(upbeat techno music) >> Hey everyone, and welcome back to theCUBE's coverage of Snowflake Summit '22 live from Caesar's Forum in beautiful, warm, and sunny Las Vegas. I'm Lisa Martin. I got the Chris and Chris show, next. Bear with me. Chris Degnan joins us again. One of our alumni, the Chief Revenue Officer at Snowflake. Good to have you back, Chris. >> Thank you for having us. >> Lisa: Chris Grusz also joins us. Director of Business Development AWS Marketplace and Service Catalog at AWS. Chris and Chris, welcome. >> Thank you. >> Thank you. >> Thank you. Good to be back in person. >> Isn't it great. >> Chris G: It's so much better. >> Chris D: Yeah. >> Nothing like it. So let's talk. There's been so much momentum, Chris D, at Snowflake the last few years. I mean the momentum at this show since we launched yesterday, I know you guys launched the day before with partners, has been amazing. A lot of change, and it's like this for Snowflake. Talk to us about AWS working together with Snowflake and some of the benefits in it from your customer. And then Chris G, I'll go to you for the same question. >> Chris G: Yep. >> You know, first of all, it's awesome. Like, I just, you know, it's been three years since I've had a Snowflake Summit in person, and it's crazy to see the growth that we've seen. You know, I can't, our first cloud that we ever launched on top of was, was AWS, and AWS is our largest cloud, you know, in in terms of revenue today. And they've been, they just kind of know how to do it right. And they've been a wonderful partner all along. There's been challenges, and we've kind of leaned in together and figured out ways to work together, you know, and to solve those challenges. So, been a wonderful partnership. >> And talk about it, Chris G, from your perspective obviously from a coopetition perspective. >> Yep. >> AWS has databases, cloud data forms. >> Chris G: Yeah. >> Talk to us about it. What was the impetus for the partnership with Snowflake from AWS's standpoint? >> Yeah, well first and foremost, they're building on top of AWS. And so that, by default, makes them a great partner. And it's interesting, Chris and I have been working together for, gosh, seven years now? And the relationship's come a really long way. You know, when we first started off, we were trying to sort out how we were going to work together, when we were competing, and when we're working together. And, you know, you fast forward to today, and it's just such a good relationship. Because both companies work backwards from customers. And so that's, you know, kind of in both of our DNA. And so if the customer makes that selection, we're going to support them, even from an AWS perspective. When they're going with Snowflake, that's still a really good thing for AWS, 'cause there's a lot of associated services that Snowflake either integrates to, or we're integrating to them. And so, it's really kind of contributed to how we can really work together in a co-sell motion. >> Talk to us, talk about that. The joint GOTO market and the co-selling motion from Snowflake's perspective, how do customers get engaged? >> Well, I think, you know, typically we, where we are really good at co-selling together is we identify on premise systems. So whether it's, you know, some Legacy UDP system, some Legacy database solution, and they want to move to the cloud? You know, Amazon is all in on getting everyone to the cloud. And I think that's their approach they've taken with us is saying we're really good at accelerating that adoption and moving all these, you know, massive workloads into the cloud. And then to Chris's point, you know, we've integrated so nicely into things like SageMaker and other tool sets. And we, we even have exciting scenarios where they've allowed us to use, you know, some of their Amazon.com retail data sets that we actually use in data sharing via the partnership. So we continue to find unique ways to partner with our great friends at Amazon. >> Sounds like a very deep partnership. >> Chris D: Yeah. Absolutely. >> Chris G: Oh, absolutely, yeah. We're integrating into Snowflake, and they're integrating to AWS. And so it just provides a great combined experience for our customers. And again, that's kind of what we're both looking forward from both of our organizations. >> That customer centricity is, >> Yeah. >> is I think the center of the flywheel that is both that both of you, your companies have. Chris D, talk about the the industry's solutions, specific, industry-specific solutions that Snowflake and AWS have. I know we talked yesterday about the pivot from a sales perspective >> Chris D: Yes. >> That snowflake made in recent months. Talk to us about the industries that you are help, really targeting with AWS to help customers solve problems. >> Yeah. I think there's, you know, we're focused on a number of industries. I think, you know, some of the examples, like I said, I gave you the example of we're using data sharing to help the retail space. And I think it's a really good partnership. Because some of the, some companies view Amazon as a competitor in the retail space, and I think we kind of soften that blow. And we actually leverage some of the Amazon.com data sets. And this is where the partnership's been really strong. In the healthcare space, in the life sciences space, we have customers like Anthem, where we're really focused on helping actually Anthem solve real business problems. Not necessarily like technical problems. It's like, oh no, they want to get, you know, figure out how they can get the whole customer and take care of their whole customer, and get them using the Anthem platform more effectively. So there's a really great, wonderful partnership there. >> We've heard a lot in the last day and a half on theCUBE from a lot of retail customers and partners. There seems to be a lot of growth in that. So there's so much change in the retail market. I was just talking with Click and Snowflake about Urban Outfitters, as an example. And you think of how what these companies are doing together and obviously AWS and Snowflake, helping companies not just pivot during the pandemic, but really survive. I mean, in the beginning with, you know, retail that didn't have a digital presence, what were they going to do? And then the supply chain issues. So it really seems to be what Snowflake and its partner Ecosystem is doing, is helping companies now, obviously, thrive. But it was really kind of like a no-go sort of situation for a lot of industries. >> Yeah, and I think the neat part of, you know, both the combined, you know, Snowflake and AWS solution is in, a good example is DoorDash, you know. They had hyper growth, and they could not have handled, especially during COVID, as we all know. We all used DoorDash, right? We were just talking about it. Chipotle, like, you know, like (laughter) and I think they were able to really take advantage of our hyper elastic platforms, both on the Amazon side and the Snowflake side to scale their business and meet the high demand that they were seeing. And that's kind of some of the great examples of where we've enabled customer growth to really accelerate. >> Yeah. Yeah, right. And I'd add to that, you know, while we saw good growth for those types of companies, a lot of your traditional companies saw a ton of benefit as well. Like another good example, and it's been talked about here at the show, is Western Union, right? So they're a company that's been around for a long time. They do cross border payments and cross currency, you know, exchanges, and, you know, like a lot of companies that have been around for a while, they have data all over the place. And so they started to look at that, and that became an inhibitor to their growth. 'Cause they couldn't get a full view of what was actually going on. And so they did a lengthy evaluation, and they ended up going with Snowflake. And, it was great, 'cause it provided a lot of immediate benefits, so first of all, they were able to take all those disparate systems and pull that into Snowflake. So they finally had a single source of the truth, which was lacking before that. So that was one of the big benefits. The second benefit, and Chris has mentioned this a couple times, is the fact that they could use data sharing. And so now they could pull in third data. And now that they had a holistic view of their entire data set, they could pull in that third party data, and now they could get insights that they never could get before. And so that was another large benefit. And then the third part, and this is where the relationship between AWS and Snowflake is great, is they could then use Amazon SageMaker. So one of the decisions that Western Union made a long time ago is they use R for their data science platform, and SageMaker supports R. And so it really allowed them to dovetail the skill sets that they had around data science into SageMaker. They could now look across all of Snowflake. And so that was just a really good benefit. And so it drove the cost down for Western Union which was a big benefit, but the even bigger benefit is they were now able to start to package and promote different solutions to their customers. So they were effectively able to monetize all the data that they were now getting and the information they were getting out of Snowflake. And then of course, once it was in there, they could also use things like Tableau or ThoughtSpot, both of which available in AWS Marketplace. And it allowed them to get all kinds of visualization of data that they never got in the past. >> The monetization piece is, is interesting. It's so challenging for organizations, one, to get that single source view, to be able to have a customer 360, but to also then be able to monetize data. When you're in customer conversations, how do you help customers on that journey, start? Because the, their competitors are clearly right behind them, ready to take first place spot. How do you help customers go, all right this is what we're going to do to help you on this journey with AWS to monetize your data? >> I think, you know, it's everything from, you know, looking at removing the silos of data. So one of the challenges they've had is they have these Legacy systems, and a lot of times they don't want to just take the Legacy systems and throw them into the cloud. They want to say, we need a holistic view of our customer, 360 view of our customer data. And then they're saying, hey, how can we actually monetize that data? That's where we do everything from, you know, Snowflake has the data marketplace where we list it in the data marketplace. We help them monetize it there. And we use some of the data sets from Amazon to help them do that. We use the technologies like Chris said with SageMaker and other tool sets to help them realize the value of their data in a real, meaningful way. >> So this sounds like a very strategic and technical partnership. >> Yeah, well, >> On both sides. >> It's technical and it's GOTO market. So if you take a look at, you know, Snowflake where they've built over 20 integrations now to different AWS services. So if you're using S3 for object storage, you can use Snowflake on top of that. If you want to load up Snowflake with Glue which is our ETL tool, you can do that. If you want to use QuickSite to do your data visualization on top of Snowflake, you can do that. So they've built integration to all of our services. And then we've built integrations like SageMaker back into Snowflake, and so that supports all kinds of specific customer use cases. So if you think of people that are doing any kind of cloud data platform workload, stuff like data engineering, data warehousing, data lakes, it could be even data applications, cyber security, unistore type things, Snowflake does an excellent job of helping our customers get into those types of environments. And so that's why we support the relationship with a variety of, you know, credit programs. We have a lot of co-sell motions on top of these technical integrations because we want to make sure that we not only have the right technical platform, but we've got the right GOTO market motion. And that's super important. >> Yeah, and I would add to that is like, you know one of the things that customers do is they make these large commitments to Amazon. And one of the best things that Amazon did was allow those customers to draw down Snowflake via the AWS Marketplace. So it's been wonderful to his point around the GOTO market, that was a huge issue for us. And, and again, this is where Amazon was innovative on identifying the ways to help make the customer have a better experience >> Chris G: Yeah. >> Chris D: and put the customer first. And this has been, you know, wonderful partnership there. >> Yeah. It really has. It's been a great, it's been really good. >> Well, and the customers are here. Like we said, >> Yep. >> Yes. Yes they are. >> we're north of 10,000 folks total, and customers are just chomping at the bit. There's been so much growth in the last three years from the last time, I think I heard the 2019 Snowflake Summit had about 1500 people. And here we are at 10,000 plus now, and standing-room-only keynote, the very big queue to get in, people turned away, pushed back to an overflow area to be able to see that, and that was yesterday. I didn't even get a chance to see what it was like today, but I imagine it was probably the same. Talk about the, when you're in customer conversations, where do you bring, from a GTM perspective, Where do you bring Snowflake into the conversation? >> Yeah >> Obviously, there's Redshift there, what does that look like? I imagine it follows the customer's needs, challenges. >> Exactly. >> Compelling events. >> Yeah. We're always going to work backwards from the customer need, and so that is the starting point for kindling both organizations. And so we're going to, you know, look at what they need. And from an AWS perspective, you know, if they're going with Snowflake, that's a very good thing. Right? 'Cause one of the things that we want to support is a selection experience to our AWS customers and make sure that no matter what they're doing, they're getting a very good, supported experience. And so we're always going to work backwards from the customer. And then once they make that technology decision, then we're going to support them, as I mentioned, with a whole bunch of co-sell resources. We have technical resources in the field. We have credit programs and in, you know, and, of course, we're going to market in a variety of different verticals as well with Snowflake. If you take a look at all the industry clouds that Snowflake has spun up, financial services and healthcare, and media entertainment, you know, those are all very specific use cases that are very valuable to an AWS customer. And AWS is going more and more to market on a vertical approach, and so Snowflake really just fits right in with our overall strategy. >> Right. Sounds like very tight alignment there. That mission alignment that Frank talked about yesterday. I know he was talking about that with respect to customers, but it sounds like there's a mission alignment between AWS and Snowflake. >> Mission alignment, yeah. >> I live that every week. (laughter) >> Sorry if I brought up a pain point. >> Yeah. Little bit. No. >> Guys, what's, in terms of use cases, obviously we've been here for a couple days. I'm sure you've had tremendous feedback, >> Chris G: Yeah. >> from, from customers, from partners, from the ecosystem. What's next, what can we expect to hear next? Maybe give us a preview of re:Invent in the few months. >> Preview of re:Invent. Yeah. No, well, one of the things we really want to start doing is just, you know, making the use case of, of launching Snowflake on AWS a lot easier. So what can we do to streamline those types of experiences? 'Cause a lot of times we'll find that customers, once they buy a third party solution like Snowflake, they have to then go through a whole series of configuration steps, and what can we do to streamline that? And so we're going to continue to work on that front. One of the other places that we've been exploring with Snowflake is how we work with channel partners. And, you know, when we first launched Marketplace it was really more of an app store model that was ISVs on one side and channel partners on the other, and there wasn't really a good fit for channel partners. And so four years ago we retrofitted the platform and have opened it up to resellers like an SHI or SIs like Salam or Deloitte who are top, two top SIs for Snowflake. And now they can use Marketplace to resell those technologies and also sell their services on top of that. So Snowflake's got a big, you know, practice with Salam, as I mentioned. You know, Salam can now sell through Marketplace and they can actually sell that statement of work and put that on the AWS bill all by virtue of using Marketplace, that automation platform. >> Ease of use for customers, ease of use for partners as well. >> Yes. >> And that ease of use is it's no joke. It's, it's not just a marketing term. It's measurable and it's about time-to-value, time-to-market, getting customers ahead of their competition so that they can be successful. Guys, thanks for joining me on theCUBE today. Talking about AWS and >> Nice to be back. Nice to be back in person. >> Isn't it nice to be back. It's great to be actually sitting across from another human. >> Exactly. >> Thank you so much for your insights, what you shared about the partnership and where it's going. We appreciate it. >> Thank you. >> Cool. Thank you. >> Thank you. >> All right guys. For Chris and Chris, I'm Lisa Martin, here watching theCUBE live from Las Vegas. I'll be back with my next guest momentarily, so stick around. (Upbeat techno music)

(upbeat music) >> Hello everyone, welcome back to theCUBE's coverage of splunk.com 2021 virtual. We're here live in the Splunk studios. We're all here gettin all the action, all the stories. Garth Fort, senior vice president, Chief Product Officer at Splunk is here with me. CUBE alumni. Great to see you. Last time I saw you, we were at AWS now here at Splunk. Congratulations on the new role. >> Thank you. Great to see you again. >> Great keynote and great team. Congratulations. >> Thank you. Thank you. It's a lot of fun. >> So let's get into the keynote a little bit on the product. You're the Chief Product Officer. We interviewed Shawn Bice, who's also working with you as well. He's your boss. Talk about the, the next level, cause you're seeing some new enhancements. Let's get to the news first. Talk about the new enhancements. >> Yeah, this was actually a really fun keynote for me. So I think there was a lot of great stuff that came out of the rest of it. But I had the honor to actually showcase a lot of the product innovation, you know, since we did .conf last year, we've actually closed four different acquisitions. We shipped 43 major releases and we've done hundreds of small enhancements, like we're shipping code in the cloud every six weeks and we're shipping new versions twice a year for our Splunk Enterprise customers. And so this was kind of like if you've seen that movie Sophie's Choice, you know, where you have to pick one of your children, like this was a really hard, hard thing to pick. Cause we only had about 25 minutes, but we did like four demos that I think landed really well. The first was what we call ingest actions and you know, there's customers that are using, they start small with gigabytes and they go to terabytes and up to petabytes of data per day. And so they wanted tools that allow them to kind of modify filter and then route data to different sort of parts of their infrastructure. So that was the first demo. We did another demo on our, our visual playbook editor for SOAR, which has improved quite a bit. You know, a lot of the analysts that are in the, in the, in the SOC trying to figure out how to automate responses and reduce sort of time to resolution, like they're not Python experts. And so having a visual playbook editor that lets them drag and drop and sort of with a few simple gestures create complex playbooks was pretty cool. We showed some new capabilities in our APM tool. Last year, we announced we acquired a company called Plumbr, which has expertise in basically like code level analysis and, and we're calling it "Always On" profiling. So we, we did that demo and gosh, we did one more, four, but four total demos. I think, you know, people were really happy to see, you know, the thing that we really tried to do was ground all of our sort of like tech talk and stuff that was like real and today, like this is not some futuristic vision. I mean, Shawn did lay out some, some great visions, visionary kind of pillars. But, what we showed in the keynote was I it's all shipping code. >> I mean, there's plenty of head room in this market when it comes to data as value and data in motion, all these things. But we were talking before you came on camera earlier in the morning about actually how good Splunk product and broad and deep the product portfolio as well. >> Garth: Yeah. >> I mean, it's, I mean, it's not a utility and a tooling, it's a platform with tools and utilities. >> Garth: Yeah >> It's a fully blown out platform. >> Yeah. Yeah. It is a platform and, and, you know, it's, it's one that's quite interesting. I've had the pleasure to meet a couple of big customers and it's kind of amazing, like what they do with Splunk. Like I was meeting with a large telco on the east coast and you know, they actually, for their set top boxes, they actually have to figure out in real time, which ads to display and the only tool they could find to process 15 million events in real time, to decide what ad to display, was Splunk. So that was, that was like really cool to hear. Like we never set out to be like an ad tech kind of platform and yet we're the only tool that operates at that level of scale and that kind of data. >> You know, it's funny, Doug Merritt mentioned this in my interview with him earlier today about, you know, and he wasn't shy about it, which was great. He was like, we're an enabling platform. We don't have to be experts in all these vertical industries >> Garth: Yep >> because AI takes care of that. That's where the machine learning >> Garth: Yeah >> and the applications get built. So others are trying to build fully vertically integrated stacks into these verticals when in reality they don't have to, if they don't want it. >> Yeah, and Splunk's kind of, it's quite interesting when you look across our top 100 customers, you know, Doug talks about like the, you know, 92 of the fortune 100 are kind of using Splunk today, but the diversity across industries and, you know, we have government agencies, we have, you know, you name the retail or the vertical, you know, we've got really big customers, they're using Splunk. And the other thing that I kind of, I was excited about, we announced the last demo I forgot was TruSTAR integration with Enterprise Security. That's pretty cool. We're calling that Splunk Threat Intelligence. And so That was really fun and we only acquired, we closed the acquisition to TruSTAR in May, but the good news is they've been a partner with us like for 18 months before we actually bought em. And so they'd already done a lot of the work to integrate. And so they had a running start in that regard, But other, one other one that was kind of a, it was a small thing. I didn't get to demo it, but we talked about the, the content pack for application performance monitoring. And so, you know, in some ways we compete in the APM level, but in many ways there's a ton of great APM vendors out there that customers are using. But what they wanted us to do was like, hey, if I'm using APM for that one app, I still want to get data out of that and into Splunk because Splunk ends up being like the core repository for observability, security, IT ops, Dev Sec Ops, et cetera. It's kind of like where the truth, the operational truth of how your systems works, lives in Splunk. >> It's so funny. The Splunk business model has actually been replicated. They call it data lake, whatever you want to call it. People are bringing up all these different metaphors. But at the end of the day, if you guys can create a value proposition where you can have data just be, you know, stored and dumped and dumped into whatever they call it stored in a way >> Garth: We call it ingest >> Ingested, ingested. >> Garth: Not dumped. >> Data dump. >> Garth: It's ingested. >> Well, I mean, well you given me a plan, but you don't have to do a lot of work to store just, okay, we can only get to it later, >> Garth: Yep. >> But let the machines take over >> Garth: Yep. >> With the machine learning. I totally get that. Now, as a pro, as a product leader, I have to ask you your, your mindset around optimization. What do you optimize for? Because a lot of times these use cases are emerging. They just pop out of nowhere. It's a net new use case that you want to operationalize. So balancing the headroom >> Yep. >> Or not to foreclose those new opportunities for customers. How are customers deciding what's important to them? How do you, because you're trying to read the tea leaves for the future >> Garth: A little bit, yeah. >> and then go, okay, what do our customers need, but you don't want to foreclose anything. How do you think about product strategy around that? >> There's a ton of opportunity to interact with customers. We have this thing called the Customer Advisory Board. We run, I think, four of them and we run a monthly. And so we got an opportunity to kind of get that anecdotal data and the direct contact. We also have a portal called ideas.splunk.com where customers can come tell us what they want us to build next. And we look at that every month, you know, and there's no way that we could ever build everything that they're asking us to, but we look at that monthly and we use it in sort of our sprint planning to decide where we're going to prioritize engineering resources. And it's just, it's kind of like customers say the darndest things, right? Sometimes they ask us for stuff and we never imagined building it in a million years, >> John: Yeah. >> Like that use case around ads on the set top box, but it's, it's kind of a fun place to be like, we, we just, before this event, we kind of laid out internally what, you know, Shawn and I kind of put together this doc, actually Shawn wrote the bulk of it, but it was about sort of what do we think? Where, where can we take Splunk to the next three to five years? And we talked about these, we referred to them as waves of innovation. Cause you know, like when you think about waves, there's multiple waves that are heading towards the beach >> John: Yeah. >> in parallel, right? It's not like a series of phases that are going to be serialized. It's about making a set of investments. that'll kind of land over time. And, and the first wave is really about, you know, what I would say is sort of, you know, really delivering on the promise of Splunk and some of that's around integration, single sign-on things about like making all of the Splunk Splunk products work together more easily. We've talked a lot in the Q and a about like edge and hybrid. And that's really where our customers are. If you watch the Koby Avital's sort of customer keynote, you know, Walmart by necessity, given their geographic breadth and the customers they serve has to have their own infrastructure. They use Google, they use Azure and they have this abstraction layer that Koby's team has built on top. And they use Splunk to manage kind of, operate basically all of their infrastructure across those three clouds. So that's the hybrid edge scenario. We were thinking a lot about, you mentioned data lakes. You know, if you go back to 2002, when Splunk was founded, you know, the thing we were trying to do is help people make sense of log files. But now if you talk to customers that are moving to cloud, everybody's building a data lake and there's like billions of objects flowing into millions of these S3 buckets all over the place. And we're kind of trying to think about, hey, is there an opportunity for us to point our indexing and analytics capability against structured and unstructured data and those data lakes. So that that'll be something we're going to >> Yeah. >> at least start prototyping pretty soon. And then lastly, machine learning, you know, I'd say, you know, to use a baseball metaphor, like in terms of like how we apply machine learning, we're like in the bottom of the second inning, >> Yeah. >> you know, we've been doing it for a number of years, but there's so much more. >> There's so, I mean, machine learning is only as good as the data you put into the machine learning. >> Exactly, exactly. >> And so if you have, if you have gap in the data, the machine learning is going to have gaps in it. >> Yeah. And we have, we announced a feature today called auto detect. And I won't go into the gory details, but effectively what it does is it runs a real-time analytics job over whatever metrics you want to look at and you can do what I would consider more statistics versus machine learning. You can say, hey, if in a 10 minute period, like, you know, we see more errors than we see on average over the last week, throw an alert so I can go investigate and take a look. Imagine if you didn't have to figure out what the right thresholds were, if we could just watch those metrics for you and automatically understand the seasonality, the timing, is it a weekly thing? Is it a monthly thing? And then like tell you like use machine learning to do the anomaly detection, but do it in a way that's more intelligent than just the static threshold. >> Yeah. >> And so I think you'll see things like auto detect, which we announced this week will evolve to take advantage of machine learning kind of under the covers, if you will. >> Yeah. It was interesting with cloud scale and the data velocity, automations become super important. >> Oh yeah. >> You don't have a lot of new disciplines emerge, like explainable AI is hot right now. So you got, the puck is coming. You can see where the puck is going. >> Yeah >> And that is automation at the app edge or the application layer where the data has got to be free-flowing or addressable. >> Garth: Yeah. >> This is something that is being talked about. And we talked about data divide with, with Chris earlier about the policy side of things. And now data is part of everything. It's part of the apps. >> Garth: Yeah. >> It's not just stored stuff. So it's always in flight. It should be addressable. This is what people want. What do you think about all of that? >> No, I think it's great. I actually just can I, I'll quote from Steve Schmidt in, in sort of the keynote, he said, look like security at the end of the day is a human problem, but it kind of manifests itself through data. And so being able to understand what's happening in the data will tell you, like, is there a bad actor, like wreaking havoc inside of my systems? And like, you can use that, the data trail if you will, of the bad actor to chase them down and sort of isolate em. >> The digital footprints, if you will, looking at a trail. >> Yeah. >> All right, what's the coolest thing that you like right now, when you look at the treasure trove of, of a value, as you look at it, and this is a range of value, Splunk, Splunk has had customers come in with, with the early product, but they keep the customers and they always do new things and they operationalize it >> Garth: Yep. >> and another new thing comes, they operationalize it. What's the next new thing that's coming, that's the next big thing. >> Dude that is like asking me which one of my daughters do I love the most, like that is so unfair. (laughing) I'm not going to answer that one. Next question please. >> Okay. All right. Okay. What's your goals for the next year or two? >> Yeah, so I just kind of finished roughly my first 100 days and it's been great to, you know, I had a whole plan, 30, 60, 90, and I had a bunch of stuff I wanted to do. Like I'm really hoping, sort of, we get past this current kind of COVID scare and we get to back to normal. Cause I'm really looking forward to getting back on the road and sort of meeting with customers, you know, you can meet over Zoom and that's great, but what I've learned over time, you know, I used to go, I'd fly to Wichita, Kansas and actually go sit down with the operators like at their desk and watch how they use my tools. And that actually teaches you. Like you, you come up with things when you see, you know, your product in the hands of your customer, that you don't get from like a CAB meeting or from a Zoom call, you know? >> John: Yeah, yeah. >> And so being able to visit customers where they live, where they work and kind of like understand what we can do to make their lives better. Like that's going to, I'm actually really excited to gettin back to travel. >> If you could give advice to CTO, CISO, or CIO or a practitioner out there who are, who is who's sitting at their virtual desk or their physical desk thinking, okay, the pandemic, were coming through the pandemic. I want to come out with a growth strategy, with a plan that's going to be expansive, not restrictive. The pandemic has shown what's what works, what doesn't work. >> Garth: Sure. >> So it's going to be some projects that might not get renewed, but there's doubling down on, certainly with cloud scale. What would advice would you give that person when they start thinking about, okay, I got to get my architecture right. >> Yeah. >> I got to get my playbooks in place. I got to get my people aligned. >> Yeah >> What's what do you see as a best practice for kind of the mindset to actual implementation of data, managing the data? >> Yeah, and again, I'm, I'm, this is not an original Garth thought. It actually came from one of our customers. You know, the, I think we all, like you think back to March and April of 2020 as this thing was really getting real. Everybody moved as fast as they could to either scale up or scale scaled on operations. If you were in travel and hospitality, you know, that was, you know, you had to figure how to scale down quickly and like what you could shut down safely. If you were like in the food delivery business, you had to figure out how you could scale up, like Chipotle hit two, what is it? $2 billion run rate on delivery last year. And so people scrambled as fast as they could to sort of adapt to this new world. And I think we're all coming to the realization that as we sort of exit and get back to some sense of new normal, there's a lot of what we're doing today that's going to persist. Like, I think we're going to have like flexible rules. I don't think everybody's going to want to come back into the office. And so I think, I think the thing to do is you think about returning to whatever this new normal looks like is like, what did we learn that was good. And like the pandemic had a silver lining for folks in many ways. And it sucked for a lot. I'm not saying it was a good thing, but you know, there were things that we did to adapt that I think actually made like the workplace, like stronger and better. And, and sort of. >> It showed that data's important, internet is important. Didn't break, the internet didn't break. >> Garth: Correct. >> Zoom was amazing. And the teleconferencing with other tools. >> But that's kind of, just to sort of like, what did you learn over the last 18 months that you're going to take for it into the next 18 years? You know what I mean? Cause there was a lot of good and I think people were creative and they figured out like how to adapt super quickly and take the best of the pandemic and turn it into like a better place to work. >> Hybrid, hybrid events, hybrid workforce, hybrid workflows. What's what's your vision on Splunk as a tier one enterprise? Because a lot of the news that I'm seeing that's, that's the tell sign to me in terms of this next growth wave is big SI deals, Accenture and others are yours working with and you still got the other Partnerverse going. You have the ecosystems emerging. >> Garth: Yep. >> That's a good, that means your product's enabling people to make money. >> Garth: Yeah. Yeah, yeah, yeah. >> And that's a good thing. >> Yeah, BlueVoyant was a great example in the keynote yesterday and they, you know, they've really, they've kind of figured out how, you know, most of their customers, they serve customers in heavily regulated industries kind of, and you know, those customers actually want their data in a Splunk tenant that they own and control and they want to have that secure boundary around that. But BlueVoyant's figured out how they can come in and say, hey, I'm going to take care of the heavy lifting of the day-to-day operations, the monitoring of that environment with the security. So, so BlueVoyant has done a great job sort of pivoting and figuring out how they can add value to customers and do, you know, because they they're managing not just one Splunk instance, but they're managing 100s of Splunk cloud instances. And so they've got best practices and automation that they can play across their entire client base. And I think you're going to see a lot more of that. And, and Teresa's just, Teresa is just, she loves Partners, absolutely loves Partners. And that was just obvious. You could, you could hear it in her voice. You could see it in her body language, you know, when she talked about Partnerverse. So I think you'll see us start to really get a lot more serious. Cause as big as Splunk is like our pro serve and support teams are not going to scale for the next 10,000, 100,000 Splunk customers. And we really need to like really think about how we use Partners. >> There's a real growth wave. And I, and I love the multiples wave in parallel because I think that's what everyone's consensus on. So I have to ask you as a final question, what's your takeaway? Obviously, there's been a virtual studio here where all the Splunk executives and, and, and customers and partners are here. TheCUBE's here doing all the presentations, live by the way. It was awesome. What would you say the takeaway is for this .conf, for the people watching and consuming all the content online? A lot of asynchronous consumption would be happening. >> Sure. >> What's your takeaway from this year's Splunk .conf? >> You know, I, it's hard cause you know, you get so close to it and we've rehearsed this thing so many times, you know, the feedback that I got and if you look at Twitter and you look at my Slack and everything else, like this felt like a conf that was like kind of like a really genuine, almost like a Splunk two dot O. But it's sort of true to the roots of what Splunk was true to the product reality. I mean, you know, I was really careful with my team and to avoid any whiff of vaporware, like what were, what we wanted to show was like, look, this is Splunk, we're acquiring companies, you know, 43 major releases, you know, 100s of small ones. Like we're continuing to innovate on your behalf as fast as we can. And hopefully this is the last virtual conf. But even when we go back, like there was so much good about the way we did this this week, that, you know, when we, when we broke yesterday on the keynote and we were sitting around with the crew and it kind of looking at that stage and everything, we were like, wow, there is a lot of this that we want to bring to an in-person event as well. Cause so for those that want to travel and come sit in the room with us, we're super excited to do that as soon as we can. But, but then, you know, there may be 25, 50, 100,000 that don't want to travel, but can access us via this virtual event. >> It's like a time. It's a moment in time that becomes a timeless moment. That could be, >> Wow, did you make that up right now? >> that could be an NFT. >> Yeah >> We can make a global cryptocurrency. Garth, great to see you. Of course I made it up right then. So, great to see you. >> Air bump, air bump? Okay, good. >> Okay. Garth Fort, senior vice president, Chief Product Officer. In theCUBE here, we're live on site at Splunk Studio for the .conf virtual event. I'm John Furrier. Thanks for watching. >> All right. Thank you guys. (upbeat music)

>>From the Bellagio hotel in Las Vegas, it's the queue covering UI path forward for brought to you by UI path. >>We'll go back to the cubes coverage of UI paths forward for big customer event. You know, this company has always bucked the trend and they're doing it again. They're having a live event, physical event. There are customers here, partners, technologists. I'm here with Lisa Martin, my co-host for the show. And we're going to talk about testing. It's a new market for UI path. If anybody knows anything about testing, it's kind of this mundane, repetitive process ripe for automation geared vice-chairs. Here's the senior vice president of testing products at UI path and Matt Elisa. Who's the product marketing lead at UI path. Gents. Welcome to the cube. Thanks for coming on. Thanks for having us feminists. Explain to us how you guys think about testing both from an internal perspective and how you're going to market. >>Yeah, well, testing has been around for a long time, right? 25 years or so when, when I came to UI pass, the first thing I looked at was like, how do our customers test RPA? And it's quite interesting. We did a survey actually with 1500 people and, uh, 27% said that they wouldn't test at all. And I thought that's really interesting. RPA is a business critical software that runs in your production environment and you probably have to test. So we came up with this idea that we create the test suite we're using, you know, proven technology from UI pass. And, and we built this offering and brought this into the market for RPA testing and for application testing. So we do both. And of course we use it internally as well. I mean, that will be, you know, eat your own dog food or drink your own champagne, I guess. Yeah. >>Well, think about it. If you, if you automate, if you, if there's an ROI to automate a process, there's gotta be an ROI to verify that it's going to work before it goes into production too. And so it's amazing that a lot of companies are not doing this and they're doing it manually, um, today. >>So, so, but so, but parts of testing have been automated, haven't they with regression testing. So can, can you guys take us through kind of the before and after and how you're approaching it versus the traditional way? >>Yeah, absolutely. I mean, like I said, testing is not new, right? Um, but still when you look at the customers, they're not out to meeting more than I would say, 30, 40% of the manual tests. So still a lot of Stan manually, which I think, and we talked about this right manual testing is the, the original RPA. It's a tedious, repetitive tasks that you should not do manually. Right? And so what we are trying to bring in is now we're talking about this new role, it's called a digital tester. The digital tester is an empowered. We could call a manual tester, who's able to build automation and we believe that this will truly increase the automation, even in the existing testing market. And it's going to be, I don't want to use the word game-changer, but it's gonna change. Uh, the way testing is done. Yeah. >>And we're, we're applying, um, all the capabilities of UI path and delivering those testers, just like we would for HR team or a, or a, a finance and accounting team. But testing even has they understand this more, they've been doing this for 20 years. They understand automation and we're going to get them things like process mining so they can figure out what tests they need to run from production data. We're going to give them task mining so they can make more human-like tests test. Exactly. Like I used to be a tester, uh, and I ran a test team. And what I used to do is I have to go out to a warehouse and I'd have to go watch people as they entered orders, to make sure I was testing it the right way. So they would like click. We usually thought they were clicking things, whether you're using hotkeys, that's just an example of what they were doing. But now we can do task, task mining to get that remotely, pull that data in and do tests and make more realistic tests. >>How much of the there's so much potential there? I think you were saying that only 27% are actually doing testing. So there's so much opportunity. I'm curious, where are your conversations within the customer organization? We know that automation is a board level investor topic. Where are you? Where are those discussions with the testing folks, the RPA folks, helping them come together? >>Well, that's interesting. The question we typically, on the IP side, we talked to the cos by the people that are professionally developing those RPAs, but very easily, we get introduced to the test side of the house. And then usually there's a joint meeting where the test people are there, the RPA people are there. And that's why we are talking about this is going to convert somehow, right? They are in different departments today. But if you think about it, if five years down the road, maybe 10 years, they might be an automation discipline for the entire enterprise. So if that answered your question about, >>Yeah, >>Yeah. And we have a customer coming presenting this afternoon, Chipola and they're gonna be talking about how they, both of the teams are using a test teams and the RPA teams. And they built a reusable component library that, so when they built RPA team built their automations, they put them in a reusable library and the test team is able to recreate their tests much faster, reusing about 70% of the components. And so when the, when you think of automation, they're thinking about automating the application, not automating a process or a test so that people can use those like Lego blocks and build it if they're doing so, they could even, even it automation, if they wanted to start doing it, automation, they could pull those components out and use those. >>This is game changing is quality because so often, because in this day and age of agile, it's like move fast and break things. A lot of things break. And when we heard this morning in the keynotes, how you guys are pushing code like a couple of times a week, I mean, it's just a constant. And then you do two big releases. Okay. I get, I get it for the on-prem. But when you're pushing code that fast, you don't have time to test everything. There's a lot of stuff that's unknown. And so to the extent that you can compress all those checkboxes, now I can focus on the really important things that sometimes are architectural. How do you expect applying RPA to testing is going to affect the quality? Or maybe you got some examples. Chipotle. You just mentioned what, >>First of all, I mean, when you say we pushing code like bi-weekly or so, right. We're talking about continuous development. That's what it's called. Right? It's agile. You have sprint cycles, you continue to bring new code, new code, new code, and you test all the increments with it. So it's not that you building up a huge backlog for the testing on the RPA side. What I see is that there will be a transformation about the process, how they develop RPA at the moment. It's still done very much, I would say, in a waterfall issue, which is agree, >>A big bang waterfall. >>Yeah. It will transition. We already have partners that apply agile methodologies to their actually RPA development. And that's going to change that. >>Okay. So it's not so it's quality for those that are in testing obviously, but, but it's, but for the waterfall guys, it's, it's compressing the time to value. Oh yeah. That's going to be the big key. Yeah. That's really where it's coming. >>But he said his Chipotle is, was able to reuse 70% of the automation components. Right. That's huge. I mean, you have to think about it. 70% can be reused from testing to RPA and vice versa. That's a huge acceleration. Also on the IPA side, you can automate more processes faster. If you have components that you can trust. >>So you were a tester. Yeah. So you were a cost center. Yes, exactly. >>Unnecessary. What's the budget. >>So could you think RPA and automation can flip that mindset? Yes, >>Totally. And that's one of the things we want to do is we want to turn testing from a cost center to a value center, give testers a new career paths, even because really testers before all you could do is you could be more technical. Maybe you become a developer or you could be a manager, but you couldn't really become like an automation architect or a senior automation person. And now we're giving them a whole different career path to go down. So it's really exciting >>Because I know when I came out of college, I had a job offer and I wanted to be a developer, a programmer. We call them back then. And the only job I could get was as a tester. And I was like, oh, this is miserable. I'm not doing this, but there's a, there was a growth path there. They were like, Hey, do this for two or three years, maybe five years. I was like, forget it. I'm going into sales and marketing. But so what's the, what's the growth path today for the tester. And how do you see this >>Changing? So you want to go, you want to, I can take that one. No, you take it. I mean, I did it, so really it's, I mean, we're going to be giving these guys, the testing market has been kind of not innovating for years and years and years. And so we're going to be giving these guys some new tools to make them more powerful, make even the cause. Testing is a kind of a practice that is, you know, like, like you said, you didn't like testing. I didn't like testing either. Actually I hate testing. So I automated it. Right. So, um, and so that was the first thing I did. And so I think we're going to give these guys some new tools, some ways to grow their career and some ways to be even better testers, but like, like, like we talked about process mining, test mining, like maybe they're maybe they're testing the wrong things. Maybe they're not testing, you know, maybe, you know, there, cause there's kind of this test, everything mentality where we need to test everything and the whole release instead of like focusing in on what changed. And so I think we'll be able to help them really focus on the testing and the quality to make it more efficient as well. However, >>So T to defend the testers, right test is a very skilled people. Yes. They know their business, they know what to test and how to test in a way that nobody else knows that it's something we sometimes underestimate. They are not developers, so they don't write code or they don't build automations typically. But if we can equip them with tools that they can build out information, you have the brain and the muscle together, you know what I mean? You don't have to delegate the automation to some, whatever team that is maybe outsourced even you can do it. In-house and I think to some extent, that was also the story of Chipotle, right? Yeah. Yeah. They were in sourcing again because they're building their own >>And it saved them time because they have deal is handoffs, you know, to an external third party to do the testing for them. And so they pulled it all in made things much more streamlined and efficient. How >>Is that? It seems like a big cultural shift within any type of organization in any industry we're using as an example here, how does UI path help facilitate that cultural shift? Cause that's big and we're talking about really reducing, um, or speeding time to value. >>Right. Right. And it is a lot of the agile methodology is like, we're starting. So it's kind of like, we're going back in time, you know, and we're teaching these people, you know, the RPA community, all of the things that we learned from software development. Right. And so we're going to bring applying that to this. And so all those agile mindset, the th the agile values, you know, those are the things that are going to help them kind of come together. And that's one of the things that Julie talked about is one of the things is they had a kind of agile mindset, a can-do attitude that pulled them together. >>I think one thing that will really helps with changing the culture is empowering the people. If you give them the tools that they can do, they will do, and that will change the culture. I don't think it can come from top down. It needs to come from within and from the people. And that's what we see also with RPA, by the way, is adopted on department level and D build automations. And then at some point it becomes maybe an enterprise wide initiative, right. But somebody in HR had this idea and started >>The other thing too, is Matt, you mentioned this, you could go to a third party. So what years ago? In the early two thousands, we had a software company. We would use a company called agile on. They were us. I don't know if you ever heard of them. They're basically, we're a job shop. And we would throw our code over the very waterfall, throw the code over the fence. It was a black box and it was very asynchronous. And it would come back, you know, weeks later. And they say, I fix this, fix this, but we didn't have the analytics we didn't have. There was no transparency. Had we had that. We would have maybe come up with new ideas or a way to improve it because we knew the product way better. And so if you can bring that, in-house now you've got much better visibility. So what, what analytics are analytics a piece of this? >>Is that something that is so, I mean, I'll give you an example, SAP systems, right? When you have SAP systems, customers apply transports like five or 10 a day. Every transport can change the system in a way that you might break the automation. We have the possibility to actually not only understand what's going on in this system with process mining, but we also have the possibility to do change, impact, money, and change impact. Mining tells me with every process, every transport I apply, what has changed, and we can pinpoint the test cases that you need to run. So instead of running a thousand test cases, every time we pinpoint 50 of them and you know exactly what has changed. Yeah. >>That's right. Cause a lot of times you don't know what you don't know. And you're saying the machine is basically saying focus on these areas that are going to give you the biggest, that's kind of Amdahl's law, isn't it focus on the areas that are going to get the most return. Yeah. So this is a new business for UI path. You guys are targeting this as a market segment. Can you tell us more about that? >>We joined about two years ago. It takes some time to build something, right. There was a lot of proven technology there. And then we lounged, uh, I think it wasn't July last year, which was more like a, uh, private lounge. We, we didn't make much noise around it and it's gaining a lot of traction. So it's several hundred customers have already jumped on their test bandwagon, if you can call it this way. And yeah, this, this year we were pushing full speed into the testing market as well, because we see the benefits that customers get when they use both like the story from Chipotle. It has other customers like Cisco and, and more, when you hear the stories, what they were able to achieve. I mean, that's a no-brainer I think for any customer who wants to improve the automation. Yeah. >>Well, and also we're taking production grade automation and giving it to the testers and we're giving them this advanced AI so they can automate things. They weren't able to automate before, like Citrix virtual virtualized machines, point of sale systems, like 12 layer, any other business would have, they can automate all those things now that they couldn't do before, as well as everything else. And then they can also the testing tools, they talked about fragmentation this morning. That's another problem is there's a tool for mobile. There's a tool for this. There's a tool for API APIs. You have all these tools, you have to learn all these languages. We're going to give them one. They can learn and use and apply to all their technologies. And it's easy to use and it's easy to use. Yeah. >>That's kind of been the mantra of UI path for very long time, easy to use making, making RPA simple. We've got 8,000 plus customers. You mentioned a few of them. We're going to have some of them on the program this week. How do you expect good question for you that stat that you mentioned from that survey in the very beginning of our conversation, how do you expect that needle to move in the next year? Because we're seeing so much acceleration because of the pandemic. >>That's a really good question because the questions that we had in the, after we had the first hundred, right? The values didn't change that much. So we have now 1500 and you would assume that is pretty stable from the data. It didn't change that much. So we're still at 27% that are not testing. And that's what we see as our mission. We want to change that no customer that has more than, I dunno, five processes in production should not like not test that's crazy and we can help. And that's our mission. So, but the data is not changing. That's the interesting part. >>I know, I know we're out of time, but, but we're how do you price this? Is it a, is it a set? Is it a subscription? Is it a usage based model? How, how do you, >>It's fully included in the UI pass tool suite. So it means it's on the cloud and on-prem the pricing is the same. We are using this. There >>It is. Yeah. >>It's the same components. Like, like we're using studio for automation, we're using orchestrator, but we're using robots. We have cloud test manager on prem test manager. It's just a part of the >>Value, add that you're putting into the platform. Yeah, yeah, >>Exactly. Yeah. There are components that are priced. Yes. But I mean, it's part of the platform, how it is delivered. >>Yeah. So I paid for that module and you turn it on and use it. So it's a subscription. It could be an annual term if I want multi-year term. I can do that. Exactly. Good. Great guys. Thanks so much for coming on the Cuban and good luck with this. Thank you. Great, great innovations. Okay. Keep it right there at Dave Volante for Lisa Martin, we'll be back with our coverage of UI path forward for, from the Bellagio in Las Vegas. Keep it right there.

>>From the Bellagio hotel in Las Vegas, it's the cube covering UI path forward for brought to you by UI path. >>We'll go back to the cubes coverage of UI paths forward for big customer event. You know, this company has always bucked the trend and they're doing it again. They're having a live event, physical event. There are customers here, partners, technologists. I'm here with Lisa Martin, my co-host for the show. And we're going to talk about testing. It's a new market for UI path. If anybody knows anything about testing, it's kind of this mundane, repetitive process ripe for automation geared vice-chairs. Here's the senior vice president of testing products at UI path and Matt Elisa. Who's the product marketing lead at UI path. Gents. Welcome to the cube. Thanks for coming on. Thanks for having a feminist Likert. Explain to us how you guys think about testing both from an internal perspective and how you're going to market. >>Yeah, well, testing has been around for a long time, right? 20 twenty-five years or so when, when I came to UI pass, the first thing I looked at was like, how do our customers test RPA? And it's quite interesting. We did a survey actually with 1500 people and, uh, 27% said that they wouldn't test at all. And I thought that's really interesting. RPA is a business critical software that runs in your production environment and you probably have to test. So we came up with this idea that we create the test suite. We're using, you know, proven technology from UI pass. And, and we built this offering and brought us into market for RPA testing in for application testing. So we do both. And of course we use it internally as well. I mean, that will be, you know, eat your own dog food or drink your own champagne, I guess. So >>I want to think about it. If you, if you automate, if you, if there's an ROI to automate a process, there's gotta be an ROI to verify that it's going to work before it goes into production too. And so it's amazing that a lot of companies are not doing this and they're doing it manually, um, today. >>So, so, but so, but parts of testing have been automated, haven't they with regression testing. So can, can you guys take us through kind of the before and after and how you're approaching it versus the traditional? >>Yeah, absolutely. I mean, like I said, testing is not new, right? Um, but still when you look at the customers, they're not out to meeting more than I would say, 30, 40% of the manual tests. So still a lot of Stan manually, which I think, and we talked about this right manual testing is the, the original RPA. It's a tedious, repetitive tasks that you should not do manually. Right? And so what we are trying to bring in is now we're talking about this new role it's called the digital tester. The digital tester is an empowered. We could call a manual tester, who's able to build automation and we believe that this will truly increase the automation, even in the existing testing market. And it's going to be, I don't want to use the word game changer, but it's going change. Uh, the way testing is done. Yeah. >>And we're, we're applying, um, all the capabilities of UI path and delivering those to testers, just like we would for HR team or a, or a, a finance and accounting team. But testing even has they understand this more, they've been doing this for 20 years. They understand automation and we're going to give them things like process mining so they can figure out what tests they need to run from production data. We're going to give them task mining so they can make more human-like tests test. Exactly. Like I used to be a tester and I ran a test team. And what I used to do is I have to go out to a warehouse and I'd have to go watch people as they entered orders, to make sure I was testing it the right way. So they would like click. We usually thought they were clicking things, but they were using hotkeys. That's just an example of what they were doing. But now we can do task task mining to get that remotely, pull that data in and do tests and make more realistic tests. >>So much of the there's so much potential there. I think you were saying that only 27% are actually doing testing. So there's so much opportunity. I'm curious, where are your conversations within the customer organization? We know that automation is a board level investor topic. Where are you? Where are those discussions with the testing folks, the RPA folks, helping them come together? >>Well, that's interesting. The question, uh, we typically on the IPS, have we talked to the cos, right? The people that are professionally developing those RPAs, but very easily, we get introduced to the test side of the house. And then usually there's a joint meeting where the test people are there, the RPA people are there. And that's why we are talking about this is going to convert somehow, right? The are in different departments today. But if you think about it, five years down the road, maybe 10 years, they might be at an automation discipline for the entire enterprise. So if that answered your question about, >>Yeah. >>Going to require a cultural shift. Yeah. And we have a customer coming presenting this afternoon. and they're gonna be talking about how they, both of the teams are using a test teams and the RPA teams. And they built a reusable component library that, so when they built RPA team built their automations, they put them in a reusable library and the test team is able to recreate their test much faster reusing about 70% of the components. And so when the, when you think of automation, they're thinking about automating the application, not automating a process or a test so that people can use those like Lego blocks and build it if they're doing so, they could even, even it automation, if they wanted to start with an it automation, they could pull those components out and use those. >>I think this is game changing is quality because so often, because in this day and age of agile, it's like move fast and break things. A lot of things break. And when we heard this morning in the keynotes, how you guys are pushing code like a couple of times a week, I mean, it's just a constant. And then you do two big releases. Okay. I get, I get it for the on-prem. But when you're pushing code that fast, you don't have time to test everything. There's a lot of stuff that's unknown. And so to the extent that you can compress all those check boxes, now I can focus on the really important things that sometimes are architectural. How do you expect applying RPA to testing is going to affect the quality? Or maybe you've got some examples. Chipotle, you just mentioned, >>First of all, I mean, when you say we pushing code like bi-weekly or so, right. We're talking about continuous development. That's what it's called. Right? It's agile. You have sprint cycles, you continue to bring new code, new code, new code, and you test all the increments with it. So it's not that you building up a huge backlog for the testing on the IPA side. What I see is that there will be a transformation about the process, how they develop RPA at the moment. It's still done very much, I would say, in a waterfall way, which is agree. A big bang waterfall. Yeah. It will transition. We already have partners that apply agile methodologies to their actually RPA development. And that's going to change that. >>Okay. So it's not so it's quality for those that are in testing obviously, but, but it's, but for the waterfall guys, it's, it's compressing the time to value. Oh yeah. That's going to be the big key. That's really worth. >>I mean, what he said is Chipotle is, was able to reuse 70% of the automation components. Right. That's huge. I mean, you have to think about it. 70% can be reused from testing to RPA and vice versa. That's a huge acceleration. Also on the RPA side, you can automate more processes faster. If you have components that you can trust. >>So you were a tester. Yeah. So you were a cost center. Yes, exactly. >>Unnecessary. What's the budget. >>So could you think RPA and automation can flip that mindset? >>Yeah, totally. And that's one of the things we want to do is we want to turn testing from a cost center to a value center, give testers a new career paths, even because really testers before all you could do is you could be more technical. Maybe you become a developer or you can be a manager, but you couldn't really become like an automation architect or a senior automation person. And now we're giving them a whole different career path to go down. So it's really exciting. >>'cause I know when I came out of college, I had a job offer and I wanted to be a developer, a programmer. We called them back then. And the only job I could get was as a tester. And I was like, oh, this is miserable. I'm not doing this, but there's a, there was, there's a growth path there. They were like, Hey, do this for two or three years, maybe five years. I was like, forget it. I'm going into sales and marketing. But so what's the, what's the growth path today for the tester. And how do you see this changing? >>So you want to go, you want to, I can take that one. No, you take it. So that's a really, yeah. I mean, I did it, so really it's, I mean, we're going to be giving these guys, the testing market has been kind of not innovating for years and years and years. And so we're going to be giving these guys some new tools to make them more powerful, make even the cause. Testing is a kind of a practice that is, you know, like, like you said, you, you didn't like testing. I didn't like testing either. Actually I hate testing. So I automated it. So, um, and so that was the first thing I did. And so I think we're going to give these guys some new tools, some ways to grow their career and some ways to be even better testers, but like, like, like we've talked about process mining, test mining, like maybe they're maybe they're testing the wrong things. Maybe they're not testing, you know, maybe, you know, there, cause there's kind of this test, everything mentality we're we need to test everything and the whole release instead of like focusing in on what changed. And so I think we'll be able to help them really focus on the testing and the quality to make it more efficient as well. >>Go ahead. So do to defend the testers, right? Test is a very skilled people. Yes. They know their business, they know what to test and how to test in a way that nobody else knows that it's something we sometimes underestimate. They are not developers so that they don't write code and they don't build automations typically. But if we can equip them with tools that they can build out information, you have the brain and the muscle together, you know what I mean? You don't have to delegate the automation to some, whatever team that is maybe outsourced even you can do it. In-house and I think to some extent, that was also the story of Portland sourcing again, because they're building their own automation. Yeah. >>And it saved them time because they have deal is handoffs, you know, to an external third party to do the testing for them. And so they pulled it all in made things much more streamlined and efficient. How >>Is that? It seems like a big cultural shift within any type of organization in any industry we're using Chipola as an example here, how does your path help facilitate that cultural shift? Because that's big and we're talking about really reducing, um, or speeding time to value. >>Right. Right. And it is a lot of the agile methodologies like we're starting. So it's kind of like, we're going back in time, you know, and we're teaching these people, you know, the RPA community, all of the things that we learned from software development. Right. And so we're going to be applying that to this. And so all those agile mindset, the th the agile values, you know, those are the things that are going to help them kind of come together. And that's one of the things that Julie talked about is one of the things is they had a, kind of an agile mindset, a can-do attitude that pulled them down. >>And I think one thing that will really helps with changing the culture is empowering the people. If you give them the tools that they can do, they will do, and that will change the culture. I don't think it can come from top down. It needs to come from within and from the people. And that's what we see also with RPA, by the way, is adopted on department level and D build automations. And then at some point it becomes maybe an enterprise wide initiative, right. But somebody in HR had this idea and started >>The other thing too, is Matt, you mentioned this you'd go to a third party. So years ago in the early two thousands, we had a software company. We would use a company called agile on. They were, so I don't know if you ever heard of them. They're basically, we're a job shop. And we would throw our code over the very waterfall, throw the code over the fence. It was a black box and it was very asynchronous. And it would come back, you know, weeks later. And they say, oh, I fixed this, fixed this, but we didn't have the analytics we didn't have. There was no transparency had we had that. We would have maybe come up with new ideas or have way to improve it because we knew the product way better. And so if you can bring that, in-house now you've got much better visibility. So what, what analytics are our analytics a piece of this? And is that something? Yeah. >>Yeah. So, I mean, they'll give you an example, SAP systems, right? When you have SAP systems, customers apply transports like five or 10 a day. Every transport can change the system in a way that you might break the automation. We have the possibility to actually not only understand what's going on in this system with process mining, but we also have the possibility to do change, impact, money, and change impact. Mining tells me with every process, every transport I apply, what has changed, and we can pinpoint the test cases that you need to run. So instead of running a thousand test cases, every time we pinpoint 50 of them and you know exactly what has changed. Yeah. >>That's right. Because a lot of times you don't know what you don't know. And you're saying the machine is basically saying focus on these areas that are going to give you the biggest, that's kind of Amdahl's law. Isn't it focus on the areas that going to get the most return. Yeah. So this is a new business for UI path. You guys are targeting this as a market segment. Can you tell us more about that? >>We joined about two years ago. It takes some time to build something, right. There was a lot of proven technology there. And then we lounged, uh, I think it wasn't July last year, which was more like a private lounge. We, we didn't make much noise around it and it's gaining a lot of traction. So it's several hundred customers have already jumped on that test bandwagon, if you can call it this way. And yeah, this, this year we are pushing full speed into the testing market as well, because we see the benefits that customers get when they use both like the story from Chipotle. It has other customers like Cisco and, and more, when you hear the stories, what they were able to achieve. I mean, that's a no-brainer I think for any customer who wants to improve the automation. Yeah. >>Well, and also we're taking production grade automation and giving it to the testers and we're giving them this advanced AI so they can automate things. They weren't able to automate before, like Citrix virtual virtualized machines, point of sale systems, like 12 layer, any other business would have, they can automate all those things now that they couldn't do before, as well as everything else. And then they can also the testing tools, they talked about fragmentation this morning. That's another problem is there's a tool for mobile. There's a tool for this. There's a tool for API APIs and you have all these tools. You have to learn all these languages. We're going to give them one that they can learn and use and apply to all their technologies. And it's easy to use and it's easy to use. Yeah. >>That's kind of been the mantra of UiPath for very long time, easy to use making, making RPA simple. We've got 8,000 plus customers. You mentioned a few of them. We're going to have some of them on the program this week. How do you expect good question for you that stat that you mentioned from that survey in the very beginning of our conversation, how do you expect that needle to move in the next year? Because we're seeing so much acceleration because of the pandemic. >>A really good question, because the questions that we had in the beginning after we had the first hundred, right? The values didn't change that much. So we have now 1500 and you would assume that is pretty stable from the data. It didn't change that much. So we're still at 27% that are not testing. And that's what we see as our mission. We want to change that no customer that has more than, I dunno, five processes in production should not like not test that's crazy and we can help. And that's our mission. So, but the data is not changing. That's the interesting part. >>And I know, I know we're out of time, but, but we're how do you price this? Is it a, is it a set? Is it a subscription? Is it a usage based model? How >>It's fully included in the UI pass tool suite. So it means it's on the cloud and on-prem the pricing is the same. We are using this. There it is. Yeah. It's the same components. Like, like we're using studio for automation, we're using orchestrator, but we're using robots. We have cloud test manager on prem test manager. It's just a part of the, >>So it's a value add that you're putting into the platform. Yeah, yeah, exactly. >>Yeah. Th there are components that are priced. Yes. But I mean, it's part of the platform, how, >>But it's a module. So I paid for that module and you turn it on and then they can use it. So it's a subscription. It could be an annual term if I want multi-year term, I can do that. Exactly. Good. Great guys. Thanks so much for coming on the Cuban and good luck with this. Thank you. Great, great innovations. Okay. Keep it right there at Dave Volante for Lisa Martin, we'll be back with our coverage of UI path forward for, from the Bellagio in Las Vegas. Keep it right there.

(bright music) >> Welcome back to HPE Discover 2021. My name is Dave Vellante and you're watching theCUBE's virtual coverage of HPE's big customer event. Of course, the virtual edition and we're going to dig into transformations, the role of technology and the role of senior technology leadership. Look, let's face it, HPE has gone through a pretty dramatic transformation itself in the past few years so it makes a great example in case study and with me is Rashmi Kumar who is the senior vice president and CIO at HPE, Rashmi welcome come on inside theCUBE. >> Hi Dave nice to be here. >> Well it's been almost a year since COVID you know changed the world as we know it. How would you say the role of the CIO specifically in generally IT has changed? I mean you got digital, zero trust has gone from buzzword to mandate, digital, everybody was you know complacent about digital in many ways and now it's really accelerated, remote work, hybrid, how do you see it? >> Absolutely, as I said in the last Discover that COVID has been the biggest reason to accelerate digital transformation in the companies. I see CIO's role has changed tremendously in the last 15 months. It's no more just keep the operations running, that's become a table stake. Our roles have become not only to create digital customer experience, engage with our customers in different ways, but also to transform the company operations from inside out to be able to give that digital experience from beginning to end of the customer engagement going forward. We have also become responsible for switching our strategies around the companies as the COVID hit in different parts of the world at different times and how companies structured their operations to go from one region to another, a global company like HPE had to look into its supply chain differently, had to look into strategies to mitigate the risk that was created because of the supply chain disruptions, as well as you go to taking care of our employees. How do you create this digital collaboration experience where teams can still come together and make the work happen for our end customers? How do we think about future employee engagement when people are not coming into these big buildings and offices and working together, but how do you create the same level of collaboration, coordination, as well as delivery of faster, good and services which is enabled by technology going forward. So CIO and IT's role has gone from giving a different level of customer experience to different level of employee experience, as well as enabling day-to-day operations of the companies. CEOs have realized that digital is the way to go forward, it does not matter what industry you are in and now CIOs have their seat at the table to define what the future of every company now which is a technology company irrespective you are in oil and gas, or mining, or a technical product, or a car or a mobility company, end of the day you have to act and behave like a technology company. >> So I want to ask you about that because you've been a CIO at a leading technology provider now for the last three years and you've had previous roles and were, you know non-technical, technology, you know, selling to IT companies and as you point out those worlds are coming together. Everybody's a technology company today. How do you think that changes the role of the CIO because it would always seem to me that there was a difference between a CIO at a tech company you know what I mean by that and a CIO at sort of every other company is, are those two worlds converging? >> Absolutely and it's interesting you pointed out that I have worked in many different industries from healthcare and pharma, to entertainment, to utilities and now at a technology company. End of the day the issues that IT deals with are pretty similar across the organization. What is different here is now my customers are people like me in other industries and I have little bit of an advantage because just having the experience across various ecosystem even that HPE look I was fortunate at HPE because of Antonio's leadership we had top-down mandate to transform how we did business and I talked about my NextGEN IT program in last year's CUBE interview. But at the same time while we were changing our customer, partner's experience from ordering, to order processing, to supply chain, to finance, we decided this pivot of becoming as a service company. And if you think about that pivot, it's pretty common. If it was a technology company or non-technology company. At HPE we were very used to selling a product and coming back three years later at the time of refresh of infrastructure or hardware. That's no more true for us. Now we are becoming an as a service or a subscription company and IT played a major role to enable that quote-to-cash experience which is very different than the traditional experience, around how we stay connected with our customer, how we proactively understand their behavior. I always talk about this term digital exhaust which results into data, which can result into better insight and you can not only upsell, cross-sell because now you have more data about your product usage, but first and foremost give what your customer wants in a much better way because you can proactively understand their needs and wants because you are providing a digital product versus a physical product. So this is the change that most of the companies are now going through. If you look at Domino's transition, they are pizza sellers but they did better because they had better digital experience. If you look at Chipotle, these are food service companies. Ikea which is a furniture manufacturer, across the board we have helped our customers and industries to understand how to become a more digital provider. And remember when HPE says edge to cloud platform as a service, edge is the product, the customers is what we deal with and how do we get that, help them get that data, understand how the product is behaving and then get the information to cloud for further analysis and understanding from the data that comes out of the products that they sell. >> I think you've been at HPE now I think around three years and I've been watching of course for decades, you know HPE, well HP then HPE is, I feel like it's entering now that sort of third phase of its transformation, your phase one was okay we got to figure out how to deal or operate as separate companies, okay, that took some time and then it was okay, now how do we align our resources? And you know what are the waves that we're going to ride? And how do we take our human capital, our investments and what bets do we place? And you're all in on as a service and now it's like okay, you know how do we deliver on all those promises? So pretty massive transformations. You talked about edge to cloud as a service so you've got this huge pivot in your business. What's the technology strategy to support that transformation? >> Yeah, that's a great question. So as I mentioned first, your second phase which was becoming a stand-alone company was the NextGEN IT program where we brought in S4 and 60 related ecosystem application where even in the traditional business there was a realization that we were 120 billion company, we are a 30 billion company, we need different types of technologies as well as more integrated across our product line, across the globe and we, I'm very happy to report that we are the last leg of NextGEN IT transformation. Where we have brought in new customer experience through low-touch or no-touch order processing, a very strong S4 capabilities where we are now able to run all global orders across all our hardware and services business together and I'm happy to report that we have been able to successfully run through the transformation which a typical company of our size would take five or six years to do in around close to three years. But at the same time while we were building this foundation and the capabilities to be able to do order management supply chain and data and analytics platforms, we also made the pivot to go to as a service. Now for as a service and subscription selling, it needs a very different quote-to-cash experience for our customers. And that's where we had bring in platforms like BRIM to do subscription billing, convergent charging and a whole different way to address. But we were lucky to have this transformation completed on which we could bolt on this new capability and we had the data analytics platform built which now these as a service products can also use to drive better insight into our customer behavior as well as how they're using our product real time for our operations teams. >> Well they say follow the money, in theCUBE we love to say follow the data. I mean data is obviously a crucial component of competitive advantage, business value, so talk a little bit more about the role of data, I'm interested in where IT fits. You know a lot of companies they'll have a chief data officer, or a CIO, sometimes they're separate sometimes they work, you know for each other, or CDO works for CIO, how do you guys approach the whole data conversation? >> Yeah that's a great question and has been top of the mind of a lot of CEOs, CIOs, chief digital officers in many different companies. The way we have set it up here is we do have a chief data officer and we do have a head of technology and platform and data lake within IT. Look the way I see is that I call the term data torture. If they have multiple data lakes, if they have multiple data locations and the data is not coming together at one place at the first time that it comes out to the source system, we end up with data swamps and it's very difficult to drive insights, it's very difficult to have single version of truth. So HPE had two-pronged approach. First one was as part of this NextGEN IT transformation we embarked upon the journey first of all to define our customers and products in a very uniform way across the globe. It's called entity master data and product master data program. These were very, very difficult program. We are now happy to report that we can understand the customer from cold stage to servicing stage beginning to end across all our system. It's been a tough journey but it was effort well spent. At the same time while we were building this master data capability we also invested time in our analytics platform. Because we are generating so much data now globally as one footprint, how do we link our data lake to our SAP and Salesforce and all these systems where our customer data flows through and create analytics and insight from it from our customers or our operations team. At the same time we also created a chief data officer role where the responsibility is really to drive business from understanding what decision making and analytics they need around product, around customer, around their usage around their experience to be able to drive better alignment with our customers and products going forward. So this creates efficiencies in the organization. If you have a leader who is taking care of your platforms and data, building single source of truth and you have a leader who is propagating this mature notion of handling data as enterprise data and driving that focus on understanding the metrics and the insight that the businesses need to drive better customer alignment, that's when we gain those efficiencies and behind the scenes the chief data officer and the data leader within my organization work very, very closely to understand each other needs, sometimes art of the possible, where do we need the data processing? Is it at the edge? Is it in the cloud? What's the best way to drive the technology and the platform forward? And they kind of rely on each other's knowledge and intelligence to give us superior results. And I have done data analytics in many different companies, this model works. Where you have focus on insight and analytics without, because data without insight is of no value. But at the same time you need clean data, you need efficient, fast platforms to process that insight at the functional non-functional requirement that our business partners have. And that's how we have established in here and we have seen many successes recently as of now. >> I want to ask you a kind of a harder, maybe it's not a harder question it's a weird question around single version of the truth. 'Cause it's clearly a challenge for organizations and there's many applications, workloads that require that single version of the truth, the operational systems, the transaction systems, the HR, the Salesforce and clearly you have to have a single version of the truth. I feel like, however we're on the cusp of a new era where business lines see an opportunity for whatever, their own truth to work with a partner to create some kind of new data product. And it's early days in that but I wonder, maybe not the right question for HPE but I wonder if you see it with in your ecosystems where it's yes, single version of truth is sort of one class of data and analytics got to have that nailed down, data quality, everything else. But then there's this sort of artistic version of the data where business people need more freedom, they need more latitude to create. Are you seeing that? Maybe you can help me put that into context. >> That's a great question Dave and I'm glad you asked it so. I think Tom Davenport, who is known in the data space talks about the offensive and the defensive use cases of leveraging data. I think the piece that you talked about where it's clean, it's pristine, it's quality, it's all that, most of those offer the offensive use cases where you are improving companies' operations incrementally because you have very clean data, you have very good understanding of how my territories are doing, how my customers are doing, how my products are doing, how am I meeting my SLAs or how my financials are looking, there's no room for failure in that area. The other area is though which works on the same set of data. It's not a different set of data but the need is more around finding needles in the haystack to come up with new needs, new wants in customers or new business models that we go with. The way we have done it is we do take this data, take out what's not allowed for everybody to be seen and then what we call is a private space but that's this entire data available to our business leader not real time, because the need is not as real time because they are doing more, what we call this predictive analytics to be able to leverage the same data set and run their analytics. And we work very closely with business units, we educate them, we tell them how to leverage this data set and use it and gather their feedback to understand what they need in that space to continue to run with their analytics. I think as we talk about hindsight, insight and foresight, hindsight and insight happens more from this clean data lakes where you have authenticity, you have quality and then most of the foresight happens in a different space where the users have more leverage to use data in many different ways to drive analytics and insights which is not readily available. >> Great thank you for that. That's an interesting discussion. You know digital transformation it's a journey and it's going to take you know many years. I know a lot of ways, not a lot of ways, 2020 was a forced march to digital you know. If you weren't a digital business you were out of business and so you really didn't have much time to plan. So now organizations are stepping back saying, okay, let's really lean into our strategy, the journey and along the way, there's going to be blind spots, there's bumps in the road, when you look out what are the potential disruptions that you see maybe in terms of how companies are currently approaching their digital transformations? >> That's a great question Dave and I'm going to take a little bit more longer-term view on this topic, right? And what's top of my mind recently is the whole topic of ESG, environmental, social and governance. Most of the companies have governance in place right? Because they are either public companies, or they're under some kind of scrutiny from different regulatory bodies or whatnot even if you're a startup you need to do things with our customers and whatnot. It has been there for companies, it continues to be there. We the public companies are very good at making sure that we have the right compliance, right privacy, right governance in place. Now we'll talk about cybersecurity I think that creates a whole new challenge in that governance space, however we have the setup within our companies to be able to handle that challenge. Now, when we go to social, what happened last year was really important. And now as each and every company we need to think about what are we doing from our perspective to play our part in that and not only the bigger companies, leaders at our level I would say that between last March and this year I have hired more than 400 people during pandemic which was all virtual, but me and my team have made sure that we are doing the right thing to drive inclusion and diversity which is also very big objective for HPE and Antonio himself has been very active in various round tables in US at the World Economic Forum level and I think it's really important for companies to create that opportunity, remove that disparity that's there for the underserved communities. If we want to continue to be successful in this world to create innovative product and services we need to sell it to the broader cross section of populations and to be able to do that we need to bring them in our fold and enable them to create that equal consumption capabilities across different sets of people. HPE has taken many initiatives and so are many companies. I feel like the momentum that companies have now created around the topic of equality is very important. I'm also very excited to see that a lot of startups are now coming up to serve that 99% versus just the shiny ones as you know in the Bay Area to create better delivery methods of food or products right? But the third piece which is environmental is extremely important as well. As we have seen recently in many companies and where even the dollar or the economic value is flowing are around the companies which are serious about environmental. HPE recently published it's a Living Progress Report, we have been in the forefront of innovation to reduce carbon emissions, we help our customers through those processes. Again, if we don't, if our planet is on fire none of us will exist right? So we all have to do that every little part to be able to do better. And I'm happy to report I myself as a person solar panels, battery, electric cars, whatever I can do. But I think something more needs to happen right? Where as an individual I need to pitch in but maybe utilities will be so green in the future that I don't need to put panels on my roof which again creates a different kind of race going forward. So when you ask me about disruptions, I personally feel that successful company like ours have to have ESG top of their mind and think of product and services from that perspective, which creates equal opportunity for people, which creates better environment sustainability going forward and you know our customers, our investors are very interested in seeing what we are doing to be able to serve that cause for bigger cross section of companies. And I'm most of the time very happy to share with my CIO cohort around how our HPEFS capabilities creates or feeds into the circular economy, how much e-waste we have recycled or kept it off of landfills, our green lake capabilities, how it reduces the e-waste going forward, as well as our sustainability initiatives which can help other CIOs to be more carbon neutral going forward as well. >> You know that's a great answer Rashmi thank you for that 'cause I got to tell you I hear a lot of mumbo jumbo about ESG but that was a very substantive, thoughtful response that I think tech companies in particular are, have to lead and are leading in this area. So I really appreciate that sentiment. I want to end with a very important topic which is cyber it's, obviously you know escalated in the news the last several months, it's always in the news but, you know 10 or 15 years ago there was this mentality of failure equals fire. And now we realize, hey they're going to get in, it's how you handle it. Cyber has become a board-level topic. You know years ago there was a lot of discussion, oh you can't have the SecOps team working for the CIO because that's like the fox watching the hen house that's changed. It's been a real awakening, a kind of a rude awakening so the world is now more virtual, you've got a secure physical assets. I mean any knucklehead can now become a ransomware attacker, they can buy ransomware as a service in the dark web so that's something we've never seen before. You're seeing supply chains get hacked and self-forming malware I mean it's a really scary time. So you've got these intellectual assets it's a top priority for organizations. Are you seeing a convergence of the CISO role, the CIO role, the line of business roles relative to sort of prior years in terms of driving security throughout organizations? >> Yeah this is a great question and this was a big discussion at my public board meeting a couple of days ago. It's, as I talk about many topics, if you think digital, if you think data, if you think ESG, it's no more one organization's business, it's now everybody's responsibility. I saw a Wall Street Journal article a couple of days ago where somebody has compared cyber to 9/11 type scenario that if it happens for a company that's the level of impact you feel on your operations. So, you know all models are going to change where CISO reports to CIO, at HPE we are also into product security and that's why CISO is a peer of mine who I work with very closely, who also worked with product teams where we are saving our customers from lot of pain in this space going forward and HPE itself is investing enormous amount of efforts and time in coming out of products which are secure and are not vulnerable to these types of attacks. The way I see it is CISO role has become extremely critical in every company and a big part of that role is to make people understand that cybersecurity is also everybody's responsibility. That's why an IT we propagate DevSecOps, as we talk about it we are very, very careful about picking the right products and services. This is one area where companies cannot shy away from investing. You have to continuously looking at cybersecurity architecture, you have to continuously look at and understand where the gaps are and how do we switch our product or service that we use from the providers to make sure our companies stay secure. The training not only for individual employees around anti-phishing or what does cybersecurity mean, but also to the executive committee and to the board around what cyber security means, what zero trust means, but at the same time doing drive-ins. We did it for business continuity and disaster recovery before, now it is time we do it for a ransomware attack and stay prepared. As you mentioned and we all say in tech community, it's always if not when. No company can take them their chest and say, "oh we are fully secure," because something can happen going forward. But what is the readiness for something that can happen? It has to be handled at the same risk level as a pandemic, or a earthquake, or a natural disaster and assume that it's going to happen and how as a company we will behave when something like this happens. So I'm huge believer in the framework of protect, detect, govern and respond as these things happen. So we need to have exercises within the company to ensure that everybody's aware of the part that they play day to day but at the same time when some event happen and making sure we do very periodic reviews of IT and cyber practices across the company, there is no more differentiation between IT and OT. That was 10 years ago. I remember working with different industries where OT was totally out of reach of IT and guess what happened? WannaCry and Petya and XP machines were still running your supply chains and they were not protected. So, if it's a technology it needs to be protected. That's the mindset people need to go with. Invest in education, training, awareness of your employees, your management committee, your board and do frequent exercises to understand how to respond when something like this happen. See it's a big responsibility to protect our customer data, our customer's operations and we all need to be responsible and accountable to be able to provide all our product and services to our customers when something unforeseen like this happens. >> Rashmi you're very generous with your time thank you so much for coming back in theCUBE it was great to have you again. >> Thank you Dave, it was really nice chatting with you. >> And thanks for being with us for our ongoing coverage of HPE Discover '21. This is Dave Vellante you're watching the virtual CUBE, the leader in digital tech coverage we'll be right back. (bright music)

>>Welcome back to HP discover 2021 My name is Dave Volonte and you're watching the cubes, virtual coverage of H. P. S. Big customer event. Of course, the virtual edition, we're gonna dig into transformations the role of technology in the role of senior technology leadership. Look, let's face it, H P. E. Has gone through a pretty dramatic transformation itself in the past few years. So it makes a great example in case study and with me is rashmi kumari who is the senior vice president and C. I. O. At HP rashmi welcome come on inside the cube. >>Dave Nice to be here. >>Well, it's been almost a year since Covid changed the world as we know it. How would you say the role of the CEO specifically and generally it has changed. I mean you got digital Zero Trust has gone from buzzword to >>mandate >>digital. Everybody was complacent about digital in many ways and now it's really accelerated remote work hybrid. How do you see it? >>Absolutely. As I said in the last discover that Covid has been the biggest reason to accelerate digital transformation in the company's I. C. C. I O. S role has changed tremendously in the last 15 months. It's no more just keep the operations running that's become a table stick. Our roles have become not only to create digital customer experience engaged with our customers in different ways, but also to transform the company operations from inside out to be able to give that digital experience from beginning to end off the customer engagement going forward. We have also become responsible for switching our strategies around the companies as the Covid. Covid hit in different parts of the world at different times and how companies structured their operations to go from one region to another. A global company like H. B had to look into its supply chain differently. Had to look into strategies to mitigate the risk that was created because of the supply chain disruptions as well as you go to taking care of our employees. How do you create this digital collaboration experience where teams can still come together and make the work happen for our end customers? How do we think about future employee engagement when people are not coming into these big buildings and offices and working together, But how to create the same level of collaboration coordination as well as delivery or faster uh goods and services which is enabled by technology going forward. So see I. O. And I. T. S. Role has gone from giving a different level of customer experience to a different level of employee experience as well as enabling day to day operations of the company's. Ceos have realized that digital is the way to go forward. It does not matter what industry you are in and now see a as have their seat at the table to define what the future of every company now, which is a technology company respective you are in oil and gas or mining or a technical product or a card or a mobility company. End of the day you have to act and behave like a technology company. >>So I want to ask you about that because you've you've been a Ceo and uh you know, leading technology provider now for the last three years and you've had previous roles and where you know non technical technology, you know, selling to I. T. Companies and as you point out those worlds are coming together, everybody is a technology company today. How do you think that changes the role of the C. I. O. Because it would always seem to me that there was a difference between A C. I. O. And a tech company. You know what I mean by that? And the C. I. O. It's sort of every other company is those two worlds converging. >>Absolutely. And it's interesting you pointed out that I have worked in many different industries from healthcare and pharma to entertainment to utilities. Um And now at a technology company end of the day um The issues that I. T. Deals with are pretty similar across the organization. What is different here is now my customers are people like me in other industries and I have a little bit of an advantage because just having the experience across various ecosystem. Even at H. B. Look I was fortunate um at H. B. Because of Antonio's leadership, we have topped out mandate to transform how we did business. And I talked about my next gen IT program in last year's cube interview. But at the same time while we were changing our customer partners experience from ordering to order processing to supply chain to finance. Uh We decided this pivot of becoming as a service company. And if you think about that pivot it's pretty common if it was a technology company or non technology company at HP. We were very used to selling a product and coming back three years later at the time of refresh of infrastructure or hardware. That's no more true for us now we are becoming as a service or a subscription company and I. T. Played a major role to enable that quote to cash experience. Which is very different than the traditional experience around how we stay connected with our customer, how we proactively understand their behavior. I always talk about this term. Um Digital exhaust which results into data which can result into better insight and you can not only Upsell cross l because now you have more data about your product usage, but first and the foremost give what your customer wants in a much better way because you can proactively understand their needs and wants because you are providing a digital product versus a physical product. So this is the change that most of the companies are now going through. If you look at Domino's transition, there are pills a sellers but they did better because they had better digital experience. If you look at Chipotle, these are food service companies I. K which is a furniture manufacturer across the board. We have helped our customers and industries to understand how to become a more digital provider. And and remember when uh hp says edge to cloud platform as a service edges the product, the customers who we deal with and how do we get that? Help them get their data to understand how the product is behaving and then get the information to cloud for further analysis. Um and understanding from the data that comes out of the products that gets up, >>I think you've been HP now think around three years and I've been watching of course for decades. Hp. Hp then HP is I feel like it's entering now the sort of third phase of its transformation, your phase one was okay, we gotta figure out how to deal or or operate as a separate companies. Okay. That took some time and then it was okay. Now how do we align our resources and you know, what are the waves that we're gonna ride? And how do we how do we take our human capital, our investments and what bets do we place and and all in on as a service. And now it's like okay how do we deliver on all those promises? So pretty massive transformations. You talked about edge to cloud as a service so you've got this huge pivot in your in your business. What's the technology strategy to support that transformation? >>Yeah that's a that's a great question. So as I mentioned first your second phase which was becoming a stand alone company was the next N. I. T. Program very broad and um S. Four and 60 related ecosystem application. We're even in the traditional business there was a realization that we were 100 20 billion company. We are 30 billion company. We need different types of technologies as well as more integrated across our product line across the globe. And um we I'm very happy to report that we are the last leg of next in I. T. Transformation where we have brought in new customer experience through low touch or not touch order pressing. A very strong as four capabilities. Where we are now able to run all global orders across all our hardware and services business together. And I'm happy to report that we have been able to successfully run through the transformation which a typical company of our size would take five or six years to do in around close to three years. But at the same time while we were building this foundation and the capabilities to be able to do other management, supply chain and data and analytics platforms. We also made the pivot to go to as a service now for as a service and subscription selling. It needs a very different quote to Kazakh cash experience for our customers and that's where we had to bring in um platforms like brim to do um subscription building, convergent charging and a whole different way to address. But we were lucky to have this transformation completed on which we could bolt on this new capability and we had the data and another X platform built which now these as a service products can also use to drive better insight into our customer behavior um as well as how they're using our product a real time for our operations teams. >>Well they say follow the money in the cube. We love to say follow the day to day is obviously a crucial component of competitive advantage business value. So you talk a little bit more about the role of data. I'm interested I'm interested in where I. T. Fits uh you know a lot of companies that have a Chief data officer or Ceo sometimes they're separate. Sometimes they they work you know for each other or Cdo works for C. I. O. How do you guys approach the whole data conversation? >>Yeah that's a that's a great question and has been top of the mind of a lot of C E O C I O S. Chief digital officers in many different companies. The way we have set it up here is do we do have a chief data officer and we do have a head of uh technology and platform and data within I. T. Look. The way I see is that I call the term data torture if we have multiple data lakes, if we have multiple data locations and the data is not coming together at one place at the first time that it comes out of the source system, we end up with data swamps and it's very difficult to drive insights. It's very difficult to have a single version of truth. So HP had two pronged approach. First one was as part of this next gen i. T. Transformation we embarked upon the journey first of all to define our customers and products in a very uniform way across the globe. It's called entity Master Data and Product Master Data Program. These were very very difficult program. We are now happy to report that we can understand the customer from code stage to servicing stage beginning to end across all our system. It's been a tough journey but it was a effort well spent at the same time while we were building this message capability, we also invest the time in our analytics platform because we are generating so much data now globally as one footprint. How do we link our data link to R. S. A. P. And Salesforce and all these systems where our customer data flows through and create analytics and insight from it from our customers or our operations team. At the same time, we also created a chief data officer role where the responsibility is really to drive business from understanding what decision making an analytics they need around product, around customer, around their usage, around their experience to be able to drive better alignment with our customers and products going forward. So this creates efficiencies in the organization. If you have a leader who is taking care of your platforms and data building single source of truth and you have a leader who is propagating this mature notion of handling data as enterprise data and driving that focus on understanding the metrics and the insight that the businesses need to drive better customer alignment. That's when we gain those efficiencies and behind the scenes, the chief data officer and the data leader within my organization worked very, very closely to understand each other needs sometimes out of the possible where do we need the data processing? Is it at the edge? Is it in the cloud? What's the best way to drive the technology and the platform forward? And they kind of rely on each other's knowledge and intelligence to give us give us superior results. And I have done data analytics in many different companies. This model works where you have focused on insight and analytics without because data without insight is of no value, but at the same time you need clean data. You need efficient, fast platforms to process that insight at the functional nonfunctional requirements that are business partners have and that's how we have established in here and we have seen many successes recently. As of now, >>I want to ask you a kind of a harder maybe it's not harder question. It's a weird question around single version of the truth because it's clearly a challenge for organizations and there's many applications workloads that require that single version of the truth. The operational systems, the transaction systems, the HR the salesforce. Clearly you have to have a single version of the truth. I feel like however we're on the cusp of a new era where business lines see an opportunity for whatever their own truth to work with a partner to create some kind of new data product. And it's early days in that. But I want to and maybe not the right question for HP. But I wonder if you see it with in your ecosystems where where it's it's yes, single version of truth is sort of one class of data and analytics gotta have that nail down data quality, everything else. But then there's this sort of artistic version of the data where business people need more freedom. They need more latitude to create. Are you seeing that? And maybe you can help me put that into context. >>Uh, that's a great question. David. I'm glad you asked it. So I think tom Davenport who is known in the data space talks about the offensive and the defensive use cases of leveraging data. I think the piece that you talked about where it's clean, it's pristine, it's quality. It's all that most of those offer the offensive use cases where you are improving company's operations incrementally because you have very clean that I have very good understanding of how my territories are doing, how my customers are doing how my products are doing. How am I meeting my sls or how my financials are looking? There's no room for failure in that area. The other area is though, which works on the same set of data. It's not a different set of data, but the need is more around finding needles in the haystack to come up with new needs, new ones and customers or new business models that we go with. The way we have done it is we do take this data take out what's not allowed for everybody to be seen and then what we call is a private space. But that's this entire data available to our business leader, not real time because the need is not as real time because they're doing more what we call this predictive analytics to be able to leverage the same data set and run their analytics. And we work very closely with business in its we educate them. We tell them how to leverage this data set and use it and gather their feedback to understand what they need in that space to continue to run with their with their analytics. I think as we talk about hindsight insight and foresight hindsight and insight happens more from this clean data lakes where you have authenticity, you have quality and then most of the foresight happens in a different space where the users have more leverage to use data in many different ways to drive analytics and insights which is not readily available. >>Thank you for that. That's interesting discussion. You know digital transformation. It's a journey and it's going to take many years. A lot of ways, not a lot of ways 2020 was a forced March to digital. If you weren't a digital business, you were out of business and you really didn't have much time to plan. So now organizations are stepping back saying, okay let's really lean into our strategy the journey and along the way there's gonna be blind spots, there's bumps in the road when you look out what are the potential disruptions that you see maybe in terms of how companies are currently approaching their digital transformations? That's a great question. >>Dave and I'm going to take a little bit more longer term view on this topic. Right in what's top of my mind um recently is the whole topic of E. S. G. Environmental, social and governance. Most of the companies have governance in place, right? Because they are either public companies or they're under some kind of uh scrutiny from different regulatory bodies or what not. Even if you're a startup, you need to do things with our customers and what not. It has been there for companies. It continues to be there. We the public companies are very good at making sure that we have the right compliance, right privacy, right governance in in in place. Now we'll talk about cyber security. I think that creates a whole new challenge in that governance space. However, we have the set up within our companies to be able to handle that challenge. Now, when we go to social, what happened last year was really important. And now as each and every company, we need to think about what are we doing from our perspective to play our part in that. And not only the bigger companies leaders at our level, I would say that Between last March and this year, I have hired more than 400 people during pandemic, which was all virtual, but me and my team have made sure that we are doing the right thing to drive inclusion and diversity, which is also very big objective for h P E. And Antonio himself has been very active in various round tables in us at the world Economic forum level and I think it's really important for companies to create that opportunity, remove that disparity that's there for the underserved communities. If we want to continue to be successful in this world too, create innovative products and services, we need to sell it to the broader cross section of populations and to be able to do that, we need to bring them in our fold and enable them to create that um, equal consumption capabilities across different sets of people. Hp has taken many initiatives and so are many companies. I feel like uh, The momentum that companies have now created around the topic of equality is very important. I'm also very excited to see that a lot of startups are now coming up to serve that 99% versus just the shiny ones, as you know, in the bay area to create better delivery methods of food or products. Right. The third piece, which is environmental, is extremely important as well as we have seen recently in many companies and where even the dollar or the economic value is flowing are around the companies which are serious about environmental HP recently published its living Progress report. We have been in the forefront of innovation to reduce carbon emissions, we help our customers, um, through those processes. Again, if we do, if our planet is on fire, none of us will exist, right. So we all have to do that every little part to be able to do better. And I'm happy to report, I myself as a person, solar panels, battery electric cars, whatever I can do, but I think something more needs to happen right where as an individual I need to pitch in, but maybe utilities will be so green in the future that I don't need to put panels on my roof, which again creates a different kind of uh waste going forward. So when you ask me about disruptions, I personally feel that successful company like ours have to have E. S. G. Top of their mind and think of products and services from that perspective, which creates equal opportunity for people, which creates better environment sustainability going forward. And, you know, our customers are investors are very interested in seeing what we are doing to be able to serve that cause uh for for bigger cross section of companies, and I'm most of the time very happy to share with my C I. O cohort around how are H. P E F s capabilities creates or feeds into the circular economy, how much e waste we have recycled or kept it off of landfills are green capabilities, How it reduces the evils going forward as well as our sustainability initiatives, which can help other, see IOS to be more um carbon neutral going forward as well. >>You know, that's a great answer, rashmi, thank you for that because I gotta tell you hear a lot of mumbo jumbo about E S G. But that was a very substantive, thoughtful response that I think, I think tech companies in particular are have to lead in our leading in this area. So I really appreciate that sentiment. I want to end with a very important topic which is cyber. It's obviously, you know, escalated in, in the news the last several months. It's always in the news, but You know, 10 or 15 years ago there was this mentality of failure equals fire. Now we realize, hey, they're gonna get in, it's how you handle it. Cyber has become a board level topic, you know? Years ago there was a lot of discussion, oh, you can't have the sec ops team working for the C. I. O. Because that's like the Fox watching the Henhouse, that's changed. Uh it's been a real awakening, a kind of a rude awakening. So the world is now more virtual, you've gotta secure physical uh assets. I mean, any knucklehead can now become a ransomware attack, er they can, they can, they can buy ransomware as a services in the dark, dark web. So that's something we've never seen before. You're seeing supply chains get hacked and self forming malware. I mean, it's a really scary time. So you've got these intellectual assets, it's a top priority for organizations. Are you seeing a convergence of the sea? So roll the C. I. O. Roll the line of business roles relative to sort of prior years in terms of driving security throughout organizations. >>This is a great question. And this was a big discussion at my public board meeting a couple of days ago. It's as as I talk about many topics, if you think digital, if you think data, if you think is you, it's no more one organizations, business, it's now everybody's responsibility. I saw a Wall Street Journal article a couple of days ago where Somebody has compared cyber to 9-11-type scenario that if it happens for a company, that's the level of impact you feel on your on your operations. So, you know, all models are going to change where C so reports to see IO at H P E. We are also into products or security and that's why I see. So is a peer of mine who I worked with very closely who also worked with product teams where we are saving our customers from a lot of pain in this space going forward. And H. B. E. Itself is investing enormous amount of efforts in time in coming out of products which are which are secured and are not vulnerable to these types of attacks. The way I see it is see So role has become extremely critical in every company and the big part of that role is to make people understand that cybersecurity is also everybody's responsibility. That's why in I. T. V. Propagate def sec ups. Um As we talk about it, we are very very careful about picking the right products and services. This is one area where companies cannot shy away from investing. You have to continuously looking at cyber security architecture, you have to continuously look at and understand where the gaps are and how do we switch our product or service that we use from the providers to make sure our companies stay secure The training, not only for individual employees around anti phishing or what does cybersecurity mean, but also to the executive committee and to the board around what cybersecurity means, what zero trust means, but at the same time doing drive ins, we did it for business continuity and disaster recovery. Before now at this time we do it for a ransomware attack and stay prepared as you mentioned. And we all say in tech community, it's always if not when no company can them their chest and say, oh, we are fully secured because something can happen going forward. But what is the readiness for something that can happen? It has to be handled at the same risk level as a pandemic or earthquake or a natural disaster. And assume that it's going to happen and how as a company we will behave when when something like this happen. So I'm here's believer in the framework of uh protect, detect, govern and respond um as these things happen. So we need to have exercises within the company to ensure that everybody is aware of the part that they play day today but at the same time when some event happen and making sure we do very periodic reviews of I. T. And cyber practices across the company. There is no more differentiation between I. T. And O. T. That was 10 years ago. I remember working with different industries where OT was totally out of reach of I. T. And guess what happened? Wanna cry and Petra and XP machines were still running your supply chains and they were not protected. So if it's a technology it needs to be protected. That's the mindset. People need to go with invest in education, training, um awareness of your employees, your management committee, your board and do frequent exercises to understand how to respond when something like this happen. See it's a big responsibility to protect our customer data, our customers operations and we all need to be responsible and accountable to be able to provide all our products and services to our customers when something unforeseen like this happens, >>Russian, very generous with your time. Thank you so much for coming back in the CUBA is great to have you again. >>Thank you. Dave was really nice chatting with you. Thanks >>for being with us for our ongoing coverage of HP discover 21 This is Dave Volonte, you're watching the virtual cube, the leader in digital tech coverage. Be right back. >>Mm hmm, mm.

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back, you're ready. Jeff Frick here with theCUBE. We are still getting through the year of 2020. It's still the year of COVID and there's no end in sight I think until we get to a vaccine. That said, we're really excited to have one of our favorite guests. We haven't had him on for a while. I haven't talked to him for a long time. He used to I think have the record for the most CUBE appearances of probably any CUBE alumni. We're excited to have him joining us from his house in Palo Alto. Bill Schmarzo, you know him as the Dean of Big Data, he's got more titles. He's the chief innovation officer at Hitachi Vantara. He's also, we used to call him the Dean of Big Data, kind of for fun. Well, Bill goes out and writes a bunch of books. And now he teaches at the University of San Francisco, School of Management as an executive fellow. He's an honorary professor at NUI Galway. I think he's just, he likes to go that side of the pond and a many time author now, go check him out. His author profile on Amazon, the "Big Data MBA," "The Art of Thinking Like A Data Scientist" and another Big Data, kind of a workbook. Bill, great to see you. >> Thanks, Jeff, you know, I miss my time on theCUBE. These conversations have always been great. We've always kind of poked around the edges of things. A lot of our conversations have always been I thought, very leading edge and the title Dean of Big Data is courtesy of theCUBE. You guys were the first ones to give me that name out of one of the very first Strata Conferences where you dubbed me the Dean of Big Data, because I taught a class there called the Big Data MBA and look what's happened since then. >> I love it. >> It's all on you guys. >> I love it, and we've outlasted Strata, Strata doesn't exist as a conference anymore. So, you know, part of that I think is because Big Data is now everywhere, right? It's not the standalone thing. But there's a topic, and I'm holding in my hands a paper that you worked on with a colleague, Dr. Sidaoui, talking about what is the value of data? What is the economic value of data? And this is a topic that's been thrown around quite a bit. I think you list a total of 28 reference sources in this document. So it's a well researched piece of material, but it's a really challenging problem. So before we kind of get into the details, you know, from your position, having done this for a long time, and I don't know what you're doing today, you used to travel every single week to go out and visit customers and actually do implementations and really help people think these through. When you think about the value, the economic value, how did you start to kind of frame that to make sense and make it kind of a manageable problem to attack? >> So, Jeff, the research project was eyeopening for me. And one of the advantages of being a professor is, you have access to all these very smart, very motivated, very free research sources. And one of the problems that I've wrestled with as long as I've been in this industry is, how do you figure out what is data worth? And so what I did is I took these research students and I stick them on this problem. I said, "I want you to do some research. Let me understand what is the value of data?" I've seen all these different papers and analysts and consulting firms talk about it, but nobody's really got this thing clicked. And so we launched this research project at USF, professor Mouwafac Sidaoui and I together, and we were bumping along the same old path that everyone else got, which was inched on, how do we get data on our balance sheet? That was always the motivation, because as a company we're worth so much more because our data is so valuable, and how do I get it on the balance sheet? So we're headed down that path and trying to figure out how do you get it on the balance sheet? And then one of my research students, she comes up to me and she says, "Professor Schmarzo," she goes, "Data is kind of an unusual asset." I said, "Well, what do you mean?" She goes, "Well, you think about data as an asset. It never depletes, it never wears out. And the same dataset can be used across an unlimited number of use cases at a marginal cost equal to zero." And when she said that, it's like, "Holy crap." The light bulb went off. It's like, "Wait a second. I've been thinking about this entirely wrong for the last 30 some years of my life in this space. I've had the wrong frame. I keep thinking about this as an act, as an accounting conversation. An accounting determines valuation based on what somebody is willing to pay for." So if you go back to Adam Smith, 1776, "Wealth of Nations," he talks about valuation techniques. And one of the valuation techniques he talks about is valuation and exchange. That is the value of an asset is what someone's willing to pay you for it. So the value of this bottle of water is what someone's willing to pay you for it. So everybody fixates on this asset, valuation in exchange methodology. That's how you put it on balance sheet. That's how you run depreciation schedules, that dictates everything. But Adam Smith also talked about in that book, another valuation methodology, which is valuation in use, which is an economics conversation, not an accounting conversation. And when I realized that my frame was wrong, yeah, I had the right book. I had Adam Smith, I had "Wealth of Nations." I had all that good stuff, but I hadn't read the whole book. I had missed this whole concept about the economic value, where value is determined by not how much someone's willing to pay you for it, but the value you can drive by using it. So, Jeff, when that person made that comment, the entire research project, and I got to tell you, my entire life did a total 180, right? Just total of 180 degree change of how I was thinking about data as an asset. >> Right, well, Bill, it's funny though, that's kind of captured, I always think of kind of finance versus accounting, right? And then you're right on accounting. And we learn a lot of things in accounting. Basically we learn more that we don't know, but it's really hard to put it in an accounting framework, because as you said, it's not like a regular asset. You can use it a lot of times, you can use it across lots of use cases, it doesn't degradate over time. In fact, it used to be a liability. 'cause you had to buy all this hardware and software to maintain it. But if you look at the finance side, if you look at the pure play internet companies like Google, like Facebook, like Amazon, and you look at their valuation, right? We used to have this thing, we still have this thing called Goodwill, which was kind of this capture between what the market established the value of the company to be. But wasn't reflected when you summed up all the assets on the balance sheet and you had this leftover thing, you could just plug in goodwill. And I would hypothesize that for these big giant tech companies, the market has baked in the value of the data, has kind of put in that present value on that for a long period of time over multiple projects. And we see it captured probably in goodwill, versus being kind of called out as an individual balance sheet item. >> So I don't think it's, I don't know accounting. I'm not an accountant, thank God, right? And I know that goodwill is one of those things if I remember from my MBA program is something that when you buy a company and you look at the value you paid versus what it was worth, it stuck into this category called goodwill, because no one knew how to figure it out. So the company at book value was a billion dollars, but you paid five billion for it. Well, you're not an idiot, so that four billion extra you paid must be in goodwill and they'd stick it in goodwill. And I think there's actually a way that goodwill gets depreciated as well. So it could be that, but I'm totally away from the accounting framework. I think that's distracting, trying to work within the gap rules is more of an inhibitor. And we talk about the Googles of the world and the Facebooks of the world and the Netflix of the world and the Amazons and companies that are great at monetizing data. Well, they're great at monetizing it because they're not selling it, they're using it. Google is using their data to dominate search, right? Netflix is using it to be the leader in on-demand videos. And it's how they use all the data, how they use the insights about their customers, their products, and their operations to really drive new sources of value. So to me, it's this, when you start thinking about from an economics perspective, for example, why is the same car that I buy and an Uber driver buys, why is that car more valuable to an Uber driver than it is to me? Well, the bottom line is, Uber drivers are going to use that car to generate value, right? That $40,000, that car they bought is worth a lot more, because they're going to use that to generate value. For me it sits in the driveway and the birds poop on it. So, right, so it's this value in use concept. And when organizations can make that, by the way, most organizations really struggle with this. They struggle with this value in use concept. They want to, when you talk to them about data monetization and say, "Well, I'm thinking about the chief data officer, try not to trying to sell data, knocking on doors, shaking their tin cup, saying, 'Buy my data.'" No, no one wants your data. Your data is more valuable for how you use it to drive your operations then it's a sell to somebody else. >> Right, right. Well, on of the other things that's really important from an economics concept is scarcity, right? And a whole lot of economics is driven around scarcity. And how do you price for scarcity so that the market evens out and the price matches up to the supply? What's interesting about the data concept is, there is no scarcity anymore. And you know, you've outlined and everyone has giant numbers going up into the right, in terms of the quantity of the data and how much data there is and is going to be. But what you point out very eloquently in this paper is the scarcity is around the resources to actually do the work on the data to get the value out of the data. And I think there's just this interesting step function between just raw data, which has really no value in and of itself, right? Until you start to apply some concepts to it, you start to analyze it. And most importantly, that you have some context by which you're doing all this analysis to then drive that value. And I thought it was really an interesting part of this paper, which is get beyond the arguing that we're kind of discussing here and get into some specifics where you can measure value around a specific business objective. And not only that, but then now the investment of the resources on top of the data to be able to extract the value to then drive your business process for it. So it's a really different way to think about scarcity, not on the data per se, but on the ability to do something with it. >> You're spot on, Jeff, because organizations don't fail because of a lack of use cases. They fail because they have too many. So how do you prioritize? Now that scarcity is not an issue on the data side, but it is this issue on the people resources side, you don't have unlimited data scientists, right? So how do you prioritize and focus on those opportunities that are most important? I'll tell you, that's not a data science conversation, that's a business conversation, right? And figuring out how you align organizations to identify and focus on those use cases that are most important. Like in the paper we go through several different use cases using Chipotle as an example. The reason why I picked Chipotle is because, well, I like Chipotle. So I could go there and I could write it off as research. But there's a, think about the number of use cases where a company like Chipotle or any other company can leverage your data to drive their key business initiatives and their key operational use cases. It's almost unbounded, which by the way, is a huge challenge. In fact, I think part of the problem we see with a lot of organizations is because they do such a poor job of prioritizing and focusing, they try to solve the entire problem with one big fell swoop, right? It's slightly the old ERP big bang projects. Well, I'm just going to spend $20 million to buy this analytic capability from company X and I'm going to install it and then magic is going to happen. And then magic is going to happen, right? And then magic is going to happen, right? And magic never happens. We get crickets instead, because the biggest challenge isn't around how do I leverage the data, it's about where do I start? What problems do I go after? And how do I make sure the organization is bought in to basically use case by use case, build out your data and analytics architecture and capabilities. >> Yeah, and you start backwards from really specific business objectives in the use cases that you outline here, right? I want to increase my average ticket by X. I want to increase my frequency of visits by X. I want to increase the amount of items per order from X to 1.2 X, or 1.3 X. So from there you get a nice kind of big revenue hit that you can plan around and then work backwards into the amount of effort that it takes and then you can come up, "Is this a good investment or not?" So it's a really different way to get back to the value of the data. And more importantly, the analytics and the work to actually call out the information. >> The technologies, the data and analytic technologies available to us. The very composable nature of these allow us to take this use case by use case approach. I can build out my data lake one use case at a time. I don't need to stuff 25 data sources into my data lake and hope there's someone more valuable. I can use the first use case to say, "Oh, I need these three data sources to solve that use case. I'm going to put those three data sources in the data lake. I'm going to go through the entire curation process of making sure the data has been transformed and cleansed and aligned and enriched and met of, all the other governance, all that kind of stuff this goes on. But I'm going to do that use case by use case, 'cause a use case can tell me which data sources are most important for that given situation. And I can build up my data lake and I can build up my analytics then one use case at a time. And there is a huge impact then, huge impact when I build out use case by use case. That does not happen. Let me throw something that's not really covered in the paper, but it is very much covered in my new book that I'm working on, which is, in knowledge-based industries, the economies of learning are more powerful than the economies of scale. Now think about that for a second. >> Say that again, say that again. >> Yeah, the economies of learning are more powerful than the economies of scale. And what that means is what I learned on the first use case that I build out, I can apply that learning to the second use case, to the third use case, to the fourth use case. So when I put my data into my data lake for my first use case, and the paper covers this, well, once it's in my data lake, the cost of reusing that data in a second, third and fourth use cases is basically, you know marginal cost is zero. So I get this ability to learn about what data sets are most important and to reapply that across the organization. So this learning concept, I learn use case by use case, I don't have to do a big economies of scale approach and start with 25 datasets of which only three or four might be useful. But I'm incurring the overhead for all those other non-important data sets because I didn't take the time to go through and figure out what are my most important use cases and what data do I need to support those use cases. >> I mean, should people even think of the data per se or should they really readjust their thinking around the application of the data? Because the data in and of itself means nothing, right? 55, is that fast or slow? Is that old or young? Well, it depends on a whole lot of things. Am I walking or am I in a brand new Corvette? So it just, it's funny to me that the data in and of itself really doesn't have any value and doesn't really provide any direction into a decision or a higher order, predictive analytics until you start to manipulate the data. So is it even the wrong discussion? Is data the right discussion? Or should we really be talking about the capabilities to do stuff within and really get people focused on that? >> So Jeff, there's so many points to hit on there. So the application of data is what's the value, and the queue of you guys used to be famous for saying, "Separating noise from the signal." >> Signal from the noise. Signal from a noise, right. Well, how do you know in your dataset what's signal and what's noise? Well, the use case will tell you. If you don't know the use case and you have no way of figuring out what's important. One of the things I use, I still rail against, and it happens still. Somebody will walk up my data science team and say, "Here's some data, tell me what's interesting in it." Well, how do you separate signal from noise if I don't know the use case? So I think you're spot on, Jeff. The way to think about this is, don't become data-driven, become value-driven and value is driven from the use case or the application or the use of the data to solve that particular use case. So organizations that get fixated on being data-driven, I hate the term data-driven. It's like as if there's some sort of frigging magic from having data. No, data has no value. It's how you use it to derive customer product and operational insights that drive value,. >> Right, so there's an interesting step function, and we talk about it all the time. You're out in the weeds, working with Chipotle lately, and increase their average ticket by 1.2 X. We talk more here, kind of conceptually. And one of the great kind of conceptual holy grails within a data-driven economy is kind of working up this step function. And you've talked about it here. It's from descriptive, to diagnostic, to predictive. And then the Holy grail prescriptive, we're way ahead of the curve. This comes into tons of stuff around unscheduled maintenance. And you know, there's a lot of specific applications, but do you think we spend too much time kind of shooting for the fourth order of greatness impact, instead of kind of focusing on the small wins? >> Well, you certainly have to build your way there. I don't think you can get to prescriptive without doing predictive, and you can't do predictive without doing descriptive and such. But let me throw a really one at you, Jeff, I think there's even one beyond prescriptive. One we're talking more and more about, autonomous, a ton of analytics, right? And one of the things that paper talked about that didn't click with me at the time was this idea of orphaned analytics. You and I kind of talked about this before the call here. And one thing we noticed in the research was that a lot of these very mature organizations who had advanced from the retrospective analytics of BI to the descriptive, to the predicted, to the prescriptive, they were building one off analytics to solve a problem and getting value from it, but never reusing this analytics over and over again. They were done one off and then they were thrown away and these organizations were so good at data science and analytics, that it was easier for them to just build from scratch than to try to dig around and try to find something that was never actually ever built to be reused. And so I have this whole idea of orphaned analytics, right? It didn't really occur to me. It didn't make any sense into me until I read this quote from Elon Musk, and Elon Musk made this statement. He says, " I believe that when you buy a Tesla, you're buying an asset that appreciates in value, not depreciates through usage." I was thinking, "Wait a second, what does that mean?" He didn't actually say it, "Through usage." He said, "He believes you're buying an asset that appreciates not depreciates in value." And of course the first response I had was, "Oh, it's like a 1964 and a half Mustang. It's rare, so everybody is going to want these things. So buy one, stick it in your garage. And 20 years later, you're bringing it out and it's worth more money." No, no, there's 600,000 of these things roaming around the streets, they're not rare. What he meant is that he is building an autonomous asset. That the more that it's used, the more valuable it's getting, the more reliable, the more efficient, the more predictive, the more safe this asset's getting. So there is this level beyond prescriptive where we can think about, "How do we leverage artificial intelligence, reinforcement, learning, deep learning, to build these assets that the more that they are used, the smarter they get." That's beyond prescriptive. That's an environment where these things are learning. In many cases, they're learning with minimal or no human intervention. That's the real aha moment. That's what I miss with orphaned analytics and why it's important to build analytics that can be reused over and over again. Because every time you use these analytics in a different use case, they get smarter, they get more valuable, they get more predictive. To me that's the aha moment that blew my mind. I realized I had missed that in the paper entirely. And it took me basically two years later to realize, dough, I missed the most important part of the paper. >> Right, well, it's an interesting take really on why the valuation I would argue is reflected in Tesla, which is a function of the data. And there's a phenomenal video if you've never seen it, where they have autonomous vehicle day, it might be a year or so old. And he's got his number one engineer from, I think the Microprocessor Group, The Computer Vision Group, as well as the autonomous driving group. And there's a couple of really great concepts I want to follow up on what you said. One is that they have this thing called The Fleet. To your point, there's hundreds of thousands of these things, if they haven't hit a million, that are calling home reporting home every day as to exactly how everyone took the Northbound 101 on-ramp off of University Avenue. How fast did they go? What line did they take? What G-forces did they take? And every one of those cars feeds into the system, so that when they do the autonomous update, not only are they using all their regular things that they would use to map out that 101 Northbound entry, but they've got all the data from all the cars that have been doing it. And you know, when that other car, the autonomous car couple years ago hit the pedestrian, I think in Phoenix, which is not good, sad, killed a person, dark tough situation. But you know, we are doing an autonomous vehicle show and the guy who made a really interesting point, right? That when something like that happens, typically if I was in a car wreck or you're in a car wreck, hopefully not, I learned the person that we hit learns and maybe a couple of witnesses learn, maybe the inspector. >> But nobody else learns. >> But nobody else learns. But now with the autonomy, every single person can learn from every single experience with every vehicle contributing data within that fleet. To your point, it's just an order of magnitude, different way to think about things. >> Think about a 1% improvement compounded 365 times, equals I think 38 X improvement. The power of 1% improvements over these 600,000 plus cars that are learning. By the way, even when the autonomous FSD, the full self-driving mode module isn't turned on, even when it's not turned on, it runs in shadow mode. So it's learning from the human drivers, the human overlords, it's constantly learning. And by the way, not only they're collecting all this data, I did a little research, I pulled out some of their job search ads and they've built a giant simulator, right? And they're there basically every night, simulating billions and billions of more driven miles because of the simulator. They are building, he's going to have a simulator, not only for driving, but think about all the data he's capturing as these cars are riding down the road. By the way, they don't use Lidar, they use video, right? So he's driving by malls. He knows how many cars are in the mall. He's driving down roads, he knows how old the cars are and which ones should be replaced. I mean, he has this, he's sitting on this incredible wealth of data. If anybody could simulate what's going on in the world and figure out how to get out of this COVID problem, it's probably Elon Musk and the data he's captured, be courtesy of all those cars. >> Yeah, yeah, it's really interesting, and we're seeing it now. There's a new autonomous drone out, the Skydio, and they just announced their commercial product. And again, it completely changes the way you think about how you use that tool, because you've just eliminated the complexity of driving. I don't want to drive that, I want to tell it what to do. And so you're saying, this whole application of air force and companies around things like measuring piles of coal and measuring these huge assets that are volume metric measured, that these things can go and map out and farming, et cetera, et cetera. So the autonomy piece, that's really insightful. I want to shift gears a little bit, Bill, and talk about, you had some theories in here about thinking of data as an asset, data as a currency, data as monetization. I mean, how should people think of it? 'Cause I don't think currency is very good. It's really not kind of an exchange of value that we're doing this kind of classic asset. I think the data as oil is horrible, right? To your point, it doesn't get burned up once and can't be used again. It can be used over and over and over. It's basically like feedstock for all kinds of stuff, but the feedstock never goes away. So again, or is it that even the right way to think about, do we really need to shift our conversation and get past the idea of data and get much more into the idea of information and actionable information and useful information that, oh, by the way, happens to be powered by data under the covers? >> Yeah, good question, Jeff. Data is an asset in the same way that a human is an asset. But just having humans in your company doesn't drive value, it's how you use those humans. And so it's really again the application of the data around the use cases. So I still think data is an asset, but I don't want to, I'm not fixated on, put it on my balance sheet. That nice talk about put it on a balance sheet, I immediately put the blinders on. It inhibits what I can do. I want to think about this as an asset that I can use to drive value, value to my customers. So I'm trying to learn more about my customer's tendencies and propensities and interests and passions, and try to learn the same thing about my car's behaviors and tendencies and my operations have tendencies. And so I do think data is an asset, but it's a latent asset in the sense that it has potential value, but it actually has no value per se, inputting it into a balance sheet. So I think it's an asset. I worry about the accounting concept medially hijacking what we can do with it. To me the value of data becomes and how it interacts with, maybe with other assets. So maybe data itself is not so much an asset as it's fuel for driving the value of assets. So, you know, it fuels my use cases. It fuels my ability to retain and get more out of my customers. It fuels ability to predict what my products are going to break down and even have products who self-monitor, self-diagnosis and self-heal. So, data is an asset, but it's only a latent asset in the sense that it sits there and it doesn't have any value until you actually put something to it and shock it into action. >> So let's shift gears a little bit and start talking about the data and talk about the human factors. 'Cause you said, one of the challenges is people trying to bite off more than they can chew. And we have the role of chief data officer now. And to your point, maybe that mucks things up more than it helps. But in all the customer cases that you've worked on, is there a consistent kind of pattern of behavior, personality, types of projects that enables some people to grab those resources to apply to their data to have successful projects, because to your point there's too much data and there's too many projects and you talk a lot about prioritization. But there's a lot of assumptions in the prioritization model that you can, that you know a whole lot of things, especially if you're comparing project A over in group A with project B, with group B and the two may not really know the economics across that. But from an individual person who sees the potential, what advice do you give them? What kind of characteristics do you see, either in the type of the project, the type of the boss, the type of the individual that really lends itself to a higher probability of a successful outcome? >> So first off you need to find somebody who has a vision for how they want to use the data, and not just collect it. But how they're going to try to change the fortunes of the organization. So it always takes a visionary, may not be the CEO, might be somebody who's a head of marketing or the head of logistics, or it could be a CIO, it could be a chief data officer as well. But you've got to find somebody who says, "We have this latent asset we could be doing more with, and we have a series of organizational problem challenges against which I could apply this asset. And I need to be the matchmaker that brings these together." Now the tool that I think is the most powerful tool in marrying the latent capabilities of data with all the revenue generating opportunities in the application side, because there's a countless number, the most important tool that I found doing that is design thinking. Now, the reason why I think design thinking is so important, because one of the things that design thinking does a great job is it gives everybody a voice in the process of identifying, validating, valuing, and prioritizing use cases you're going to go after. Let me say that again. The challenge organizations have is identifying, validating, valuing, and prioritizing the use cases they want to go after. Design thinking is a marvelous tool for driving organizational alignment around where we're going to start and what's going to be next and why we're going to start there and how we're going to bring everybody together. Big data and data science projects don't die because of technology failure. Most of them die because of passive aggressive behaviors in the organization that you didn't bring everybody into the process. Everybody's voice didn't get a chance to be heard. And that one person who's voice didn't get a chance to get heard, they're going to get you. They may own a certain piece of data. They may own something, but they're just waiting and lay, they're just laying there waiting for their chance to come up and snag it. So what you got to do is you got to proactively bring these people together. We call this, this is part of our value engineering process. We have a value engineering process around envisioning where we bring all these people together. We help them to understand how data in itself is a latent asset, but how it can be used from an economics perspective, drive all those value. We get them all fired up on how these can solve any one of these use cases. But you got to start with one, and you've got to embrace this idea that I can build out my data and analytic capabilities, one use case at a time. And the first use case I go after and solve, makes my second one easier, makes my third one easier, right? It has this ability that when you start going use case by use case two really magical things happen. Number one, your marginal cost flatten. That is because you're building out your data lake one use case at a time, and you're bringing all the important data lake, that data lake one use case at a time. At some point in time, you've got most of the important data you need, and the ability that you don't need to add another data source. You got what you need, so your marginal costs start to flatten. And by the way, if you build your analytics as composable, reusable, continuous learning analytic assets, not as orphaned analytics, pretty soon you have all the analytics you need as well. So your marginal cost flatten, but effect number two is that you've, because you've have the data and the analytics, I can accelerate time to value, and I can de-risked projects as I go use case by use case. And so then the biggest challenge becomes not in the data and the analytics, it's getting the all the business stakeholders to agree on, here's a roadmap we're going to go after. This one's first, and this one is going first because it helps to drive the value of the second and third one. And then this one drives this, and you create a whole roadmap of rippling through of how the data and analytics are driving this value to across all these use cases at a marginal cost approaching zero. >> So should we have chief design thinking officers instead of chief data officers that really actually move the data process along? I mean, I first heard about design thinking years ago, actually interviewing Dan Gordon from Gordon Biersch, and they were, he had just hired a couple of Stanford grads, I think is where they pioneered it, and they were doing some work about introducing, I think it was a a new apple-based alcoholic beverage, apple cider, and they talked a lot about it. And it's pretty interesting, but I mean, are you seeing design thinking proliferate into the organizations that you work with? Either formally as design thinking or as some derivation of it that pulls some of those attributes that you highlighted that are so key to success? >> So I think we're seeing the birth of this new role that's marrying capabilities of design thinking with the capabilities of data and analytics. And they're calling this dude or dudette the chief innovation officer. Surprise. >> Title for someone we know. >> And I got to tell a little story. So I have a very experienced design thinker on my team. All of our data science projects have a design thinker on them. Every one of our data science projects has a design thinker, because the nature of how you build and successfully execute a data science project, models almost exactly how design thinking works. I've written several papers on it, and it's a marvelous way. Design thinking and data science are different sides of the same coin. But my respect for data science or for design thinking took a major shot in the arm, major boost when my design thinking person on my team, whose name is John Morley introduced me to a senior data scientist at Google. And I was bottom coffee. I said, "No," this is back in, before I even joined Hitachi Vantara, and I said, "So tell me the secret to Google's data science success? You guys are marvelous, you're doing things that no one else was even contemplating, and what's your key to success?" And he giggles and laughs and he goes, "Design thinking." I go, "What the hell is that? Design thinking, I've never even heard of the stupid thing before." He goes, "I'd make a deal with you, Friday afternoon let's pop over to Stanford's B school and I'll teach you about design thinking." So I went with him on a Friday to the d.school, Design School over at Stanford and I was blown away, not just in how design thinking was used to ideate and bring and to explore. But I was blown away about how powerful that concept is when you marry it with data science. What is data science in its simplest sense? Data science is about identifying the variables and metrics that might be better predictors of performance. It's that might phrase that's the real key. And who are the people who have the best insights into what values or metrics or KPIs you might want to test? It ain't the data scientists, it's the subject matter experts on the business side. And when you use design thinking to bring this subject matter experts with the data scientists together, all kinds of magic stuff happens. It's unbelievable how well it works. And all of our projects leverage design thinking. Our whole value engineering process is built around marrying design thinking with data science, around this prioritization, around these concepts of, all ideas are worthy of consideration and all voices need to be heard. And the idea how you embrace ambiguity and diversity of perspectives to drive innovation, it's marvelous. But I feel like I'm a lone voice out in the wilderness, crying out, "Yeah, Tesla gets it, Google gets it, Apple gets it, Facebook gets it." But you know, most other organizations in the world, they don't think like that. They think design thinking is this Wufoo thing. Oh yeah, you're going to bring people together and sing Kumbaya. It's like, "No, I'm not singing Kumbaya. I'm picking their brains because they're going to help make their data science team much more effective and knowing what problems we're going to go after and how I'm going to measure success and progress. >> Maybe that's the next Dean for the next 10 years, the Dean of design thinking instead of data science, and who knew they're one and the same? Well, Bill, that's a super insightful, I mean, it's so, is validated and supported by the trends that we see all over the place, just in terms of democratization, right? Democratization of the tools, more people having access to data, more opinions, more perspective, more people that have the ability to manipulate the data and basically experiment, does drive better business outcomes. And it's so consistent. >> If I could add one thing, Jeff, I think that what's really powerful about design thinking is when I think about what's happening with artificial intelligence or AI, there's all these conversations about, "Oh, AI is going to wipe out all these jobs. Is going to take all these jobs away." And what we're actually finding is that if we think about machine learning, driven by AI and human empowerment, driven by design thinking, we're seeing the opportunity to exploit these economies of learning at the front lines where every customer engagement, every operational execution is an opportunity to gather not only more data, but to gather more learnings, to empower the humans at the front lines of the organization to constantly be seeking, to try different things, to explore and to learn from each of these engagements. I think it's, AI to me is incredibly powerful. And I think about it as a source of driving more learning, a continuous learning and continuously adapting an organization where it's not just the machines that are doing this, but it's the humans who've been empowered to do that. And my chapter nine in my new book, Jeff, is all about team empowerment, because nothing you do with AI is going to matter of squat if you don't have empowered teams who know how to take and leverage that continuous learning opportunity at the front lines of customer and operational engagement. >> Bill, I couldn't set a better, I think we'll leave it there. That's a great close, when is the next book coming out? >> So today I do my second to last final review. Then it goes back to the editor and he does a review and we start looking at formatting. So I think we're probably four to six weeks out. >> Okay, well, thank you so much, congratulations on all the success. I just love how the Dean is really the Dean now, teaching all over the world, sharing the knowledge and attacking some of these big problems. And like all great economics problems, often the answer is not economics at all. It's completely really twist the lens and don't think of it in that, all that construct. >> Exactly. >> All right, Bill. Thanks again and have a great week. >> Thanks, Jeff. >> All right. He's Bill Schmarzo, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (gentle music)

>>from around the globe. It's the Cube with digital coverage of you. I path live the release show brought to you by you. I path everyone. Welcome back. This is Dave Vellante for the human. We've been covering the r P a market now for quite some time. Yeah, I pastor said this huge announcement. Then we're gonna update you on the space. As you know, we've been quantifying this with our partners at ET are one of the areas that you I path is obviously focused on talking about scaling. If you wanna win in r p. A. You got a scale you want to scale. You gotta have cloud, though. Eric Metaphor is here. He's the director of product management. That you either path, We're gonna talk Cloud. Tara. Good to see you. Thanks for coming on. >>Thank you. Didn't get to see you as well. >>Yes. So you know, you guys have this huge announcement. Um, there are really four major components, if you will. That you extended the core platform. You talked about more automation? More ai smarter robots. The whole end to end. When you guys talk about the what? Sometimes it gets a little buzz, wordy, but that hyper automation, it's got to be end end. You've got to take a systems view, and then you got to put the tools in the hands of regular people. If you want to have a robot for every person, it's got to be simple. You've got to democratize uh, R p A. So my question is, where does the cloud fit into all this? >>You know, the cloud is the one that wraps it all up together. So for us, it's very important to make it easy for people to start instantly. You when you when you start to decide that you want to do in investment in r P A. And you really want to get started very quickly. And the second thing is, you eventually want to grow that are being investment, and the cloud makes it super easy for you to scale out of it. So Cloud makes it easy for you to start instantly and scaling. >>You think about the cloud you know, kind of started with. I guess it's sort of started with Salesforce back in 1999 kind of pre cloud. But certainly, you know so many functions and software areas have been cloud ified. You saw it with the email. You certainly where you start with I t s m. Which was kind of a heavy lift. You certainly see it with With HR. You seen it with data protection and backup. You see, do you see r p A. Is kind of the next big wave of cloud ification. >>Well, I absolutely think that cloud is gonna be very big in our be a. So from our perspective, when you start thinking about are you really thinking about automation? You want your automation is the light up and to save you money and to cut time for you and the investment thing that's going to remind what's going through your mind is not setting up infrastructure and, you know, configuring machines and selling software to make our p possible that the cloud makes it super easy for you to just cut down that I t infrastructure investment and go right ahead into what you really care about, which is the automation. So I think it's gonna be big that we allow you to just go directly to automation. I want RB start thinking about automation for good infrastructure lead that blood. >>So there's obviously you hear a lot of narrative in the marketplace about Cloud Cloud native. You see some companies or dogmatic will never do on had Frank's loop in a little while ago. So we're not doing our friend. That's Ah, that's a halfway house. You guys have taken a different approach, obviously started on Prem and now you're moving to the cloud. What? What's your philosophy on that? You know, Why wouldn't everybody do Cloud >>makes sense. So for us, we're very pragmatic about We believe that customers are different stages in their cloud adoption. Some people are extremely cloud friendly and have already put in place at plans for making sure that everything is already in the cloud. There are companies that are cloud native. They were born in the cloud that if you go a nascent install a piece of software in the local server, they would just laugh at you. So that's on one end of the spectrum and you want to make sure that those people can take full capability of our. On the other hand, there are people who are still, you know, coming from on Prem servers who are trying to move to the cloud to have plans to move to the cloud who would like to try some components in the CLI. But they still have some legacy systems that exist on Prem or a lot of systems that exist, and we want to make sure that those people are also able to take care of our key. And on the other end of the spectrum, there are industries or some companies and some industries that just are not ready for the cloud at all. And from our perspective, we want to democratize our key. We want our P available for everyone. So it is our philosophy that we're going to give you a multitude of the women options. If you want on Prem all the way, we got it. If you want cloud all the way we got, you want the hybrid assistant, We got it. We're just going to make it possible for you. And the deployment choice is your choice >>and the experience on Prem and Cloud. It's substantially identical. Would you say it's completely identical? What? What's the Delta? >>That is absolutely one of our goals. It is absolutely a gore for Google for you. I have to make sure that if you are an on Prem customer and you are starting to use some cloud, that your experiences seamless between on premises and in the cloud if you are a cloud customer and you have some components that still exist on Prem, if you want to use them, is very important for us that you have that common experience between both. So our software is designed with a common experience of the core, and it's actually the same software that runs from a user experience perspective in the cloud and on premises. Now, obviously, a lot of the infrastructure is different than a lot of the security aspects are different. But the user experience itself is, you know, consistently the same and intentionally that way. >>So when people talk about cloud or not, this is often site, you know, several things. Clearly, Layton sees a factor. If your data lives on, you know, on Prem, maybe you want to do things on Prem. There's local laws, data sovereignty. Uh, there's there's corporate edicts. Okay, we're not going to the cloud now. Maybe with Covad, that's that's changing somewhat, but so what are you hearing from customers just in terms of the rationale on Prem versus Cloud Hybrid. What are some of the decision points? >>Those are all good points, Dave. That's exactly the kind of stuff that we hear from our customers. So I think the main things that we hear in terms of cloud is about security people rightfully. So. When you start talking about cloud that they start asking, Can I really trust you as a vendor? With my data, I'm giving you my sales data. I'm giving you my HR data. I mean, this is some confidential information. Can I really trust you with that data? So that's one thing we absolutely, I start taking care off with large focus on security, and I can definitely dive deeper into that if you want. In addition to that, privacy and data sovereignty and where data lives is a big deal. So from our perspective, we host your data as an enterprise customer in three different locations. We host demand. We have servers in the United States. We have servers in Europe. We have servers in Japan, and as a customer you get to choose where data lives and we keep it the way you thoughts s so that kind of helps with data sovereignty because some countries, as you mentioned or some cos there's mentioned really have strict rules about that. Also, that helps with the legacy aspect. So if you're a customer in Japan, you would really prefer to use our Japan Data Center as opposed to a You know, your it's simple. >>The customers care, like where your cloud infrastructure lives. Are they asking you about that? They did they? Did they probe you on that? I mean specifically in terms of your cloud partner, like maybe you could talk about that a little bit. >>Absolutely. People definitely care about who we use and where the data is going to lie. And so from our perspective, for example, we're partnered with Microsoft and all our infrastructures. They don't Microsoft Azure, and, uh, we use data centers from Microsoft Azure through the whole start stuff, and that's a really good for multiple reads as it provides some very good uptime and reliability guarantees. In addition to that, they have service around the world that we can utilize so that we can expect, for example, for a next frontier becomes for example, in Australia, New Zealand. Then we want to create a reason they're being on top of Azure really allows us to go and spend that off pretty quickly and help customers that way. >>So we don't want things about Cloud is you can you can experiment very cheaply and you can fail fast and then iterating. So one of the things that struck me about your announcement was your community edition. I always look for, you know, Is there a community edition? Is that in addition, free for life? Is it neutered? In other words, can I actually do anything with it? Um, so I was happy to see that you guys had that. And also happy to see I mean, you've got I think it's early days for you, but I think that you have 200 enterprise customers, two orders of magnitude greater than that from the community edition. Did I get that right? >>That's great. Yes, absolutely. So when you think about the automation, cloud comes into play. But we have a community, the one for community, and that is the free version. And it's as you said, it's not like a pretrial or three limited time or something. It was just free as in free, free forever. We're gonna keep it to you. That's all you need. Just use it. No questions asked. You know, be happy with it. And the community edition actually is, is a fully functional product, and it allows you to do to get to attended robots one unattended robot and you get the option of connecting up the studio to two studios for designing animations. Work with those s so it's it's it allows it. That's all your automation needs, like small automation needs. Just go ahead and use it. If you're a small company or an individual or a small team, just use the community edition and you want to use that production. That's fine. No problem. That's all you need to go for it, then the and we have tens of thousands of community customers that are actively using the product. I'm not talking about the people that have ever signed up on left. Those are a lot more than that, but I'm talking about actually actively using the product. We've got tens of thousands of users as they're using it every month and built on that same infrastructure comes our Enterprise Edition, and the Enterprise edition is a basically the same infrastructure. But it adds a number of capabilities that are useful for a larger enterprise, of course, the most important of which is an uptime guarantee. So, you know, with the community edition, obviously we keep the service. Often we have very good response times, but with enterprise, you actually get enough time guarantee. In addition to that, you get access to our sport. They have a dedicated support team that works 24 7 around the clock and multiple reasons, and you get guaranteed response times with the mass away. On top of that, you get to be able to purchase is many robots as you want, and the list goes on and on and on. And it's very easy to go. Like you said, from playing with community, working with community, using that doing a trial, it's instinct as well. You just click a button, and all of a sudden, now you have five robots that each night that you can use for 60 days, and from there you can just go ahead and buy. >>We'll talk more about the uptime guarantee is that basically Azure s l A. So the data layer on top of that, you know, what are we measuring there? You could add some color. >>Absolutely. A startup and guarantee is built on top of azure, obviously. But we provide our own uptime guarantee regardless of what happens in the underlying system. Eso From our perspective, we guarantee that the service is going to be out at a specific amount of time. So we measure the number of minutes every month that the service should actually be up. And we measure the number of ministers service experiences, any kind of downtime. And we measure down. I'm in multiple ways. So we do outside in testing. Or, you know, if you're a customer, are you able to reach our service or not that we do incredible detail the monitoring and reporting on the life off our service itself from inside and And we look at any minute, any of those the services that we use underlying services are not responding to customers, and we count those down as well. So and we guarantee that there's a specific number of minutes that the service will be down, and that's >>it. And So if I understand it, you're really taking an application. You It's not the light on the server. It's It's the it's Can I get to you as a customer? Yeah. My state, my service, your >>perspective. Are you able to reach our service and do what you're expected to do with it? That's what you as a customer, really care about and in turn, that's the right way for us to be measuring up. >>And if I don't? If you don't hit, that s L. A. What? I get re credit. So how does that >>work? Absolutely, we have in our agreements and provision for penalties on the outside, we don't hit that escalate. And similarly for support. You know, if you call support, you're guaranteed depending on the severity of the issue on the back of contract that sport. Uh, you know how many minutes it takes us for somebody to be engaged with you on that issue? And we have very good numbers and hitting. That s L. A. And if we don't? They're also penalties on the outside for that. I think this is a real enterprise services you'd expect. >>Yeah, Great. I want to get to take some examples. You've got a couple 100 customers I think you mentioned should hopefully was up and running very quickly. I think you had some other examples, but but what can you share with us in terms of actual experience that your customers have seen? >>Absolutely. So we were thinking a very measured and careful approach, actually launching our service. So even though our service literally just launched last week fully to the world, we have actually been enabling enterprise customers we've been. We started a private preview with four customers and back in April of last year. And then we extended that to a public preview for any customer to try our service, as is no payment but also no guarantees. Back in the day, I want to say June of last year and then it took us all the way to December to feel comfortable that the services of the place where when we launch it, customers are going to get an excellent quality. And that's when we did a what we call a limited availability where we started on boarding enterprise customers step by step. We started with 10. We went to 50. We went to 100 now we have a couple 100 customers that already signed up, using the enterprise product every day as a guaranteed service and getting that Sele's that we've promised. So this was the time when we said, You know what? I think we've definitely been meeting our SL A's for five months running. Now we feel very comfortable lunch the rest, >>even if it's anecdotal. Have you discerned appreciable change in people's attitude toward cloud as a result of over? >>Absolutely, absolutely. I mean, it's just the aspect of working from home and having a lot of people just not available either demand infrastructure or demand servers made. A lot of people think about what is the best way to continue to run on information while they're at it. And obviously there's nothing easier than granting access to someone in the cloud to access a service from home. Then, if you were to bring them access to an on premise service with BP and all that stuff, and then if you want the provisioning new robots and machines and you have to do that again on premises, you know it's it's it's a lot more complicated. So a lot of customers are really starting to look at the cloud so many of the conversations that we would have, obviously we come in and they would ask us about the capabilities of our system. They would ask about security that ask a lot of things and those discussions, you know, anywhere between, you know, a few days to sometimes a few months. You know, some customers is just a great for those. The volume of customers that's asking about cloud is definitely increasing good for us. Obviously the number of deals were signed exalts increasing. But most importantly, I think that the number of customers that are benefiting from the value of starting instantly like cloud scale easily in the cloud. And you mentioned the example of Chipotle. And while that was engagement that we had obviously for the core of it, you know, they were very impressed with what they were able to do with The came in and falling and team had budgeted about two weeks to get started and set up everything so that they can build on top of that in their automation. And they, they chose wisely, do start in the cloud and As a result, they were done and all set up in one day. So it is definitely a huge difference between what you're able to get in the cloud person. So what you're doing? >>Well, you have passed all about automation. And and so is the cloud. The superpowers of this decade Cloud data A You guys were, you know, at the heart of all those Eric. Thanks so much for coming on the cube in explaining sort of your cloud angle. Really Appreciate your time. >>Thank you very much, Dave. It's a pleasure to be here. >>Alright? And thank you for watching everybody. More coverage on the r p. A market analysis digging into your past recent announcement stable people want to have right back right after this short break. Yeah, yeah, yeah, yeah, yeah

>> Announcer: Live from Orlando, Florida, it's theCUBE, covering Microsoft Ignite, brought to you by Cohesity. >> Good morning, everyone, and welcome back to theCUBE's live coverage of Microsoft Ignite. 26,000 people from around the world have descended onto the Orlando, here in Orlando, for Microsoft Ignite. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We are joined by Christina Warren. She is the senior cloud advocate at Microsoft. Thank you so much for coming on the show. >> Thanks so much for having me. >> So I'd love to have you talk a little bit about your work as a senior cloud advocate. And you are responsible for a lot of the video strategy of Channel 9. >> Yeah, I am. So we have a cloud advocacy scene within developer relations, and most of our advocates are focused on either kind of a specific technology area or a specific audience. I'm a little bit different in that I am kind of of a, I call myself, I'm kind of a jack of all trades, master of none. So I go across a lot of different technology areas, but I look at our video content and our video strategies that we have at Channel 9 and our YouTube channel, Microsoft Developer, and some of our other channels, and I think about what are they types of stories we want to tell, what's content do we want to create for our audience, and how can we bring new developers into our ecosystem, as well. >> So what are those stories? I mean, what do you, what are you hearing from customers and what are you hearing also from colleagues at Microsoft that say, "Here's something that we need to tell the world about"? >> Yeah, so I think it's really interesting. I think there are a lot of things. One, there, we were talking a little bit before the show. There's kind of an insatiable, I think, need for a lot of people how to get started, whether it's getting started coding, if you're wanting to learn Python or learn JavaScript or something else, or even if you're just wanting to, you're starting to get into infrastructure, and you're wanting to learn, okay, how do I, you know, spin things up on the cloud. How do I set things up? And having some of that base fundamentals content is really important, but I also think sometimes it's about troubleshooting, and it's about figuring out what are the new services. What can this do for me? And I think a lot of times, when I think about the stories we want to tell, it's not, oh look at how great our service or our product is but it's this is designed to ease my pain points and make my life as a developer or an ops person easier. >> Christina, in the early days, everybody thought that, you know, the promise of cloud was it, it was supposed to be simple and inexpensive, and unfortunately, we learned it is neither of those things by default, so, you know, how do we help people to go from, you know, it's only 20% of applications today are in the cloud. You know, really simplicity is something we need to attack, and education is one of those areas that we need, you know. Give us some examples of some of the things that your team's doing to try to help us get to the majority of environments and work loads. >> Yeah, so a great example is, you know, .NET Core 3.0 launched a couple of months ago, and there's been a big push there with cloud-native apps and cloud-native applications, and so we have like a new video series, The Cloud Native Show, that my colleague Shane Boyer heads up, where they go through kind of all the steps of cloud-native development. And what's great about this is that you have existing .NET developers who have not, to your point, you know, 80% of applications are still not on the cloud, so they're going from that older environment. And then this is saying, you can take the skills you already have, but this is how you think about these things in this new environment. And for a lot of things when it comes to tech, if you're, the way I can always think about things is the next generation of developers, they don't know a non-cloud world. They're literally cloud-first, and I think that's an important thing for all of us to consider is that the next generation developers, the kids who are in high school now, the kids who are in college, they don't know, you know, the pain of having to provision and deal with their own, you know, servers or data centers. They've only known the cloud. And so, but that's an interesting opportunity both to create cloud-first content for them, but for the people who have been using things to say, okay, what you've already been doing, there are changes, but you're not starting from zero, and you can take some of the things you already know and just move that into, into the new world. >> Yeah, well, one of the interesting things we've found this week is that when we talk about engaging with Microsoft, it's not just .NET, it's not just Windows, or Azure. We talked yesterday with Donovan Brown and Scott Hanselman, and it's you know, any app, any language, your tools, pulling those off together. That's really challenging from, you know, creating content out there, because, you know, you're not going to recreate the entire internet there. So how do you tie in what you're doing with other resources and have that, you know, communication, collaboration out there in the industry? >> So a lot of it I think from what I do and what a lot of us do, I look, I used to be a journalist, so I look at what's interesting to me and what stories I would want to tell and what things I would want to know more about. And so, you know, Visual Studio Online, which was announced this week, massive announcement. I'm super excited about that. I am super excited about what that means, and I know that the audience is going to be excited about that. So I look at an announcement like that, and I'm like okay, what kind of content can we work with with those product teams to do? What sort of tutorials would I like to build? What things would I want to know more about if, if I were, you know, really experienced or just getting started? And I think some other areas are, for instance, Windows Subsystem for Linux 2, WSL 2 will be coming out in the future. That's a great opportunity for people who are both familiar with Linux and might not be familiar with Linux to kind of get started and using Windows as their development platform. And so when I see trends like that happening or things around, you know, containers, you know, Kubernetes, you know, containerize all the things, start thinking about, okay, what are the opportunities? What are cool examples? What would I want to see as somebody who, who's tuning in? That's what I always try to think about is what would. I just try to think about it like a journalist. You know, what would an interesting story be to tell from my perspective? What would I want to know more about? And then we can go from there and work with the product groups and work with some of the other teams to make sure that we can tell those stories. >> So, I'm curious. As a former journalist, you spent a decade as a digital editor and reporter and commentator. What made you want to make the leap to big tech? >> You know, okay, so media is not a great place right now. So that's number one. Number two, you know, I was very technical as a journalist, and it was interesting because when I made that transition, I then had to really actually shore up my tech skills. And I said, okay, I have some of the basics, but I really need to like double down and invest in myself and invest in learning more. But I always, even when I was a journalist, I loved telling developer stories, and I loved advocating for developers. Even when I was, I was working at really mainstream places like Mashable, where, you know, they would send me to developer conferences, and I wouldn't just go to the press things. I would want to go to the sessions and talk to the developers and find out, okay, what are you excited about? What are the opportunities you see to build things? What's coming around that has you excited? I've always loved that. And so when the opportunity presented itself for me to be able to do that at Microsoft, I was like, oh, you know, I'd never considered that before, but that's really interesting, and that would be a interesting way of maybe seeing if I can do something else. >> One of the skills that you, that you, is common between what you do now and as work as a journalist is breaking down this technical language and making it accessible for a wider audience, particularly at more mainstream publications. What is your advice for people in terms of how to do that? Because on this show, we have a lot of technically-minded people who can really go deep into technology. But how do you then make it accessible? What is, what is your advice? >> I always try to think of who is your muse as someone who might not know what's, all the intricacies that are going on but is an intelligent person that can understand. So for me, I always use my mom. Now this was easier when I was a tech journalist than it is what I do now because she understands even less what I do now, but I try to think about, okay, how would I explain this to her? She doesn't need to know all of the intricacies, the nitty-gritty. But how could I explain something to her that would be technically accurate but would get the basic idea? And I think a lot of times when it comes to breaking down content, it's just getting to the essence of what problem is this solving, what is this doing that's better or worse, and how does it do it and in starting from there. And it, a lot of times it just takes a lot of work, and you figure out as you go along what getting feedback from users, frankly, based on they might be asking more clarifying questions, or maybe they'll want to know more about something or less about something else. This is confusing for me. And just modulating that as you go along. >> Yeah, Christina, it makes me laugh, actually. When I started blogging, my mother was one of the people that would read, and she would say, "Oh, yes, I heard about this cloud thing before. "I watched it on NPR." It's a nuanced and complicated message. I actually, I roll my eyes a little bit back at the old Microsoft to the cloud videos there, because it was like it didn't resonate. It's the stories that you're telling these days. How do you balance there's the outcomes is, yes, we want to, you know, solve, you know, some of the great challenges and help healthcare, but, you know, underneath, there's some nitty-gritty developer and infrastructure things that get solved. How do you make sure there's the connections between, you know, what the products do and the outcomes? >> Yeah, that's really interesting. You're right, it is a challenge. I think the, the important thing here is not every message has to have all of those components. So you can tell different stories. You could tell one story that's just more focused on the outcome and is just more focused on the opportunity and what's happening in healthcare, and you could have another story that might be about this is what's going on underneath that is allowing those things to happen. >> Yeah, do you, do you have any favorite, you know, outcome stories from Microsoft? >> Gosh, you know, yesterday, during Scott Hanselman's developer keynote, he was, he was, I didn't even know about the Chipotle case study. That was so interesting to me and seeing what they're doing with the different technology. That's, that was a really, that's just the first one that comes to mind I thought was really cool. I'm really excited about the opportunities we have in Quantum, and I'm really excited about opportunities in healthcare because, you know, I think we've all been to the doctor, and we've seen how much IT and how much tech infrastructure could help not just the process of diagnosing and helping things but just, even just the minutia of data entry and record delivery and keeping track of everything. So there, a lot of the things we've done there have been really interesting. >> One of the things you said is you love telling developer stories, and I'm a journalist, too. And I cover entrepreneurs, and I feel the same way about telling entrepreneur stories. Talk about some of the common characteristics you've seen. I mean, we can't obviously generalize an entire population of people, but talk about what you have seen as sort of the common elements of their personalities and their approach to solving problems. >> Right, so I think it's interesting. When I think about any developers, which are a little bit different than enterprise devs, although there are some similarities, you know, you start with, and I know for me when I start first started coding and when I first started building websites and then other things, like, for me, I wanted to either solve a problem, or I wanted to create something that other people could, could see. And so a lot of times that probably one of the more commonalities is, you know, developers, they're in many ways wanting to scratch their own itch. I wanted to do something, I couldn't figure out how to do it, so I built this myself, found out other people were using it, too, and I added features to it. I mean, I think that's what's so great about open-source is that people have the opportunity to collaborate either contributing code or even, you know, doing bug reports and or sharing ideas. And so I, one of the more common elements is I wanted to do something, or I had a really interesting idea, and I didn't see anyone else doing it, and so I just decided to build it myself. It's not that different from entrepreneurs, right. Like it's I see, I see a business opportunity, I see a business I want to do, so I'm going to build it. And that's, wanting to build things is probably the most common thing I see. >> Yeah, Christina, any common conversations or things that are coming up that, you know, people that aren't at this show, you'd like to share? >> Oh, gosh, I mean, I think there's been so much good stuff. I mentioned Visual Studio Online, which I think is really exciting because I'm really excited about being able to like be on my iPad and also code. Like, that's going to be really great. Also, I think the Arc stuff, the Azure Arc stuff is really interesting, the idea of being able to not just be focused on, you know, one platform, but being able to control all of your infrastructure no matter where it is is really, really, that's a really compelling story. That's something that makes me really excited because I love to just automate and simplify things, so anything that can make, you know, the life easier, I think is great. >> As a former journalist, I'd love your thoughts on the state of news today. I know you said you got out of it because it's not a great career path, but the overreach of social media, the spread of fake news, the real and perceived media biases. I'm interested in your thoughts about where we are today, particularly as it relates to coverage of technology. >> It's interesting. I think in some ways technology. For a really long time, most technology coverage was almost cheerleaderish. You could even look back even 20 years when the dot-com crash happened, and I was in high school then, but I was following all of that avidly. The after flow of that, the business press was maybe a little bit burned, but the technology press was still very much gung ho and was still very much cheerleading. That's changed a little bit as we've started to have to grapple with some of the consequences, good and bad, that happened with tech and with the internet. Right now, I almost feel like maybe we've gone a little bit over the edge a little bit more, and some of the critiques are fair, and some of them maybe are just, you know, it's popular to kind of be more negative. So that's been an interesting change, I think to see. You're right, though, when it comes to the spreading of kind of misinformation or people just reading things in headlines, it's really difficult I think, for people to find authoritative voices and things they can trust. Weirdly, though, I do actually think this is an opportunity for the big tech companies to help. This is things that AI could really play a big role in. These are things that could really kind of help, you know, recognize patterns of scan bots and of other things that aren't there and filter that out. But I think even when, I still feel good about journalism as a medium. I still think that the press is one of the most important assets we have, and even when we are going through shakier times, there are opportunities. I think that we will, it'll find it's way. And honestly, I really do think that technology is one of those things that will help get the real things, the important stories out there. >> All right, so, Christina, I guess final word is how should people think of Microsoft in 2019? >> We're here to help. You know, I think that we are, we are a technology company that is, that is creating the tools so that you can build and solve the problems that you need to solve. >> All right, that's a, that's a great note to end on. Thank you so much for coming on the show, Christina. >> Thank you so much for having me. >> Stay tuned for more of theCUBE's live coverage of Microsoft Ignite coming up in just a little bit. (upbeat instrumental music)

>> Announcer: Live from Orlando, Florida it's theCUBE! Covering Microsoft Ignite, brought to you by Cohesity. >> Hello, and happy taco Tuesday CUBE viewers! You are watching theCUBE's live coverage of Microsoft's Ignite here in Orlando, Florida. I'm your host Rebecca Knight, along with Stu Miniman. We're joined by Scott Hanselman, he is the partner program manager at Microsoft. Thank you so much for coming on theCUBE! >> Absolutely, my pleasure! >> Rebecca: And happy taco Tuesday to you! Will code for tacos. >> Will code for tacos. >> I'm digging it, I'm digging it >> I'm a very inexpensive coder. >> So you are the partner program manager, but you're really the people's programmer at Microsoft. Satya Nadella up on the main stage yesterday, talking about programming for everyone, empowering ordinary citizen developers, and you yourself were on the main stage this morning, "App Development for All", why is this such a priority for Microsoft at this point in time? >> Well there's the priority for Microsoft, and then I'll also speak selfishly as a priority for me, because when we talk about inclusion, what does that really mean? Well it is the opposite of exclusion. So when we mean inclusion, we need to mean everyone, we need to include everyone. So what can we do to make technology, to make programming possible, to make everyone enabled, whether that be something like drag and drop, and PowerApps, and the Power platform, all the way down to doing things like we did in the keynote this morning with C# on a tiny micro-controller, and the entire spectrum in between, whether it be citizen programmers in Excel using Power BI to go and do machine learning, or the silly things that we did in the keynote with rock, paper scissors that we might be able to talk about. All of that means including everyone and if the site isn't accessible, if Visual Studio as a tool isn't accessible, if you're training your AI in a non-ethical way, you are consciously excluding people. So back to what Satya thinks is why can't everyone do this? SatyaSacha thinks is why can't everyone do this? Why are we as programmers having any gate keeping, or you know, "You can't do that you're not a programmer, "you know, I'm a programmer, you can't have that." >> So what does the future look like, >> Rebecca: So what does the future look like, if everyone knows how to do it? I mean, do some imagining, visioning right now about if everyone does know how to do this, or at least can learn the building blocks for it, what does technology look like? >> Well hopefully it will be ethical, and it'll be democratized so that everyone can do it. I think that the things that are interesting, or innovative today will become commoditized tomorrow, like, something as simple as a webcam detecting your face, and putting a square around it and then you move around, and the square, we were like, "Oh my God, that was amazing!" And now it's just a library that you can download. What is amazing and interesting today, like AR and VR, where it's like, "Oh wow, I've never seen augmented reality work like that!" My eight-year-old will be able to do it in five years, and they'll be older than eight. >> So Scott, one of the big takeaways I had from the app dev keynote that you did this morning was in the past it was trying to get everybody on the same page, let's move them to our stack, let's move them to our cloud, let's move them on this programming language, and you really talked about how the example of Chipotle is different parts of the organization will write in a different language, and there needs to be, it's almost, you know, that service bus that you have between all of these environments, because we've spent, a lot of us, I know in my career I've spent decades trying to help break down those silos, and get everybody to work together, but we're never going to have everybody doing the same jobs, so we need to meet them where they are, they need to allow them to use the tools, the languages, the platforms that they want, but they need to all be able to work together, and this is not the Microsoft that I grew up with that is now an enabler of that environment. The word we keep coming back to is trust at the keynote. I know there's some awesome, cool new stuff about .net which is a piece of it, but it's all of the things together. >> Right, you know I was teaching a class at Mesa Community College down in San Diego a couple of days ago and they were trying, they were all people who wanted jobs, just community college people, I went to community college and it's like, I just want to know how to get a job, what is the thing that I can do? What language should I learn? And that's a tough question. They wonder, do I learn Java, do I learn C#? And someone had a really funny analogy, and I'll share it with you. They said, well you know English is the language, right? Why don't the other languages just give up? They said, you know, Finland, they're not going to win, right? Their language didn't win, so they should just give up, and they should all speak English, and I said, What an awful thing! They like their language! I'm not going to go to people who do Haskell, or Rust, or Scala, or F#, and say, you should give up! You're not going to win because C won, or Java won, or C# won. So instead, why don't we focus on standards where we can inter-operate, where we can accept that the reality is a hybrid cloud things like Azure Arc that allows us to connect multiple clouds, multi-vendor clouds together. That is all encompassing the concept of inclusion, including everyone means including every language, and as many standards as you can. So it might sound a little bit like a Tower of Babel, but we do have standards and the standards are HTTP, REST, JSON, JavaScript. It may not be the web we deserve, but it's the web that we have, so we'll use those building block technologies, and then let people do their own thing. >> So speaking of the keynote this morning, one of the cool things you were doing was talking about the rock, paper, scissors game, and how it's expanding. Tell our viewers a little bit more about the new elements to rock, paper, scissors. >> So folks named Sam Kass, a gentleman named Sam Kass many, many years ago on the internet, when the internet was much simpler web pages, created a game called Rock, Paper, Scissors, Lizard, Spock, and a lot of people will know that from a popular TV show on CBS, and they'll give credit to that show, in fact it was Sam Kass and Karen Bryla who created that, and we sent them a note and said, "Hey can I write a game about this?" And we basically built a Rock, Paper, Scissors, Lizard, Spock game in the cloud containerized at scale with multiple languages, and then we also put it on a tiny device, and what's fun about the game from a complexity perspective is that rock, paper, scissors is easy. There's only three rules, right? Paper covers rock, which makes no sense, but when you have five, it's hard! Spock shoots the Rock with his phaser, and then the lizard poisons Spock, and the paper disproves, and it gets really hard and complicated, but it's also super fun and nerdy. So we went and created a containerized app where we had all different bots, we had node, Python, Java, C#, and PHP, and then you can say, I'm going to pick Spock and .net, or node and paper, and have them fight, and then we added in some AI, and some machine learning, and some custom vision such that if you sign in with Twitter in this game, it will learn your patterns, and try to defeat you using your patterns and then, clicking on your choices and fun, snd then, clicking on your choices and fun, because we all want to go, "Rock, Paper, Scissors shoot!" So we made a custom vision model that would go, and detect your hand or whatever that is saying, this is Spock and then it would select it and play the game. So it was just great fun, and it was a lot more fun than a lot of the corporate demos that you see these days. >> All right Scott, you're doing a lot of different things at the show here. We said there's just a barrage of different announcements that were made. Love if you could share some of the things that might have flown under the radar. You know, Arc, everyone's talking about, but some cool things or things that you're geeking out on that you'd want to share with others? >> Two of the things that I'm most excited, one is an announcement that's specific to Ignite, and one's a community thing, the announcement is that .net Core 3.1 is coming. .net Core 3 has been a long time coming as we have began to mature, and create a cross platform open source .net runtime, but .net Core 3.1 LTS Long Term Support means that that's a version of .net core that you can put on a system for three years and be supported. Because a lot of people are saying, "All this open source is moving so fast! "I just upgraded to this, "and I don't want to upgrade to that". LTS releases are going to happen every November in the odd numbered years. So that means 2019, 2021, 2023, there's going to be a version of .net you can count on for three years, and then if you want to follow that train, the safe train, you can do that. In the even numbered years we're going to come out with a version of .net that will push the envelope, maybe introduce a new version of C#, it'll do something interesting and new, then we tighten the screws and then the following year that becomes a long term support version of .net. >> A question for you on that. One of the challenges I hear from customers is, when you talk about hybrid cloud, they're starting to get pulled apart a little bit, because in the public cloud, if I'm running Azure, I'm always on the latest version, but in my data center, often as you said, I want longer term support, I'm not ready to be able to take that CICD push all of the time, so it feels like I live, maybe call it bimodal if you want, but I'm being pulled with the am I always on the latest, getting the latest security, and it's all tested by them? Or am I on my own there? How do you help customers with that, when Microsoft's developing things, how do you live in both of those worlds or pull them together? >> Well, we're really just working on this idea of side-by-side, whether it be different versions of Visual Studio that are side-by-side, the stable one that your company is paying for, and then the preview version that you can go have side-by-side, or whether you could have .net Core 3, 3.1, or the next version, a preview version, and a safe version side-by-side. We want to enable people to experiment without fear of us messing up their machine, which is really, really important. >> One of the other things you were talking about is a cool community announcement. Can you tell us a little bit more about that? >> So this is a really cool product from a very, very small company out of Oregon, from a company called Wilderness Labs, and Wilderness Labs makes a micro-controller, not a micro-processor, not a raspberry pie, it doesn't run Linux, what it runs is .net, so we're actually playing Rock, Paper, Scissors, Lizard, Spock on this device. We've wired it all up, this is a screen from our friends at Adafruit, and I can write .net, so somehow if someone is working at, I don't know, the IT department at Little Debbie Snack Cakes, and they're making WinForms applications, they're suddenly now an IOT developer, 'cause they can go and write C# code, and control a device like this. And when you have a micro-controller, this will run for weeks on a battery, not hours. You go and 3D print a case, make this really tiny, it could become a sensor, it could become an IOT device, or one of thousands of devices that could check crops, check humidity, moisture wetness, whatever you want, and we're going to enable all kinds of things. This is just a commodity device here, this screen, it's not special. The actual device, this is the development version, size of my finger, it could be even smaller if we wanted to make it that way, and these are our friends at Wilderness Labs. and they had a successful Kickstarter, and I just wanted to give them a shout out, and I just wanted to give them a shoutout, I don't have any relationship with them, I just think they're great. >> Very cool, very cool. So you are a busy guy, and as Stu said, you're in a lot of different things within Microsoft, and yet you still have time to teach at community college. I'm interested in your perspective of why you do that? Why do you think it's so important to democratize learning about how to do this stuff? >> I am very fortunate and I think that we people, who have achieved some amount of success in our space, need to recognize that luck played a factor in that. That privilege played a factor in that. But, why can't we be the luck for somebody else, the luck can be as simple as a warm introduction. I believe very strongly in what I call the transitive value of friendship, so if we're friends, and you're friends, then the hypotenuse can be friends as well. A warm intro, a LinkedIn, a note that like, "Hey, I met this person, you should talk to them!" Non-transactional networking is really important. So I can go to a community college, and talk to a person that maybe wanted to quit, and give a speech and give them, I don't know, a week, three months, six months, more whatever, chutzpah, moxie, something that will keep them to finish their degree and then succeed, then I'm going to put good karma out into the world. >> Paying it forward. >> Exactly. >> So Scott, you mentioned that when people ask for advice, it's not about what language they do, is to, you know, is to,q you know, we talk in general about intellectual curiosity of course is good, being part of a community is a great way to participate, and Microsoft has a phenomenal one, any other tips you'd give for our listeners out there today? >> The fundamentals will never go out of style, and rather than thinking about learning how to code, why not think about learning how to think, and learning about systems thinking. One of my friends, Kishau Rogers, talked about systems thinking, I've hade her on my podcast a number of times, and we were giving a presentation at Black Girls Code, and I was talking to a fifteen-year-old young woman, and we were giving a presentation. It was clear that her mom wanted her to be there, and she's like, "Why are we here?" And I said, "All right, let's talk about programming "everybody, we're talking about programming. "My toaster is broken and the toast is not working. "What do you think is wrong?" Big, long, awkward pause and someone says, "Well is the power on?" I was like, "Well, I plugged a light in, "and nothing came on" and they were like, "Well is the fuse blown?" and then one little girl said "Well did the neighbors have power?", And I said, "You're debugging, we are debugging right?" This is the thing, you're a systems thinker, I don't know what's going on with the computer when my dad calls, I'm just figuring it out like, "Oh, I'm so happy, you work for Microsoft, "you're able to figure it out." >> Rebecca: He has his own IT guy now in you! >> Yeah, I don't know, I unplug the router, right? But that ability to think about things in the context of a larger system. I want toast, power is out in the neighborhood, drawing that line, that makes you a programmer, the language is secondary. >> Finally, the YouTube videos. Tell our viewers a little bit about those. you can go to D-O-T.net, so dot.net, the word dot, you can go to d-o-t.net, so dot.net, the word dot, slash videos and we went, and we made a 100 YouTube videos on everything from C# 101, .net, all the way up to database access, and putting things in the cloud. A very gentle, "Mr. Rodgers' Neighborhood" on-ramp. A lot of things, if you've ever seen that cartoon that says, "Want to draw an owl? "Well draw two circles, "and then draw the rest of the fricking owl." A lot of tutorials feel like that, and we don't want to do that, you know. We've got to have an on-ramp before we get on the freeway. So we've made those at dot.net/videos. >> Excellent, well that's a great plug! Thank you so much for coming on the show, Scott. >> Absolutely my pleasure! >> I'm Rebecca Knight, for Stu Miniman., stay tuned for more of theCUBE's live coverage of Microsoft Ignite. (upbeat music)

>> Narrator: Live from Orlando, Florida, it's theCUBE, covering Microsoft Ignite. Brought to you by, Cohesity. >> Good morning everyone and welcome back to theCUBE's live coverage of Microsoft Ignite. We are here in the Orange County Convention Center. I'm your host Rebecca Knight, along with Stu Miniman. Stu, this is Microsoft's Big Show. 26,000 people from around the globe, all descending on Orlando. This is the big infrastructure show. Thoughts, impressions, now that we're on day two of a three day show. >> Yeah, Rebecca. Last year I had this feeling that it was a little bit too much talking about the Windows 10 transition and the latest updates to Office 365. I could certainly want to make sure that we really dug in more to what's going on with Azure, what's happening in 6the developer space. Even though they do have a separate show for developers, it's Microsoft build. They actually have a huge partner show. And so, Microsoft has a lot of shows. So it's, what is this show that is decades old? And really it is the combination of Microsoft as a platform today. Satya Nadella yesterday talked about empowering the world. This morning, Scott Hanselman was in a smaller theater, talking about app devs. And he came out and he's like, "Hey, developers, isn't it a little bit early for you this morning?" Everybody's laughing. He said, "Even though we're kicking off at 9:00 a.m., Eastern." He said, "That's really early, especially for anybody coming from the West Coast." He was wearing his Will Code For Tacos shirt. And we're going to have Scott on later today, so we'll talk about that. But, where does Microsoft sit in this landscape? Is something we've had. I spent a lot of time looking at the cloud marketplace. Microsoft has put themselves as the clear number two behind AWS. But trying to figure out because SaaS is a big piece of what Microsoft does. And they have their software estate in their customer relationship. So how many of those that are what we used to call window shops. And you had Windows people are going to start, Will it be .NET? Will it be other operating systems? Will it come into Azure? Where do they play? And the answer is, Microsoft's going to play a lot of places. And what was really kind of put on with the point yesterday is, it's not just about the Microsoft solutions, it is about the ecosystem, they really haven't embraced their role, very supportive of open source. And trust is something that I know both you and I have been pointing in on because, in the big tech market, Microsoft wants to stand up and say, "We are the most trusted out there. And therefore, turn to us and we will help you through all of these journeys." >> So you're bringing up so many great points and I want to now go through each and every one of them. So, absolutely, we are hearing that this is the kinder, gentler Microsoft, we had Dave Totten on yesterday. And he was, as you just described, just talking about how much Microsoft is embracing and supporting customers who are using a little bit of Microsoft here, a little bit of other companies. I'm not going to name names, but they're seemingly demanding. I just want best to breed, and this is what I'm going to do. And Microsoft is supporting that, championing that. And, of course we're seeing this as a trend in the broader technology industry. However, it feels different, because it's Microsoft doing this. And they've been so proprietary in the past. >> Yeah, well, and Rebecca, it's our job on theCUBE actually, I'm going to name names. (laughs) And actually Microsoft is-- >> Okay. >> Embracing of this. So, the thing I'm most interested in at the show was Azure Arc. And I was trying to figure out, is this a management platform? And at the end of the day really, it is, there's Kubernetes in there, and it's specifically tied to applications. So they're going to start with databases specifically. My understanding, SQL is the first piece and saying, it sounds almost like the next incarnation of platform as a service to our past. And say, I can take this, I can put it on premises in Azure or on AWS. Any of those environments, manage all of them the same. Reminds me of what I hear from VMware with Hangzhou. Vmworld, Europe is going on right now in Barcelona. Big announcement is to the relationship with VMware on Azure. If I got it right, it's actually in beta now. So, Arc being announced and the next step of where Microsoft and VMware are going together, it is not a coincidence. They are not severing the ties with VMware. VMware, of course partners with all the cloud providers, most notably AWS. Dave Totten yesterday, talked about Red Hat. You want Kubernetes? If you want OpenShift, if you are a Red Hat customer and you've decided that, the way I'm going to leverage and use and have my applications run, are through OpenShift, Microsoft's is great. And the best, most secure place to run that environment is on Azure. So, that's great. So Microsoft, when you talk about choice, when you talk about flexibility, and you talk about agility cause, it is kinder and gentler, but Satya said they have that tech intensity. So all the latest and greatest, the new things that you want, you can get it from Microsoft, but they are also going to meet you where you are. That was Jeremiah Dooley, the Azure advocate, said that, "There's, lots of bridges we need to make, Microsoft has lots of teams. It's not just the DevOps, it's not just letting the old people do their own thing, from your virtualization through your containerization and everything in between microservices server list, and the like. Microsoft has teams, they have partners. Sure that you could buy everything in Microsoft, but they know that there are lots of partners and pieces. And between their partners, their ecosystem, their channel, and their go-to-market, they're going to pull this together to help you leverage what you need to move your business forward. >> So, next I want to talk about Scott Hanselman who was up on the main stage, we're going to have him on the show and he was as you said, adorned in coder dude, attire with a cool t-shirt and snappy kicks. But his talk was app development for everyone. And this is really Microsoft's big push, democratizing computing, hey, anyone can do this. And Satya Nadella, as we've talked about on the show. 61% of technologist's jobs are not in the technology industry. So this is something that Microsoft sees as a trend that's happening in the employment market. So they're saying, "Hey, we're going to help you out here." But Microsoft is not a hardware company. So how does this really change things for Microsoft in terms of the products and services-- >> Well right, >> It offers. >> So really what we're talking about here, we're talking about developers right? 61% of jobs openings for developers are outside the tech sector. And the high level message that Scott had is your tools, your language, your apps. And what we have is, just as we were talking about choice of clouds, it's choice of languages. Sure they'd love to say .NET is wonderful, but you want your Java, your PHP, all of these options. And chances are, not only are you going to use many of them, but even if you're working on a total solution, different groups inside your company might be using them and therefore you need tools that can spam them. The interesting example they use was Chipotle. And if there's a difference between when you're ordering and going through the delivery service, and some of the back-end pieces, and data needs to flow between them, and it can't be, "Oh wait, I've got silos of my data, I've got silos of all these other environments." So, developer tools are all about, having the company just work faster and work across environments. I was at AnsibleFest show earlier this year. And, Ansible is one of those tools that actually, different roles where you have to have the product owner, the developer, or the the operations person. They all have their way into that tool. And so, Microsoft's showing some very similar things as to, when I build something, it's not, "Oh, wait, we all chose this language." And so many of the tools was, " Okay, well, I had to standardize on something." But that didn't fit into what the organization needed. So I need to be able to get to what they all had. Just like eventually, when I'm picking my own taco, I can roll it, bowl it, soft or hard shell-- >> It was a cool analogy. >> And choose all my toppings in there. So it is Taco Tuesday here-- >> Yes. >> At Microsoft Ignite and the developers like their choices of tools, just like they like their tacos. >> And they like their extra guac. So going back to one of the other points you made at the very opening. And this is the competitive dynamic that we have here. We had David Davis and Scott Lowe on yesterday from a ActualTech Media. Scott was incredibly bullish about Microsoft. And saying it could really overtake AWS, not tomorrow, but within the next decade. Of course, the choice for JEDI certainly could accelerate that. What do you make of it? I mean, do you think that's still pie in the sky here? AWS is so far ahead. >> So look, first of all, when you look at the growth rates, first of all, just to take the actual number, we know what AWS's, revenue is. Last quarter, AWS did $9 billion. And they're still growing at about a 35% clip. When I look at Microsoft, they have their intelligent cloud bucket, which is Azure, Windows Server, SQL Server and GitHub. And that was 10.8 billion. And you say, "Oh, okay, that's really big." But last year, Azure did about $12 billion dollars. So, AWS is still two to three times larger when you look at infrastructure as a service. But SaaS hugely important piece of what's going on in the cloud opportunity. AWS really is more of the platform and infrastructure service, they absolutely have some of the PaaS pieces. Azure started out as PaaS and has this. So you're trying to count these buckets, and Azure is still growing at, last quarter was 64%. So if you look at the projection, is it possible for Azure to catch up in the next three years? Well, Azure's growth rate is also slowing down, so I don't think it matters that much. There is a number one and a number two, and they're both clear, valid choices for a customer. And, this morning at breakfast, I was talking to a customer and they are very heavily on Microsoft shop. But absolutely, they've got some AWS on the side. They're doing Azure, they've got a lot of Azure, being here at our Microsoft show. And when I go to AWS, even when I talked to the companies that are all in on AWS, " Oh, you got O 365?" "Of course we do." "Oh, if you're starting to do O 365, are there any other services that you might be using out of Azure?" "Yeah, that's possible." I know Google is in the mix. Ali Baba's in the mix. Oracle, well, we're not going to talk about Oracle Cloud, but we talked about Oracle, because they will allow their services to run on Azure specifically. We talked about that a lot yesterday, especially how that ties into JEDI. So, look, I think it is great when we have a healthy competitive marketplace. Today really, it is a two horse race. It is, AWS and Azure are the main choices for customers. Everyone else is really a niche player. Even a company like IBM, there's good solutions that they have, but they play in a multi cloud world. Google has some great data services, and absolutely a important player when you talk about multi cloud for all they've done with Kubernetes and Istio. I'm going to be at Kube Con in a couple of weeks and Google is front and center there. But if you talk about the general marketplace, Microsoft has a lot of customers, they had a lot of applications and therefore, can they continue to mature that market and grow their environment? Absolutely. AWS has so many customers, they have the marketplace is stronger. It's an area that I want to dig in a little bit more at this show is the Azure Marketplace, how much we talked about the ecosystem. But, can I just procure through the cloud and make it simpler? Big theme we've talked about is, cloud in the early days was supposed to be cheap and simple. And it is neither of those things. So, how do we make it easier, so that we can go from the 20% of applications in the public cloud, up to 50% or more? Because it is not about all everything goes to the public cloud, but making customers put the applications and their data in the right place at the right time with the right services. And then we haven't even talked about edge computing which Microsoft has a big push on, especially with their partners. We talked to HP, a little bit about that yesterday. But really the surface area that this show and Microsoft covers is immense and global. >> It is indeed, and we are going, this is our second day of three days of coverage and we're going to be getting into all of those things. We've got a lot of great guests. We have Cute Host, Keith Townsend, Dave Cahill, a former Wikibon guy, a lot of other fantastic people. So I'm excited to get it on with you today, Stu. >> Thank you, Rebecca. Great stuff. >> I'm Rebecca Knight, for Stu Miniman. Stay tuned for more of theCUBE's live coverage of Microsoft Ignite. (upbeat music`)

>> live from New York. It's the Q covering AWS Global Summit 2019 brought to you by Amazon Web service is >> Welcome back. I'm stupid like co host Cory Quinn. And we're here at the end of a summit in New York City, where I'm really happy to welcome to the program first time guests, but somebody that has a nap, it's on my phone. So, Andy thing, who's the CEO of Door Dash, gave a great presentation this morning. Thanks so much for joining us. >> Absolutely happy to be here, guys. >> All right, so, you know, before we dig into the kind of your Amazon stack, bring us back. You talked about 2013. You know, your mission of the company will help empower local businesses. I think most people know, you know, door dash delivery from my local businesses. Whether that is a small place or, you know, chipotle o r like there. And I love little anecdote that you said the founders actually did the first few 100 deliveries, but it gives a little bit of the breath of the scope of the business now. >> Absolutely. I mean, when we started in 2013 you know, we started out of Ah dorm on Stanford campus and, like you said, we're doing the first couple 100 deliveries ourselves. But, you know, fast forwarding to today you were obviously at a much, much different level of scale. And I think one thing that I mentioned about it, Mikey No, a cz just We've been trying to keep up pays and more than doubling as a business every year. And it's a really fascinating industry that were in in the on demand delivery space in particular, I mean, Dara, the CEO of uber himself, said in May, which is a month and 1/2 ago. He said that you know, the food delivery industry may become bigger than a ride hailing industry someday. >> So just just one quick question on kind of food delivery. Because when I think back when I was in college, I worked at a food truck. It was really well known on campus, and there are people that 20 years later they're like stew. I remember you serving me these sandwiches, and I loved it in the community and we gather and we talk today on campus. Nobody goes to that place anymore, you know, maybe I know my delivery person more than I know the person that's making it. So I'm just curious about the relationship between local businesses and the people. How that dynamic changing the gig? Economy? I mean, yeah, you guys were right in the thick of it. No, it's a >> great question. I think. You know, for merchants, a lot of the things that we talk to them about it is you're actually getting access to customers who wouldn't even walk by your store in the first place. And I think that's something that they find to be very captivating. And it shows in the store sales data when they start partnering with the door dash. But we've also tried to building our products to really get customers to interact with the physical neighborhoods. Aaron the most concrete example of that as we launch a product called In Store in Star Pickup Chronic, where you can order online, skip the line and pick up the order yourself in the store, and I think a way we can build the AB experience around that, you know, you're gonna actually start building kind of a geospatial. Browse experience for customers with the door dash app, which means that they can get a little bit more familiarity with what's around them, as opposed to just kind of looking at it on their phones themselves. All right, >> so the logistics of this, you know, are not trivial. You talked about 325% order growth. You know, your database is billions of rose. You know, just the massive scale massive transaction. Therefore, you know, as a you know, your nap on. You know the scale you're at technology is pretty critical to your environment. So burgers inside that a little? >> Yeah. I mean, we're fortunate enough, and you and I are talking before the show. I mean, we're kind of born on the cloud way started off, actually on Roku. Uh, back in 2013 we adopted eight of us back in 2015. And there's just so many different service is that Amazon Web services has been able to provide us and they've added more overtime. I think the one that I talked about, uh was one that actually came out only in early 2018 which is the Aurora Post product. Um, we've been able to sail our databases scale up our analytics infrastructure. We've also used AWS for things like, you know, really time data streaming. They have the cloudwatch product where it gives us a lot of insight into the kind of our servers are behaving. And so the eight of us ecosystem in of itself is kind of evolving, and we feel like we've grown with them and they're growing with us. So it's been a great synergy over the past couple of years >> as you take a look at where you started and where you've wound up. Can you use that to extrapolate a little bit further? As far as what shortcomings you seeing today? That, ideally, would be better met by a cloud provider or at this point is it's such a simpatico relationship is you just alluded to where you just see effectively your continued to grow in the same simple directions just out of, I guess, happenstance. Yeah, it is a >> good question. I think there are some shortcomings. For example, eight of us just recently launched and chaos, which is their in house coffin solution. We're looking for something that's kind of a lot more vetted, right? So we're considering Do we adopt eight of us version or do we try to do it in house, or do we go with 1/3 party vendor? That's >> confidence. Hard to say no to these days. >> Yeah, exactly. And I think, you know, we want to make sure that we are building our infrastructure in a way that way, feel confident in can scale. I mean, with Aurora Post Chris, it's done wonders for us, but we've also kind of been the Pi. One of the pioneers were eight of us for scaling that product, and I think we got kind of lucky in some ways they're in terms of how it's been ableto pan out. But we want to make sure the stakes are a lot higher for us now. And so you know, when we have issues, millions of people face issues, so we want to make sure that we're being more thoughtful about it. Eight of us certainly has matured a lot over the past couple of years, but we're keeping our options open and we want to do what's best for our customers. Eight of us more often than not has a solution, but sometimes we have the you consider other solutions and consider the back that AWS may or may not. So some of the future problems. >> Oh yeah, it's, I think, that it's easy to overlook. Sometimes with something like a food delivery service. It's easy to make jokes about it about what you're too lazy to cook something. And sure, when I was younger, absolutely then I had a child. And when she wasn't going to sleep when she was a baby, I only had one hand. How do I How do I feed myself? There's an accessibility story. People aren't able to easily leave the house, so it's not just people aren't able to get their wings at the right time. This starts becoming impacting for people. It's an important need. >> Yeah, and I think it's been awesome to see just how quickly it's been adopted. And I think another thing about food delivery that you know people don't necessarily remember about today is it was Premier Li, just the very dense urban area phenomenon, like obviously in Manhattan, where we are today who delivers existed forever. But the suburbs is where the vast, vast majority of the growth of the industry has been and you know It's just awesome to see how this case has flourished with all different kinds of people. >> I have to imagine there's a lot of analytics that are going on for some of these. You said. In the rural areas, the suburban areas you've got, it's not as dense. And how do you make sure you optimize for people that are doing so little? So what are some of the challenges you're facing their in house technology helping? >> Exactly? Yeah. I mean, with our kind of a business, it's really important for us again to the lowest level of detail, right? Just cause we're going through 100 25% year on year in 2019 maybe we're growing faster in certain parts of the United States and growing slower and others, and that's definitely the case. And so, uh, one of the awesome things that we've been able to leverage from our cloud infrastructure is just the ability to support riel, time data access and our business operators across Canada. In the United States, they're constantly trying to figure out how are we performing relative to the market in our particular locality, meaning not just, you know, the state of New York. But Manhattan, in which district in Manhattan. Um, all that matters with a business like ours. Where is this? A hyper local economy? And so I think the real time infrastructure, particularly with things like with Aurora the faster up because we're able to actually get a lot of Reed. It's too these red because because it's not affecting our right volume. So that's been really powerful. And it's allowed our business operators to just really run in Sprint. >> So, Andy, I have to imagine just data is one of the most important things of your business. How do you look at that as an asset is their, You know, new things. That new service is that you could be putting out there both for the merchants as well for the customers. Absolutely. I think one of >> the biggest ones we try to do is you know, we never give merchant direct access to the customer data because we want to protect the customer's information, but we do give them inside. That's how they can increase their sales and target customers. I haven't used them before, So one of the biggest programs we launched over the past few years is what we call Try me free so merchants can actually target customers who've never place an order from their store before and offer them a free delivery for their order from that store. So that's a great way for merchants acquire new customers. And it's simple concept for them to understand. And over time we definitely want to be able to personalize the ability to target the sort of promotions on. So we have a lot of data to do that on. We also have data in terms of what customers like what they don't like in terms of their order behavior in terms of how they're raiding the food, the restaurant. So that kind of dynamic is something that is pretty interesting Data set for us to have. You know, you look at a other local companies out there like Yelp, Google Maps. They don't actually have verified transaction information, whereas we d'oh. So I think it's really powerful. Merchants actually have that make decisions. >> It's a terrific customer experience. It almost seems to some extent to be aligned with the Amazons Professor customer obsession leadership principle to some extent, and the reason I bring that up is you mentioned you started on Hiroko and then in 2015 migrated off to AWS. Was it a difficult decision for you to decide first to eventually go all in on a single provider? And secondly, to pick AWS as that provider It wasn't >> a hard decision for us to go to. Ah, no cloud provider. That was, you know, ready to like showtime. It's a hero is more of a student project kind of scale at that time. I don't know what they're doing today. Um, but I think a doubt us at the time was still very, very dominant and that we're considering Azure and G C P. I think was kind of becoming a thing back then made of us. It was always the most mature, and they've done a great job of keeping their lead in this space. Uh, Google, an azure have cropped up. Obviously, Oracle clouds coming up Thio and were considered I mean, we consider the capabilities of something like Google Cloud their machine. Learning soft service is a really powerful. They actually have really sophisticated, probably more so than a W s kubernetes service is actually more sophisticated. I guess it's built in house at Google. That makes sense. But, you know, we've considered landscape out there, but AWS has served a lot of our knees up to this point. Um, and I think it's gonna be a very dynamic industry with the cloud space. And there's so much at stake for all these different companies. It's fascinating to just be a part of it and kind of leverage. It >> s o nd I'm guessing, you know, when you look at some of your peers out there and you know, when a company files in s one and every goes, Oh, my God, Look at their cloud, Bill. You know, how do you look at that balance? You send your keynote this morning. You know, you like less than a handful of engineers working on the data infrastructure. So you know that line Item of cloud you know, I'm guessing is nontrivial from your standpoint. So how do you look at that? Internally is how do you make sure you keep control and keep flexibility and your options Yet focus on your core business and you know not, you know, that the infrastructure piece >> of it that was such a great question, because it's something that way we think about that trade off a lot. Obviously. In the early days, what really mattered ultimately is Do we have product market bid? Do we have? Do we have something that people will care about? Right. So optimizing around costs obviously was not prudent earlier on. Now we're in a such a large scale, and obviously the bills very big, uh, that, you know, optimizing the cost is very real thing, um, and part of what keeps, you know, satisfied with staying on one provider is kind of a piece of set up. And what you already have figured there? Um and we don optimization is over the years wear folks on financing now who basically looking at Hey, where are areas were being extremely inefficient. Where are areas that we could do? Bookspan, this is not just on AWS with is on all our vendors. Obviously eight of us is one of our biggest. I'm not the biggest line item there. Um, and we just kind of take it from there, and there's always trade offs you have to make. But I know there's companies out there that are trying to sell the value proposition of being ableto optimize your cloud span, and that is definitely something that there's a lot of. I'm sure there's a lot of places to cut costs in that we don't know about. And so, yeah, I think that's something that way we're being mindful of. >> Yeah, it's a challenge to you See across the board is that there's a lot of things you can do programmatically with a blind assessment of the bill. But without business inside, it becomes increasingly challenging. And you spoke to it yourself. Where you're not going to succeed or fail is a business because the bill winds up getting too high. Unless you're doing something egregious, it's a question of growth. It's about ramping, and you're not gonna be able to cost optimize your way to your next milestone unless something is very strange with your business. So focusing on it in due course is almost always the right answer. >> Yeah, I mean, when I think about increasing revenue or deep recent costs nine times out of 10 we're trying to provide more value, right, so increasing revenues, usually they go to option for us, but they're sometimes where it's obvious. Hey, there's a low hanging fruit and cutting costs, and if it's relatively straightforward to do, then let's do it. I think with all the cloud infrastructure that we've been able to build on top of, we've been able to focus a lot of our energy and efforts on innovating, building new things, cementing our industry position. And, yeah, I think it's been awesome. On top >> of what? Want to give you the final word? Any addressing insights in your business? You know, it's like I like food and I like eating out and, you know, it feels like, you know, we've kind of flatten the world in lot is like, You know, I think it was like, uh, like, 556 years ago. The first time I went white and I got addressed to Pok. Everybody in California knows, okay, but I live on the East Coast now. I've got, like, three places within half an hour of me that I could get it. So you know those kind of things. What insight to you seeing you know what's changing in the marketplace? What? What's exciting you these >> days? Yeah, I mean, for us, we've definitely seen phenomenon where different food trans kind of percolate across different areas. I'm going to start in one region and then spread out across the entire United States or even Canada. I would say I don't way try to have as much emergence election on a platform. It's possible so that no matter what the new hot hottest trend is that more likely than not, we're gonna have what you want on the platform. And I think what's really exciting to us over the next couple years is you know, last year we actually started way started satisfying grocery delivery. So, uh, in fact, we power a lot of grocery deliveries for Walmart today, which is exciting, and a lot of other grocers lined up as well. We're gonna see how far we can take our logistics capabilities from that standpoint, But really, we want to want to have as many options as possible for our customers. >> Anything. Thanks so much for joining us. Congressional Congratulations on the progress with your death for Cory Quinn. I'm stupid and we'll be back here with more coverage from eight of US summit in New York City. 2019. Thanks is always watching. Cute

live from Miami Beach Florida Biman 2019 brought to you by beam welcome back to sunny Miami everybody you're watching the cube the leader in live tech coverage we like to go out to the events extract the signal from the noise and we're here at Vemma on 2019 I'm Dave Volante with my co-host this is day 2 Peter Burris and I have been covering wall-to-wall coverage with the cube folks from net APIs are here Jeff McCullough who's the vice president of Americas partner sales for net app and our good friend Keith Norby who runs alliances for net up guys great to see you thanks for coming on thanks for having us so Keith let's start with you V has been a partner of yours for a while now you guys go to market together year you have always been very partner friendly particularly when it comes to data protection but what's the state of the partnership today yeah this is something that we'd looked at a couple years ago and got into a very much more strategic relationship with veem over a year ago kind of work through a lot of ways to reconnect and establish a better together and this is something that we think is a strategic opportunity is kind of backed by a lot of the data you see at this show talking about you know organizations are gonna change roughly 60% of the organization is going to change their platform because of cost complexity reasons and together we've been working with Veeam to figure out how to deliver data protection for a data fabric and and IDC validates that in a number of ways that we can unpack here on this on the show or in the conversations with customers and and we've gotten great reaction to it and Jeff you lead America's partner sales from North America South America the whole kit and kaboodle talk more about your role sure well my responsibility is net at partners I am I'm successful when our partners successful are successful so everything I do is all around putting our partners in the position of you know executing being successful within that brand certainly being profitable right having profitable strong businesses and and growing right growing and taking taking market share and and helping them expand and grow their respective business law you guys have dramatically increased the percentage of your sales that come through the channel over the last you know 10 10 12 years yes pretty significantly and there's a fundamental part of your strategy stager at this executive level so yeah for sure you know channel is its core to what we do you know when we go to market you know with developing our products or executing our marketing plans it's all around how do we go execute with partners right whether it's the tools the partners need the pricings the programs to help them go engage in the market that leads to man generation and we're at various stages in all these but you know what I think you'll see consistently from the partners that you know certainly will talk and talk about their net businesses we generally lead in profitability across our partner base and we absolutely lead in terms of total profitability when you include things like services attached and how we go and execute on us partner delivered services strategy so you know from I always say NetApp is it's not just a product category it's a whole economy for our channel and it puts people to work it allows them to expand and grow their teams and it's it's a critical part of many many of the partners that are here today at veeneman certain v-mon and and certainly in the marketplace and your partner friendly and assess that you don't have a huge services organization that's competing with your channel i mean that's a jerk yeah we put partner services in the forefront of everything we do Keith you talked about better together yeah what does that mean just in terms of engineering integration go to market I mean how did you sort over the last two years you know get better together what specific actions were you guys taking I think you got to look at it first from kind of the customer in the markets in and you got a look at what's the dynamic that requires change right that sort of shapes what your PRD and your Mardis are to make a product in this case you know we've got platforms that have incredible snapshot technologies so to me it really starts there with simplifying the way that you get the first copy of data and then simply working with the strengths that veem has and their platforms and making sure that we have great option ality between our replication and other snapshot technologies their replication tech to be able to give a level of flexibility for this data fabric to come to life you know no matter if you've got the traditional data center that's got these enterprise apps like at sa P Hana or others or you built the next generation data center like on that FH CI and you're building up scale out via more private cloud or you've got the hyper scalar cloud you know with our cloud volumes you know we have options on how we get data throughout the copy process of primary to secondary to you know cloud and tertiary data so you know to us it was about really making that as simple and as pre-wired as possible via the api's and then really making that easy for partners to go and grab on to to make it easy for someone to buy us because you always want to build something that people want to buy no one wants to be sold any of this stuff and so building the right thing that people want to buy the next step then with Jeff and reason why is so critical to this is getting that ready for the partners be able to have an easy process with their customers that frankly they love people hate to be sold they love to buy yeah let's talk about they love to buy one of the challenges that the entire industry has is we move through the significant transformation is customers user organizations or themselves in the midst of huge transformations institutional transformations technology transformations relationship with their business transformation mission transformation just starting with this whole role that the channel is has been playing it's going to play how will the channel be an increasing source of value add in the deal yeah how's that playing out to help these customers you know smooth their changes yeah and I think you know I was just watching the news this morning right target announced their earnings and a big part of their earnings announcement was the improvement they made in customer interaction through digital platforms right the ability to order online pick up in the store or order online and have it delivered same-day right and these are and it's just you know one example you can go down the list of customers that have really used transformation to change their business right and you know Chipotle who's trans you know they've transformed burritos now and a lot of their successes come through digital transformation platforms so you know the evidence is overwhelming that digital transformation drives better results and we've done a lot of study at this right we we have lots of detail around customers that know how to use data and you know that the basic fact is one out of ten customers is in a position to actually leverage data effectively right this is all of the research we've done along you know with partners with with other companies the other nine need help and this is where channel partners come in this is what I tell partners all the time is this digital transformation wave is real the results are real and the customers need to move is is real and so they play a role in can play a role in helping customers accelerate that digital transformation and so our portfolio is all around accelerating customers and their ability to leverage data to transform their business and partners through both of the portfolio that they sell but then the partner driven services that we promote and drive you know really stand out in the forefront of being able to help a customer execute these these really tough strategies and in you know the thing that reason why customers love partners is partners bring choices right and you know for us as vendors we have to deal with the other side of that which is partners have choices and who they sell so we represent a portfolio that is forward thinking it aligns to where the market is going the lines to the tough problems that customers have and it's you know in its a position that allows partners to be profitable and and make money helping customers transform and deliver their own success but it's got to be more than just partners cat create choices and here's one explain what I mean by that it's increasingly your typical CIO medium-sized company large size company which is where we spend most of our time is thinking in terms of what is going to bring me value today and also generate a stream of value for me in the future so I need choice now but options for the future that are relevant and meaningful and so partners increasingly have to be part of that options equation how are they going to create options for customers and you know one of the nice things about the relationship that you have the theme is that you are a partner to veem and presumably you're going to help Veen customers create additional types of options through this expanding folio of value that you guys have so so talk about that dynamic because it really requires an even greater dependency on that customer partner engagement including you know the dependency the beam has on on you guys yeah doing it maybe start with just the veem partnership partnership yeah I think you know which we create the conditions with which I think a partner comes to life with what we've tried to do in in the product building solutions and then trying to develop the go-to-market around the partners ability to go meet the market and what the market is asking for in such you know the partners have natural services on the front side of the assessments a bit like trying to help you plan your 401 K they help you like see what kind of data you don't even see we have a wealth of partners that just have incredible skills there and then as they take that through our solution we do everything we can to make that process easy to match our technology to that design requirement and then afterwards the partners always have these these great capabilities for things like you know a one call or a managed service to help take even more complexity off the table for people to just live with the ability to have data protected across all spectrums of where they have data live so the partner equation is definitely getting more complicated right if you dial back you know half a decade decade you had guys who sold hardware boxes they livox sellers we love them but and they moved a lot of a lot of product and they worked with you okay now the cloud comes in you guys they're going you know software-defined so you can run your services in the cloud you know or you run it on Prem you've got hybrid so it's a complicated equation much more so than it was in the past so how are you seeing the partners evolve and transform you know beyond the sort of box selling mentality of course you know VMware specialists you get those guys at sa P maybe Oracle but yeah but it's even more than that now with cloud isn't it oh yeah yeah you know cloud is you know kind of the third big disruptive wave in the channel right if you think of kind of client-server is the big first disruptive way of virtualization the second disruptive way to now cloud just purely from a channel perspective the third big one and maybe the biggest right because it is completely changing the dynamics and the economics of how partners operate and you know and we've been looking at this for you know for a long time and certainly as we move our portfolio as we transition our portfolio to be cloud enabled and native to the cloud it creates options but but you know the market is moving from you know deal based revenue to reoccurring revenue and what I see partners moving to is various various degrees of reoccurring revenue strategies whether they're setting up their own MSP business and they're opening up shop and they're doing data protection on demand or they are doing managed services on premise and they're charging customer or they're buying out the infrastructure I'm charging a customer once a month or they're selling services in the cloud and in what I think is also interesting and you can see the kind of the direction where the industry of a channel is going is when you look at the acquisitions that partners are making not only of each other but of software development right IP there are going out and buying software development because the the the long term opportunity is not just selling the infrastructure it's selling a solution solving a big problem right which could be this digital transformation opportunity but it's it's more than just sure I can I can upgrade your servers it's their digital transformation right it is you know you know kind of clouds not really a destination right everybody thinks clouds the destination I got to get to you know it's not a destination it's a tool in the bag that you know customer is going to use and certainly a partner is going to leverage cloud to create a money stream write a business model that is sustainable and can grow but it's super dynamically different than what we do you know what they're doing today so you guys talk about profitability before you had a point go ahead and I say balance all that against I think we're the volume the mass of the volume is even though the hyper scalars have a tremendous amount of growth it is still VM based it is still kind of on-prem based and so there's still in this two-year window of change the vast majority of the opportunity is going to be on Prem but you also have to factor in how you involve the cloud and that strategy as what ratmir would called second wave right of beams strategy and we're right in the heart of that I mean there isn't any greater strength than what we're doing as a company with NetApp than what we're doing with cloud and it's just a natural way for us to extend you know a partner's capability a customer's ability to buy what they what you'd want to get from NetApp and beam together well and what the hyper scales have done is they've changed the way in which people consume technology absolutely understand and NetApp is a great case study of a company that's moving through that process from a product orientation to a services orientation the key I want to come back to this notion of how the NetApp relationship with Veen creates new classes of options for Ravine customers as they thought try to think about data protection differently because precisely because it's Dave said you have expanded your portfolio you are going to market with a different value proposition than a couple years ago how is that playing out in your conversations with customers as they think about moving from a data protection that's focused on devices to a data protection that's focused on delivery of digital services yeah well it's not a great topic to talk about where do you start with that organically I think you look at the way people try to operate and deal with the operations of data protection you know it really starts there because you know cloud is really about IT operations what we've done is really try to simplify that stack to get beyond it being one single endpoint of technology so it's not just about how we take data sets you know from say a net F as or a net of HCI and bring it through Veeam to another thousand or eseries and then off to the cloud you know it's beyond just the basic technology it's much more operational and it's in its nature so if you look at all the stuff they're talking about here with VOA and all the discovery elements that they're doing to help make it easier one of the one of the areas that IDC caught particularly in one of our benefit statements on taking complexity off the table is our ability to have autodiscover of yemm's you know it's it's ways that you could make much more autonomy and orchestration of operations kind of come to life as a way of you doing this technology together that's only just one of the example points that we have on this better together with veem taking the heart of their core technology and where they're being you know pervade of in in not just a VM centric crowd but also hyper-v and some of the other things they talked about that's kind of the top of their rationalize stack and then bringing that down through the heart of our data fabric portfolio and saying you know any one point at which you're at we were able to put these things together at the heart of the first step and we kind of mapped this customer journey out in our presentation to the attendees here was this customer journey from the current form of complexities you have you know and moving that all the way through to snapshot integration platform selection of which ones would make sense for what scenario how we work through veem x' data replication and management technologies our data replication our data fabric technologies to get from one endpoint to the other so and then ultimately you gotta be able talk about the ability to restore or you really shouldn't be talking about backup all right we got a wrap but I'm gonna ask you guys each question Jeff from trip reports so from your standpoint you talkin sales momentum with partners what are you gonna tell your colleagues and Keith obviously the partnership with Veen what what are you gonna tell your colleagues when you get back home yeah so so for me it's you know this is we've talked about transformation this you know I think our relationship with Veeam and the strategies that we're executing is all around transforming data protection right and it's really around this concept of simplification and I think as we were chatting before before we started taping the you know simple simple matters right simplification or simple is really attractive feature and you know our ability to simplify data protection for customers in partnership with Veeam deliver solution that's you know clearly world-class and you know NetApp bringing world-class technology to the table it's a great partnership it creates an opportunity for us to go and have conversations with customers that made me never thought of NetApp before and and it's you know an opportunity for us to open a lot of doors and certainly for me what I care about it's an opportunity for our partners to open a lot of doors yeah I would just say listen we worked from our joint CEOs together so George and ratmir starting this like joint bond of alignment all the way down through product solutions feel Geo's channels we're gonna have explosive growth together you know we're gonna go address this market that is looking to change we've got something we're bringing together and it's absolutely better together great power players aligning at the top all the way down through the channel to the partners into the cloud bringing you all the data here the cube Jeff and Keith thanks very much for coming on the cube keep it right to everybody Peter Burris and I will be back with our next guest right after this short break we're live from Miami at Vemma in 2019 over a pack

produced from the cube studios this is strong by science in-depth conversations about science based training sports performance and all things health and wellness here's your host max smart [Music] [Applause] [Music] all right thank you guys tune in today I have the one and only Dean of big data the man the myth the legend bill Schwarz oh also my dad is the CTO of Hitachi van Tara and IOC in analytics he has a very interesting background because he is the well he's known as the Dean of big data but also the king of the court and all things basketball related when it comes to our household and unlike most people in the data world and I want to say most as an umbrella term but a some big bill has an illustrious sports career playing at Coe College the Harvard of the Midwest my alma mater as well but I think having that background of not just being computer science but where you have multiple disciplines involved when it comes to your jazz career you had basketball career you have obviously the career Iran now all that plays a huge role in being able to interpret and take multiple domains and put it into one so thank you for being here dad yeah thanks max that's a great introduction I rep reciate that no it's it's wonderful to have you and for our listeners who are not aware bill is referring him is Bill like my dad but I call my dad the whole time is gonna drive me crazy bill has a mind that thinks not like most so he he sees things he thinks about it not just in terms of the single I guess trajectory that could be taken but the multiple domains that can go so both vertically and horizontally and when we talk about data data is something so commonly brought up in sports so commonly drop in performance and athletic development big data is probably one of the biggest guess catchphrases or hot words or sayings that people have nowadays but doesn't always have a lot of meaning to it because a lot of times we get the word big data and then we don't have action out of big data and bill specialty is not just big data but it's giving action out of big data with that going forward I think a lot of this talk to be talking about how to utilize Big Data how do you guys data in general how to organize it how to put yourself in a situation to get actionable insights and so just to start it off Becky talked a little bit on your background some of the things you've done and how you develop the insights that you have thanks max I have kind of a very nos a deep background but I've been doing data analytics a long time and I was very fortunate one of those you know Forrest Gump moments in life where in the late 1980s I was involved in a project at Procter & Gamble I ran the project where we brought in Walmart's point of sales data for the first time into a what we would now call a data warehouse and for many of this became the launching point of the data warehouse bi marketplace and we can trace the effect the origins of many of the BI players to that project at Procter & Gamble in 87 and 88 and I spent a big chunk of my life just a big believer in business intelligence and data warehousing and trying to amass data together and trying to use that data to report on what's going on and writing insights and I did that for 20 25 years of my life until as you probably remember max I was recruited out Business Objects where I was the vice president of analytic applications I was recruited out of there by Yahoo and Yahoo had a very interesting problem which is they needed to build analytics for their advertisers to help those advertisers to optimize or spend across the Yahoo ad network and what I learned there in fact what I unlearned there was that everything that I had learned about bi and data warehouse and how you constructed data warehouses how you were so schema centric how everything was evolved around tabular data at Yahoo there was an entirely different approach the of my first introduction to Hadoop and the concept of a data Lake that was my first real introduction into data science and how to do predictive analytics and prescriptive analytics and in fact it was it was such a huge change for me that I was I was asked to come back to the TD WI data world Institute right was teaching for many years and I was asked to do a keynote after being at Yahoo for a year or so to share sort of what were the observations what did I learn and I remember I stood up there in front of about 600 people and I started my presentation by saying everything I've taught you the past 20 years is wrong and it was well I didn't get invited back for 10 years so that probably tells you something but it was really about unlearning a lot about what I had learned before and probably max one of the things that was most one of the aha moments for me was bi was very focused on understanding the questions that people were trying to ask an answer davus science is about us to understand the decisions they're trying to take action on questions by their very nature our informative but decisions are actionable and so what we did at Yahoo in order to really drive the help our advertisers optimize your spend across the Yahoo ad network is we focus on identifying the decisions the media planners and buyers and the campaign managers had to make around running a campaign know what what how much money to allocate to what sides how much how many conversions do I want how many impressions do I want so all the decisions we built predictive analytics around so that we can deliver prescriptive actions to these two classes of stakeholders the media planners and buyers and the campaign managers who had no aspirations about being analysts they're trying to be the best digital marketing executives or you know or people they could possibly be they didn't want to be analysts so and that sort of leads me to where I am today and my my teaching my books my blogs everything I do is very much around how do we take data and analytics and help organizations become more effective so everything I've done since then the books I've written the teaching I do with University of San Francisco and next week at the National University of Ireland and Galway and all the clients I work with is really how do we take data and analytics and help organizations become more effective at driving the decisions that optimize their business and their operational models it's really about decisions and how do we leverage data and analytics to drive those decisions so what would how would you define the difference between a question that someone's trying to answer versus a decision but they're trying to be better informed on so here's what I'd put it I call it the Sam test I am and that is it strategic is it actionable is it material and so you can ask questions that are provocative but you might not fast questions that are strategic to the problems you're trying to solve you may not be able to ask questions that are actionable in a sense you know what to do and you don't necessarily ask questions that are material in the sense that the value of that question is greater than the cost of answering that question right and so if I think about the Sam test when I apply it to data science and decisions when I start mining the data so I know what decisions are most important I'm going through a process to identify to validate the value and prioritize those decisions right I understand what decisions are most important now when I start to dig through the data all this structured unstructured data across a number different data sources I'm looking for I'm trying to codify patterns and relationships buried in that data and I'm applying the Sam test is that against those insights is it strategic to the problem I'm trying to solve can I actually act on it and is it material in the sense that it's it's it's more valuable to act than it is to create the action around it so that's the to me that big difference is by their very nature decisions are actually trying to make a decision I'm going to take an action questions by their nature are informative interesting they could be very provocative you know questions have an important role but ultimately questions do not necessarily lead to actions so if I'm a a sport coach I'm writing a professional basketball team some of the decisions I'm trying to make are I'm deciding on what program best develops my players what metrics will help me decide who the best prospect is is that the right way of looking at it yeah so we did an exercise at at USF too to have the students go through an exercise - what question what decisions does Steve Kerr need to make over the next two games he's playing right and we go through an exercise of the identifying especially in game decisions exercise routes oh no how often are you gonna play somebody no how long are they gonna play what are the right combinations what are the kind of offensive plays that you're gonna try to run so there's a know a bunch of decisions that Steve Kerr is coach of the Warriors for example needs to make in the game to not only try to win the game but to also minimize wear and tear on his players and by the way that's a really good point to think about the decisions good decisions are always a conflict of other ideas right win the game while minimizing wear and tear on my players right there's there are there are all the important decisions in life have two three or four different variables that may not be exactly the same which is by this is where data science comes in the data science is going to look across those three or four very other metrics against what you're going to measure success and try to figure out what's the right balance of those given the situation I'm in so if going back to the decision about about playing time well think about all the data you might want to look at in order to optimize that so when's the next game how far are they in this in this in the season where do they currently sit ranking wise how many minutes per game has player X been playing looking over the past few years what's there you know what's their maximum point so there's there's a there's not a lot of decisions that people are trying to make and by the way the beauty of the decisions is the decisions really haven't changed in years right what's changed is not the decisions it's the answers and the answers have changed because we have this great bound of data available to us in game performance health data you know all DNA data all kinds of other data and then we have all these great advanced analytic techniques now neural networks and unstructured supervised machine learning on right all this great technology now that can help us to uncover those relationships and patterns that are buried in the data that we can use to help individualize those decisions one last point there the point there to me at the end when when people talk about Big Data they get fixated on the big part the volume part it's not the volume of big data that I'm going to monetize it's the granularity and what I mean by that is I now have the ability to build very detailed profiles going back to our basketball example I can build a very detailed performance profile on every one of my players so for every one of the players on the Warriors team I can build a very detailed profile it the details out you know what's their optimal playing time you know how much time should they spend before a break on the feet on the on the on the court right what are the right combinations of players in order to generate the most offense or the best defense I can build these very detailed individual profiles and then I can start mission together to find the right combination so when we talk about big it's not the volume it's interesting it's the granularity gotcha and what's interesting from my world is so when you're dealing with marketing and business a lot of that when you're developing whether it be a company that you're trying to find more out about your customers or your startup trying to learn about what product you should develop there's tons of unknowns and a lot of big data from my understanding it can help you better understand some patterns within customers how to market you know in your book you talk about oh we need to increase sales at Chipotle because we understand X Y & Z our current around us now in the sports science world we have our friend called science and science has helped us early identify certain metrics that are very important and correlated to different physiological outcomes so it almost gives us a shortcut because in the big data world especially when you're dealing with the data that you guys are dealing with and trying to understand customer decisions each customer is individual and you're trying to compile all together to find patterns no one's doing science on that right it's not like a lab work where someone is understanding muscle protein synthesis and the amount of nutrients you need to recover from it so in my position I have all these pillars that maybe exist already where I can begin my search there's still a bunch of unknowns with that kind of environment do you take a different approach or do you still go with the I guess large encompassing and collect everything you can and siphon after maybe I'm totally wrong I'll let you take it away no that's it's a it's a good question and what's interesting about that max is that the human body is governed by a series of laws we'll say in each me see ology and the things you've talked about physics they have laws humans as buyers you know shoppers travelers we have propensity x' we don't have laws right I have a propensity that I'm gonna try to fly United because I get easier upgrades but I might fly you know Southwest because of schedule or convenience right I have propensity x' I don't have laws so you have laws that work to your advantage what's interesting about laws that they start going into the world of IOT and this concept called digital twins they're governed by laws of physics I have a compressor or a chiller or an engine and it's got a bunch of components in it that have been engineered together and I can actually apply the laws I can actually run simulations against my digital twins to understand exactly when is something likely to break what's the remaining useful life in that product what's the severity of the the maintenance I need to do on that so the human body unlike the human psyche is governed by laws human behaviors are really hard right and we move the las vegas is built on the fact that human behaviors are so flawed but body mate but bat body physics like the physics that run these devices you can actually build models and one simulation to figure out exactly how you know what's the wear and tear and what's the extensibility of what you can operate in gotcha yeah so that's when from our world you start looking at subsystems and you say okay this is your muscular system this is your autonomic nervous system this is your central nervous system these are ways that we can begin to measure it and then we can wrote a blog on this that's a stress response model where you understand these systems and their inferences for the most part and then you apply a stress and you see how the body responds and even you determine okay well if I know the body I can only respond in a certain number of ways it's either compensatory it's gonna be you know returning to baseline and by the mal adaptation but there's only so many ways when you look at a cell at the individual level that that cell can actually respond and it's the aggregation of all these cellular responses that end up and manifest in a change in a subsystem and that subsystem can be measured inferential II through certain technology that we have but I also think at the same time we make a huge leap and that leap is the word inference right we're making an assumption and sometimes those assumptions are very dangerous and they lead to because that assumptions unknown and we're wrong on it then we kind of sway and missed a little bit on our whole projection so I like the idea of looking at patterns and look at the probabilistic nature of it and I'm actually kind of recently change my view a little bit from my room first I talked about this I was much more hardwired and laws but I think it's a law but maybe a law with some level of variation or standard deviation and it we have guardrails instead so that's kind of how I think about it personally is that something that you say that's on the right track for that or how would you approach it yeah actually there's a lot of similarities max so your description of the human body made up of subsystems when we talk to organizations about things like smart cities or smart malls or smart hospitals a smart city is comprised of a it's made up of a series of subsystems right I've got subsystems regarding water and wastewater traffic safety you know local development things like this look there's a bunch of subsystems that make a city work and each of those subsystems is comprised of a series of decisions or clusters of decisions with equal use cases around what you're trying to optimize so if I'm trying to improve traffic flow if one of my subsystems is practically flow there are a bunch of use cases there about where do I do maintenance where do I expand the roads you know where do I put HOV lanes right so and so you start taking apart the smart city into the subsystems and then know the subsystems are comprised of use cases that puts you into really good position now here's something we did recently with a client who is trying to think about building the theme park of the future and how do we make certain that we really have a holistic view of the use cases that I need to go after it's really easy to identify the use cases within your own four walls but digital transformation in particular happens outside the four walls of an organization and so what we what we're doing is a process where we're building journey maps for all their key stakeholders so you've got a journey map for a customer you have a journey map for operations you have a journey map for partners and such so you you build these journey maps and you start thinking about for example I'm a theme park and at some point in time my guest / customer is going to have a pity they want to go do something you want to go on vacation at that point in time that theme park is competing against not only all the other theme parks but it's competing against major league baseball who's got things it's competing against you know going to the beach in Sanibel Island just hanging around right there they're competing at that point and if they only start engaging the customer when the customers actually contacted them they must a huge part of the market they made you miss a huge chance to influence that person's agenda and so one of the things that think about I don't know how this applies to your space max but as we started thinking about smart entities we use design thinking and customer journey match there's a way to make certain that we're not fooling ourselves by only looking within the four walls of our organization that we're knocking those walls down making them very forest and we're looking at what happens before somebody engages it with us and even afterwards so again going back to the theme park example once they leave the theme park they're probably posting on social media what kind of fun they had or fun they didn't have they're probably making plans for next year they're talking to friends and other things so there's there's a bunch of stuff we're gonna call it afterglow that happens after event that you want to make certain that you're in part of influencing that so again I don't know how when you combined the data science of use cases and decisions with design thinking of journey Maps what that might mean to do that your business but for us in thinking about smart cities it's opened up all kinds of possibilities and most importantly for our customers it's opened up all kinds of new areas where they can create new sources of value so anyone listening to this need to understand that when the word client or customer is used it can be substituted for athlete and what I think is really important is that when we hear you talk about your the the amount of infrastructure you do for an idea when you approach a situation is something that sports science for in my opinion especially across multiple domains it's truly lacking what happens is we get a piece of technology and someone says go do science while you're taking the approach of let's actually think out what we're doing beforehand let's determine our key performance indicators let's understand maybe the journey that this piece of technology is going to take with the athlete or how the athletes going to interact with this piece of technology throughout their four years if you're in the private sector right that afterglow effect might be something that you refer to as a client retention and their ability to come back over and over and spread your own word for you if you're in the sector with student athletes maybe it's those athletes talking highly about your program to help with recruiting and understanding that developing athletes is going to help you know make that college more enticing to go to or that program or that organization but what really stood out was the fact that you have this infrastructure built beforehand and the example I give I spoke with a good number of organizations and teams about data utilization is that if if you're to all of a sudden be dropped in the middle of the woods and someone says go build a cabin now how was it a giant forest I could use as much wood as I want I could just keep chopping down trees until I had something that had with a shelter of some sort right even I could probably do that well if someone said you know what you have three trees to cut down to make a cabin you could become very efficient and you're going to think about each chop in each piece of wood and how it's going to be used and your interaction with that wood and conjunction with that woods interaction with yourself and so when we start looking at athlete development and we're looking at client retention or we're looking at general health and wellness it's not just oh this is a great idea right we want to make the world's greatest theme park and we want to make the world's greatest training facility but what infrastructure and steps you need to take and you said stakeholders so what individuals am i working with am I talking with the physical therapist am i talking with the athletic trainer am I talking with the skill coach how does the skill coach want the data presented to them maybe that's different than how the athletic trainer is going to have a day to present it to them maybe the sport coach doesn't want to see the data unless something a red flag comes up so now you have all these different entities just like how you're talking about developing this customer journey throughout the theme park and making sure that they have a you know an experience that's memorable and causes an afterglow and really gives that experience meaning how can we now take data and apply it in the same way so we get the most value like you said on the granular aspect of data and really turn that into something valuable max you said something really important and one of the things that let me share one of many horror stories that that that comes up in my daily life which is somebody walking up to me and saying hey I got a client here's their data you know go do some science on it like well well what the heck right so when we created this thing called the hypothesis development canvas our sales teams hate it or do the time our data science teams love it because we do all this pre work we just say we make sure we understand the problem we're going after the decision they're trying to make the KPI is it's what you're going to measure success in progress what are they the operational and financial business benefits what are the data sources we want to consider here's something by the way that's it's important that maybe I wish Boeing would have thought more about which is what are the costs of false positives and false negatives right do you really understand where your risks points are and the reason why false positive and false negatives are really important in data science because data size is making predictions and by virtue of making predictions we are never 100% certain that's right or not predictions hath me built on I'm good enough well when is good enough good enough and a lot of that determination as to when is good enough good enough is really around the cost of false positives and false negatives think about a professional athlete like the false the you know the ramifications of overtraining professional athlete like a Kevin Durant or Steph Curry and they're out for the playoffs as huge financial implications them personally and for the organization so you really need to make sure you understand exactly what's the cost of being wrong and so this hypothesis development canvas is we do a lot of this work before we ever put science to the data that yeah it's it's something that's lacking across not just sports science but many fields and what I mean by that is especially you referred to the hypothesis canvas it's a piece of paper that provides a common language right it's you can sit it out before and for listeners who aren't aware a hypothesis canvas is something bill has worked and developed with his team and it's about 13 different squares and boxes and you can manipulate it based on your own profession and what you're diving into but essentially it goes through the infrastructure that you need to have setup in order for this hypothesis or idea or decision to actually be worth a damn and what I mean by that is that so many times and I hate this but I'm gonna go in a little bit of a rant and I apologize that people think oh I get an idea and they think Thomas Edison all son just had an idea and he made a light bulb Thomas Edison's famous for saying you know I did you know make a light bulb I learned was a 9000 ways to not make a light bulb and what I mean by that is he set an environment that allowed for failure and allowed for learning but what happens often people think oh I have an idea they think the idea comes not just you know in a flash because it always doesn't it might come from some research but they also believe that it comes with legs and it comes with the infrastructure supported around it that's kind of the same way that I see a lot of the data aspect going in regards to our field is that we did an idea we immediately implement and we hope it works as opposed to set up a learning environment that allows you to go okay here's what I think might happen here's my hypothesis here's I'm going to apply it and now if I fail because I have the infrastructure pre mapped out I can look at my infrastructure and say you know what that support beam or that individual box itself was the weak link and we made a mistake here but we can go back and fix it