(upbeat music) >> Welcome back to Supercloud22. This is an open community event, and it's dedicated to tracking the future of cloud in the 2020s. Supercloud is a term that we use to describe an architectural abstraction layer that hides the underlying complexities of the individual cloud primitives and APIs and creates a common experience for developers and users irrespective of where data is physically stored or on which cloud platform it lives. We're now going to explore the nuances of going to market in a world where data architectures span on premises across multiple clouds and are increasingly stretching out to the edge. Paula Hansen is the President and Chief Revenue Officer at Alteryx. And the reason we asked her to join us for Supercloud22 is because first of all, Alteryx is a company that is building a form of Supercloud in our view. If you have data in a bunch of different places and you need to pull in different data sets together, you might want to filter it or blend it, cleanse it, shape it, enrich it with other data, analyze it, report it out to your colleagues. Alteryx allows you to do that and automate that life cycle. And in our view is working to break down the data silos across clouds, hence Supercloud. Now, the other reason we invited Paula to the program is because she's a rockstar female in tech, and since day one at theCube, we've celebrated great women in tech, and in this case, a woman of data, Paula Hansen, welcome to the program. >> Thank you, Dave. I am absolutely thrilled to be here. >> Okay, we're going to focus on customers, their challenges and going to market in this cross cloud, multi-cloud, Supercloud world. First, Paula, what's changing in your view in the way that customers are innovating with data in the 2020s? >> Well, I think we've all learned very clearly over these last two years that the global pandemic has altered life and business as we know it. And now we're in an interesting time from a macroeconomic perspective as well. And so what we've seen is that every company in every industry has had to pivot and think about how they meet redefined customer expectations and an ever evolving competitive landscape. There really isn't an industry that wasn't reshaped in some way over the last couple of years. And we've been fortunate to work with companies in all industries that have adapted to this ever changing environment by leveraging Alteryx to help accelerate their digital transformations. Companies know that they need to unlock the full potential of their data to be able to move quickly to pivot and to respond to their customer's needs, as well as manage their businesses most efficiently. So I think nothing tells that story better than sharing a customer example with you, Dave. We love to share stories of our very innovative customers. And so the one that I'll share with you today in regards to this is Delta Airlines, who we're all very familiar with. And of course Delta's goal is to always keep their airplanes in the air flying passengers and getting people to their destinations efficiently. So they focus on the maintenance of their aircraft as a necessary part of running their business and they need to manage their maintenance stops and the maintenance of their aircraft very efficiently and effectively. So we work with them. They leverage our platform to automate all the processes for their aircraft maintenance centers. And so they've built out a fully automated reporting system on our platform leveraging tons of data. And this gives their service managers and their aircraft technicians foresight into what's happening with their scheduling and their maintenance processes. So this ensures that they've got the right technicians in the service center when the aircrafts land and that everything across that process is fully in place. And previously because of data silos and just complexity of data, this process would've taken them many many hours in each independent service center, and now leveraging Alteryx and the power of analytics and bringing all the data together. Those centers can do this process in just minutes and get their planes back in the air efficiently and delivering on their promises to their customers. So that's just one of many examples that we have in terms of the way the Alteryx analytics automation helps customers in this new age and helping to really unlock the power of their data. >> You know, Paul, that's an interesting example. Because in a previous life I worked with some airlines and people maybe don't realize this but, aircraft maintenance is the mission critical application for carriers. It's not the booking system. Because we've been there before, we show you there's a problem when you're booking or sometimes it's unfortunate, but people they get de booked. But the aircraft maintenance is the one that matters the most and that keeps planes in the air. So we hear all the time, you just mention it. About data silos and how problematic they are. So, specifically how are you seeing customers thinking about busting the data silos? >> Yeah, that's right, it's a big topic right now. Because companies realize that business processes that they run their business with, is very cross-functional in nature and requires data across every department in the enterprise. And you can't keep data locked in one department. So if you think of business processes like pay to procure or quote to cash, these are business processes that companies in every industry run their business. And that requires them to get data from multiple departments and bring all of that data together seamlessly to make the best business decisions that they can make. So what our platform does is, and is really well known for, is being very easy for users number one, and then number two, being really great at getting access to data quickly and easily from all those data silos, really, regardless of where it is. We talk about being everywhere. And when we say that we mean, whether it's on-prem, in your legacy applications and databases, or whether it's in the cloud with of course, all the multiple cloud platforms and modern cloud data warehouses. Regardless of where it is, we have the ability to bring that data together across hundreds of different data sources, bring it together to help drive insights and ultimately help our customers make better decisions, take action, and deliver on the business outcomes that they all are trying to drive within their respective industries. And what's- >> You know- >> Go ahead. >> Please carry on. >> Well, I was just going to say that what I do think has really sort of a tipping point in the last six months in particular is that executives themselves are really demanding of their organizations, this democratization of data. And the breaking down of the silos and empowering all of the employees across their enterprise regardless of how sophisticated they are with analytics to participate in the analytic opportunity. So we've seen some really cool things of late where executives, CEOs, chief financial officers, chief data officers are sponsoring events within their organizations to break down these silos and encourage their employees to come together on this democratization opportunity of democratization of data and analytics. And there's a shortage of data scientists on top of this. So there's no way that you're going to be able to hire enough data scientists to make sense of all this data running around your enterprise. So we believe with our platform we empower people regardless of their skillset. And so we see executives sponsoring these hackathons within their environments to bring together people to brainstorm and ideate on use cases, to share examples of how they leverage our platform and leverage the data within their organization to make better decisions. And it's really quite cool. Companies like Stanley Black & Decker, Ingersoll Rand, Inchcape PLC, these are all companies that the executive team has sponsored these hackathon events and seen really powerful things come out of it. As an example Ingersoll Rand sponsored their Alteryx hackathon with all of their data workers across various different functions where the data exists. And they focused on both top line revenue use cases as well as bottom line efficiency cases. And one of the outcomes was a use case that helped with their distribution center in north America and bringing all the data together across their various applications to reduce the amount of over ordering and under ordering of parts and more effectively manage their inventory within that distribution center. So, really cool to see this is now an executive level board level conversation. >> Very cool, a hackathon bringing people together for collaboration. A couple things that you said I want to comment on. Again, one of the reasons why we invited you guys to come on is, when you think about on-prem data and anybody who follows theCube and my breaking analysis program, knows we're big fans of Zhamak Dehghani's concept of data mesh. And data mesh is supposed to be inclusive. It doesn't matter if it's an S3 bucket, Oracle data base, or data warehouse, or data lake, that's just a note on the data mesh. And so it should be inclusive and Supercloud should include on-prem data to the extent that you can make that experience consistent. We have a lot of technical sessions here at Supercloud22, we're focusing now and go to market and the ecosystem. And we live in a world of multiple partners exploding ecosystems. And a lot of times it's co-opetition. So Paula, when you joined Alteryx you brought a proven go to market discipline to the company. Alignment with the customer, playbooks, best practice of sales, et cetera. And we've seen the results. It's a big reason why Mark Anderson and the board promoted you to president just after 10 months. Summarize how you approached the situation at Alteryx when you joined last spring. >> Yeah, I think first we were really intentional about what part of the market, what type of enterprises get the most benefit from the innovation that we deliver? And it's really clear that it's large enterprises. That the more complex a company is, most likely the more data they have and oftentimes the more decentralized that data is. And they're also really all trying to figure out how to remain competitive by leveraging that data. So, the first thing we did was be very intentional that we're focused on the enterprise and building out all of the capability required to be able to serve the enterprise. Of course, essential to all of that is having a platform capability because enterprises require that. So, with Suresh Vittal our Chief Product Officer, he's been fantastic in building out an end to end analytic platform that serves a wide range of analytic capabilities to a wide range of users. And then of course has this flexibility to operate both on-prem and in the cloud which is very important. Because we see this hybrid environment in this multicloud environment being something that is important to our customers. The second thing that I was really focused on was understanding how do you have those conversations with customers when they all are in maybe different types of backgrounds? So the way that you work with a business analyst in the office of finance or supply chain or sales and marketing, is different than the way that you serve a data scientist or a data engineer in IT. The way that you talk to a business owner who wants not to really understand the workflow level of data but wants to understand the insights of data, that's a different conversation. When you want to have a conversation of analytics for all or democratization of analytics at the executive level with the chief data officer or a CIO, that's a whole different conversation. And so we've built very specific sales plays to be able to have those conversations bring the relevant information to the relevant person so that we're really making sure that we explain the value proposition of the platform. Fully understand their world, their language and can work with them to deliver the value to them. And then the third thing that we did, was really heavily invest in our partnerships and you referenced this day. It's a a broad ecosystem out there. And we know that we have to integrate into that broad data ecosystem. and be a good partner to serve our customers. So, we've invested both in technology integration as well as go to market strategies with cloud data warehouse companies like Snowflake and Databricks, or RPA companies like UiPath and Blue Prism, as well as a wide range of other application and all of the cloud platforms because that's what our customers expect from us. So that's been a really important sort of third pillar of our strategy in making sure that from a go to market perspective, we understand where we fit in the ecosystem and how we collectively deliver on value to our joint customers. >> So that's super helpful. What I'm taking away from this is you didn't come to it with a generic playbook. Frank Lyman always talks about situation leadership. You assess the situation and applied that and a great example of partners is Snowflake and Databricks, these sort of opposites, but trying to solve similar problems. So you've got to be inclusive of all that. So we're trying to sort of squint through this Paula and say, okay, are there nuances and best practices beyond some of the the things that you just described that are unique to what we call Supercloud? Are there observations you can make with respect to what's different in this post isolation economy? Specifically in managing remote employees and of course remote partners, working with these complex ecosystems and the rise of this multi-cloud world, is it different or is it same wine new bottle? >> Well, I think it's both common from the on-prem or pre-cloud world, but there's also some differences as well. So what's common is that companies still expect innovation from us and still want us to be able to serve a wide range of skill sets. So our belief is that regardless of the skill set that you have, you can participate in the analytics opportunity for your company and unlocking the potential of your data. So we've been very focused since our inception to build out a platform that really serves this wide range of capabilities across the enterprise space. What's perhaps changed more or continues to evolve in this cloud world is just the flexibility that's required. You have to be everywhere. You have to be able to serve users wherever they are and be able to live in a multi-cloud or super cloud world. So when I think of cloud, I think it just unlocks a whole bigger opportunity for Alteryx and for companies that want to become analytic leaders. Because now you have users all over the globe, many of them looking for web-based analytic solutions. And of course these enterprises are all in various places on their journey to cloud and they want a partner and a platform that operates in all of those environments, which is what we do at Alteryx. So, I think it's an exciting time. I think that it's still very early in the analytic market and what companies are going to do to leverage their data to drive their transformation. And we're really excited to be a part of it. >> So last question is, I said up front we always like to celebrate women in tech. How'd you get into tech.? You've got a background, you've got somewhat of a technical background of being technical sales. And then of course rose up throughout your career and now have a leadership position. I called you a woman of data. How'd you get into it? Where'd you find the love of data? Give us the background and help us inspire some of the young women out there. >> Oh, well, but I'm super passionate about inspiring young women and thinking about the future next generation of women that can participate in technology and in data specifically. I grew up loving math and science. I went to school and got an electrical engineering degree but my passion around technology hasn't been just around technology for technology's sake, my passion around technology is what can it enable? What can it do? What are the outcomes that technology makes possible? And that's why data is so attractive because data makes amazing things possible. I shared some of those examples with you earlier but it not only can we have effect with data in businesses and enterprise, but governments globally now are realizing the ability for data to really have broad societal impact. And so I think that that speaks to women many times. Is that what does technology enable? What are the outcomes? What are the stories and examples that we can all share and be inspired by and feel good and and inspired to be a part of a broader opportunity that technology and data specifically enables? So that's what drives me. And those are the conversations that I have with the women that I speak with in all ages all the way down to K through 12 to inspire them to have a career in technology. >> Awesome, the more people in STEM the better, and the more women in our industry the better. Paula Hansen, thanks so much for coming in the program. Appreciate it. >> Thank you, Dave. >> Okay, keep it right there for more coverage from Supercloud 22, you're watching theCube. (upbeat music)

>>Please >>welcome back to HP discover 2021 the virtual version. My name is Dave Volonte and you're watching the cube and we're here with Guido appenzeller who's the C. T. O. Of the data platforms group at Intel. Guido. Welcome to the cube. Come on in. >>Thanks. Dave. I appreciate it's great to be here today. So >>I'm interested in your role at the company. Let's talk about that. Your brand new. Tell us a little bit about your background. What attracted you to intel and what's your role here? >>Yeah. So I'm, you know, I grew up in the startup ecosystem of Silicon Valley came from my PhD and and and never left and uh you know, built software companies, worked at software companies worked at the embassy for a little bit and I think my, my initial reaction when the intel recruiter called me, it was like you got the wrong phone number, right? I'm a software guy that's probably not who you're looking for. And uh you know, we had a good conversation I think at Intel, you know, there's a, there's a realization that you need to look at what intel builds more as an overall system from novel systems perspective right, that you have the software stack and then the hardware components that we're getting more and more intricately linked and you know, you need the software to basically bridge across the different hardware components that intel is building. So I'm here now is the CEO for the data platform school. So that builds the data center for Arts here at Intel. And it's a really exciting job. These are exciting times that intel, you know, with, with Pat, you got a fantastic uh you know, CEO at the home, I worked with him before at december, so a lot of things to do. Um but I think a very exciting future. >>Well, I mean the data center is the wheelhouse of intel. I mean of course you, your ascendancy was a function of the pcs and the great volume and how you change that industry. But really data centers is where they, I mean I remember the days of people that until will never be the data center, it's just a toy and of course your dominant player there now. So your initial focus here is is really defining the vision. Uh and and I'd be interested in your thoughts on the future, what the data center looks like in the future, where you see intel playing a role. What what are you seeing is the big trends there. You know, Pat Pat Gelsinger talks about the waves. He says if you don't ride the waves you're gonna end up being driftwood. So what are the waves you're driving? What's different about the data center of the future? >>That's right. You want to surf the waves? Right? That's the way to do it. So look, I like to look at this in sort of in terms of major macro trends. Right? And I think the biggest thing that's happening um in the market right now is the cloud revolution. Right? And I think we're halfway through or something like that and this transition from the classic uh client server type model, uh you know that we're with enterprises running their own data centers to more of a cloud model where something is, you know, run by by hyper scale operators or it may be run you know by uh by an enterprise themselves that message to the absolute there's a variety of different models, but the provisioning models have changed, right? The it's it's much more of a turnkey type service. And when when we started out on this journey, I think the we build data centers the same way that we built them before. Although you know the way to deliver it had really changed. Right? That's going to morph a service model and we're really now starting to see the hardware diverge right there actually. Silicon that we need to build or to address these use cases diverge. And so I think one of the things that is kind of the most interesting for me is really to think through how does intel in the future build silicon? That's that's built for clouds. You know, like on prem clouds. Edge clouds, hyper scale cloud but basically built for these new use cases that have emerged. So >>just kind of quick aside, I mean to me, the definition of cloud is changing. It's evolving. It used to be this set of remote services in a hyper scale data center. It's now, you know, that experience is coming on prem it's connecting across clouds. It's moving out to the edge, it's supporting, you know, all kinds of different workloads. How do you see that? It's evolving Cloud. >>Yeah, I think, I mean, there's the biggest difference to me is that sort of a cloud starts with this idea that the infrastructure operator and the tenant are separate, right? And that is actually has major architectural implications. I mean, just to, you know, this is a perfect analogy, but if I build a single family home, right, where everything is owned by one party, uh you know, I want to be able to walk from the kitchen to the living room pretty quickly, if that makes sense? Right, sorry. In my house here has actually open kitchen, it's the same room essentially. If you're building a hotel where your primary goal is to have guests, you pick a completely different architecture, right? The kitchen from from your restaurants where the cooks are busy preparing the food and the dining room where the guests are sitting there separate. Right? I mean, the hotel staff has a dedicated place to work and the guests have a dedicated places to mingle, but they don't overlap typically. I think it's the same thing with architecture in the clouds. Right? That's you know, initially the assumption was it's all one thing. And now suddenly we're starting to see, you know, like a much much cleaner separation of these different different areas. I think a second major influences that the type of workloads we're seeing. It's just evolving incredibly quickly. Right? I mean, you know, 10 years ago, you know, things were mostly monolithic today. You know, most new workloads are micro service base and that that has a huge impact in uh you know, where where CPU cycles are spent, you know, a way we need to put in accelerators, you know, how we how we build silicon for that too. Give you an idea, I mean there's some really good research out of google and facebook where they run numbers. For example, if you just take a a standard system and you run a micro service based application, written a micro service based architecture, you can spend anywhere from, I want to say 25 in some cases over 80% of your CPU cycles. Just an overhead. Right. And just on marshalling the marshaling the protocols and uh the encryption and decryption of the packets and your service match that sits in between all these things. So I created a huge amount of overhead so for us, 80% go into these, into these overhead functions. Really our focus suddenly needs to be uh how do we enable um, that kind of infrastructure? >>Yeah, So let's talk a little bit more about workloads if we can. I mean the overhead, there's also sort of as the software, as the data center becomes software defined, you know, thanks thanks to your good work at VM where there's a lot of cores that are supporting that software defined data center and then >>that's exactly right as >>well. You mentioned micro services, container based applications, but but as well, you know, aI is coming into play and what it is, you know, a i is this kind of amorphous, but it's really data oriented workloads versus kind of general purpose CRP and finance and HCM So those workloads are exploding and then we can maybe talk about the edge. How are you seeing the workload mix shift and how is intel playing there? >>Look, I think the trend you're talking about is definitely Right, Right. We're getting more and more data centric, you know, shifting the data around becomes a larger and larger part of the overall workload in the data center. Ai is getting a ton of attention. Right? It's look, if I talked to the most operators, aI is still emerging category. Right. I mean, we're seeing, I'd say five, maybe 10% percent of workloads being A. I. Um it's growing the very high value workloads right now, very challenging workloads. Um but you know, it's still a smaller part of the overall mix. Now, Edge edge is big and it's too big. It's big. And it's complicated because of the way I think about edges. It's not just one homogeneous market, it's really a collection of separate sub markets, right? It's very heterogeneous, you know, it runs on a variety of different hardware. All right. It can be everything from, you know, a little a little server that's families that's strapped to a phone, telephone pole with an antenna on top of, you know, to greater micro cell. Or it can be, you know, something that's running inside a car, Right. I mean, you know, uh, modern cars has a small little data center inside, it can be something that runs in the industrial factory floor, right. The network operators, there's a pretty broad range of verticals that all looks slightly different in, in their requirements. And uh, you know, and it's, I think it's really interesting, right? It's one of those areas that really creates opportunities for, for vendors like, like HPV right to, to, to really shine and and address this, this heterogeneity with a, with a broad range of solutions. Very excited to work together with them in that space. >>Yeah, I'm glad you brought HP into the discussion because we're here at HP discover I want to connect them. But so my question is, what's the role of the data center in this, this world of edge? How do you see it? >>Yeah. Look, I think in a sense, what the cloud revolution is doing is that it's showing us a leads to polarisation of a classic data into edge and clout. That makes sense. Right. It's splitting right before this was all mingled a little bit together. If my data centers in my basement anyways, you know what the edge, what's data says the same thing. Right? At the moment I'm moving some workloads in the clouds. I don't even know where they're running anymore than some other workloads that have to have a certain sense of locality. I need to keep closely. Right. And there's some workloads, you just just can't move into the cloud, right? I mean, there's uh if I'm generating a lot of time on the video data that I have to process, it's financially completely unattractive to shift all of that, you know, to, to essential location. I want to do this locally. Right? Will I ever connect my smoke detector with my sprinkler system via the cloud? No, I won't write just for if things go bad, right, they may not work anymore. So I need something that does this locally. So I think as many reasons, you know, why, why you want to keep something on, on premises And I think it's, I think it's a growing market, right? It's very exciting. You know, we're doing some some very good stuff with friends at hp. You know, the they have the pro line dl 1, 10, 10, 10 plus server with our latest third generation z johnson them uh, the open ran, you know, which is the radio access network for the telco space HP Edge Line service. Also, the third generation says it's a really nice products there that I think can really help addressing enterprises carriers, a number of different organizations. You know, these these alleged use cases, Can you >>explain you mentioned open randy rand. So we essentially think of that as kind of the software to find telco. >>Yeah, exactly. It's a software defined cellular. Right. I mean, actually, I learned a lot about that of the recent months, You know, when, when, when I was taking these classes at stanford, you know, these things were still dying down in analogue, Right. That basically a radio signal will be processed in a long way and, and digested. And today, typically the radio signal is immediately digitized and all the processing of the radio signal happens happens digitally and uh, you know, it happens on servers, right? Um, something HP servers and uh, you know, it's, it's a really interesting use case where we're basically now able to do something in a much, much more efficient way by moving it to a digital, more modern platform. And it turns out you can actually visualize these servers and, you know, run a number of different cells inside the same server. Right? It's really complicated because you have to have fantastic real time guarantees, very sophisticated software stack. But it's, it's really fascinating news case. >>You know, a lot of times we have these debates and it may be somewhat academic, but I'd love to get your thoughts on the debate is about, okay, how much data that that is, you know, processed and inferred at the edge is actually gonna come back to the cloud most of the day, is going to stay at the edge. A lot of it's not even gonna be persisted. And the counter to that is so that's sort of the negative for the data center. But the counter that is, they're gonna be so much data. Even a small percentage of all the data that we're going to create is going to create so much more data, you know, back in the cloud, back in the data center. What's your take on that? >>Look? I think there's different applications that are easier to do in certain places. Right? I mean, look, going to a large cloud has a couple of advantages. You have a very complete software ecosystem around you, you know, lots of different services. Um, you have four. If you need very specialized hardware. If I want to run a big learning task where something need 1000 machines. Right. And then this runs for a couple of days and then I don't need to do that for for another month or two. Right. For that is really great. There's on demand infrastructure, right? Having having all this capability up there, uh you know, at the same time it costs money to send the data up there, Right. If I just look at the hardware cost is much, much cheaper to to build myself, you know, in my own data center or in the edge. Um so I think we'll we'll see, you know, customers picking and choosing what they want to do. Where. Right. And and there's a role for both. Right. Absolutely. And so, you know, I think there's there's certain categories, I mean, at the end of the day, um, why do I absolutely need to have something at the edge? And there's a couple of, I think good, good use cases. I mean one is, let me ask you a few phrases, but I think it's three primary reasons. Right? Um, one is simply a bandwidth, Right? What I'm saying? Okay, my my video data, like I have have 100 and four K video cameras, you know, with 60 frames a second feet, there's no way I'm going to move into the cloud. It's just cost prohibitive. I have a hard time getting a line that allows you to do this right. Um, there might be latency, right. If I don't want to reliably react in a very short period of time, I can't do that in the cloud. I need to do this locally with me. Um, I can't even do this in my data center. This has to be very, very closely coupled. And then there's this idea of faith sharing, I think, you know, that if I want to make sure that if things go wrong right, uh, the system is still intact, right. You know, anything that's an emergency kind of backup, emergency type procedure, right? If things go wrong, I can't rely on there'll be a good internet connection, I need to handle things things locally. Like, you know, that's the smoke detector and sprinkler system. Right? And so for for, for all of these, right, there's good reasons why we need to move things close to the edge. So I think there'll be a creative tension between the two, Right? But both are huge markets and I think there's, there's great opportunities for, for hp ahead to uh, you know, to, to work on these two cases. >>Yeah, for sure. Top brand in that compute business. So before we wrap up today, you know, thinking about your, your role, I mean part of your role is the trend spotter. You're right, you gotta, you're, you're kind of driving innovation, riding, surfing the waves as you said, you know, skating to the park, all >>the all my perfect crystal ball right here, Yeah, got all the cliches. >>Right? Yes, yeah. Right foot's a little pressure on you. But so what are some of the things that you're overseeing that you're, you're looking towards in terms of innovation projects, particularly obviously in the data center space, what's really exciting you >>look, I mean there's a lot of them and I pretty much all the, you know, the interesting ideas I get from talking to customers, right? You talk to to the sophisticated customers, you try to understand the problems that are trying to solve that they cancel right now and that that gives you ideas to just to pick a couple. Right? I mean, one thing, what area I'm probably thinking about a lot is how can we built in a sense, better accelerators for the infrastructure functions. Right. So, so no matter if I run an edge cloud or I run a big public cloud, I want to find ways how I can, I can reduce the amount of CPU cycles I I spent on, you know, Microsoft's marshalling the marshaling service mesh, you know, storage acceleration and these things like that. Right? So clearly, if this is a large chunk of the overall uh cycle budget, right? We need to find ways to, to to shrink that right to to make this more efficient. Right? So that I think so this basically infrastructure function acceleration, it sounds probably as unsexy as any topic could sound, but I think this is actually really, really interesting area. One of the big levers we have right now in the data set. >>I would agree. I think that's actually really exciting because you actually can pick up a lot of the wasted cycles now and that's that drops right to the bottom line. But >>exactly. I mean it's you know, it's kind of funny. I mean we're still measuring so much with speck and rates of Cpus right performances like, well, They may actually make measuring the wrong thing, right? If 80% of the cycles of my upper spent an overhead right then the speed of the CPU doesn't matter as much. Right? It's other functions that end. So that's one um the second big one is memory is becoming a bigger and bigger issue. Right? And and it's it's memory cost because you know, memory prices, they used to have declined the same rate that, you know, our core counts and and and you know, Fox speeds increased. That's no longer the case. That we've run to some scaling limits there some physical scaling limits where memory prices are becoming stagnant and this is becoming a major pain point for everybody was building servers. Right. So I think we need to find ways how we can leverage memory more efficiently. Right, share memory more efficiently. We have some really cool ideas and in that space that we're working on. >>Yeah, let me just sorry to interrupt. But Pat hinted to that and your big announcement, I mean you talk about system on package I think is what he used to talk about what I call disaggregated memory and better sharing of that memory resource. And I mean that seems to be a clear benefit of value creation for the industry. >>Exactly, right. I mean, if this becomes a larger for our customers, this becomes a larger part of the overall cost, right? We want to help them address that issue. And you know, and then the third one is um, you know, we're seeing more and more data center operators effectively power limited. Right? So we need to reduce the overall power of systems or, you know, uh maybe to some degree, just figure out better ways of cooling these systems. But I think there's a there's a lot of innovation that can be done their right to both make these data centers more economical, but also to make them a little more green today, data centers have gotten big enough that if you look at the total amount of energy that we're spending in this world is mankind. Right. A chunk of that is going just to data centers. Right. And so if we're spending energy at that scale, right. I think we have to start thinking about how can we build data centers that are more energy officials? I'll do the same thing with less energy in the future. >>Well, thank you for for laying those out. I mean you guys have been long term partners with with HP and now of course H P E. I'm sure Gelsinger's really happy to have you on board Guido. I would be and thanks so much for coming on the cube. >>It's great to be here. Great to be at the HP show. Thanks >>For being with us for HP Discover 2021 the virtual version. You're watching the Cube, the leader in digital tech coverage. Right back.

>>From around the globe. It's the cube with digital coverage of IBM. Think 2021 brought to you by IBM. >>Welcome to the cubes coverage of IBM. Think the digital experience I'm Lisa Martin. I've got two guests with me here today. Ryan Dennings joins us manager of ECM solutions at auto owners insurance company, Ryan, welcome to the program. Thank you. And Michelle Christianson is here as well. VP of enterprise report management practice at end choice, Michelle. It's good to have you on the program. Thank you. Thank you. So let's, let's go ahead and start with you. You guys are a customer of and choice and IBM, talk to us a little bit about auto owners company. I know this is a fortune 500. This was founded in 1916. You've got about nearly 3 million policy holders, but give us an overview of auto owners insurance. >>Sure. So I don't want to said insurance is an insurance company. That's headquartered in Lansing, Michigan. We write insurance in 26 States throughout the United States. Um, just by our name being auto owners insurance, which is how we started. Um, we write all personal lines, commercial lines and also have a life insurance company, >>So comprehensive and that across those nearly 3 million policy holders. Michelle, tell us a little bit about end choice. I know this, you guys are an IBM gold business partner, but this is end choices first time on the cubes. So give us a background. Sure, sure. Great. So in choice are an IBM gold business partner. Uh, we have had 28 years success with IBM as a business partner. Our headquarters are in areas, um, Austin, Texas, and, uh, Tempe, Arizona, as well as Shelton Connecticut. We cover all of North America and we are a hundred percent focused on the IBM digital business automation space. We have about 500 customers now that we've helped, uh, through the years. And we continue to be a leading support provider as well as an implementation partner with all the IBM solutions. And talk to me a little bit Michelle, about how it is that you work with with, um, auto owners. >>So we assisted auto owners recently in their digital transformation journey and they were, uh, dealing with an antiquated product and wanted to get for moving forward, you know, provided better customer satisfaction, um, experience, um, for their clients agents. And so we partnered with them and with IBM and bringing them a content manager on demand solution as well as navigator and several other products within the IBM digital business automation portfolio. Excellent client. Oh, sorry Michelle, go ahead. Nope. That's that's fine. All right, Ryan, tell us a little bit about auto owners, your relationship with IBM and choice and how is it helping you to address some, the challenges in the market today? >>Sure. So I don't know if this has a long-term relationship with IBM. Um, originally starting back as we go as a mainframe customer and then, you know, more recently, um, helping us with different modern technology initiatives. Uh, they were instrumental in the nineties when we created our initial web offerings. And then more recently they've been helping us with our digital business automation, which has helped us to, um, mature our content, offering it. >>So you have had a long standing relationship with IBM. Right. And you mentioned the nineties, ah, a time when we didn't have to wear a mask on our faces. So a couple of decades it goes back. Yeah. >>Yes. For sure. Yes. Even further than that back, you know, back into the seventies from the mainframe side of things, >>Uh, the seventies, another good time. All right. So Michelle had talked to me a little bit about what end choices doing with IBM solutions to help auto owners from a digital transformation perspective is as I said, this is a company that was founded in 1916. And I always love to hear how history companies like that are actually working with technology companies to facilitate that transformation a lot harder than it sounds well. That's correct. Just as I mentioned, we're focused on helping customers develop their strategies, their digital strategy and creating those transformative solutions. So we're helping organizations like auto owners, um, with their journey by first realizing, um, their existing, existing, digital state, what challenges they might have and what needs they might need. And then we break that down or we deconstruct those technical and process. And finally we re-invent, um, their strategic offering with modern capabilities. >>So we're focused on technologies like RPA machine learning, artificial intelligence, they're more efficient, scalable, and secure. So any way we can bring those technologies into the equation we go forward. So this offers us, our clients, um, smarter and more into intuitive interfaces, creating basically a better user experience and a better user experience then becomes disruptive to their competition. So they gain a better place in the market space. Ryan talked to us about that process as much as you were involved in it. I liked that Michelle said, you know, we kind of look at the environment, we deconstruct it and then we reinvent it. Talk to me about how IBM and enChoice have ha has helped auto owners to do that so that your digital infrastructure is much more modern. And I presume much more resilient when there are market dynamics like we're living in now. >>Yeah, for sure. So, you know, we've, we've gone through a couple of transformation journeys at auto owners with IBM. Um, when I started the team about seven years ago, we originally started using file NATS and data cap and case manager and content aggregator, um, as our first, um, movement from a traditional, um, platform that we had for content management into a more modern platform. And that helped us a lot to improve our business process, um, improve how we capture content and bring it into the system and make it actionable more recently, we've been working with Michelle and the team on our, um, migration to a content management on demand platform. And that's really going to be transformative in terms of how we're able to present content and documents and bills, um, to our agents and customers, um, to be able to transform that content and show it in ways that are, um, important, um, for our customers to be able to see it to, um, engage from, with auto owners in a, in a digital era. >>So Ryan, just a couple of questions on that is that, is that a facilitation of like the digitization of processes that had some paper involved cause you guys have about 48,000 agents. So a lot of folks, a lot of content, tell me a little bit more about how, um, that like content manager on demand, for example, and what you're doing with ETF, how has that really revolutionizing and driving part of that digital transformation? >>Sure. So, uh, you know, there's two parts to that in terms of that content management management on demand journey. Um, one is the technology portion of it, but IBM's provided and that suite of software gives us some functionality that we haven't had in the past. Um, specifically some functionality around searching and searchability of our content, um, that will make it easier for people to find the content that they're looking for, um, ability to implement, uh, records management policies and other things that help us manage that content more effectively, um, as well as, um, some different options to be able to present the content, uh, to our customers and agents in a, in a better and more modern way. Um, and I'm choices role rolling that has really been, sorry, guide us on that journey, um, to help us make the right choices along the way on the project and help us get to a successful implementation and production. >>Excellent. Michelle, talk to me about hybrid cloud AI data, a big theme of, uh, IBM think is your, how is enChoice using hybrid cloud and AI, you mentioned some of the ways, but kind of break into that a little bit more about how you're helping customers like auto owners and others really take advantage of those modern technologies. Well, sure, sure. So, um, of course with the Calpec offerings that IBM has come forward with and where we focus in the cloud Pak for automation, um, several of those offerings are, some of them are, um, uh, built specifically to, uh, survive or to, to, um, be hosted in a hybrid environment. And as we working with auto owners, um, transforming their platforms going forward, for example, they just invested in, in a, um, a, uh, I just lost the word here. I, they just invested in a new platform mainframe platform where they're going to be leveraging the red hats and from there they'll drive forward into containerization. >>So, um, Ryan mentioned, uh, some of the ways that we'll be presenting the content for his agents and his customers and a particular, um, that entire viewing platform itself can be moved to a containerization state. So, um, so it's going to be a lot easier for him to transition into that and to maintain it and to management manage it. And of course, um, just that whole, um, the ease of function around it will be a lot easier. So we are in our area as an IBM business partner. Um, we work with, uh, these solutions to try to stay ahead of the game, to try to be able to assist our customers to understand what makes sense, when is it time to move into those? Um, it's great to take advantage of the new stuff, but nobody wants to be, you know, the bleeding game. We want to be the leading game. >>And, um, so that's some of the areas we focus with our clients to really stay tight with the labs tight with IBM and understanding their strategies and convey those and educate our customers on those excellent leading edge. Ran, talk to me a little bit. I love this a bank, uh, sorry. Uh, an insurance company from the early 19 hundreds moving into the using container technology. I'll have stories like that. Talk to me a little bit about hybrid cloud AI and how those technologies are going to be facilitators of the continuation of the digital transformation and probably enabling more opportunities for your agents to meet more needs from, from your policy holders. >>Yeah, for sure. So, uh, first and foremost, um, we were a red hat open shift, uh, customer before IBM acquired them and we were doing microservices development and things like that on the platform. Um, and then we were super excited about IBM's digital business automation strategy to, uh, move to cloud pack, um, and have that available for software products to run on OpenShift. Um, at the end of last year, we updated our license thing so that we can move in that direction and we're starting to, um, deploy, um, digital business automation products on our OpenShift platform, which is super exciting for me. It's going to make for faster upgrades, more scalability. Um, just a lot of ease of use things, um, for my team, um, to make their jobs easier, but also easier for us to adapt new upgrades and software offerings from IBM. Um, there's also a number of products that are in the, um, containerized or OpenShift only offering as they're initially coming out, whether it's mobile capture or automated document processing, um, the same a couple, um, and those are both things that we're looking at auto owners to continue to mature in this space and be able to offer more functionality to our associates, our customers, and our agents, um, to continue to grow the business >>Very forward-thinking uh, awesome Ryan, thanks for sharing with us. What auto insurance or auto owners insurance is doing, how you're being successful and how, how you've done so much transformation already. I want to throw the last question to Michelle. Take us out Michelle with what's next from end choices perspective in terms of your digital transformation. Um, well we have been a hundred percent focus on helping all of our customers develop their digital strategy and, uh, and creating their own transformative solutions. So as we continue to work with our clients, take them through the journey. Um, as I mentioned before, we try to encourage them not to focus on the, the technology itself, but really to focus on creating their exceptional customer experience when driving their digital strategy. And we see ourselves as, you know, helping transform our clients experience such that, you know, customer experience becomes what enChoice does best. >>So we see not only our own organization going through the transformation, but making sure that we're taking our clients with us and with 500 clients, we're, we're really busy. So that's always good. That is good. It sounds like the last year has been, uh, very fruitful for you. And I love that you mentioned customer experience, Michelle. I think that is so important and as well as employee experience, but having a good customer experience, especially these days. Table-stakes I thank you both so much for sharing what you guys are doing with IBM solutions, the transformation that you're both of your companies are on, and we look forward to hearing what's to come. Thank you both for your time. Thank you. Thank you for Rand Dunnings and Michelle Christiansen. I'm Lisa Martin. You're watching the cubes coverage of IBM. Think that digital experience.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome back to the cubes Coverage of AWS reinvent 2020 The digital edition. I'm Lisa Martin, and I'm welcoming back one of our Cube alumni. Paul Saville joins me the S VP of product management and services from Lumen Technologies. Paul, welcome back to the Cube. >>Thank you, Lisa. It's great to be here. >>Last time I got to go to an event was aws reinvent 2019. You were there, but when you were there, you were with centurylink Centurylink. Lumen, What's the correlation? >>Yeah, well, thanks for asking that question. Yes. So we did Rand rebrand our company to loom in technologies. And there's a reason for that because, really, a few years ago, centurylink was largely a consumer telecom business. It's roughly half of its business was in the consumer space, delivering home broadband services, voice services. The other half of the business was around enterprise services and telecom services. But now our company has grown, and we've become much more than that. Now the consumer side of our business is much smaller it's. It's less than 25% of our business overall, and we brought in many more capabilities and technologies. And so we really felt like we were at a point where we and talking to our customers and doing brand analysis around the world because we're now a global, uh, company that has operations in over 100 countries around the world. Um, we felt like we needed to change that branding to represent who we are as terms of that, that large enterprise services company that does a lot more than just telecom services. And so that's why we came up with the name of Lumen Technologies. And as I said, the consumer side, the business still has a centurylink brand. But now the Enterprise Services piece of our company is called Lumen. >>So as that's transpired during this very dynamic time, just give me a little bit of perspective from your customers. How are they embracing this reading? Because we know rebrand is far more than simply rebranding product names and things like that. >>Yes, yeah, I think our customers we're really embracing it. Well, I mean, we've got great feedback from them on the new naming approach and our customers love the name. And but they also more than just the name they love, the idea of, of what we're doing and how we're positioning, how we're transforming our company to really represent what we do as being a company that delivers a platform for managing and distributing digital applications and digital assets across the world. And as you as this audience really knows, uh, enterprises values arm or and MAWR being being determined by their digital assets, whether that is content or whether it's applications. Or it could be, um, processes and things that the intellectual property that that companies own. And when we thought about our company and what it was that we really do for our customers, it really boils down to that is that customers trust us to move their their most valuable digital assets around the world to place them where they need to be when they need to be secured them in place and remove them when they don't need them there anymore. >>And that trust is absolutely critical. I want to get your perspective on something I noticed on Lumens website saying powering progress and the promise of the fourth Industrial Revolution. First of all, what is the promise of the fourth Industrial Revolution? And how is Lumen positioned to deliver progress on it? >>Yeah, So the fourth Industrial Revolution. Some of the audience may not understand what we mean by that when there's really been been. Up to now, there have been three industrial or industrial revolutions. The last one was the advent of the Internet and electron ICS And, you know, looming in its history plays a big role in the third Industrial Revolution because of the build out of the global Internet. You know, we operate one of the largest public Internet networks in the world, and but now we see that technology is pacing. Is taking a ramp up in the next phase of leveraging technologies like artificial intelligence and machine learning i O. T technologies technologies that that require applications and data that need to be distributed in a much more wide basis because computers happening everywhere in the fourth Industrial Revolution. And when we say that we're enabling that and we're enabling the promise of that, we're looking at what we do as having a platform that enables enterprise customers to create capabilities that leverage Fourth Industrial Revolution Technologies and distribute those around the world on a dynamic basis in a real time basis, in in in the fashion of How Cloud has evolved over the last few years. >>So how are you guys working together with AWS to enable customers to be able to leverage that technology that power the ability to get data that they need all across the globe as quickly as possible? >>Yes, so we worked with AWS and a number of ways in that front. You know, of course, AWS makes some great products that are based in the cloud. And they do all these technologies that are speaking about in terms of artificial intelligence and machine learning and video analytics or things and tools that AWS is built to be run out of their out of their cloud services. But Lemon works with AWS in that distribution aspect of it, and taking those assets and those applications and making them operate on a much widely distributed basis and dropping them on customer premise locations at the deep edge in into different markets wherever it makes the most sense for customers, from a performance and economic standpoint to be running those, uh, those next generation types of applications. And so we work with in combination with a W s to build those solutions into end for customers. Lumen has a professional services I t services organization also, that helps customers put together complex solutions involving Internet of things. So we, for instance, we just deployed a factory environment that has a million square foot factory with high level of automation that's run using these types of analytics tools where we're we're putting together the integration on the factory floor back to, uh, the cloud a cloud like aws. >>So in the last, you know, nine months of the world being in such a different place with businesses overnight suddenly having to dio almost 100% remote operations, how does the technology that you just talked about? How does that facilitate a business to keep up and running to not just be able to survive and continue to pivot as they need to during this time, but also to be able to really become the drivers of tomorrow? >>Yes, you know, and from our position is having, you know, over 100,000 enterprise customers and operating in regions over the world are perspective. We've really been able to see how our customers have survived and thrived and those who have not thrived so well through this whole cove it pandemic. And, you know, one of the keys for the companies that have really kind of excelled during this time has been there how far along they were in the adoption curve of cloud technologies and things like the Fourth Industrial Revolution types of technologies. Because those companies were able to dynamically scale up re shift, their resource is they were able to act remotely and control things remotely without having to have humans on premise on site engaging. Um, you know, some of the factory things that we've seen some of the work from home situations that we've seen those companies that were not operating with the kind of flexibility and scale that the cloud environment and the the four ir environment enables have really have really struggled, while the others have really been able to step up on bond, even outperform in many ways from where they were before. >>Yeah, we've been talking for months on the Cube about this acceleration of digital transformation that this pandemic has really forced and seen those companies to your point. Those that were already poised to be agile to adopted are in a much better position. One of the companies I was talking to you recently has Webcams all over the globe, and they're providing, um, you could get it throughout your Apple TV or I think, in Amazon Fire Stick where you can have these virtual experiences going into what's going on in Paris right now, of course, helping us live vicariously since we can't travel. But that's the whole proliferation of the edge and the amount of data that's being generated and process at the edge to the cloud to the core and getting that quickly to the consumer, whether it's a business or an actual consumer, what are you guys doing to help your business is your customers leverage the edge in a in an efficient way so that this accelerated pace that we're living in is actually able to help them. Dr Value. >>Yeah, we we have seen a really uptick in terms of edge opportunities since the Kobe pandemic hit and s so I can give you a great example of one that we that we recently just publicly announced its with a interesting situation with a company called Cyber Reef. Cyber Reef Builds has security technology that they help protect school systems and kids that are now being educated at home instead of in the public schools. Physically, they're they're they're at home, and those kids need protection from the Internet because they're on the Internet all day now. And Cyber Reef provides security tools for the public school systems to help protect those Children and what they're doing and making sure that there focused on school and not, you know, getting. They're having bad actors reached them through the public Internet. They're doing that That is an edge application because they needed to place their security software control tools very close to the edge deep into these markets, with good connection into public Internet and close proximity to the eyeballs of these, uh, these schoolchildren that around in the area, and so they have deployed across the country across our footprint, their their their platform, basically on on our platform to support those deployments toe help our Children as they get educated, >>so important. And if you think about a year ago when we were all in Vegas for reinvent 2019, we wouldn't even have thought we would need something of that scale. I'm here we are with this massive need and companies like Lumet and A W s being able to enable that. Talk to me a little bit about though what you guys are doing with a W s outpost is that part of what you just talked about? >>It wasn't for that example that I just gave, but we are working a lot with AWS outpost. And so we have we see aws outpost, a za key part of our total edged portfolio of solutions that we that we deliver. We have been, uh, investing a lot in our data centers across the world, because looming has hundreds of data centers that are deeply distributed into all of these markets around the world and working with aided without the ws on certifying those locations as outpost deployment, uh, locations. We have also used that I T services organization that that can provide consultation and I t management services for our enterprise customers. Thio. We've been certifying them on outpost configurations. So we've been training our I T professionals on, uh, the AWS solution and on the outpost solution in getting those certification credentials so that we can bring joint products to market with AWS that involved outposts as part of the solution and build in the end capabilities that combine our our services and capabilities with AWS and outpost for for combined solution. >>And can that combined solution to help your customers your joint customers get faster access to their data? Because we know data volume is only going up and up and up, and businesses need to be able to gain insights in real time. Is this the technology that could help get faster insights or access data faster? >>Absolutely. You know, that's and that's one of the key value propositions of ah, a solution like an outpost. Is that because you can drop them pretty much anywhere in the world that you that you need to put compute close to the point of digital interaction? Then, uh, it makes an ideal solution for customers that, uh, that want to work in that AWS environment and also leverage all of the other tools that eight of us can bring to bear from the cloud, uh, platform that that they that they offer but yeah, the place and compute close to that. That point of digital interaction is what it's all about, and it isn't just driven by performance, and performance is a really key part of it because they wanna have that fast interaction at the edge. But there are other things there, too. I mean, sometimes there are economics that play out for many companies that just make it make more sense to act on on compute or storage that it sits, sits more centrally, too many notes that could be aggregated in a market to that one essential location. We're running across use cases where customers, uh, they want to keep that data local because of governance issues or because of privacy issues or because of some kind of a regulatory requirement that they've got that they don't. They need to know exactly where that that data resides at all times, and it needs to be localized in a certain market or country. And eso they're the types of reasons why they would want to use an outpost to really there's there numerous. >>So last question. When you're talking with customers, I imagine the conversations quite different the last nine months or so. Maybe even the level of which you're having these conversations has gone up to the C suite or maybe even to the board. What do you what's your advice to businesses in any industry that really need to move forward quickly, transform to be able to start harnessing the power that four er can deliver but are just not sure where to start. >>Yeah, so, you know, we're just my advice is that they're gonna have to embrace the future embrace that, you know, embrace change. We're Look, we we have never been in a period of time where the pace of change has been assed fast as it is now, and it's not going to slow down. And so you do have to embrace that. But when you But if you're sitting there struggling, I appreciate the dilemma that they're in because, like, Well, where do I start? What do I what do I try? The thing is that that you can you you should pick a project that you can manage and deploy it. But when you deploy it and test it, make sure that you've got really measurable results. that you have really clear KP eyes of what you're trying to achieve and what you know. Are you out for financial goals or you out for performance improvement? Are you out for I t. Greater I t agility. Build the measures around that, Then test the technology that you want to try because we find that some companies approach it and they're kind of like doing it as a science experiment. And then they go, Wow, this was This was cool. It was a good science experiment, but it didn't, but it didn't wind up. They didn't capture the the actual benefit of it. And so then they don't They can't go in and prove it in anymore. And it's kind of like it sets them back because they didn't take that extra preparation >>and businesses in any industry. Nobody has. Has the time Thio face a setback because there's gonna be somebody right behind you in the rear view mirror who's gonna be smaller, agile, more nimble to take advantage. Paul. Great advice for businesses in every industry, and thank you for talking to us about what Lumen Technologies is what you guys are doing with a W s to help customers really embrace the capabilities of the Fourth Industrial Revolution. We appreciate your time. >>All right. Thank you. And thank you to the Cuba. It's good to see you all again. >>Good to see you too. Glad you're safe. And hopefully next time we'll get to see you in person soon For Paul Saville. I'm Lisa Martin. You're watching the cubes coverage of aws reinvent 2020? Yeah.

>> From Cambridge, Massachusetts, it's The Cube, covering MIT Chief data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, everybody. We're here at Tang building at MIT for the MIT CDOIQ Conference. This is the 13th annual MIT CDOIQ. It started as a information quality conference and grew through the big data era, the Chief Data Officer emerged and now it's sort of a combination of those roles. That governance role, the Chief Data Officer role. Critical for organizations for quality and data initiatives, leading digital transformations ans the like. I'm Dave Vallante with my cohost Paul Gillin, you're watching The Cube, the leader in tech coverage. Mark Chrisco is here, the deputy, sorry, Principle Deputy Director for Enterprise Information at the Department of Defense. Good to see you again, thanks for coming on. >> Oh, thank you for having me. >> So, Principle Deputy Director Enterprise Information, what do you do? >> I do data. I do acquisition data. I'm the person in charge of lining the acquisition data for the programs for the Under Secretary and the components so a strong partnership with the army, navy, and air force to enable the department and the services to execute their programs better, more efficiently, and be efficient in the data management. >> What is acquisition data? >> So acquisition data generally can be considered best in the shorthand of cost schedule performance data. When a program is born, you have to manage, you have to be sure it's resourced, you're reporting up to congress, you need to be sure you have insight into the programs. And finally, sometimes you have to make decisions on those programs. So, cost schedule performance is a good shorthand for it. >> So kind of the key metrics and performance metrics around those initiatives. And how much of that is how you present that data? The visualization of it. Is that part of your role or is that, sort of, another part of the organization you partner with, or? >> Well, if you think about it, the visualization can take many forms beyond that. So a good part of the role is finding the authoritative trusted source of that data, making sure it's accurate so we don't spend time disagreeing on different data sets on cost schedule performance. The major programs are tremendously complex and large and involve and awful lot of data in the a buildup to a point where you can look at that. It's just not about visualizing, it's about having governed authoritative data that is, frankly, trustworthy that you can can go operate in. >> What are some of the challenges of getting good quality data? >> Well, I think part of the challenge was having a common lexicon across the department and the services. And as I said, the partnership with the services had been key in helping define and creating a semantic data model for the department that we can use. So we can have agreement on what it would mean when we were using it and collecting it. The services have thrown all in and, in their perspective, have extended that data model down through their components to their programs so they can better manage the programs because the programs are executed at a service level, not at an OSD level. >> Can you make that real? I mean, is there an example you can give us of what you mean by a common semantic model? >> So for cost schedule, let's take a very simple one, program identification. Having a key number for that, having a long name, a short name, and having just the general description of that, were in various states amongst the systems. We've had decades where, however the system was configured, configured it the way they wanted to. It was largely not governed and then trying to bring those data sets together were just impossible to do. So even with just program identification. Since the majority of the programs and numbers are executed at a service level, we worked really hard to get the common words and meanings across all the programs. >> So it's a governance exercise the? >> Yeah. It is certainly a governance exercise. I think about it as not so much as, in the IT world or the data world will call it governance, it's leadership. Let's settle on some common semantics here that we can all live with and go forward and do that. Because clearly there's needs for other pieces of data that we may or may not have but establishing a core set of common meanings across the department has proven very valuable. >> What are some of the key data challenges that the DOD faces? And how is your role helping address them? >> Well in our case, and I'm certain there's a myriad of data choices across the department. In our place it was clarity in and the governance of this. Many of the pieces of data were required by statute, law, police, or regulation. We came out of eras where data was the piece of a report and not really considered data. And we had to lead our ways to beyond the report to saying, "No, we're really "talking about key data management." So we've been at this for a few years and working with the services, that has been a challenge. I think we're at the part where we've established the common semantics for the department to go forward with that. And one of the challenges that I think is the access and dissemination of knowing what you can share and when you can share it. Because Michael Candolim said earlier that the data in mosaic, sometimes you really need to worry about it from our perspective. Is too much publicly available or should we protect on behalf of the government? >> That's a challenge. Is the are challenge in terms of, I'm sure there is but I wonder if you can describe it or maybe talk about how you might have solved it, maybe it's not a big deal, but you got to serve the mission of the organization. >> Absolutely. >> That's, like, number one. But at the same time, you've got stakeholders and they're powerful politicians and they have needs and there's transparency requirements, there are laws. They're not always aligned, those two directives, are they? >> No, thank goodness I don't have to deal with misalignments of those. We try to speak in the truth of here's the data and the decisions across the organization of our reports still go to congress, they go to congress on an annual basis through the selected acquisition report. And, you know, we are better understanding what we need to protect and how to advice congress on what should be protected and why. I would not say that's an easy proposition. The demands for those data come from the GAO, come from congress, come from the Inspector General and having to navigate that requires good access and dissemination controls and knowing why. We've sponsored some research though the RAND organization to help us look and understand why you have got to protect it and what policies, rules, and regulations are. And all those reports have been public so we could be sure that people would understand what it is. We're coming out of an era where data was not considered as it is today where reports were easily stamped with a little rubber stamp but data now moves at the velocities of milliseconds not as the velocity of reports. So we really took a comprehensive look at that. How do you manage data in a world where it is data and it is on infrastructures like data models. >> So, the future of war. Everybody talks about cyber as the future of war. There's a lot of data associated with that. How does that change what you guys do? Or does it? >> Well, I think from an acquisition perspective, you would think, you know. In that discussion that you just presented us, we're micro in that. We're equipping and acquiring through acquisitions. What we've done is we make sure that our data is shareable, you know? Open I, API structures. Having our data models. Letting the war fighters have our data so they could better understand where information is here. Letting other communities to better help that. By us doing our jobs where we sit, we can contribute to their missions and we've aways been every sharing in that. >> Is technology evolving to the point where, let's assume you could dial back 10 or 15 years and you had the nirvana of data quality. We know how fast technology is changing but is it changing as an enabler to really leverage that quality of data in ways that you might not have even envision 10 or 15 years ago? >> I think technology is. I think a lot of this is not in tools, it's now in technique and management practices. I think many of us find ourselves rethinking of how to do this now that you have data, now that you have tools that you can get them. How can you adopt better and faster? That requires a cultural change to organization. In some cases it requires more advanced skills, in other cases it requires you to think differently about the problems. I always like to consider that we, at some point, thought about it as a process-driven organization. Step one to step two to step three. Now process is ubiquitous because data becomes ubiquitous and you could refactor your processes and decisions much more efficiently and effectively. >> What are some of the information quality problems you have to wrestle with? >> Well, in our case, by setting a definite semantic meaning, we kicked the quality problems to those who provide the authoritative data. And if they had a quality problem, we said, "Here's your data. "We're going to now use it." So it spurs, it changes the model of them ensuring the quality of those who own the data. And by working with the services, they've worked down through their data issues and have used us a bit as the foil for cleaning up their data errors that they have from different inputs. And I like to think about it as flipping the model of saying, "It's not my job to drive quality, "it's my job to drive clarity, "it's their job to drive the quality into the system." >> Let's talk about this event. So, you guys are long-time contributors to the event. Mark, have you been here since the beginning? Or close to it? >> Um... About halfway through I think. >> When the focus was primarily on information quality? >> Yes. >> Was it CDOIQ at the time or was it IQ? >> It was the very beginnings of CDOIQ. It was right before it became CDOIQ. >> Early part of this decade? >> Yes. >> Okay. >> It was Information Quality Symposium originally, is that was attracted you to it? >> Well, yes, I was interested in it because I think there were two things that drew my interest. One, a colleague had told me about it and we were just starting the data journey at that point. And it was talking about information quality and it was out of a business school in the MIT slenton side of the house. And coming from a business perspective, it was not just the providence of IT, I wanted to learn form others because I sit on the business side of the equation. Not a pure IT-ist or technology. And I came here to learn. I've never stopped learning through my entire journey here. >> What have you learned this week? >> Well, there's an awful lot I learned. I think it's been... This space is evolving so rapidly with the law, policy, and regulation. Establishing the CDOs, establishing the roles, getting hear from the CDOs, getting to hear from visions, hear from Michael Conlan and hear from others in the federal agencies. Having them up here and being able to collaborate and talk to them. Also hearing from the technology people, the people that're bringing solutions to the table. And then, I always say this is a bit like group therapy here because many of us have similar problems, we have different start and end points and learning from each other has proven to be very valuable. From the hallway conversations to hearing somebody and seeing how they thought about the products, seeing how commercial industry has implemented data management. And you have a lot of similarity of focus of people dealing with trying to bring data to bring value to the organizations and understanding their transformations, it's proven invaluable. >> Well, what did the appointment of the DOD's first CDO last year, what statement did that make to the organization? >> That data's important. Data are important. And having a CDO in that and, when Micheal came on board, we shared some lessons learned and we were thinking about how to do that, you know? As I said, I function in a, arguably a silo of the institution is the acquisition data. But we were copying CDO homework so it helped in my mind that we can go across to somebody else that would understand and could understand what we're trying to do and help us. And I think it becomes, the CDO community has always been very sharing and collaborative and I hold that true with Micheal today. >> It's kind of the ethos of this event. I mean, obviously you guys have been heavily involved. We've always been thrilled to cover this. I think we started in 2013 and we've seen it grow, it's kind of fire marshal full now. We got to get to a new facility, I understand. >> Fire marshal full. >> Next year. So that's congratulations to all the success. >> Yeah, I think it's important and we've now seen, you know, you hear it, you can read it in every newspaper, every channel out there, that data are important. And what's more important than the factor of governance and the factor of bringing safety and security to the nation? >> I do feel like a lot in, certainly in commercial world, I don't know if it applies in the government, but a lot of these AI projects are moving really fast. Especially in Silicon Valley, there's this move fast and break things mentality. And I think that's part of why you're seeing some of these big tech companies struggle right now because they're moving fast and they're breaking things without the governance injected and many CDOs are not heavily involved in some of these skunk works projects and it's almost like they're bolting on governance which has never been a great formula for success in areas like governance and compliance and security. You know, the philosophy of designing it in has tangible benefits. I wonder if you could comment on that? >> Yeah, I can talk about it as we think about it in our space and it may be limited. AI is a bit high on the hype curve as you might imagine right now, and the question would be is can it solve a problem that you have? Well, you just can't buy a piece of software or a methodology and have it solve a problem if you don't know what problem you're trying to solve and you wouldn't understand the answer when it gave it to you. And I think we have to raise our data intellectualism across the organization to better work with these products because they certainly represent utility but it's not like you give it with no fences on either side or you open up your aperture to find basic solution on this. How you move forward with it is your workforce has got to be in tune with that, you have to understand some of the data, at least the basics, and particularly with products when you get the machine learning AI deep learning, the models are going to be moving so fast that you have to intellectually understand them because you'll never be able to go all the way back and stubby pencil back to an answer. And if you don't have the skills and the math and the understanding of how these things are put together, it may not bring the value that they can bring to us. >> Mark, thanks very much for coming on The Cube. >> Thank you very much. >> Great to see you again and appreciate all the work you guys both do for the community. All right. And thank you for watching. We'll be right back with our next guest right after this short break. You're watching The Cube from MIT CDOIQ.