>>Hello. Welcome back to the cubes coverage of splunk.com virtual this year. I'm John ferry, host of the cube. And one of the great reasons of great reasons of being on site with the team here is we have to bring remote guests in real guests from all no stories, too small. We bring people into the cube to have the right conversations. We've got Brooke Cunningham area, VP of global partner marketing experience. Brooke, welcome to the cube. Thanks for coming on. >>Hey, thank you, John. This is my sixth dot conflict, but this is actually my first time being on the cube. So I'm delighted. >>Great to have you on these new hybrid events. We can bring people in. You don't have to be here. All the execs are here, the partners are here. Great news is happening all around the world. You guys just announced a new partner program for the cloud called partner verse program. This is kind of, you know, mostly partner news is okay. Okay. Partner news partner ecosystem. But I think this is an important story because Splunk is kind of going to the next level of scale. That's to me is my observations walking away from the keynote, a lot of the partners, great technology, great platform, a lot of growth with cloud. We had formula one on you guys have a growing ecosystem. What is the new announcement partner versus about? >>Yes. Thanks, John. And you are spot on. We are growing for scale and Splunk's partner ecosystem is 2200 strong and we were so delighted to have so much partner success highlighted today on the keynotes. And specifically we have announced an all new spunk Splunk partner program called the Splunk partner verse. So we're taking it to new frontiers for our partners, really built for the cloud to help our partners lean into those cloud transformations with their customer. >>Great. Fro can you walk me through some of the numbers inside the numbers for a second? How many partners do you have and what is this program about specifically? >>Yeah, so 2200 partners that we featured some amazing stories in the keynotes today, around some of the momentum we have with partners like AWS, a center blue buoyant, a partner that just recently rearchitected all of their managed services from Splunk enterprise to Splunk cloud, because as they put it, Splunk is the only solution that can truly offer that hybrid solution for their customers. So all new goodness for our partners to help them lean in, to get enabled around all of the Splunk products, as well as to differentiate, differentiate their offerings with a new badging system. And we're going to help our partners really take that to the market by extending and expanding our marketing and creating an all new solutions catalog for our partners to differentiate themselves to their customers. >>You mentioned a couple things I want to double down on this badging thing, get in some of the nuances, but I want to just point out that, you know, and get your reaction to this when you see growth. And I saw this early on with AWS early on, when they performing, when you start to see the ecosystem grow like this, you start to see more enablement. You see more, money-making going on more, more, um, custom solutions, more agility you. So you started to see these things develop around you guys. So what does all this badging mean? How what's in it for me as a partner? Like how do I win on this? >>Yeah, great question. So first of all, John partner listening is a big part of what we do here at Splunk. And it's specifically a major part of what I do in my role. So we create a lot of forums to get that real deal partner feedback. What do they need to be successful with their customers? Especially as Splunk continues to expand our portfolio. And we heard some really clear feedback from our partners. Number one, they need more enablement faster, especially all those new products. They really want to get enabled around new product areas like observability, their customers are asking for it. They secondly told us that being able to differentiate themselves to customers was key. And that showing that they had core expertise around specific solution areas, types of services, as well as specializations. For example, some of our partners that are authorized learning partners, they really want it to be able to showcase these skills and differentiate that to their customers in the market. And it's not a role for us at Splunk to really help them do that. And so we took that feedback and really incorporated it into this new program, badging specifically will help to address some of those things I mentioned. So for example, a lot of badging around those use case areas, security, observability, AOD migrations, as well as specializations. Like I mentioned, for things like, uh, partners that are doing, uh, learning specific partners that are really helping us extend our reach in, in different international markets and so on. >>Okay. Let me just ask a question on the badge if you don't mind. Um, so you mentioned, you mentioned almost like you were going through like verticals is badging to be much more about discovery from a client customer, uh, end user customer standpoint. Are you looking to create kind of much more categorical differentiation is what's the, what, what's the purpose of the badge? Cause I noticed it was like different verticals. I heard security and >>Yeah, so I would say it's think of it as both. So for example, our partners go to market with us in many different ways. Some of them are selling servicing building. So there'll be partner motion badges to really differentiate the different ways that they're supporting customers from a go-to-market approach and then additional badging to help really identify some of those specialization areas around whether that's clunky use cases, specializations and more, uh, for example, a specific badge that we're rolling out right here at.com is around cloud migrations and partners will be able to get started to get engaged on that badge in preparation for our full-scale launch in February, we'll, they'll start to be able to take advantage of learning pathways, get their teams skilled up, and that will then unlock some new incentives as well as, uh, benefits that they can take advantage of things like accessing or of the Splunk's I've experience and the proof of concept platform and really giving their teams more, uh, capability. And, >>You know, I such a recent cross in the hallway here at dot confidence. She was, she and I were talking about how AI and data is enabling a lot of people to create these solutions. So, you know, you got kind of this almost like Amazon web services dynamic, where it's growing really fast and we're hearing stories, how data is driving value. We had formula one on the cube, the keynotes were giving some examples as you start to see this momentum kind of scaling up to the next level, if you're enabling customers, which you are with data, the monetization or the economic shifts, right? So it's healthy ecosystems, the partners create solutions, they deal with the customer, they're making some money, right? So, so can you share your vision on the unit on the economic equation of how partners are tapping into this? Because I almost imagine, um, a thousand flowers are blooming and then you start to see more value being created and Splunk also gets a cut of it, but there's, there should be that kind of deck. And you can talk about that. >>Yeah, absolutely. In fact, one of the things that I have the opportunity to do with our partners is study our partners, success and profitability. And some of the things that we learned from those studies with our partners is that what's really helping our partners to grow their practices with Blanca and their profitability with that business is really the stickiness that they have with their customers, being able to deliver solutions and services and really be those subject matter experts for their customers. And we know that our most successful and profitable partners are servicing their customers across the Splunk cases. So for example, many of our partners came from a security background and they are super deep, super knowledgeable around security, and they are trusted by their customers as the, you know, subject matter experts around security. And so many of them are starting to lean in on some of the new, additional use cases. Observability is a hot topic with our partners right now it's a new and emerging use cases case for them to transition some of the same sets of data that they are addressing in their current appointments with our customers and bring new value with those new use cases. But that's where we're seeing partner profitability growth. >>I love the channel dynamic. There we go, indirect and real and value creation. I got to ask you about the day-to-day dynamic. Of course we all know about the mark injuries and story. Software's eating the world, okay. Software ate the world. Okay. Now that's done. Now we're data is continuing to drive the value proposition. And so that's going to have an impact on how customer your partners serve their customers, ultimately your customer at the end of the day. How, how is that happening? And from a success standpoint, how would you talk to, uh, where people are on the progress of bringing the most innovative solutions? What, where's the headroom, where do you see that going Brook >>There's? I would say there's just endless opportunity here. And we just see so much innovation in our partner ecosystem to create purpose built solutions for their customers business problems. And that's where I think the value of the data comes to life. Really turning that data into doing as is really the Matic for all the things that we're talking about here, uh, at.com 21, that our partners really see these opportunities and then can replicate some of those same solutions for other customers in the same spaces. So for example, you know, really specialized solutions for healthcare where they're, uh, providing, you know, access to all the data across the hospital, or, um, you heard in guard's keynote about unlocking the value of SAP data. This is just a huge opportunity accessing all that data and really turning that data into doing. And we'll be talking even more about the new SAP relationship and the value for the partner ecosystem to go address those FP data sets in their customers. We'll be talking more about that on our partner feature session, which is tomorrow in day two of dotcom. >>Well, you guys to have a nice mix of business in the partner ecosystem from, you know, small boutiques to high-end system integrators and everything in between, I noticed you're doing a lot with censure. Could you talk about how you guys are partnering with the large global system integrators because they're becoming their own clouds. So, you know, as Jerry Chen at Greylock says, are these castles being built in the cloud with real competitive advantage with data? Again, this is a new phenomenon in the past really two years, you're starting to see explosion of, of scale and refactoring business models with data. What's your, what's your reaction to that? >>Absolutely. In fact, we are really leading in with some of these global systems integrators, and you've heard this exciting news in Theresa Carlson's portion of the keynote earlier today, where we've announced a partner, a center partner business group together. And we're so excited about the center and Splunk partner business group. It's going to elevate the Splunk and essential partnership eCenter has invested in thousands and thousands of joint professionals that are skilled up on flunk. They are building a purpose patients. We have so many amazing examples where Splunk and essential work together to solve real life problems. For example, there's a joint solution that helps address anti-human trafficking. Uh, there's a joint solution that helped with vaccine tracking. I mean, just really powerful examples that are just really extending value to customers and solving real life, data problems. >>Well, you guys have a lot of momentum, bro. Congratulations on the success and partner versus we're going to follow it again. It was built for the cloud. I know it's in the headline. It says flunked launches, new partner program for the cloud. Was there a partner program for the on premises and what's different about on the cloud? Was it kind of new, everything is cloud what's that? What does that mean? That statement? Yeah, >>Absolutely. So we, you know, as we've all seen, customers are leaning into the class that growth to the movement, to the cloud, just accelerated during COVID. And so part of that feedback that I referenced earlier that we heard from our partners, they said, we need help. We need help moving faster. And so that's really the underpinning of the all-new Splunk partner vers program is to really that acceleration to skill up our partners and give them the tools to be successful. And so with that, we did want to rebrand and reinvigorate it to really signal this newness. And as it was mentioning earlier, when we were talking about the badges, it's really about making sure we're providing the partners the right enablement so that they can be ready and able to support their customers on this journey, to the cloud, as well as the access, the resources, the support and the marketing so that they can be successful and really featured their expertise and value in the market. >>Well, Brooke, I want to get one final question before we go. Cause I know you have a lot of experience in the partner ecosystems and over your career. And we just interviewed the formula one CEO, uh, Zach brown, and, and they've been very popular with the, with the Netflix series driving to survive. And I was joking with him driving value with data as channel partners and your partners look to the post pandemic survive and thrive trend that people are going through right now. What should they be thinking about when they look at partner versus, and how Splunk can help them drive an advantage, not only just survive, but to actually drive to an advantage. >>I, I just see this as an opportunity for partners that haven't already leaned into the cloud and helping their customers migrate to the cloud now is the time rapid five acceleration is just essential for organizations to reach their most critical missions and their outcomes. And this one partner versus program is a significant move forward for Splunk partners. And we want to pursue a massive market opportunity focused on the cloud with our partners, for our customers. So I just really encourage our partners to engage, participate and join us on this journey. >>Well, it's a lot of evidence to support this vision. Uh, with pandemic, we saw refab replatforming and refactoring the businesses in the cloud at speeds, that unprecedented deployments. So, uh, cloud can, can bring that scale and speed to the table. It's really incredible. So thank you very much for coming on the cube remotely. Thanks have you had, >>Thank you. This was a delight. Really appreciate the time, John and very excited to have my first opportunity to be a >>Okay. You're a cube alumni. We are here in the studios, Splunk studios for their virtual event here with all the top executives and partners bringing in guests remotely. It's a virtual event. So we'll be back in person. I'm Jennifer, the cube. Thanks for watching.

>> Announcer: Live, from Washington DC, it's the CUBE. Covering .conf2017. Brought to you by Splunk. >> And welcome back here on theCUBE. John Walls and Dave Vellante, we're in Washington DC for .conf2017, Splunk's annual get together coming up to the nation's capital for the first time. This is the eighth year for the show, and 7,000 plus attendees, 65 countries, quite a wide menu of activities going on here. We'll get into that a little bit later on. We're joined now by a couple of gentlemen, Michael Arahuleta who is the Vice President of Engineering at Atlassian, Michael, thank you for being with us. >> Thank you, actually it's Director of Business Development. >> John: Oh, Director of Business Development, my apologies >> He's doin' a great job >> My apologies. >> I don't need that. >> Oh very good. And Brooke Gravitt, who I believe is the VP of Engineering, >> There ya go. >> And the Chief Software Architect at Forty8Fifty. >> Yep, how ya doin'? >> No promotions or job assignments, I've gotcha on the right path there? >> Yeah, yeah. >> Good deal, alright. Thank you for joining us, both of you. First off, let's just set the stage a little bit for the folks watching at home, tell us a little bit about your company, descriptions, core competencies, and your responsibilities, and then we'll get into the intersection, of why the two of you are here. So Michael, why don't you lead off. >> So Atlassian, we, in our simplest form, right, we make team collaboration software. So our goal as a company is to really help make the tools that companies use to collaborate and communicate internally. Our primary focus, and kind of our bread and butter has always been making the tools that software companies use to turn around and make their software. Which is a great position to be in, and an increasingly we're seeing ourselves expand into providing that team collaboration software products like Jira, Confluence, BitBucket, and now, the new introduction of a product called Stride, which is a real time team collaboration product, not just for technical teams, but we're really seeing a great opportunity to empower all teams 'cause every team in every organization needs a better way to communicate and get things done. That's really what Atlassian core focus is all about. >> John: Gotcha. Brooke, if you would. >> Yeah, so Forty8Fifty Labs, we're the software development and DevOps focused subsidiary of Veristor Systems based out of Atlanta. We focus primarily on four key partners, which would be Atlassian, Splunk, QA Symphony, and Red Hat, and primarily, we do integrations and extensibility around products that these guys provide as well as hosting, training, and consulting on DevOps and Atlassian products. >> So the ideal state in your worlds is you've got -- true DevOps, Agile, infrastructure as code, I'll throw all the buzzwords out at ya, but essentially you're not tossing code from the development team into the operations team who them hacks the code, messes it up, points fingers, all that stuff is in part anyway what you're about eliminating, >> Right. >> And getting to value sooner. Okay, so that's the sort of end state Nirvana. Many companies struggle with that obviously, You got, what, Gartner has this term, bimodal IT, which everybody, you know, everybody criticizes but it's sort of true. You've got hybrid clouds, you've got, you know, different skillsets, what is the state of, Agile development, DevOps, where are we in terms of organizational maturity? Wonder if you guys could comment. >> I'll start with that right, I think -- Even though we've been talking about DevOps for a while and companies like Atlassian and Splunk, we live and breathe it. I still think when you look at the vast majority of enterprises, we're still at the early stages of effectively implementing this. I think we're still really bringing the right definition to what DevOps is, we're kind of go through those cycles where either a buzzword gets hot, everybody glams onto it, but no one really knows what it means. I think we're really getting into that truly understanding what DevOps means. I know we've been working hard at Atlassian to really define that strong ecosystem of partners. We really see ourselves as kind of in the middle of that DevOps lifecycle, and we integrate with so many great solutions around monitoring and logging, testing, other operational softwares, and things of that nature to really complete that DevOps lifecycle. I think we're really just now finally seeing it come together and finally starting to see even larger organizations, very large Fortune 100 companies talk about how they know they've got to get away from Waterfall, they've got to embrace Agile, and they've got to get to a true DevOps culture, and I think that's where Atlassian is very strong, devs have loved us for a long time. Operations teams are really learning to embrace Atlassian as well. I think we're really going to great position to be at that mesh of what truly is DevOps as it really emerges in the next couple years. >> Brooke, people come to Forty8Fifty, and they say, alright, teach me how to fish in the DevOps world, is that right? >> Yeah, absolutely. I mean, one of the challenges that you have in large enterprises is bringing these two groups of people together, and one of the easy ways is to go out and buy a tool, I think the harder and more difficult challenge that they face is the culture change that's required to really have a successful DevOps transformation. So we do a little bit of consulting in that area with workshops with folks like Gene Kim, Gary Gruver, Jez Humble that we bring in who are sort of industry icons for that sort of DevOps transformation. To assist, based on our experiences ourselves in previous companies or engagements with customers where we've been successful. >> So the cloud native guys, people who are doing predominantly cloud, or smaller companies, tech companies presumably, have glommed onto this, what about the sort of the Fortune 1000, the Global 2000, what are we seeing in terms of their adoption, I mean, you mentioned Waterfall before, you talk to some application development heads will say, well listen, we got to protect some of our Waterfall, because it's appropriate. What are you seeing in the sort of traditional enterprise? >> We see the traditional enterprise really embracing Agile in a very aggressive way. Obviously they wouldn't be working with Atlassian if they weren't, so our view is probably a little bit tilted. Companies that engage with us are the more open to that. But we're definitely seeing that the far and away the vast majority in the reports that we get from our partners like Forty8Fifty Labs is that increasingly larger and larger companies are really aggressively looking to embrace Agile, bring these methodologies in, and the other simple truth is with the way Atlassian sells -- the way we sell our products online, we have always sort of grown kind of bottoms up inside a lot of these large organizations, so where officially IT may still be doing something else, they're always countless smaller teams within the organization that have embraced Atlassian, are using Atlassian products, and then, a year down the road, or two years down the road, we tend to then emerge as the defacto solution for the organization after we kind of spread through all these different groups within the company. It's a great growth strategy, a lot are trying to replicate it. >> Okay, what's the Splunk angle? What do you guys do with Splunk, and how does it affect your business? >> Mike: Do you want to start? >> Sure, so, we're both a partner of Splunk, a customer of Splunk, and we use it in our own products in terms of our hosting, and support methodologies that we leverage at Forty8Fifty. We use the product day in and day out, and so with Atlassian, we have pulled together a connector that is -- one half of it is a Splunk app, it's available on Splunk base, and the other part is in the Atlassian marketplace, which allows us to send events from Juris Service Desk, ticketing events, over to Splunk to be indexed. You have a data model that ties in and allows you to get some metrics out of those events, and then the return trip is to -- based on real time searches, or alerts, or things that you have -- you're very interested in reports, you can trigger issues to be created inside of Jira. >> I think the only thing to add to that, so definitely, that's been a great relationship and partnership, and we're seeing an increasing number of our partners also become partners with Splunk and vice versa, which is great. The other strong side to this as well, is our own internal use of Splunk. So, we as a company, we always like to empower our different teams to pick whatever solution they want to use, and embrace that, and really give that authority to the individual teams. However, with logging, we were having a huge problem where all of our different teams were using over a whole host variety of different logging solutions, and frankly not to go into all the details, it was a mess. Our security team decided to embrace Splunk and start using Splunk, and really got a lot of value out of the solution and fell in love with the solution. Which says a lot, because our security team doesn't normally like much of anything, especially if it's not homegrown. That was a huge statement there, and then quickly Splunk now has spread to our cloud team which is growing rapidly as our cloud scales dramatically. Our developers are using it for troubleshooting, our SREs and our support team for incident management, and it's even spread to our marketplace, which is one of the larger marketplaces out there today for third party apps. Then the new product, Stride, for team collaboration is going to be very dependent on Splunk for logging as well. It's become that uniform fabric. I even heard a dev use a term which I've never heard a dev talk about logs and talk about log love, which is no PR, that is the direct statement from a developer, which I thought was amazing to hear. 'cause you know, they just want to code and make stuff, they don't want to deal when it actually breaks and have to fix it. But with Splunk they've actually -- They're telling me they actually enjoy that. So that's a great -- >> That's more than the answer is in the logs, that's there's value in our logs, right? >> Yeah, a ton of value, right? Because at the end of the day, these alerts are coming in and then we use tools like the Forty8Fifty Labs tool to get those tickets into Jira. Those logs and things are coming in, that means there's an issue and there's something to be resolved and there's customer pain. So the quicker we can resolve that, that log is that first indicator of what's going on in the cloud and in our platforms to help us figure out how do we keep that customer happy? This isn't just work, and just a task, this is about delivering customer value and that log can be that first indicator. The sooner you can get something resolved, the sooner the customer's back to getting stuff done and that's really our focus as a company, right? How do we enable people to get things done? >> Excuse me, when you are talking about your customers, what are their pain points? Today? I mean, big data's getting bigger and more capabilities, you've got all kinds of transport problems and storage problems, and security problems, so what are the pain points for the people who are just trying to get up to speed, trying to get into the game, and that the kind of services you're trying to bring to them to open their eyes. >> I think if you look at the value stream mapping and time to market for most businesses, where Splunk and Atlassian play in is getting that fast feedback. The closer in to the development side, the left hand side of value stream that you can pull in, key metrics, and get an understanding of where issues are, that actually -- it's much less expensive to fix problems in development than when they're in production, obviously. Rolling things like Splunk that can be used as a SIM to do some security analysis on, whether it be product code or business process early, rather than end up with a data breach or finding something after it's already in production. That kind of stuff, those are the challenges that a lot of the companies are facing is -- especially when the news, if you look at all the things that are goin on from a security perspective, taking these two products and being able to detect things that are going on, trends, any sort of unusual activity, and immediately having that come back for somebody in a service desk to work on either as a security incident or if it's a developer finding a bug early in the lifecycle, and augmenting your sort of infrastructure as code, the build out of the infrastructure itself. Being able to log all that data, and look at the metrics around that to help you build more robust enterprise class platforms for your teams. >> We've been sort of joking earlier about how the big data, nobody really talks about big data anymore, interestingly, Splunk who used to never talk about big data is now talking about big data, cause they're kind of living it. It's almost like same wine, new bottle with machine learning and AI and deep learning are all kind of the new big data buzzwords, but my question is, as practitioners, you were describing a situation where you can sort of identify a problem, maybe get an alert, and then manually I guess remediate that problem, how far away are we from -- so the machines automating that remediation? Thoughts on that? >> Am I first up? >> You guys kind of -- >> We've done a lot of automated remdediation. Close with remediation is what you call it. The big challenge is, it's a multi-disciplinary effort, so you might have folks that need to have expertise between network and systems and the application stack, maybe load balancing. There's a lot of different pieces there, so step one is you got to have folks that have the capacity to actually create the automation for their domain of expertise, and then you need to have sort of that cross platform DevOps mindset of being able to pull that together and the coordinator role of let's orchastrate all of the automations, and then hopefully out of that, combined with machine learning, some of the stuff that you can do in AWS, or with IBM's got out. You can take some of that analysis and be a little bit smarter about running the automation. In terms of whether that's scaling things up, or when -- For example, if you're in a financial industry and you've got a webpage that people are doing bill pay for, if you have a single website down, a web server down, out of a farm of 1000, in a traditional NOC, that would be kind of red on a dashboard. It's high, it's low priority, but it's high visibility and it's just noise, and so leveraging machine learning, people do that in Splunk to really refine what actually shows up in the NOC, that's something I think is compelling to customers. >> How are devs dealing with complexity, obviously, collaboration tools help, but I mean, the level of complexity today, versus when you think back to client server, is orders of magnitude greater for admins and developers, now you got to throw in containers and microservices, and the amount of data, is the industry keeping pace with the pace of escalation of complexity, and if so, how? >> I think we're trying. I think that's where we come into play. As this complexity increases really the only way you can solve it is through better communication and better tools to make sure that teams have the right information at their fingertips. The other challenge too is now in the world of the cloud, these teams need to be on 24/7. But you've got to kind of roll across the globe, and have your support teams in different time zones. You don't always have the right people online at the same time to be able to address, and you can't always talk directly, so that's where having the right tools and processes in place are extremely important so that team can know and know what did the team earlier do, how did they resolve this, where's the run book for this issue, and if this happens, how do we resolve it? How do we do so quickly? I think that tooling is key, and also too, this complexity is also as you guys were talking about before, being solved through some automation as well, and we're increasingly seeing that to where if this occurs and a certain thing occurs, then Jira can now automatically start to trigger some things for you, and then report back as to what it did. You're going to see more and more of that going forward as these models become more intelligent and we can redeploy, or if capacity is low, let's pull back resources, and let's not spend all this money on cloud computing platforms that we may not need because utilization is low. You're seeing all of those things start to happen and Jira as that workflow engine is that engine that's making those things happen in either an automated way at times, or just enabling people to communicate and do things in a very logical fashion. >> As ecosystem partners, how do you view the evolution of Splunk, is it becoming a application platform for you? Are you concerned about swim lanes? I wonder if you could talk about that? >> I personally, I don't see any real concerns of overlap between Splunk and Atlassian. In our view at Atlassian is, we tend to work very closely with people kind of fit into that frenemy category, and they're definitely a partner that we overlap with I think in very very few ways. If and when we ever do, I mean in a way, that's kind of something we always embrace as a company. I mean one thing we'll say a lot is overlap is better than a gap. Because if there's a gap between us and a partner, then that's going to result in customer pain. That means there's nothing that's filling that void. I'd rather have some overlap, and then give the customer the power to choose how do they want to do it. I mean, Splunk says you can probably do it this way, Atlassian says you could do it this way, as long as they can get stuff done, and that's always -- it's not a cliche from us, I mean that's a core message from Atlassian, then we're happy. Regardless if they completely embrace it our way, a little bit, a little deviation, that's not what really matters. >> Too much better than too little. >> Exactly. >> Is what it comes down to. Gentlemen, thanks for being with us. >> Thank you. >> We appreciate the time today and look forward to seeing you down the road and looking as your relationship continues. Not only between the two companies, but with Splunk as well. Thanks for being here. >> Mike: Thank you guys. >> We continue theCUBE does, live from Washington DC here at .conf2017, back with more in just a bit.

>>Hi, everyone. Welcome back to the cubes coverage of splunk.com, virtual 2021. I'm John Ford, your host of the cube. We're here with Teresa Carlson, special guests cube alumni. Who's now the president and chief growth officer of Splunk. Teresa, welcome back to the queue. >>So glad you're here with us >>As the president of Splunk. Great to see you. Great to see you. So we've had many conversations in the queue. When you were the chief of public sector of Amazon web services, you grew that business significantly over the years. We've documented on the cube and we've talked about I've written about it. Um, now Splunk, it feels a lot like AWS was back in LA a couple of years ago, where you have this amazing product everyone's using. They don't lose customers. They're getting customers they're in the middle of the security thing, which you know a lot about, and they have this large enterprise base growing. It's just a minute. Grazer leaning in Splunk is, seems to be going to the next level. >>Totally. Well, you nailed it. I would say we're definitely in a scale mode at this point at Splunk. And also to your point, our customers are so loyal to us and we're seeing actually customers with more than a million dollars doubling their spend almost with us. Uh, it's pretty cool. And now we have this cloud portfolio, which is one of my jobs, as you know, I love, I've got my cloud shirt on. I've been believer in cloud. I'm a real believer. You know, I saw the transformational effects of cloud in real time, over 11 years and bringing that here even more to utilize that in our security and observability spaces is quite phenomenal. And then you see again in a much more, uh, set of segmented workloads, how customers take advantage of this. And of course today, like no other John security is just top of mind. It's always been you and I talked earlier about how security kind of evolved over the years and public sector led some of that over time. And then commercial industry say, you know, wow, that today it's, I mean, it's more than top of mind for not just every enterprise organization and government entity, but it's also every board out there. It's something that we think about internal threat, external threat. How do we manage it? How do we get the data around it to understand it? And then how do we take action on it? >>I seen you up on stage as a senior leader here at Splunk, um, at the virtual venue at a great keynote was a lot of news. And we'll get into that in a second, but I want to ask you, knowing you personally and covering you over the years of Amazon web services, you've been a fierce competitor. Okay. But you also have been a great people, person, people loved working for you, Splunk, is it the same? We've been covering them just as long as we cover an ADFS. The culture seemed to fit because Splunk is kind of competitive, but they're kind of quiet, competitive culture. Yeah. Interesting. Tell us about, tell us about your experience. >>Well, and I think we can, yeah, we can do it in our own Spanky way. I'm learning new it's six minutes today that I've been as blind quiches and believable that I've been here this long already, but, uh, Splunk has a very quirky culture, which I led. They have a lot of fan. They have a big following and I'm so sorry that everyone couldn't attend in person, but the virtual social media feeds are off the charts. I mean, I'm just, I'm having so much fencing high already. They come together. It's a real community, but, uh, yeah, on the competition front, here's what reminds me so much about my old world is that I always love that when somebody wakes up and realizes that it's a huge industry and they want to participate. And that's kind of what happened when I was at AWS and now it's blank. >>I'm like, Hey, all these companies are waking up and saying, data's this real thing. It's like a $90 billion plus industry and growing, and then data with security. Hello, are you kidding me? So I feel like really that's kind of what's happened. And Splunk has such a unique set of tools and solutions that just work, they work. And that's what customers, I have heard that statement from customers and partners so much that it just works. And the other thing that's pretty unique about us, I would say John is our ability to navigate between an on-prem world and a cloud world in a unique set of areas like IOT, edge computing. So wherever customer's data is multiple clouds, we're able to take advantage of that for the customer. So they make the choice of where that data comes from and they use the splint tooling then to be able to get those insights and information >>Well, great to have you on the Cuban grid, that's swung to have you, and they're going to be lucky to have you going to do a lot stuff, knowing you and knowing the Splunk community and the team here. A great team. Now talking about the announcements, look at what's going on. Obviously security is still in everything. Yep. A couple of things, rebranding of the partner versus sends a huge message of the ecosystem. You know, that movie you've seen that movie before, um, digital journey for customer success. Again, they have tons of customers that have been with them from beginning and new customers, but they've got to go government action going on here. Whereas you know, a lot about the government logging in monetization program. >>Yeah. Well, as you know, the government, uh, you got 11, but they do continually come up with N fended mandates. And my government customers always have said, oh my gosh, I've got another unfunded mandate. So we're really helping them at that because yes, while it's infested in this budget this year, as it states, they know how important it is. And I do think this initiative is something that is going to have a waterfall effect into the commercial industries. Also just like a lot of these things do and around security, uh, but it's important that we help our government customer made as best as they can. So we've come up with, I think, a very unique offering that they can take advantage of for Splunk and we're going to be out there helping them every way. And, and hopefully John L also helped them learn more about cross governmental, what they're doing and how they can understand from their logs and metrics even more about how to protect. Yes. >>One of the things that we've talked about before in the past, but how cloud-scale, and as creates ecosystems, Amazon VMware, you seeing all these ecosystems that have been thriving for, for decades, Splunk has an ecosystem developing very, very fast. Their partners are, are loyal and they're making money with them. And they're being delivered solutions as data becomes the new enablement. How do you see the role of the partners that growing? How do you see them evolving over time? >>Well, let me just tell you, I'm, I'm a real believer in the partner community. I mean, firsthand over the years, my time at Microsoft at AWS, I saw it as an unbelievable force multiplier to your business. And I mean that, and they do things that you don't even think of. I, you know, I'm always amazed at partners. I'm like, oh, you're using the tool for that. Wow. So while we are broadly good, we're, we're very good at what we do, but we cannot understand every horizontal or vertical industry out there. And the reason it's important to have partners, they can take you to places that you never dreamed. And for us, if you look at the categories, we need our CSP or cloud service providers to be able to really help us make sure that we take advantage of the cloud platforms that are out there and our primary, we AWS, and then Google cloud. >>Uh, and then after that we work, we work with both those a migration. You saw Steve Schmidt today. Good friend of mine love Steve. And the work we're doing. And you saw, we were one of the first migration partners with AWS. You'll see us continue that program. We'll work together to continue to look for security services jointly that we can offer. And we're a customer of theirs. They're a customer of ours. It makes a good partnership. And then additionally, you have, uh, you have your MSPs, right? Your managed service providers. And today we talked about blue buoyant who had multiples, and these are partners out there that have a unique offering for me, generally managed security or observability in the marketplace. They take the Splunk toolkit, they add to it and they have it off, offered out to their customers. Um, and then you have your largest size like Accenture. I'm so excited about that. First of all, led Julie Sweet. She's an amazing CEO and leader. Uh, and w in what they're doing with this, they've been a long-standing partner of ours, but now they've actually made us part of their, one of their 11 business groups. So it's Accenture plus Splunk, and now they'll take us into all of their industries together. So it's huge. And, you know, >>Does that mean cause, cause this is a business deal. This isn't just like a, you know, some sort of deal where you guys saying we're going together. This is a specific division. >>That's right. That's right. So they have a leaven partners that they work with. AWS is one of them. SAP's one of them. Uh, IBM's one of them, Salesforce, I believe is one of them. And they have, they have experts at Accenture that can go into customers to implement tools and services for customers at the enterprise level. And so they have selected. Splunk is one of those business partners that you heard Paul today talk about. We already have 400 customers together and growing, we will expand that, but it's a joint effort of both go-to-market selling and technical resources that will deliver. But for Splunk, again, it's back to that horizontal and vertical slicing where they can take us into security practice that they have chosen. Splunk is one of their security offerings and it's important that we really support them. But also in the splint, a partner verse, we're going to do some new things. >>John, if I just first take and talk about it, we've had a great partner program, but now we're going to Korea's credits, uh, technology, architecture, tooling support, uh, getting in, you know, to certify themselves, to be pro serve ready for those migrations and modernizations. But also really what we heard from a lot of them is they need more training and education remaster to understand our new cloud offerings. And that makes sense. So it's more digital and more cloud oriented with these partners. And then guess what they would love for us to talk about how great they are and we should. So when we get them out there that helps our customers really understand the offerings they have in the marketplace >>At Brooke honeymoon was saying she didn't do a lot more listening and they're working on this next level partner verse. I found that really interesting, all sorts of Katie beyond key. I talked with she's the SVP of customer success, something you're I know you're obsessed about. You always work backwards from the customers as the AWS way. How do you view customer stuff? Because you have a lot of different customers, you have diverse customers. What's important. What are you going to keep Katie's on top of this, but what's your view. >>We ha we do have a lot of different customers. However, we have a concentration of the largest, most important and influential customers in the world. So our customer base is very large enterprise oriented, multiple departments within that enterprise take advantage of Splunk. We work with 90 to the 100 fortune 100 companies, and we've worked with them for a long time. And like I said, we're continuing to see them use more of splice, not less as blank. And the way that that happens is, and I hear from him, I sit and talk to him and they're like, now we're using Splunk in these multiple departments and we need to bring it all together at the enterprise level for the C-suite to look at it. Now, I know it sounds a little strange John, but that's changed a bit over the years. And that is because, you know, if you look at big spenders at an enterprise, he spends a lot of money because they need to at dev, you know, uh, security, right. Security infrastructure, and they need to monitor all that. They need to understand it, but guess what they want, understand it now at the corporate level. And they need it at the CIO, they need at the Cisco level for threat analysis. And then now boards want more and more that information they want to roll up of what's happening. So we're seeing a trend where the C-suite, the senior executives really are much more interested in Splunk. It used to be very departmental. >>I'll throw another wrench in the equation. There is one developers want shifting left. They want real time data security policy in the development, CDC at pipelining. So another problem. Yeah. >>Yeah. And developers lever tools. And again, they're, they're another unique group I should totally talk about. That takes your tools to another level and really fears that ways within their customer set to take advantage of the tooling. >>He's a great to see you. Congratulations on a new opportunity here. And the leadership at Splunk, um, really perfectly poised to take the growth of the cloud. That's. So I have to ask you, what's your mission? What's your mission for the next year as you come on? You're six months in what's the, >>Well, for us, here's blankets, continuing to scale, really listening to our customers and partners. It sounds, I don't want it to sound like a cliche. We really are spending time listening and working back, Sean and I are working. He's their president of technology products and technology. He and I are working very closely to look at features and functionality that we need to be talking about. Uh, it is about taking advantage of the partner community in a way to support them, to help again, get us into new areas of the business. And then lastly, continue to make sure that we have the training and education for customers directly because our tools and technologies are evolving. And if I've learned anything over the last 11 years is cloud is a step change for a lot of customers and they're still hybrid. So it's important that we meet them where they are, but help them get over that bridge so that they have that full digital journey. So that's what you're going to see me focused on. I'm super excited. >>I was talking with Claire, the CMO just before you leave, I want to get your reaction. This event went virtual the last minute. It became a studio here in Silicon valley. You're a media company now Splunk. Yeah. >>It's like it. I mean, it is amazing what we accomplished today. Uh, I, you know, I don't want to pre give numbers, but we had way, way over 20,000 today, online and, uh, growing. So the numbers we're still looking at, but it was unbelievable. And we had, I think we had had like 22,000 registered and we even got more. So people joined in, they stay, they watched the keynote, there were out narrow specialty sessions. And I all agree, like it was pretty cool. It was a step change because we were thinking about doing it in person. We took a pulse and we said, you know, we think we can actually do a better job this year because of COVID steel. If we do it all virtually and it turned out and we have you, so look at this, you're like, we have you here. And I love your cool backdrop here, John. Yeah. >>Well, you guys do a great job. You guys are a media company. Now you're telling your own stories direct. There's a lot of stories to tell. Thank you for coming on the cube. Great to see you >>Again. John's great to see you because the >>Cubes coverage here at.com 2021 virtual I'm John for your host of the cube. Thanks for watching.

>> Good day, everyone. Thank you for joining me. I'm Cindy Maike, joined by Rick Taylor of Cloudera. We're here to talk about predictive maintenance for the public sector and how to increase asset service reliability. On today's agenda, we'll talk specifically around how to optimize your equipment maintenance, how to reduce costs, asset failure with data and analytics. We'll go into a little more depth on what type of data, the analytical methods that we're typically seeing used, the associated- Brooke will go over a case study as well as a reference architecture. So by basic definition, predictive maintenance is about determining when an asset should be maintained and what specific maintenance activities need to be performed either based upon an assets actual condition or state. It's also about predicting and preventing failures and performing maintenance on your time on your schedule to avoid costly unplanned downtime. McKenzie has looked at analyzing predictive maintenance costs across multiple industries and has identified that there's the opportunity to reduce overall predictive maintenance costs by roughly 50% with different types of analytical methods. So let's look at those three types of models. First, we've got our traditional type of method for maintenance, and that's really about uncorrective maintenance, and that's when we're performing maintenance on an asset after the equipment fails. The challenges with that is we end up with unplanned downtime. We end up with disruptions in our schedules, as well as reduce quality around the performance of the asset. And then we started looking at preventive maintenance and preventative maintenance is really when we're performing maintenance on a set schedule. The challenges with that is we're typically doing it regardless of the actual condition of the asset, which has resulted in unnecessary downtime and expense. And specifically we're really now focused on condition-based maintenance, which is looking at leveraging predictive maintenance techniques based upon actual conditions and real time events and processes. Within that, we've seen organizations and again, source from McKenzie, have a 50% reduction in downtime, as well as overall 40% reduction in maintenance costs. Again, this is really looking at things across multiple industries, but let's look at it in the context of the public sector and based upon some activity by the department of energy several years ago, they really looked at what does predictive maintenance mean to the public sector? What is the benefit of looking at increasing return on investment of assets, reducing, you know, reduction in downtime as well as overall maintenance costs. So corrective or reactive based maintenance is really about performing once there's been a failure and then the movement towards preventative, which is based upon a set schedule. We're looking at predictive where we're monitoring real-time conditions. And most importantly is now actually leveraging IOT and data and analytics to further reduce those overall down times. And there's a research report by the department of energy that goes into more specifics on the opportunity within the public sector. So Rick, let's talk a little bit about what are some of the challenges regarding data, regarding predictive maintenance? >> Some of the challenges include having data silos, historically our government organizations and organizations in the commercial space as well, have multiple data silos. They've spun up over time. There are multiple business units and note, there's no single view of assets. And oftentimes there's redundant information stored in these silos of information. Couple that with huge increases in data volume, data growing exponentially, along with new types of data that we can ingest there's social media, there's semi and unstructured data sources and the real time data that we can now collect from the internet of things. And so the challenge is to collect all these assets together and begin to extract intelligence from them and additional insights and and that in turn, then fuels machine learning and what we call artificial intelligence, which enables predictive maintenance. Next slide. >> Cindy: So let's look specifically at, you know, the types of use cases and I'm going to- Rick and I are going to focus on those use cases, where do we see predictive maintenance coming in to the procurement facility, supply chain, operations and logistics? We've got various level of maturity. So, you know, we're talking about predictive maintenance. We're also talking about using information, whether it be on a connected asset or a vehicle doing monitoring to also leveraging predictive maintenance on how do we bring about looking at data from connected warehouses facilities and buildings? I'll bring an opportunity to both increase the quality and effectiveness of the missions within the agencies to also looking at looking at cost efficiency, as well as looking at risk and safety. And the types of data, you know, that Rick mentioned around, you know, the new types of information. Some of those data elements that we typically have seen is looking at failure history. So when has an asset or a machine or a component within a machine failed in the past? We've also looking at bringing together a maintenance history, looking at a specific machine. Are we getting error codes off of a machine or assets looking at when we've replaced certain components to looking at how are we actually leveraging the assets? What were the operating conditions? Pulling up data from a sensor on that asset? Also looking at the features of an asset, whether it's, you know, engine size it's make and model, where's the asset located? To also looking at who's operated the asset, you know, whether it be their certifications, what's their experience, how are they leveraging the assets? And then also bringing in together some of the pattern analysis that we've seen. So what are the operating limits? Are we getting service reliability? Are we getting a product recall information from the actual manufacturer? So Rick, I know the data landscape has really changed. Let's, let's go over looking at some of those components. >> Rick: Sure. So this slide depicts sort of the, some of the inputs that inform a predictive maintenance program. So we've talked a little bit about the silos of information, the ERP system of record, perhaps the spares and the service history. So we want, what we want to do is combine that information with sensor data, whether it's a facility and equipment sensors, or temperature and humidity, for example. All this stuff is then combined together and then used to develop machine learning models that better inform predictive maintenance, because we do need to take into account the environmental factors that may cause additional wear and tear on the asset that we're monitoring. So here are some examples of private sector maintenance use cases that also have broad applicability across the government. For example, one of the busiest airports in Europe is running Cloudera on Azure to capture secure and correlate sensor data collected from equipment within the airport. The people moving equipment more specifically, the escalators, the elevators, and the baggage carousels. The objective here is to prevent breakdowns and improve airport efficiency and passenger safety. Another example is a container shipping port. In this case, we use IOT data and machine learning to help customers recognize how their cargo handling equipment is performing in different weather conditions to understand how usage relates to failure rates and to detect anomalies in transport systems. These all improve port efficiency. Another example is Navistar. Navistar is a leading manufacturer of commercial trucks, buses, and military vehicles. Typically vehicle maintenance, as Cindy mentioned, is based on miles traveled or based on a schedule or a time since the last service. But these are only two of the thousands of data points that can signal the need for maintenance. And as it turns out, unscheduled maintenance and vehicle breakdowns account for a large share of the total cost for vehicle owners. So to help fleet owners move from a reactive approach to a more predictive model, Navistar built an IOT enabled remote diagnostics platform called On Command. The platform brings in over 70 sensor data feeds for more than 375,000 connected vehicles. These include engine performance, trucks speed, acceleration, coolant temperature and break ware. This data is then correlated with other Navistar and third-party data sources, including weather, geolocation, vehicle usage, traffic, warranty, and parts inventory information. So the platform then uses machine learning and advanced analytics to automatically detect problems early and predict maintenance requirements. So how does the fleet operator use this information? They can monitor truck health and performance from smartphones or tablets and prioritize needed repairs. Also, they can identify that the nearest service location that has the relevant parts, the train technicians and the available service space. So sort of wrapping up the benefits. Navistar's helped fleet owners reduce maintenance costs by more than 30%. This same platform has also used to help school buses run safely and on time. For example, one school district with 110 buses that travel over a million miles annually reduce the number of tows needed year over year, thanks to predictive insights, delivered by this platform. So I'd like to take a moment and walk through the data life cycle as depicted in this diagram. So data ingest from the edge may include feeds from the factory floor or things like connected vehicles, whether they're trucks, aircraft, heavy equipment, cargo vessels, et cetera. Next, the data lands on a secure and governed data platform where it is combined with data from existing systems of record to provide additional insights. And this platform supports multiple analytic functions working together on the same data while maintaining strict security, governance and control measures. Once processed the data is used to train machine learning models, which are then deployed into production, monitored and retrained as needed to maintain accuracy. The process data is also typically placed in a data warehouse and use to support business intelligence analytics and dashboards. And in fact, this data life cycle is representative of one of our government customers doing condition-based maintenance across a variety of aircraft. And the benefits they've discovered include; less unscheduled maintenance and a reduction in mean man hours to repair, increased maintenance efficiencies, improved aircraft availability, and the ability to avoid cascading component failures, which typically costs more in repair cost and downtime. Also, they're able to better forecast the requirements for replacement parts and consumables and last, and certainly very importantly, this leads to enhanced safety. This chart overlays the secure open source Cloudera platform used in support of the data life cycle we've been discussing. Cloudera data flow, provides the data ingest, data movement and real time streaming data query capabilities. So data flow gives us the capability to bring data in from the asset of interest, from the internet of things. While the data platform provides a secure governed data lake and visibility across the full machine learning life cycle eliminates silos and streamlines workflows across teams. The platform includes a integrated suite of secure analytic applications. And two that we're specifically calling out here are Cloudera machine learning, which supports the collaborative data science and machine learning environment, which facilitates machine learning and AI and the Cloudera data warehouse, which supports the analytics and business intelligence, including those dashboards for leadership Cindy, over to you. >> Cindy: Rick, Thank you. And I hope that Rick and I provided you some insights on how predictive maintenance condition-based maintenance is being used and can be used within your respective agency, bringing together data sources that maybe you're having challenges with today, bringing that more real-time information in from a streaming perspective, blending that industrial IOT, as well as historical information together to help actually optimize maintenance and produce costs within each of your agencies. To learn a little bit more about Cloudera and our, what we're doing from a predictive maintenance, please visit us at Cloudera.com/Solutions/PublicSector And we look forward to scheduling a meeting with you. And on that, we appreciate your time today and thank you very much.

>>Hi, this is Cindy Mikey, vice president of industry solutions at caldera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and shad we'll go over reference architecture and a case study. So by definition, fraud, waste and abuse per the government accountability office is fraud. Isn't an attempt to obtain something about value through unwelcome misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal benefit. So as we look at fraud, um, and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically from the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external, uh, perpetrators again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically about 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from permit out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, um, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, there's a broad stroke areas. What are the actual use cases that our agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use crate, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at, you know, social services, uh, to public safety, to also the, um, our, um, uh, additional agency methods, we're gonna use focused specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of, um, unemployment insurance fraud, uh, benefit fraud, as well as payment and integrity. So fraud has it it's, um, uh, underpinnings inquiry, like you different on government agencies and difficult, different analytical methods, and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models. We're typically looking at historical type information, but if we're actually trying to look at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case that shad is going to talk about later is how do I look at more of that? >>Real-time that streaming information? How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that, uh, behavioral, uh, that's unstructured data, whether it be camera analysis and so forth. So for quite a different variety of data and the, the breadth and the opportunity really comes about when you can integrate and look at data across all different data sources. So in a looking at a more extensive, uh, data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities, uh, to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be investigating the forms that they've provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes on increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits, uh, or potential fraud to also looking at areas of under-reported tax information? So there you might be pulling in some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, um, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific, like a constituent, are there areas where we're seeing, uh, >>Um, other >>Aspects of, of fraud potentially being occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, uh, agent-based modeling techniques, where we're looking at simulation Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, uh, the public sector. Um, and again, that really, uh, lends itself to a new opportunities. And on that, I'm going to turn it over to chef to talk about, uh, the reference architecture for, uh, doing these buckets. >>Thanks, Cindy. Um, so I'm gonna walk you through an example, reference architecture for fraud detection using, uh, Cloudera's underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or novelists behavior within our datasets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so incomes, clutters platform, and this reference architecture that needs to be for you. >>So, uh, let's start on the left-hand side of this reference architecture with the collect phase. So fraud detection will always begin with data collection. We need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create our normal behavior profiles. And these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, thinking, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different velocities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jason or a binary format, right? So this is a data collection challenge that can be solved with cluttered data flow, which is a suite of technologies built on a patch NIFA in mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to, uh, you know, downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geolocation that's in that transaction data can be enriched with previously known locations of that very same individual. And all of that enriched data can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stricted to Kafka and coffin. It's going to serve as that central repository of syndicated services or a buffer zone, right? >>So coffee is going to pretty much provide you with, uh, extremely fast resilient and fault tolerance storage. And it's also gonna give you the consumer APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transformed data within your buffer zone, uh, allowed that, you know, 17. So you can store that data in a distributed file system, give you that historical context that you're going to need later on for machine learning, right? So the next step in the architecture is to leverage a cluttered SQL stream builder, which enables us to write, uh, streaming SQL jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer in real time. Uh I'll you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage kudu, uh, while EDA or, you know, exploratory data analysis and visualization, uh, can all be enabled through clever visualization technology. >>All right, so we've filtered, we've analyzed and we've explored our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, uh, even deep learning techniques with neural networks. And these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real-time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. >>Uh, and this entire pipeline is powered by clutters technology, right? And so, uh, the IRS is one of, uh, clutter's customers. That's leveraging our platform today and implementing, uh, a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of historical facts, data. Um, and one of the neat things with the IRS is that they've actually recently leveraged the partnership between Cloudera and Nvidia to accelerate their spark based analytics and their machine learning, uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, um, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real-time perspective, looking at anomalies, being able to do some of those on detection, uh, looking at neural network analysis, time series information. So next steps we'd love to have additional conversation with you. You can also find on some additional information around, I have caught areas working in the, the federal government by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining us Sheva and I today. We greatly appreciate your time and look forward to future progress. >>Good day, everyone. Thank you for joining me. I'm Sydney. Mike joined by Rick Taylor of Cloudera. Uh, we're here to talk about predictive maintenance for the public sector and how to increase assets, service, reliability on today's agenda. We'll talk specifically around how to optimize your equipment maintenance, how to reduce costs, asset failure with data and analytics. We'll go into a little more depth on, um, what type of data, the analytical methods that we're typically seeing used, um, the associated, uh, Brooke, we'll go over a case study as well as a reference architecture. So by basic definition, uh, predictive maintenance is about determining when an asset should be maintained and what specific maintenance activities need to be performed either based upon an assets of actual condition or state. It's also about predicting and preventing failures and performing maintenance on your time on your schedule to avoid costly unplanned downtime. >>McKinsey has looked at analyzing predictive maintenance costs across multiple industries and has identified that there's the opportunity to reduce overall predictive maintenance costs by roughly 50% with different types of analytical methods. So let's look at those three types of models. First, we've got our traditional type of method for maintenance, and that's really about our corrective maintenance, and that's when we're performing maintenance on an asset, um, after the equipment fails. But the challenges with that is we end up with unplanned. We end up with disruptions in our schedules, um, as well as reduced quality, um, around the performance of the asset. And then we started looking at preventive maintenance and preventative maintenance is really when we're performing maintenance on a set schedule. Um, the challenges with that is we're typically doing it regardless of the actual condition of the asset, um, which has resulted in unnecessary downtime and expense. Um, and specifically we're really now focused on pre uh, condition-based maintenance, which is looking at leveraging predictive maintenance techniques based upon actual conditions and real time events and processes. Um, within that we've seen organizations, um, and again, source from McKenzie have a 50% reduction in downtime, as well as an overall 40% reduction in maintenance costs. Again, this is really looking at things across multiple industries, but let's look at it in the context of the public sector and based upon some activity by the department of energy, um, several years ago, >>Um, they've really >>Looked at what does predictive maintenance mean to the public sector? What is the benefit, uh, looking at increasing return on investment of assets, reducing, uh, you know, reduction in downtime, um, as well as overall maintenance costs. So corrective or reactive based maintenance is really about performing once there's been a failure. Um, and then the movement towards, uh, preventative, which is based upon a set schedule or looking at predictive where we're monitoring real-time conditions. Um, and most importantly is now actually leveraging IOT and data and analytics to further reduce those overall downtimes. And there's a research report by the, uh, department of energy that goes into more specifics, um, on the opportunity within the public sector. So, Rick, let's talk a little bit about what are some of the challenges, uh, regarding data, uh, regarding predictive maintenance. >>Some of the challenges include having data silos, historically our government organizations and organizations in the commercial space as well, have multiple data silos. They've spun up over time. There are multiple business units and note, there's no single view of assets. And oftentimes there's redundant information stored in, in these silos of information. Uh, couple that with huge increases in data volume data growing exponentially, along with new types of data that we can ingest there's social media, there's semi and unstructured data sources and the real time data that we can now collect from the internet of things. And so the challenge is to collect all these assets together and begin to extract intelligence from them and insights and, and that in turn then fuels, uh, machine learning and, um, and, and what we call artificial intelligence, which enables predictive maintenance. Next slide. So >>Let's look specifically at, you know, the, the types of use cases and I'm going to Rick and I are going to focus on those use cases, where do we see predictive maintenance coming into the procurement facility, supply chain, operations and logistics. Um, we've got various level of maturity. So, you know, we're talking about predictive maintenance. We're also talking about, uh, using, uh, information, whether it be on a, um, a connected asset or a vehicle doing monitoring, uh, to also leveraging predictive maintenance on how do we bring about, uh, looking at data from connected warehouses facilities and buildings all bring on an opportunity to both increase the quality and effectiveness of the missions within the agencies to also looking at re uh, looking at cost efficiency, as well as looking at risk and safety and the types of data, um, you know, that Rick mentioned around, you know, the new types of information, some of those data elements that we typically have seen is looking at failure history. >>So when has that an asset or a machine or a component within a machine failed in the past? Uh, we've also looking at bringing together a maintenance history, looking at a specific machine. Are we getting error codes off of a machine or assets, uh, looking at when we've replaced certain components to looking at, um, how are we actually leveraging the assets? What were the operating conditions, uh, um, pulling off data from a sensor on that asset? Um, also looking at the, um, the features of an asset, whether it's, you know, engine size it's make and model, um, where's the asset located on to also looking at who's operated the asset, uh, you know, whether it be their certifications, what's their experience, um, how are they leveraging the assets and then also bringing in together, um, some of the, the pattern analysis that we've seen. So what are the operating limits? Um, are we getting service reliability? Are we getting a product recall information from the actual manufacturer? So, Rick, I know the data landscape has really changed. Let's, let's go over looking at some of those components. Sure. >>So this slide depicts sort of the, some of the inputs that inform a predictive maintenance program. So, as we've talked a little bit about the silos of information, the ERP system of record, perhaps the spares and the service history. So we want, what we want to do is combine that information with sensor data, whether it's a facility and equipment sensors, um, uh, or temperature and humidity, for example, all this stuff is then combined together, uh, and then use to develop machine learning models that better inform, uh, predictive maintenance, because we'll do need to keep, uh, to take into account the environmental factors that may cause additional wear and tear on the asset that we're monitoring. So here's some examples of private sector, uh, maintenance use cases that also have broad applicability across the government. For example, one of the busiest airports in Europe is running cloud era on Azure to capture secure and correlate sensor data collected from equipment within the airport, the people moving equipment more specifically, the escalators, the elevators, and the baggage carousels. >>The objective here is to prevent breakdowns and improve airport efficiency and passenger safety. Another example is a container shipping port. In this case, we use IOT data and machine learning, help customers recognize how their cargo handling equipment is performing in different weather conditions to understand how usage relates to failure rates and to detect anomalies and transport systems. These all improve for another example is Navistar Navistar, leading manufacturer of commercial trucks, buses, and military vehicles. Typically vehicle maintenance, as Cindy mentioned, is based on miles traveled or based on a schedule or a time since the last service. But these are only two of the thousands of data points that can signal the need for maintenance. And as it turns out, unscheduled maintenance and vehicle breakdowns account for a large share of the total cost for vehicle owner. So to help fleet owners move from a reactive approach to a more predictive model, Navistar built an IOT enabled remote diagnostics platform called on command. >>The platform brings in over 70 sensor data feeds for more than 375,000 connected vehicles. These include engine performance, trucks, speed, acceleration, cooling temperature, and break where this data is then correlated with other Navistar and third-party data sources, including weather geo location, vehicle usage, traffic warranty, and parts inventory information. So the platform then uses machine learning and advanced analytics to automatically detect problems early and predict maintenance requirements. So how does the fleet operator use this information? They can monitor truck health and performance from smartphones or tablets and prioritize needed repairs. Also, they can identify that the nearest service location that has the relevant parts, the train technicians and the available service space. So sort of wrapping up the, the benefits Navistar's helped fleet owners reduce maintenance by more than 30%. The same platform is also used to help school buses run safely. And on time, for example, one school district with 110 buses that travel over a million miles annually reduce the number of PTOs needed year over year, thanks to predictive insights delivered by this platform. >>So I'd like to take a moment and walk through the data. Life cycle is depicted in this diagram. So data ingest from the edge may include feeds from the factory floor or things like connected vehicles, whether they're trucks, aircraft, heavy equipment, cargo vessels, et cetera. Next, the data lands on a secure and governed data platform. Whereas combined with data from existing systems of record to provide additional insights, and this platform supports multiple analytic functions working together on the same data while maintaining strict security governance and control measures once processed the data is used to train machine learning models, which are then deployed into production, monitored, and retrained as needed to maintain accuracy. The process data is also typically placed in a data warehouse and use to support business intelligence, analytics, and dashboards. And in fact, this data lifecycle is representative of one of our government customers doing condition-based maintenance across a variety of aircraft. >>And the benefits they've discovered include less unscheduled maintenance and a reduction in mean man hours to repair increased maintenance efficiencies, improved aircraft availability, and the ability to avoid cascading component failures, which typically cost more in repair cost and downtime. Also, they're able to better forecast the requirements for replacement parts and consumables and last, and certainly very importantly, this leads to enhanced safety. This chart overlays the secure open source Cloudera platform used in support of the data life cycle. We've been discussing Cloudera data flow, the data ingest data movement and real time streaming data query capabilities. So data flow gives us the capability to bring data in from the asset of interest from the internet of things. While the data platform provides a secure governed data lake and visibility across the full machine learning life cycle eliminates silos and streamlines workflows across teams. The platform includes an integrated suite of secure analytic applications. And two that we're specifically calling out here are Cloudera machine learning, which supports the collaborative data science and machine learning environment, which facilitates machine learning and AI and the cloud era data warehouse, which supports the analytics and business intelligence, including those dashboards for leadership Cindy, over to you, Rick, >>Thank you. And I hope that, uh, Rick and I provided you some insights on how predictive maintenance condition-based maintenance is being used and can be used within your respective agency, bringing together, um, data sources that maybe you're having challenges with today. Uh, bringing that, uh, more real-time information in from a streaming perspective, blending that industrial IOT, as well as historical information together to help actually, uh, optimize maintenance and reduce costs within the, uh, each of your agencies, uh, to learn a little bit more about Cloudera, um, and our, what we're doing from a predictive maintenance please, uh, business@cloudera.com solutions slash public sector. And we look forward to scheduling a meeting with you, and on that, we appreciate your time today and thank you very much.

>>Bach from Las Vegas. It's the cube covering AWS executive summit brought to you by extension. >>Welcome back everyone to the cubes live coverage of the Accenture executive summit here at the Venetian in Las Vegas. We are part of AWS reinvent. I'm your host, Rebecca Knight. We are joined by two guests for this segment. We have Prisaad Sanker and he is a senior managing director global ICI lead. Thank you so much for coming on the show. Personal and Larry soccer, global managing director ICI offerings. Thank you so much for coming on Larry. So present to minister with you this week marks the one year anniversary of intelligent cloud infrastructure, a group that you lead at Accenture. Tell us a little bit more about, about, first of all why this group was formed and the journey you've had this year, the highest, the highs and lows. >>Sure, sure. So first, first of all, thank you for having us. Um, so as you mentioned, December 1st will be one year of having formed this group. And the reason we did that was because all of our clients are going through a journey of digital transformation. And it's very important for us to be able to support that journey. So there are different elements that we have to bring together around cloud as well as infrastructure. So we brought together this group, which was actually in different parts of Accenture as one particular group, and we call it intelligent cloud. And infrastructure consists of 30,000 people pretty much in every part of the world supporting all different industries. And this is a way for us to bring together not just cloud computing, but also areas like networking, workplace, digital, other digital businesses that we need to be able to support in order to be able to help our clients through their journey of transformation. >>So this, this group was formed at a time of tremendous change and upheaval and the landscape. Talk to us a little bit about, we hear so much about digital transformation, our company's ready. What's the, let us into the client mindset. >>Yeah. So what happens is, you know, different industries obviously are progressing at different speeds. All of our clients are always worried about being disrupted within their industries, either by an existing competitor all by a completely new competitor that doesn't exist. You know, all the stories about, you know, the big companies that existed and almost vanished overnight. So that's something that keeps CEOs and CIO is awake at night just worrying about that. And so digital transformation is very important for them to be relevant to their client. It's all about bringing new products to their clients and also the speed with which they can actually do that. It's no longer enough to be a fast follower. You have to be an innovator. And cloud is the way that this innovation will happen for our clients. And so it's very important for us to be able to bring our group together. We are able to support that journey for our clients. Leary >>want to bring you into this conversation a little bit. It'd be what will be required for enterprises to make this big transition. I mean, he was talking about how you need to be an innovator. You can't just be a fast follower. >> Well, I mean a lot of times I look at it just given the size, the scale of most of our clients who are really up market, most of them don't have the option to just do a rip and replace and just reinvent themselves completely. So it really is how do I very rapidly modernize and transform my business to take advantage of it? And it really needs to start with your application landscape and end data. So how do I start to look at all the possibilities of the AWS is and start to re-imagine, reinvent Duke, use cloud native technologies. Also a significant amount of their estates are already running in legacy environments. >>We get the mainframe or other environments. How do you digitally decouple those so that you can extract value out of that? And ultimately those decisions of apps and data that are going to drive cloud deployments and architectures and data gravity really becomes the key decision factor to decide where do I place this day? And it was a great example today if you saw Jesse's keynote, he announced Achla where they're actually starting to look at how do I move compute and the processing closer to the actual datasets. So actually inverting the problem and moving closer to the data. And then we see that trend starting to proliferate on the other part of the keynote that was very interesting was the five G announcement. And first you heard about AWS pushing into local zones where they were getting much distributing it out closer to them, reduce latency and really starting to push out. >>So ultimately we seen the whole landscape being transformed by data, these new application architectures and where that data resides and out to traditional world that we've known of hybrid with public and private is really transforming with the Amazon outputs, with the BMCs and stuff like that into much more one about shared and dedicated infrastructure. Then the big, the next real big thing that starts to happen then is this whole explosion of IOT. So as price performance goes down with Moore's law, we can start to see a lot more cost effective IOT solutions. And all of a sudden a world that was very centralized, you know, running up in the, in the world of the Amazons had the public cloud is not going to be much more distributed to a lot more of that compute over time gets moved out there. So we've seen a very rapidly evolving landscape. Apps and data are ultimately driving our cloud clients cloud and infrastructure investments. And they're really just trying to figure out how they can rapidly transform their environments to take advantage of this new landscape. >> So both of you are describing this exceedingly complex environment that is changing dizzying speed. I mean, just even this morning, but the Andy Jassy on stage for three hours with all of the new products and services that AWS has coming out with. What is AWS? What is ICI and Accenture doing to help clients navigate this, this, this, this landscape? Prisaad you know, our >>team is, it's not just enough for infrastructure and cloud to be a horizontal function as it used to be. We feel that, you know, one of the things that Accenture really brings to the table is our industry differentiation. Spent a lot of time analyzing the industries that our clients are in. So we've actually changed the team of ICI to be three different things. The first is to be industry led, so it's no longer good enough to be a horizontal function. We have to understand the needs of each industry and really look at how cloud and infrastructure will support that industry. The second is all about intelligence. And Larry just talked about the proliferation of data, but it's also bringing artificial intelligence, making networks much more smart, you know, really infusing intelligence into everything we do. And the third is the concept of being invisible because our clients are expecting infrastructure to just be there all the time. >>They don't really have to understand how it works, but it has to be there. It's just like going to into room and turning on a switch and you expect electricity to be there. So infrastructure has to be very much like, because it has to be ubiquitous, it has to be just available all the time. So those are the things that we are trying to bring to our clients to make it very specific for and very industry specific for for our clients. And this goes into areas like cloud computing. It goes into 5g edge is going to be a big part of what is going to happen in various industries. And as Larry talked about, IOT devices are going to be just proliferating. It's going to be billions of IOT devices. There's trillions of dollars being spent. In fact, I think the spend on IOT is probably bigger than any other area that I have seen probably in my working lifetime. So it's going to be an exciting time to come for us. >>I mean, we tend to think about artificial intelligence as this futuristic Jetsons kind of thing, but really it's, it's here. And now, Larry, can you talk a little bit about how companies are using AI and having an impact already on their businesses? I mean obviously you see a lot of AI being used for different use cases. We saw some great examples today in Jesse's keynote and we're seeing a use for video analytics for example. And AI to try to figure out predictive maintenance type activity. So there's obviously a lot of business use cases. I think what's interesting from our perspective as well is a lot of the operational use cases. So if you take a look at it with all these new innovations, the rapid pace of change that we're seeing with cloud infrastructure, that application landscape, we've started to rely pretty heavily first on analytics to how do we, how do we figure out what's going on, how do we operate efficiently, how do we make sure we don't put the businesses grist, you know, really pivoting from reactive to proactive and predictive operations. >>We've obviously automated everything as much as we can. I've see AI actually playing a very interesting role in how we optimize these environments over time. So as you get a much more complex environment, much more dynamic, and with containers, Coobernetti's, serverless compute dynamic networks that Prisaad was talking about with software defined networking, AI is going to be the only way we can tune and optimize that over time. So you've obviously got all the business use cases that we see in healthcare that we see in mining, predictive operations and stuff like that. But how we actually use AI internally is going to be critical to how we actually be able to manage cloud and infrastructure and really optimize it over time. >>W what is the client? What's, what's on your minds of your customers right now? We know that only 20% of companies out there have really adopted the cloud. Two thirds have really yet to capture the benefits of the cloud. What are you hearing from them? What are they saying to you? What are their pain points? >>So I think, you know, all of our clients realize that ultimately the cloud is going to be where they will be at. You know, data centers are existing today, but at some point, you know, everybody's going to move to the cloud. Most of our clients have taken the easier workloads and you know, the easy part has already been done. That's the first 20% but 80% of the work still remains. And that's the more complicated work that has to come. So they're looking to us to give them the right solutions. And then there's a variety of other factors to be considered. For example, they have to look at security issues. They have to understand that, you know, there are data privacy aspects to be considered. So really it's a question of matching the right private and public options. And as Larry also mentioned, probably only 30 40% of the data will actually sit in the central cloud. Most of the other data is actually gonna move out to the edge with IOT devices and so on. So data gravity, where does your dataset, where does your compute sit? And Andy talked about it as well today in his keynote address. These are all things that are going to keep evolving and I think that's going to really change the landscape. >>I think they, I think they all see the power of cloud. I mean, which in my mind it's really around the innovation cycles. You know, what you look at the pace that they're innovating with with RDS and Redshift. So they all see that power. I think the biggest thing, they struggle with our skills. And culture because how do you upskill, retrain the organization, everything from the new technologies, how to architect in the new world where it's very ephemeral, dynamic, a serverless world. How do you start to adopt those technologies? How do you operationalize it? How do you go beyond just agile and really do true dev sec ops where you're integrating security and operations built in from the ground floor. And a lot of times he's a cultural change is one of the things we see in cloud and infrastructure operations for example, is how do you take develop operators who used to be eyes on glass, looking at console's turn them into developers where they, you know, they're writing the next analytic algorithms to get to predictive there they're automating automation scripts to improve operations and ultimately tuning the AI engines that optimize it. >>And I think that skills and culture barrier is probably the hardest thing for them to overcome. And how do you just, you can't just go to the cloud, you've got to behave differently. It really have to transform how you use it, how you operate and really transform the organization and culture. >>So these change management challenges, where do you even start? Because as you said, the adopting the technology is almost the easy part, or at least the most straightforward, but really getting everyone on board and really changing people's mindsets and mentalities and dispositions and the way that they collaborate with each other and collaborate cross-functionally. So what have you learned within ICI to, to help companies? And what's your advice? >>I think, I think there are three aspects that you have to get right. In fact, I was talking to one of the CEOs of a very large client of ours, and I think you have to get three things right and you've got to get them aligned and moving at the same time. The first obviously is the technology. So you have to understand what makes sense for you, for your industry. Make the right bets because if you make a wrong decision, then you know you're going to set yourself back. So getting the technology right obviously is important. The second is operating model, making sure that you get that the right operating model in place and kicked off right, right upfront. And the third, like Larry said, is transforming your workforce. So making sure that people are, you know, have all the right skill sets when you actually have the operating model and the technology ready. So it's very important to bring all those three aspects together and a company like Accenture, with our background around consulting, around change management, around technology, we're uniquely positioned understanding our client's industries and really bringing all of those three aspects together so that we're able to position our clients to take that journey forward. >>Larry, in terms of next year's Excenture executive summit, look into your crystal ball. You've already talked about a lot of emerging technologies, IOT, edge computing have talked a lot about AI. Of course. What do you think are going to be the hot topics? Looking ahead this this year in ice with an ICI, you >>touched on earlier, I think everyone's going to be talking about data gravity. As you get these bigger and bigger data sets, it becomes, you know, the network's always going to be the bottleneck. So even with Moore's law, stretching from 18 months to 24 the amount of data we produce, particularly with IOT and edge, is really going to transform things. And even though we've got massive network upgrades like 5g coming along, it will never be enough. I mean, that comes along every 12 years. We're seeing a doubling of price performance who competed? I think data gravity, you can start to see a very different landscape where it used to be public and private and now edge is really going to be obliterated to much more seamless architecture. Then there was a lot of the keynote today, and if you start to take a look at local zones and some of the announcements today, they were ready. Amazon was heading there with green Greengrass so you can have much more seamlessness. And how do I get compute closer to the processing? You're gonna be talking a lot about clustering, clustering, compute around datasets versus the other way around. So I think we're gonna see, and I think that's going to happen pretty fast. Usually a lot of this stuff we've been talking about IOT for years. I do think we're on the tipping point. I think we're about to see exponential growth just as price performance >>comes together. Some of the technologies had gotten gotten there, but, but I think that the whole focus on data and data gravity is what you're going to hear a lot about next year. I can't wait to hear the AWS reinvent band. Do a little pink Floyd or something like that for data gravity. We'll Larry and Prisaad. Thank you so much for coming on the cube. It was a pleasure having you on. Thanks for Brooke. I'm Rebecca night's stay tuned for more of the cubes live coverage of the Accenture executive summit.

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen, brought to you by Del Technologies and its ecosystem partners. >> Hello. Welcome back to the Cube. At least a market with Dave Alonso. We are at Del Technologies World. This is our third day of coverage. As John has been saying, This is a cannon double cannon of Q content. We are pleased to welcome back a couple of alumni to keep. We've got Beth failing Presidents data Protection division from Italians. It's great to have you back. And Sherrod Rastogi also welcome back S VP of data protection product management Guys, Lots of news. The last three days, fifteen thousand or so people. Lot of partners. We've been hearing nothing but tremendous amount of positivity and also appreciation from your customers and partners for all of this collaboration within the Della Technologies company with partners. Some of the news, though, that you were on the keynote stage give us some anecdotes that you've heard from customers and partners the last few days about where Del Technologies is going. >> Yeah, I'm happy too. And you know, a big announcements this week. We're a power protect software and the power protect extra hundred appliance. And what we're hearing from customers is this is exactly what we needed to do because the demands on data protection are changing with more more. Brooke look being distributed with data being more more important and with the risks being more more prevalent that they were looking for us to take a bold step and introduce this next generation software to find platform. And so the feedback you're getting is you've done what you needed to do, and they're looking forward to learning more. >> So I wonder if we could sort of explore a little bit this concept of data management. So data management lead needs different things to different people. Sure, if your database person maybe maybe different from a person who's doing data protection, what does it mean in a data protection context? I think >> first of all, you know, having visibility off your data all across your infrastructure that resides in the edge. The court a cloud across multiple applications in physical virtual environments, right? So having full facility that I think is one component second is not the ability to move the data across seamlessly across any socially target but it is on track in the cloud. Robert Cloud. I think that sort of a second element, the third and probably the most important is how do you actually get value from the data, right? Already, Actually, not only unable to protect it, but make it available at the right time, right place for the right application and be able to use it because, as you know, data is the fuel of the modern visual economy. On making it available is really, really critical. And that to me. So you're combining all of that is what I would consider it at management to be. >> So double click on that. I mean, could you be more specific about the attributes of, you know, a modern data management system? So I >> would say, you know, any modern technology may be modular FBI driven, you know, it really sort of the automate scale performance coverage. All those attributes, I think are very important for any more than data protection product and be able to meet the needs of our customers. You know, high scale hi coverage and rapidly, >> and that gives you a cloud like experience presumably allows you to scale out many a performance. I've seen some of the conversations that start associating with that or scale in place Bath. You talked about that? Yeah, Well, yeah. I want to explore a little bit about your business because you know who knew? Who would have predicted a few years ago? The data protection would always because all of a sudden become this hot space veces diving in hundreds and hundreds of millions of dollars being spent. And of course, you're the biggest player. So everybody wants a piece of your hide. And so and you got a portfolio. It goes back up llegado days. They have amar stuff data, domaine et cetera, et cetera. She had a sort of make sure that that was logical for your customers. Protect those customers that have made investment of you, but also shoma roadmap. Jeff Clark comes in, says, Okay, we're going to simplify, you know, marching orders. Your business in a very rapid time has transformed. Can you talk about that? What's what's taking place in your business? >> Absolutely, David, it's so interesting even comparing last year to this year, right? We're at this pivot point where we're building on the legacy of Trust and I T and knowledge and experience that we have. But we're now setting the foundation to be number one and data protection and data management for the next ten years. Introducing this new set of products were able to bring a customer's forward. We call it the path to power. So in addition to that, bring new customers into the family. We're looking for all those aspects of modern day to management, with simplicity, with multi cloud, with automation and with the new use cases where it's more than just back up. It's CCD are its analytics. It's testing toe. It's validation. So this is whole spectrum of things that we can expand into now that we have this new platform. It's really exciting. >> It is exciting. And yesterday the under Armour video was very cool, and one of the things that they set in there is that there they're leveraging data for brand reputation. I mean, they've got under Armour has incredible brand ambassadors Tom Brady, Steph Curry. But looking at it as not just a business ever. But this is actually tied to our brand reputation, did. It is so incredibly pivotal to the lifeblood of a business. It has to be protected. >> Yeah, and that's a big theme. And you probably something too. But, you know, in this day and time data is no longer something that maybe people in I'd worry about write It is now the lifeblood of most of our customers, corporations and at the same time list, like the threat of malware are very prevalent. And so things like what we've done with cyber recovery always were working with our customers to protect their data. In a survey we just did. With twenty two hundred I t professionals, twenty eight percent of them had had some data loss in the last twelve months. So the risk of data loss is real. And we take our responsibility very seriously to help our customers protect from that risk. >> So I like this message to any source. Any target, any s l a. I would I would had any workload and because on so talk about you're differentiation in the marketplace, that would be great, because it's hard sometimes, you know, squint through all the marketing. And so what makes you guys different specifically thinking >> about Delhi emcee Indiana production historically has its strength in dealing with complex work clothes at high scale, with high performance on having a wide coverage of work has been a strength and actually had very low cost, very efficient, right? So that string we sort of carry on into the future. And what we're adding on is I would say that the next degree off simplification and ease off ease off, install, upgrade use. Making those work was very, very simple, right? So I think that's another dimension. We are God. We're adding our dimension, what we call multi dimensional scale, which is both scale up and scale out the same time when you actually add more notes and more cubes, you are not only capacity, but he also improved performance, right? That's it, architecturally, a fundamentally different way in Harvey approach it. So I think that's an element of innovation, and I think on performance we're introducing our first all flash off Lions industry first, So we're super excited about that. And so I think it just helped our customers in terms of restore interactions store Do those work was a lot faster. Those are some other elements in which we continue innovating. >> That's great. Yeah, so you talk about the power protect X four hundred, which is your flesh. John Rose said something on stage. Beth, I want to ask you, Teo, sort of add some color. Hey, said this is not just secondary storage. It's protected. Managed infrastructure, >> huh? That's great face. >> What? What did he mean by that? And what should we take away? >> I mean, it shows how we're broadening the use cases that these products can help satisfy. And so much of what we're talking about Del Technologies is a simplified infrastructure across the board, not thinking about just point products, but giving the customer that experience of a seamless extendable infrastructure. So protected managed infrastructure means that your infrastructure, something you have, can confidence it's protected and that you also are not just dealing with all of these pieces and parts. But I can think of it has a managed whole. I think that that helps out and talk to John about that. But that's what I take away from what he's saying. >> If I can just add to that, I would say Like, you know, data management is sort of the perfect glue across the whole del technology infrastructure, but a server storage bm We're, you know, eighty, you know, infrastructure pivotal, right? Data management data productions are off, cuts across everything, and we can bring everything together. So >> I would like to add something to that if I make it. You know Beth on sure Art as well. Data protection Backup was always OK. We gotta back it up. Who's gonna? Okay, Bump bolted on. And what's happening is the lines are blurring. Primary storage, secondary storage. You're seeing back up in the e r. Use cases. You talked about analytics and, you know, so many new emerging. That's why it is so exciting. And so because those lines are blurring, you get more value out of the system. It goes beyond just insurance. And that means this could be a lot of money being made here >> if there is. And it is also a really important need, write one thing that we haven't touched on. But I also think it's really important is with our protect we're helping combine self service with centralized governance. So what I mean by that is, if you're a V a madman or Oracle Adnan or a sequel admin, you know, you could have control over protecting your data, but we pair that with a single, you know, governance model. So if I'm the person is responsible for my company's entire, you know, data set, I can still make sure that everything's happening is it should be. And there are no anomalies, so we're really making it as easy as possible, for the business is within our customers to protect and manage their data but not making it the Wild West. Because somebody in the end is accountable for saying I know where all the data is, and I know it's protected, so it's having both of those users. >> So as data protection has really elevated, the stay was saying to become its way beyond an insurance policy. This is absolutely table stakes because data has so much value and so much value that organisations haven't even been able to extract it right, how the conversation within the customer base changed. It's not just to the admin girl or guy anymore. Rightness is Are you saying this really leveled up Tio? Maybe a senior level C level challenge as our business imperative that the state of must be protected and readily accessible at any time. Who are you talking to? >> So answer quickly that I lied to you when we're talking to the eye to decision makers. So seo no, that level data protection strategy has become something that they have in their priority list, right? It's not really in any way what it was maybe five or ten years ago. Now it's something that there's cord of what they hold as their responsibilities, executives and and that's great. It's great to have those kind of conversations because it's strategic. >> Another conversation. Just an example from yesterday, while speaking with one of the chief architects at a major company, they're really talking about cyber security on How do you use Extend? You know what we offer into a full solution across their technology. Do address, you know, doesn't use case right. So I think it's expanding beyond just back up and protection to true protection off the data. Very most mission critical data is available and not just protected. They also want to talk about how can you recover that real quickly in very quick time, so that your operation, when you do have that cyber, if and when you have that attack So I think it's just expanding toe touch. A lot more customers, I would say Our people buying, buying decision makers across >> so that when I talk to people in division I sense a renewed energy. A renewed focus. I mean, GMC before Del. Tell'Em Steve always been really good. Taking engineering resource is to getting products out to the market. But But I I see again more focused effort here and one of the exam to keep pushing on. Is this notion of cloud model so beyond? Just okay, there's a target. How do we now get to that? You know, data protection is a service small. I know that you're working toward that. I know it's, you know, a lot of it's It's early days there, but you've got to be a leader in that, I presume. So. I want to keep watching that pushing that I won if you guys could comment on what coming >> on, both things that you said. First of all, there's absolutely a level of excitement and focus and confidence in what we're doing in the product groups. I'm really changing the way we're developing software so that we have a new customer value coming out every quarter. And they were having clarity between the top level strategies. White downs, what individual engineers are working on. So that's fun and excited because we're truly transforming the way we're developing Product says point one. And the second one, absolutely here, that theme throughout all of what we're talking about. You heard a nun day one, No, giving people that cloud that experience infrastructure has a service which certainly includes data management and data protection so they can consume it in a way that fit step business that scales with business That's automated, that doesn't require, you know, massive manual steps and is more what people expect today was a cloud like experience, even for them on from data centers. Clearly, that's where we're moving. And this one more point is you know, people really want automation they don't wanna have to think about. Did I remember to protect everything? They want the system to do that for them. So you'LL see more of that from us as well. You know how we helping them with machine learning? An A I an automation so they can have confidence that all of the assets are protected even if they haven't remember to do it all. >> I mean, I just add to it. I've bean at Delhi emcee for about a year. >> It >> has been a fantastic journey waiting. It's exciting. It's been awesome. Awesome experience. I totally see the >> focus. And I think that renewed focus the cloud like a model and the innovation. They all go hand in hand because the old waterfall model of okay, we're gonna develop properties shipment every year, eighteen months. Whatever it is that doesn't fly anymore. People want innovations, and now they want to push code every day. Right? So our baby, every quarter at least. >> Yeah. Yeah. Facing new energy to the engineers as well. >> So I mean, I understand that many of your team, if not your entire engineering team, has been trained in agile. Is that my getting it right? Is that right? >> Yeah, yeah, >> not just not just like internal train. You guys brought in outside people and really took him through some formal training. Right >> way have in multiple different kinds of training. And we have lots of communications inside to get people coaching. And it's not just a process book that we're following its really a different way of thinking about how you bring customer value in small increments, staying in a good known stay and making sure that we're maximizing our engineering capacity. >> That's big. And I wish we had more time cause that's cultural train. Yeah, yeah, that you guys are really driving. And we also didn't have time to touch on partners, but it can imagine there's a lot of excitement and your huge partner community about what you guys are doing This. Congratulations on all the announcement is gonna have to have you back because there's just so much more to dig into. But back Sherrod, Thank you for joining David me this afternoon on the you go. >> Thank you so much >> for our pleasure. For Dave Volonte and Lisa Martin. You're watching the Cube live from Day three of Del Technologies, World twenty nineteen on the Cube. Thanks for watching