>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Worldwide Public sector. >>Welcome to the Cube. Virtual. This is our coverage of aws reinvent 2020. Specialized programming for worldwide public sector. I'm Lisa Martin. Got a couple of guests here from No. Veta, please welcome Steven Adelman, principal computer scientists, and Kevin Healed, vice president of Information Exploitation. Gentlemen, welcome to the Cube. >>Thank you. >>Thank you for having us. >>Alright, guys. So? So, Kevin, we're going to start with you. Give our audience an introduction to Nevada. What do you What do you guys do? Who are you? How do you play in the public sector Government space, >>right? Yeah. Thank you, Lisa. Eso, Nevada Nevada is a technology services company focused on government solutions. So primarily national security solutions. Eso think customers such as Doody, the intelligence community, FBI, law enforcement and things like that about 13 1300 employees worldwide, primarily in our in our field. Clear resource is, um, that really focused on cloud for solutions for our customers. So solving the tough mission challenges our customers have, so that could be in technology solutions such as Data Analytics A I M L i O T. Secure Workloads, full spectrum cyber Cobb video processing. Really anything that's a high end technology solution or something we do for the government. We have been a privilege. We have. It's a privilege to be a partner with AWS for for some time now. In fact, I think the first reinvent we may have been to Stephen was six years ago. Five years ago, two >>1012 or 13 >>s So we've we've we've been around for a while, really kind of enjoying it and certainly sad that we're missing an in person reinvent this year, but looking forward to doing it virtually so, we're actually advanced your partner with AWS with a machine learning and government competency. Andi really kind of thio pump the m l side of that. That was one of our first companies with compasses with AWS and led by a center of excellence that I have in my division that really focuses on machine learning and how we applied for the Michigan. And so, um, really, we focus on protecting the nation and protecting our activities in the country >>and on behalf of the country. We thank you, Steven. Give me a little bit of information from a double click perspective as computer scientists. What are some of the key challenges that no, that helps its customers to solve. And how do you do that with a W s? >>Yeah, Thank you. So really as, ah, company, that is is data first. So our initial love and and still are kind of strongest competency is in applying solutions to large data sets. And as you can imagine, uh, the bigger the data set them or compute you need the the more resource is you need and the flexibility from those resource is is truly important, which led us very early, as especially in the government space and public sector space to be in early. A doctor of cloud resource is because of the fact that, you know, rather than standing up a 200 node cluster at at many millions of dollars, we could we could spend up a W s resource is process a big data set, and then and then get the answers an analyst or on operator needed and then spin down. Those resource is when When when that kind of compute wasn't needed. And that is really, uh, kind of informed how we do our work Azaz Nevadans that that cloud infrastructure and now pushing into the edge compute space. Still kind of keeping those cloud best practices in play to get access to more data. That the two, the two biggest, I think revolutions that we've seen with regards to using data to inform business processes and missions has been that that cloud resource that allows us to do so much with so less and so much more flexibly and then the idea of cheap compute making it to the edge and the ability to apply sensors thio places where you know it would been a would have been, you know, operational cost prohibitive to do that and then, ironically, those air to things that aren't necessarily data analytics or machine learning focused but man, did they make it easier to collect that data and process that data and then get the answers back out. So that really has has has kind of, uh, shaped a lot of the way Nevada has grown as a company and how we serve our customers. >>So coming back over to you lets. One of the things that we've been talking about almost all year is just the acceleration in digital transformation and how much faster organizations, private sector, public sector need to innovate to stay relevant, to stay competitive. How do you are you working with government customers to help them innovate so quickly? >>You know, we're very fortunate that a set of customers that focuses actually innovation it's focuses. I rad on. Do you know we can't do the cool things we do without those customer relationships that really encourage us to, um, to try new things out and, quite frankly, fail quickly when we need Thio. And so, by establishing that relationship, what we've been able to do is to blend agile development. Actual acquisition with government requirements process, right? If if you know the typical stereotype of government work is it's this very stovepiped hard core acquisition process, right? And so we have been fortunate to instead try quick win kind of projects. And so one of the biggest things we do is partner with our government customers and try to find it difficult, um, challenged to solve over 6 to 12 month time, right? So instead of making this long four or five year acquisition cycles like show me, right. How can we solve this problem? And then we partner with the mission partner show success in six months show that we can do it with a smaller part of money, and then as we're able to actually make that happen, it expands in something bigger, broader, and then we kind of bringing together a coalition of the willing, if you will in the government and saying, Okay, are there other stakeholders to care about this problem, bring them on, bring their problems and bringing together? You know, we can't do that with some of the passionate people we have, like Stevens. A perfect example. When we talk about a car in the projects we're doing here, Stevens passion for this technology partner with our customers having these challenges and try to enhance what they're doing is a powerful combination. And then the last thing that we're able to is a company is we actually spend a decent amount of our own dollar dollars on I rad S O. R and D that we fund ourselves. And so, while finding those problems and spending government dollars in doing that. We also have spent our own dollars on machine learning Coyote sensor next Gen five g and things like that and how those compartment together partner together to go back to the government. >>Yeah, yeah, So I would even say, You know, there's this. There's a conventional wisdom that government is slow in plotting and a little bit behind commercial best practices. But there are There are pockets in growing pockets across the government, Um, where they're really they're really jumping ahead of, ah, lot of processes and getting in front of this curve and actually are quite innovative. And and because they kind of started off from behind, they could jump over a lot of kind of middle ground legacy technologies. And they're really innovating. As Kevin said with With With the card platform, we're partnering with um P E O Digital in the Air Force in South C, D. M and Air Force security forces as that kind of trifecta of stakeholders who all want toe kind of saw a mission problem and wanted to move forward quickly and leave the legacy behind and and really take a quantum leap forward. And if anything, they're they're driving us Thio, Innovate Mawr Thio Introduce more of those kind of modern back practices on bond. Nevada as a company loves to find those spots in the government sector where we've got those great partners who love what we're doing. And it's this great feedback loop where, um, where we can solve hard technical problems but then see them deployed to some really important and really cool and impactful missions. And we tend to recruit that that set that kind of nexus of people who want to both solve a really difficult problem but want to see it executed in a really impactful way as well. I mean, that really grates a great bond for us, and and I'm really excited to say that that a lot of the government it is really taking a move forward in this this this realm. And I think it's it's just good for our country and good for the missions that they support. >>Absolutely. And it's also surprising because, as you both said, you know, there is this expectation that government processes or lengthy, you know, laborious, um, not able to be turned around quickly. But as Kevin, you just said, you know helping customers. Government agencies get impact within 6 to 12 months versus 4 to 5 years. So you talked about Picard? Interesting name. Kevin. Tell me a little bit more about that technology and what it is that you guys deliver. That's unique. >>Well, honestly, it's probably best to start with Stephen. I can give you the high level. This is Stevens vision. I have to give him credit for that. And I will say way have lots of fun. Acronym. So it isn't Actually, it isn't backward. Um, right. Stephen doesn't actually stand for something. >>It stands for Platform for Integrated, a C three and Responsive for defense on >>Guy. You know >>that the Star Trek theme is the leg up from the last set of programs I had, >>which were >>my little ponies. So >>Oh, wow. That's a definite stuff in a different direction. Like >>it? Part of the great thing about working in the government is you get to name things, cool things, so but t get to your question eso So Picard really sprung out of this idea that I had a few years ago that the world but for our spaces, the Department of defense and the federal government was going to see a massive influx of the desire to consume sensors from from areas of responsibility, from installations and, frankly, from battlefields. Um, but they were gonna have to do it. In a way, um, uh, that presented some real challenges that you couldn't just kind of throw compute editor, throw traditional I t processes at it. You know, we have legacy sensors that are 40 years old sitting on installations. You know, old program, a logical controllers or facilities control systems that were written in cobalt in the seventies, right in the world are not even I, p based, most of them bond. Then on the other end of the spectrum, you have seven figure sensors that air, you know, throwing out megabits of second of data that are mounted to the back of jeeps. Right, That that air bouncing through the desert today. But we'll be bouncing through the jungle tomorrow, and you have to find all of those kind of in combined all of those together, um, and kind of create a cohesive data center for data set set for you know, the mission for, um, you know what we call a user to find common operating picture for a person. Thio kind of combine all of those different resource is and make it work for them. And so we found a great partner with security forces. Um, they realized that they wanted Thio to make a quantum leap forward. They had this idea that the next defender So there are there, like a military police outfit that the next defender was going to be a data driven defender and they were gonna have to win the information war war as much as they had to kind of dominate physical space. And they immediately got what we were trying to achieve, and it was just just great synergy. And then we've piled on some other elements, and we're really moving that platform forward to to kind of take every little bit of information we can get from the areas of responsibility and get it into a you know, your modern Data Lake, where they can extract information from all that data. >>Kevin, as the VP of information exploitation, that's a very interesting title. How are you helping government organizations to win the war on information? Leverage that information to make a big impact fast. >>Yeah. I mean, I think a lot of it is is that we try to break down the barriers between systems on data so that we can actually enable that data to fuse together to find and get insights into it. You know, as ML and I have become trendy topics, you know, they're very data hungry operations. And I think what Steven has done with the card and his team is really we want to be able to make those sensors seamless from a plug and play perspective that Aiken plug in a new sensor. It's a standards based, uh, interface that sends that data back so that we can and take it back to the user to find Operation Picture and make some decisions based off of that data. Um, you know, what's more is that data could even refused with more than the data that Stevens collecting off the sensors. It could be commercial data, other government data and I think is Davis. As Stephen said earlier, you have to get it back. And as long as you've gotten back in Labour's share with some of our mission partners, then you can do amazing things with it. And, you know, Stephen, I know you have some pretty cool ideas and what we're gonna do on the edge, right? How do we do some of this work of the edge where a sensor doesn't allow us to pull out that data back? >>Yeah, and and Thio follow on to what you were kind of referring to with regards to thio handling heterogeneous data from different sensors. Um, one of the main things that our government customers and we have seen is that there are a lot of historically there are a lot of vertical solutions where you know, the sensor, the platform, and then the data Laker kind of all part of this proprietary stack. And we quickly realized that that just doesn't work. And so one of the major thrust of that card platform was to make sure that we had ah, platform by which we could consume data through adapters from essentially any sensor speaking. Any protocol with any style data object, Whether that was an industry standard or a proprietary protocol, we could quickly interested and bring it into our Data lake. And then to pile on to what Kevin was talking about with compute. Right? So you have, uh, like, almost like a mass locks hierarchy of needs when it comes to cyber data or thio this coyote data or kind of unified data, Um, you know, you wanna turn it into basic information, alerts alarms, then you want to do reporting on it, or analytics or some some higher level workflow function. And then finally, you probably want to perform some analytics or some trending or sort of anomaly detection on it. And and that gets more computational e intensive each step of the way. And so you gotta You gotta build a platform that allows you to to both take some of that high level compute down to the edge, but also then bring some of that data up into the clouds where you could do that processing, and you have to have kind of fun jubilate e between that and so that hard platform allows you to kind of bring GP use and high processing units down to the edge and and make that work. Um, but then also and then as maybe even a first passive to rule out some of the most you know, some of the boring gated in the video Analytics platform. We call it Blue Sky and Blue Ocean. Right, so you're recording lots of video. That's not that interesting. How do you filter that out? So you're only sending the information The interesting video up eso You're not wasting bandwidth on stuff that just doesn't matter on DSO. It's It's a lot of kind of tuning these knobs and having a flexible enough platform that you could bring Compute down when you need it. And you could bring data up to compute on Big Cloud while you need it, and just kind of finding a way to tune that that that really does. I mean it. You know, that's a lot of words about how you do that. But what that comes to is flexible hardware and being able to apply those dev ops and C I. C D platform characteristics to that edge hardware and having a unified platform that allows you to kind of orchestrate your applications in your services all the way up and down your stack, from micro controllers to a big cloud instant creation. >>You make it sound so easy. Steven Kevin. Let's wrap it up with you in terms of like making impacts and going forward. We know the edge has exploded, even mawr, during this very interesting year. And that's going to be something that's probably going to stay, um, stay as a permanent impact or effect. What are some of the things that we can expect in 2021 in terms of how you're able to help government organizations capitalize on that, find things faster, make impact faster? >>Yeah. I mean, I think the cool thing we're seeing is that there's a lot more commoditization of sensors. There's a lot more censored information. And so let's use lighters. Example. We you know, things were getting cheaper, and so we can all of a sudden doom or or more things at the edge, and we ever would have expected. Right when you know Steven's team is integrating camera data and fence data from 40 years ago, you know, it's just saying on off it's not do anything fancy. But now we you know, you know, Stephen, I camera whether Metro you gave him before was, but the cost of light are has dropped so significantly that we can now then deploy that we can actually roll it out there and not being locked in their proprietary, uh, system. Um, so I see that being very powerful, you know? Also, I can see where you start having sensors interact with each other, right? So one sensor finds one thing and then a good example that we've started thio experiment with. And I think Steve, you could touch on it is using triggering a sensor, triggers a drone to actually investigate what's going on and then therefore, hybrid video back and then automatically can investigate instead of having to deploy a defender to actually see what happened at that. At that end, Points dio e don't know. There's it's amore detail you can provide there. >>Yeah, No. So exactly that Kevin. So So the power of the sensor is is something something old that that gives you very uninteresting Data like a one or a zero on on or off can detect something very specific and then do something kind of high speed, like task a drone to give you a visual assessment and then run object detection or facial recognition on, you know, do object detection to find a person and do facial recognition on that person to find out if that's a patrol walking through a field or a bad guy trying Thio invade your space. Um and so it's really the confluence and the gestalt of all of these sensors in the analytics working together, Um, that really creates the power from very simple, simple delivery. I think, um, there's this, You know, this idea that you know, ah 100 bytes of data is not that important. But when you put a million sensors giving you 100 bytes of data, you can truly find something extremely powerful. And then when you kind of and you make those interactions sing, um, it's amazing. Tow us the productivity that we can produce and the kind of fidelity of response that we can give thio actors in the space whether that's a defender trying to defend the base or a maintenance person trying thio proactively replace the fan or clean the fan on an H vac system. So So you know, you know, there isn't a fire at a base or for, uh, interesting enough. One of the things that we we've been able to achieve is we've taken maintenance data for helicopter engines and And we've been able to proactively say, Hey, you need to You need to take care of this part of the helicopter engine. Um and it saves money. It saves downtimes. It keeps the birds in the air. And it's a relatively simple algorithm that we were able to achieve. And we were able to do that with the maintenance people, bring them along in this endeavor and create analytics that they understood and could trust on DSO. I think that's really the power of this base. >>Tremendous power. I wish we had more time to to dig into it. Guys, thank you so much for sharing. Not just your insights, what nobody is doing but your passion for what you're doing and how you're making such an impact. Your passion is definitely palpable. Steven. Kevin, Thank you for joining me today. >>Thank you >>for my guests. I'm Lisa Martin. You're watching the Cube? Virtual. Yeah,

>> Announcer: Live from San Francisco it's the CUBE covering Spark Summit 2017 brought to you by Data Bricks. >> Welcome back to the CUBE, we're continuing to take about two people who are not just talking about things but doing things. We're happy to have, from Novetta, the Director of Predictive Analytics, Mr. Rob Lantz. Rob, welcome to the show. >> Thank you. >> And off to my right, George, how are you? >> Good. >> We've introduced you before. >> Yes. >> Well let's talk to the guest. Let's get right to it. I want to talk to you a little bit about what does Novetta do and then maybe what apps you're building using Spark. >> Sure, so Novetta is an advanced analytics company, we're medium sized and we develop custom hardware and software solutions for our customers who are looking to get insights out of their big data. Our primary offering is a hard entity resolution engine. We scale up to billions of records and we've done that for about 15 years. >> So you're in the business end of analytics, right? >> Yeah, I think so. >> Alright, so talk to us a little bit more about entity resolution, and that's all Spark right? This is your main priority? >> Yes, yes, indeed. Entity resolution is the science of taking multiple disparate data sets, traditional big data, and taking records from those and determining which of those are actually the same individual or company or address or location and which of those should be kept separate. We can aggregate those things together and build profiles and that enables a more robust picture of what's going on for an organization. >> Okay, and George? >> So what did you do... What was the solution looking like before Spark and how did it change once you adopted Spark? >> Sure, so with Spark, it enabled us to get a lot faster. Obviously those computations scaled a lot better. Before, we were having to write a lot of custom code to get those computations out across a grid. When we moved to Hadoop and then Spark, that made us, let's say able to scale those things and get it done overnight or in hours and not weeks. >> So when you say you had to do a lot of custom code to distribute across the cluster, does that include when you were working with MapReduce, or was this even before the Hadoop era? >> Oh it was before the Hadoop era and that predates my time so I won't be able to speak expertly about it, but to my understanding, it was a challenge for sure. >> Okay so this sounds like a service that your customers would then themselves build on. Maybe an ETL customer would figure out master data from a repository that is not as carefully curated as the data warehouse or similar applications. So who is your end customer and how do they build on your solution? >> Sure, so the end customer typically is an enterprise that has large volumes of data that deal in particular things. They collect, it could be customers, it could be passengers, it could be lots of different things. They want to be able to build profiles about those people or companies, like I said, or locations, any number of things can be considered an entity. The way they build upon it then is how they go about quantifying those profiles. We can help them do that, in fact, some of the work that I manage does that, but often times they do it themselves. They take the resolve data and that gets resolved nightly or even hourly. They build those profiles themselves for their own purpose. >> Then, to help us think about the application or the use case holistically, once they've built those profiles and essentially harmonized the data, what does that typically feed into? >> Oh gosh, any number of things really. Oh, shoot. We've got deployments in AWS in the cloud, we've got deployments, lots of deployments on premises obviously. That can go anywhere from relational databases to graph query language databases. Lots of different places from there for sure. >> Okay so, this actually sounds like everyone talks now about machine learning and forming every category of software. This sounds like you take the old style ETL, where master data was a value add layer on top, and that was, it took a fair amount of human judgment to do. Now, you're putting that service on top of ETL and you're largely automating it, probably with, I assume, some supervised guidance, supervised training. >> Yes, so we're getting into the machine learning space as far as entity extraction and resolution and recognition because more and more data is unstructured. But machine learning isn't necessarily a baked in part of that. Actually entity resolution is a prerequisite, I think, for quality machine learning. So if Rob Lantz is a customer, I want to be able to know what has Rob Lantz bought in the past from me. And maybe what is Rob Lantz talking about in social media? Well I need to know how to figure out who those people are and who's Rob Lantz and who's Robert Lantz is a completely different person, I don't want to collapse those two things together. Then I would build machine learning on top of that to say, right, now what's his behavior going to be in the future. But once I have that robust profile built up, I can derive a lot more interesting features with which to apply the machine learning. >> Okay, so you are a Data Bricks customer and there's also a burgeoning partnership. >> Rob: Yeah, I think that's true. >> So talk to us a little bit about what are some of the frustrations you had before adopting Data Bricks and maybe why you choose it. >> Yeah, sure. So the frustrations primarily with a traditional Hadoop environment involved having to go from one customer site to another customer site with an incredibly complex technology stack and then do a lot of the cluster management for those customers even after they'd already set it up because of all the inner workings of Hadoop and that ecosystem. Getting our Spark application installed there, we had to penetrate layers and layers of configuration in order to tune it appropriately to get the performance we needed. >> David: Okay, and were you at the keynote this morning? >> I was not, actually. >> Okay, I'm not going to ask you about that then. >> Ah. >> But I am going to ask you a little bit about your wishlist. You've been talking to people maybe in the hallway here, you just got here today but, what do you wish the community would do or develop, what would you like to learn while you're here? >> Learning while I'm here, I've already picked up a lot. So much going on and it's such a fast paced environment, it's really exciting. I think if I had a wishlist, I would want a more robust ML Lib, machine learning library. All the things that you can get on traditional, in scientific computing stacks moved onto a Spark ML Lib for easier access. On a cluster would be great. >> I thought several years ago ML Lib took over from Mahoot as the most active open source community for adding, really, I thought, scale out machine learning algorithms. If it doesn't have it all now, or maybe all is something you never reach, kind of like Red Queen effect, you know? >> Rob: For sure, for sure. >> What else is attracting these scale out implementations of the machine learning algorithms? >> Um? >> In other words, what are the platforms? If it's not Spark then... >> I don't think it exists frankly, unless you write your own. I think that would be the way to go. That's the way to go about it now. I think what organizations are having to do with machine learning in a distributed environment is just go with good enough, right. Whereas maybe some of the ensemble methods that are, actually aren't even really cutting edge necessarily, but you can really do a lot of tuning on those things, doing that tuning distributed at scale would be really powerful. I read somewhere, and I'm not going to be able to quote exactly where it was but, actually throwing more data at a problem is more valuable than tuning a perfect algorithm frankly. If we could combine the two, I think that would be really powerful. That is, finding the right algorithm and throwing all the data at it would get you a really solid model that would pick up on that signal that underlies any of these phenomena. >> David: Okay well, go ahead George. >> I was going to ask, I think that goes back to, I don't know if it was Google Paper, or one of the Google search quality guys who's a luminary in the machine learning space says, "data always trumps algorithms." >> I believe that's true and that's true in my experience certainly. >> Once you had this machine learning and once you've perhaps simplified the multi-vendor stack, then what is your solution start looking like in terms of broadening its appeal, because of the lower TCO. And then, perhaps embracing more use cases. >> I don't know that it necessarily embraces more use cases because entity resolution applies so broadly already, but what I would say is will give us more time to focus on improving the ER itself. That's I think going to be a really, really powerful improvement we can make to Novetta entity analytics as it stands right now. That's going to go into, we alluded to before, the machine learning as part of the entity resolution. Entity extraction, automated entity extraction from unstructured information and not just unstructured text but unstructured images and video. Could be a really powerful thing. Taking in stuff that isn't tagged and pulling the entities out of that automatically without actually having to have a human in the loop. Pulling every name out, every phone number out, every address out. Go ahead, sorry. >> This goes back to a couple conversations we've had today where people say data trumps algorithms, even if they don't say it explicitly, so the cloud vendors who are sitting on billions of photos, many of which might have house street addresses and things like that, or faces, how do you make better... How do you extract better tuning for your algorithms from data sets that I assume are smaller than the cloud vendors? >> They're pretty big. We employ data engineers that are very experienced at tagging that stuff manually. What I would envision would happen is we would apply somebody for a week or two weeks, to go in and tag the data as appropriate. In fact, we have products that go in and do concept tagging already across multiple languages. That's going to be the subject of my talk tomorrow as a matter of fact. But we can tag things manually or with machine assistance and then use that as a training set to go apply to the much larger data set. I'm not so worried about the scale of the data, we already have a lot, a lot of data. I think it's going to be getting that proof set that's already tagged. >> So what you're saying is, it actually sounds kind of important. That actually almost ties into what we hear about Facebook training their messenger bot where we can't do it purely just on training data so we're going to take some data that needs semi-supervision, and that becomes our new labeled set, our new training data. Then we can run it against this broad, unwashed mass of training data. Is that the strategy? >> Certainly we would get there. We would want to get there and that's the beauty of what Data Bricks promises, is that ability to save a lot of the time that we would spend doing the nug work on cluster management to innovate in that way and we're really excited about that. >> Alright, we've got just a minute to go here before the break, so I wanted to ask you maybe, the wish list question, I've been asking everybody today, what do you wish you had? Whether it's in entity resolution or some other area in the next couple of years for Novetta, what's on your list? >> Well I think that would be the more robust machine learning library, all in Spark, kind of native, so we wouldn't have to deploy that ourselves. Then, I think everything else is there, frankly. We are very excited about the platform and the stack that comes with it. >> Well that's a great ending right there, George do you have any other questions you want to ask? Alright, we're just wrapping up here. Thank you so much, we appreciate you being on the show Rob, and we'll see you out there in the Expo. >> I appreciate it, thank you. >> Alright, thanks so much. >> George: It's good to meet you. >> Thanks. >> Alright, you are watching the CUBE here at Spark Summit 2017, stay tuned, we'll be back with our next guest.

>>Welcome back here on the Cube. I'm John Walls. We are in Las Vegas at the Venetian, and this is Reinvent 22 in the Executive Summit sponsored by Accenture. Glad to have you with us here as we continue our conversations. I'm joined by Paul Puckett, who's the former director of the Enterprise Cloud Management Services at the US Army. Paul, good to see you sir. Hey, you as well, John. Thank you. And Justin, she who is managing director and cloud go to market lead at Accenture Federal Services. Justin, good morning to you. Good morning, John. Yeah, glad to have you both here on the cube. First time too, I believe, right? Yes sir. Well, welcome. I wish we had some kind of baptism or indoctrination, but I'll see what I can come up with in the next 10 minutes for you. Let's talk about the Army, Paul. So enterprise cloud management, US Army. You know, I can't imagine the scale we're talking about here. I can't imagine the solutions we're talking about. I can't imagine the users we're talking about. Just for our folks at home, paint the picture a little bit of what kind of landscape it is that you have to cover with that kind of title. >>Sure. The United States Army, about 1.4 million people. Obviously a global organization responsible for protecting and defending the United States as part of our sister services in the Department of Defense. And scale often comes up a lot, right? And we talk about any capability to your solution for the United States Army scale is the, the number one thing, but oftentimes people overlook quality first. And actually when you think of the partnership between the Army and Accenture Federal, we thought a lot when it came to establishing the enterprise Cloud management agency that we wanted to deliver quality first when it came to adopting cloud computing and then scale that quality and not so much be afraid of the, the scale of the army and the size that forces us to make bad decisions. Cuz we wanted to make sure that we proved that there was opportunity and value in the cloud first, and then we wanted to truly scale that. And so no doubt, an immense challenge. The organization's been around for now three years, but I think that we've established irreversible momentum when it comes to modernization, leveraging cloud computing >>For the army. So let's back up. You kind of threw it in there, the ecma. So this agency was, was your a collaboration, right? To create from the ground up and it's in three years in existence. So let's just talk about that. What went into that thinking? What went into the planning and then how did you actually get it up and run into the extent that it is today? >>Sure. Well, it was once the enterprise cloud management office. It was a directorate within the, the CIO G six of the United States Army. So at the headquarters, the army, the chief information Officer, and the G six, which is essentially the military arm for all IT capability were once a joint's organization and the ECMO was created to catalyze the adoption of cloud computing. The army had actually been on a, a cloud adoption journey for many years, but there wasn't a lot of value that was actually derived. And so they created the ecma, well, the ECMO at the time brought me in as the director. And so we were responsible for establishing the new strategy for the adoption of cloud. One of the components of that strategy was essentially we needed an opportunity to be able to buy cloud services at scale. And this was part of our buy secure and build model that we had in place. And so part of the buy piece, we put an acquisition strategy together around how we wanted to buy cloud at scale. We called it the cloud account management optimization. OTA >>Just rolls right off the >>Tongue, it just rolls right off the tongue. And for those that love acronyms, camo, >>Which I liked it when I was say cama, I loved that. That was, that was, >>You always have to have like a tundra, a little >>Piece of that. Very good. It was good. >>But at the time it was novetta, no, Nevada's been bought up by afs, but Novea won that agreement. And so we've had this partnership in place now for just about a year and a half for buying cloud computing net scale. >>So let's talk about, about what you deal with on, on the federal services side here, Justin, in terms of the army. So obviously governance, a major issue, compliance, a major issue, security, you know, paramount importance and all that STEM leads up to quality that Paul was talking about. So when you were looking at this and keeping all those factors in, in your mind, right? I mean, how many, like, oh my God, what kind of days did you have? Oh, well, because this was a handful. >>Well, it was, but you could see when we were responding to the acquisition that it was really, you know, forward thinking and forward leaning in terms of how they thought about cloud acquisition and cloud governance and cloud management. And it's really kind of a sleepy area like cloud account acquisition. Everyone's like, oh, it's easy to get in the cloud, you know, run your credit card on Amazon and you're in, in 30 seconds or less. That's really not the case inside the federal government, whether it's the army, the Air Force or whoever, right? Those, those are, they're real challenges in procuring and acquiring cloud. And so it was clear from, you know, Paul's office that they understood those challenges and we were excited to really meet them with them. >>And, and how, I guess from an institutional perspective, before this was right, I I assume very protective, very tight cloistered, right? You, you, in terms of being open to or, or a more open environment, there might have been some pushback was they're not. Right? So dealing with that, what did you find that to be the case? Well, so >>There's kind of a few pieces to unpacking that. There's a lot of fear in trepidation around something you don't understand, right? And so part of it is the teaching and training and the, and the capability and the opportunity in the cloud and the ability to be exceptionally secure when it comes to no doubt, the sensitivity of the information of the Department of Defense, but also from an action acquisition strategy perspective, more from a financial perspective, the DOD is accustomed to buying hardware. We make these big bets of these big things to, to live in today's centers. And so when we talk about consuming cloud as a utility, there's a lot of fear there as well, because they don't really understand how to kind of pay for something by the drink, if you will, because it incentivizes them to be more efficient with their utilization of resources. >>But when you look at the budgeting process of the d od, there really is not that much of incentive for efficiency. The p PPE process, the planning program, budgeting, execution, they care about execution, which is spending money and you can spend a lot of money in the cloud, right? But how are you actually utilizing that? And so what we wanted to do is create that feedback loop and so the utilization is actually fed into our financial systems that help us then estimate into the future. And that's the capability that we partnered with AFS on is establishing the closing of that feedback loop. So now we can actually optimize our utilization of the cloud. And that's actually driving better incentives in the PPE >>Process. You know, when you think about these keywords here, modernized, digitized, data driven, so on, so forth, I, I don't think a lot of people might connect that to the US government in general just because of, you know, it's a large intentionally slow moving bureaucratic machine, right? Is that fair to characterize it that way? It >>Is, but not in this case. Right? So what we done, >>You you totally juxtapose that. Yeah. >>Yeah. So what we've done is we've really enabled data driven decision making as it relates to cloud accounts and cloud governance. And so we have a, a tool called Cloud Tracker. We deployed for the army at a number of different classifications, and you get a full 360 view of all of your cloud utilization and cloud spend, you know, really up to date within 24 hours of it occurring, right? And there a lot of folks, you know, they didn't never went into the console, they never looked at what they were spending in cloud previously. And so now you just go to a simple web portal and see the entire entirety of the army cloud spend right there at your fingertips. So that really enables like better decision making in terms of like purchasing savings plans and reserved instances and other sorts of AWS specific tools to help you save money. >>So Paul, tell me about Cloud Tracker then. Yeah, I mean from the client side then, can you just say this dashboard lays it out for you right? In great detail about what kind of usage, what kind of efficiencies I assume Yeah. What's working, what's not? >>Absolutely. Well, and, and I think a few things to unpack that's really important here is listen, any cloud service provider has a concept. You can see what you're actually spending. But when it comes to money in the United States government, there are different colors of money. There's regulations when it comes to how money is identified for different capabilities or incentives. And you've gotta be very explicit in how you track and how you spend that money from an auditability perspective. Beyond that, there is a move when it comes to the technology business management, which is the actual labeling of what we actually spend money on for different services or labor or software. And what Cloud Tracker allows us to do is speak the language of the different colors of money. It allows us to also get very fine grain in the actual analysis of, from a TBM perspective, what we're spending on. >>But then also it has real time hooks into our financial systems for execution. And so what that really does for us is it allows us to complete the picture, not just be able to see our spend in the cloud, but also be able to able to see that spending context of all things in the P P P E process as well as the execution process that then really empowers the government to make better investments. And all we're seeing is either cost avoidance or cost savings simply because we're able to close that loop, like I said. Yep. And then we're able to redirect those funds, retag them, remove them through our actual financial office within the headquarters of the army, and be able to repurpose that to other modernization efforts that Congress is essentially asking us to invest >>In. Right. So you know how much money you have, basically. Exactly. Right. You know how much you've already spent, you know how you're spending it, and now you how much you have left, >>You can provide a reliable forecast for your spend. >>Right. You know, hey, we're, we're halfway through this quarter, we're halfway through the, the fiscal year, whatever the case might be. >>Exactly. And the focus on expenditures, you know, the government rates you on, you know, how much have you spent, right? So you have a clear total transparency into what you're going to spend through the rest of the fiscal. Sure. >>All right. Let's just talk about the relationship quickly then about going forward then in terms of federal services and then what on, on the, the US Army side. I mean, what now you've laid this great groundwork, right? You have a really solid foundation where now what next? >>We wanna be all things cloud to the army. I mean, we think there's tremendous opportunity to really aid the modernization efforts and governance across the holistic part of the army. So, you know, we just, we want to, we wanna do it all with the Army as much as we can. It's, it's, it's a fantastic >>Opportunity. Yeah. AFS is, is in a very kind of a strategic role. So as part of the ecma, we own the greater strategy and execution for adoption of cloud on behalf of the entire army. Now, when it comes to delivery of individual capabilities for mission here and there, that's all specific to system owners and different organizations. AFS plays a different role in this instance where they're able to more facilitate the greater strategy on the financial side of the house. And what we've done is we've proven the ability to adopt cloud as a utility rather than this fixed thing, kind of predict the future, spend a whole bunch of money and never use the resource. We're seeing the efficiency for the actual utilization of cloud as a utility. This actually came out as one of the previous NDAs. And so how we actually address nda, I believe it was 2018 in the adoption of cloud as a utility, really is now cornerstone of modernization across all of the do d and really feeds into the Jo Warfighting cloud capability, major acquisition on behalf of all of the D O D to establish buying cloud as just a common service for everyone. >>And so we've been fortunate to inform that team of some of our lessons learned, but when it comes to the partnership, we just see camo moving into production. We've been live for now a year and a half. And so there's another two and a half years of runway there. And then AFS also plays a strategic role at part of our cloud enablement division, which is essentially back to that teaching part, helping the Army understand the opportunity of cloud computing, align the architectures to actually leverage those resources and then deliver capabilities that save soldier's >>Lives. Well, you know, we've, we've always known that the Army does its best work on the ground, and you've done all this groundwork for the military, so I'm not surprised, right? It's, it's a winning formula. Thanks to both of you for being with us here in the executive summit. Great conversation. Awesome. Thanks for having us. A good deal. All right. Thank you. All right. You are watching the executive summit sponsored by Accenture here at Reinvent 22, and you're catching it all on the cube, the leader in high tech coverage.