roll up. In fact, I was looking at some of the productivity stats that Smartsheet talks about on their website and they say an average per, individual user of Smartsheet will save about 300 hours a year. An organization can save up to 60,000 hours a year. >> I believe that. That's believable. I mean there's, just a technical aspect of managing a turbine. If we even talk about you know issuing a purchase order. Managing contractor labor, invoices. The tool that we're using today is a complete end to end P & L management tool. So it takes invoicing from subcontractors, labor. We are inventory tracking, we are tracking any health and safety issues. Everything from end to end, so it's really done a great job for us. >> That's all built within your Smartsheet? >> Correct. >> Wow. >> And it's all mobile, so. I mean I'm not at my site this week, but on a daily basis I have visibility to my business. You're talking about 70, 80 plus machines, that's over you know about a hundred million dollars in assets that have to be managed effectively, efficiently, and correctly. >> You have visibility into everyday from wherever you are? >> Exactly, yes. >> That's a huge transformation. So we talked about you being a renegade and other groups within GE on divisions that are curious about this. I'm curious, have you heard anything today that they have announced that excites you, or maybe was any of this part of a feedback that you provided, as we've heard all day Jeff that they're very responsive to customer feedback in terms of product innovation. Anything you're going to go back to the office and be excited, like the next generation or what's coming available soon? Is it going to enable me to do X-Y-Z now? >> That's a good question. So GE is a very tough company to change. There will be a lot of takeaways from this trip and when I go head back. After the last conversation I had with GE digital and the team, they are going to hire a new resource and set budget aside to help close the gaps that we've identified. So I think after this visit and some of the things I've learned throughout the conference and when I head back I'll only be able to identify a few more gaps that they need to fill, and I'll push that up to them probably in the next week when I get back there and hopefully they can appreciate that candid feedback and take that and run with it. >> But you were able to fund your existing project just out of your own discretionary funds? >> Exactly. I mean that's one of the benefits of Smartsheet. It costs really nothing to create something, and my job is to manage wind farms, so I've taken initiative to create, I call it a mini-ERP system using Smartsheet with an associate of mine, and it's an organic creation. It didn't take us, I mean to run three wind farms, I started last April, it probably took us less than six months to create a working system. That's awesome feedback for Smartsheet, their tools are very user-friendly. It's lightweight, it takes away the fear of coding that Excel gives to some people. If you're a new user of any application you can kind of walk into it and run with it. That's one of the reasons why we took it from nothing to something in such a short period of time. >> Wow. >> That's a ground swell in action that has some significant results. But you'd better be careful. I'm imagining your success is going to go so viral, you're going to have way more than 75 turbines and three wind farms >> That's possible. >> to manage. (laughs) >> There's been a recent acquisition and there's other sites around me that my boss is, or my directors said, "Hey, what are you doing next week?" >> Oh! (laughs) >> "Let's go visit this site for a few minutes." Okay, I know what you're getting at. >> Kind of a good problem to have, but thanks so much for stopping by and sharing with us what you're doing as a renegade. It seems pretty contagious. >> Appreciate it, thank you for having me. >> Thanks. >> Thanks. >> For Jeff Frick I'm Lisa Martin and you're watching theCUBE live from Smartsheet Engage 2018. Stick around, Jeff and I will be back to wrap up the show in just a minute. (digital music)

>> Live from New York, it's the Cube (funky music) covering the Cube New York City 2018 brought to you by SiliconANGLE Media and its ecosystem partners. (funky music) >> Everyone welcome back to theCUBE live in New York City for Cube NYC. This is our live broadcast - two days of coverage around the big data world, AI, the future of Cloud analytics. I'm John Furrier, my cohost Peter Burris. Our next guest is Paul Appleby, CEO Kinetica. Thanks for coming back to theCUBE - good to see you. >> Great to be back again and great to visit in New York City - it's incredible to be here on this really important week. >> Last time we chatted was in our big data Silicon Valley event, which is going to be renamed Cube SV, because it's not just data anymore; there's a lot of Cloud involved, a lot of new infrastructure. But analytics has certainly changed. What's your perspective now in New York as you're in here hearing all the stories around the show and you talk to customers - what's the update from your perspective? Because certainly we're hearing a lot of Cloud this year - Cloud, multi Cloud, analytics, and eyeing infrastructure, proof in the pudding, that kind of thing. >> I'm going to come back to the Cloud thing because I think that's really important. We have shifted to this sort of hybrid multi Cloud world, and that's our future - there is no doubt about it, and that's right across all spectre of computing, not just as it relates to data. But I think this evolution of data has continued this journey that we've all been on from whatever you want to call it - systems or record - to the world of big data where we're trying to gain insights out of this massive oceans of data. But we're in a world today where we're leveraging the power of analytics and intelligence, AI machine learning, to make fundamental decisions that drive some action. Now that action may be to a human to make a decision to interact more effectively with a customer, or it could be to a machine to automate some process. And we're seeing this fundamental shift towards a focus on that problem, and associated with that, we're leveraging the power of Cloud, AI, ML, and all the rest of it. >> And the human role in all this has been talked about. I've seen in the US in the political landscape, data for good, we see Facebook up there being basically litigated publicly in front of the Senate around the role of data and the elections. People are talking in the industry about the role of humans with machines is super important. This is now coming back as a front and center issue of hey, machines do great intelligence, but what about the human piece? What's your view on the human interaction component, whether it's the curation piece, the role of the citizen analyst, or whatever we're calling it these days, and what machines do to supplement that? >> Really good question - I've spent a lot of time thinking about this. I've had the incredible privilege of being able to attend the World Economic Forum for the last five years, and this particular topic of how Robotics Automation Artificial Intelligence machine learning is impacting economies, societies, and ultimately the nature of work has been a really big thread there for a number of years. I've formed a fundamental view: first of all, any technology can be used for good purposes and bad purposes, and it's - >> It always is. >> And it always is, and it's incumbent upon society and government to apply the appropriate levels of regulation, and for corporations to obviously behave the right way, but setting aside those topics - because we could spend hours talking about those alone - there is a fundamental issue, and this is this kind of conversation about what a lot of people like to describe as the fourth industrial revolution. I've spent a lot of time, because you hear people bandy that around - what do they really mean, and what are we really talking about? I've looked at every point in time where there's been an industrial revolution - there's been a fundamental shift of work that was done by humans that's now done by machines. There's been a societal uproar, and there're being new forms of work created, and society's evolved. What I look at today is yes, there's a responsibility and a regular treaside to this, but there's also a responsibility in business and society to prepare our workers and our kids for new forms of work, cause that's what I really think we should be thinking about - what are the new forms of work that are actually unlocked by these technologies, rather than what are the roles that are displaced by this steam powered engine. (laughs softly) >> Well, Paul, we totally agree with you. There's one other step in this process. It kind of anticipates each of these revolutions, and that is there is a process of new classes of asset formation. Mhm. So if you go back to when we put new power trains inside row houses to facilitate the industrial revolution in the early 1800s, and you could say the same thing about transportation, and what the trains did and whatnot. There's always this process of new asset formation that presaged some of these changes. Today it's data - data's an asset cause businesses ultimately institutionalize, or re institutionalize, their work around what they regard as valuable. Now, when we start talking about machines telling other machines what to do, or providing options or paring off options for humans so they have clear sets of things that they can take on, speed becomes a crucial issue, right? At the end of the day, all of this is going to come back to how fast can you process data? Talk to us a little bit about how that dynamic and what you guys are doing to make it possible is impacting business choices. >> Two really important things to unpack there, and one I think I'd love to touch on later, which is data as an asset class and how corporations should treat data. You talk about speed, and I want to talk about speed in the context of perishability, because the truth is if you're going to drive these incredible insights, whether it's related to a cyber threat, or a terrorist threat, or an opportunity to expand your relationship with a customer, or to make a critical decision in a motor vehicle in an autonomous operating mode, these things are about taking massive volumes of streaming data, running analytics in real time, and making decisions in real time. These are not about gleaning insights from historic pools or oceans of data; this is about making decisions that are fundamental to - >> Right now. >> The environment that you're in right now. You think about the autonomous car - great example of the industrial Internet, one we all love to talk about. The mechanical problems associated with autonomy have been solved, fundamentally sensors in cars, and the automated processes related to that. The decisioning engines - they need to be applied at scale in millions of vehicles in real time. That's an extreme data problem. The biggest problem solved there is data, and then over time, societal and regulatory change means that this is going to take some time before it comes to fruition. >> We were just saying - I think it was 100 Teslas generating 100 terabytes of data a day based on streams from its fleet of cars its customers have. >> We firmly believe that longer term, when you get to true autonomy, each car will probably generate around ten terabytes of data a day. That is an extremely complex problem to solve, because at the end of the day, this thinking that you're able to drive that data back to some centralized brain to be making those decisions for and on behalf of the cars is just fundamentally flawed. It has to happen in the car itself. >> Totally agree. >> This is putting super computers inside cars. >> Which is kind of happening - in fact, that 100 terabytes a day is in fact the data that does get back to Tesla. >> Yeah. >> As you said, there's probably 90% of the data is staying inside the car, which is unbelievable scale. >> So the question I wanted to ask you - you mentioned the industrial revolution, so every time there's a new revolution, there's an uproar, you mentioned. But there's also a step up of new capabilities, so if there's new work being developed, usually entrepreneur activity - weird entrepreneurs figured out that everyone says they're not weird anymore; it's great. But there's a step up of new capability that's built. Someone else says hey, the way we used to do databases and networks was great for moving one gig Ethernet on top of the rack; now you got 10 terabytes coming off a car or wireless spectrum. We got to rethink spectrum, or we got to rethink database. Let's use some of these GPUs - so a new step up of suppliers have to come in to support the new work. What's your vision on some of those things that are happening now - that you think people aren't yet seeing? What are some of those new step up functions? Is it on the database side, is it on the network, is it on the 5G - where's the action? >> Wow. Because who's going to support the Teslas? (Paul laughs) Who's going to support the new mobile revolution, the new iPhones the size of my two hands put together? What's your thoughts on that? >> The answer is all of the above. Let me talk about that and what I mean by that. Because you're looking at it from the technology perspective, I'd love to come back and talk about the human perspective as well, but from the technology perspective, of course leveraging power is going to be fundamental to this, because if you think about the types of use cases where you're going to have to be gigathreading queries against massive volumes of data, both static and streaming, you can't do that with historic technology, so that's going to be a critical part of it. The other part of it that we haven't mentioned a lot here but I think we should bring into it is if you think about these types of industrial Internet use cases, or IOT - even consumer Internet IOT related use cases - a lot of the decisioning has to occur out of the H. It cannot occur in a central facility, so it means actually putting the AI or ML engine inside the vehicle, or inside the cell phone tower, or inside the oil rig, and that is going to be a really big part of you know, shifting back to this very distributive model of machine lining in AI, which brings very complex questions in of how you drive governance - (John chuckles) >> And orchestration around employing Ai and ML models at massive scale, out to edge devices. >> Inferencing at the edge, certainly. It's going to be interesting to see what happens with training - we know that some of the original training will happen at the center, but some of that maintenance training? It's going to be interesting to see where that actually - it's probably going to be a split function, but you're going to need really high performing databases across the board, and I think that's one of the big answers, John, is that everybody says oh, it's all going to be in software. It's going to be a lot of hard word answers. >> Yep. >> Well the whole idea is just it's provocative to think about it and also intoxicating if you also want to go down that rabbit hole... If you think about that car, okay, if they're going to be doing century machine learning at the edge - okay, what data are you working off of? There's got to be some storage, and then what about real time data coming from other either horizontally scalable data sets. (laughs) So the question is, what do they have access to? Are they optimized for the decision making at that time? >> Mhm. >> Again, talk about the future of work - this is a big piece, but this is the human piece as well. >> Yeah. >> Are our kids going to be in a multi massive, multi player online game called Life? >> They are. >> They are now. They're on Fortnite, they're on Call of Duty, and all this gaming culture. >> But I think this is one of the interesting things, because there's a very strong correlation between information theory and thermodynamics. >> Mhm. >> They're the same exact - in physics, they are the identical algorithms and the identical equations. There's not a lot of difference, and you go back to the original revolution, you have a series of row houses, you put a power supply all the way down, you can run a bunch of looms. The big issue is entropy - how much heat are you generating? How do you get greater efficiency out of that single power supply? Same thing today: we're worried about the amount of cost, the amount of energy, the amount of administrative overhead associated with using data as an asset, and the faster the database, the more natural it is, the more easy it is to administer, the more easy it is to apply to a lot of different cases, the better. And it's going to be very, very interesting over the next few year to see how - Does database come in memory? Does database stay out over there? A lot of questions are going to be answered in the next couple years as we try to think about where these information transducers actually reside, and how they do their job. >> Yeah, and that's going to be driven yes, partially by the technology, but more importantly by the problems that we're solving. Here we are in New York City - you look at financial services. There are two massive factors in financial services going on what is the digital bank of the future look like, and how the banks interact with their customers, and how you get that true one-to-one engagement, which historically has been virtually impossible for companies that have millions or tens of millions of customers, so fundamental transformation of customer engagement driven by these advanced or excelerated analytics engines, and the pair of AI and ML, but then on the other side if you start looking at really incredibly important things for the banks like risk and spread, historically because of the volumes of data, it's been virtually impossible for them to present their employees with a true picture of those things. Now, with these accelerated technologies, you can take all the historic trading data, and all of the real time trading data, smash that together, and run real time analytics to make the right decisions in the moment of interaction with a customer, and that is incredibly powerful for both the customer, but also for the bank in mitigating risk, and they're the sorts of things we're doing with banks up and down the city here in New York, and of course, right around the world. >> So here's a question for you, so with that in mind - this is kind of more of a thought exercise - will banks even be around in 20 years? >> Wow. (laughs) >> I mean, you've got block chains saying we're going to have new crypto models here, if you take this Tesla with ten terabytes going out every second or whatever that number is. If that's the complex problem, banking should be really easy to solve. >> I think it's incumbent on boards in every industry, not just banking, to think about what existential threats exist, because there are incredibly powerful, successful companies that have gone out of existence because of fundamental shifts and buying behaviors or technologies - I think banks need to be concerned. >> Every industry needs to be concerned. >> Every industry needs to be concerned. >> At the end of the day, every board needs to better understand how they can reduce their assets specificities, right? How they can have their assets be more fungible and more applicable or appropriable to multiple different activities? Think about a future where data and digital assets are a dominant feature of business. Asset specificities go down; today their very definition of vertical industry is defined by the assets associated with bottling, the assets associated with flying, the assets associated with any number of other things. As aspect specialist needs to go down because of data, it changes even the definition of industry, let alone banking. >> Yeah, and auto industry's a great example. Will we own cars in the future? Will we confirm them as a service? >> Exactly. >> Car order manufacturers need to come to terms with that. The banks need to come to terms with the fact that the fundamental infrastructure for payments, whether it's domestic or global, will change. I mean, it is going to change. >> It's changing. It's changing. >> It has to change, and it's in the process of changing, and I'm not talking about crypto, you know, what form of digital currency exists in the future, we can argue about forever, but a fundamental underlying platform for real time exchange - that's just the future. Now, what does that mean for banks that rely heavily on payments as part of their core driver of profitability? Now that's a really important thing to come to terms with. >> Or going back to the point you made earlier. We may not have banks, but we have bankers. There's still going to be people who're providing advice in council, helping the folks understand what businesses to buy, what businesses to sell. So whatever industry they're in, we will still have the people that bring the extra taste to the data. >> Okay, we got to break it there, we've run out of time. Paul, love to chat further about future banking, all this other stuff, and also, as we live in a connected world, what does that mean? We're obviously connected to data; we certainly know there's gonnna be a ton of data. We're bringing that to you here, New York City, with Cube NYC. Stay with us for more coverage after the short break. (funky music)

>> Narrator: Live, from the Bill Graham Auditorium in San Francisco, it's theCUBE! Covering, Pure Storage Accelerate, 2018! Brought to you by: Pure Storage. (upbeat electronic music) >> Welcome back to theCUBE, we are live at Pure Storage Accelerate 2018. I am Lisa Martin, supporting the Prince look today. We're at the Bill Graham Civic Auditorium, this is a super cool building, 1915 it was built, and is the home of so many cool artists, so got to represent today. Dave Vellante's my co-host for the day. >> Well, I got to tell you, Charlie, thank you for wearing a tie. >> Yeah, well-- >> My tie's coming off. >> Okay, well, hey, look, you and me both. >> You have to wear yours-- >> Well, I do, I still have investors later. >> I'm not the only one who's representing musicians today. >> I got my tee shirt underneath here, all right. >> Oh, oh oh! >> Ladies and gentlemen, you will not want to miss this. >> Bill Graham, right, I'm on a Who, Lisa. >> "I'm on a Who", oh he said The Who! >> The Who! >> We got Roger Daltrey-- >> Charlie: Oh, that's fantastic. >> (laughing) >> Pete Townshend-- >> The Who! >> That's my deal. >> He's being so careful not to ruin his shirt with the buttons. >> The Who. >> I got to say-- >> Well done. >> Tower of Power was really my band. >> Oh, wow. >> They didn't play here, but Bill Graham was the first to sign him. >> Wow, representing. >> Well, I was an East Coast boy, so it was all the New York concerts and venues for me, but it was fantastic, I used to watch, you remember, Bill Graham presents? That was-- >> Yes! >> Yeah! >> I always thought if I found myself on stage, there'd be a couple of security guys dragging me off. >> Love that line! >> Nobody today, and you got a lot of applause, a lot of confetti. So Charlie, kick things off this morning at the Third Annual Accelerate, packed house, orange as far as the eye can see, but just a couple days ago-- >> Sea of orange. >> Exactly, sea of orange, a proud sea of orange. >> Right. >> Just two days ago, on the 21st of May, you guys announced your fiscal 19 first quarter results. Revenue up 40%, year over year, you added 300 new customers, including the U.S. Department of Energy, Paige.ai, and the really amazing transformational things they're doing for cancer research. You also shared today your NPS score: over 83! >> Correct. >> Big numbers shared today. >> These are big numbers. >> You've been the CEO for about nine months or so now, tell us what's going on, how are you sustaining this? Stocks going up? >> Right, right, stock's up about 80% year over year right now, so that's very good, but really I think it's a recognition that Pure is playing a very important role in the data processing, in the high-tech landscape, right? I think, you know, storage was really, I think up until now, really viewed as maybe an aging technology, something that was becoming commoditized, something where innovation wasn't really important, and Pure was the one company that actually thought that storage was important. As I mention in my keynote talk, you know, I really view technology as being a three-legged stool. That is, it's comprised as three elements: compute, networking, and storage. If any of one of them falls behind, you know, it becomes unbalanced, and frankly, you know, computers has advanced 10X over the last 10 years, networking has advanced more than 10X over the last 10 years, and storage didn't keep up at the same time that data was exploding, right? Pure is the one company that actually believes that there's real innovation to be had in storage. Paige.ai is a great example of that, I know it tugs on all of our heartstrings, but Paige.ai took lots of analog data, what was it, we're talking about cancer samples that were on slides, okay, they took literally millions of samples, digitized it, and fed it into an AI machine learning engine. Now, if you understand the way machine learning operates, it has to practice on thousands, or actually tens of thousands, millions, of samples. It could take all year, or it can take hours. What you want it to do is take minutes or hours, and if the data can't be fed fast enough into that engine, you know, it's going to take all year. You want your cancer pathology to be analyzed, you know, really quickly. >> Immediately. >> Immediately, right? That's what this engine can do, and it can do it because we can feed the data at it fast, at the rate it needs to be able to analyze that cancer. Data is just becoming the core of every company's business, it's becoming, if you will, the currency, it's becoming the gold mine, where companies now want to analyze their data. Right now, only about a half of 1% of the data that companies have can even be analyzed, because it's being kept in cold storage, and at Pure, we believe in no cold storage, you know, it's all got to be hot, it's all got to be available, able to be analyzed, able to be mined. >> Do you think, I got to ask you this, do you think that percentage will rise faster than the amount of data that's going to be created? Especially when you're thinking things at the edge. >> It's a great question, and I think absolutely! The reason is because it's not only the data that's being generated, or saved now, that's important. If you really want to analyze trends and get to know your customers, you know, the last five years, the last 10 years of data, is just as important. Increasingly, I think you may know this just from online banking, right, it used to be that maybe you'd have last month's checks available to you, but now you want to go back a year, you want to go back five years, and see, you know, you get audited by the IRS, they say: "Well, prove to us you did this," you need to find those checks and banks are being expected to have that information available to you. >> I got to ask you, you're what we call a tech-athlete, you were showing your tech-chops on stage, former CTO, but you've been a CEO, a board member of many prominent companies, why, Charlie, did you choose to come back in an operating role? You know, why at Pure, and why in an operating role? >> You know, I love being part of a team, it's really that. You know, I've had great fun throughout my career, but being part of a team that is focused on innovation, and is enabling, you know, not just our industry but frankly, allowing the world's business to do a better job. I mean, that's what gets me thrilled. I like working with customers every day, with our sales people, with our engineers. It's just a thrilling life! >> You did say in your keynote this morning that you leave the office, at the end of the day, with a smile, and you get to the office in the morning with a smile, that's pretty cool. >> I do, and if you asked my wife she'd tell you the same thing right, so I really enjoy being part of the team. >> Dave: So, oh, go ahead, please >> Oh, thank you sir. One of the things that Pure has done well is: partners, partnerships. We're going to be talking with NVIDIA later today, so this is going to be on, you guys just announced the new AIRI mini, and I was just telling Dave: I need to see that box, cause it looks pretty blinged out on the website. Talk to us about, though, what you guys are doing with your partnerships and how you've seen that really be represented in the successes of your customers. >> Right, well there are several different types of partnerships that we could talk about. First of all, we're 100% channel lead in our organization. We believe in the channel. You know, this is ancient history now, but when I arrived at Cisco, they were 100% direct at that time, no partners whatsoever. >> Belly to belly. >> Belly to belly, and I was very much apart of driving Cisco to be 100% partner over that period of time. So, you know, my history and belief in utilizing a channel to go to market is very well known, and my view is: the more we make our partners successful, the more we make our customers successful, the more successful we will be. But then, there are other types of partnerships as well. There are technology partnerships, like what we have with Cisco and NVIDIA, and again, we need to do more with other companies to make the solutions that we jointly provide, easier for our customers to be able to use. Then, there are system integration partners, because, let's face it, with as much technology as we build, customers often need help from experts of system integrators, to be able to pull that all together, to solve their business problems. Again, the more we can work with these system integrators, have them understand our products, train them to use them better, the better off our customers will be. >> Charlie, Pure has redefined, in my opinion, escape velocity in the storage business, it used to be getting to public, you saw that with 3PAR, Compel, Isilon, Data Domain, you guys are the first storage to hit one billion dollars since NetApp-- >> Right, 20 years ago. >> Awesome milestone, I didn't think it was possible eight years ago, to be honest, so now, okay, what's next? Can you remain an independent company? In order to remain independent, you got to grow, NetApp got to five billion in a faster growing market, you guys got to gain-share, how do you continue to do that? >> Well, you're right, each and every day we have to compete. We have to, you know, kill for what we eat. Our European sales lead calls it, our competition, on an account basis, a: knife fight in a phone booth. So the competition is tough out there, but we are bringing innovations to market, and more importantly, we're investing in the technology at a rate that I think our competitors are not going to be able to keep up with. We invest close to 20% of our revenue every year in R&D. Our competitors are in single-digits, okay, and this is a technology business, you know, eventually, if you don't keep up with the technology, you're going to lose, and so, that I think is going to allow us to continue growing and scaling. You're right, growth is important for us to be able to stay independent, but I looked very deeply at the entire industry before joining, and you know, I was in private equity for awhile, so we know how to analyze an industry, right? My view was that all of the other competitors are either no longer investing, and that's either internally, or in terms of large acquisitions, or they've already made their beds, and so I didn't really see a likely acquirer for Pure, and that was going to give us, if you will, the breathing room to be able to grow to a scale where we can continue to be independent. >> Almost by necessity! >> Almost by necessity, yeah. >> It's good to put the pressure on yourselves. >> So, in terms of where you are now, how is Pure positioned to lead storage growth in infrastructure for AI-based apps? There's this explosion of AI, right, fueled by deep-learning, and GPUs, and big data. How are you positioned to lead this charge is storage growth there? >> That's such a great question, you know, to get to the part of, you know, I started hearing about AI when I graduated college, which is a really long time ago now, and yet why is it exploding now? Well, computing has done its job, right, we're here today with NVIDIA, with GPUs that are just, you know, we're talking about, you know, giga-flops, you know, just incredible speeds of compute. Networking has done its job, we're now at 100 gigabits, and we're starting to talk about 400 gigabit per second networks, and storage hadn't kept up, right, even though data is exploding. So, we announced today, as you know, our data-centric architecture, and we believe this is an architecture that really sets our customers' data free. It sets it free in many ways. One of which, it allows it to always be hot, at a price that customers can afford, not only can afford, it's cheaper than what they're doing today, because we're collapsing tiers. No longer a hot tier, warm tier, cold tier, it's all one tier that can serve many, many needs at the same time, and so all of your applications can get access to real-time data, and access it simultaneously with the other applications, and we make sure that they get the quality of service they need, and we protect the data from being, you know, either corrupted or changed when other applications want it to be the same. So, we do what is necessary now, to allow the data to be analyzed for whether it's analytics, or AI, or machine learning, or simply to allow DEV-ops to be able to operate on real-time data, on live data, you know, without upsetting the operation's environment. >> I want to make sure I understand this, so you're democratizing tiering, essentially-- >> Charlie: Democratizing tiering. >> So how do you deal with, you know, different densities, QLC, et cetera, is that through software, is that? >> Well, so we hide that from the customer, right, so we're able to take advantage of the latest storage because we speak directly to the storage chips themselves. All of our competitors use what are called SSDs, solid state drives. Now, think about that for a moment. There's no drive in a solid state drive, these things are designed to allow Flash to mimic hard disk, but hard disk has all these disadvantages, why do you want Flash to mimic hard disk? We also set Flash free. We're able to use Flash in parallel, okay, we're able to take low quality Flash and make it look like high quality Flash, because our software adapts to whatever the specific characteristics of the flash are. So we have this whole layer of software that does nothing other than allow Flash to provide the best possible performance characteristics that Flash can provide. It allows us to mix and match, and completely hide that from the customer. >> With MVME, you're taking steps to eliminate what I call: the horrible storage stack. >> Charlie: That's exactly right. >> So, you talked earlier about the disparity between storage and the other two legs of the stool, so as you attack that bottle neck, what's the new bottle neck? Is it networking, and do you see that shaking out? >> It's a great question, I think the new bottle neck, I would actually put it at a higher layer, it's the orchestration layer that allows all this stuff to work together, in a way that requires less human interaction. There are great new technologies on the horizon, you know, Kubernetes, and Spark, and Kafka, a variety of others that will allow us to create a cloud environment, if you will, both for the applications and for the data, within private enterprises, similar to what they can get in the cloud, in many cases. >> You also talked about, innovation, and I want to ask you about the innovation equation, as both a technologist and a CEO who talks to a lot of other CEOS. We see innovation as coming from data, and the application of machine intelligence on that data, and cloud economics at scale, do you buy that? And where do you guys fit in that? >> We do buy that, although cloud economics, we believe, that we can create an environment where customers and their private data centers can also get cloud economics, and in fact, if you look at cloud economics, they're very good for some workloads, not necessarily good for other workloads. They're good at low scale, but not initially good at high scale. So, how do we allow customers to be able to easily move workloads between these different environments, depending on what their specific needs are, and that's what we view as our job, but also point something else out as well. About 30% of our sales are in the cloud providers themselves. They're in softwares that service, infrastructures that service, platforms as a service. These vendors are using our systems, so as you can see, we are already designed for cloud economics. We also already get to see how these leading-edge, very high scale customers construct their environments, and then we're able to bring that into the enterprise environment as well. >> I mean, I think we buy that. You're an arm's dealer to the cloud, you know, maybe not the tier zero to use that term, which is, but also, you're helping your On-Prem customers bring the cloud operating model to their data, cause they can't just stuff it into the cloud. >> It won't always be the right solution for everyone, now, it'll be the right solution for many, and we're doing more and more to allow the customers to bridge that, but we think that it's a multi-cloud environment, including private data centers, and we want to create as much flexibility as we can. >> Would you say Pure is going to be an enabler of companies being able to analyze way more than a half a percent of their data? >> If we don't do that, then there's no good reason for us to be in business. That is exactly what we're focused on. >> Last question for you Charlie, you've been the CEO about nine months now; cultural observations of Pure Storage? >> Oh, you know, you've seen the sea of orange that's here, and by the way, the orange is being sported not just by Puritans, not just by our employees, but by our partners and our customers as well. It's a bit infections, I have to be honest, I had one piece of orange clothing when I started this job, and you know, my mother's into it, she's sending me orange, you know, all sorts of orange clothing, some of which I'll wear, some of which I won't. My wife, everyone, there's a lot of enthusiasm about this business, it has a bit of a cult-like following, and Puritans are really very, very dedicated, not just to the customer, I mean, people become dedicated, you know, not to an entity, they become dedicated to a cause, and the cause for Pure is really to make our customers successful, and our employees feel that it's what drives them every day, it's what brings them to work, and hopefully it's what puts a smile on their face when they go home at night. >> Charlie Giancarlo, CEO of Pure Storage, thanks so much for joining us on theCUBE today! >> Thank you, thank you. >> For The Who Vallante, I'm Prince Martin, and we are live at Pure Accelerate 2018, in San Francisco, stick around, Who and I will be right back. (upbeat electronic music)

(techno music) >> (Narrator) Live from Washington, DC. It's Cube conversations with John Furrier. (techno music) >> Hello and welcome to the special exclusive Cube Conversations here in Washington, DC. I'm John Furrier host of the Cube. Here at Amazon Web Services Headquarter World Headquarters for Public Sector Summit in Arlington, Virginia. Our special guest is Tricia Davis-Muffett, who is the Director of Marketing for Worldwide Amazon Web Services. Thanks for joining me. >> Yep. >> So we see each other and reinvent Public Sector Summit, but you're always running around. You got so many things going on. >> I am. >> Big responsibility here. (Tricia laughs) >> You guys are running hard and you have great culture, Teresa's team. Competitive, like to have fun. Don't like to lose. (Tricia laughs) >> What's it like being a marketer for the fastest growing hottest product in Washington, DC and around the world? >> Yeah. I mean it's really been amazing. When I came here, I kind of took a leap of faith on the company because it's four and a half years ago that I came. I literally accepted the job before we had even gotten our first fed ramp approval. So it wasn't entirely sure that this was going be the place to go to for technology for the government, but I really loved the way that we were helping the government innovate and save money of course. I think most of us who are in Public Sector have a passion for citizens, and for making government better and so that's really what I saw in Teresa and her team that they had such a passion to do that and that the technology was going to help the government really improve the lives of citizens. It's been great. One of the things that's been amazing is the passion that our customers have for our technology. I think they get a little taste of it and they go "Wow, I can't believe what I can do "that I thought was impossible before." And so I love seeing what our customers do with the technology. >> It's something people would think might be easy to be a marketer for Amazon, but if you think about it, you have so much speed in your business. You have a cult of personality in the Cloud addiction, or Cloud value. In addition to the outcomes that are happening. >> Uh huh. >> We're a customer and one kind of knows that's pretty biased on it. We've seen the success ourselves, but you guys have a community. Everywhere you go, you're seeing Amazon as they take more territory down. Public Cloud originally, and now Enterprise, and Public Cloud, Public Sector Enterprise, Public Cloud. Each kind of wave of territory that Amazon goes in to Amazon Web Services, is a huge community. >> Yeah. >> And so that's another element. I mean Public Sector Summit last year it felt like Reinvent. So this years going to be bigger. >> Yeah. We had 65 hundred plus people attend last year, just in the Washington DC area and we've also expanded that program now and we are taking our Public Sector Summit specifically for government education non-profit around the world. So this year we will be in Brussels, and Camber, Australia. We have great adoption in Australia as well with the government there. In Singapore, Ottawa. So we're really expanding quite a bit and helping governments around the world to adopt. >> So if that's a challenge, how are you going to handle that because you guys have always been kind of with Summits. Do you coattail Summits? Do you go separate? >> No. We go separate. We actually have the Public Sector Summits we take the experience of our technology to government towns that wouldn't typically get a Summit. So for instance here in the United States of course, San Francisco and New York there's a lot of commercial businesses. We have our big Summits there, but there's not as much commercial business here in Washington DC, so really Public Sector takes the lead here. And then we focus on some of the things that really are most important to our Public Sector customers. Things like, procurement and acquisition. Things like the security and compliance that's so critical in the government sector. And then also, we do a really careful job of curating our customers, because we know that our government customers want to hear from each other. They want to hear from people who are blazing a trail within the Public Sector. They don't necessarily want to hear about what we want to say. They want to hear what their peers are doing with the technology. So last year, we had over a hundred of our Public Sector customers speaking to each other about what they were doing with the Cloud. >> And I find that's impressive. I actually commented on the Cube that week that it's interesting you let the customers do the talking. I mean, that's the best ultimate sign of success and traction. >> Yeah. And the great thing is, you know I've worked in other places in the Public Sector and government customers can be kind of shy about talking about what they're doing. You know, there are very motivated to just keep things going calmly, quietly, you know get their jobs done. But I think... >> Well, it doesn't hurt when you have the top guy at the CIA say, "Best decision we've ever made." "It's the most innovative thing we've ever done." I mean talk about being shy. >> Yeah. >> That's the CIA, by the way. That's the CIA. And we've also had, people like NASA JPL who've been very outspoken. Tom Soderstrom said that it was conservatively 1/100th of the cost of what it would have been if he had built out the infrastructure himself to build the infrastructure for his Mars landing. I mean that kind of... >> It just keeps giving. You lower prices. Okay I got to change gears, because a couple things that I've observed to every Reinvent, as being a customer and I think I've used Amazon I first came out as an entrepreneur. (inaudible) had no URL support, but that's showing my age. (Tricia laughs) But, here's the thing, you guys have enabled customers to solve problems that they couldn't solve in the past. >> (Tricia) Right. >> You mentioned NASA and then a variety of other (inaudible). But you guys are also in Public Sectors specifically are doing new things. New problems that no ones ever seen before. And society, entrepreneurship, diversity inclusion, education, non-profits. You don't think of Gov Cloud and Public Sector; you think non-profits, education. So it's kind of these sectors that are coming together. This is a new phenomenon. Can you talk and explain the dynamic behind that and the opportunity? >> Sure. I love to hear the stories of what our customers are doing when they really are tackling a problem that no one had thought of before. So for instance, at Reinvent this year, one of our Public Sector customers who spoke was Thorne. And they are using AI to crawl the dark web and help find people who are trafficking children in human trafficking, and that's a great use of AI and that's the kind of thing. It also helps our public servants because it helps to make police officers' jobs more effective. So of course we know that police officers, there are never enough police officers to go around. There's never enough detectives to look into everything that they need to and this makes them so much more effective to make the world a safer, better place. I also love some of the things about educational outcomes. Ivy Tech Community College is one of our great community college customers. And their using big data analysis to put together all of the different data sets that they have about their students and identify who might be at risk of failing a class 10 days into the semester so that they can help intervene with those students. >> Where was that class when I needed it? >> I know. >> Popup and say, "Hey homework time." >> I mean it really is looking at what kind of issues that they're having very early on with attendance, with different behavioral things. >> A great example at Reinvent with the California Community College system. That was a very interesting way. He was up there bragging like it was nobody's business. >> Yeah, and I think the community colleges that really goes into this idea of we're trying to expand opportunity for a wide-range of people. You might think of computer scientists as that's going to be all the Carnegie Mellon and Stanford and MIT people. And of course those are great contributors to computer science, but the fact is that computer science is so critical in so many aspects of life and in so many different kinds of careers. We know that one of the limiters to our own growth is going to be the talent that we have available to take advantage of the technology. We've been really working hard to expand opportunity for a wide-range of people, so that any smart person with an idea, can be using our technology, that's part of what's behind building the AWS Educate Program, which is a program to offer free computer science training to any university student or college student anywhere in the world. >> So it's a program you guys are doing? >> (Tricia) This is a program we are doing, >> What's it called again? >> AWS Educate. And it's a program that offers free credits to use AWS to any student who is enrolled in any kind of university or college anywhere around the world. >> That's a gateway drug to Cloud computing. >> Absolutely. >> Free resources. >> Yeah, and we're giving them a training path so that they can... >> So they want to write some code, or whatever they want to do. >> Yeah, and they can take different paths and learn. Okay, I want to learn a data science pathway, so I'm going to go that way. I want to learn a websites pathway. And they can go through things and build a portfolio of projects that they've actually built. >> So can they tap into some of the AWS AI tools too? >> They can tap into a wide range of tools and they have different levels of tiers of credits that they get, so it's a really great program to really open up Cloud computing. >> Now is there any limitations on that? What grade levels, is it college and above? >> Actually at Reinvent we just opened it up to students 14 and above. >> (John) Beautiful. That's awesome. >> And we also have a program called... >> How do they prove they're a student? >> Having a school, an EDU email address, or their school being registered through the program. >> (John) Okay, that's awesome. >> And then we also have another program called We Power Tech, and that really is a program to help open up the talent pool again to women to underserved communities, to people of different ethnic backgrounds who might not see themselves in technology because they don't see themselves as computer programmers on TV or whatever. >> Or they don't see their peer group in there, or some sort of might be an inclusion issue. >> Right and we're looking at if you take educate and We Power Tech, we're looking at that full pipeline of talent all the way from kids who are deciding should I pursue computer science or not, all the way through to professionals and getting them to try to stay in technology. >> So you guys are legit on this. You're not going to just check the box and focus on narrow things. A lot of companies do that, where they go oh we're targeting young girls or women. You guys are looking at the spectrum broader. >> Yep. And we're really looking at different communities and helping people to find their community in technology so that they can find supportive networks and also find people to mentor them or find people to mentor who are elsewhere. >> How big of a problem is it right now in today's culture and in the online culture to find peers and friends to do work like this? Because it just doesn't seem to me like there's been any innovation in online message groups. Seems like so 30 years ago. (Tricia laughs) >> Yeah. I think it is tough and I think there are somethings that we're trying to break through. For instance, a lot of the role models out there are the same people over and over again. We're trying to find new role models. And we find that through our customers. We find customers who are doing interesting work and we're trying to cultivate their voice and help put them on stage. >> New voices because it's new things. Machine learning, these are new disciplines. Data science across the board. >> Yeah, and one of the things that I love about the technology is it really is has democratizing affect. If you have an idea, you can make that idea happen for very little money, with just your ingenuity and your ability to stick to it. >> I got to ask you the hard question. Shouldn't be hard for you, but Amazon is gritty. It's been called gritty by me, hustling, but they're very good with their money. They don't really waste a lot in marketing. >> Yeah we're frugal. >> Very frugal, but you're very efficient, so I got to ask your favorite gorilla marketing technique. Cause you guys do more with less. >> (Tricia) We do. >> Once been criticized in Wired magazine. I remember reading years ago about they were comparing the Schwag bag to Reinvent. (Tricia laughs) Google almost gave out phones. It's kind of like typical reporter, but my point is you guys spend your money on education to engineers. You don't skip on that, but you might not put the flair onto an event, but now you guys are doing it. >> I think there are two things. So one of them is the aesthetic of our events. We typically do have a very stripped down aesthetic and we've made frugal look cool. I think that's one of the things I learned when I came here was go ahead and have the concrete floor and put quotes from customers there instead of paying to carpet it. So don't waste money on things that don't add value that's one of the core tenants of what we do in marketing. >> Get a better band instead of the rug. You guys have always had great music. >> We do always have great music. >> Tricia, tell me about your favorite program or project you've done a lot over the years. Pick your favorite child. What's your favorite? You have a lot of great stuff going on. Do you have a favorite? >> I think that my favorite is probably the City on a Cloud Innovation Challenge which is something we've done every year for the last four years. And we really went and asked cities, "Tell us what you're doing with our technology." Because we weren't sure what they were doing cause it's not very expensive for cities to run on us. We found that they were doing incredible things. They were doing water monitoring in their cities to help improve the quality of life of their citizens. They were delivering education more effectively. They were helping their transportation run in a more effective way. New York City Department of Transportation was doing really cool citizen facing apps to help them manage their transportation challenges and also cities all around the world. We've had people put in things about garbage management in Jerusalem and about lighting management in a Japanese city. We've had all kinds of really interesting stories come out and I just love hearing what the customers are doing and this year we added a Dream Big category where we said, "If you had the money, what would "you do with technology in your city?" and we've been really thrilled to be able to offer grants and fund some of those things to help cities get started. >> That's awesome. Not only is it engaging for them to engage with you through the program, it's inspirational. The use cases are everything from IOT to every computer. >> Yeah and we've also had partners submit as well, and we've learned about things like parking applications that cities are putting in place to help their citizens find better parking or all kinds of really interesting. How to keep track of the tree and do a tree census in their cities. Things like that. >> Maybe I'll borrow that and give you credit for it as a Cube question. What would you do if you had unlimited money? >> Exactly. (John laughs) Well the great part is that most of the cities find out that they can do what they want to do with very little money. They think it's going to be millions of dollars and then they realize, "Oh my gosh, it's going to be hard "for me to spend this 50 thousand dollar grant "because it doesn't cost that much." >> That's awesome and you got a big event coming up in June. Public Sector Summit again. Any preview on that? Any thing you can share? I'm sure it's a lot of things up in the air. >> A lot of really cool things. We are very excited to have some of our great customers on stage again. We're also this year going to have a pre day where we're going to feature Air and Space workloads on AWS. So that's going to be really interesting. I think we're going to have Blue Origin there and we're going to talk about what it's going to take to get to the next planet. >> And certainly that's beautiful for Cloud and also a huge robotics trend. People love to geek out on space related stuff. >> Yep. >> Awesome. Well the Cube will be there. Any numbers? Is it going to be the same location? >> It's going to be the same location at the Convention Center June 20th and 21st. We're going to have boot camps and certification labs and all that kind of stuff. I expect we'll grow again, so definitely more than seven thousand people. >> How big was the first one? >> Oh my gosh, the first one was in a little hotel conference room. I think there were a hundred and 50 people there. (Tricia laughs) >> Sounds like Reinvent happening all over again. We've seen this movie before. >> (Tricia) Yep. >> Tricia, thanks so much for coming on the Cube here. In the headquarters of Amazon Web Services Public Sector Summit in Washington DC. We're in Arlington, Virginia, right next to the nation's capital. I'm John Furrier. Thanks for watching. (techno music)

>> Announcer: Live from midtown Manhattan, it's theCUBE covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and it's ecosystem sponsors. (upbeat techno music) >> Hey, welcome back, everyone. Here live in New York City, this is theCUBE from SiliconANGLE Media Special. Exclusive coverage of the Big Data World at NYC. We call it Big Data NYC in conjunction also with Strata Hadoop, Strata Data, Hadoop World all going on kind of around the corner from our event here on 37th Street in Manhattan. I'm John Furrier, the co-host of theCUBE with Peter Burris, Head of Research at SiliconANGLE Media, and General Manager of WikiBon Research. And our next guest is one of our famous CUBE alumni, Prakash Nanduri co-founder and CEO of Paxata who launched his company here on theCUBE at our first inaugural Big Data NYC event in 2013. Great to see you. >> Great to see you, John. >> John: Great to have you back. You've been on every year since, and it's been the lucky charm. You guys have been doing great. It's not broke, don't fix it, right? And so theCUBE is working with you guys. We love having you on. It's been a pleasure, you as an entrepreneur, launching your company. Really, the entrepreneurial mojo. It's really what it's all about. Getting access to the market, you guys got in there, and you got a position. Give us the update on Paxata. What's happening? >> Awesome, John and Peter. Great to be here again. Every time I come here to New York for Strata I always look forward to our conversations. And every year we have something exciting and new to share with you. So, if you recall in 2013, it was a tiny little show, and it was a tiny little company, and we came in with big plans. And in 2013, I said, "You know, John, we're going to completely disrupt the way business consumers and business analysts turn raw data into information and they do self-service data preparation." That's what we brought to the market in 2013. Ever since, we have gone on to do something really exciting and new for our customers every year. In '14, we came in with the first Apache Spark-based platform that allowed business analysts to do data preparation at scale interactively. Every year since, last year we did enterprise grade and we talked about how Paxata is going to be delivering our self-service data preparation solution in a highly-scalable enterprise grade deployment world. This year, what's super exciting is in addition to the recent announcements we made on Paxata running natively on the Microsoft Azure HDI Spark system. We are truly now the only information platform that allows business consumers to turn data into information in a multi-cloud hybrid world for our enterprise customers. In the last few years, I came and I talked to you and I told you about work we're doing and what great things are happening. But this year, in addition to the super-exciting announcements with Microsoft and other exciting announcements that you'll be hearing. You are going to hear directly from one of our key anchor customers, Standard Chartered Bank. 150-year-old institution operating in over 46 countries. One of the most storied banks in the world with 87,500 employees. >> John: That's not a start up. >> That's not a start up. (John laughs) >> They probably have a high bar, high bar. They got a lot of data. >> They have lots of data. And they have chosen Paxata as their information fabric. We announced our strategic partnership with them recently and you know that they are going to be speaking on theCUBE this week. And what started as a little experiment, just like our experiment in 2013, has actually mushroomed now into Michael Gorriz, and Shameek Kundu, and the entire leadership of Standard Chartered choosing Paxata as the platform that will democratize information in the bank across their 87,500 employees. We are going in a very exciting way, a very fast way, and now delivering real value to the bank. And you can hear all about it on our website-- >> Well, he's coming on theCUBE so we'll drill down on that, but banks are changing. You talk about a transformation. What is a teller? An Internet of Things device. The watch potentially could be a terminal. So, the Internet of Things of people changes the game. Are the ATMs going to go away and become like broadcast points? >> Prakash: And you're absolutely right. And really what it is about is, it doesn't matter if you're a Standard Chartered Bank or if you're a pharma company or if you're the leading healthcare company, what it is is that everyone of our customers is really becoming an information-inspired business. And what we are driving our customers to is moving from a world where they're data-driven. I think being data-driven is fine. But what you need to be is information-inspired. And what does that mean? It means that you need to be able to consume data, regardless of format, regardless of source, regardless of where it's coming from, and turn it into information that actually allows you to get inside in decisions. And that's what Paxata does for you. So, this whole notion of being information-inspired, I don't care if you're a bank, if you're a car company, or if you're a healthcare company today, you need to have-- >> Prakash, for the folks watching that might not know our history as you launched on theCUBE in 2013 and have been successful every year since. You guys have really deploying the classic entrepreneurial success formula, be fast, walk the talk, listen to customers, add value. Take a minute quickly just to talk about what you guys do. Just for the folks that don't know you. >> Absolutely, let's just actually give it in the real example of you know, a customer like Standard Chartered. Standard Chartered operates in multiple countries. They have significant number of lines of businesses. And whether it's in risk and compliance, whether it is in their marketing department, whether it's in their corporate banking business, what they have to do is, a simple example could be I want to create a customer list to be able to go and run a marketing campaign. And the customer list in a particular region is not something easy for a bank like Standard Charter to come up with. They need to be able to pull from multiple sources. They need to be able to clean the data. They need to be able to shape the data to get that list. And if you look at what is really important, the people who understand the data are actually not the folks in IT but the folks in business. So, they need to have a tool and a platform that allows them to pull data from multiple sources to be able to massage it, to be able to clean it-- >> John: So, you sell to the business person? >> We sell to the business consumer. The business analyst is our consumer. And the person who supports them is the chief data officer and the person who runs the Paxata platform on their data lake infrastructure. >> So, IT sets the data lake and you guys just let the business guys go to town on the data. >> Prakash: Bingo. >> Okay, what's the problem that you solve? If you can summarize the problem that you solve for the customers, what is it? >> We take data and turn it into information that is clean, that's complete, that's consumable and that's contextual. The hardest problem in every analytical exercise is actually taking data and cleaning it up and getting it ready for analytics. That's what we do. >> It's the prep work. >> It's the prep work. >> As companies gain experience with Big Data, John, what they need to start doing increasingly is move more of the prep work or have more of the prep work flow closer to the analyst. And the reason's actually pretty simple. It's because of that context. Because the analyst knows more about what their looking for and is a better evaluator of whether or not they get what they need. Otherwise, you end up in this strange cycle time problem between people in back end that are trying to generate the data that they think they want. And so, by making the whole concept of data preparation simpler, more straight forward, you're able to have the people who actually consume the data and need it do a better job of articulating what they need, how they need it and making it presentable to the work that they're performing. >> Exactly, Peter. What does that say about how roles are starting to merge together? Cause you've got to be at the vanguard of seeing how some of these mature organizations are working. What do you think? Are we seeing roles start to become more aligned? >> Yes, I do think. So, first and foremost, I think what's happening is there is no such thing as having just one group that's doing data science and another group consuming. I think what you're going to be going into is the world of data and information isn't all-consuming and that everybody's role. Everybody has a role in that. And everybody's going to consume. So, if you look at a business analyst that was spending 80% of their time living in Excel or working with self-service BI tools like our partner's Tableau and Power BI from Microsoft, others. What you find is these people today are living in a world where either they have to live in coding scripting world hell or they have to rely on IT to get them the real data. So, the role of a business analyst or a subject matter expert, first and foremost, the fact that they work with data and they need information that's a given. There is no business role today where you can't deal with data. >> But it also makes them real valuable, because there aren't a lot of people who are good at dealing with data. And they're very, very reliant on these people to turn that data into something that is regarded as consumable elsewhere. So, you're trying to make them much more productive. >> Exactly. So, four years years ago, when we launched on theCUBE, the whole premise was that in order to be able to really drive towards a world where you can make information and data-driven decisions, you need to ensure that the business analyst community, or what I like to call the business consumer needs to have the power of being able to, A, get access to data, B, make sense of the data, and then turn that data into something that's valuable for her or for him. >> Peter: And others. >> And others, and others. Absolutely. And that's what Paxata is doing. In a collaborative, in a 21st Century world where I don't work in a silo, I work collaboratively. And then the tool, and the platform that helps me do that is actually a 21st Century platform. >> So, John, at the beginning of the session you and Jim were talking about what is going to be one of the themes here at the show. And we observed that it used to be that people were talking about setting up the hardware, setting up the clutters, getting Hadoop to work, and Jim talked about going up the stack. Well, this is one of the indicators that, in fact, people were starting to go up the stack because they're starting to worry more about the data, what it can do, the value of how it's going to be used, and how we distribute more of that work so that we get more people using data that's actually good and useful to the business. >> John: And drives value. >> And drives value. >> Absolutely. And if I may, just put a chronological aspect to this. When we launched the company we said the business analyst needs to be in charge of the data and turning the data into something useful. Then right at that time, the world of create data lakes came in thanks to our partners like Cloudera and Hortonworks, and others, and MapR and others. In the recent past, the world of moving from on premise data lakes to hybrid, multicloud data lakes is becoming reality. Our partners at Microsoft, at AWS, and others are having customers come in and build cloud-based data lakes. So, today what you're seeing is on one hand this complete democratization within the business, like at Standard Chartered, where all these business analysts are getting access to data. And on the other hand, from the data infrastructure moving into a hybrid multicloud world. And what you need is a 21st Century information management platform that serves the need of the business and to make that data relevant and information and ready for their consumption. While at the same time we should not forget that enterprises need governance. They need lineage. They need scale. They need to be able to move things around depending on what their business needs are. And that's what Paxata is driving. That's why we're so excited about our partnership with Microsoft, with AWS, with our customer partnerships such as Standard Chartered Bank, rolling this out in an enterprise-- >> This is a democratization that you were referring to with your customers. We see this-- >> Everywhere. >> When you free the data up, good things happen but you don't want to have IT be the constraint, you want to let them enable-- >> Peter: And IT doesn't want to be the constraint. >> They don't. >> This is one of the biggest problems that they have on a daily basis. >> They're happy to let it go free as long as it's in they're mind DevOps-like related, this is cool for them. >> Well, they're happy to let it go with policy and security in place. >> Our customers, our most strategic customers, the folks who are running the data lakes, the folks who are managing the data lakes, they are the first ones that say that we want business to be able to access this data, and to be able to go and make use out of this data in the right way for the bank. And not have us be the impediment, not have us be the roadblock. While at the same time we still need governance. We still need security. We still need all those things that are important for a bank or a large enterprise. That's what Paxata is delivering to the customers. >> John: So, what's next? >> Peter: Oh, I'm sorry. >> So, really quickly. An interesting observation. People talk about data being the new fuel of business. That really doesn't work because, as Bill Schmarzo says, it's not the new fuel of business, it's new sunlight of business. And the reason why is because fuel can only be used once. >> Prakash: That's right. >> The whole point of data is that it can be used a lot, in a lot of different ways, and a lot of different contexts. And so, in many respects what we're really trying to facilitate or if someone who runs a data lake when someone in the business asks them, "Well, how do you create value for the business?" The more people, the more users, the more context that they're serving out of that common data, the more valuable the resource that they're administering. So, they want to see more utilization, more contexts, more data being moved out. But again, governance, security have to be in place. >> You bet, you bet. And using that analogy of data, and I've heard this term about data being the new oil, etc. Well, if data is the oil, information is really the refined fuel or sunlight as we like to call it. >> Peter: Yeah. >> John: Well, you're riffing on semantics, but the point is it's not a one trick pony. Data is part of the development, I wrote a blog post in 1997, I mean 2007 that said data's the new development kit. And it was kind of riffing on this notion of the old days >> Prakash: You bet. >> Here's your development kit, SDK, or whatever was how people did things back then Enter the cloud, >> Prakash: That's right. >> And boom, there it is. The data now is in the process of the refinery the developers wanted. The developers want the data libraries. Whatever that means. That's where I see it. And that is the democratization where data is available to be integrated in to apps, into feeds, into ... >> Exactly, and so it brings me to our point about what was the exciting, new product innovation announcement we made today about Intelligent Ingest. You want to be able to access data in the enterprise regardless of where it is, regardless of the cloud where it's sitting, regardless of whether it's on-premise, in the cloud. You don't need to as a business worry about whether that is a JSON file or whether that's an XML file or that's a relational file. That's irrelevant. What you want is, do I have the access to the right data? Can I take that data, can I turn it into something valuable and then can I make a decision out of it? I need to do that fast. At the same time, I need to have the governance and security, all of that. That's at the end of the day the objective that our customers are driving towards. >> Prakash, thanks so much for coming on and being a great member of our community. >> Fantastic. >> You're part of our smart network of great people out there and entrepreneurial journey continues. >> Yes. >> Final question. Just observation. As you pinch yourself and you go down the journey, you guys are walking the talk, adding new products. We're global landscape. You're seeing a lot of new stuff happening. Customers are trying to stay focused. A lot of distractions whether security or data or app development. What's your state of the industry? How do you view the current market, from your perspective and also how the customer might see it from their impact? >> Well, the first thing is that I think in the last four years we have seen significant maturity both on the providers off software technology and solutions, and also amongst the customers. I do think that going forward what is really going to make a difference is one really driving towards business outcomes by leveraging data. We've talked about a lot of this over the last few years. What real business outcomes are you delivering? What we are super excited is when we see our customers each one of them actually subscribes to Paxata, we're a SAS company, they subscribe to Paxata not because they're doing the science experiment but because they're trying to deliver real business value. What is that? Whether that is a risk in compliance solution which is going to drive towards real cost savings. Or whether that's a top line benefit because they know what they're customer 360 is and how they can go and serve their customers better or how they can improve supply chains or how they can optimize their entire efficiency in the company. I think if you take it from that lens, what is going to be important right now is there's lots of new technologies coming in, and what's important is how is it going to drive towards those top three business drivers that I have today for the next 18 months? >> John: So, that's foundational. >> That's foundational. Those are the building blocks-- >> That's what is happening. Don't jump... If you're a customer, it's great to look at new technologies, etc. There's always innovation projects-- >> RND, GPOCs, whatever. Kick the tires. >> But now, if you are really going to talk the talk about saying I'm going to be, call your word, data-driven, information-driven, whatever it is. If you're going to talk the talk, then you better walk the walk by delivering the real kind of tools and capabilities that you're business consumers can adopt. And they better adopt that fast. If they're not up and running in 24 hours, something is wrong. >> Peter: Let me ask one question before you close, John. So, you're argument, which I agree with, suggests that one of the big changes in the next 18 months, three years as this whole thing matures and gets more consistent in it's application of the value that it generates, we're going to see an explosion in the number users of these types of tools. >> Prakash: Yes, yes. >> Correct? >> Prakash: Absolutely. >> 2X, 3X, 5X? What do you think? >> I think we're just at the cusp. I think is going to grow up at least 10X and beyond. >> Peter: In the next two years? >> In the next, I would give that next three to five years. >> Peter: Three to five years? >> Yes. And we're on the journey. We're just at the tip of the high curve taking off. That's what I feel. >> Yeah, and there's going to be a lot more consolidation. You're going to start to see people who are winning. It's becoming clear as the fog lifts. It's a cloud game, a scale game. It's democratization, community-driven. It's open source software. Just solve problems, outcomes. I think outcome is going to be much faster. I think outcomes as a service will be a model that we'll probably be talking about in the future. You know, real time outcomes. Not eight month projects or year projects. >> Certainly, we started writing research about outcome-based management. >> Right. >> Wikibon Research... Prakash, one more thing? >> I also just want to say that in addition to this business outcome thing, I think in the last five years I've seen a lot of shift in our customer's world where the initial excitement about analytics, predictive, AI, machine-learning to get to outcomes. They've all come into a reality that none of that is possible if you're not able to handle, first get a grip on your data, and then be able to turn that data into something meaningful that can be analyzed. So, that is also a major shift. That's why you're seeing the growth we're seeing-- >> John: Cause it's really hard. >> Prakash: It's really hard. >> I mean, it's a cultural mindset. You have the personnel. It's an operational model. I mean this is not like, throw some pixie dust on it and it magically happens. >> That's why I say, before you go into any kind of BI, analytics, AI initiative, stop, think about your information management strategy. Think about how you're going to democratize information. Think about how you're going to get governance. Think about how you're going to enable your business to turn data into information. >> Remember, you can't do AI with IA? You can't do AI without information architecture. >> There you go. That's a great point. >> And I think this all points to why Wikibon's research have all the analysts got it right with true private cloud because people got to take care of their business here to have a foundation for the future. And you can't just jump to the future. There's too much just to come and use a scale, too many cracks in the foundation. You got to do your, take your medicine now. And do the homework and lay down a solid foundation. >> You bet. >> All right, Prakash. Great to have you on theCUBE. Again, congratulations. And again, it's great for us. I totally have a great vibe when I see you. Thinking about how you launched on theCUBE in 2013, and how far you continue to climb. Congratulations. >> Thank you so much, John. Thanks, Peter. That was fantastic. >> All right, live coverage continuing day one of three days. It's going to be a great week here in New York City. Weather's perfect and all the players are in town for Big Data NYC. I'm John Furrier with Peter Burris. Be back with more after this short break. (upbeat techno music).

>> Announcer: Live from San Francisco it's theCUBE Covering Spark Summit 2017, brought to you by Databricks. >> Welcome back to theCUBE. You are watching coverage of Spark Summit 2017. It's day two, we've got so many new guests to talk to today. We already learned a lot, right George? >> Yeah, I mean we had some, I guess, pretty high bandwidth conversations. >> Yes, well I expect we're going to have another one here too, because the person we have is the founder of Predictive Analytics World, it's Eric Siegel, Eric welcome to the show. >> Hey thanks Dave, thanks George. You go by Dave or David? >> Dave: Oh you can call me sir, and that would be. >> I was calling you, should I, can I bow? >> Oh no we are bowing to you, you're the author of the book, Predictive Analytics, I love the subtitle, the Power to Predict Who Will Click, Buy, Lie or Die. >> And that sums up the industry right? >> Right, so if people are new to the industry, that's sort of an informal definition of predictive analytics, basically also known as machine learning. Where you're trying to make predictions for each individual, whether it's a customer for marketing, a suspect for fraud or law enforcement, a voter for political campaigning, a patient for healthcare. So, in general it's on that level, it's a prediction for each individual. So how does data help make those predictions? And then you can only imagine just how many ways in which predicting on that level helps organizations improve all their activities. >> Well we know you were on the keynote stage this morning. Could you maybe summarize for the CUBE audience, what a couple of the top themes that you were talking about? >> Yeah, I covered two advanced topics. I wanted to make sure this pretty technical audience was aware of because a lot of people aren't and one is called uplift modeling, so that's optimizing for persuasion for things like marketing and also for healthcare, actually. And for political campaigning. So when you do predictive analytics for targeting marketing normally sort of the traditional approach is, let's predict will this person buy if I contact them because when well its okay maybe its a good idea to spend the two dollars to send them a brochure its marketing treatment, right. But there is actually a little bit different question that would make even driving them better decisions Which is not will this person buy but would contacting them, sending them the brochure, influence them to buy, will it increase the chance that we get that positive outcome. That's a different question, and it doesn't correspond with standard predictive modeling or machine learning methods So uplift modeling, also known as net lift modeling, persuasion modeling its a way to actually create a predictive model like any other except that it's target is, is it a good idea to contact this person because it will increase the chances that they are going to have a positive outcome. So that's the first of the two. And I cram this all in 20 minutes. The other one was a little more commonly known But I think people would like to visit it and it's called P-Hacking or vast search. Where you can be fooled by randomness and data relatively easily in the era of Big Data there is this all to common pitfall where you find a predictive insight in the data and it turns out it was actually just a random perturbation. How do you know the difference? >> Dave: Fake news right? >> Okay fake news, except that in this case, it was generated by a computer, right? And then there is a statistical test that makes it look like its actually statistically significant and we should have credibility to it, on it or about it. So you can avert it, you have compensate for the fact that you are trying lots, that you are evaluating many different predictive insights or hypotheses whatever you want to call it and make sure that the one that you are believing you sort of checked for the ability that it wasn't just random luck, that's known as p-hacking. >> Alright, so uplift modeling and p-hacking. George do you want to drill on those a little bit. >> Yeah, I want to start from maybe the vocabulary of our audience where they say sort of like uplift modeling goes beyond prediction. Actually even for the second one with p-hacking is that where you're essentially playing with the parameters of the model to find the difference between correlation and causation and going from prediction to prescription? >> It's not about causation, its actually so correlation is what you get when you get a predictive insight or some component of a predictive model where you see these things connected therefore one is predictive of the other. Now the fact that does not entail causation is a really good point to remind people of as such. But even before you address that question, the first question is this correlation actually legit? Is there really a correlation between this things? Is this an actual finding? Or is it just happened to be the case in this particular sample of limited sample data that I have access to at the moment, right? So is it a real link or correlation in the first place before you even start asking any question about causality and it does have, it does related to what you alluded to with regard to tuning parameters because its closely related to this issue of overfitting. People who do predictive modeling are very familiar with overfitting. The standard practice all tools implementations of machine learning and predictive modeling do this, which is they hold the side evaluation set called test set. So you don't get to cheat, creates a predict model. It learns from the data, does the number crunching, its mostly automated, right. And it comes out with this beautiful model that does well predicting and then you evaluate, you assess it over this held aside. Oh my thing's falling off here. >> Dave: Just second on your. >> See then you evaluate it on this held aside set it was quarantine so you didn't get to cheat. You didn't get to look at it when you are creating the model. So it serves as an objective performance measure. The problem is and here is the huge irony, the things that we get from data, the predictive insights, there was one famous one that was broadcasted too loudly because its not nearly as credible as they first thought. Is that an orange used car is a better one to buy because its less likely to be a lemon. That's what it looked like in this one data set. The problem is, that when you have a single insight where its relatively simple, just talking about the car, the color to make the prediction. A predictive model is much more complex and deals with lots of other attributes not just the color, for example, make, year, model everything on that individual car, individual person, you can imagine all the attributes that's the point of the modeling process, the learning process, how do you consider multiple things. If its just a really simple thing with just based on the car color, then many of even the most advanced data science practitioners kind of forget that there is still potential to effectively overfit, that you might have found something that doesn't apply in general, only applies over this particular set of data. So that's where the trap falls and they don't necessarily hold themselves a high standard of having this held aside test set. So its kind of ironic thing, the things that most likely to make the headlines like orange cars are simpler, easier to understand, but are less well understood that they could be wrong. >> You know keying off that, that's really interesting, because we've been hearing for years that what's made, especially deep learning relevant over the last few years is huge compute up in the cloud and huge data sets. >> Yeah. >> But we're also starting to hear about methods of generating a sort of synthetic data so that if you don't have, I don't know what the term is, organic training data, and then test data, we're getting to the point where we can do high quality models with less. >> Yes, less of that training data. And did you. >> Tell us. >> Did you interview with the keynote speaker from Stanford about that? >> No, I only saw part of his. >> Yeah his speech yesterday. That's an area that I'm relatively new to but it sounds extremely important because that is the bottleneck. He called it, if data's the new oil, he's calling it the new-new oil. Which is more specific than data, it's training data. So all of the machine learning or predictive modeling methods of which we speak, are, in most cases, what's called supervised learning. So the thing that makes it supervised is you have a bunch of examples where you already know the answer. So you're trying to figure out is this picture of a cat or of a dog, that means you need to have a whole bunch of data from which to learn, the training data, where you've already got it labeled. You already know the correct answer. In many business applications just because of history you know who did or didn't respond to your marketing, you know who did or did not turn out to be fraudulent. History is experience in which to learn, it's in the data, so you do have that labeled, yes, no, like you already know the answer, you don't need to predict on them, it's in the past but you use that as training data. So we have that in many cases. But for something like classifying an image, and we're trying to figure out does this have a picture of a cat somewhere in the image, or whatever all these big image classification problems, you do need, often, a manual effort to label the data. Have the positive and negative examples, that's what's called training data, the learning data. It's actually called training data. There's definitely a bottleneck so anything that can be done to avert that bottleneck decrease the amount that we need, or find ways to make, sort of, rough training data that may serve as a building block for the modeling process this kind of thing. That's not my area of expertise, sounds really intriguing though. >> What about, and this may be further out on the horizon but one thing we are hearing about is the extreme shortage of data scientists who need to be teamed up with domain experts to figure out the knobs, the variables to create these elaborate models. We're told that even if you're doing the traditional, statistical, machine learning models, that eventually deep learning can help us identify the features or the variables just the way they sort of identify you know ears and whiskers and a nose and then figure out from that the cat. That's something that's in the near term, the medium term in terms of helping to augment what the data scientist does? >> It's in the near term and that's why everyone's excited about deep learning right now is that, basically the reason we built these machines called computers is because they automate stuff. Pretty much anything that you can think of and define well, you can program. Then you've got a machine that does it. Of course one of the things we wanted to learn, to do actually, is to learn from data. Now, it's literally really very analogous to what it means for a human to learn. You've got a limited number of examples that you're trying to draw generalizations from those. When you go to bigger scale problems where the thing you're classifying isn't just like a customer, and all the things you know about the customer, are they likely to commit fraud, yes or no. But it become a level more complex when it's an image right, image is worth a thousand words. And maybe literally more than a thousand words where it says of data if it's a high resolution. So how do you process that? Well there's all sorts of research like well we can define the thing that tries to find arcs, and circles and edges and this kind of thing, or, we can try to, once again, let that be automatic. Let the computer do that. So deep learning is a way to allow, spark is a way to make it operate quickly but there's another level of scale other than speed. The level of scale is just like how complex of a task can you leave up to the automaton, to go by itself. That's what deep learning does is it scales in that respect it has the ability to automate more layers of that complexity as far as finding those kinds of what might me domain specific features and images. >> Okay, but I'm thinking not just the, help me figure out speech to text and natural language understanding or classify. >> Anything with a signal where it's a high bandwidth amount of data coming in that you want to classify. >> OK, so could that, does that extend to I'm building a very elaborate predictive model not on, is there a cat in the video or in the picture so much as I guess you called it, is there an uplift potential and how big is that potential, in a context of making a sale on an eCommerce site. >> So what you just tapped into was when you go to marketing and many other business applications, you don't actually need to have high accuracy what you have to do is have a prediction that's better than guessing. So for example, if I get a 1% response rate to my marketing campaign, but I can find a pocket that's got 3% response rate, it may be very much rocket science to define and learn from the data how to define that specifically defined sub-segment that has a higher response rate, or whatever it is. But the 3% isn't like, I have high confidence this person's definitely going to buy, it's still just 3%, but that difference can make a huge difference and can improve the bottom line marketing by a factor of five and that kind of thing. It's not necessarily about accuracy. If you've got an image and you need to know is there a picture of a car, or is this traffic light green or red, somewhere in this image, then there's certain application areas, self driving cars what have you, it does need to be accurate right. But maybe there's more potential for it to be accurate because there's more predictability inherent to that problem. Like I can predict that there's a traffic light that has a green light somewhere in an image because there is enough label data and the nature of the problem is more tractable because it's not as challenging to find where the traffic light is, and then which color it is. You need it to scale, to reach that level of classification performance in terms of accuracy or whatever measure you use for certain applications. >> Are you seeing like new methodologies like reinforcement learning or deep learning where the models are adversarial where they make big advances in terms of what they can learn without a lot of supervision? Like the ones where. >> It's more self learning and unsupervised. >> Sort of glue yourself onto this video game screen we'll give you control of the steering wheel and you figure out how to win. >> Having less required supervision, more self-learning, anomaly detection or clustering, these are some of the unsupervised ones. When it comes to vision there are part of the process that can be unsupervised in the sense that you don't need labels on your target like is there a car in the picture. But it can still learn the feature detection in a way that doesn't have that supervised data. Although that image classification in general, on that level deep learning, is not my area of expertise. That's a very up and coming part of machine learning but it's only needed when you have these high bandwidth inputs like an entire image, high resolution, or a video, or a high bandwidth audio. So it's signal processing type problems where you start to need that kind of deep learning. >> Great discussion Eric, just a couple of minutes to go in this segment here. I want to make sure I give a chance to talk about Predictive Analytics World and what's your affiliation with that ad what do you want theCUBE audience to know? >> Oh sure, Predictive Analytics World I'm the founder it's the leading cross-vendor event focused on commercial deployment of predictive analytics and machine learning. Our main event a few times a year is a broad scope business focused event but we also have industry vertical focused specialized events just for financial services, healthcare, workforce, manufacturing and government applications of predictive analytics and machine learning. So there's a number a year, and two weeks from now in Chicago, October in New York and you can see the full agendas at PredictiveAnalyticsWorld.com. >> Alright great short commercial there. 30 seconds. >> It's the elevator pitch. >> Answered the toughest question in 30 seconds what the toughest question you got after your keynote this morning? Maybe a hallway conversation or. >> What's the toughest question I got after my keynote? >> Dave: From one of the attendees. >> Oh, the question that always comes up is how do you get this level of complexity across to non-technical people or your boss or your colleagues or your friends and family. By the way that's something I worked really hard on with the book which is meant for all readers although the last few chapters have. >> How do you get executive sponsors to get what you're doing? >> Well, as I say, give them the book. Because the point of the book is it's pop science it's accessible, it's analytically driven, it's entertaining it keeps it relevant but it does address advanced topics at the end of the book. So it sort of ends, industry overview kind of thing. The bottom line there, in general, is that you want to focus on the business impact. What I mentioned briefly a second ago if we can improve target marketing this much it will increase profit by a factor five something like that. So you start with that and then answer any questions they have about, well how does it work, what makes it credible that it really has that much potential in the bottom line. When you're a techie, you're inclined to go forward you start with the technology that you're excited about. That's my background, so that's sort of the definition of being a geek, that you're ore enamored with the technology than the value it produces. Because it's amazing that it works, and it's exciting, it's interesting, it's scientifically challenging. But, when you're talking to the decision makers you have to start with the eventual carrot at the end of the stick, which is the value. >> The business outcome. >> Yeah. >> Great, well that's going to be the last word. That might even make it onto our CUBE Gems segment, great sound bites. George thanks again, great questions and Eric the author of Predictive Analytics, the Power to Predict Who Will Click, Buy, Lie or Die. Thank you for being on the show we appreciate your time. >> Eric: Sure, yeah thank you, great to meet you. >> Thank you for watching theCUBE we'll be back in just a few minutes with our next guest here at Spark Summit 2017.

>> Announcer: Live from San Jose, California, it's The Cube. Covering Big Data, Silicon Valley, 2017. (electronic music) >> Okay, welcome back everyone, live at Silicon Valley for the big The Cube coverage, I'm John Furrier, with me Wikibon analyst George Gilbert, Bruno Aziza, who's on the CMO of AtScale, Cube alumni, and Josh Klahr VP at AtScale, welcome to the Cube. >> Welcome back. >> Thank you. >> Thanks, Brian. >> Bruno, great to see you. You look great, you're smiling as always. Business is good? >> Business is great. >> Give us the update on AtScale, what's up since we last saw you in New York? >> Well, thanks for having us, first of all. And, yeah, business is great, we- I think Last time I was here on The Cube we talked about the Hadoop Maturity Survey and at the time we'd just launched the company. And, so now you look about a year out and we've grown about 10x. We have large enterprises across just about any vertical you can think of. You know, financial services, your American Express, healthcare, think about ETNA, SIGNA, GSK, retail, Home Depot, Macy's and so forth. And, we've also done a lot of work with our partner Ecosystem, so Mork's- OEM's AtScale technology which is a great way for us to get you AtScale across the US, but also internationally. And then our customers are getting recognized for the work that they are doing with AtScale. So, last year, for instance, Yellowpages got recognized by Cloudera, on their leadership award. And Macy's got a leadership award as well. So, things are going the right trajectory, and I think we're also benefitting from the fact that the industry is changing, it's maturing on the the big data side, but also there's a right definition of what business intelligence means. This idea that you can have analytics on large-scale data without having to change your visualization tools and make that work with existing stock you have in place. And, I think that's been helping us in growing- >> How did you guys do it? I mean, you know, we've talked many times in there's some secret sauce there, but, at the time when you guys were first starting it was kind of crowded field, right? >> Bruno: Yeah. >> And all these BI tools were out there, you had front end BI tools- >> Bruno: Yep. But everyone was still separate from the whole batch back end. So, what did you guys do to break out? >> So, there's two key differentiators with AtScale. The first one is we are the only platform that does not have a visualization tool. And, so people think about this as, that's a bug, that's actually a feature. Because, most enterprises have already that stuff made with traditional BI tools. And so our ability to talk to MDX and SQL types of BI tools, without any changes is a big differentiator. And then the other piece of our technology, this idea that you can get the speed, the scale and security on large data sets without having to move the data. It's a big differentiation for our enterprise to get value out of the data. They already have in Hadoop as well as non-Hadoop systems, which we cover. >> Josh, you're the VP of products, you have the roadmaps, give us a peek into what's happening with the current product. And, where's the work areas? Where are you guys going? What's the to-do list, what's the check box, and what's the innovation coming around the corner? >> Yeah, I think, to follow up on what Bruno said about how we hit the sweet spot. I think- we made a strategic choice, which is we don't want to be in the business of trying to be Tableu or Excel or be a better front end. And there's so much diversity on the back end if you look at the ecosystem right now, whether it's Spark Sequel, or Hive, or Presto, or even new cloud based systems, the sweet spot is really how do you fit into those ecosystems and support the right level of BI on top of those applications. So, what we're looking at, from a road map perspective is how do we expand and support the back end data platforms that customers are asking about? I think we saw a big white space in BI on Hadoop in particular. And that's- I'd say, we've nailed it over the past year and a half. But, we see customers now that are asking us about Google Big Query. They're asking us about Athena. I think these server-less data platforms are really, really compelling. They're going to take a while to get adoption. So, that's a big investment area for us. And then, in terms of supporting BI front ends, we're kind of doubling down on making sure our Tableau integration is great, Power BI is I think getting really big traction. >> Well, two great products, you've got Microsoft and Tableau, leaders in that area. >> The self-service BI revolution has, I would say, has won. And the business user wants their tool of choice. Where we come in is the folks responsible for data platforms on the back end, they want some level of control and consistency and so they're trying to figure out, where do you draw the line? Where do you provide standards? Where do you provide governance, and where do you let the business lose? >> All right, so, Bruno and Josh, I want you to answer the questions, be a good quiz. So, define next generation BI platforms from a functional standpoint and then under the hood. >> Yeah, there's a few things you can look at. I think if you were at the Gartner BI conference last week you saw that there was 24 vendors in the magic quadrant and I think in general people are now realizing that this is a space that is extremely crowded and it's also sitting on technology that was built 20 years ago. Now, when you talk to enterprises like the ones we work with, like, as I named earlier, you realize that they all have multiple BI tools. So, the visualization war, if you will, kind of has been set up and almost won by Microsoft and Tableau at this point. And, the average enterprise is 15 different BI tools. So, clearly, if you're trying to innovate on the visualization side, I would say you're going to have a very hard time. So, you're dealing with that level of complexity. And then, at the back end standpoint, you're now having to deal with database from the past - that's the Teradata of this world - data sources from today - Hadoop - and data sources from the future, like Google Big Query. And, so, I think the CIO answer of what is the next gen BI platform I want is something that is enabling me to simplify this very complex world. I have lots of BI tools, lots of data, how can I standardize in the middle in order to provide security, provide scale, provide speed to my business users and, you know, that's really radically going to change the space, I think. If you're trying to sell a full stack that's integrated from the bottom all the way to visualization, I don't think that's what enterprises want anymore >> Josh, under the hood, what's the next generation- you know, key leverage for the tech, and, just the enabler. >> Yeah, so, for me the end state for the next generation GI platform is a user can log in, they can point to their data, wherever that data is, it's on Prime, it's in the cloud, it's in a relational database, it's a flat file, they can design their business model. We spend a lot of time making sure we can support the creation of business models, what are the key metrics, what are the hierarchies, what are the measures, it may sound like I'm talking about OLAP. You know, that's what our history is steeped in. >> Well, faster data is coming, that's- streaming and data is coming together. >> So, I should be able to just point at those data sets and turn around and be able to analyze it immediately. On the back end that means we need to have pretty robust modeling capabilities. So that you can define those complex metrics, so you can functionally do what are traditional business analytics, period over period comparisons, rolling averages, navigate up and down business hierarchies. The optimizations should be built in. It shouldn't be the responsibility of the designer to figure out, do I need to create indeces, do I need to create aggregates, do I need to create summarization? That should all be handled for you automatically. Shouldn't think about data movement. And so that's really what we've built in from an AtScale perspective on the back end. Point to data, we're smart about creating optimal data structure so you get fast performance. And then, you should be able to connect whatever BI tool you want. You should be able to connect Excel, we can talk the MDX Query language. We can talk Sequel, we can talk Dax, whatever language you want to talk. >> So, take the syntax out of the hands of the user. >> Yeah. >> Yeah. >> And getting in the weeds on that stuff. Make it easier for them- >> Exactly. >> And the key word I think, for the future of BI is open, right? We've been buying tools over the last- >> What do you mean by that, explain. >> Open means that you can choose whatever BI tool you want, and you can choose whatever data you want. And, as a business user there's no real compromise. But, because you're getting an open platform it doesn't mean that you have to trade off complexity. I think some of the stuff that Josh was talking about, period analysis, the type of multidimensional analysis that you need, calendar analysis, historical data, that's still going to be needed, but you're going to need to provide this in a world where the business, user, and IT organization expects that the tools they buy are going to be open to the rest of the ecosystem, and that's new, I think. >> George, you want to get a question in, edgewise? Come on. (group laughs) >> You know, I've been sort of a single-issue candidate, I guess, this week on machine learning and how it's sort of touching all the different sectors. And, I'm wondering, are you- how do you see yourselves as part of a broader pipeline of different users adding different types of value to data? >> I think maybe on the machine learning topic there is a few different ways to look at it. The first is we do use machine learning in our own product. I talked about this concept of auto-optimization. One of the things that AtScale does is it looks at end-user query patterns. And we look at those query patterns and try to figure out how can we be smart about anticipating the next thing they're going to ask so we can pre-index, or pre-materialize that data? So, there's machine learning in the context of making AtScale a better product. >> Reusing things that are already done, that's been the whole machine-learning- >> Yes. >> Demos, we saw Google Next with the video editing and the video recognition stuff, that's been- >> Exactly. >> Huge part of it. >> You've got users giving you signals, take that information and be smart with it. I think, in terms of the customer work flow - Comcast, for example, a customer of ours - we are in a data discovery phase, there's a data science group that looks at all of their set top box data, and they're trying to discover programming patterns. Who uses the Yankees' network for example? And where they use AtScale is what I would call a descriptive element, where they're trying to figure out what are the key measures and trends, and what are the attributes that contribute to that. And then they'll go in and they'll use machine learning tools on top of that same data set to come up with predictive algorithms. >> So, just to be clear there, they're hypotehsizing about, like, say, either the pattern of users that might be- have an affinity for a certain channel or channels, or they're looking for pathways. >> Yes. And I'd say our role in that right now is a descriptive role. We're supporting the descriptive element of that analytics life cycle. I think over time our customers are going to push us to build in more of our own capabilities, when it comes to, okay, I discovered something descriptive, can you come up with a model that helps me predict it the next time around? Honestly, right now people want BI. People want very traditional BI on the next generation data platform. >> Just, continuing on that theme, leaving machine learning aside, I guess, as I understand it, when we talked about the old school vendors, Care Data, when they wanted to support data scientists they grafted on some machine learning, like a parallel version of our- in the core Teradata engine. They also bought Astro Data, which was, you know, for a different audience. So, I guess, my question is, will we see from you, ultimately, a separate product line to support a new class of users? Or, are you thinking about new functionality that gets integrated into the core product. I think it's more of the latter. So, the way that we view it- and this is really looking at, like I said, what people are asking for today is, kind of, the basic, traditional BI. What we're building is essentially a business model. So, when someone uses AtScale, they're designing and they're telling us, they're asserting, these are the things I'm interested in measuring, and these are the attributes that I think might contribute to it. And, so that puts us in a pretty good position to start using, whether it's Spark on the back end, or built in machine learning algorithms on the Hadoop cluster, let's start using our knowledge of that business model to help make predictions on behalf of the customer. So, just a follow-up, and this really leaves out the machine learning part, which is, it sounds like, we went- in terms of big data we we first to archive it- supported more data retension than could do affordably with the data warehouse. Then we did the ETL offload, now we're doing more and more of the visualization, the ad-hoc stuff. >> That's exactly right. So, what- in a couple years time, what remains in the classic data warehouse, and what's in the Hadoop category? >> Well, so there is, I think what you're describing is the pure evolution, of, you know, any technology where you start with the infrastructure, you know, we've been in this for over ten years, now, you've got cloud. They are going APO and then going into the data science workbench. >> That's not official yet. >> I think we read about this, or at least they filed. But I think the direction is showing- now people are relying on the platform, the Hadoop platform, in order to build applications on top of it. And, so, I think, just like Josh is saying, the mainstream application on top of the database - and I think this is true for non-Hadoop systems as well - is always going to be analytics. Of course, data science is something that provides a lot of value, but it typically provides a lot of value to a few set of people that will then scale it out to the rest of their organization. I think if you now project out to what does this mean for the CIO and their environment, I don't think any of these platforms, Teradata or Hadoop, or Google, or Amazon or any of those, I don't think do 100% replace. And, I think that's where it becomes interesting, because you're now having to deal with a hetergeneous environment, where the business user is up, they're using Excel, they're using they're standard net application, they might be using the result of machine learning models, but they're also having to deal with the heterogeneous environment at the data level. Hadoop on Prime, Hadoop in the cloud, non-Hadoop in the cloud and non-Hadoop on Prime. And, of course that's a market that I think is very interesting for us as a simplification platform for that world. >> I think you guys are really thinking about it in a new way, and I think that's kind of a great, modern approach, let the freedom- and by the way, quick question on the Microsoft tool and Tableau, what percentage share do you think they are of the market? 50? Because you mentioned those are the two top ones. >> Are they? >> Yeah, I mentioned them, because if you look at the magic quadrant, clearly Microsoft, Power BI and Tableau have really shot up all the way to the right. >> Because it's easy to use, and it's easy to work with data. >> I think so, I think- look, from a functionality standpoint, you see Tableau's done a very good job on the visualization side. I think, from a business standpoint, and a business model execution, and I can talk from my days at Microsoft, it's a very great distribution model to get thousands and thousands of users to use power BI. Now, the guys that we didn't talk about on the last magic quadrant. People who are like Google Data Studio, or Amazon Quicksite, and I think that will change the ecosystem as well. Which, again, is great news for AtScale. >> More muscle coming in. >> That's right. >> For you guys, just more rising tide floats all boats. >> That's right. >> So, you guys are powering it. >> That's right. >> Modern BI would be safe to say? >> That's the idea. The idea is that the visualization is basically commoditized at this point. And what business users want and what enterprise leaders want is the ability to provide freedom and openness to their business users and never have to compromise security, speed and also the complexity of those models, which is what we- we're in the business of. >> Get people working, get people productive faster. >> In whatever tool they want. >> All right, Bruno. Thanks so much. Thanks for coming on. AtScale. Modern BI here in The Cube. Breaking it down. This is The Cube covering bid data SV strata Hadoop. Back with more coverage after this short break. (electronic music)