>>AWS public sector summit here in person in Washington, D. C. For two days live. Finally a real event. I'm john for your host of the cube. Got a great guest Howard Levinson from data bricks, regional vice president and general manager of the federal team for data bricks. Uh Super unicorn. Is it a decade corn yet? It's uh, not yet public but welcome to the cube. >>I don't know what the next stage after unicorn is, but we're growing rapidly. >>Thank you. Our audience knows David bricks extremely well. Always been on the cube many times. Even back, we were covering them back when big data was big data. Now it's all data everything. So we watched your success. Congratulations. Thank you. Um, so there's no, you know, not a big bridge for us across to see you here at AWS public sector summit. Tell us what's going on inside the data bricks amazon relationship. >>Yeah. It's been a great relationship. You know, when the company got started some number of years ago we got a contract with the government to deliver the data brooks capability and they're classified cloud in amazon's classified cloud. So that was the start of a great federal relationship today. Virtually all of our businesses in AWS and we run in every single AWS environment from commercial cloud to Govcloud to secret top secret environments and we've got customers doing great things and experiencing great results from data bricks and amazon. >>The federal government's the classic, I call migration opportunity. Right? Because I mean, let's face it before the pandemic even five years ago, even 10 years ago. Glacier moving speed slow, slow and they had to get modernized with the pandemic forced really to do it. But you guys have already cleared the runway with your value problems. You've got lake house now you guys are really optimized for the cloud. >>Okay, hardcore. Yeah. We are, we only run in the cloud and we take advantage of every single go fast feature that amazon gives us. But you know john it's The Office of Management and Budget. Did a study a couple of years ago. I think there were 28,000 federal data centers, 28,000 federal data centers. Think about that for a minute and just think about like let's say in each one of those data centers you've got a handful of operational data stores of databases. The federal government is trying to take all of that data and make sense out of it. The first step to making sense out of it is bringing it all together, normalizing it. Fed aerating it and that's exactly what we do. And that's been a real win for our federal clients and it's been a real exciting opportunity to watch people succeed in that >>endeavour. We have another guest on. And she said those data center huggers tree huggers data center huggers, majority of term people won't let go. Yeah. So but they're slowly dying away and moving on to the cloud. So migrations huge. How are you guys migrating with your customers? Give us an example of how it's working. What are some of the use cases? >>So before I do that I want to tell you a quick story. I've I had the luxury of working with the Air Force Chief data officer Ailene vedrine and she is commonly quoted as saying just remember as as airmen it's not your data it's the Air Force's data. So people were data center huggers now their data huggers but all of that data belongs to the government at the end of the day. So how do we help in that? Well think about all this data sitting in all these operational data stores they're getting it's getting updated all the time. But you want to be able to Federated this data together and make some sense out of it. So for like an organization like uh us citizenship and immigration services they had I think 28 different data sources and they want to be able to pull that data basically in real time and bring it into a data lake. Well that means doing a change data capture off of those operational data stores transforming that data and normalizing it so that you can then enjoy it. And we've done that I think they're now up to 70 data sources that are continually ingested into their data lake. And from there they support thousands of users doing analysis and reports for the whole visa processing system for the United States, the whole naturalization environment And their efficiency has gone up I think by their metrics by 24 x. >>Yeah. I mean Sandy carter was just on the cube earlier. She's the Vice president partner ecosystem here at public sector. And I was coming to her that federal game has changed, it used to be hard to get into you know everybody and you navigate the trip wires and all the subtle hints and and the people who are friends and it was like cloak and dagger and so people were locked in on certain things databases and data because now has to be freely available. I know one of the things that you guys are passionate about and this is kind of hard core architectural thing is that you need horizontally scalable data to really make a I work right. Machine learning works when you have data. How far along are these guys in their thinking when you have a customer because we're seeing progress? How far along are we? >>Yeah, we still have a long way to go in the federal government. I mean, I tell everybody, I think the federal government's probably four or five years behind what data bricks top uh clients are doing. But there are clearly people in the federal government that have really ramped it up and are on a par were even exceeding some of the commercial clients, U. S. C. I. S CBP FBI or some of the clients that we work with that are pretty far ahead and I'll say I mentioned a lot about the operational data stores but there's all kinds of data that's coming in at U S. C. I. S. They do these naturalization interviews, those are captured in real text. So now you want to do natural language processing against them, make sure these interviews are of the highest quality control, We want to be able to predict which people are going to show up for interviews based on their geospatial location and the day of the week and other factors the weather perhaps. So they're using all of these data types uh imagery text and structure data all in the Lake House concept to make predictions about how they should run their >>business. So that's a really good point. I was talking with keith brooks earlier directive is development, go to market strategy for AWS public sector. He's been there from the beginning this the 10th year of Govcloud. Right, so we're kind of riffing but the jpl Nasa Jpl, they did production workloads out of the gate. Yeah. Full mission. So now fast forward today. Cloud Native really is available. So like how do you see the the agencies in the government handling Okay. Re platform and I get that but now to do the reef acting where you guys have the Lake House new things can happen with cloud Native technologies, what's the what's the what's the cross over point for that point. >>Yeah, I think our Lake House architecture is really a big breakthrough architecture. It used to be, people would take all of this data, they put it in a Hadoop data lake, they'd end up with a data swamp with really not good control or good data quality. And uh then they would take the data from the data swamp where the data lake and they curate it and go through an E. T. L. Process and put a second copy into their data warehouse. So now you have two copies of the data to governance models. Maybe two versions of the data. A lot to manage. A lot to control with our Lake House architecture. You can put all of that data in the data lake it with our delta format. It comes in a curated way. Uh there's a catalogue associated with the data. So you know what you've got. And now you can literally build an ephemeral data warehouse directly on top of that data and it exists only for the period of time that uh people need it. And so it's cloud Native. It's elastically scalable. It terminates when nobody's using it. We run the whole center for Medicaid Medicare services. The whole Medicaid repository for the United States runs in an ephemeral data warehouse built on Amazon S three. >>You know, that is a huge call out, I want to just unpack that for a second. What you just said to me puts the exclamation point on cloud value because it's not your grandfather's data warehouse, it's like okay we do data warehouse capability but we're using higher level cloud services, whether it's governance stuff for a I to actually make it work at scale for those environments. I mean that that to me is re factoring that's not re platform Ng. Just re platform that's re platform Ng in the cloud and then re factoring capability for on uh new >>advantages. It's really true. And now you know at CMS, they have one copy of the data so they do all of their reporting, they've got a lot of congressional reports that they need to do. But now they're leveraging that same data, not making a copy of it for uh the center for program integrity for fraud. And we know how many billions of dollars worth of fraud exist in the Medicaid system. And now we're applying artificial intelligence and machine learning on entity analytics to really get to the root of those problems. It's a game >>changer. And this is where the efficiency comes in at scale. Because you start to see, I mean we always talk on the cube about like how software is changed the old days you put on the shelf shelf where they called it. Uh that's our generation. And now you got the cloud, you didn't know if something is hot or not until the inventory is like we didn't sell through in the cloud. If you're not performing, you suck basically. So it's not working, >>it's an instant Mhm. >>Report card. So now when you go to the cloud, you think the data lake and uh the lake house what you guys do uh and others like snowflake and were optimized in the cloud, you can't deny it. And then when you compare it to like, okay, so I'm saving you millions and millions if you're just on one thing, never mind the top line opportunities. >>So so john you know, years ago people didn't believe the cloud was going to be what it is. Like pretty much today, the clouds inevitable. It's everywhere. I'm gonna make you another prediction. Um And you can say you heard it here first, the data warehouse is going away. The Lake house is clearly going to replace it. There's no need anymore for two separate copies, there's no need for a proprietary uh storage copy of your data and people want to be able to apply more than sequel to the data. Uh Data warehouses, just restrict. What about an ocean house? >>Yeah. Lake is kind of small. When you think about this lake, Michigan is pretty big now, I think it's I >>think it's going to go bigger than that. I think we're talking about Sky Computer, we've been a cloud computing, we're going to uh and we're going to do that because people aren't gonna put all of their data in one place, they're going to have, it spread across different amazon regions or or or amazon availability zones and you're going to want to share data and you know, we just introduced this delta sharing capability. I don't know if you're familiar with it but it allows you to share data without a sharing server directly from picking up basically the amazon, you RLS and sharing them with different organizations. So you're sharing in place. The data actually isn't moving. You've got great governance and great granularity of the data that you choose to share and data sharing is going to be the next uh >>next break. You know, I really loved the Lake House were fairly sing gateway. I totally see that. So I totally would align with that and say I bet with you on that one. The Sky net Skynet, the Sky computing. >>See you're taking it away man, >>I know Skynet got anything that was computing in the Sky is Skynet that's terminated So but that's real. I mean I think that's a concept where it's like, you know what services and functions does for servers, you don't have a data, >>you've got to be able to connect data, nobody lives in an island. You've got to be able to connect data and more data. We all know more data produces better results. So how do you get more data? You connect to more data sources, >>Howard great to have you on talk about the relationship real quick as we end up here with amazon, What are you guys doing together? How's the partnership? >>Yeah, I mean the partnership with amazon is amazing. We have, we work uh, I think probably 95% of our federal business is running in amazon's cloud today. As I mentioned, john we run across uh, AWS commercial AWS GovCloud secret environment. See to us and you know, we have better integration with amazon services than I'll say some of the amazon services if people want to integrate with glue or kinesis or Sagemaker, a red shift, we have complete integration with all of those and that's really, it's not just a partnership at the sales level. It's a partnership and integration at the engineering level. >>Well, I think I'm really impressed with you guys as a company. I think you're an example of the kind of business model that people might have been afraid of which is being in the cloud, you can have a moat, you have competitive advantage, you can build intellectual property >>and, and john don't forget, it's all based on open source, open data, like almost everything that we've done. We've made available to people, we get 30 million downloads of the data bricks technology just for people that want to use it for free. So no vendor lock in. I think that's really important to most of our federal clients into everybody. >>I've always said competitive advantage scale and choice. Right. That's a data bricks. Howard? Thanks for coming on the key, appreciate it. Thanks again. Alright. Cube coverage here in Washington from face to face physical event were on the ground. Of course, we're also streaming a digital for the hybrid event. This is the cubes coverage of a W. S. Public sector Summit will be right back after this short break.

Hello, everyone, and welcome back to the special presentation from theCUBE, where we're exploring the future of cloud and its business impact in the coming decade, kind of where we've come from and where we're going. My name is Dave Vellante, and with me is a CIO/CTO/COO, and longtime colleague, Dan Sheehan. Hello, Dan, how're you doing? >> Hey, Dave, how are you doing? Thank you for having me. >> Yeah, you're very welcome. So folks, Dan has been in the technology industry for a number of years. He's overseen, you know, large-multi, tens of millions of dollar ERP application development efforts, He was a CIO of a marketing, you know, direct mail company. Dan, we met at ADVO, it seems like such a (snickers) long time ago. >> Yeah, that was a long time ago, back in Connecticut. Back in the early 2000s. >> Yeah, ancient days. But pretty serious data for back then, you know, the early 2000s, and then you did a six-year stint as a EVP and CIO at Dunkin' Brands. I remember I came out to see you when I was starting Wikibon and trying to understand. >> Oh yeah. >> You know, what the CIOs cared about. You were so helpful and thanks for that. And that was a big deal. I mean, Dunkin', 17,000 points of distribution. I mean, that was sort of a complicated situation, right? >> Oh yeah. >> So, great experience. >> I mean, when you get involved with franchisees and trying to make everybody happy, yes, that was a lot of fun. >> And then you had a number of other roles, one was as COO at Modell's, and then to fast-forward, Beacon Health. You were EVP and CIO there. And you also, it looked like you had a kind of a business and operational role. You helped the company get acquired by Anthem Blue Cross. So awesome, congrats on that. That must've been a great experience. >> It was. A year of my life, yes. (both laugh) >> You're still standing. So anyway, you can see Dan, he's like this multi-tool star, he's seen a lot of changes in the technology business. So Dan, again, welcome back. Dan Sheehan. >> Oh, thank you. >> So when you started in your career, you know, there was no cloud, right? I mean, you had to do everything. It's funny, I remember I was... You probably know Bill Rucci, CIO of Hartford Steam Boiler. I remember we were talking one day, and this again was pre-cloud and he said, you know, I'm thinking, do I really need to manage my own email? I mean, back then, we did everything. So you had to provision infrastructure so you could write apps, and that was important. That frustrated CFOs, but it was a necessary piece of the value chain. So how have you seen that sort of IT value contribution shift over the years? Let's start there. >> Ah, well, I think it comes down to demand versus capacity. If you look at where companies want to go, they want to do a lot with technology. Technology has taken on a larger role. It's no longer and has not been a, so to speak, cost center. So I think the demand for making change and driving a company forward or reducing costs, there are other executives, peers to the CIO, to the CTO that are looking to do more, and when it comes to doing more, that means more demand, and you step back and you look at what the CIO has for capacity. Looking at Quick Solution's data, solutions in the cloud is appealing, and there are, you know, times where other functions talk to a vendor and see that they can get a vertical solution done pretty quickly. They go off and take that on, or it could be, you know, a ServiceNow capability that you want to implement across the company, and you do that just like an ERP type of roll up. But the bottom line is there are solutions out there that have pushed, I would say the IT organization to look at their capacity versus demand, and sometimes you can get things done quicker with a cloud type of solution. >> So how did you look at that shadow IT as a CIO? Was it something that kind of ticked you off or like you're sort of implying that it made you better? >> Well, I think it does ultimately make you better, but I think you have to partner with the functions because if you don't, you get these types of scenarios, and I've been involved in these just as well. You are busy with, you know, fulfilling your objectives as the leader of IT, and then you get a knock on the door from, let's say marketing or operations, and they say, hey, we just purchased this X solution and we want to integrate it with A, B and C. Well, that was not on the budget or on the IT roadmap or the IT strategy that was linked to the IT, I'm sorry, to the business strategy, and all of a sudden now you have more demand versus the capacity, and then you have to go start reprioritizing. So it's more of, yeah, kind of disrupted, but at the same time, it pushed, you know, the needle of the company forward. But it's all about just working together to make it happen. And that's a lot of, you know, hard conversations when you have to start reprioritizing capacity. >> Well, so let's talk about that alignment. I mean, there's always been a sort of a schism between IT and its ability to deliver, manage demand, and the business will always want you to go faster. They want IT to develop the systems, you know, of course, for less and then they want you to eat the cost of maintaining them, so (chuckles) there's been that tension. But in many ways, that CIO's job is alignment. I mean, it seems to me anyway that schism has certainly narrowed and the cloud's been been part of that, but what do you see as that trajectory over the years and where do you see it going? >> Well, I think it's going to continue to move forward, and depending upon the service, you know, companies are going to take advantage of those services. So yes, some of the non-mission critical capabilities that you would want to move out to the cloud or have somebody else do it, so to speak, that's going to continue to happen because they should be able to do it a lot cheaper than you can, just like use you mentioned a few moments ago about email. I did not want to maintain, you know, exchange service and keeping that all up and running. I moved quickly to Microsoft 365 and that's been a world of difference, but that's just one example. But when you have mission critical apps, you're going to have to make a decision if you want to continue to house them in-house or push them out to an AWS and house them there. So maybe you don't need a large data center and you can utilize some of the best and brightest around security, around managing size of the infrastructure and getting some of their engineering help, which can help. So it just depends upon the application, so to speak, or a function that you're trying to support. And you got to really look at your enterprise architecture and see where that makes sense. So you got to have a hybrid. I see and I have, you know, managed towards a hybrid way of looking at your architecture. >> Okay, so obviously the cloud played a role in that change, and of course, you were in healthcare too so you had to be somewhat careful, >> Yep. >> With the cloud. But you mentioned this hybrid architecture. I mean, from a technologist standpoint and a business standpoint, what do you want out of, you know, you hear a hybrid, multi, all the buzz words. What are you looking for then? Is it a consistent experience? Is it a consistent security? Or is it sort of more horses for courses, where you're trying to run a workload in the right place? What's your philosophy on that? >> Well, I mean, all those things matter, but you're looking at obviously, cost, you're looking at engagement. How does these services engage? Whether it's internal employees or external clients who you're servicing, and you want to get to a cost structure that makes sense in terms of managing those services as well as those mission critical apps. So it comes down to looking at the dollars and cents, as well as what type of services you can provide. In many cases, if you can provide a cheaper and increase the overall services, you're going to go down that path. And just like we did with ServiceNow, I did that at Beacon and also at DentaQuest two healthcare companies. We were able to, you know, remove duplicated, so to speak, ticketing systems and move to one and allow a better experience for the internal employee. They can do self-service, they can look at metrics, they can see status, real-time status on where their request was. So that made a bigger difference. So you engaged the employee differently, better, and then you also reduce your costs. >> Well, how about the economics? I mean, your experience that cloud is cheaper. You hear a lot of the, you know, a lot of the legacy players are saying, oh, no cloud's super expensive. Wait till you get that Amazon bill. (laughs) What's the truth? >> Well, I think there's still a lot of maturing that needs to go on, because unfortunately, depending upon the company, so let's use a couple of examples. So let's look at a startup. You look at a startup, they're probably going to look at all their services being in the cloud and being delivered through a SaaS model, and that's going to be an expense, that's going to be most likely a per user expense per month or per year, however, they structure the contract. And right out of the gate, that's going to be a top line expense that has to be managed going forward. Now you look at companies that have been around for a while, and two of the last companies I worked with, had a lot of technical debt, had on-prem applications. And when you started to look at how to move forward, you know, you had CFOs that were used to going to buy software, capitalize in that software over, you know, five years, sometimes three years, and using that investment to be capitalized, and that would sit below the line, so to speak. Now, don't get me wrong, you still have to pay for it, it's just a matter of where it sits. And when you're running a company and you're looking at the financials, not having that cost on your operational expenses, so to speak, if you're not looking at the depreciation through those numbers, that was advantageous to a CFO many years ago. Now you come to them and say, hey, we're going to move forward with a new HR system, and it's all increasing the expense because there's nothing else to capitalize. Those are different conversations, and all of a sudden your expenses have increased, and yes, you have to make sure that the businesses behind you, with respects to an ROI and supporting it. >> Yeah, so as long as the value is there, and that's a part of the alignment. I want to ask you about cloud pricing strategies because you mentioned ServiceNow, you know, Salesforce is in there, Workday. If you look at the way these guys price, it's really not true cloud pricing in a way, cause they're going to have you sign up for an annual license, you know, a lot of times you got pay up front, or if you want a discount, you're going to have to sign up for two years or three years. But now you see guys like Snowflake coming in, you know, big high-profile IPO. They actually charge you on a consumption-based model. What are your thoughts on that? Do you see that as sort of a trend in the coming decade? >> No, I absolutely think it's going to be on a trend, because consumption means more transactions and more transactions means more computing, and they're going to look at charging it just like any other utility charges. So yes, I see that trend continuing. Did a big deal with UltiPro HR, and yeah, that was all based upon user head count, but they were talking about looking at their payroll and changing their costing on payroll down the road. With their merger, or they went from being a public company to a private company, and now looking to merge with Kronos. I can see where time and attendance and payroll will stop being looked at as a transaction, right? It's a weekly or bi-weekly or monthly, however the company pays, and yes, there is dollars to be made there. >> Well, so let me ask you as a CIO and a business, you know, COO. One of the challenges that you hear with the cloud is okay, if I get my Amazon bill, it's something that Snowflake has talked about, where you know, to me, it's the ideal model, but on the other hand, the transparency is not necessarily there. You don't know what it's going to be at the end of (mumbles) Would you rather have more certainty as to what that bill's going to look like? Or would you rather have it aligned with consumption and the value to the business? >> Well, you know, that's a great question, because yes, I mean, budgets are usually built upon a number that's fixed. Now, no, don't get me wrong. I mean, when I look at the wide area network, the cost for internet services, yes, sometimes we need to increase and that means an increase in the overall cost, but that consumption, that transactional, that's going to be a different way of having to go ahead and budget. You have to budget now for the maximum transactions you anticipate with a growth of a company, and then you need to take a look at that you know, if you're budgeting. I know we were on a calendar fiscal year, so we started up budgeting process in August and we finalized at sometime in the end of October, November for the proceeding year, and if that's the case, you need to get a little bit better on what your consumptions are going to be, because especially if you're a public company, going out on the street with some numbers, those numbers could vary based upon a high transaction volume and the cost, and maybe you're not getting the results on the top end, on the revenue side. So I think, yeah, it's going to be an interesting dilemma as we move forward. >> Yeah. So, I mean, it comes back to alignment, doesn't it? I mean, I know in our small example, you know, we're doing now, we were used to be physical events with theCUBE, now it's all virtual events and our Amazon bill is going through the roof because we're supporting all these users on these virtual events, and our CFO's like, well, look at this Amazon bill, and you say, yeah, but look at the revenue, it's supporting. And so to your point, if the revenue is there, if the ROI is there, then it makes sense. You can kind of live with it because you're growing with it, but if not, then you really got to question it. >> Yeah. So you got to need to partner with your financial folks and come up with better modeling around some of these transactional services and build that into your modeling for your budget and for your, you know, your top line and your expenses. >> So what do you think of some of these SaaS companies? I mean, you've had a lot of experience. They're really coming at it from largely an application perspective, although you've managed a lot of infrastructure too. But we've talked about ServiceNow. They've kind of mopped up in the ITSM. I mean, there's nobody left. I mean, ServiceNow has sort of taken over the whole (mumbles) You know, Salesforce, >> Yeah. >> I guess, sort of similarly, sort of dominating the CRM space. You hear a lot of complaints now about, you know, ServiceNow pricing. There is somebody the other day called them the Oracle of ITSM. Do you see that potentially getting disrupted by maybe some cloud native developers who are developing tools on top? You see in, like, for instance, Datadog going after Splunk and LogRhythm. And there seem to be examples popping up. Well, what's your take on all this? >> No, absolutely. I think cause, you know, when we were talking about back when I first met you, when I was at the ADVO, I mean, Oracle was on it's, you know, rise with their suite of capabilities, and then before you know it, other companies were popping up and took over, whether it was Firstbeat, PeopleSoft, Workday, and then other companies that just came into play, cause it's going to happen because people are going to get, you know, frustrated. And yes, I did get a little frustrated with ServiceNow when I was looking at a couple of new modules because the pricing was a little bit higher than it was when I first started out. So yes, when you're good and you're able to provide the right services, they're going to start pricing it that way. But yes, I think you're going to get smaller players, and then those smaller players will start grabbing up, so to speak, market share and get into it. I mean, look at Salesforce. I mean, there are some pretty good CRMs. I mean, even, ServiceNow is getting into the CRM space big time, as well as a company like Sugar and a few others that will continue to push Salesforce to look at their pricing as well as their services. I mean, they're out there buying up companies, but you just can't automatically assume that they're going to, you know, integrate day one, and it's going to take time for some of their services to come and become reality, so to speak. So yes, I agree that there will be players out there that will push these lager SaaS companies, and hopefully get the right behaviors and right pricing. >> I've said for years, Dan, that I've predicted that ServiceNow and Salesforce are on a collision course. It didn't really happen, but it's starting to, because ServiceNow, the valuation is so huge. They have to grow into other markets much in the same way that Salesforce has. So maybe we'll see McDermott start doing some acquisitions. It's maybe a little tougher for ServiceNow given their whole multi-instance architecture and sort of their own cloud. That's going to be interesting to see how that plays out. >> Yeah. Yeah. You got to play in that type of architecture, let's put it that way. Yes, it'll be interesting to see how that does play out. >> What are your thoughts on the big hyperscalers; Amazon, Microsoft, Google? What's the right strategy there? Do you go all in on one cloud like AWS or are you more worried about lock-in? Do you want to spread your bets across clouds? How real is multi-cloud? Is it a strategy or more sort of a reality that you get M and A and you got shadow IT? What's your take on all that? >> Yeah, that's a great question because it does make you think a little differently around you know, where to put all your eggs. And it's getting tougher because you do want to distribute those eggs out to multiple vendors, if you would, service providers. But, you know, for instance we had a situation where we were building a brand new business intelligence data warehouse, and we decided to go with Microsoft as its core database. And we did a bake-off on business analytic tools. We had like seven of them at Beacon and we ended up choosing Microsoft's Power BI, and a good part of that reason, not all of it, but a good part of it was because we felt they did everything else that the Tableau's and others did, but, you know, Microsoft would work to give, you know, additional capabilities to Power BI if it's sitting on their database. So we had to take that into consideration, and we did and we ended up going with Power BI. With Amazon, I think Amazon's a little bit more, I'll put it horizontal, whereby they can help you out because of the database and just kind of be in that data center, if you would, and be able to move some of your homegrown applications, some of your technical debt over to that, I'll say cloud. But it'll get interesting because when you talk about integration, when you talk about moving forward with a new functionality, yeah, you have to put your architecture in a somewhat of a center point, and then look to see what is easier, cheaper, cost-effective, but, you know, what's happening to my functionality over the next three to five years. >> But it sounds like you'd subscribe to a horses for courses approach, where you put the right workload in the right cloud, as opposed to saying, I'm going to go all in on one cloud and it's going to be, you know, same skillset, same security, et cetera. It sounds like you'd lean toward the former versus going all in with, you know, MANO cloud. >> Yeah, I guess again, when I look at the architecture. There will be major, you know, breaks if you would. So yes, there is somewhat of a, you know, movement to you know, go with one horse. But, you know, I could see looking back at the Beacon architecture that we could, you know, lift and put the claims adjudication capabilities up in Amazon and then have that conduct, you know, the left to right claims processing, and then those transactions could then be moved into Microsoft's data warehouse. So, you know, there is ways to go about spreading it out so that you don't have all those eggs in one basket and that you reduce the amount of risk, but that weighed heavily on my mind. >> So I was going to ask you, how much of a factor lock-in is it? It sounds like it's more, you know, spreading your eggs around, as you say and reducing your risk as opposed to, you know, worried about lock-in, but as a CIO, how worried are you about lock-in? Where is that fit in the sort of decision tree? >> Ah, I mean, I would say it's up there, but unfortunately, there's no number one, there's like five number ones, if you would. So it's definitely up there and it's something to consider when you're looking at, like you said, the cost, risk integration, and then time. You know, sometimes you're up against the time. And again, security, like I said. Security is a big key in healthcare. And actually security overall, whether you're retail, you're going to always have situations no matter what industry, you got to protect the business. >> Yeah, so I want to ask you about security. That's the other number one. Well, you might've been a defacto CSO, but kind of when we started in this business security was the problem of the security teams, and you know, it's now a team sport. But in thinking about the cloud and security, how big of a concern is the cloud? Is it just more, you're looking for consistency and be able to apply the corporate edicts? Are there other concerns like the shared responsibility model? What are your thoughts on security in the cloud? >> Well, it probably goes back to again, the industry, but when I looked at the past five years in healthcare, doing a lot of work with the CMS and Medicaid, Medicare, they had certain requirements and certain restrictions. So we had to make sure that we follow those requirements. And when you got audited, you needed to make sure that you can show that you are adhering to their requirements. So over the past, probably two years with Amazon's government capabilities that those restrictions have changed, but we were always looking to make sure that we owned and managed how we manage the provider and member data, because yes, we did not want to have obviously a breach, but we wanted to make sure we were following the guidelines, whether it's state or federal, and then and even some cases healthcare guidelines around managing that data. So yes, top of mind, making sure that we're protecting, you know, in my case so we had 37 million members, patients, and we needed to make sure that if we did put it in the cloud or if it was on-prem, that it was being protected. And as you mentioned, recently come off of, I was going to say Amazon, but it was an acquisition. That company that was looking at us doing the due diligence, they gave us thumbs up because of how we were managing the data at the lowest point and all the different levels within the architecture. So Anthem who did the acquisition, had a breach back in, I think it was 2015. That was top of mind for them. We had more questions during the due diligence around security than any other functional area. So it is critical, and I think slowly, some of that type of data will get up into the cloud, but again, it's going to go through some massive risk management and security measures, and audits, because how fragile that is. >> Yeah, I mean, that could be a deal breaker in an acquisition. I got two other questions for you. One is, you know, I know you follow the technologies very closely, but there's all the buzz words, the digital transformation, the AI, these new SaaS models that we talked about. You know, a lot of CIOs tell me, look, Dave, get the business right and the technology is the easy part. It's people, it's process. But what are you seeing in terms of some of this new stuff coming out, there's machine learning, you know, obviously massive scale, new cloud workloads. Anything out there that really excites you and that you could see on the horizon that could be, you know, really change agents for the next decade? >> Yeah, I think we did some RPA, robotics on some of the tasks that, you know, where, you know, if the analysis types of situations. So I think RPA is going to be a game changer as it continues to evolve. But I agree with what you just said. Doing this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as I have the right requirements, so that goes back to people. Making sure we have the partnership that goes back to leadership and the people. And then the change management aspects. Right out of the gate, you should be worrying about how is it going to affect and then the adoption and engagement. Because adoption is critical, because you can go create the best thing you think from a technology perspective, but if it doesn't get used correctly, it's not worth the investment. So I agree, whether it's digital transformation or innovation, it still comes down to understanding the business model and injecting and utilizing technology to grow or reduce costs, grow the business or reduce costs. >> Yeah, usage really means value. Sorry, my last question. What's the one thing that vendors shouldn't do? What's the vendor no-no that'll alienate CIO's? >> To this day, I still don't like, there's a company out there that starts with an O. I still don't like it to that, every single technology module, if you would, has a separate sales rep. I want to work with my strategic partners and have one relationship and that single point of contact that spark and go back into their company and bring me whatever it is that we're looking at so that I don't get, you know, for instance from that company that starts with an O, you know, 17 calls from 17 different sales reps trying to sell me 17 different things. So what irritates me is, you know, you have a company that has a lot of breadth, a lot of, you know, capability and functional, you know that I may want. Give me one person that I can deal with. So a single point of contact, then that makes my life a lot easier. >> Well, Dan Sheehan, I really appreciate you spending some time on theCUBE, it's always a pleasure catching up with you and really appreciate you sharing your insights with our audience. Thank you. >> Oh, thank you, David. I appreciate the opportunity. You have a great day. >> All right. You too. And thank you for watching everybody. This is Dave Vellante for theCUBE on Cloud. Keep it right there. We'll be back with our next guest right after the short break. Awesome, Dan.

>> Go on my lead. >> Dan: All right, very good. >> Five, four. Hello, everyone, and welcome back to the special presentation from theCUBE, where we're exploring the future of cloud and its business impact in the coming decade, kind of where we've come from and where we're going. My name is Dave Vellante, and with me is a CIO/CTO/COO, and longtime colleague, Dan Sheehan. Hello, Dan, how're you doing? >> Hey, Dave, how are you doing? Thank you for having me. >> Yeah, you're very welcome. So folks, Dan has been in the technology industry for a number of years. He's overseen, you know, large-multi, tens of millions of dollar ERP application development efforts, He was a CIO of a marketing, you know, direct mail company. Dan, we met at ADVO, it seems like such a (snickers) long time ago. >> Yeah, that was a long time ago, back in Connecticut. Back in the early 2000s. >> Yeah, ancient days. But pretty serious data for back then, you know, the early 2000s, and then you did a six-year stint as a EVP and CIO at Dunkin' Brands. I remember I came out to see you when I was starting Wikibon and trying to understand. >> Oh yeah. >> You know, what the CIOs cared about. You were so helpful and thanks for that. And that was a big deal. I mean, Dunkin', 17,000 points of distribution. I mean, that was sort of a complicated situation, right? >> Oh yeah. >> So, great experience. >> I mean, when you get involved with franchisees and trying to make everybody happy, yes, that was a lot of fun. >> And then you had a number of other roles, one was as COO at Modell's, and then to fast-forward, Beacon Health. You were EVP and CIO there. And you also, it looked like you had a kind of a business and operational role. You helped the company get acquired by Anthem Blue Cross. So awesome, congrats on that. That must've been a great experience. >> It was. A year of my life, yes. (both laugh) >> You're still standing. So anyway, you can see Dan, he's like this multi-tool star, he's seen a lot of changes in the technology business. So Dan, again, welcome back. Dan Sheehan. >> Oh, thank you. >> So when you started in your career, you know, there was no cloud, right? I mean, you had to do everything. It's funny, I remember I was... You probably know Bill Rucci, CIO of Hartford Steam Boiler. I remember we were talking one day, and this again was pre-cloud and he said, you know, I'm thinking, do I really need to manage my own email? I mean, back then, we did everything. So you had to provision infrastructure so you could write apps, and that was important. That frustrated CFOs, but it was a necessary piece of the value chain. So how have you seen that sort of IT value contribution shift over the years? Let's start there. >> Ah, well, I think it comes down to demand versus capacity. If you look at where companies want to go, they want to do a lot with technology. Technology has taken on a larger role. It's no longer and has not been a, so to speak, cost center. So I think the demand for making change and driving a company forward or reducing costs, there are other executives, peers to the CIO, to the CTO that are looking to do more, and when it comes to doing more, that means more demand, and you step back and you look at what the CIO has for capacity. Looking at Quick Solution's data, solutions in the cloud is appealing, and there are, you know, times where other functions talk to a vendor and see that they can get a vertical solution done pretty quickly. They go off and take that on, or it could be, you know, a ServiceNow capability that you want to implement across the company, and you do that just like an ERP type of roll up. But the bottom line is there are solutions out there that have pushed, I would say the IT organization to look at their capacity versus demand, and sometimes you can get things done quicker with a cloud type of solution. >> So how did you look at that shadow IT as a CIO? Was it something that kind of ticked you off or like you're sort of implying that it made you better? >> Well, I think it does ultimately make you better, but I think you have to partner with the functions because if you don't, you get these types of scenarios, and I've been involved in these just as well. You are busy with, you know, fulfilling your objectives as the leader of IT, and then you get a knock on the door from, let's say marketing or operations, and they say, hey, we just purchased this X solution and we want to integrate it with A, B and C. Well, that was not on the budget or on the IT roadmap or the IT strategy that was linked to the IT, I'm sorry, to the business strategy, and all of a sudden now you have more demand versus the capacity, and then you have to go start reprioritizing. So it's more of, yeah, kind of disrupted, but at the same time, it pushed, you know, the needle of the company forward. But it's all about just working together to make it happen. And that's a lot of, you know, hard conversations when you have to start reprioritizing capacity. >> Well, so let's talk about that alignment. I mean, there's always been a sort of a schism between IT and its ability to deliver, manage demand, and the business will always want you to go faster. They want IT to develop the systems, you know, of course, for less and then they want you to eat the cost of maintaining them, so (chuckles) there's been that tension. But in many ways, that CIO's job is alignment. I mean, it seems to me anyway that schism has certainly narrowed and the cloud's been been part of that, but what do you see as that trajectory over the years and where do you see it going? >> Well, I think it's going to continue to move forward, and depending upon the service, you know, companies are going to take advantage of those services. So yes, some of the non-mission critical capabilities that you would want to move out to the cloud or have somebody else do it, so to speak, that's going to continue to happen because they should be able to do it a lot cheaper than you can, just like use you mentioned a few moments ago about email. I did not want to maintain, you know, exchange service and keeping that all up and running. I moved quickly to Microsoft 365 and that's been a world of difference, but that's just one example. But when you have mission critical apps, you're going to have to make a decision if you want to continue to house them in-house or push them out to an AWS and house them there. So maybe you don't need a large data center and you can utilize some of the best and brightest around security, around managing size of the infrastructure and getting some of their engineering help, which can help. So it just depends upon the application, so to speak, or a function that you're trying to support. And you got to really look at your enterprise architecture and see where that makes sense. So you got to have a hybrid. I see and I have, you know, managed towards a hybrid way of looking at your architecture. >> Okay, so obviously the cloud played a role in that change, and of course, you were in healthcare too so you had to be somewhat careful, >> Yep. >> With the cloud. But you mentioned this hybrid architecture. I mean, from a technologist standpoint and a business standpoint, what do you want out of, you know, you hear a hybrid, multi, all the buzz words. What are you looking for then? Is it a consistent experience? Is it a consistent security? Or is it sort of more horses for courses, where you're trying to run a workload in the right place? What's your philosophy on that? >> Well, I mean, all those things matter, but you're looking at obviously, cost, you're looking at engagement. How does these services engage? Whether it's internal employees or external clients who you're servicing, and you want to get to a cost structure that makes sense in terms of managing those services as well as those mission critical apps. So it comes down to looking at the dollars and cents, as well as what type of services you can provide. In many cases, if you can provide a cheaper and increase the overall services, you're going to go down that path. And just like we did with ServiceNow, I did that at Beacon and also at DentaQuest two healthcare companies. We were able to, you know, remove duplicated, so to speak, ticketing systems and move to one and allow a better experience for the internal employee. They can do self-service, they can look at metrics, they can see status, real-time status on where their request was. So that made a bigger difference. So you engaged the employee differently, better, and then you also reduce your costs. >> Well, how about the economics? I mean, your experience that cloud is cheaper. You hear a lot of the, you know, a lot of the legacy players are saying, oh, no cloud's super expensive. Wait till you get that Amazon bill. (laughs) What's the truth? >> Well, I think there's still a lot of maturing that needs to go on, because unfortunately, depending upon the company, so let's use a couple of examples. So let's look at a startup. You look at a startup, they're probably going to look at all their services being in the cloud and being delivered through a SaaS model, and that's going to be an expense, that's going to be most likely a per user expense per month or per year, however, they structure the contract. And right out of the gate, that's going to be a top line expense that has to be managed going forward. Now you look at companies that have been around for a while, and two of the last companies I worked with, had a lot of technical debt, had on-prem applications. And when you started to look at how to move forward, you know, you had CFOs that were used to going to buy software, capitalize in that software over, you know, five years, sometimes three years, and using that investment to be capitalized, and that would sit below the line, so to speak. Now, don't get me wrong, you still have to pay for it, it's just a matter of where it sits. And when you're running a company and you're looking at the financials, not having that cost on your operational expenses, so to speak, if you're not looking at the depreciation through those numbers, that was advantageous to a CFO many years ago. Now you come to them and say, hey, we're going to move forward with a new HR system, and it's all increasing the expense because there's nothing else to capitalize. Those are different conversations, and all of a sudden your expenses have increased, and yes, you have to make sure that the businesses behind you, with respects to an ROI and supporting it. >> Yeah, so as long as the value is there, and that's a part of the alignment. I want to ask you about cloud pricing strategies because you mentioned ServiceNow, you know, Salesforce is in there, Workday. If you look at the way these guys price, it's really not true cloud pricing in a way, cause they're going to have you sign up for an annual license, you know, a lot of times you got pay up front, or if you want a discount, you're going to have to sign up for two years or three years. But now you see guys like Snowflake coming in, you know, big high-profile IPO. They actually charge you on a consumption-based model. What are your thoughts on that? Do you see that as sort of a trend in the coming decade? >> No, I absolutely think it's going to be on a trend, because consumption means more transactions and more transactions means more computing, and they're going to look at charging it just like any other utility charges. So yes, I see that trend continuing. Did a big deal with UltiPro HR, and yeah, that was all based upon user head count, but they were talking about looking at their payroll and changing their costing on payroll down the road. With their merger, or they went from being a public company to a private company, and now looking to merge with Kronos. I can see where time and attendance and payroll will stop being looked at as a transaction, right? It's a weekly or bi-weekly or monthly, however the company pays, and yes, there is dollars to be made there. >> Well, so let me ask you as a CIO and a business, you know, COO. One of the challenges that you hear with the cloud is okay, if I get my Amazon bill, it's something that Snowflake has talked about, where you know, to me, it's the ideal model, but on the other hand, the transparency is not necessarily there. You don't know what it's going to be at the end of (mumbles) Would you rather have more certainty as to what that bill's going to look like? Or would you rather have it aligned with consumption and the value to the business? >> Well, you know, that's a great question, because yes, I mean, budgets are usually built upon a number that's fixed. Now, no, don't get me wrong. I mean, when I look at the wide area network, the cost for internet services, yes, sometimes we need to increase and that means an increase in the overall cost, but that consumption, that transactional, that's going to be a different way of having to go ahead and budget. You have to budget now for the maximum transactions you anticipate with a growth of a company, and then you need to take a look at that you know, if you're budgeting. I know we were on a calendar fiscal year, so we started up budgeting process in August and we finalized at sometime in the end of October, November for the proceeding year, and if that's the case, you need to get a little bit better on what your consumptions are going to be, because especially if you're a public company, going out on the street with some numbers, those numbers could vary based upon a high transaction volume and the cost, and maybe you're not getting the results on the top end, on the revenue side. So I think, yeah, it's going to be an interesting dilemma as we move forward. >> Yeah. So, I mean, it comes back to alignment, doesn't it? I mean, I know in our small example, you know, we're doing now, we were used to be physical events with theCUBE, now it's all virtual events and our Amazon bill is going through the roof because we're supporting all these users on these virtual events, and our CFO's like, well, look at this Amazon bill, and you say, yeah, but look at the revenue, it's supporting. And so to your point, if the revenue is there, if the ROI is there, then it makes sense. You can kind of live with it because you're growing with it, but if not, then you really got to question it. >> Yeah. So you got to need to partner with your financial folks and come up with better modeling around some of these transactional services and build that into your modeling for your budget and for your, you know, your top line and your expenses. >> So what do you think of some of these SaaS companies? I mean, you've had a lot of experience. They're really coming at it from largely an application perspective, although you've managed a lot of infrastructure too. But we've talked about ServiceNow. They've kind of mopped up in the ITSM. I mean, there's nobody left. I mean, ServiceNow has sort of taken over the whole (mumbles) You know, Salesforce, >> Yeah. >> I guess, sort of similarly, sort of dominating the CRM space. You hear a lot of complaints now about, you know, ServiceNow pricing. There is somebody the other day called them the Oracle of ITSM. Do you see that potentially getting disrupted by maybe some cloud native developers who are developing tools on top? You see in, like, for instance, Datadog going after Splunk and LogRhythm. And there seem to be examples popping up. Well, what's your take on all this? >> No, absolutely. I think cause, you know, when we were talking about back when I first met you, when I was at the ADVO, I mean, Oracle was on it's, you know, rise with their suite of capabilities, and then before you know it, other companies were popping up and took over, whether it was Firstbeat, PeopleSoft, Workday, and then other companies that just came into play, cause it's going to happen because people are going to get, you know, frustrated. And yes, I did get a little frustrated with ServiceNow when I was looking at a couple of new modules because the pricing was a little bit higher than it was when I first started out. So yes, when you're good and you're able to provide the right services, they're going to start pricing it that way. But yes, I think you're going to get smaller players, and then those smaller players will start grabbing up, so to speak, market share and get into it. I mean, look at Salesforce. I mean, there are some pretty good CRMs. I mean, even, ServiceNow is getting into the CRM space big time, as well as a company like Sugar and a few others that will continue to push Salesforce to look at their pricing as well as their services. I mean, they're out there buying up companies, but you just can't automatically assume that they're going to, you know, integrate day one, and it's going to take time for some of their services to come and become reality, so to speak. So yes, I agree that there will be players out there that will push these lager SaaS companies, and hopefully get the right behaviors and right pricing. >> I've said for years, Dan, that I've predicted that ServiceNow and Salesforce are on a collision course. It didn't really happen, but it's starting to, because ServiceNow, the valuation is so huge. They have to grow into other markets much in the same way that Salesforce has. So maybe we'll see McDermott start doing some acquisitions. It's maybe a little tougher for ServiceNow given their whole multi-instance architecture and sort of their own cloud. That's going to be interesting to see how that plays out. >> Yeah. Yeah. You got to play in that type of architecture, let's put it that way. Yes, it'll be interesting to see how that does play out. >> What are your thoughts on the big hyperscalers; Amazon, Microsoft, Google? What's the right strategy there? Do you go all in on one cloud like AWS or are you more worried about lock-in? Do you want to spread your bets across clouds? How real is multi-cloud? Is it a strategy or more sort of a reality that you get M and A and you got shadow IT? What's your take on all that? >> Yeah, that's a great question because it does make you think a little differently around you know, where to put all your eggs. And it's getting tougher because you do want to distribute those eggs out to multiple vendors, if you would, service providers. But, you know, for instance we had a situation where we were building a brand new business intelligence data warehouse, and we decided to go with Microsoft as its core database. And we did a bake-off on business analytic tools. We had like seven of them at Beacon and we ended up choosing Microsoft's Power BI, and a good part of that reason, not all of it, but a good part of it was because we felt they did everything else that the Tableau's and others did, but, you know, Microsoft would work to give, you know, additional capabilities to Power BI if it's sitting on their database. So we had to take that into consideration, and we did and we ended up going with Power BI. With Amazon, I think Amazon's a little bit more, I'll put it horizontal, whereby they can help you out because of the database and just kind of be in that data center, if you would, and be able to move some of your homegrown applications, some of your technical debt over to that, I'll say cloud. But it'll get interesting because when you talk about integration, when you talk about moving forward with a new functionality, yeah, you have to put your architecture in a somewhat of a center point, and then look to see what is easier, cheaper, cost-effective, but, you know, what's happening to my functionality over the next three to five years. >> But it sounds like you'd subscribe to a horses for courses approach, where you put the right workload in the right cloud, as opposed to saying, I'm going to go all in on one cloud and it's going to be, you know, same skillset, same security, et cetera. It sounds like you'd lean toward the former versus going all in with, you know, MANO cloud. >> Yeah, I guess again, when I look at the architecture. There will be major, you know, breaks if you would. So yes, there is somewhat of a, you know, movement to you know, go with one horse. But, you know, I could see looking back at the Beacon architecture that we could, you know, lift and put the claims adjudication capabilities up in Amazon and then have that conduct, you know, the left to right claims processing, and then those transactions could then be moved into Microsoft's data warehouse. So, you know, there is ways to go about spreading it out so that you don't have all those eggs in one basket and that you reduce the amount of risk, but that weighed heavily on my mind. >> So I was going to ask you, how much of a factor lock-in is it? It sounds like it's more, you know, spreading your eggs around, as you say and reducing your risk as opposed to, you know, worried about lock-in, but as a CIO, how worried are you about lock-in? Where is that fit in the sort of decision tree? >> Ah, I mean, I would say it's up there, but unfortunately, there's no number one, there's like five number ones, if you would. So it's definitely up there and it's something to consider when you're looking at, like you said, the cost, risk integration, and then time. You know, sometimes you're up against the time. And again, security, like I said. Security is a big key in healthcare. And actually security overall, whether you're retail, you're going to always have situations no matter what industry, you got to protect the business. >> Yeah, so I want to ask you about security. That's the other number one. Well, you might've been a defacto CSO, but kind of when we started in this business security was the problem of the security teams, and you know, it's now a team sport. But in thinking about the cloud and security, how big of a concern is the cloud? Is it just more, you're looking for consistency and be able to apply the corporate edicts? Are there other concerns like the shared responsibility model? What are your thoughts on security in the cloud? >> Well, it probably goes back to again, the industry, but when I looked at the past five years in healthcare, doing a lot of work with the CMS and Medicaid, Medicare, they had certain requirements and certain restrictions. So we had to make sure that we follow those requirements. And when you got audited, you needed to make sure that you can show that you are adhering to their requirements. So over the past, probably two years with Amazon's government capabilities that those restrictions have changed, but we were always looking to make sure that we owned and managed how we manage the provider and member data, because yes, we did not want to have obviously a breach, but we wanted to make sure we were following the guidelines, whether it's state or federal, and then and even some cases healthcare guidelines around managing that data. So yes, top of mind, making sure that we're protecting, you know, in my case so we had 37 million members, patients, and we needed to make sure that if we did put it in the cloud or if it was on-prem, that it was being protected. And as you mentioned, recently come off of, I was going to say Amazon, but it was an acquisition. That company that was looking at us doing the due diligence, they gave us thumbs up because of how we were managing the data at the lowest point and all the different levels within the architecture. So Anthem who did the acquisition, had a breach back in, I think it was 2015. That was top of mind for them. We had more questions during the due diligence around security than any other functional area. So it is critical, and I think slowly, some of that type of data will get up into the cloud, but again, it's going to go through some massive risk management and security measures, and audits, because how fragile that is. >> Yeah, I mean, that could be a deal breaker in an acquisition. I got two other questions for you. One is, you know, I know you follow the technologies very closely, but there's all the buzz words, the digital transformation, the AI, these new SaaS models that we talked about. You know, a lot of CIOs tell me, look, Dave, get the business right and the technology is the easy part. It's people, it's process. But what are you seeing in terms of some of this new stuff coming out, there's machine learning, you know, obviously massive scale, new cloud workloads. Anything out there that really excites you and that you could see on the horizon that could be, you know, really change agents for the next decade? >> Yeah, I think we did some RPA, robotics on some of the tasks that, you know, where, you know, if the analysis types of situations. So I think RPA is going to be a game changer as it continues to evolve. But I agree with what you just said. Doing this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as I have the right requirements, so that goes back to people. Making sure we have the partnership that goes back to leadership and the people. And then the change management aspects. Right out of the gate, you should be worrying about how is it going to affect and then the adoption and engagement. Because adoption is critical, because you can go create the best thing you think from a technology perspective, but if it doesn't get used correctly, it's not worth the investment. So I agree, whether it's digital transformation or innovation, it still comes down to understanding the business model and injecting and utilizing technology to grow or reduce costs, grow the business or reduce costs. >> Yeah, usage really means value. Sorry, my last question. What's the one thing that vendors shouldn't do? What's the vendor no-no that'll alienate CIO's? >> To this day, I still don't like, there's a company out there that starts with an O. I still don't like it to that, every single technology module, if you would, has a separate sales rep. I want to work with my strategic partners and have one relationship and that single point of contact that spark and go back into their company and bring me whatever it is that we're looking at so that I don't get, you know, for instance from that company that starts with an O, you know, 17 calls from 17 different sales reps trying to sell me 17 different things. So what irritates me is, you know, you have a company that has a lot of breadth, a lot of, you know, capability and functional, you know that I may want. Give me one person that I can deal with. So a single point of contact, then that makes my life a lot easier. >> Well, Dan Sheehan, I really appreciate you spending some time on theCUBE, it's always a pleasure catching up with you and really appreciate you sharing your insights with our audience. Thank you. >> Oh, thank you, David. I appreciate the opportunity. You have a great day. >> All right. You too. And thank you for watching everybody. This is Dave Vellante for theCUBE on Cloud. Keep it right there. We'll be back with our next guest right after the short break. Awesome, Dan.

>>from around the >>globe. It's the Cube with digital coverage of AWS reinvent 2020 Special coverage sponsored by AWS Worldwide Public Sector >>Hi and welcome to the Q Virtual and our coverage of AWS reinvent 2020 with special coverage of the public sector. I'm your host, Rebecca >>Knight. >>Today we have two guests for our segment. We have Kevin McCallum Jr. He is the chief technology officer at Maximus. Thanks for joining us, Kevin, and we have way. And we have Steve Zimmerman, who is the vice president of consulting services at Insight. Thank you so much for coming on the show. Steve. >>Thank you for having us appreciate it. >>So I want to start by asking. You both have to tell us a little bit more about your company's. Kevin. Let's start with you. Tell us a little bit more about Maximus. >>Yes, Thanks for having me. Maximus is a 40 year old company. We partner with state, federal and local governments to provide communities with critical health and human service programs. We leverage extensive experience to develop high quality services and solutions that are cost effective and tailored to their unique needs. One of the things that we do is offer government's ability to programs rapidly and scalable so that we can focus on the automation and their operations. We do services from Medicare to Medicaid, Welford work, and we have comprehensive solutions. Help the government's run effectively and efficiently. >>Great, Steve, tell us a little bit about insight. >>Yeah, sure. Um, Insight is a Fortune 500 company, you know, in 2020 will roughly do you know, probably a plus billion dollars in revenue. Global company. You know, we have thousands of treaty GIC relationships, but I'd say we have probably a couple 100 partners. We focus on one of those key partners to us is a W s. Right. As we go to market, Azzawi start, you know, working with our customers around transformation, of which we're gonna talk a little bit about that today with Kevin as it relates, Thio incite public sector. It's >>a pretty sizable >>part of our business. You know, we'll do about $1.5 billion in revenue. We have 200 plus contract vehicles, will work out there over 500 plus teammates, and we're seeing that business grow quarter over quarter, 20% growth. So It's a big investment for us and really looking forward to hearing Kevin talk about Maximus, uh, to the team, because obviously it's a big lever for us for inside public sector to get the word out there about the great transformation work. What you do with our customers. >>That's a great segue. So let's go back to you, Kevin, and talk a little bit about Maximus. Cloud transformation. Why did you hire insight for help you with this? >>Yeah, A Z We started our journey. One of the things we realized is as we were moving to the cloud is the experience. We needed a trusted partner and we ran an RFP process looking for partners out there that have done it that have done major data center programs. You're moving large companies, you know, We're moving about 6000 workloads 160 plus applications. So it was not a light or easy project and insight fit that. Aziz, We went through the interview process. It became very clear that they have done this for Fortune 500 companies in the past and their experience is beneficial to helping us drive to the future and the other factors is we wanted to make sure that once we were done with the project, we had the experience internally that they helped us with Thio drive forward. >>So talking about the importance of a trusted partner, which is such a key component of digital transformation cloud journeys tell us a little bit about the the strategy tied to the data center transformation and why you chose AWS. >>Sure. So, as we started doing our research, we did analysis across all of the cloud providers who were out there. AWS is clear leader in the marketplace. Their technology is better aligned with what Maximus has as the underlying technologies were, ah, majority of Lennox Base. We also have windows. We have Oracle, which, with the AWS depth on breath of our offerings, tied better to what we had. The other thing we were looking to do is get rid of our monolithic off the shelf products and use mawr of the cloud based products that are out there. Amazon has a very deep, uh, native technology that allows you to replace your old services where you had to bolt on or purchase another product to something that is integrated and streamlined, you know, down Thio, how do you monitor your systems? How do you do logs things like that. And, you know, as we looked at the time frame, we had to deliver this. They had to be able to grow with us. So as we were building out, new infrastructure were able to build where previously internally. With data centers, you have to buy infrastructure. You wait for it to arrive, you install it. Amazon has it at the click of a ah button. So we're able Thio basically have environment stood up in a day rather than having to wait weeks for it. So and the last thing was up time. So you know Amazon. They're five nines plus in up time and most of our contracts or three nines or better requirements. We had to find a bender that had multiple availability zones and regions that allowed us to be flexible in how we deployed. >>So talking about the convenience and the ability to streamline, and also the need for flexibility in the covert era. Of course, the word hybrid work environments has taken on a new meaning. But I want to ask you about how you see the hybrid era in the long term affecting Maximus. >>Yeah. Since Maximus is a government contractor, we will always be in a hybrid, uh, set up. So some of our contracts are very restrictive, especially when you get into our S d. O. D. And some of those agencies you have a fed ramp requirement is right. Well, with some of the federal agencies. So some of those components about to stay internally So where we can force, uh, you know, moving to the cloud because of the flexibility we have to deploy, that is the right will go. Um, co vid has introduced a new complexity. When it started back in March, you know, Maximus had 30,000 or so employees, and we instantly were thrown into You gotta make those employees get those employees to work from home. So we used Amazon's workspace Thio push our employees to work from home, where, you know, some of the employees and some of our contracts are customer owned equipment. So we couldn't actually take that equipment home. So we had to move to a B y o d model on Amazon workspaces in order to get the users to work from home and the complexity that, with what Amazon has to offer, allowed us to quickly move over 25,000 employees on the Amazon workspaces and work from home and then keeping the data center migration moving in the middle of it has also been, ah, challenge. So we will, in our federal space, still have internal data centers. Integration points that Amazon offers with their inter connects is key toe. How we make it a seamless process because we may have a business unit has stuff sitting in the data center and at Amazon, and they have to look at the seamless package. >>Steve, I want to bring you in here a little bit into this conversation. Cloud transformation, digital transformation. These are These are difficult and huge undertaking in the best of times. How does this pandemic this health crisis emergency. How has that affected the way you help your clients the way you work with your clients? Collaborate, communicate, talk a little bit about the effect of Kobe on this on the >>eso I would. I'll answer the question in a couple different ways, so I would agree with Kevin because, you know, forget about what we do with our customers. You know, we had a pivot really quick to write all remote workforce. You know, I think about my team, you know, 1000 plus teammates. Everyone's 80% travel all gone like, um, and I write eso everybody working remote. Everybody work from their homes. And but the challenging part was working with our customers. And, you know, I look at you know, I looked at with Kevin. You know, I've never met Kevin in person, you know, frankly, and there's teammates have come on to our to the project and execute executing this program remotely, so it makes it that much harder working with the customer. Um, you know, doing more video chats. You know, our methodology is built to be all remote. We have a proprietary tool called snap start that allows to bail scan environments. All that things done. Remote migrations could be done remote. The hard part is when you have to go on site because there's this stuff you have to go on site for around physical inventory to look at the equipment, but it just makes it that much harder. You know, I think he taking advantage of these video tools like we're doing today. You know, I can't tell me how many Skype You know how many calls have been on with Kevin like this and with his peers and with his leadership. But communication is really important program like this because, you know, in a program like this, there will be problems, right? And there will be challenges and, you know, getting on a call on being I will look at Kevin face to face and see what his reaction is really key. But you gotta work that much harder. You gotta work that much harder now in the pandemic. You know, I have other projects right now leaving with this other projects that, frankly, we have sold all remote and we're doing it all remote. And what I'm seeing with the bidam IQ is an acceleration of digital transformation. So, other similar projects like we're doing with Kevin. We're doing for other large fortune 500 companies because it's an acceleration of Hey, look, we gotta be old digital now, so it'll be interesting to see you know how the pandemic effects is long term because it is definitely accelerating out their digital transformation if you haven't done it, you're in trouble because it's gonna eat your company alive. >>Mhm. So, Kevin, he's talking. He talked a little bit about she talked a little bit about the importance of communication, particularly when work so many people are working from home. Um, talk a little bit of about other best practices that have emerged. Things that you have noticed. Things that you advice you would have to your peers. I mean, a Z we heard from Steve. If you're not there yet, you're in trouble. But for the for the people, for the executives out there who are watching this, What advice would you have for them? >>Yeah, I think that you know this this is brought to light. You know, there was always a view that you had to be in an office on a white board and actual actually functioning in that fashion. So, you know, before the pandemic, I was traveling three weeks a month on now, not traveling. I feel that I actually get more work done. I actually feel that I'm closer to the team just because we've introduced a lot of different digital channels. So now we have slack we have teams we do zoom. I require everybody to be on a on video, whereas previously before the pandemic you'd rarely have anybody on video. Um, and you've seen Ah, transformation is people pick up the phone a lot quicker than they did in the past. So it is, actually, I believe, brought the team closer together because now you know, everybody's on. Um, the downside of it is everybody's on all the time. So you've also had to have people step away from work because generally when they take PTO, they leave the office that go somewhere with their family. Now it's your kind of at home. There's not much to dio. You kinda have to force them to take the time off. One of the major factors that has has been interesting is we're doing this transformation in the middle of co vid with moving. All of our resource is the home. So we've we've had to take pauses, toe focus on getting everybody to work from home. Okay, now their work from home back to the project. And, you know, it's kind of a change the timeline a little bit, but in the end, you know we have some hard deadlines to meet. So it's been an interesting transition. You >>know, Kevin, um, I wanna agree with you two points is, uh you know, I think we're also getting not only your time, but also senior leadership, that I think, frankly, we never would have gotten, you know, I'm talking, you know, your peers and your leadership, Like I would fly for those meetings. I think about all the time that I've saved. But then again, it never ends, right? Never. It begins and never ends. And, you know, one of the things I'm concerned about is you know, the long term burnout factor for these folks because and depending on what state you're in, it never ends. You don't have anywhere to go, right. And you know, I think about teammates. I think you know, Kevin, I have talked about this related to our project like burdens and really thing right now for sure. 889 months into this thing. It's a real thing. Is people they have to focus on. Is is work sometimes. So it's a it's a concern for all of us is a project team is we start looking at the executing. This continue to execute this program for the next year. >>And it really highlights the importance of visionary leadership and a leader who cares who is empathetic, who is checking in with his or her team and making sure that the colleagues feel appreciated and cared for. I want you both to just give us look into your crystal ball is a little bit and talk about the where you see things 12, 24 months from now. Hopefully there will be a vaccine and we will return to somewhat of a of a new normal. Um, talk a little bit about where you see the Maximus transformation in two years. Absolutely. Yeah. Start with you. >>So s so you know, our cloud migration. We have some hard deadlines through next year, so we have a focus with insight to get that completed by September next year because our data center contracts are up and we've got to get out. You know, one of the the advantages of where we're headed is to move into more of a Dev ops model where you know you're able thio enable groups that have previously not been able to do work just do thio. The infrastructure was set up your now, enabling them to do deployments, get into production and have full stack ownership. That's really where our focus is. Is enablement of the teams that couldn't do the work previously because now you're in a different type of environment. Um, the other thing is being able thio be more agile. So as we move forward into the cloud journey, we as a company are consort contracts quicker. We are part of the, you know, contract tracing on unemployment insurance. We've done a lot of contracts with states that you know previously most of our contracts or anywhere from a 62 120 day startup. These contracts and contact tracing and covert projects. We've had to start them up in three days. That's having 500 employees online on workspaces on Genesis Cloud and fully functional, and it has been a challenge. But it also has introduced a a better way to do business because now we can we can move quicker for our customers and we can get contracts where they come and say, Hey, I need something in the next couple days. If you look further down the road. You know, it's taking the advantage of what Amazon has to offer, you know, moving from arm or monolithic programs like, you know, we sit on Oracle on Lenox today. You know, we could move into Aurora, which opens up the doors and floodgates, because then you manage, er a little differently. You manage your data a little differently. That's really where I think the the market's going and where we can actually transform our business. Even better, Thio, where we could be more flexible. We can start up quicker and, you know, be doom or things for our customers. >>The final word from you >>e I think it's gonna be a hybrid world, right? It's at least in the short term. And you know, we believe it's all about the workload and getting those workloads or applications, you know, in in the right spot, whether it be public or private and helping our customers with that journey, you know, just a pile on with Kevin talked about around Dev ops. Once you get a guy to get once you get all the stuff over there, you still got to manage it, Whether it's in a W. S or, you know, on Prem. You still gotta have a process to do that. So we see a lot of opportunity around the Modern I t operations and helping with that way. We want to continue to be a trusted partner. Thio Maximus. It's been a great relationship, but I want to thank Kevin and his his leadership team for trusting in us. And we look forward, Um, or more success with him in the future. >>Excellent. Thank you both so much. Kevin and Steve, thanks so much for coming on the Cube. >>Absolutely. Thank you. >>I'm your host, Rebecca. Night. Stay tuned. For more of the Cube virtual coverage of AWS reinvent with special coverage of the public sector.

>>Bach from Las Vegas. It's the cube covering KWS executive sub brought to you by extension. >>Welcome back everyone to the cubes live coverage of the Accenture executives summit here in Las Vegas, part of AWS reinvent. I'm your host, Rebecca Knight. We're joined by two guests for this segment. We have Mamadou BA. He is the senior director of cloud technology at Anthem. Thanks so much for coming on the show. Mamadou and Sean mulch and Donnie, he is AWS security lead at Accenture. Thank you so much Sean. Thank you for having us. Rebecca, glad to be with you. So let's start with you mama. Do tell our viewers a little bit about Anthem, the business. >>Sure. So Anthem is a healthcare company. We're serving around 40 million members and we're committed to simplifying healthcare and make it more accessible and affordable for people. >>So committed to simplifying healthcare, which is, I'm imagining the driving force for your cloud journey, but, but what were some of the other factors that led you to the cloud? It's >>really, we want to make healthcare more accessible for people and more affordable. We want to meet our consumers where they are and meet them using mediums that they want us to use. So it's going through all the data we have. We have 40 million members who serving today looking at the data and find the ways to build customized and personalized experiences to meet them where they are and how they want to be met and also improve to health care for them. >>So what kinds of personalized customized experiences are you talking about and what does the cloud enable? >>So really when you look at, we have a variety of members, young children to adults and people who are Medicare and Medicaid, they have various needs. When you look at people's medical needs, you look at their financial needs, their social needs. What works for me might not work for your, might not work for him. So it's understanding the person as a whole and meeting them where they want to be a mentor really. >>So Sean, how does does, does Accenture, what does Accenture bring to this partnership? How are you helping Anthem realize its goals? >>Sure. So, I mean, I would say this happens under the guise of cloud and at Anthem broadly as well. Right? So Accenture, Anthem is, has Accenture is one of its largest partners. We're proud to be one of, have Anthem is one of our largest clients of course, and all the way from a lot of the outsourcing operations from the business operations side providing cost-effective business operations for addressing all those millions of subscribers that they have to of course helping them innovate both within cloud, within a lot of their other technology needs on premise from a lot of, they're from a lot of like transformations in technology. That's, I would say that covers the gamut specifically within, I'd say where we're helping both strategically and operationally on a strategic front. This includes mapping some of the business needs to um, how to various cloud technologies, uh, where it's a multicloud and a hybrid cloud approach, but also specifically on AWS and, and also about how we can help empower Anthem to realize its cloud journey and potential there with their workforce. >>We, their cloud technology organization and how we empower that movement going forward. Uh, there are a number of other drivers on the operational side and that includes of course, minimizing any future technical debt. Um, and that's, that's a big journey of course, or a big pattern. I would say that that is prevalent across multiple clients, but also realizing comprehensive monitoring, save preventive guardrails for services that then allows developers to have the freedom to experiment, to enable rapid prototyping. And also of course, uh, transparent, uh, operations from a cost perspective. So these would be a couple of ways. >>So mama, do you talk about the ways in which you are innovating in this cloud space? What are, what are some of the most exciting projects that you're working on? Right. So >>we have a, a large number of projects, but NTM as a whole, since we're serving 40 plus million members, we have thousands of applications, petabytes of data. So some of the projects we're working on today, we have a landing zone on AWS and we have some applications in AWS. What we need to meet our application teams. Also internally, we need to help them focus on the business drivers focused on healthcare. So we're working on providing them a nimble platform so they're not worried about day to day it and providing them a self service catalog. And we understand that there's a lot of complexity in healthcare or when you have all this data you need to make sure it's secure. There's a lot of regulatory challenges, so we don't want our application teams to have to deal with all those things. So it's really putting together, identifying the services, AI services, machine learning services, container and serverless, and building a framework for them to have access to all those services that are preapproved and make those self-service for the application teams. >>So that's our service catalog project and allowed them to use all that in an AWS account where they're self sufficient. So we were working closely with Accenture on their end. What we found was while the technology is very valuable, the people and process aspect of it, it's we have to get alignment across all the internal divisions, working closely and bringing our security teams on the table, our data teams, our operation teams, and working together to say how can we empower our developers internally to focus on business deliverables? So building that catalog, provide them a reference, a provider for reference architecture or reference implementation, identifying skills gaps and recognizing them, working with HR to hire new talent and reskill our existing talent, but also leveraging our partners to bring in that talent and give us various ways of looking at the same problem. >>So I saw you Shawn, nodding along with what a lot of mama do was talking about in terms of the alignment. Can you talk about that challenge and how you work with clients to make sure that you are bringing people along? Because the people and the processes are the most important part, but they're often the hardest part too. >>They're are definitely the hardest part. And of course we, I mean behind every grade success story, there's so many challenges, right? And, and one of the things we do of course is not just try to bring our best people that are technically sharp for Anthem, but that understand the client that understand the business needs. For example, it's not just about technology, but it's also about how it's applied to support certain business operations like mergers and acquisitions or as a strategy grows from one cloud to multi-cloud. So it's about bringing those folks that help align or understand those goals organizationally and how they're realized technically. In addition to that, I would say it's also bonding very, very, very closely with leadership, with architects, with operations personnel and the developers and engineers at Anthem to work side by side and realizing many of these goals or many of our shared goals and Anthem's overall vision. And >>the good thing there is really the cloud is aligned with the corporate strategy. So there's a lot of leadership alignment. And what we found is really trying to find that balance between autonomy and alignment. One, the teams to be autonomous. We're providing them with self-service, want them to innovate and get to market quickly, but we also want them to be aligned with the company and enterprise best practices and regulatory standards, so it's a fine balance, but I think we're making great progress with our partners. The processes are being reevaluated. Every process we were saying because we've done it this way for all these years and we were successful at doing it, doesn't mean that that's the way forward. We want to bring everyone together and think of a process holistically, not this is my team, I'm doing this and passing it to the next team. It's bring your best people and let's solve the problem together. >>Right. At the same time, I would say it's not siloed again between say architecture, operations and security either before or after. It's about bringing, I would say these, these three legs of that stool together or are together throughout the process and I think that's something we've done as well. One of the things we've done is establish a tiger team essentially right for to, to power through some of our challenges as we build out a new landing zone. As we move towards implementing some of these self capabilities and plan for migration of I would say a hundreds or potentially thousands of applications to the cloud. It's about getting security to shape policy, getting buy in from there as well. Ensuring that when design decisions are made from an architecture perspective, we take into consideration not just the operational side of Anthem but the operational arm of Accenture that supports and enables some of that work as well and how we can make that their lives easier and how we can make a, minimize any risks of the business, any disruptions, outages, et cetera, by way of good design and by getting their buy in and making sure that every internal stakeholders are, >>yeah. Yeah. Really our um, our emphasis is on quality by design, by bringing the right stakeholders, help architect it properly, and then have some process control and monitoring in place and having some key metrics that we look at. How long is it taking a developer to get an AWS account? How long does it take them to get access to a service that they need to meet? That business function letter is an AI service or a server less the application that they're trying to build. Evaluating those and then trying to improve our process >>and by keeping everyone in the loop, I mean it's this dynamic process that is that I'm sure is very complicated, but by with everyone on the same page, they then feel more engaged in the process and that they matter more, which, which also I'm sure drives productivity. Yes. >>Times w whenever you have a lot of people, sometimes there's no agreement on the decision, but you have to be at a point where when you come to an agreement, you might not have a hundred percent consensus all the time, but if 70 or 80% agree, the other people still feel included, their needs have been heard, their concerns will be addressed one way or the other, and they're willing to move forward with the group. It's not because I didn't get my way. I'm not supporting the business. They understand that and there's some trade offs. >>So I wanted to, I want to switch gears here and talk a little bit about security because health health care data represents some of the biggest security breaches of industry data. So how, how biz cloud infrastructure and your security processes and practices help help counteract that. >>Sure. So before you even get in the account do account is designed to meet all our Hampton security best practices and are based on our AWS agreement. Those best practices listed on there and working with our partners to make sure that by the time you get in an account, it's secure, you only have access to services we gave you. And for each of those services we do a full analysis on it, look at the various attack patterns. For instance, I do encryption and just ensure that the developers have a safe environment to experiment and develop. That's why we're building the self service catalog. It's a self service, but we put the services in there after we evaluated them, we feel comfortable with them. Some services, let's say some HIPAA eligible services. We want to ensure if your application is a HIPAA applicate eligible application, you, you're using those services, so having to control them process in place before you even get to account once you get it. And we have detective and preventive controls in place to alert us in case of any, anyone trying to use a service they're not supposed to use. >>Sean, I want to ask you about some research that Accenture did in 2017 the healthcare industry will be one of the top two industries to face the most digital disruption and the next three years. This was part of the technology vision survey. What, how, how do you even begin to to talk to clients through this, hold their hands through this enormously disruptive period in the healthcare industry. What's your advice and what do you think about the role of big data and analytics going forward? >>Right, absolutely. I think so. There's definitely a tremendous amount of disruption and then it's where a number of large, some of our large clients enterprises really have to go through their own transformational process, their own disruption process for the better, right. As you have a number of different start ups as you have a number of different new entrance into the field and one of the things they cloud technologies do is oftentimes it's not necessarily a first mover advantage, but it's, it's actually the lowest common denominator that if you're not using some of these services, whether it's the predictive capabilities for example, or some of the other analytics capabilities that are offered. So whether it's predict, whether it's Sage maker, et cetera, within AWS and other capabilities, these are really the new foundation and so many companies either no matter of size are actually leveraging these to build for a better experience. And one of the things we are looking at is how we can work with our clients to actually get them there as soon as possible and or use that again as the lowest common denominator and build their own differentiators bill bring to bear some of their experience throughout. Uh, I would say a years potentially decades been valuable experience products of services and actually turbocharge them for lack of a better word, >>mamma do large scale cloud transformation, innovation. This is a monumental challenge. How do you, but it's also a balancing act. How do you make sure that you are balancing the needs in adjacent areas like applications and onboarding and dev ops? How do you, >>so it's, it's really having that alignment and everyone understanding that this is a part of our corporate mission. We're trying to improve health care and reduce the cost, make it more affordable, improve people's lives. So all the teams that are leaders are coming together. Like you mentioned, we have a cloud tiger team and saying for my business unit or my application teams, these are the capabilities I need to support on AWS can do enterprise build up platform for me so I can focus on my business. So it's bringing people together, understanding where they are. Some application teams are more mature than others. Finding really ways to understand our internal customers. Also because we have many application teams and business divisions and having a process while working with, you can have application migration, we can help you migrate to the cloud, but that's not the goal. >>We want to help you understand the services you're using. It's enabling the application teams and providing them with a reference architecture or sometimes reference implementation team. We have a cloud enablement team for instance, where it's an internal consulting group where you go in and say, this is my application, helped me find the best way to move this application to cloud and the best way to improve it over time. So it's bringing everyone together and working closely with HR, the training teams, the vendor management teams, there's, it's almost everyone has to come together to scale this. If it's one team, it's easy to do it, but when you want to make it enterprise wide you have to really scale it and have the leaders aligned. Everyone contributing to it. It is all about alignment. >>It is. It is. It definitely is. Great. Yeah. Just wanted to comment earlier about the piece on security as well. Right, so we talked about, of course he talked about mama was talked about the service catalog, service introduction, so one of the things we do is as part of that alignment, getting everybody's thoughts in terms of how we see this working. Looking at that picture holistically, also looking at what is the, what is the consumer experience? Was the desired experience, is that how do we secure that? How do we make sure that it's frictionless and internally, how does that translate into all of the giving the developers freedom and having those guard but still having some guardrails in place as well as some comprehensive visibility and monitoring. There are about a good dozen services if not more, that provide different points of data metrics, alarms within AWS, but how do we do all of this at scale, at Anthem scale, and then back to the self service perspective. Not just enable security and as part of the organization to monitor, but how every part of the organization is accountable for ensuring security, be it an application team, be it part of the dev sec ops process, be at the networking teams, infrastructure teams, et cetera. So how is everybody informed and how do we bring that level of self service, not just from an application onboarding or migration perspective, but also from a security perspective. >>Yeah. Yeah. It's all about really enabling the application teams also because we can tell you you need to do these five things before you go to production, but if you don't know how to do them, you will not get to production. Instead of doing that, providing you some references, providing you have people you can talk to that can help you go through that. And everyone collaborating as let's help this application team get to production instead of we need to do these things before we approve you. Great. And they're from an alignment perspective. Again, we've gotten folks from cloud strategy, operating model and governance, architecture, um, operations, the actual network team, uh, in different parts of security. Yeah. Database of course, database, data, warehouses, et cetera. And then different parts of security, be it all the way from encryption, key management, the preventive side of things to more of the operational side as well. >>And how all of these folks come together with, if I may add some fantastic executive support on the end in front, um, across, across our board, um, to make things a reality. And I think it's been, we didn't, we didn't start with that model. We did that model out of necessity because when we started our cloud journey, we did have multiple teams taking care of their area. They did their job properly, but then there were some tickets waiting in queues. And it was when you look at the end to end process, it was slowing down the application teams. So we said, how do we help accelerate this stuff? Let's bring everyone together. Not, I did my work and I'm giving it to the next year, but let's collaborate and make sure we're doing the work as one team. >>Well, mama do. Sean, thank you so much. I've really fascinating conversation about re-imagining healthcare and how the cloud helps us do that. Thank you. Thank you so much for having us. Stay tuned for more of the cubes live coverage of the Accenture executive summit coming up in just a little bit.

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officers Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. I'm Dave Vellante and you're watching theCUBE, the leader in live tech coverage. This is the end of the day panel at the IBM Chief Data Officer Summit. This is the 10th CDO event that IBM has held and we love to to gather these panels. This is a data all-star panel and I've recruited Seth Dobrin who is the CDO of the analytics group at IBM. Seth, thank you for agreeing to chip in and be my co-host in this segment. >> Yeah, thanks Dave. Like I said before we started, I don't know if this is a promotion or a demotion. (Dave laughing) >> We'll let you know after the segment. So, the data all-star panel and the data all-star awards that you guys are giving out a little later in the event here, what's that all about? >> Yeah so this is our 10th CDU Summit. So two a year, so we've been doing this for 5 years. The data all-stars are those people that have been to four at least of the ten. And so these are five of the 16 people that got the award. And so thank you all for participating and I attended these like I said earlier, before I joined IBM they were immensely valuable to me and I was glad to see 16 other people that think it's valuable too. >> That is awesome. Thank you guys for coming on. So, here's the format. I'm going to introduce each of you individually and then ask you to talk about your role in your organization. What role you play, how you're using data, however you want to frame that. And the first question I want to ask is, what's a good day in the life of a data person? Or if you want to answer what's a bad day, that's fine too, you choose. So let's start with Lucia Mendoza-Ronquillo. Welcome, she's the Senior Vice President and the Head of BI and Data Governance at Wells Fargo. You told us that you work within the line of business group, right? So introduce your role and what's a good day for a data person? >> Okay, so my role basically is again business intelligence so I support what's called cards and retail services within Wells Fargo. And I also am responsible for data governance within the business. We roll up into what's called a data governance enterprise. So we comply with all the enterprise policies and my role is to make sure our line of business complies with data governance policies for enterprise. >> Okay, good day? What's a good day for you? >> A good day for me is really when I don't get a call that the regulators are knocking on our doors. (group laughs) Asking for additional reports or have questions on the data and so that would be a good day. >> Yeah, especially in your business. Okay, great. Parag Shrivastava is the Director of Data Architecture at McKesson, welcome. Thanks so much for coming on. So we got a healthcare, couple of healthcare examples here. But, Parag, introduce yourself, your role, and then what's a good day or if you want to choose a bad day, be fun the mix that up. >> Yeah, sounds good. Yeah, so mainly I'm responsible for the leader strategy and architecture at McKesson. What that means is McKesson has a lot of data around the pharmaceutical supply chain, around one-third of the world's pharmaceutical supply chain, clinical data, also around pharmacy automation data, and we want to leverage it for the better engagement of the patients and better engagement of our customers. And my team, which includes the data product owners, and data architects, we are all responsible for looking at the data holistically and creating the data foundation layer. So I lead the team across North America. So that's my current role. And going back to the question around what's a good day, I think I would say the good day, I'll start at the good day. Is really looking at when the data improves the business. And the first thing that comes to my mind is sort of like an example, of McKesson did an acquisition of an eight billion dollar pharmaceutical company in Europe and we were creating the synergy solution which was based around the analytics and data. And actually IBM was one of the partners in implementing that solution. When the solution got really implemented, I mean that was a big deal for me to see that all the effort that we did in plumbing the data, making sure doing some analytics, is really helping improve the business. I think that is really a good day I would say. I mean I wouldn't say a bad day is such, there are challenges, constant challenges, but I think one of the top priorities that we are having right now is to deal with the demand. As we look at the demand around the data, the role of data has got multiple facets to it now. For example, some of the very foundational, evidentiary, and compliance type of needs as you just talked about and then also profitability and the cost avoidance and those kind of aspects. So how to balance between that demand is the other aspect. >> All right good. And we'll get into a lot of that. So Carl Gold is the Chief Data Scientist at Zuora. Carl, tell us a little bit about Zuora. People might not be as familiar with how you guys do software for billing et cetera. Tell us about your role and what's a good day for a data scientist? >> Okay, sure, I'll start by a little bit about Zuora. Zuora is a subscription management platform. So any company who wants to offer a product or service as subscription and you don't want to build your billing and subscription management, revenue recognition, from scratch, you can use a product like ours. I say it lets anyone build a telco with a complicated plan, with tiers and stuff like that. I don't know if that's a good thing or not. You guys'll have to make up your own mind. My role is an interesting one. It's split, so I said I'm a chief data scientist and we work about 50% on product features based on data science. Things like churn prediction, or predictive payment retries are product areas where we offer AI-based solutions. And then but because Zuora is a subscription platform, we have an amazing set of data on the actual performance of companies using our product. So a really interesting part of my role has been leading what we call the subscription economy index and subscription economy benchmarks which are reports around best practices for subscription companies. And it's all based off this amazing dataset created from an anonymized data of our customers. So that's a really exciting part of my role. And for me, maybe this speaks to our level of data governance, I might be able to get some tips from some of my co-panelists, but for me a good day is when all the data for me and everyone on my team is where we left it the night before. And no schema changes, no data, you know records that you were depending on finding removed >> Pipeline failures. >> Yeah pipeline failures. And on a bad day is a schema change, some crucial data just went missing and someone on my team is like, "The code's broken." >> And everybody's stressed >> Yeah, so those are bad days. But, data governance issues maybe. >> Great, okay thank you. Jung Park is the COO of Latitude Food Allergy Care. Jung welcome. >> Yeah hi, thanks for having me and the rest of us here. So, I guess my role I like to put it as I'm really the support team. I'm part of the support team really for the medical practice so, Latitude Food Allergy Care is a specialty practice that treats patients with food allergies. So, I don't know if any of you guys have food allergies or maybe have friends, kids, who have food allergies, but, food allergies unfortunately have become a lot more prevalent. And what we've been able to do is take research and data really from clinical trials and other research institutions and really use that from the clinical trial setting, back to the clinical care model so that we can now treat patients who have food allergies by using a process called oral immunotherapy. It's fascinating and this is really personal to me because my son as food allergies and he's been to the ER four times. >> Wow. >> And one of the scariest events was when he went to an ER out of the country and as a parent, you know you prepare your child right? With the food, he takes the food. He was 13 years old and you had the chaperones, everyone all set up, but you get this call because accidentally he ate some peanut, right. And so I saw this unfold and it scared me so much that this is something I believe we just have to get people treated. So this process allows people to really eat a little bit of the food at a time and then you eat the food at the clinic and then you go home and eat it. Then you come back two weeks later and then you eat a little bit more until your body desensitizes. >> So you build up that immunity >> Exactly. >> and then you watch the data obviously. >> Yeah. So what's a good day for me? When our patients are done for the day and they have a smile on their face because they were able to progress to that next level. >> Now do you have a chief data officer or are you the de facto CFO? >> I'm the de facto. So, my career has been pretty varied. So I've been essentially chief data officer, CIO, at companies small and big. And what's unique about I guess in this role is that I'm able to really think about the data holistically through every component of the practice. So I like to think of it as a patient journey and I'm sure you guys all think of it similarly when you talk about your customers, but from a patient's perspective, before they even come in, you have to make sure the data behind the science of whatever you're treating is proper, right? Once that's there, then you have to have the acquisition part. How do you actually work with the community to make sure people are aware of really the services that you're providing? And when they're with you, how do you engage them? How do you make sure that they are compliant with the process? So in healthcare especially, oftentimes patients don't actually succeed all the way through because they don't continue all the way through. So it's that compliance. And then finally, it's really long-term care. And when you get the long-term care, you know that the patient that you've treated is able to really continue on six months, a year from now, and be able to eat the food. >> Great, thank you for that description. Awesome mission. Rolland Ho is the Vice President of Data and Analytics at Clover Health. Tell us a little bit about Clover Health and then your role. >> Yeah, sure. So Clover is a startup Medicare Advantage plan. So we provide Medicare, private Medicare to seniors. And what we do is we're because of the way we run our health plan, we're able to really lower a lot of the copay costs and protect seniors against out of pocket. If you're on regular Medicare, you get cancer, you have some horrible accident, your out of pocket is infinite potentially. Whereas with Medicare Advantage Plan it's limited to like five, $6,000 and you're always protected. One of the things I'm excited about being at Clover is our ability to really look at how can we bring the value of data analytics to healthcare? Something I've been in this industry for close to 20 years at this point and there's a lot of waste in healthcare. And there's also a lot of very poor application of preventive measures to the right populations. So one of the things that I'm excited about is that with today's models, if you're able to better identify with precision, the right patients to intervene with, then you fundamentally transform the economics of what can be done. Like if you had to pa $1,000 to intervene, but you were only 20% of the chance right, that's very expensive for each success. But, now if your model is 60, 70% right, then now it opens up a whole new world of what you can do. And that's what excites me. In terms of my best day? I'll give you two different angles. One as an MBA, one of my best days was, client calls me up, says, "Hey Rolland, you know, "your analytics brought us over $100 million "in new revenue last year." and I was like, cha-ching! Excellent! >> Which is my half? >> Yeah right. And then on the data geek side the best day was really, run a model, you train a model, you get ridiculous AUC score, so area under the curve, and then you expect that to just disintegrate as you go into validation testing and actual live production. But the 98 AUC score held up through production. And it's like holy cow, the model actually works! And literally we could cut out half of the workload because of how good that model was. >> Great, excellent, thank you. Seth, anything you'd add to the good day, bad day, as a CDO? >> So for me, well as a CDO or as CDO at IBM? 'Cause at IBM I spend most of my time traveling. So a good day is a day I'm home. >> Yeah, when you're not in an (group laughing) aluminum tube. >> Yeah. Hurdling through space (laughs). No, but a good day is when a GDPR compliance just happened, a good day for me was May 20th of last year when IBM was done and we were, or as done as we needed to be for GDPR so that was a good day for me last year. This year is really a good day is when we start implementing some new models to help IBM become a more effective company and increase our bottom line or increase our margins. >> Great, all right so I got a lot of questions as you know and so I want to give you a chance to jump in. >> All right. >> But, I can get it started or have you got something? >> I'll go ahead and get started. So this is a the 10th CDO Summit. So five years. I know personally I've had three jobs at two different companies. So over the course of the last five years, how many jobs, how many companies? Lucia? >> One job with one company. >> Oh my gosh you're boring. (group laughing) >> No, but actually, because I support basically the head of the business, we go into various areas. So, we're not just from an analytics perspective and business intelligence perspective and of course data governance, right? It's been a real journey. I mean there's a lot of work to be done. A lot of work has been accomplished and constantly improving the business, which is the first goal, right? Increasing market share through insights and business intelligence, tracking product performance to really helping us respond to regulators (laughs). So it's a variety of areas I've had to be involved in. >> So one company, 50 jobs. >> Exactly. So right now I wear different hats depending on the day. So that's really what's happening. >> So it's a good question, have you guys been jumping around? Sure, I mean I think of same company, one company, but two jobs. And I think those two jobs have two different layers. When I started at McKesson I was a solution leader or solution director for business intelligence and I think that's how I started. And over the five years I've seen the complete shift towards machine learning and my new role is actually focused around machine learning and AI. That's why we created this layer, so our own data product owners who understand the data science side of things and the ongoing and business architecture. So, same company but has seen a very different shift of data over the last five years. >> Anybody else? >> Sure, I'll say two companies. I'm going on four years at Zuora. I was at a different company for a year before that, although it was kind of the same job, first at the first company, and then at Zuora I was really focused on subscriber analytics and churn for my first couple a years. And then actually I kind of got a new job at Zuora by becoming the subscription economy expert. I become like an economist, even though I don't honestly have a background. My PhD's in biology, but now I'm a subscription economy guru. And a book author, I'm writing a book about my experiences in the area. >> Awesome. That's great. >> All right, I'll give a bit of a riddle. Four, how do you have four jobs, five companies? >> In five years. >> In five years. (group laughing) >> Through a series of acquisition, acquisition, acquisition, acquisition. Exactly, so yeah, I have to really, really count on that one (laughs). >> I've been with three companies over the past five years and I would say I've had seven jobs. But what's interesting is I think it kind of mirrors and kind of mimics what's been going on in the data world. So I started my career in data analytics and business intelligence. But then along with that I had the fortune to work with the IT team. So the IT came under me. And then after that, the opportunity came about in which I was presented to work with compliance. So I became a compliance officer. So in healthcare, it's very interesting because these things are tied together. When you look about the data, and then the IT, and then the regulations as it relates to healthcare, you have to have the proper compliance, both internal compliance, as well as external regulatory compliance. And then from there I became CIO and then ultimately the chief operating officer. But what's interesting is as I go through this it's all still the same common themes. It's how do you use the data? And if anything it just gets to a level in which you become closer with the business and that is the most important part. If you stand alone as a data scientist, or a data analyst, or the data officer, and you don't incorporate the business, you alienate the folks. There's a math I like to do. It's different from your basic math, right? I believe one plus one is equal to three because when you get the data and the business together, you create that synergy and then that's where the value is created. >> Yeah, I mean if you think about it, data's the only commodity that increases value when you use it correctly. >> Yeah. >> Yeah so then that kind of leads to a question that I had. There's this mantra, the more data the better. Or is it more of an Einstein derivative? Collect as much data as possible but not too much. What are your thoughts? Is more data better? >> I'll take it. So, I would say the curve has shifted over the years. Before it used to be data was the bottleneck. But now especially over the last five to 10 years, I feel like data is no longer oftentimes the bottleneck as much as the use case. The definition of what exactly we're going to apply to, how we're going to apply it to. Oftentimes once you have that clear, you can go get the data. And then in the case where there is not data, like in Mechanical Turk, you can all set up experiments, gather data, the cost of that is now so cheap to experiment that I think the bottleneck's really around the business understanding the use case. >> Mm-hmm. >> Mm-hmm. >> And I think the wave that we are seeing, I'm seeing this as there are, in some cases, more data is good, in some cases more data is not good. And I think I'll start it where it is not good. I think where quality is more required is the area where more data is not good. For example like regulation and compliance. So for example in McKesson's case, we have to report on opioid compliance for different states. How much opioid drugs we are giving to states and making sure we have very, very tight reporting and compliance regulations. There, highest quality of data is important. In our data organization, we have very, very dedicated focus around maintaining that quality. So, quality is most important, quantity is not if you will, in that case. Having the right data. Now on the other side of things, where we are doing some kind of exploratory analysis. Like what could be a right category management for our stores? Or where the product pricing could be the right ones. Product has around 140 attributes. We would like to look at all of them and see what patterns are we finding in our models. So there you could say more data is good. >> Well you could definitely see a lot of cases. But certainly in financial services and a lot of healthcare, particularly in pharmaceutical where you don't want work in process hanging around. >> Yeah. >> Some lawyer could find a smoking gun and say, "Ooh see." And then if that data doesn't get deleted. So, let's see, I would imagine it's a challenge in your business, I've heard people say, "Oh keep all the, now we can keep all the data, "it's so inexpensive to store." But that's not necessarily such a good thing is it? >> Well, we're required to store data. >> For N number of years, right? >> Yeah, N number of years. But, sometimes they go beyond those number of years when there's a legal requirements to comply or to answer questions. So we do keep more than, >> Like a legal hold for example. >> Yeah. So we keep more than seven years for example and seven years is the regulatory requirement. But in the case of more data, I'm a data junkie, so I like more data (laughs). Whenever I'm asked, "Is the data available?" I always say, "Give me time I'll find it for you." so that's really how we operate because again, we're the go-to team, we need to be able to respond to regulators to the business and make sure we understand the data. So that's the other key. I mean more data, but make sure you understand what that means. >> But has that perspective changed? Maybe go back 10 years, maybe 15 years ago, when you didn't have the tooling to be able to say, "Give me more data." "I'll get you the answer." Maybe, "Give me more data." "I'll get you the answer in three years." Whereas today, you're able to, >> I'm going to go get it off the backup tapes (laughs). >> (laughs) Yeah, right, exactly. (group laughing) >> That's fortunately for us, Wells Fargo has implemented data warehouse for so many number of years, I think more than 10 years. So we do have that capability. There's certainly a lot of platforms you have to navigate through, but if you are able to navigate, you can get to the data >> Yeah. >> within the required timeline. So I have, astonished you have the technology, team behind you. Jung, you want to add something? >> Yeah, so that's an interesting question. So, clearly in healthcare, there is a lot of data and as I've kind of come closer to the business, I also realize that there's a fine line between collecting the data and actually asking our folks, our clinicians, to generate the data. Because if you are focused only on generating data, the electronic medical records systems for example. There's burnout, you don't want the clinicians to be working to make sure you capture every element because if you do so, yes on the back end you have all kinds of great data, but on the other side, on the business side, it may not be necessarily a productive thing. And so we have to make a fine line judgment as to the data that's generated and who's generating that data and then ultimately how you end up using it. >> And I think there's a bit of a paradox here too, right? The geneticist in me says, "Don't ever throw anything away." >> Right. >> Right? I want to keep everything. But, the most interesting insights often come from small data which are a subset of that larger, keep everything inclination that we as data geeks have. I think also, as we're moving in to kind of the next phase of AI when you can start doing really, really doing things like transfer learning. That small data becomes even more valuable because you can take a model trained on one thing or a different domain and move it over to yours to have a starting point where you don't need as much data to get the insight. So, I think in my perspective, the answer is yes. >> Yeah (laughs). >> Okay, go. >> I'll go with that just to run with that question. I think it's a little bit of both 'cause people touched on different definitions of more data. In general, more observations can never hurt you. But, more features, or more types of things associated with those observations actually can if you bring in irrelevant stuff. So going back to Rolland's answer, the first thing that's good is like a good mental model. My PhD is actually in physical science, so I think about physical science, where you actually have a theory of how the thing works and you collect data around that theory. I think the approach of just, oh let's put in 2,000 features and see what sticks, you know you're leaving yourself open to all kinds of problems. >> That's why data science is not democratized, >> Yeah (laughing). >> because (laughing). >> Right, but first Carl, in your world, you don't have to guess anymore right, 'cause you have real data. >> Well yeah, of course, we have real data, but the collection, I mean for example, I've worked on a lot of customer churn problems. It's very easy to predict customer churn if you capture data that pertains to the value customers are receiving. If you don't capture that data, then you'll never predict churn by counting how many times they login or more crude measures of engagement. >> Right. >> All right guys, we got to go. The keynotes are spilling out. Seth thank you so much. >> That's it? >> Folks, thank you. I know, I'd love to carry on, right? >> Yeah. >> It goes fast. >> Great. >> Yeah. >> Guys, great, great content. >> Yeah, thanks. And congratulations on participating and being data all-stars. >> We'd love to do this again sometime. All right and thank you for watching everybody, it's a wrap from IBM CDOs, Dave Vellante from theCUBE. We'll see you next time. (light music)

>> Live from Time Square in New York City, it's theCUBE, covering IBM's Change the Game, Winning with AI. (theCUBE theme music) Brought to you by IBM. >> Hello everybody, welcome back to theCUBE's special presentation. We're covering IBM's announcement. Changing the Game, Winning with AI is the theme of IBM. And IBM has these customer meet-ups, analyst meet-ups, partner meet-ups and they do this in conjunction with Strata every year. And theCUBE has been there covering 'em. I'm Dave Vellante with us is James Wade, who's the Director of Application Hosting at Guidewell, and Hemanth Manda, who's the Director of Platform Offerings at IBM. Gentlemen, welcome to theCUBE thanks for coming on. >> Thank you. >> Hemanth, let's start with you. Platform offerings. A lot of platforms inside of IBM. What do you mean platform offerings? Which one are you responsible for? >> Yeah, so IBM's data and analytics portfolio is pretty wide. It's close to six billion dollar business. And we have hundred plus products. What we are trying to do, is we're trying to basically build a platform through IBM Cloud Private for Data. Bring capabilities that cuts across our portfolio and build upon it. We also make it open. Support multiple clouds and support other partners who wants to run on the platform. So that's what I'm leading. >> Okay, great and we'll come back and talk about that. But James, tell us more about Guidewell. Where are you guys based? What'd you do and what's your role? >> Guidewell is the largest insurer in the sate of Florida. We have about six and a half million members. We also do about 38, 39% of the government processing for MediCare, MediCaid claims. Very large payer. We've also recently moved in over the provider space. We actually have clinics throughout the state of Florida where our members can go in and actually get services there. So we're actually morphing as a company, away from just an insurance company, really to a healthcare company. Very exciting time to be there. We've doubled in size in the last six years from a six billion dollar company to a, I mean from an eight billion dollar company to an 18 billion dollar company. >> So both health insurer and provider, bringing those two worlds together. And the thinking there is just more efficient, you'd be able to drive efficiencies obviously out of your business, right? >> Yup, yes. I mean, the ultimate goal for us is just to have better health outcomes for our members. And the way you deliver that is, one, you do the insurance right, you do it well. You make sure that their processed and handled properly, that they're getting all the services that they need. But two, from a provider space, how do you take the information that you have about your members and use them in a provider space to make sure they're getting the right prescriptions at the right time, for the right situations that they're having, whatever's going on in their life. >> And keeping cost down. I mean, there's a lot of finger pointing in the industry. If you bring those two areas together, you know, now they got a single throat to choke, >> That's right, we get that too. (laughing) >> Buck stops with you. Okay, and you're responsible for the entire application portfolio across the insurance and the clinical side? >> Yes, I have, you know, be it both sides, we have Guidewell as the holding company, we have multiple companies underneath it. So all of those companies roll up into a single kind of IT infrastructure. And I manage that for them, for the entire company. >> Okay. Talk about the big drivers in you business. Obviously on the insurance side, it's the claims system is the life blood, the agency system to deal with, the channel. And now of course, you've got the clinical thing to worry about, but so, talk about sort of the drivers of your business and what's changing. >> Right, I mean, the biggest change we've had, obviously in last few years, has been the Affordable Care Act. It changed the way that, you know, from a group policy where if you're a big corporation and you work for a big corporation, that company actually buys insurance for you and provides it to their employees. Well now the individual market has grown significantly. We're still a group policy insurance company, don't get me wrong, we have a great portfolio of companies that we work with, but we also now sell directly to individuals. So they're in the consumer space directly. And that's just a different way of interacting with folks. You have to have sales sites. You have to have websites that are up, where folks can come and browse your products. You have to interface with government websites. Like CMS has their site where they set up and you're able to buy products through that. So it's really changed our marketing and sales channels completely. And on the back side, the volume of growth, I mean, with the new individual insurance market we've grown in size significantly in our number of members. And that's really stressed our IT systems, it's stressed our database environment. And it's really stressed our ability to kind of analyze the thing that we're doing. And make sure that we're processing claims efficiently and making sure that the members are getting what they expect from us. So, the velocity and change in size has really stressed us. >> Yeah, so you got the Affordable Care Act and some uncertainties around that, the regulations around that. You've got things like EMR and meaningful use that you got to worry about. So a lot of complexity in the application portfolio. And Hemanth, I imagine this is not a unique discussion that you have with some of your insurance clients and healthcare folks, although, you guys are a little different in that you're bringing those two worlds together. But your thoughts on what you're seeing the marketplace. >> Yeah, so I mean, this is not unique because the data is exploding and there are multiple data sources spread across multiple clouds. So in terms of trying to get a sense of where the data is, how to actually start leveraging it, how to govern it, how to analyze it, is a problem that is across all industry verticals. And especially as we are going through digital transformation right, trying to leverage and monetize your data becomes even more important. So. >> Yeah, so, well let's talk a little bit about the data. So your data, like a lot of companies, you must have a lot of data silos. And we have said on theCUBE a lot, that the innovation engine in the future is data. Applying machine intelligence to that data. Using cloud models, whether that cloud is in a private cloud or a public cloud or now even at the edge. But having a cloud-like experience for scale and agility is critical. So, that seems to be the innovation, whereas, last 20, 30 years the innovation has been you know kind of Moore's Law and being able to get the latest and greatest systems, so I can get data out of my data warehouse faster. So change in the innovation engine driven by data what are you seeing James? >> I mean, absolutely. Again, we go back to the mission of the company. It's to provide better health outcomes for our members, right. And IT, and using the data that we collect more effectively and efficiently, allows us to do that. I mean we, if you take, you know, across the board, you may have four or five doctors that you're working with and they've prescribed multiple things to you, but they're not talking. They have no idea what your other doctor is doing with you, unless you tell 'em and a lot of people forget. So just as an example, we would know as the payer, what you've been prescribed, what you've been using for multiple years. If we see something, using AI, machine learning, that you've just been prescribed is going to have a detrimental impact to something else that you're doing, we can alert you. We can send you SMS messages, we can send you emails, we could alert your doctors. Just to say, hey this could be a problem and it could cause a prescription collision and you can end up in the hospital or worse. And that's just one example of the things that we look at everyday to try to better the outcome for our members. But, you know, that's just the first layer. What else can you do with that? Are there predictive medicines? Are there things we could alert your doctors to, that we're seeing from other places, or populations, that kind of match, you know, your current, you know, kind of what you look like, what you do, what you think, what you're using. All the information we have about you, can we predict health outcomes down the future and let your doctors know? So, exciting time to be in this industry. >> Let's talk about the application architecture to support that outcome, because you know, you're not starting from a green field. You probably got some Cobalt running and it works, you can't mess with that stuff. And traditionally you built, especially in a regulated industry, you're building applications that are hardened. And as I said you have this data silo that really, you know, it's like, it works, don't touch it. How much of a challenge is it for you to enter this sort of new era? And how are you getting there? I'd like to understand, IBM's role as well. >> Well we, it's very challenging, number one. You have your, I don't want to call it legacy 'cause that makes it sound bad, but you do have kind of your legacy environments where we're collecting the information. It's kind of like the silos that have gathered the information, the sales information, the claims information, that type of stuff. But those may not be the best systems currently, to actually do the processing and the data analysis and having the machine learning run against it. So we have, you know, really complex ETL, you know, moving data from our kind of legacy environments in to these newer open source models that you guys support with, you know, IBM Cloud Private for Data. But basically, moving into these open source areas where we can kind of focus our tools on it and learn from that data. So that, you know, having your legacy environment and moving it to the new environment where you can do this processing, has been a challenge. I mean the velocity of change in the new environment, the types of databases that are out there Hadoop and then the products that you guys have that run through the information, that's one of the bigger challenges that we have. Our company is very supportive of IT, they give us plenty of budget, they give us plenty of resources. But even with all of the support that we get, the velocity of change in the new environment, in the AI space and the machine learning, is very difficult to keep up with. >> Yeah and you can't just stop doing what your doing in the existing environment, you still got to make changes to it. You got regulatory, you got hippo stuff that you've got to deal with. So you can't just freeze your code there. So, are things like containers and, you know, cloud native techniques coming into play? >> Absolutely, absolutely. We're developing all, you know, we kind of drew a line in the sand, our CIO about two years ago, line in the sand, everything that we develop now is in our cloud-first strategy. That doesn't necessarily mean it's going to go into the external cloud. We have an internal cloud that we have. And we have a very large power environment at Guidewell. Our mainframe is still sort of a cloud-like infrastructure. So, we developed it to be cloud native, cloud-first. And then if it, you know, more than likely stays in our four walls, but there's also the option that we can move it out. Move it to various clouds that are out there. As an IBM Cloud, Amazon, Microsoft, Google, any of those clouds. So we're developing with a cloud-first strategy all of the new things. Now, like you said, the legacy side, we have to maintain. I mean, still the majority of our business is processing claims for our members, right, and that's still in that kind of legacy environment. Runs on a mainframe in the power environment today. So we have to keep it up and running as well. >> How large of organization are you, head count wise? >> We have about 2,100 IT people at Guidewell. Probably a 17,000 person organization. So there is a significant percentage of the population of our employees that are IT directly. >> I was at a, right 'cause it is a IT heavy business, always has been. I was at a conference recently and they threw out a stat that the average organization has eight clouds. And I said, "we're like a 60 person company "and we have eight clouds." I mean you must have 8,000 clouds. (laughing) Imagine when you through in the SAS and so forth. But, you mentioned a number of other clouds. You mentioned IBM Cloud and some others. So, it's a multi-cloud world. >> Yes, yes. >> Okay, so I'm interested in how IBM is approaching that, right. You're not just saying, okay, IBM Cloud or nothing, I think, you know. And cloud is defined on-prem, off-prem, maybe now at the edge, your thoughts. >> Yeah, so, absolutely, I think that is our strategy. We would like to support all the clouds out there, we don't want to discriminate one versus the other. We do have our own public cloud, but what our strategy is, to support our products and platforms on any cloud. For example, IBM Cloud Private for Data, it can run in the data center, it can provide the benefits of the cloud within your firewall. But if you want to deploy it on any other public cloud infrastructures, such as Amazon or Red Hat OpenStack, we do support it. We are also looking to expand that support to Microsoft and Google in the future. So we are going forward with the multi-cloud strategy. Also, if you look at IBM's strength, right, we have significant on-premise business, right, that's our strength. So we want to basically start with enterprise-out. So by focusing on private cloud, and making sure that customers can actually move their offerings and products to private cloud, we are essentially providing a path for our customers and clients to move cloud, embrace cloud. So that's been our approach. >> So James, I'm interested in how you guys look at cloud-first. When you say cloud-first, first of all, I'm hearing, it's not about where it goes, it's about the experience. So we're going to bring the cloud model to the data, wherever the data lives. It's in the public cloud, of course it's cloud. If we bring it on-prem, we want a cloud-like experience. How do you guys looks at that cloud-like experience? Is it utility pricing, is it defined in sort of agility terms? Maybe you could elaborate. >> Actually, we're trying to go with the agility piece first, right. The hardest thing right now is to keep up with the pace that customers demand. I mean, you know, my boss Paul Stallings always talks about, you know, consumer-grade is now the industrial strength. Now you go home at night, your network at home is very fast to your PC. Your phone, you just hit an app, you always expect it to work. Well, we have to be able to provide that same level of support and reliability in the applications we're deploying inside of our infrastructure. So, to do that, you have to be fast, you have to be agile. And our cloud-first being, how do you get things to market faster, right. So you can build service faster build out your networks faster and build you databases faster. Already have like defined sizes, click a button and it's there. On-demand infrastructure, much like they do in the public loud, We want to have that internally. But second, and our finance department would tell you, is that, you know, most important is the utility piece. So once you can define these individuals modules that you can hit a button and immediately spin up and instantiate, you should be able to figure out what that cost the company. How do you define what a server cost? Total cost of ownership through the lifetime that server is for the company. Because if we can lower thar cost, if we can do these things very well, automate 'em, get the data where it needs to be, spin up quickly, we can reduce our administrative cost and then pass those savings right back to our members. You know, if we can find a way to save your grandmother $20 a month off her health insurance, that can make a lot of difference in a person's life, right. Just by cutting our cost on the IT side, we can deliver savings back to the company. And that's very key to us. >> And in terms of sort of what goes where, I guess it's a function of the physics, right, if there's latencies involved, the economics, which you mentioned are critical obviously in your business. And I guess the laws, you know, the edicts of the government-- >> Yes and the various contracts that you sign with companies. I mean, there's some companies that we deal with it in the state of Florida that want their data to stay in that sate of Florida. Well if you move it out to a various cloud provider, you don't know which data center that it's in. So you have to go, there's the laws and regulations based on your contracts. But you're exactly right. It's what have you signed up for, what've you agreed to, what are your member comfortable with as to where the data can actually go? >> How does IBM help Guidewell and other companies sort of mange through that complexity? >> Yeah, absolutely. So I think, in addition to what James mentioned, right, it's also about agility. Because for example, if you look at insurance applications, there's a specific time period where you probably would expect 10x of load, right. So you should be able to easily scale up and down. And also, as you're changing your business model, if you have new laws, or if you want to go after new businesses, you should be able to easily embrace that, right. So cloud provides sort of flexibility and elasticity and also the agility. So that's one. The other thing that you mentioned around regulation, especially in healthcare and also too with financial services industry. So what we're trying to do is, on our platform, we would like to actually have industry-specific accelerators. We've been working with fortune 500 companies for the last 30, 40 years. So we've gained a depth of knowledge that we currently have within our company. So we want to basically start exposing the accelerators. And this is on our roadmap and will be available fairly quickly. So that's one approach we're taking. The other approach we're taking is, we're also working with our business partners and technology partners because we do believe, in today's world, you cannot go after an opportunity all by yourself. You need to build an ecosystem and that's what we're doing. We're trying to work with, basically, specialty vendors who might be focused on that particular vertical, who can bring the depth in knowledge that we might not be having. And work with them and team up, so that they can build their solutions on top of the platform. So that's another approach that we're taking. >> So I got to ask you, I always ask this question of customers. Why IBM? >> I mean, this, you guys have been a part of our business for so long. You have very detailed sales guys that are embed really with our IT folks. You understand our systems. You understand what we do, when we do it, why we do it. You understand our business cycle. IBM really invests in their customers and understanding what they're doing, what they need to be done. And quite honestly, you guys bring some ideas to the table we haven't even thought of. You have such a breadth of understanding, and you're dealing with so many other companies, you'll see things out there that could be a nugget that we could use. And IBM's never shied of bringing that to us. Just a history and a legacy of really bringing innovative solutions to us to really help our business. And very companies out there really get to know a company's business, as well as IBM does. >> Hemanth I'll give you the last word. We got Change the Game, Winning with AI tonight You go to IBM.com/winwithAI and register there. I just did, I'm part of the analyst program. So, Hemanth, last word for you. >> Yeah, so, I think the world is changing really fast and unless enterprises embrace cloud and embrace artificial intelligence and cloud base their data to monetize new business models, it very hard to compete. Like, digital transformation is impacting every industry vertical, including IBM. So, I think going after this opportunistically is critical. And IBM Cloud Private for Data, the platform provides this. And please join us today, it's going to be a great event. And I look forward to meeting you guys, thank you. >> Awesome, and definitely agree. It's all about your digital meets data, applying machine intelligence, machine learning, AI, to that data. Being able to run it in a cloud-like model so you can scale, you can be fast. That's the innovation sandwich for the future. It's not just about the speed of the processor, or the size of the disk drive, or the flash or whatever is. It's really about that combination. theCUBE bringing you all the intelligence we can find. You're watching CUBE NYC. We'll be right back right after this short break. (theCUBE theme music)

>> Live, from Washington DC, it's theCube, covering the AWS Public Sector Summit 2018. Brought to you by Amazon Web Services, and its ecosystem partners. (techno music) >> Welcome back everyone it's theCube's exclusive coverage here, day two of the Amazon Web Sources public sector summit. This is the public sector across the globe. This is their reinvent, this is their big event. I'm John Furrier, Stu Miniman, and also David Vellante's been here doing interviews. Our next guest is, we got Doug Van Dyke, he's the director of U.S. Federal Civilian and Non Profit Sectors of the group, welcome to theCube, good to see you. >> John, thank you very much for having me. >> So you've been in the federal, kind of game, and public sector for a while. You've known, worked with Theresa, at Microsoft before she came to Reinvent. >> 15 years now. >> How is she doing? >> She's doing great, we saw her on main stage yesterday. Force of nature, love working with her, love working for her. This is, like you were saying, this is our re-invent here in D.C. and 14,000 plus, 15,000 registrations, she's on the top of her game. >> What I'm really impressed with her and your team as well, is the focus on growth, but innovation, right? it's not just about, knock down the numbers and compete. Certainly you're competing against people who are playing all kinds of tricks. You got Oracle out there, you got IBM, we've beaten at the CIA. It's a street battle out there in this area in D.C. You guys are innovative, in that you're doing stuff with non-profits, you got mission driven, you're doing the educate stuff, so it's not just a one trick pony here. Take us through some of the where you guys heads are at now, because you're successful, everyone's watching you, you're not small anymore. What's the story? >> So, I think the differentiator for us is our focus on the customers. You know, we've got a great innovation story at the Department of Veterans Affairs with vets.gov. So five years ago if a veteran went out to get the services that the government was going to provide them, they've have to pick from 200 websites. It just wasn't to navigate through 200 websites. So, the innovation group at Veteran's Affairs, the digital services team, figured out, let's pull this all together under a single portal with vets.gov. It's running on AWS, and now veterans have a single interface into all the services they want. >> Doug, one of the things I've been impressed, my first year coming to this. I've been to many other AWS shows, but you've got all these kind of overlapping communities. Of course, the federal government, plus state and local, education. You've got this civilian agencies, so give us a little bit of flavor about that experience here at the show. What trends your hearing from those customers. >> So what's great for me is I've been here almost six and a half years, and I've seen the evolution. And you know, there were the early customers who were just the pioneers like Tom Soderstrom, from JPL, who was on main stage. And then we saw the next wave where there were programs that needed a course correction, like Center for Medicare Medicaid with Healthcare.gov. Where Amazon Web Services came in, took over, helped them with the MarketPlace, you know, get that going. And now we're doing some great innovative things at CMS, aggregating data from all 50 states, about 75 terabytes, so they can do research on fraud, waste, and abuse that they couldn't do before. So we're helping our customers innovate on the cloud, and in the cloud, and it's been a great opportunity. >> Oh my God, I had the pleasure of interviewing Tom Soderstrom two years ago. >> Okay. >> Everybody gets real excited when you talk about space. It's easy to talk about innovation there, but you know, talk about innovation throughout the customers, because some people will look at it, and be like, oh come on, government and their bureaucracy, and they're behind. What kind of innovation are you hearing from your customers? >> So there's an exciting with Department of Energy. They, you know there's a limited amount of resources that you have on premise. Well, they're doing research on the large Hadron Collider in Cern, Switzerland. And they needed to double the amount of capacity that they had on premise. So, went to the AWS cloud, fired up 50,000 cores, brought the data down, and they could do research on it. And so, we're making things possible that couldn't be done previously. >> What are some of the examples that government entities and organizations are doing to create innovation in the private sector? Cause the private sector's been the leader to the public sector, and know you're seeing people starting to integrate it. I mean, half the people behind us, that are exhibiting here, are from the commercial side doing business in the public sector. And public sector doing, enabling action in the private sector. Talk about that dynamic, cause it's not just public sector. >> Right. >> Can you just share your? >> These public, private. Great example with NOAA, the National Oceanic Atmospheric Administration. They have a new program called NEXRAD. It's the next generation of doppler radar. They have 160 stations across the world, collecting moisture, air pressure, all of the indicators that help predict the weather. They partner with us at AWS to put this data out, and through our open data program. And then organizations like the Weather Bug can grab that information, government information, and use it to build the application that you have on your I-Phone that predicts the weather. So you know whether to bring an umbrella to work tomorrow. >> So you're enabling the data from, or stuff from the public, for private, entrepreneurial activity? >> Absolutely. >> Talk about the non-profits. What's going on there? Obviously, we heard som stuff on stage with Teresa. The work she's showcasing, a lot of the non-profit. A lot of mission driven entrepreneurships happening. Here in D.C, it's almost a Silicon Valley like dynamic, where stuff that was never funded before is getting funded because they can do Cloud. They can stand it up pretty quickly and get it going. So, you're seeing kind of a resurgence of mission driven entrepreneurships. What is the nonprofit piece of it look now for AWS? How do you talk about that? >> Sure. Well again, one of the areas that I'm really passionate about being here, and being one of the people who helped start our nonprofit vertical inside of AWS, we now have over 12, I'm sorry, 22,000 nonprofits using AWS to keep going. And the mission of our nonprofit vertical is just to make sure that no nonprofit would ever fail for lack of infrastructure. So we partnered with Tech Soup, which is an organization that helps vet and coordinate our Cloud credits. So nonprofits, small nonprofit organizations can go out through Tech Soup, get access to credits, so they don't have to worry about their infrastructure. And you know we.. >> Free credits? >> Those credits, with the Tech Soup membership, they get those, yeah, and using the word credit, it's more like a grant of AWS cloud. >> You guys are enabling almost grants. >> Yes, cloud grants. Not cash grants, but cloud grants. >> Yeah, yeah great. So, how is that converting for you, in your mind? Can you share some examples of some nonprofits that are successful? >> Sure. A great presentation, and I think it was your last interview. A game changer. Where these smaller nonprofits can have a really large impact. And, but then we're also working with some of the larger nonprofits too. The American Heart Association, that built their precision medicine platform to match genotype, phenotype information, so we can further cardiovascular research. They have this great mission statement, they want to reduce cardiovascular disease by 20 percent by 2020. And we're going to help them do that. >> You guys are doing a great job, I got to say. It's been fun to watch, and now, we've been covering you guys for the past two years now, here at the event. A lot more coming on, in D.C. The CIA went in a few years ago. Certainly a shot heard around the cloud. That's been well documented. The Department of Defense looking good off these certain indicators. But, what's going on in the trends in the civilian agencies? Can you take a minute to give an update on that? >> Yeah, so I started earlier saying I've seen the full spectrum. I saw the very beginning, and then I've seen all the way to the end. Where, I think it was three years ago at this event, I talked to Joe Piva, who is the former CIO for the Department of Commerce ITA, the International Trade Association. He had data center contracts coming up for renewal. And he made a really brave decision to cancel those contracts. So he had 18 months to migrate the entire infrastructure for ITA over on to AWS. And you know, there's nothing like an impending date to move. So, we've got agencies that are going all in on AWS, and I think that's just a sign of the times. >> Data centers, I mean anyone who were startup nine years into it, we've never had a data center. I think most startups don't.. >> Born in the cloud. >> Born in the cloud. Thanks so much Dave, for coming on. Appreciate the time. Congratulations on your success. AWS public sector doing great, global public sector. You guys are doing great. Building nations, we had Baharain on as well. Good luck, and the ecosystems looks good. You guys did a good job. So, congratulations. >> John, Stu, thank you very much for having me here today. >> Live coverage here, we are in Washington D.C. For Cube. Coverage of AWS Public Sector Summit. We'll be back with more. Stay with us, we've got some more interviews after this short break. (techno music)

>> Announcer: Live from Washington, D.C., it's theCUBE covering AWS Public Sector Summit 2017. Brought to you by Amazon Web Services and it's partner ecosystem. >> Welcome back, live here on theCUBE along with John Furrier, I'm John Walls. Welcome to AWS Public Sector Summit 2017. Again, live from Washington, D.C., your nation's capital, our nation's capital. With us now is our host for the week, puts on one heck of a show, I'm want to tell you, 10,000 strong here, jammed into the Washington Convention Center, Theresa Carlson from World Wide Public Sector. Nice to have you here, Theresa. >> Hi, good afternoon. >> Thanks for joining us. >> Love theCUBE and thank you for being here with us today. >> Absolutely. >> All week in fact. >> It's been great, it really has. Let's just talk about the show first off. Way back, six years ago, we could probably get everybody there jammed into our little area here, just about I think. >> Pretty much. >> Hard to do today. >> That's right. >> How do you feel about when you've seen this kind of growth not only of the show, but in your sector in general? >> I think at AWS we're humbled and excited and, on a personal level because I was sort of given the charge of go create this Public Sector business world-wide, I'm blown away, I pinch myself every time because you did hear my story. The first event, we had about 50 people in the basement of some hotel. And then, we're like, okay. And today, 10,000 people. Last year we had it at the Marriott Wardman Park and we shut down Connecticut Avenue so we knew we needed to make a change. (laughing) But it's great, this is really about our customers and partners. This is really for them. It's for them to make connections, share, and the whole theme of this is superheroes and they are our superheroes. >> One of the heroes you had on the stage today, John Edwards from the CIA, one of your poster-children if you will for great success and that kind of collaboration, said something to the effect of quote, "The best decision we ever made at the CIA "was engaging with AWS in that partnership." When you hear something like that from such a treasured partner, you got to feel pretty good. >> You just have to drop the microphone, boom, and you're sort of done. They are doing amazing work and their innovation levels are really leading, I would say, in the US Public Sector for sure and also, not just in US Public Sector but around the world. Their efforts of what they're doing and the scale and reach at which they're doing it so that's pretty cool. >> John, you've talked about the CIA moment, I'd like to hear the story, share with Theresa. >> Oh, you're going to steal my thunder here? >> No, I'm setting you up. That's what a good partner does. It's all yours. >> Well, John, we've talked multiple times already so I'll say it for the third time. The shot heard around the cloud was my definition of seminal moment, in big mega-trends there's always a moment. It was when Obama tweeted, Twitter grew, plane landing on the Hudson, there's always a seminal moment in major trends that make or break companies. For you guys, it was the CIA. Since then, it's just been a massive growth for you guys. That deal was interesting because it validated Shadow IT, validated the cloud, and it also unseated IBM, the behemoth sales organization that owned the account. In a way, a lot of things lined up. Take us through what's happened then, and since then to now. >> Well, you saw between yesterday at Werner Vogels' keynote and my keynote this morning, just the breadth and depth of the type of customers we have. Everything from the UK government, GCHQ, the Department of Justice with the IT in the UK, to the centers for Medicare for HHS, to amazing educational companies, Cal. Polytech., Australian Tax Office. That's just the breadth and depth of the type of customers we have and all of their stories were impactful, every story is impactful in their own way and across whatever sector they have. That really just tells you that the type of workloads that people are running has evolved because I remember in the early days, when you and I first talked, we talked about what are the kind of workloads and we were talking a little bit about website hosting. That's, of course, really evolved into things like machine learning, artificial intelligence, a massive scale of applications. >> Five or six years ago when we first chatted at re:Invent, it's interesting 'cause now this is the size of re:Invent what it was then so you're on a same trajectory from a show size. Again, validation to the growth in Public Sector. But I was complimenting you on our opening today, saying that you're tenacious because we've talked early days, it was a slog in the early days to get going in the cloud, you were knocking on a lot of doors, convincing people, hey, the future's going to look his way and I don't want to say they slammed the proverbial door in your face but it was more of, woah, they don't believe the cloud is ever going to happen for the government. Share some of those stories because now, looking back, obviously the world has changed. >> It has and, in fact, it's changed in many aspects of it, from policy makers, which I think would be great for you all to have on here sometime to get their perspective on cloud, but policy makers who are now thinking about, we just had a new modernization of IT mandate come out in the US Federal Government where they're going to give millions and millions of dollars toward the modernization of IT for US Government agencies which is going to be huge. That's the first time that's ever happened. To an executive order around cyber-security which is pretty much mandated to look at cloud and how you use it. You're seeing thing like that to even how grants are given where it used to be an old-school model of hardware only to now use cloud. Those ideas and aspects of how individuals are using IT but also just the procurements that are coming out. The buying vehicles that you're seeing come out of government, almost all of them have cloud now. >> John and I were talking about D.C. and the political climate. Obviously, we always talk about it on my show, comment on that. But, interesting, theCUBE, we could do damage here in D.C.. So much target-rich environment for content but more than ever, to me, is the tech scene here is really intrinsically different. For example, this is not a shiny new toy kind of trend, it is a fundamental transformation of the business model. What's interesting to me is, again, since the CIA shot heard around the cloud moment, you've seen a real shift in operating model. So the question I have for you, Theresa, if you can comment on this is: how has that changed? How has the procuring of technology changed? How has he human side of it changed? Because people want to do a good job, they're just on minicomputers and mainframes from the old days with small incremental improvement over the years in IT but now to a fundamental, agile, there's going to be more apps, more action. >> You said something really important just a moment ago, this is a different kind of group than you'll get in Silicon Valley and it is but it's very enterprise. Everybody you see here, every project they work on, we're talking DoD, the enterprise of enterprises. They have really challenging and tough problems to solve every day. How that's changed, in the old days here in government, they know how to write acquisitions for a missile or a tank or something really big in IT. What's changing is their ability to write acquisitions for agile IT, things like cloud utility based models, moving fast, flywheel approach to IT acquisitions. That's what's changing, that kind of acquisition model. Also, you're seeing the system integrator community here change. Where they were, what I call, body shops to do a lot of these projects, they're having to evolve their IT skills, they're getting much more certified in areas of AWS, at the system admin to certified solution architects at the highest level, to really roll these projects out. So training, education, the type of acquisition, and how they're doing it. >> What happened in terms of paradigm shift, mindset? Something had to happen 'cause you brought a vision to the table but somebody had to buy it. Usually, when we talk about legacy systems, it was a legacy mindset too, resistant, reluctant, cautious, all those things. >> Theresa: Well, everything gets thrown out. >> What happened? Where did it tip the other way? Where did it go? >> I think, over time, it's different parts of the government but culture is the hardest thing to, always, change. Other elements of any changes, you get there, but culture is fundamentally the hardest thing. You're seeing that. You've always heard us say, you can't fight gravity, and cloud is the new normal. That's for the whole culture. People are like, I cannot do my project anymore without the use of cloud computing. >> We also have a saying, you can't fight fashion either, and sometimes being in fashion is what the trends are going on. So I got to ask you, what is the fashion statement in cloud these days with your customers? Is it, you mentioned there, moving much down in the workload, is it multi-cloud? Is it analytics? Where's the fashionable, cool action right now? >> I think, here, right now, the cool thing that people really are talking about are artificial intelligence and machine learning, how they take advantage of that. You heard a lot about recognition yesterday, Poly and Lex, these new tools how they are so differentiating anything that they can possibly develop quickly. It's those kind of tools that really we're hearing and of course, IOT for state and local is a big deal. >> I got to ask you the hard question, I always ask Andy a hard question too, if he's watching, you're going to get this one probably at re:Invent. Amazon is a devops culture, you ship code fast and you make all these updates and it's moving very, very fast. One of the things that you guys have done well, but I still think you need some work to do in terms of critical analysis, is getting the releases out that are on public cloud into the GovCloud. You guys have shortened that down to less than a year on most things. You got the east region now rolled out so full disaster recovery but government has always been lagging behind most commercial. How are you guys shrinking that window? When do you see the day when push button commercial, GovCloud are all lockstep and pushing code to both clouds? >> We could do that today but there's a couple of big differentiators that are important for the GovCloud. That is it requires US citizenship, which as you know, we've talked about the challenges of technology and skills. That's just out there, right? At Amazon Web Services, we're a very diverse company, a group of individuals that do our coding and development, and not all of them are US citizens. So for these two clouds, you have to be a US citizen so that is an inhibitor. >> In terms of developers? In terms of building the product? >> Not building but the management aspect. Because of their design, we have multiple individuals managing multiple clouds, right? Now, with us, it's about getting that scale going, that flywheel for us. >> So now it's going to be managed in the USA versus made in the USA with everything as a service. >> Yeah, it is. For us, it's about making sure, number one, we can roll them out, but secondly, we do not want to roll services into those clouds unless they are critical. We are moving a lot faster, we rolled in a lot more services, and the other cool thing is we're starting to do some unique things for our GovCloud regions which, maybe the next time, we can talk a little bit more about those things. >> Final question for me, and let John jump in, the CIA has got this devops factory thing, I want you to talk about it because I think it points to the trend that's encouraging to me at least 'cause I'm skeptical on government, as you know. But this is a full transformation shift on how they do development. Talk about these 4000 developers that got rid of their development workstations, are now doing cloud, and the question is, who else is doing it? Is this a trend that you see happening across other agencies? >> The reason that's really important, I know you know, in the old-school model, you waited forever to provision anything, even just to do development, and you heard John talk about that. That's what he meant on this sort of workstation, this long period of time it took for them to do any kind of development. Now, what they do is they just use any move they have and they go and they provision the cloud like that. Then, they can also not just do that, they can create armies of cores or Amazon machine images so they have super-repeatable tools. Think about that. When you have these super-repeatable tools sitting in the cloud, that you can just pull down these machine images and begin to create both code and development and build off those building blocks, you move so much faster than you did in the past. So that's sort of a big trend, I would say they're definitely leading it. But other key groups are NASA, HHS, Department of Justice. Those are some of the key, big groups that we're seeing really do a lot changes in their dev. >> I got to ask you about the-- >> Oh, I have to say DHS, also DHS on customs and border patrols, they're doing the same, really innovators. >> One of the things that's happening which I'm intrigued by is the whole digital transformation in our culture, right, society. Certainly, the Federal Government wants to take care of the civil liberties of the citizens. So it's not a privacy question, it's more about where smart cities is going. We're starting to see, I call, the digital parks, if you will, where you're starting to see a digital park go into Yosemite and camping out and using pristine resources and enjoying them. There's a demand for citizens to democratize resources available to them, supercomputing or datasets, what's your philosophy on that? What is Amazon doing to facilitate and accelerate the citizen's value of technology so it can be in the hands of anyone? >> I love that question because I'll tell you, at the heart of our business is what we call citizen service, paving the way for disruptive innovation, making the world a better place. That's through citizen's services and they're access. For us, we have multiple things. Everything from our dataset program, where we fund multiple datasets that we put up on the cloud and let everybody take advantage of them, from the individual student to the researcher, for no fee. >> John F.: You pick up the cost on that? >> We do, we fund, we put those datasets in completely, we allow them to go and explore and use. The only time they would ever pay is if they go off and start creating their own systems. The most highly curated datasets up there right now are pretty much on AWS. You heard me talk about the earth, through AWS Earth that we have that shows the earth. We have weather datasets, cancer datasets, we're working with so many groups, genomic, phenotypes, genomes of rice, the rice genome that we've done. >> So this is something that you see that you're behind, >> Oh, completely. >> you're passionate about and will continue to do? >> Because you never know when that individual student or small community school is out there and they can access tools that they never could've accessed before. The training and education, that creativity of the mind, we need to open that up to everybody and we fundamentally believe that cloud is a huge opportunity for that. You heard me tell the 1000 genomes story in the past of where took that cancer dataset or that genome dataset from NIH, put it into AWS for the first time, the first week we put it up we had 3200 new researchers crowdsource on that dataset. That was the first time, that I know of, that anyone had put up a major dataset for researchers. >> And the scale, certainly, is a great resource. And smart cities is an interesting area. I want to get your thoughts on your relationship with Intel. They have 5G coming out, they have a full network transformation, you're going to have autonomous vehicles out there, you're going to have all kinds of digital. How are you guys planning on powering the cloud and what's the role that Intel will play with you guys in the relationship? >> Of course, serverless computing comes into play significantly in areas like that because you want to create efficiencies, even in the cloud, we're all about that. People have always said, oh, AWS won't do that 'cause that's disrupting themselves. We're okay with disrupting ourselves if it's the right thing. We also don't want to hog resourcing of these tools that aren't necessary. So when it comes to devices like that and IOT, you need very efficient computing and you need tools that allow that efficient computing to both scale but not over-resource things. You'll see us continue to have models like that around IOT, or lambda, or serverless computing and how we access and make sure that those resources are used appropriately. >> We're almost out of time so I'd like to shift over if we can. Really impressed with the NGO work, the non-profit work as well and your work in the education space. Just talk about the nuance, differences between working with those particular constituents in the customer base, what you've learned and the kind of work you're providing in those silos right now. >> They are amazing, they are so frugal with their resources and it makes you hungry to really want to go out and help their mission because what you will find when you go meet with a lot of these not-for-profits, they are doing some of the most amazing work that even many people have really not heard of and they're being so frugal with how they resource and drive IT. There's a program called Feed the World and I met the developer of this and it's like two people. They've fed millions of people around the world with like three developers and creating an app and doing great work. To everything from like the American Heart Association that has a mission, literally, of stopping heart disease which is our number one killer around the world. When you meet them and you see the things they're doing and how they are using cloud computing to change and forward their mission. You heard us talk about human trafficking, it's a horrible, misunderstood environment out there that more of us need to be informed on and help with but computing can be a complete differentiator for them, cloud computing. We give millions of dollars of grants away, not just give away, we help them. We help them with the technical resourcing, how they're efficient, and we work really hard to try to help forward their mission and get the word out. It's humbling and it's really nice to feel that you're not only doing things for big governments but you also can help that individual not-for-profit that has a mission that's really important to not only them but groups in the world. >> It's a different level of citizen service, right? I mean, ocean conservancy this morning, talking about that and tidal change. >> What's the biggest thing that, in your mind, personal question, obviously you've been through from the beginning to now, a lot more growth ahead of you. I'm speculating that AWS Public Sector, although you won't disclose the numbers, I'll find a number out there. It's big, you guys could run the table and take a big share, similar to what you've done with startup and now enterprise market. Do you have a pinch-me moment where you go, where are we? Where are you on that spectrum of self-awareness of what's actually happening to you and this world and your team? In Public Sector, we operate just like all of AWS and all of Amazon. We really have treated this business like a startup and I create new teams just like everybody else does. I make them frugal and small and I say go do this. I will tell you, I don't even think about it because we are just scratching the surface, we are just getting going, and today we have customers in 155 countries and I have employees in about 25 countries now. Seven years ago, that was not the case. When you're moving that fast, you know that you're just getting going and that you have so much more that you can do to help your customers and create a partner ecosystem. It's a mission for us, it really is a mission and my team and myself are really excited, out there every day working to support our customers, to really grow and get them moving faster. We sort of keep pushing them to go faster. We have a long way to go and maybe ask me five years from now, we'll see. >> How about next year? We'll come back, we'll ask you again next year. >> Yeah, maybe I'll know more next year. >> John W.: Theresa, thank you for the time, very generous with your time. I know you have a big schedule over the course of this week so thank you for being here with us once again on theCUBE. >> Thank you. >> Many time CUBE alum, Theresa Carlson from AWS. Back with more here from the AWS Public Sector Summit 2017, Washington, D.C. right after this. (electronic music)

>> Welcome to the Intel AI Lounge. Today, we're very excited to share with you the Precision Medicine panel discussion. I'll be moderating the session. My name is Kay Erin. I'm the general manager of Health and Life Sciences at Intel. And I'm excited to share with you these three panelists that we have here. First is John Madison. He is a chief information medical officer and he is part of Kaiser Permanente. We're very excited to have you here. Thank you, John. >> Thank you. >> We also have Naveen Rao. He is the VP and general manager for the Artificial Intelligence Solutions at Intel. He's also the former CEO of Nervana, which was acquired by Intel. And we also have Bob Rogers, who's the chief data scientist at our AI solutions group. So, why don't we get started with our questions. I'm going to ask each of the panelists to talk, introduce themselves, as well as talk about how they got started with AI. So why don't we start with John? >> Sure, so can you hear me okay in the back? Can you hear? Okay, cool. So, I am a recovering evolutionary biologist and a recovering physician and a recovering geek. And I implemented the health record system for the first and largest region of Kaiser Permanente. And it's pretty obvious that most of the useful data in a health record, in lies in free text. So I started up a natural language processing team to be able to mine free text about a dozen years ago. So we can do things with that that you can't otherwise get out of health information. I'll give you an example. I read an article online from the New England Journal of Medicine about four years ago that said over half of all people who have had their spleen taken out were not properly vaccinated for a common form of pneumonia, and when your spleen's missing, you must have that vaccine or you die a very sudden death with sepsis. In fact, our medical director in Northern California's father died of that exact same scenario. So, when I read the article, I went to my structured data analytics team and to my natural language processing team and said please show me everybody who has had their spleen taken out and hasn't been appropriately vaccinated and we ran through about 20 million records in about three hours with the NLP team, and it took about three weeks with a structured data analytics team. That sounds counterintuitive but it actually happened that way. And it's not a competition for time only. It's a competition for quality and sensitivity and specificity. So we were able to indentify all of our members who had their spleen taken out, who should've had a pneumococcal vaccine. We vaccinated them and there are a number of people alive today who otherwise would've died absent that capability. So people don't really commonly associate natural language processing with machine learning, but in fact, natural language processing relies heavily and is the first really, highly successful example of machine learning. So we've done dozens of similar projects, mining free text data in millions of records very efficiently, very effectively. But it really helped advance the quality of care and reduce the cost of care. It's a natural step forward to go into the world of personalized medicine with the arrival of a 100-dollar genome, which is actually what it costs today to do a full genome sequence. Microbiomics, that is the ecosystem of bacteria that are in every organ of the body actually. And we know now that there is a profound influence of what's in our gut and how we metabolize drugs, what diseases we get. You can tell in a five year old, whether or not they were born by a vaginal delivery or a C-section delivery by virtue of the bacteria in the gut five years later. So if you look at the complexity of the data that exists in the genome, in the microbiome, in the health record with free text and you look at all the other sources of data like this streaming data from my wearable monitor that I'm part of a research study on Precision Medicine out of Stanford, there is a vast amount of disparate data, not to mention all the imaging, that really can collectively produce much more useful information to advance our understanding of science, and to advance our understanding of every individual. And then we can do the mash up of a much broader range of science in health care with a much deeper sense of data from an individual and to do that with structured questions and structured data is very yesterday. The only way we're going to be able to disambiguate those data and be able to operate on those data in concert and generate real useful answers from the broad array of data types and the massive quantity of data, is to let loose machine learning on all of those data substrates. So my team is moving down that pathway and we're very excited about the future prospects for doing that. >> Yeah, great. I think that's actually some of the things I'm very excited about in the future with some of the technologies we're developing. My background, I started actually being fascinated with computation in biological forms when I was nine. Reading and watching sci-fi, I was kind of a big dork which I pretty much still am. I haven't really changed a whole lot. Just basically seeing that machines really aren't all that different from biological entities, right? We are biological machines and kind of understanding how a computer works and how we engineer those things and trying to pull together concepts that learn from biology into that has always been a fascination of mine. As an undergrad, I was in the EE, CS world. Even then, I did some research projects around that. I worked in the industry for about 10 years designing chips, microprocessors, various kinds of ASICs, and then actually went back to school, quit my job, got a Ph.D. in neuroscience, computational neuroscience, to specifically understand what's the state of the art. What do we really understand about the brain? And are there concepts that we can take and bring back? Inspiration's always been we want to... We watch birds fly around. We want to figure out how to make something that flies. We extract those principles, and then build a plane. Don't necessarily want to build a bird. And so Nervana's really was the combination of all those experiences, bringing it together. Trying to push computation in a new a direction. Now, as part of Intel, we can really add a lot of fuel to that fire. I'm super excited to be part of Intel in that the technologies that we were developing can really proliferate and be applied to health care, can be applied to Internet, can be applied to every facet of our lives. And some of the examples that John mentioned are extremely exciting right now and these are things we can do today. And the generality of these solutions are just really going to hit every part of health care. I mean from a personal viewpoint, my whole family are MDs. I'm sort of the black sheep of the family. I don't have an MD. And it's always been kind of funny to me that knowledge is concentrated in a few individuals. Like you have a rare tumor or something like that, you need the guy who knows how to read this MRI. Why? Why is it like that? Can't we encapsulate that knowledge into a computer or into an algorithm, and democratize it. And the reason we couldn't do it is we just didn't know how. And now we're really getting to a point where we know how to do that. And so I want that capability to go to everybody. It'll bring the cost of healthcare down. It'll make all of us healthier. That affects everything about our society. So that's really what's exciting about it to me. >> That's great. So, as you heard, I'm Bob Rogers. I'm chief data scientist for analytics and artificial intelligence solutions at Intel. My mission is to put powerful analytics in the hands of every decision maker and when I think about Precision Medicine, decision makers are not just doctors and surgeons and nurses, but they're also case managers and care coordinators and probably most of all, patients. So the mission is really to put powerful analytics and AI capabilities in the hands of everyone in health care. It's a very complex world and we need tools to help us navigate it. So my background, I started with a Ph.D. in physics and I was computer modeling stuff, falling into super massive black holes. And there's a lot of applications for that in the real world. No, I'm kidding. (laughter) >> John: There will be, I'm sure. Yeah, one of these days. Soon as we have time travel. Okay so, I actually, about 1991, I was working on my post doctoral research, and I heard about neural networks, these things that could compute the way the brain computes. And so, I started doing some research on that. I wrote some papers and actually, it was an interesting story. The problem that we solved that got me really excited about neural networks, which have become deep learning, my office mate would come in. He was this young guy who was about to go off to grad school. He'd come in every morning. "I hate my project." Finally, after two weeks, what's your project? What's the problem? It turns out he had to circle these little fuzzy spots on these images from a telescope. So they were looking for the interesting things in a sky survey, and he had to circle them and write down their coordinates all summer. Anyone want to volunteer to do that? No? Yeah, he was very unhappy. So we took the first two weeks of data that he created doing his work by hand, and we trained an artificial neural network to do his summer project and finished it in about eight hours of computing. (crowd laughs) And so he was like yeah, this is amazing. I'm so happy. And we wrote a paper. I was the first author of course, because I was the senior guy at age 24. And he was second author. His first paper ever. He was very, very excited. So we have to fast forward about 20 years. His name popped up on the Internet. And so it caught my attention. He had just won the Nobel Prize in physics. (laughter) So that's where artificial intelligence will get you. (laughter) So thanks Naveen. Fast forwarding, I also developed some time series forecasting capabilities that allowed me to create a hedge fund that I ran for 12 years. After that, I got into health care, which really is the center of my passion. Applying health care to figuring out how to get all the data from all those siloed sources, put it into the cloud in a secure way, and analyze it so you can actually understand those cases that John was just talking about. How do you know that that person had had a splenectomy and that they needed to get that pneumovax? You need to be able to search all the data, so we used AI, natural language processing, machine learning, to do that and then two years ago, I was lucky enough to join Intel and, in the intervening time, people like Naveen actually thawed the AI winter and we're really in a spring of amazing opportunities with AI, not just in health care but everywhere, but of course, the health care applications are incredibly life saving and empowering so, excited to be here on this stage with you guys. >> I just want to cue off of your comment about the role of physics in AI and health care. So the field of microbiomics that I referred to earlier, bacteria in our gut. There's more bacteria in our gut than there are cells in our body. There's 100 times more DNA in that bacteria than there is in the human genome. And we're now discovering a couple hundred species of bacteria a year that have never been identified under a microscope just by their DNA. So it turns out the person who really catapulted the study and the science of microbiomics forward was an astrophysicist who did his Ph.D. in Steven Hawking's lab on the collision of black holes and then subsequently, put the other team in a virtual reality, and he developed the first super computing center and so how did he get an interest in microbiomics? He has the capacity to do high performance computing and the kind of advanced analytics that are required to look at a 100 times the volume of 3.2 billion base pairs of the human genome that are represented in the bacteria in our gut, and that has unleashed the whole science of microbiomics, which is going to really turn a lot of our assumptions of health and health care upside down. >> That's great, I mean, that's really transformational. So a lot of data. So I just wanted to let the audience know that we want to make this an interactive session, so I'll be asking for questions in a little bit, but I will start off with one question so that you can think about it. So I wanted to ask you, it looks like you've been thinking a lot about AI over the years. And I wanted to understand, even though AI's just really starting in health care, what are some of the new trends or the changes that you've seen in the last few years that'll impact how AI's being used going forward? >> So I'll start off. There was a paper published by a guy by the name of Tegmark at Harvard last summer that, for the first time, explained why neural networks are efficient beyond any mathematical model we predict. And the title of the paper's fun. It's called Deep Learning Versus Cheap Learning. So there were two sort of punchlines of the paper. One is is that the reason that mathematics doesn't explain the efficiency of neural networks is because there's a higher order of mathematics called physics. And the physics of the underlying data structures determined how efficient you could mine those data using machine learning tools. Much more so than any mathematical modeling. And so the second thing that was a reel from that paper is that the substrate of the data that you're operating on and the natural physics of those data have inherent levels of complexity that determine whether or not a 12th layer of neural net will get you where you want to go really fast, because when you do the modeling, for those math geeks in the audience, a factorial. So if there's 12 layers, there's 12 factorial permutations of different ways you could sequence the learning through those data. When you have 140 layers of a neural net, it's a much, much, much bigger number of permutations and so you end up being hardware-bound. And so, what Max Tegmark basically said is you can determine whether to do deep learning or cheap learning based upon the underlying physics of the data substrates you're operating on and have a good insight into how to optimize your hardware and software approach to that problem. >> So another way to put that is that neural networks represent the world in the way the world is sort of built. >> Exactly. >> It's kind of hierarchical. It's funny because, sort of in retrospect, like oh yeah, that kind of makes sense. But when you're thinking about it mathematically, we're like well, anything... The way a neural can represent any mathematical function, therfore, it's fully general. And that's the way we used to look at it, right? So now we're saying, well actually decomposing the world into different types of features that are layered upon each other is actually a much more efficient, compact representation of the world, right? I think this is actually, precisely the point of kind of what you're getting at. What's really exciting now is that what we were doing before was sort of building these bespoke solutions for different kinds of data. NLP, natural language processing. There's a whole field, 25 plus years of people devoted to figuring out features, figuring out what structures make sense in this particular context. Those didn't carry over at all to computer vision. Didn't carry over at all to time series analysis. Now, with neural networks, we've seen it at Nervana, and now part of Intel, solving customers' problems. We apply a very similar set of techniques across all these different types of data domains and solve them. All data in the real world seems to be hierarchical. You can decompose it into this hierarchy. And it works really well. Our brains are actually general structures. As a neuroscientist, you can look at different parts of your brain and there are differences. Something that takes in visual information, versus auditory information is slightly different but they're much more similar than they are different. So there is something invariant, something very common between all of these different modalities and we're starting to learn that. And this is extremely exciting to me trying to understand the biological machine that is a computer, right? We're figurig it out, right? >> One of the really fun things that Ray Chrisfall likes to talk about is, and it falls in the genre of biomimmicry, and how we actually replicate biologic evolution in our technical solutions so if you look at, and we're beginning to understand more and more how real neural nets work in our cerebral cortex. And it's sort of a pyramid structure so that the first pass of a broad base of analytics, it gets constrained to the next pass, gets constrained to the next pass, which is how information is processed in the brain. So we're discovering increasingly that what we've been evolving towards, in term of architectures of neural nets, is approximating the architecture of the human cortex and the more we understand the human cortex, the more insight we get to how to optimize neural nets, so when you think about it, with millions of years of evolution of how the cortex is structured, it shouldn't be a surprise that the optimization protocols, if you will, in our genetic code are profoundly efficient in how they operate. So there's a real role for looking at biologic evolutionary solutions, vis a vis technical solutions, and there's a friend of mine who worked with who worked with George Church at Harvard and actually published a book on biomimmicry and they wrote the book completely in DNA so if all of you have your home DNA decoder, you can actually read the book on your DNA reader, just kidding. >> There's actually a start up I just saw in the-- >> Read-Write DNA, yeah. >> Actually it's a... He writes something. What was it? (response from crowd member) Yeah, they're basically encoding information in DNA as a storage medium. (laughter) The company, right? >> Yeah, that same friend of mine who coauthored that biomimmicry book in DNA also did the estimate of the density of information storage. So a cubic centimeter of DNA can store an hexabyte of data. I mean that's mind blowing. >> Naveen: Highly done soon. >> Yeah that's amazing. Also you hit upon a really important point there, that one of the things that's changed is... Well, there are two major things that have changed in my perception from let's say five to 10 years ago, when we were using machine learning. You could use data to train models and make predictions to understand complex phenomena. But they had limited utility and the challenge was that if I'm trying to build on these things, I had to do a lot of work up front. It was called feature engineering. I had to do a lot of work to figure out what are the key attributes of that data? What are the 10 or 20 or 100 pieces of information that I should pull out of the data to feed to the model, and then the model can turn it into a predictive machine. And so, what's really exciting about the new generation of machine learning technology, and particularly deep learning, is that it can actually learn from example data those features without you having to do any preprogramming. That's why Naveen is saying you can take the same sort of overall approach and apply it to a bunch of different problems. Because you're not having to fine tune those features. So at the end of the day, the two things that have changed to really enable this evolution is access to more data, and I'd be curious to hear from you where you're seeing data come from, what are the strategies around that. So access to data, and I'm talking millions of examples. So 10,000 examples most times isn't going to cut it. But millions of examples will do it. And then, the other piece is the computing capability to actually take millions of examples and optimize this algorithm in a single lifetime. I mean, back in '91, when I started, we literally would have thousands of examples and it would take overnight to run the thing. So now in the world of millions, and you're putting together all of these combinations, the computing has changed a lot. I know you've made some revolutionary advances in that. But I'm curious about the data. Where are you seeing interesting sources of data for analytics? >> So I do some work in the genomics space and there are more viable permutations of the human genome than there are people who have ever walked the face of the earth. And the polygenic determination of a phenotypic expression translation, what are genome does to us in our physical experience in health and disease is determined by many, many genes and the interaction of many, many genes and how they are up and down regulated. And the complexity of disambiguating which 27 genes are affecting your diabetes and how are they up and down regulated by different interventions is going to be different than his. It's going to be different than his. And we already know that there's four or five distinct genetic subtypes of type II diabetes. So physicians still think there's one disease called type II diabetes. There's actually at least four or five genetic variants that have been identified. And so, when you start thinking about disambiguating, particularly when we don't know what 95 percent of DNA does still, what actually is the underlining cause, it will require this massive capability of developing these feature vectors, sometimes intuiting it, if you will, from the data itself. And other times, taking what's known knowledge to develop some of those feature vectors, and be able to really understand the interaction of the genome and the microbiome and the phenotypic data. So the complexity is high and because the variation complexity is high, you do need these massive members. Now I'm going to make a very personal pitch here. So forgive me, but if any of you have any role in policy at all, let me tell you what's happening right now. The Genomic Information Nondiscrimination Act, so called GINA, written by a friend of mine, passed a number of years ago, says that no one can be discriminated against for health insurance based upon their genomic information. That's cool. That should allow all of you to feel comfortable donating your DNA to science right? Wrong. You are 100% unprotected from discrimination for life insurance, long term care and disability. And it's being practiced legally today and there's legislation in the House, in mark up right now to completely undermine the existing GINA legislation and say that whenever there's another applicable statute like HIPAA, that the GINA is irrelevant, that none of the fines and penalties are applicable at all. So we need a ton of data to be able to operate on. We will not be getting a ton of data to operate on until we have the kind of protection we need to tell people, you can trust us. You can give us your data, you will not be subject to discrimination. And that is not the case today. And it's being further undermined. So I want to make a plea to any of you that have any policy influence to go after that because we need this data to help the understanding of human health and disease and we're not going to get it when people look behind the curtain and see that discrimination is occurring today based upon genetic information. >> Well, I don't like the idea of being discriminated against based on my DNA. Especially given how little we actually know. There's so much complexity in how these things unfold in our own bodies, that I think anything that's being done is probably childishly immature and oversimplifying. So it's pretty rough. >> I guess the translation here is that we're all unique. It's not just a Disney movie. (laughter) We really are. And I think one of the strengths that I'm seeing, kind of going back to the original point, of these new techniques is it's going across different data types. It will actually allow us to learn more about the uniqueness of the individual. It's not going to be just from one data source. They were collecting data from many different modalities. We're collecting behavioral data from wearables. We're collecting things from scans, from blood tests, from genome, from many different sources. The ability to integrate those into a unified picture, that's the important thing that we're getting toward now. That's what I think is going to be super exciting here. Think about it, right. I can tell you to visual a coin, right? You can visualize a coin. Not only do you visualize it. You also know what it feels like. You know how heavy it is. You have a mental model of that from many different perspectives. And if I take away one of those senses, you can still identify the coin, right? If I tell you to put your hand in your pocket, and pick out a coin, you probably can do that with 100% reliability. And that's because we have this generalized capability to build a model of something in the world. And that's what we need to do for individuals is actually take all these different data sources and come up with a model for an individual and you can actually then say what drug works best on this. What treatment works best on this? It's going to get better with time. It's not going to be perfect, because this is what a doctor does, right? A doctor who's very experienced, you're a practicing physician right? Back me up here. That's what you're doing. You basically have some categories. You're taking information from the patient when you talk with them, and you're building a mental model. And you apply what you know can work on that patient, right? >> I don't have clinic hours anymore, but I do take care of many friends and family. (laughter) >> You used to, you used to. >> I practiced for many years before I became a full-time geek. >> I thought you were a recovering geek. >> I am. (laughter) I do more policy now. >> He's off the wagon. >> I just want to take a moment and see if there's anyone from the audience who would like to ask, oh. Go ahead. >> We've got a mic here, hang on one second. >> I have tons and tons of questions. (crosstalk) Yes, so first of all, the microbiome and the genome are really complex. You already hit about that. Yet most of the studies we do are small scale and we have difficulty repeating them from study to study. How are we going to reconcile all that and what are some of the technical hurdles to get to the vision that you want? >> So primarily, it's been the cost of sequencing. Up until a year ago, it's $1000, true cost. Now it's $100, true cost. And so that barrier is going to enable fairly pervasive testing. It's not a real competitive market becaue there's one sequencer that is way ahead of everybody else. So the price is not $100 yet. The cost is below $100. So as soon as there's competition to drive the cost down, and hopefully, as soon as we all have the protection we need against discrimination, as I mentioned earlier, then we will have large enough sample sizes. And so, it is our expectation that we will be able to pool data from local sources. I chair the e-health work group at the Global Alliance for Genomics and Health which is working on this very issue. And rather than pooling all the data into a single, common repository, the strategy, and we're developing our five-year plan in a month in London, but the goal is to have a federation of essentially credentialed data enclaves. That's a formal method. HHS already does that so you can get credentialed to search all the data that Medicare has on people that's been deidentified according to HIPPA. So we want to provide the same kind of service with appropriate consent, at an international scale. And there's a lot of nations that are talking very much about data nationality so that you can't export data. So this approach of a federated model to get at data from all the countries is important. The other thing is a block-chain technology is going to be very profoundly useful in this context. So David Haussler of UC Santa Cruz is right now working on a protocol using an open block-chain, public ledger, where you can put out. So for any typical cancer, you may have a half dozen, what are called sematic variance. Cancer is a genetic disease so what has mutated to cause it to behave like a cancer? And if we look at those biologically active sematic variants, publish them on a block chain that's public, so there's not enough data there to reidentify the patient. But if I'm a physician treating a woman with breast cancer, rather than say what's the protocol for treating a 50-year-old woman with this cell type of cancer, I can say show me all the people in the world who have had this cancer at the age of 50, wit these exact six sematic variants. Find the 200 people worldwide with that. Ask them for consent through a secondary mechanism to donate everything about their medical record, pool that information of the core of 200 that exactly resembles the one sitting in front of me, and find out, of the 200 ways they were treated, what got the best results. And so, that's the kind of future where a distributed, federated architecture will allow us to query and obtain a very, very relevant cohort, so we can basically be treating patients like mine, sitting right in front of me. Same thing applies for establishing research cohorts. There's some very exciting stuff at the convergence of big data analytics, machine learning, and block chaining. >> And this is an area that I'm really excited about and I think we're excited about generally at Intel. They actually have something called the Collaborative Cancer Cloud, which is this kind of federated model. We have three different academic research centers. Each of them has a very sizable and valuable collection of genomic data with phenotypic annotations. So you know, pancreatic cancer, colon cancer, et cetera, and we've actually built a secure computing architecture that can allow a person who's given the right permissions by those organizations to ask a specific question of specific data without ever sharing the data. So the idea is my data's really important to me. It's valuable. I want us to be able to do a study that gets the number from the 20 pancreatic cancer patients in my cohort, up to the 80 that we have in the whole group. But I can't do that if I'm going to just spill my data all over the world. And there are HIPAA and compliance reasons for that. There are business reasons for that. So what we've built at Intel is this platform that allows you to do different kinds of queries on this genetic data. And reach out to these different sources without sharing it. And then, the work that I'm really involved in right now and that I'm extremely excited about... This also touches on something that both of you said is it's not sufficient to just get the genome sequences. You also have to have the phenotypic data. You have to know what cancer they've had. You have to know that they've been treated with this drug and they've survived for three months or that they had this side effect. That clinical data also needs to be put together. It's owned by other organizations, right? Other hospitals. So the broader generalization of the Collaborative Cancer Cloud is something we call the data exchange. And it's a misnomer in a sense that we're not actually exchanging data. We're doing analytics on aggregated data sets without sharing it. But it really opens up a world where we can have huge populations and big enough amounts of data to actually train these models and draw the thread in. Of course, that really then hits home for the techniques that Nervana is bringing to the table, and of course-- >> Stanford's one of your academic medical centers? >> Not for that Collaborative Cancer Cloud. >> The reason I mentioned Standford is because the reason I'm wearing this FitBit is because I'm a research subject at Mike Snyder's, the chair of genetics at Stanford, IPOP, intrapersonal omics profile. So I was fully sequenced five years ago and I get four full microbiomes. My gut, my mouth, my nose, my ears. Every three months and I've done that for four years now. And about a pint of blood. And so, to your question of the density of data, so a lot of the problem with applying these techniques to health care data is that it's basically a sparse matrix and there's a lot of discontinuities in what you can find and operate on. So what Mike is doing with the IPOP study is much the same as you described. Creating a highly dense longitudinal set of data that will help us mitigate the sparse matrix problem. (low volume response from audience member) Pardon me. >> What's that? (low volume response) (laughter) >> Right, okay. >> John: Lost the school sample. That's got to be a new one I've heard now. >> Okay, well, thank you so much. That was a great question. So I'm going to repeat this and ask if there's another question. You want to go ahead? >> Hi, thanks. So I'm a journalist and I report a lot on these neural networks, a system that's beter at reading mammograms than your human radiologists. Or a system that's better at predicting which patients in the ICU will get sepsis. These sort of fascinating academic studies that I don't really see being translated very quickly into actual hospitals or clinical practice. Seems like a lot of the problems are regulatory, or liability, or human factors, but how do you get past that and really make this stuff practical? >> I think there's a few things that we can do there and I think the proof points of the technology are really important to start with in this specific space. In other places, sometimes, you can start with other things. But here, there's a real confidence problem when it comes to health care, and for good reason. We have doctors trained for many, many years. School and then residencies and other kinds of training. Because we are really, really conservative with health care. So we need to make sure that technology's well beyond just the paper, right? These papers are proof points. They get people interested. They even fuel entire grant cycles sometimes. And that's what we need to happen. It's just an inherent problem, its' going to take a while. To get those things to a point where it's like well, I really do trust what this is saying. And I really think it's okay to now start integrating that into our standard of care. I think that's where you're seeing it. It's frustrating for all of us, believe me. I mean, like I said, I think personally one of the biggest things, I want to have an impact. Like when I go to my grave, is that we used machine learning to improve health care. We really do feel that way. But it's just not something we can do very quickly and as a business person, I don't actually look at those use cases right away because I know the cycle is just going to be longer. >> So to your point, the FDA, for about four years now, has understood that the process that has been given to them by their board of directors, otherwise known as Congress, is broken. And so they've been very actively seeking new models of regulation and what's really forcing their hand is regulation of devices and software because, in many cases, there are black box aspects of that and there's a black box aspect to machine learning. Historically, Intel and others are making inroads into providing some sort of traceability and transparency into what happens in that black box rather than say, overall we get better results but once in a while we kill somebody. Right? So there is progress being made on that front. And there's a concept that I like to use. Everyone knows Ray Kurzweil's book The Singularity Is Near? Well, I like to think that diadarity is near. And the diadarity is where you have human transparency into what goes on in the black box and so maybe Bob, you want to speak a little bit about... You mentioned that, in a prior discussion, that there's some work going on at Intel there. >> Yeah, absolutely. So we're working with a number of groups to really build tools that allow us... In fact Naveen probably can talk in even more detail than I can, but there are tools that allow us to actually interrogate machine learning and deep learning systems to understand, not only how they respond to a wide variety of situations but also where are there biases? I mean, one of the things that's shocking is that if you look at the clinical studies that our drug safety rules are based on, 50 year old white guys are the peak of that distribution, which I don't see any problem with that, but some of you out there might not like that if you're taking a drug. So yeah, we want to understand what are the biases in the data, right? And so, there's some new technologies. There's actually some very interesting data-generative technologies. And this is something I'm also curious what Naveen has to say about, that you can generate from small sets of observed data, much broader sets of varied data that help probe and fill in your training for some of these systems that are very data dependent. So that takes us to a place where we're going to start to see deep learning systems generating data to train other deep learning systems. And they start to sort of go back and forth and you start to have some very nice ways to, at least, expose the weakness of these underlying technologies. >> And that feeds back to your question about regulatory oversight of this. And there's the fascinating, but little known origin of why very few women are in clinical studies. Thalidomide causes birth defects. So rather than say pregnant women can't be enrolled in drug trials, they said any woman who is at risk of getting pregnant cannot be enrolled. So there was actually a scientific meritorious argument back in the day when they really didn't know what was going to happen post-thalidomide. So it turns out that the adverse, unintended consequence of that decision was we don't have data on women and we know in certain drugs, like Xanax, that the metabolism is so much slower, that the typical dosing of Xanax is women should be less than half of that for men. And a lot of women have had very serious adverse effects by virtue of the fact that they weren't studied. So the point I want to illustrate with that is that regulatory cycles... So people have known for a long time that was like a bad way of doing regulations. It should be changed. It's only recently getting changed in any meaningful way. So regulatory cycles and legislative cycles are incredibly slow. The rate of exponential growth in technology is exponential. And so there's impedance mismatch between the cycle time for regulation cycle time for innovation. And what we need to do... I'm working with the FDA. I've done four workshops with them on this very issue. Is that they recognize that they need to completely revitalize their process. They're very interested in doing it. They're not resisting it. People think, oh, they're bad, the FDA, they're resisting. Trust me, there's nobody on the planet who wants to revise these review processes more than the FDA itself. And so they're looking at models and what I recommended is global cloud sourcing and the FDA could shift from a regulatory role to one of doing two things, assuring the people who do their reviews are competent, and assuring that their conflicts of interest are managed, because if you don't have a conflict of interest in this very interconnected space, you probably don't know enough to be a reviewer. So there has to be a way to manage the conflict of interest and I think those are some of the keypoints that the FDA is wrestling with because there's type one and type two errors. If you underregulate, you end up with another thalidomide and people born without fingers. If you overregulate, you prevent life saving drugs from coming to market. So striking that balance across all these different technologies is extraordinarily difficult. If it were easy, the FDA would've done it four years ago. It's very complicated. >> Jumping on that question, so all three of you are in some ways entrepreneurs, right? Within your organization or started companies. And I think it would be good to talk a little bit about the business opportunity here, where there's a huge ecosystem in health care, different segments, biotech, pharma, insurance payers, etc. Where do you see is the ripe opportunity or industry, ready to really take this on and to make AI the competitive advantage. >> Well, the last question also included why aren't you using the result of the sepsis detection? We do. There were six or seven published ways of doing it. We did our own data, looked at it, we found a way that was superior to all the published methods and we apply that today, so we are actually using that technology to change clinical outcomes. As far as where the opportunities are... So it's interesting. Because if you look at what's going to be here in three years, we're not going to be using those big data analytics models for sepsis that we are deploying today, because we're just going to be getting a tiny aliquot of blood, looking for the DNA or RNA of any potential infection and we won't have to infer that there's a bacterial infection from all these other ancillary, secondary phenomenon. We'll see if the DNA's in the blood. So things are changing so fast that the opportunities that people need to look for are what are generalizable and sustainable kind of wins that are going to lead to a revenue cycle that are justified, a venture capital world investing. So there's a lot of interesting opportunities in the space. But I think some of the biggest opportunities relate to what Bob has talked about in bringing many different disparate data sources together and really looking for things that are not comprehensible in the human brain or in traditional analytic models. >> I think we also got to look a little bit beyond direct care. We're talking about policy and how we set up standards, these kinds of things. That's one area. That's going to drive innovation forward. I completely agree with that. Direct care is one piece. How do we scale out many of the knowledge kinds of things that are embedded into one person's head and get them out to the world, democratize that. Then there's also development. The underlying technology's of medicine, right? Pharmaceuticals. The traditional way that pharmaceuticals is developed is actually kind of funny, right? A lot of it was started just by chance. Penicillin, a very famous story right? It's not that different today unfortunately, right? It's conceptually very similar. Now we've got more science behind it. We talk about domains and interactions, these kinds of things but fundamentally, the problem is what we in computer science called NP hard, it's too difficult to model. You can't solve it analytically. And this is true for all these kinds of natural sorts of problems by the way. And so there's a whole field around this, molecular dynamics and modeling these sorts of things, that are actually being driven forward by these AI techniques. Because it turns out, our brain doesn't do magic. It actually doesn't solve these problems. It approximates them very well. And experience allows you to approximate them better and better. Actually, it goes a little bit to what you were saying before. It's like simulations and forming your own networks and training off each other. There are these emerging dynamics. You can simulate steps of physics. And you come up with a system that's much too complicated to ever solve. Three pool balls on a table is one such system. It seems pretty simple. You know how to model that, but it actual turns out you can't predict where a balls going to be once you inject some energy into that table. So something that simple is already too complex. So neural network techniques actually allow us to start making those tractable. These NP hard problems. And things like molecular dynamics and actually understanding how different medications and genetics will interact with each other is something we're seeing today. And so I think there's a huge opportunity there. We've actually worked with customers in this space. And I'm seeing it. Like Rosch is acquiring a few different companies in space. They really want to drive it forward, using big data to drive drug development. It's kind of counterintuitive. I never would've thought it had I not seen it myself. >> And there's a big related challenge. Because in personalized medicine, there's smaller and smaller cohorts of people who will benefit from a drug that still takes two billion dollars on average to develop. That is unsustainable. So there's an economic imperative of overcoming the cost and the cycle time for drug development. >> I want to take a go at this question a little bit differently, thinking about not so much where are the industry segments that can benefit from AI, but what are the kinds of applications that I think are most impactful. So if this is what a skilled surgeon needs to know at a particular time to care properly for a patient, this is where most, this area here, is where most surgeons are. They are close to the maximum knowledge and ability to assimilate as they can be. So it's possible to build complex AI that can pick up on that one little thing and move them up to here. But it's not a gigantic accelerator, amplifier of their capability. But think about other actors in health care. I mentioned a couple of them earlier. Who do you think the least trained actor in health care is? >> John: Patients. >> Yes, the patients. The patients are really very poorly trained, including me. I'm abysmal at figuring out who to call and where to go. >> Naveen: You know as much the doctor right? (laughing) >> Yeah, that's right. >> My doctor friends always hate that. Know your diagnosis, right? >> Yeah, Dr. Google knows. So the opportunities that I see that are really, really exciting are when you take an AI agent, like sometimes I like to call it contextually intelligent agent, or a CIA, and apply it to a problem where a patient has a complex future ahead of them that they need help navigating. And you use the AI to help them work through. Post operative. You've got PT. You've got drugs. You've got to be looking for side effects. An agent can actually help you navigate. It's like your own personal GPS for health care. So it's giving you the inforamation that you need about you for your care. That's my definition of Precision Medicine. And it can include genomics, of course. But it's much bigger. It's that broader picture and I think that a sort of agent way of thinking about things and filling in the gaps where there's less training and more opportunity, is very exciting. >> Great start up idea right there by the way. >> Oh yes, right. We'll meet you all out back for the next start up. >> I had a conversation with the head of the American Association of Medical Specialties just a couple of days ago. And what she was saying, and I'm aware of this phenomenon, but all of the medical specialists are saying, you're killing us with these stupid board recertification trivia tests that you're giving us. So if you're a cardiologist, you have to remember something that happens in one in 10 million people, right? And they're saying that irrelevant anymore, because we've got advanced decision support coming. We have these kinds of analytics coming. Precisely what you're saying. So it's human augmentation of decision support that is coming at blazing speed towards health care. So in that context, it's much more important that you have a basic foundation, you know how to think, you know how to learn, and you know where to look. So we're going to be human-augmented learning systems much more so than in the past. And so the whole recertification process is being revised right now. (inaudible audience member speaking) Speak up, yeah. (person speaking) >> What makes it fathomable is that you can-- (audience member interjects inaudibly) >> Sure. She was saying that our brain is really complex and large and even our brains don't know how our brains work, so... are there ways to-- >> What hope do we have kind of thing? (laughter) >> It's a metaphysical question. >> It circles all the way down, exactly. It's a great quote. I mean basically, you can decompose every system. Every complicated system can be decomposed into simpler, emergent properties. You lose something perhaps with each of those, but you get enough to actually understand most of the behavior. And that's really how we understand the world. And that's what we've learned in the last few years what neural network techniques can allow us to do. And that's why our brain can understand our brain. (laughing) >> Yeah, I'd recommend reading Chris Farley's last book because he addresses that issue in there very elegantly. >> Yeah we're seeing some really interesting technologies emerging right now where neural network systems are actually connecting other neural network systems in networks. You can see some very compelling behavior because one of the things I like to distinguish AI versus traditional analytics is we used to have question-answering systems. I used to query a database and create a report to find out how many widgets I sold. Then I started using regression or machine learning to classify complex situations from this is one of these and that's one of those. And then as we've moved more recently, we've got these AI-like capabilities like being able to recognize that there's a kitty in the photograph. But if you think about it, if I were to show you a photograph that happened to have a cat in it, and I said, what's the answer, you'd look at me like, what are you talking about? I have to know the question. So where we're cresting with these connected sets of neural systems, and with AI in general, is that the systems are starting to be able to, from the context, understand what the question is. Why would I be asking about this picture? I'm a marketing guy, and I'm curious about what Legos are in the thing or what kind of cat it is. So it's being able to ask a question, and then take these question-answering systems, and actually apply them so that's this ability to understand context and ask questions that we're starting to see emerge from these more complex hierarchical neural systems. >> There's a person dying to ask a question. >> Sorry. You have hit on several different topics that all coalesce together. You mentioned personalized models. You mentioned AI agents that could help you as you're going through a transitionary period. You mentioned data sources, especially across long time periods. Who today has access to enough data to make meaningful progress on that, not just when you're dealing with an issue, but day-to-day improvement of your life and your health? >> Go ahead, great question. >> That was a great question. And I don't think we have a good answer to it. (laughter) I'm sure John does. Well, I think every large healthcare organization and various healthcare consortiums are working very hard to achieve that goal. The problem remains in creating semantic interoperatability. So I spent a lot of my career working on semantic interoperatability. And the problem is that if you don't have well-defined, or self-defined data, and if you don't have well-defined and documented metadata, and you start operating on it, it's real easy to reach false conclusions and I can give you a classic example. It's well known, with hundreds of studies looking at when you give an antibiotic before surgery and how effective it is in preventing a post-op infection. Simple question, right? So most of the literature done prosectively was done in institutions where they had small sample sizes. So if you pool that, you get a little bit more noise, but you get a more confirming answer. What was done at a very large, not my own, but a very large institution... I won't name them for obvious reasons, but they pooled lots of data from lots of different hospitals, where the data definitions and the metadata were different. Two examples. When did they indicate the antibiotic was given? Was it when it was ordered, dispensed from the pharmacy, delivered to the floor, brought to the bedside, put in the IV, or the IV starts flowing? Different hospitals used a different metric of when it started. When did surgery occur? When they were wheeled into the OR, when they were prepped and drapped, when the first incision occurred? All different. And they concluded quite dramatically that it didn't matter when you gave the pre-op antibiotic and whether or not you get a post-op infection. And everybody who was intimate with the prior studies just completely ignored and discounted that study. It was wrong. And it was wrong because of the lack of commonality and the normalization of data definitions and metadata definitions. So because of that, this problem is much more challenging than you would think. If it were so easy as to put all these data together and operate on it, normalize and operate on it, we would've done that a long time ago. It's... Semantic interoperatability remains a big problem and we have a lot of heavy lifting ahead of us. I'm working with the Global Alliance, for example, of Genomics and Health. There's like 30 different major ontologies for how you represent genetic information. And different institutions are using different ones in different ways in different versions over different periods of time. That's a mess. >> Our all those issues applicable when you're talking about a personalized data set versus a population? >> Well, so N of 1 studies and single-subject research is an emerging field of statistics. So there's some really interesting new models like step wedge analytics for doing that on small sample sizes, recruiting people asynchronously. There's single-subject research statistics. You compare yourself with yourself at a different point in time, in a different context. So there are emerging statistics to do that and as long as you use the same sensor, you won't have a problem. But people are changing their remote sensors and you're getting different data. It's measured in different ways with different sensors at different normalization and different calibration. So yes. It even persists in the N of 1 environment. >> Yeah, you have to get started with a large N that you can apply to the N of 1. I'm actually going to attack your question from a different perspective. So who has the data? The millions of examples to train a deep learning system from scratch. It's a very limited set right now. Technology such as the Collaborative Cancer Cloud and The Data Exchange are definitely impacting that and creating larger and larger sets of critical mass. And again, not withstanding the very challenging semantic interoperability questions. But there's another opportunity Kay asked about what's changed recently. One of the things that's changed in deep learning is that we now have modules that have been trained on massive data sets that are actually very smart as certain kinds of problems. So, for instance, you can go online and find deep learning systems that actually can recognize, better than humans, whether there's a cat, dog, motorcycle, house, in a photograph. >> From Intel, open source. >> Yes, from Intel, open source. So here's what happens next. Because most of that deep learning system is very expressive. That combinatorial mixture of features that Naveen was talking about, when you have all these layers, there's a lot of features there. They're actually very general to images, not just finding cats, dogs, trees. So what happens is you can do something called transfer learning, where you take a small or modest data set and actually reoptimize it for your specific problem very, very quickly. And so we're starting to see a place where you can... On one end of the spectrum, we're getting access to the computing capabilities and the data to build these incredibly expressive deep learning systems. And over here on the right, we're able to start using those deep learning systems to solve custom versions of problems. Just last weekend or two weekends ago, in 20 minutes, I was able to take one of those general systems and create one that could recognize all different kinds of flowers. Very subtle distinctions, that I would never be able to know on my own. But I happen to be able to get the data set and literally, it took 20 minutes and I have this vision system that I could now use for a specific problem. I think that's incredibly profound and I think we're going to see this spectrum of wherever you are in your ability to get data and to define problems and to put hardware in place to see really neat customizations and a proliferation of applications of this kind of technology. >> So one other trend I think, I'm very hopeful about it... So this is a hard problem clearly, right? I mean, getting data together, formatting it from many different sources, it's one of these things that's probably never going to happen perfectly. But one trend I think that is extremely hopeful to me is the fact that the cost of gathering data has precipitously dropped. Building that thing is almost free these days. I can write software and put it on 100 million cell phones in an instance. You couldn't do that five years ago even right? And so, the amount of information we can gain from a cell phone today has gone up. We have more sensors. We're bringing online more sensors. People have Apple Watches and they're sending blood data back to the phone, so once we can actually start gathering more data and do it cheaper and cheaper, it actually doesn't matter where the data is. I can write my own app. I can gather that data and I can start driving the correct inferences or useful inferences back to you. So that is a positive trend I think here and personally, I think that's how we're going to solve it, is by gathering from that many different sources cheaply. >> Hi, my name is Pete. I've very much enjoyed the conversation so far but I was hoping perhaps to bring a little bit more focus into Precision Medicine and ask two questions. Number one, how have you applied the AI technologies as you're emerging so rapidly to your natural language processing? I'm particularly interested in, if you look at things like Amazon Echo or Siri, or the other voice recognition systems that are based on AI, they've just become incredibly accurate and I'm interested in specifics about how I might use technology like that in medicine. So where would I find a medical nomenclature and perhaps some reference to a back end that works that way? And the second thing is, what specifically is Intel doing, or making available? You mentioned some open source stuff on cats and dogs and stuff but I'm the doc, so I'm looking at the medical side of that. What are you guys providing that would allow us who are kind of geeks on the software side, as well as being docs, to experiment a little bit more thoroughly with AI technology? Google has a free AI toolkit. Several other people have come out with free AI toolkits in order to accelerate that. There's special hardware now with graphics, and different processors, hitting amazing speeds. And so I was wondering, where do I go in Intel to find some of those tools and perhaps learn a bit about the fantastic work that you guys are already doing at Kaiser? >> Let me take that first part and then we'll be able to talk about the MD part. So in terms of technology, this is what's extremely exciting now about what Intel is focusing on. We're providing those pieces. So you can actually assemble and build the application. How you build that application specific for MDs and the use cases is up to you or the one who's filling out the application. But we're going to power that technology for multiple perspectives. So Intel is already the main force behind The Data Center, right? Cloud computing, all this is already Intel. We're making that extremely amenable to AI and setting the standard for AI in the future, so we can do that from a number of different mechanisms. For somebody who wants to develop an application quickly, we have hosted solutions. Intel Nervana is kind of the brand for these kinds of things. Hosted solutions will get you going very quickly. Once you get to a certain level of scale, where costs start making more sense, things can be bought on premise. We're supplying that. We're also supplying software that makes that transition essentially free. Then taking those solutions that you develop in the cloud, or develop in The Data Center, and actually deploying them on device. You want to write something on your smartphone or PC or whatever. We're actually providing those hooks as well, so we want to make it very easy for developers to take these pieces and actually build solutions out of them quickly so you probably don't even care what hardware it's running on. You're like here's my data set, this is what I want to do. Train it, make it work. Go fast. Make my developers efficient. That's all you care about, right? And that's what we're doing. We're taking it from that point at how do we best do that? We're going to provide those technologies. In the next couple of years, there's going to be a lot of new stuff coming from Intel. >> Do you want to talk about AI Academy as well? >> Yeah, that's a great segway there. In addition to this, we have an entire set of tutorials and other online resources and things we're going to be bringing into the academic world for people to get going quickly. So that's not just enabling them on our tools, but also just general concepts. What is a neural network? How does it work? How does it train? All of these things are available now and we've made a nice, digestible class format that you can actually go and play with. >> Let me give a couple of quick answers in addition to the great answers already. So you're asking why can't we use medical terminology and do what Alexa does? Well, no, you may not be aware of this, but Andrew Ian, who was the AI guy at Google, who was recruited by Google, they have a medical chat bot in China today. I don't speak Chinese. I haven't been able to use it yet. There are two similar initiatives in this country that I know of. There's probably a dozen more in stealth mode. But Lumiata and Health Cap are doing chat bots for health care today, using medical terminology. You have the compound problem of semantic normalization within language, compounded by a cross language. I've done a lot of work with an international organization called Snowmed, which translates medical terminology. So you're aware of that. We can talk offline if you want, because I'm pretty deep into the semantic space. >> Go google Intel Nervana and you'll see all the websites there. It's intel.com/ai or nervanasys.com. >> Okay, great. Well this has been fantastic. I want to, first of all, thank all the people here for coming and asking great questions. I also want to thank our fantastic panelists today. (applause) >> Thanks, everyone. >> Thank you. >> And lastly, I just want to share one bit of information. We will have more discussions on AI next Tuesday at 9:30 AM. Diane Bryant, who is our general manager of Data Centers Group will be here to do a keynote. So I hope you all get to join that. Thanks for coming. (applause) (light electronic music)