>> Welcome to this IBM Watson Health Client Conversation. Here, we are probing the dynamics of the relationship between IBM and its key clients. We're looking back and we're also exploring the present situation. And we're going to talk about the future state of healthcare as well. My name is Dave Vellante from theCUBE and with me are Ryad Rondo who's the Associate director of Data Management at Veristat and Tim Watson, IBM Watson Health. Welcome gents. Tim, any relation? >> (chuckles) If I had a nickel for every time I was asked that question I'd be a wealthy man. >> Well, relationships and trust. I mean, they're pretty fundamental to any partnership and the pandemic certainly has tested us, and we've had to rely on those personal and professional relationships to get us through COVID. So let me start Ryad by asking you, how did the partnership with IBM support you last year? >> Last year as you know was particular year for our industry. So the relationship with our provider was key of the success of any studies we had last year with the new world we had. So we were working very close with IBM Clinical, and I think collaboration was key for successful (indistinct). >> So Tim, I wonder if you could talk about some of the things that you've done with Ryad and his team, maybe some of the things that you accomplished in 2020 anything that stands out. And then maybe take it from there and query Ryad on some of the more important topics that are top of mind for you. >> Yeah, absolutely. I think in 2020, we all know it was a challenging year but IBM actually put together a really good program to support our clients as as it relates to COVID-19 trials. And Veristat did a great job of taking advantage of that with a number of their clients that offered a free promotion for 18 months of a subscription to support individual sponsors in their efforts and trying to find a vaccine that supports the whole world out there. So I think we put together a program that Veristat and their clients were able to take advantage of as it relates to COVID. But in addition to that the platform supported their efforts to maintain the clinical trials that were ongoing. And that was actually probably even bigger challenge for the Veristat team. >> Yeah, so maybe do a little mock session here. Tim you're used to role-playing so let's do a little role-playing. So we're in 2021 you guys are sitting down, unfortunately you're not face-to-face, but imagine you were and Ryad, talk about the objectives that you have in 2021, as you think about your relationship with Watson Health and Tim I'd love for you to respond in real time as to how you're going to help Ryad. Ryad kick it off, what are you trying to get done in 2021? What's the priority? >> Let me take a step back from 2020 and I go to 2021. I think one of the biggest challenge we have in 2021 with studies we had, is the extreme rapid startup of several projects we had. So we needed to start, design and push studies live in the record time. We were able to design a study in a week, another study in two weeks from the protocol to the goal of the study. And all that was with the collaboration of IBM, of course. And 2020 actually brought a change of the approach the client have to the study. So now they are more willing to use more electronic solutions than before. 2020 forced our client and the industry in general to look at the solutions offered electronically by the ADC provider by IBM Clinical. So right now in 2021, we will be leveraging those solutions, I'm thinking about monitoring module, I'm thinking about ePRO, I'm thinking about eConsent which is coming soon and I'm thinking about visualization as well. So these solutions provided by the system are now more acceptable than before and we will be used in 2021. Visualization is in the top list of these solutions eConsent comes with it as well. >> Alright so Tim, how are you going to help? How's Watson Health going to be a great partner in 2021 and beyond? >> Well, I think IBM is continuing to focus on what do these solutions mean going forward and how can we extend the functionality of our platform out there? So with the release of eConsent that's something that I believe Veristat can take advantage of. And the near term is just a matter of getting the Veristat team educated on our eConsent functionality to be able to offer that out to their clients. And visualization is another area that we've had a number of discussions with Veristat on over the past 12 months and leveraging the tool set that we are able to bring to the table with smart reports and how that can provide additional value and then finding that balance where we can get them off the ground quickly maybe with some pre-packaged reports but also educating their team so that they're able to take that tool set and be able to extend that functionality to their clients. >> So Ryad what's the situation like? I wonder if you could think pre pandemic, post pandemic. A lot of clients that I talked to, they would talk the digital game, but in reality it's not that simple. You know things are done a certain way and then I've often called it the forced march to become digital. And that's kind of what happened to us. And so I'm curious as to your sense as to what the climate was like pre and post? How much if at all, I have to believe that everybody's digital strategies were compressed, but was it months? Was it years? And it was sort of overnight we had to make the changes. So it was like a Petri dish. You really didn't have time to plan, you just did. So by how much was that digital transformation compressed and what were the learnings and how do you see taking that forward? >> We historically would go to clients with solutions and you know human nature resists to change. So when we go offering electronic solutions before the pandemic, we always had to define, to use a lot of argument actually to explain to the client that this is the way to go. This is the time to do use more electronic solutions. With the pandemic, the fear forced the client to use these solutions. And they realized that it's working. They realize that we can do it. We can do it very well. Even for complex study, solutions are available and can be used. We also was forced somehow to shorten our timeline to find best way to push studies live in as short as possible timelines without jeopardizing the quality, finding solutions. Splitter is one of the solutions IBM can offer so this is one of the solution we used. The release of the ECRS done later in the edit. IBM offered the capability to do that without jeopardizing the quality. >> So Tim, maybe you could chime in here. I mean, that's really important point. We had sort of no choice but to rush into digital and electronic last year how do you help clients maintain that quality? Maybe you guys could talk amongst yourselves as to the kinds of things you did to maintain that both, when things were going crazy and they somewhat still are. And then how do you preserve that going forward maybe turn the dial maybe a little bit based on your learnings. >> I think one of the advantages that our platform brings to the table is the flexibility. And that flexibility is what Veristat was able to take advantage of in different situations, in different parts of the platform. So whether that was the ability to design trials very quickly and be very flexible with the rollout of that trial to address specific timelines or just the different areas of the platform like ePRO to be able to extend things out to their clients as well so that patients are entering data into the clinical trial so that they're not having to go visit sites necessarily out there. So there's a lot of things that we have within the platform that our clients are able to take advantage of that really came into full focus in the year of 2020. >> Does that resonate with you Ryad? Do you trust what Tim just said? Does it give you a good feeling based on your experience? How confident are you that IBM can deliver on that objective? >> Yeah, actually the pressure we received from the industry in general, in the last year and still this year is to always shorten with high quality deliverables. So we were able to use the flexibility IBM system offers to achieve that goal. Splitting release performing a complex MSU successfully. So all these features and the flexibility we have with the status and flexibility we have with the user roles all these features and flexibilities was key performing that high quality MSU complex updates and in the record time. >> I wonder if you could each talk about sort of personally and bring it professional if you like, but how have you changed as a result of the pandemic and how has it helped you position for what's coming ahead? Ryad maybe you you could start. >> One of the things I'd like to say about what happened last year is that, it has never been easier for me to explain what I do. Historically, when I asked what do they exactly? I had to spend hours explaining what I do. Now I tell them, do you know what's the phase three phase two we are all waiting for the vaccine? That's what we do. So that's I think the number one success of the year for me. Honestly, it's just proud to work in an industry like that. Proud to work in a company like Veristat who cares about the quality who cares about providing the safety of the patient using the best system in the market working with IBM Clinical in this case. We're working to achieve that goal. I think 2020 gave me that just another level of proud maybe, if I may say. Partnership with client, partnership with IBM offered free for COVID studies for an 18-month program. So all of these just confirm that we are, I am personally in the right place, right company, having the right partnership to help humanity actually get better. >> Thank you for that, that's great. And Tim, you too you're obviously part of that, but I wonder if you could comment. >> Well, I'd like to kind of echo just what Ryad had said that professionally I feel we play a very small part. The Veristat team, the Sponsors team they do all the hard work, trying to find new medications, new vaccines to bring to life. But it means a lot to play a small part in that process to offer a technology that helps them do that quicker. And if we can get those drugs and vaccines to market quicker then that's going to have a very positive impact on the world as a whole. So it's a very exciting time to be in this industry. >> Undoubtedly. Ryad what can IBM do to help you near-term, mid-term and long-term? What are some of the most important things that Tim and his company can help with? >> Yeah, as Tim mentioned, we will have eConsent coming soon. I'm sure IBM will have other electronic solution coming soon so partnership, support and training. So education between our two entities between Veristat and IBM to use this new feature now became key for the success of any study. So I expect partnership support on an education from them. We've been successfully doing that and hoping that we'll continue in this case. >> Tim, any comments on that? >> Well, we have a great relationship with Veristat and we try to have that same kind of relationship with all of our clients. We meet regularly with governance meetings. That's a great time to share new information, to revisit old questions that may still be out there. And so, we're going to continue to offer the additional training options to our clients to allow them to leverage that platform so that they can then cater to their clients, the sponsors that are contracting with them for their clinical trials. >> So Ryad, Tim did say lower the price. That's a good sign. What about that though? What about value for investment? How Ryad would you grade IBM's track record in that regard? If you had to put a grade on it, you know, A, B, C, D, E, F. >> I would put a good grade, actually. I think it's the right balance. You know, a client expect always to pay less, but we are the experts, we are doing the job. And we have to guide them, make the right decision. We tell them why they should pick that or that system. And what are they getting for the price they're paying for. Right now we didn't have much trouble selling IBM and there is something common I think so, is to revise the price to be more specific for study, I think will help the client actually. Will help selling the products. >> So if you had to put a letter grade on it what would you give them? >> A grade? (chuckles) >> A grade, come on A, B? >> An A. >> An A, you'd give them an A? >> Yes. Solid A, 4.0 that's great, Tim you got a-- >> A minus to just leave some space for improvement. (Tim chuckles) >> Okay A minus just because, hold the carrot out there right? That's good, it's okay. Tim, how do you feel about that? You don't mind having a little extra incentive, right? >> No, absolutely not. It's always great to work with the Veristat team and we have I think a great relationship and there are certainly opportunities that we can hopefully work together. And If the price needs to be addressed then we can address it and win the business. >> Tim, anything I missed? Anything that you feel like there's a gap there that you want to cover that I didn't touch on? >> No, I think we're good. >> Great, awesome. Well, great conversation guys. I really appreciate it and thanks for the good work that you guys are doing on behalf of everybody who's living through this. It's a critical time and it's amazing how your industry has responded so thank you for that. And thank you for spending some time with us. You're watching Client Conversations with IBM Watson Health.

>>Welcome to this IBM Watson health client conversation here. We're probing the dynamics of the relationship between IBM and its clients. And we're looking back, we're exploring the present situation and discussing the future state of healthcare. My name is Dave Volante from the cube and with me are Alex Dillard. Who's a senior director data analysis at blue choice, blue choice health plan, and Darryl decode, who is IBM with IBM Watson health. Of course. Welcome gentlemen. Good to see you. Thanks for coming on. >>Hey, >>So, you know, you think about lasting relationships. They're the foundation to any partnership and this past year, and it's tested all of us. We've had to rely on both personal and professional relationships to get us through the pandemic. So Alex, let me start with you. How has the partnership with IBM supported you in 2020? >>Well, uh, I've just a piece of a larger puzzle. Uh, the relationship that Darrell and I have had is confined to IBM Watson health, but blue cross blue shield, South Carolina, which food choice is a wholly owned subsidiary of has had a standing relationship with IBM on the it side. Uh, we are a mainframe shop, uh, about 70% of our it infrastructure is on a mainframe. And, uh, that puts us as a segment one client for IBM, we're in the top 300 of all of their clients in the Americans. And more specifically we're the fourth largest, um, uh, Linux on Z shop in the world. So, uh, we've got a lot of diversification at blue cross blue shield of South Carolina and the mainframe and the vastness of that. It infrastructure reflects that, uh, diversification. We are more than just a crossing the shield. Uh, that's typically what people think of is insurance when they think of crossing shield, but we also have a division that does a lot of subcontract work for government programs, uh, track air, which is the military healthcare, uh, claims processing and Medicare claims processing. >>We were a subcontractor of other folks that use our back office, it infrastructure to, to run their claims through. So that's, that's the larger, um, aspect of our relationship that, that blue cross blue shield of South Carolina house with IBM, uh, as it relates to Watson health, we have been a client since 1994 and obviously that predates the IBM proper. Uh, we were a client of med stat and then Truven, who then, uh, was bought by IBM. So we have used the products from Watson health throughout our system to support provider profiling, uh, count group reporting, um, and ad hoc analysis and to some extent to, uh, support our value-based products with, uh, ACO and PCMH, >>Uh, products. >>Awesome. Thank you for that. So Daryl is very long-term relationship. Obviously, if people forget sometimes that, uh, how IBM has modernized the Z Alex talked about, uh, Linux on the mainframe. That's pretty cool. I wonder if you could talk about specifically the things that, that you've done with Alex in his, in his, in his team, you know, thinking back last year, what were your accomplishments that you really stand out? >>Yeah, so, so one thing that jumps to mind is, uh, given the long standing relationship, I relied heavily on Alex to help us work through a multi-year renewal. And it was, it was a, um, a good adventure for us. We, we were able to laugh along the way. We certainly had some, some phone calls that, that were a little bit challenging, but the great thing about it was that the relationship that Alex and I had, he really views it as a partnership. And that was just so encouraging and uplifting. So to me, from my perspective, that was absolutely, uh, one of the highlights of my year and working through even through the pandemic and all that, we figured it out. >>So you guys, when you get together, go ahead, please. >>That's what I had as well. Um, you know, the, the unique thing about the Watson health contract is because it involves data. Uh, we take the stance that it's an it contract, so I'm on the business side. So I've got to just, as Daryl has to navigate it with me, we've got to navigate a large of your it bureaucracy. Um, and, uh, it, it was challenging. Um, you know, the business people kind of smooth the tracks and then you get the lawyers involved in, it just goes haywire. So, um, we were able to navigate that. Um, uh, so yeah, so it was a big accomplishment. So Alex, it's not real sexy to talk about, but we got it done >>Well. So Alex you're, you're in sales, so you're, you're used to role playing. So imagine you're, you're, you're sitting down, uh, sorry, Darryl. You're used to, role-playing out. Imagine you're sitting down with Alex and you're thinking about 20, 21 planning, so, you know, take it away. W what do you, what would you ask, what would you talk about or share with us? >>Yeah, yeah, absolutely. So, so I, I know that, you know, one of the key objectives is, uh, continued to ingest, engage with your members and you have key business strategies. I know you recently migrated over to a new PBM, and so there, there's some complexities that come with that. Um, but just, you know, Alex, if you don't mind, why don't you share a little bit about kind of your, your perspective on what 2020 would hold for you in your organization? Well, I think that due to the pandemic, we are, I personally kick the, can down the road on a couple of things, particularly >>Having a strategic roadmap discussion, um, you know, uh, I was going to get into this later, but I enjoy doing things face-to-face rather than, uh, over the phone or, or virtually. And so, uh, I guess I was a little too optimistic about maybe being able to get together late 2020 to have that strategic roadmap discussion. Um, I think, uh, given what has developed with, um, the pandemic and vaccines and stuff, I may, I may be able to get everybody on the same page later this year, hopefully. Uh, but certainly we want to have a strategic roadmap discussion. Um, we license, uh, Watson Hills, uh, cat group insights, uh, tool, which we use for employer group reporting. And we are currently in the beginning stages of rolling that out to our external clients, whether it's agents, brokers, um, those types of folks. And then it vanished we as our core product that we use for analysis, and that product is transitioning to what is called health insights. And so from an analytical standpoint, my staff and the staff of our cluster areas will need to sort of move to health insights since that's where it's going, uh, from an analytic standpoint. So we're going to work on that as well. Um, and then some more detailed things around database rebuilds and stuff like that. Those are all sort of on the roadmap for 2021. >>Yeah. So, you know, you talk about strategic planning and you think about the way planning used to be. I mean, sometimes you take a longer term horizon, maybe that's five years, you know, technology cycles, you know, even though they go very fast, but you see major technology shifts, they're like go through these seven year cycles, you see that in financial world. And then with the, with the pandemic, we're talking about seven day cycles, you know, how do I support people work from home? Do I open the store or not? You know, it's a day-to-day type of thing. So I wonder if you could each talk about personally and professionally w how, how is 2020, you know, changed you and maybe position you for, for what's ahead, maybe Alex, you could start, >>Well, you know, I'm an analyst, so I always fall back to the numbers. What are the numbers show us, um, you know, people can have four perceptions, but, uh, the numbers give us a reality. So the reality is that a year ago, pre pandemic, uh, just 13% of blue cross blue shield employees were working from home a hundred percent, uh fast-forward to today. And that number is now 87%. So think about, uh, just the lift from a it infrastructure to support that we almost, all of those people are using Citrix to get in to our network. Uh, we're using a remote desktop. So you've got this pipeline that probably had to go from, you know, this small, to huge, to get all this bandwidth, all this data and everything. So you've got that huge lift. Um, and then it affects different areas, um, differently. Uh, I don't have any first-line staff, any staff that are member facing, so I didn't really have to navigate, you know, how do these people talk to our member? >>How does staff talk to our members on the phone when they're at home, as opposed to in the office, and, you know, is there background noise, things like that. So I've got analysts, uh, they're just crunching numbers. Um, but my, my, my personal, uh, feeling was I like doing managing by walking around, you know, stopping and talking to other, working on. So that went away and I like face-to-face meetings, as I've mentioned, and that went away. So it was really a culture change for me personally, it was a culture change for our organization. Uh, and, and now we're having conversations with executive management that, you know, if you've got staff who have been doing a good job and they remain productive, you know, give me a reason they got to come back in, which is just, as you told me that I'm going to be the case a year ago, I would have been, you know, flabbergasted, but that's where we are right now. >>And so on a personal standpoint, you know, I went home for a little while and then came back. And so my wife also works for blue cross blue shield of South Carolina. Um, so, you know, she set up in the dining room working, uh, I have my own book in our living room working, and then we've got a great side, you know, the school is not in session, you know, in person. So he's doing virtual learning. So combine all those things, and you've got all kinds of crazy things that could happen. Uh, and then you've got staff who are in the same situation. Um, so it was a lot to handle. And the longer it goes on the novelty of working from home wears off, and you kind of realize, you know, I can't go do this. I can't go out to eat. I can't do all types of things that I used to do. And so that affects your mental health. So as, as a leader, um, of my small area, and then our executives really had to become more, uh, uh, in, in people's faces. So we've got, we've done a lot more video, uh, messages, a lot more emails. Um, I have been tasked with being very deliberate about checking on how everything is going at their house. Are they getting what they need? Um, you know, how are they feeling? Are they getting up and exercising, all those things that you took for granted, uh, beforehand. >>Yeah. So Daryl, anything you'd add to that in terms of specifically in terms of how you might, how you might change the way in which you interact with your clients generally, uh, an Alex specifically, Alex likes, face to face, you know, we can't wait. All right. >>Yeah, yeah. It's funny. We never quite got to do it Alex, but we were talking about doing a virtual happy hour at one point too, to just celebrate the success. Um, but for me, you know, typically I would travel and visit Alex face-to-face on maybe a monthly basis. And so it it's been really hard for me. I didn't realize how, how much I enjoyed that in-person interaction. And so that, that was something that I I've been, you know, working through and finding ways to, to still interact with people. And I'm certainly making, making the best of, of the video phone calls and, you know, that sort of thing. So, uh, just work working to maintain those relationships. >>I wonder if I could ask you when, when, when this thing, when we're through the pandemic, what do you expect the work from home percentage? I think I heard 13% prior to the pandemic, 87% today. What do you think is going to be post pandemic? >>That is a good question. Um, it, it may go back to maybe 60% at home. I think, I think there will be a simple majority, uh, working from home. Um, that's, that's from our planning, uh, space planning standpoint. That's, that's what we are, uh, what we're expecting, um, if, if production stays, um, at acceptable levels, um, >>Do you feel like productivity was negatively impacted positive? It will be impacted or it's kind of weird. >>Yeah. All the metrics that we track show that it was, it was sustained and in some areas even better. Uh, and if you really think about, um, sort of your typical day when you work from home, I found, uh, that I was logged on an hour earlier. That's probably what's happening with other staff as well is they're, they're motivated to get up and, and get online, uh, earlier. >>Yeah. Mostly tech leaders that I talk to share that sentiment, that the productivity is actually improved. So Darryl, I presume you see the same thing in your observation space. Yeah. >>Yeah. I, I do. And, and I have other clients too, and, and, and they are definitely looking at ways to continue to work remotely. I know that for a lot of people who are in the office all the time, uh, having a little bit more flexibility when you work from home can be a good thing. And, and like you said, you, you have to make sure that the productivity is still there and the productivity is up. Um, but I, I could see that the trend continuing absolutely >>I'd love for you to, to look at Darryl and say, and tell him what the kinds of things that IBM can do to help you both today, immediately 20, 21, and in the future and a Darryl, how, how your, how you'll respond. >>Well, I'll tell you that. Um, so in 2020, what, what changed most dramatically for us as a health plan? Uh, and, and I, it echoes what we see across the country is the gigantic shift in telehealth. Um, you know, if, if, again, if you look at the numbers, uh, our telehealth visits per thousand, so that's the number of visits per thousand members in a given month, went up 1472%. And so, you know, the common response to that is, well, you know, your visits overall probably, you know, were flat because, uh, you know, they just weren't happening in that. And that's not necessarily true for us. So if you look at visits overall, they written down four and a half percent. Um, so there was a shift, but it, it was not a big enough shift to account for, uh, visits overall sustaining the level that they were pre pandemic. >>Um, so as we look into 2021, uh, we will be investigating how we can maintain, uh, the, uh, the accessibility of our healthcare providers via telehealth. Um, you know, one of the projects that we started in 2020, uh, was based upon the choosing wisely campaign. So if you're not familiar with choosing wisely, it's a very well thought out process. It involves many, many provider specialties and its sole target is to reduce low value care. Uh, so we took it upon ourselves to Institute sort of a mirror of that plan or that program at, at blue cross here in South Carolina. And so as we moved to 2021, obviously those low value services just because of the pandemic were reduced, uh, and some of the high-value care was reduced as well. And so what we are going to try to do is bring back habit, bring back that high value care, but not bring back that low value care and so low value care or things like vitamin D testing. Uh, it can be other things like, um, uh, CT for head headaches, um, imaging for low abdominal pain, things like that. So, uh, we want to focus on low, uh, eliminating what value care, bringing back high value care, >>Okay, Dale, you're up? How are you going to help Alex achieve that? So, so good news is, is that we've got the analytic warehouse and the database where all of the data is captured. And so we we've got the treasure trove of information and data. And so what we'll do is we'll come alongside Alex and his team will do the analytics, we'll provide the analytic methods measures, and we'll also help him uncover where perhaps those individuals may be, who had postponed care, um, because of the pandemic. And so we can put together strategies to help make sure that they get the care that they need. Uh, I also a hundred percent agree that tele-health hopefully is something that will continue because I do think that that is a good way and efficient way to get care for people. Um, and, you know, as a, as, as a way to, to address some of their needs and, and in, in a safe way too. >>So, um, I, I look forward to working with Alec and his team over this coming year. I think there is, uh, knowing Alex and, and the partnership and his readiness to be a client reference for us. You know, those are all great, um, recognition of how he partners with us. And we really value and appreciate, uh, the relationship that we have with blue cross blue shield, South Carolina and, and blue choice. Excellent. Daryl's right. The, the, the database we use already has some of that low value care measures baked into it. And so throughout 2020, I've worked with our analytic consulting team. Uh, it's under Daryl too, to talk about what's on the product product roadmap for adding to the cadre of live low value care measures inside advantage suite. Uh, so that's something that we'll actively be, um, uh, discussing because certainly, you know, we're, we're obviously not the only client only health plan clients. So there may be other plans that have priorities that very different made very differently than ours. Uh, so we want to give them what we're studying, what we're interested in, so they can just add it in to all their other client feedback, uh, for advantage suites, roadmap. Excellent. >>Look, my last question, Alex is how would you grade IBM, if you had to take a bundle of sort of attributes, you know, uh, delivery, uh, value for service relationship, uh, et cetera, how would you grade the job that IBM is doing? >>I, the thing that I enjoy most about working with IBM and Darryl specifically, is that they're always challenging us to look at different things. Um, things that sometimes we hadn't considered, because obviously it may be an issue for another health plan client or an employer client that they've got. Uh, they tell us, this is what we're seeing. You know, you should look at it. Uh, a lot of times they do some of the foundational work in producing a report to show us what they're seeing in our data that is similar to what is in some of their other clients data. So that's refreshing to be, uh, challenged by IBM to look at things that we may not be, uh, looking at, uh, or maybe missing, because we've got our eye on the ball on something else you >>Care to put a letter grade on that. >>Oh, definitely. Definitely. Thank you. >>Well, Darryl, congratulations, that says a lot and, uh, we have to leave it there and one at a time, but, but Daryl, anything that I didn't ask Alex, that you, you wanted me to, >>So, um, Alex re able to keep your tennis game up during the pandemic? Uh, I, yes, I tried as, as often as my wife would let me good. I would play every time I was asked, but, uh, yeah, so I, I did have to temper it a little bit, although when you spend all day with her and, and my son, you know, she may be a little more, uh, lenient on letting me leave the house. Well, maybe she's >>Yeah. The tribute to the late great comedian Mitch Hedberg, who says, uh, you know, when I, I played tennis, I played against the wall walls. Really good, hard to beat if it's pandemic appropriate. >>Oh, that's good. That's a true statement. And there was a lot of that going on, a lot of that play and playing against the wall. >>Hey, thanks so much, stay safe and really appreciate the time. Thank you. >>Thank you. Thank you. You're >>Really welcome. It was a great conversation and thank you for watching and spending some time with client conversations with IBM Watson health.

>> Welcome to this IBM Watson Health client conversation. And we're probing the dynamics of the relationship between IBM and it's clients. We're going to look back at some of the challenges of 2020 and look forward to, you know, present year's priorities. We'll also touch on the future state of healthcare. My name is Dave Vellante. I'll be your host and I'm from theCUBE. And with me are Doctor Alex Towbin, who's Associate Chief Clinical Operations and Informatics at Cincinnati ChilDoctoren's Hospital and John Chrisman of course from IBM Watson health. Welcome gentlemen, Good to see you. Thanks for coming on. >> Thanks for having us. >> Yeah, thanks for having me. >> Yeah I know from talking to many clients around the world, of course virtually this past year, 11 months or so that relationships with technology partners they've been critical over during the pandemic to really help folks get through that. Not that we're through it yet but, we're still through the year now, there's I'm talking professionally and personally and Doctor Towbin, I wonder if you could please talk about 2020 and what role the IBM partnership played in helping Cincinnati children's, you know press on in the face of incredible challenges? >> Yeah, I think our story of 2020 really starts before the pandemic and we were fortunate to be able to plan a disaster and do disaster drill scenarios. And so, as we were going through those disaster drill scenarios, we were trying to build a solution that would enable us to be able to work if all of our systems were down and we worked with IBM Watson Health to design that solution to implement it, it involves using other solutions from our primary one. And we performed that disaster drill in the late January, early February timeframe of 2020. And while that drill had nothing to do with COVID it got us thinking about how to deal with a disaster, how to prepare for a disaster. And so we've just completed that and COVID was coming on the horizon. I'm starting to hear about it coming into the U.S for the first time. And we took that very seriously on our department. And so, because we had prepared for this this disaster drill had gone through the entire exercise and we built out different scenarios for what could happen with COVID what would be our worst case scenarios and how we would deal with them. And so we were able to then bring that to quickly down to two options on how our department and our hospital would handle COVID and deal with that within the radiology department and like many other sites that becomes options of working from home or working in a isolated way and an and an office scenario like where I'm sitting now and we planned out both scenarios and eventually made the decision. Our decision at that point was to work in our offices. We're fortunate to have private offices where we can retreat to and something like that. And so then our relationship with IBM was helpful and that we needed to secure more pieces of hardware. And so even though IBM is our PACS vendor and our enterprise imaging vendor, they also help us to secure the high resolution monitors that are needed. And we needed a large influx of those during the pandemic and IBM was able to help us to get those. >> Wow! So yeah you were able to sort of test your organization resilience before the pandemic. I mean, John, that's quite an accomplishment for last year. I'm sure there are many others. I wonder if one of you could pick it up from here and bring your perspectives into it and, you know maybe ask any questions that you would like to ask them. >> Yeah, sure, Doctor Towbin, that's great that we were able to help you with the hardware and procure things. So I'm just curious before the pandemic how many of the radiologists ever got to read from home, was that a luxury back then? And then post pandemic, are you guys going to shift to how many are on-site versus remote? >> Yeah, so we have a couple of scenarios. We've had talk about it both from our PACS perspective as well as from our VNA enterprise imaging perspective from PACS perspective we always designed our solution to be able to work from a home machine. Our machines, people would access that through a hospital-based VPN. So they would log in directly to VPN and then access the PACS that way. And that worked well. And many of our radiologists do that particularly when they're on call works best for our neuroradiologist who are on call a little bit more frequently. And so they do read from home in that scenario. With enterprise imaging and are used to the enterprise viewer and iConnect access. We always wanted that solution to work over the internet. And so it's set up securely through the internet but not through the VPN. And we have radiologists use that as a way to view studies from home, even not from home, so it can be over one of their mobile devices, such as an iPad and could be at least reviewing studies then. We, for the most part for our radiologist in the hospital that's why we made the decision to stay in the hospital. At COVID time, we have such a strong teaching mission in our department in such a commitment to the education of our trainees. We think that hospital being in the hospital is our best way to do that, it's so hard. We find to do it over something like zoom or other sharing screen-sharing technology. So we've stayed in and I think we'll continue to stay in. There will be some of those needs from a call perspective for example, reading from home, and that will continue. >> And then what's your success been with this with the technology and the efficiency of reading from home? Do you feel like you're just as efficient when you're at home versus onsite? >> The technology is okay. The, our challenges when we're reading from the PACS which is the preferred way to do it rather than the enterprise archive, the challenge is we have to use the PACS So we have to be connected through VPN which limits our bandwidth and that makes it a little bit slower to read. And also the dictation software is a little bit slower when we're doing it. So moving study to study that rapid turnover doesn't happen but we have other ways to make, to accelerate the workflow. We cashed studies through the worklist. So they're on the machine, they load a little bit more rapidly and that works pretty well. So not quite as fast, but not terrible. >> We appreciate your partnership. I know it's been going on 10 years. I think you guys have a policy that you have to look at the market again every 10 years. So what do you think of how the market's changed and how we've evolved with the VNA and with the zero footprint viewers? A lot of that wasn't available when you initially signed up with Amicas years ago, so. >> Yeah, we signed up so we've been on this platform and then, you know now the IBM family starting in 2010, so it's now now 11 years that we're, we've been on as this version of the PACS and about eight, seven or eight years from the iConnect platform. And through that, we've seen quite an evolution. We were one of the first Amicas clients to be on version six and one of the largest enterprises. And that went from, we had trouble at the launch of that product. We've worked very closely with Amicas then to merge. And now IBM from the development side, as well as the support side to have really what we think is a great product that works very well for us and drives our entire workflow all the operations of our department. And so we've really relished that relationship with now IBM. And it's been a very good one, and it's allowed us to do the things like having disaster drill planning that we talked about earlier as far as where I see the market I think PACS in particular is on the verge of the 3.0 version as a marketplace. So PACSS 1 one was about building the packs, I think, and and having electronic imaging digital imaging, PACS 2.0 is more of web-based technology, getting it out of those private networks within a radiology department. And so giving a little bit more to the masses and 3.0 is going to be more about incorporating machine learning. I really see that as the way the market's going to go and to where I think we're at the infancy of that part of the market now about how do you bring books in for machine learning algorithms to help to drive workflow or to drive some image interpretation or analysis, as far as enterprise imaging, we're on the cusp of a lot there as well. So we've been really driving deep with enterprise imaging leading nationally enterprise imaging and I have a role in the MSAM Enterprise Imaging Community. And through all of that work we've been trying to tackle works well from enterprise imaging point of view the challenges are outside of radiology, outside of cardiology and the places where we're trying to deal with medical photos, the photographs taken with a smart device or a digital camera of another type, and trying to have workflow that makes sense for providers not in those specialty to that don't have tools like a DICOM modality workloads store these giant million-dollar MRI scanners that do all the work for you, but dealing with off the shelf, consumer electronics. So making sure the workflow works for them, trying to tie reports in trying to standardize the language around it, so how do we tag photos correctly so that we can identify relevancy all of those things we're working through and are not yet standard within our, within the industry. And so we're doing a lot there and trying and seeing the products in the marketplace continuing to evolve around that on the viewer side, there's really been a big emergence as you mentioned about the zero footprint viewers or the enterprise viewer, allowing easy access easy viewing of images throughout the enterprise of all types of imaging through obtained in the enterprise and will eventually incorporate video pathology. The market is also trying to figure out if there can be one type of viewer that does them all that and so that type of universal viewer, a viewer that cardiologists can use the same as a radiologist the same as a dermatologist, same as a pathologist we're all I think a long way away from that. But that's the Marcus trying to figure those two things out. >> Yeah, I agree with you. I agree with your assessment. You talked about the non DICOM areas, and I know you've you've partnered with us, with ImageMover and you've got some mobile device capture taking place. And you're looking to expand that more to the enterprise. Are you also starting to use the XDS registry? That's part of the iConnect enterprise archive, or as well as wrapping things in DICOM, or are you going to stick with just wrapping things in DICOM? >> Yeah, so far we've been very bunched pro DICOM and using that throughout the enterprise. And we've always thought, or maybe we've evolved to think that there is going to be a role for XDS are I think our early concerns with XDS are the lack of other institutions using it. And so, even though it's designed for portability if no one else reads it, it's not portable. If no one else is using that. But as we move more and more into other specialties things like dermatology, ophthalmology, some of the labeling that's needed in those images and the uses, the secondary uses of those images for education, for publication, for dermatology workflow or ophthalmology workflow, needs to get back to that native file and the DICOM wrap may not make sense for them. And so we've been actively talking about switching towards XDS for some of the non DICOM, such as dermatology. We've not yet done that though. >> Given the era children's hospital has the impact on your patient load, then similar to what regular adult hospitals are, or have you guys had a pretty steady number of studies over the last year? >> In relay through the pandemic, we've had, it has been decreased, but children fortunately have not been as severely affected as adults. There is definitely disease in children and we see a fair amount of that. There are some unique things that happen in kids but that fortunately rare. So there's this severe inflammatory response that kids can get and can cause them to get very sick but it is quite rare. Our volumes are, I think I'm not I think our volumes are stable and our advanced imaging things like CT, MRI, nuclear medicine, they're really most decreased in radiography. And we see some weird patterns, inpatient volumes are relatively stable. So our single view chest x-rays, for example, have been stable. ER, visits are way down because people are either wearing masks, isolating or not wanting to come to the ER. So they're not getting sick with things like the flu or or even common colds or pneumonias. And so they're not coming into the ER as much. So our two view x-rays have dropped by like 30%. And so we were looking at this just yesterday. If you follow the graphs for the two we saw a dip of both around March, but essentially the one view chest were a straight line and the two view chest were a straight line and in March dropped 30 to 50% and then stayed at that lower level. Other x-rays are on the, stay at that low level side. >> Thanks, I know in 2021 we've got a big upgrade coming with you guys soon and you're going to stay in our standalone mode. I understand what the PACSS and not integrate deeply to the VNA. And so you'll have a couple more layers of storage there but can you talk about your excitement about going to 8.1 and what you're looking forward to based on your testimony. >> Yeah we're actually in, we're upgrading as we're talking which is interesting, but it's a good time for talking. I'm not doing that part of the work. And so our testing has worked well. I think we're, we are excited. We, you know, we've been on the product as I mentioned for over 10 years now. And for many of those years we were among the first, at each version. Now we're way behind. And we want to get back up to the latest and greatest and we want to stay cutting edge. There've been a lot of reasons why we haven't moved up to that level, but we do. We're very careful in our testing and we needed a version that would work for us. And there were things about previous versions that just didn't and as you mentioned, we're staying in that standalone mode. We very much want to be on the integrated mode in our future because enterprise imaging is so important and understanding how the comparisons fit in with the comparison in dermatology or chest wall deformity clinic, or other areas how those fit into the radiology story is important and it helped me as a radiologist be a better radiologist to see all those other pictures. So I want them there but we have to have the workflow, right. And so that's the part that we're still working towards and making sure that that fits so we will get there. It'll probably be in the next year or two to get to that immigrating mode. >> As you, look at the number of vendors you have I think you guys prefer to have less vendor partners than than more I know in the cardiology area you guys do some cardiology work. What has been the history or any, any look to the future of that related to enterprise imaging? Do you look to incorporate more of that into a singular solution? >> Cardiology is entirely part of our enterprise imaging solution. We all the cardiology amendments go to our vendor neutral archive on the iConnect platform. All of them are viewed across the enterprise using our enterprise viewer. They have their unique specialty viewer which is, you know, fine. I'm a believer that specialty, different specialties, deserve to have their specialty viewers to do theirs specialty reads. And at this point I don't think the universal viewer works or makes sense until we have that. And so all the cardiology images are there. They're all of our historical cardiology images are migrated and part of our enterprise solution. So they're part of the entire reference the challenge is they're just not all in PACSS. And so that's where, you know, an example, great example, why we need to get to this to the integrated mode to be able to see those. And the reason we didn't do that is the cardiology archive is so large to add a storage to the PACS archive. Didn't make sense if we knew we were going to be in an integrated mode eventually, and we didn't want to double our PACS storage and then get rid of it a couple of years later. >> So once you're on a new version of merge PACS and you're beyond this, what are your other goals in 2021? Are you looking to bring AI in? Are you using anybody else's AI currently? >> Yeah, we do have AI clinical it's phone age, so it's not not a ton of things but we've been using it clinically, fully integrated, it launches. When I open a study, when I opened a bone age study impacts it launches we have a bone age calculator as well that we've been using for almost two decades now. And so that we have to use that still but launching that automatically includes the patient's sex and birth date, which are keys for determining bone age, and all that information is there automatically. But at the same time, the images are sent to the machine learning algorithm. And in the background the machine determines a bone age that in the background it sends it straight to our dictation system and it's there when we opened the study. And so if I agree with that I signed the report and we're done. If I disagree, I copy it from my calculator and put it in until it takes just a couple of clicks. We are working on expanding. We've done a lot of research in artificial intelligence and the department. And so we've been things are sort of in the middle of translation of moving it from the research pure research realm to the clinical realm, something we're actively working on trying to get them in. Others are a little bit more difficult. >> That's the question on that John, Doctor, when you talk about injecting, you know machine intelligence into the equation. >> Yeah. >> What, how do you sort of value that? Does that give you automation? Does it improve your quality? Does it speed the outcome and maybe it's all of those but how do you sort of evaluate the impact to your organisation? >> I there's a lot of ways you can do it. And you touched on one of my favorite one of my favorite talking points, in a lot of what we've been doing and early machine learning is around image interpretation helping me as a radiologist to see a finding. Unfortunately, most of the things are fairly simple tasks that it's asking us to do. Like, is there a broken bone? Yes or no, I'm not trying to sound self-congratulatory or anything, but I'm really good at finding broken bones. I get, I've been doing it for a long time and, and radio, you know so machines doing that, they're going to perform as well as I can perform, you know, and that's the goal. Maybe they'll perform a little bit better maybe a little bit worse but we're talking tiny increments there they're really to me, not much value of that it's not something I would want. I don't value that at a time where I think machine learning can have real value around more on some of the things that you mentioned. So can it make me more efficient? Can it do the things that are so annoying that and they'd take, they're so tedious that they make me unhappy. A lot of little measurements for example are like that an example. So in a patient with cancer, we measure a little tumors everywhere and that's really important for their care, but it's tedious and so if a machine could do that in an automated way and I checked it that, you know, patient when because he or she can get that good quality care and I have a, you know, a workflow efficiency game. So that one's important. Another one that would be important is if the machine can see things I can't see. So I'm really good at finding fractures. I'm not really good at understanding what all the pixels mean and, you know in that same patient with cancer, oh what do all the pixels mean in that tumor? I know it's a tumor. I can see the tumor, I can say it's a tumor but sometimes those pixels have a lot of information in them and may give us prognosis, you know, say that this patient may, maybe this patient will do well with this specific type of chemotherapy or a specific or has a better prognosis with one with one drug compared to another. Those are things that we can't usually pick out. You know, it's beyond the level of that are I can perceive that one is really the cutting edge of machine learning. We're not there yet and then the other thing are things that, you know just the behind the scenes stuff that I don't necessarily need to be doing, or, you know so it's the non interpretive artificial intelligence. >> Dave: Right. >> And that's what I've been also trying to push. So an example of when the algorithms that we've been developing here we check airways. And this is a little bit historical in our department, but we want to make sure we're not missing a severe airway infection. That can be deadly, it's incredibly rare. Vaccines have made it go away completely but we still check airways. And so what happens is the technologist takes the x-ray. They come in to ask us if it's okay, we are interrupted from what we're doing. We open up the study, say yes or no. Okay, not okay, if it's not okay they go back, take another study. Then come back to us again and say, is it okay or not? And we repeat this a couple of times it takes them time that they don't need to spend and takes us time. And so we have, we've built an algorithm where the machine can check that and their machine is as good or a little bit worse than us, but give can give that feedback. >> Dave: Got it. >> The challenge is getting that feedback to the technologist quickly. And so that's, that's I think part for us to work on stuff. >> Thank you for that. So, John, we've probably got three or four minutes left. I'll let you bring it home and appreciate that Doctor Towbin >> I think one of the biggest impacts probably I knew this last year with the pandemic, Doctor Towbin is this, I know you're a big foodie. So having been to some good restaurants and dinners with the hot nurse in a house how's the pandemic affected you personally. And some of the things you like to do outside of work. >> Everything is shut down. And everything has changed. I have not left the house since March besides come to work and my family hasn't either. And so we're hardcore quarantining and staying you know, staying out and keeping it home. So we've not gone out to dinner or done much else. >> So its DoorDash and Uber Eats or just learned to cook at home. >> It's all cooking at home. We're fortunate, my wife loves to cook. My kids love to cook. I enjoy cooking, but I don't have the time as often. So we've done a lot of different are on our own experimenting. Maybe when the silver lining one of the things I've really relished about all this is all this time I get to spend with my family. And that closeness that we've been able to achieve because of being confined in our house the whole time. And so I've played get to play video games with my kids every night. We'd been on a big Fortnite Keck lately since it's been down making. So we've been playing that every night since we've watched movies a lot. And so as a family, we've, I it's something I'll look back fondly even though it's been a very difficult time but it's been an enjoyable time. >> I agree, I've enjoyed more family time this year as well, but final question is in 2021, beyond the PACS upgrade what are the top other two projects that you want to accomplish with us this year? And how can we help you? >> I think our big one is are the big projects are unexpanded enterprise imaging. And so we want to continue rolling out to other areas that will include eventually incorporating scopes, all the images from the operating room. We need to be able to get into pathology. I think the pathology is really going to be a long game. Unfortunately, I've been saying that already for 10 years and it's still probably another 10 years ago but we need to go. We can start with the gross pathology images all the pictures that we take for tumor boards and get those in before we start talking about whole slide scanning and getting in more of the more of the photographs in the institution. So we have a route ambulatory but we need inpatient and ER. >> All right one last question. What can IBM do to be a better partner for you guys? >> I think it's keep listening keep listening and keep innovating. And don't be afraid to be that innovative partner sort of thinking as the small company that startup, rather than the giant bohemoth that can sometimes happen with large companies, it's harder. It is fear to turn quickly, but being a nimble company and making quick decisions, quick innovations. >> Great, quick question. How would you grade IBM, your a tough grader? >> It depends on what I am a tough grader but it depends on what, you know as the overall corporate partnership? >> Yeah the relationship. >> I'd say it's A minus. >> Its pretty good. >> I think, I mean, I, we get a lot of love from IBM. I'm talking specifically in the imaging space. I not, maybe not, I don't know as much on the hardware side but we, yeah, we have a really good relationship. We feel like we're listened to and we're valued. >> All right, well guys, thanks so much. >> So even if it's not an A plus- >> Go ahead. >> I think there's some more to, you know, from the to keep innovating side there's little things that we just let you know we've been asking for that we don't always get but understand the company has to make business decisions not decisions on what's best for me. >> Of course got to hold that carrot out too. Well thanks guys, really appreciate your time. Great conversation. >> Yeah, thank you. >> All right and thank you for spending some time with us. You're watching client conversations with IBM Watson Health.

>> Welcome to this IBM Watson Health "Client Conversation." We're probing the dynamics of the relationships between IBM and its clients. And we're going to look back, we're going to explore the present situation and we're going to discuss the future state of healthcare. My name is Dave Vellante from theCUBE and with me are Dr. David Huelsman, who is a radiologist at TriHealth, which is a provider of healthcare in hospitals and John Kritzman who is with of course IBM Watson Health. Gentlemen welcome. Thanks so much for coming on. >> Thank you. >> Yeah, thanks for having us. >> Doctor let me say you're welcome. Let me start with you. As an analyst and a TV host in the tech industry, we often focus so much on the shiny new toy, the new widget, the new software. But when I talk to practitioners, almost to a person, they tell me that the relationship and trust are probably the most important elements of their success, in terms of a vendor relationship. And over the last year, we've relied on both personal and professional relationships to get us through some of the most challenging times any of us have ever seen. So, Dr. Huelsman, let me ask you, and thinking about the challenges you faced in 2020, what does partnership mean to you and how would you describe the relationship with IBM? >> Well, it is exactly the reason why when we started our journey on this enterprise imaging project at TriHealth, that we very early on made the decision We only wanted one vendor. We didn't want to do it piecemeal, like say get a vendor neutral archive from one organization, and the radiology viewer from another. We wanted to partner with the chosen vendor and develop that long-term relationship, where we learn from each other and we mutually benefit each other, in sort of not just have a transactional relationship, but that we share the same values. We share the same vision. And that's what stood out to us is Watson Health imagings vision, mirrored TriHealth's in what we were trying to achieve with our enterprise imaging project. >> You know, let me follow up with that if I could. A lot of times you hear the phrase, "Single throat to choke" and it's kind of a pejorative, right? It's a really negative term. And the way you just described that Dr. Huelsman is you were looking for a partnership. Yeah, sure. Maybe it was more manageable and maybe it was a sort of Singletree, but it was really about the partnership, going forward in a shared vision and really shared ownership of the outcome. Is that a fair characterization? >> Yeah, how about more positive is "One hand to shake." >> Wow, yeah, I love it. (chuckles) One hand to shake. I'm going to steal that line. That's good. I like it. Keep it positive. Okay, John, when you think about the past 12 months and I know you have history with TriHealth, and more recently have rejoined the account, but how would you kind of characterize that relationship and particularly anything you can add about the challenges of the 2020? What stands out to you? >> Yeah, I think going back to your one hand to shake or one vendor to hug all that's not allowed during COVID, but we're excited to be back working with you, I am in particular. And at the beginning of this sales process and RFP when you guys were looking for that vendor partner, we did talk to you about the journey, the journey with AI that we already had mature products on the vendor neutral archive side and all the product pieces that you were looking for. And I know you've recently went live over the last year and you've been working through, crawling through and learning to walk and starting to run, hopefully. And at some point we'll get to the end of the marathon, where you'll have all the AI pieces that you're looking for. But this journey has been eyeopening for all of us, from using consultants in the beginning, to developing different team members to help make you successful. So I think I've been tracking this from the outside looking in, and I'm happy to be back, more working direct with you this year to help ensure your longterm success. >> Yeah, that's great John. You have some history there. I'm going to probe that a little bit. So doctor, you talked about this enterprise imaging project. I presume that's part of, that's one of the vectors of this journey that you're on. What are you trying to accomplish in the sort of near term and midterm in 2021? John mentioned AI, is there a data element to this? Are there other, maybe more important pressing things? What are your main goals for 2021? >> Sure. Well, where we are, where we've started, the first step was getting all of our imaging stored consistently in the same place and in the same way. We had like many health system, as you grow, you acquire facilities, you acquire physician practices and they all have their own small packs system, different ways of storing the data. And so it becomes very unwieldy to be a large organization and try to provide a consistent manner of your physicians interacting with the data, with the imaging in the same way. And so it was a very large dissatisfier in our EMR to, oh if you wanted to see cardiovascular imaging, it's this tab. If you wanted to see radiology, it was this tab. If you wanted to see that, oh you got to go to the media tab. And so our big goal is, okay, let's get the enterprise archive. And so the Watson enterprise archive is to get all of our imaging stored in the same place, in the same way. And so that then our referring physicians and now with our patients as well, that you can view all the imaging, access it the same way and have the same tools. And so that's the initial step. And we're not even complete with that first step, that's where COVID and sort of diverting resources, but it's there, it's that foundation, it's there. And so currently we have the radiology, cardiology, orthopedics and just recently OB-GYN, all of those departments have their images stored on our Watson Enterprise Archive. So the ultimate goal was then any imaging, including not just what you typically think of radiology, but endoscopy and arthroscopy and those sort of images, or wound care images, in that any image, any picture in our organization will be stored on the archive. So that then when we have everything on that archive, it's easier to access consistently with the same tools. But it's also one of the large pieces of partnering with with Watson Health Imaging, is the whole cognitive solutions and AI piece. Is that, well now we're storing all the data in a consistent manner, you can access it in a consistent manner, well then we hope to analyze it in a consistent manner and to use machine learning, and the various protocols and algorithms that Watson Health Imaging develops, to employ those and to provide better care. >> Excellent, thank you for that. John, I wonder if you could add to that? I mean, you've probably heard this story before from other clients, as well as TriHealth, I call it EMR chaos. What can you add to this conversation? I'm particularly interested in what IBM Watson Health brings to the table. >> Sure, we've continued to work with TriHealth. And like we said earlier, you do have to walk before you can run. So a lot of this solution being put in place, was getting that archive stood up and getting all the images transferred out of the legacy systems. And I think that we're nearly done with that process. Doing some find audits, able to turn off some of the legacy systems. So the data is there for the easier to do modalities first, the radiology, the cardiology, the OB, as Dr. Huelsman mentioned and the ortho. And now it's really getting to the exciting point of really optimizing everything and then starting to bring in other ologies from the health system, trying to get everything in that single EMR view. So there was a lot of activity going on last year with optimizing the system, trying to fine tune hanging protocols, make the workflow for everybody, so that the systems are efficient. And I think we will continue on that road this year. We'll continue down further with other pieces of the solution that were not implemented yet. So there's some deeper image sharing pieces that are available. There are some pieces with mobile device image capture and video capture that can be deployed. So we look forward to working in 2021 on some of those areas, as well as the increased AI solutions. >> So Dr. Huelsman I wonder if you could double click on that. I mean if you're talking to IBM, what are the priorities that you have? What do you, what do you really need from Watson Health to get there? >> So I spoke with Daniel early last week, and sort of described it as now we have the foundation, we sort of have the skeleton and now it's time to put meat on the bones. And so what we're excited about is the upcoming patient synopsis would be the first piece of AI cognitive solutions that Watson Health Imaging provides. And it's sort of that partnership of we're not expecting it to be perfect, but is it better than we have today? There is no perfect solution, but does it improve our current workflow? And so we'll be very interested of when we go live with patients synopsis of does this help? Is this better than what we have today? And the focus then becomes partnering with Watson Health Imaging is how do we make it better for ourselves? How do we make it better for you? I think we're a large health organization and typically we're not an academic or heavy research institution, but we take care of a lot of patients. And if we can work together, I think we'll find solutions. It's really that triple aim of how to provide better care, at cheaper costs, with a better experience. And that's what we're all after. And what's your version of patient, the current version of patients synopsis, and okay does it work for us? Well, even if it does, how do we make it better? Or if it doesn't, how do we make it work? And I think if we work together, make it work for TriHealth, you can make it work at all your community-based health organizations. >> Yeah. So, John that brings me to, Dr. Huelsman mentioned a couple of things in terms of the outcomes. Lower costs, better patient experience, et cetera. I mean, generally for clients, how do you measure success? And then specifically with regard to TriHealth, what's that like? What's that part of the partnership? >> Yes, specifically with TriHealth, the measure of success will be when Dr. Huelsman is able to call and be a super reference for us, and have these tools working to his satisfaction. And when he's been able to give us great input from the customer side, to help improve the science side of it. So today he's able to launch his epic EMR in context and he has to dig through the data, looking for those valuable nuggets and with using natural language processing, when he has patients synopsis, that will all be done for him. He'll be able to pull up the study, a CT of the head for instance and he'll be able to get those nuggets of information using natural language processing that Watson services and get the valuable insights without spending five or 10 minutes interrogating the EMR. So we look forward to those benefits for him, from the data analytics side, but then we also look forward to in the future, delivering other AI for the imaging side, to help him find the slices of interest and the defects that are in that particular study. So whether that's with our partner AI solutions or as we bring care advisers to market. So we look forward to his input on those also. >> Can you comment on that Dr. Huelsman? I would imagine that you would be really looking forward to that vision that John just laid out, as well as other practitioners in your organization. Maybe you could talk about that, is that sort of within your reach? What can you tell us? >> Well, absolutely. That was sort of the shared vision and relationship that we hope for and sort of have that shared outlook is we have all this data, how do we analyze it to improve, provide better care cheaper? And there's no way to do that without you harnessing technology. And IBM has been on the cutting edge of technology for my lifetime. And so it's very exciting to have a partnership with WHI and IBM. There's a history, there's a depth. And so how do we work together to advance, because we want the same things. What impressed me was sure, radiology and AI has been in the news and been hyped and some think over-hyped, and what have you. Everyone's after that Holy grail. But it's that sense of you have the engineers that you talk to, but there is an understanding that don't design the system for the engineers, design it for the end user. Design it for the radiologist. Talk to the end user, because it can be the greatest tool in the world, but I can tell you as a radiologist, if it interrupts my workflow, if it interrupts my search pattern for looking at images, it doesn't help me and radiologists won't use it. And so just having a great algorithm won't help. It is how do you present it to the end user? How do I access it? How can I easily toggle on and off, or do I have to minimize and maximize, and log into a different system. We talked earlier is one throat to choke, or one vendor to hug, we only want one interface. Radiologists and users just want to look at their... They have the radiology viewer, they have their PACS, we look at it all day and you don't want to minimize that and bring up something else, you want to keep interacting with what you're used to. And the mouse buttons do the same thing, it's a mouse click away. And that's what the people at Watson Health Imaging that we've interacted with, they get it. They understand that's what a radiologist would want. They want to continue interacting with their PACS, not with a third vendor or another program or something else. >> I love that. That ton of outside in thinking, starting with the radiologist, back to the engineer, not the reverse. I think that's something that IBM, and I've been watching IBM for a long time, it's something that IBM has brought to the table with its deep industry expertise. I maybe have some other questions, but John I wanted to give you an opportunity. Is there anything that you would like to ask Dr. Huelsman that maybe I haven't touched on yet? >> Yeah. Being back on your account this year, what do you see as a success? What would you count as a success at the end of 2021, if we can deliver this year for you? >> The success would be say, at the end of the year, we've got the heavy hitters, all stored on the archive. Do we pick up all the little, we've got the low hanging fruit, now can we go after the line placement imaging and the arthroscopy and dioscomy, and all those smaller volume in pickups, that we truly get all of our imaging stored on that archive. And then the even larger piece is then do we start using the data on the archive with some cognitive solution? I would love to successfully implement, whether it's patient synopsis or one of the care advisors, that we start using sort of the analytics, the machine learning, some AI component that we successfully implement and maybe share good ideas with you. And sure we intend to go live with patient synopsis next month. I would love it by the end of the year, if the version that we're using patients synopsis and we find it helpful. And the version we use is better than what we went live with next month, because of feedback that we're able to give you. >> Great we looked forward to working with you on that. I guess, personally, with the pandemic in 2020, what have you become, I guess in 2020 that maybe you weren't a year ago before the pandemic, just out of curiosity? >> I'm not sure if we're anything different. A mantra that we've used in the department of radiology at TriHealth for a decade, "Improved service become more adaptable." And we're a service industry, so of course we want to improve service, but be adaptable, become more adaptable. And COVID certainly emphasize that need to be adaptable, to be flexible and the better tools we have. It was great early in the COVID when we had the shutdowns, we found ourselves, we have way more radiologists than we had studies that needed interpreted. So we were flexible all often and be home more. Well, the referring physicians don't know like, well is Dr. Huelsman working today? We don't expect them to look up our schedules. If I get a page that, Hey, can you take a look at this? It was great that at that time I didn't have a home workstation, but I had iConnect access. Before there was no way for me to access the images without getting on a VPN and logging on, it takes 10, 15 minutes before I'm able. Instead I could answer the phone, and I'm not going to say, "Oh, I'm sorry, I'm not at the hospital day, call this number someone else will help you." I have my iPad, go to ica.trihealth.com logged on, I'm looking at the images two minutes later. And so the ease of use, the flexibility, it helped us become adaptable. And I anticipate with we're upgrading the radiology viewer and the iConnect access next month as well, to try to educate our referring physicians, of sort of the image sharing capabilities within that next version of our viewer. Because telehealth has become like everywhere else. It's become much more important at TriHealth during this pandemic. And I think it will be a very big satisfier for both referring physicians and patients, that those image sharing capabilities, to be able to look at the same image, see the annotation that either the radiologist or the referring physician, oncologist, whoever is wanting to share images with the patient and the patient's family, to have multiple parties on at the same time. It will be very good. >> With the new tools that you have for working from home with your full workstation, are you as efficient reading at home? >> Yes. >> And having full access to the PACS as in-house? >> Absolutely. >> That's great to hear. Have you been able to take advantage of using any of the collaboration tools within iConnect, to collaborate with a referring physician, where he can see your pointer and you can see his, or is that something we need to get working? >> Hopefully if you ask me that a year from now, the answer will be yes. >> So does that exit a radiologist? Does that help a radiologist communicate with a referring physician? Or do you feel that that's going to be a- >> Absolutely. We still have our old school physicians that we love who come to the reading room, who come to the department of radiology and go over studies together. But it's dwindling, it's becoming fewer and fewer as certain individuals retire. And it's just different. But the more direct interaction we can have with referring physicians, the better information they can give us. And the more we're interacting directly, the better we are. And so I get it, they're busy, they don't want to, they may not be at the hospital. They're seeing patients at an outpatient clinic and a radiologist isn't even there, that's where that technology piece. This is how we live. We're an instantaneous society. We live through our phone and so great it's like a FaceTime capability. If you want to maintain those personal relationships, we're learning we can't rely on the orthopedist or whomever, whatever referring physician to stop by our reading room, our department. We need to make ourselves available to them and make it convenient. >> That market that you working in Cincinnati, we have a luxury of having quite a few customers with our iConnect solutions. There's been some talk between the multiple parties, of potentially being able to look across the other sites and using that common tool, but being able to query the other archives. Is that something that you'd in favor of supporting and think would add value so that the clinicians can see the longitudinal record? >> Yes And we already have that ability of we can view care everywhere in our EMR. So we don't have the images right away, but we can see other reports. Again, it's not convenient. It's not a click away, but it's two, three, four clicks away. But if I see, if it's one of my search patterns of I just worked the overnight shift last week and then you get something through the ER and there's no comparisons, and it's an abnormal chest CT. Well, I look in Care Everywhere. Oh, they had a chest CT at a different place in the city a year ago, and I can see the report. And so then at that time I can request, and it can take an hour or so, but look back and the images will be accessible to me. But so how do we improve on that? Is to make the images, that I don't have to wait an hour for the images. If we have image sharing among your organizations that can be much quicker, would be a big win. >> As you read in your new environment, do you swivel your chair and still read out of any other specialty systems, for any types of studies today? >> No, and that was a huge win. We used to have a separate viewing system for mammography and we were caught like there were dedicated viewing stations. And so even though we're a system, the radiologist working at this hospital, had to read the mammograms taken at that hospital. And one at the other hospital could only read the ones taken at that hospital. And you couldn't share the workload if it was heavy at one site and light at the other. Well, now it's all viewed through the radiology viewer if you merge PACS, in not just general radiology, but impressed. It has been so much better world that the workflow is so much better, that we can share the work list and be much more efficient. >> Do you feel that in your, your new world, that you're able to have less cherry picking between the group, I guess? Do you feel like there's less infighting or that the exams are being split up evenly through the work list? Or are you guys using some sort of assignment? >> No. And I'm curious with our next version of PACS, the next version of merge packs of 008. I forget which particular >> John: 008. >> It's 008, yeah. I know there's the feature of a smart work list to distribute the exams. Currently, we just have one. It's better than what we have before. It's one large list. We've subdivided, teased out some things that not all of the radiologist read of like MSK and cardiac and it makes it more convenient. But currently it is the radiologist choose what study they're going to open next. To me how I personally attack the list is I don't look at the list. Some radiologists can spend more time choosing what they're going to read next than they do reading. (chuckles) And so if you don't even look, and so the feature I love is just I don't want to take my eyes off my main viewer. And I don't want to swivel my chair. I don't want to turn my head to look at the list, I want everything right in front of me. And so currently the way you can use it is I never look at the list. I just use the keyboard shortcuts of, okay, well I'm done with that study. I mark it, there's one button I click on my mouse that marks it dictated, closes it and brings up the next study on the list. >> Hey guys, I got to jump in. We're running up against the clock, but John if you've got any final thoughts or Dr. Huelsman, please. >> Sure. Dr. Huelsman, I guess any homework for me? What are the top two or three things I can help you with in 2021 to be successful? >> Keep us informed of what you're working on, of what's available now. What's coming next, and how soon is it available? And you let us see those things? And we'll give you a feedback of hey, this is great. And we'll try to identify things, if you haven't thought of them, hey, this would be very helpful. >> Gents, great conversation. Gosh we could go on for another 45 minutes. And John you really have a great knowledge of the industry. And Dr. Huelsman, thanks so much for coming on. Appreciate it. >> Thank you. >> You're welcome >> And thanks for spending some time with us. You're watching "Client Conversations" with IBM Watson Health.

>>Welcome to this IBM Watson health client conversation here. We're probing the dynamics of the relationship between IBM and its clients. And we're looking back, we're going to explore the present. We're going to discuss the future state of healthcare. My name is Dave Volante from the Cuban with me are Robert Stell horn. Who's associate director, H E O R at sukha, otherwise known as pharmaceuticals, America and Rena Felton. Who's with of course, IBM Watson health. Welcome folks. Great to have you. Hi, so like strong relationships, as we know, they're the foundation of any partnership. And of course over the past year, we've had to rely on both personal and professional relationships to get us through some of the most challenging times, if not the most challenging times of our lives. So let me start with you, Robert, how has the partnership with IBM helped you in 2020? >>I think it was just a continuation of the excellent relationship we have with Rena and IBM. Um, starting in March, we had really a shift to an all remote, uh, workplace environment. And I think that constant communication with Rina and IBM helped that situation because she kept us up to date with, uh, additional products and offerings. And basically we came up with some additional solutions towards the end of the year. So we're gonna watch >>Pick it up from here. Let's go, let's go a little bit deeper and maybe you can talk about some of the things that you've done with Robert and his team and, and maybe some of the accomplishments that you're most proud of in 2020. >>No, absolutely. And I have to kind of echo what you first said about the foundation and our partnerships being the foundation, um, of our past present and future. So I do want to take the opportunity to thank Rob again for joining us today. It is, um, I know, you know, with his kids home and remote learning, um, it's a lot, uh, to, to ask in addition to, you know, your day to day work. So, so thank you, Rob. Um, I guess the question that I have for you is what would be the greatest accomplishment, um, that Watseka and IBM Watson had in 2020? >>I would say it was the addition of the linked claims EMR data, the LDCD product that we were able to license in-house, uh, thanks to your attention and to show the advantages and the strengths of that data. We are able to license that in to our, uh, set up assets we have internally. And what that's gonna allow us to do is really find out more information about the patients. Uh, we're existing users of the Mark IBM, uh, market scan data. Um, this is going to allow us to tie into those same patients and find out more about them. Um, in particular, uh, a lot of our products are in the mental health space and a lot about standing questions we have are why are the patients getting different products? And with the notes are available in that link data. We're going to now be able to tap into more information about what is happening with the patient. >>Okay. Can I ask a question on that? Um, if you guys don't mind, I mean, you know, when you, when you hear about, you know, uh, EMR, uh, in the early days, it was a lot about meaningful use and getting paid. It sounds like you guys are taking it much deeper and as a, as a, you know, as an individual, right, you're, you're really happy to hear that this information is now going to be used to really improve, uh, healthcare is, do I have that right? Is that, you know, kind of the nature of where you guys are headed? >>Well, I think ultimately it's the, the, the, the main goal is to help the patients and provide the products that can really, um, help them in their daily lives. So, um, really with this data, now, we're going to be able to tap into more of the why, um, exist in claims data. We cannot really get that information, why VC information, about what diagnoses they incurred during their treatment history. And we also can see, uh, different prescriptions that are given to them, but now we're going to be able to tie that together and get more understanding to really see more focused treatment pattern for them. >>So, Reno, w w you sit down with Rob, do you have like a, sort of a planning session for 2021? Why don't you sort of bring us up to, uh, to what your thinking is there and how you guys are working together this year? >>Yeah, no, absolutely. Um, actually, before we get to that, I wanted to kind of add onto what Rob was saying as well. It's interesting given, you know, the pandemic in 2020 and what the LCD data is going to do, um, to really be able to look back. And as Rob mentioned, looking specifically at mental health, the ability to look back and start looking at the patients and what it's really done to our community and what it's really done to our country, um, and looking at patients, you know, looking back at, at sort of their, their patient journey and where we are today. Um, but Rob and I talk all the time, we talk all the time, we probably talk three or four times a day sometimes. So I would say, um, we, we text, uh, we do talk and have a lot of our strategic, um, sessions, uh, our outlook for 2021 and what the data strategy is for Otsuka. Um, in addition, additional data assets to acquire from IBM, as well as how can we sort of leverage brander IBM, um, assets like our red hat, our OpenShift, our cloud-based solutions. So, you know, Rob and I are constantly talking and we are, um, looking for new ways to bring in new solutions into Otsuka. Um, and you know, yeah, we, we, we talk a lot. What do you think, Rob? >>I think we have an excellent partnership. Uh, basically, um, I think their relationship there is excellent. Um, we have excellent communication and, you know, I find when there's situations where I may be a bind Reno's is able to help out instantly. Um, so it's, it's really a two way street and it's an excellent partnership. >>I wonder if I could double click on that. I mean, relative to maybe some of your, I mean, I'm sure you have lots of relationships with lots of different companies, but, but what makes it excellent specifically with regard to IBM? Is there, is there anything unique Rob, that stands out to you? >>It would be the follow-up, um, really, it's not just about, uh, delivering the data and say, okay, here you have your, your product work with it in basically the, the, the vendor disappears, it's the constant followup to make sure that it's being used in any way they can help and provide more information to really extract the full value out of it. >>So I'm gonna forget to ask you guys, maybe each of you, you know, both personally and professionally, I feel like, you know, 20, 20 never ended it just sort of blended in, uh, and, and, but some things have changed. We all talk about, geez, what's going to be permanent. How have you each been affected? Um, how has it helped you position for, for what's coming in in the years ahead, maybe Reena, you could start and then pick it up with, with Rob. >>Oh man. Um, you know, 2020 was definitely challenging and I think it was really challenging given the circumstances and in my position where I'm very much used to meeting with our customers and having lunch and really just kind of walking down the hallways and bumping into familiar faces and really seeing, you know, how we can provide value with our solutions. And so, you know, that was all stripped in 2020. Uh, so it's been, it's been quite challenging. I will say, working with Rob, working with some of my other customers, um, I've had, uh, I've had to learn the resilience and to be a little bit more relentless with phone calls and follow ups and, and being more agile in my communications with the customers and what their needs are, and be flexible with calendars because there's again, remote learning and, and, um, and the like, so I think, you know, positioned for 2021 really well. Um, I am excited to hopefully get back out there and start visiting our customers. But if not, I certainly learned a lot and just, um, the follow-up and again, the relentless phone calls and calling and checking up on our customers, even if it's just to say, hi, see how everyone's doing a mental check sometimes. So I think that's, that's become, um, you know, what 2020 was, and, and hopefully, you know, what, 2021 will be better and, uh, kind of continue on that, that relentless path. >>What do you think, Rob? Hi, how are you doing? >>I would echo a lot of Rina's thoughts and the fact of, yeah, definitely miss the in-person interaction. In fact, I will say that I remember the last time I was physically in the office that Scott, it was to meet with Rina. So I distinctively remember that they remember the date was March, I believe, March 9th. So it just shows how this year as has been sort of a blur, but at the same time, you remember certain milestones. And I think it's because of that relationship, um, we've developed with IBM that I can remember those distinctive milestones and events that took place. >>So Rob, I probably should have asked you upfront, maybe tell us a little bit about Alaska, uh, maybe, maybe give us the sort of quick soundbite on where you guys are mostly focused. Sure. >>Oh, it's guys, uh, a Japanese pharmaceutical company. The focus is in mental health and nephrology, really the two main business areas. Um, my role at guys to do the internal research and data analytics within the health economics and outcomes research group. Um, currently we are transitioned to a, uh, name, which is global value and real world evidence. Um, fact that transition is already happened. Um, so we're going to have more of a global presence going forward. Um, but my role is really to, uh, do the internal research across all the brands within the company. >>So, so Rena, I wonder this, thank you for that, Robert. I wonder if you could think, thinking about what you know about Scott and your relationship with Robin, your knowledge of, of the industry. Uh, there's so much that IBM can bring to the table. Rob was talking about data earlier, talking about EMR, you were talking about, you know, red hat and cloud and this big portfolio you have. So I wonder if you could sort of start a conversation for our audience just around how you guys see all those assets that you have and all the knowledge, all that data. How do you see the partnership evolving in the future to affect, uh, the industry and the, in the future of healthcare? >>Well, I would love to see, um, the entire, uh, uh, platform, um, shift to, to the IBM cloud, um, and certainly, you know, leverage the cloud pack and analytics that, that we have to offer, um, baby steps most definitely. Um, but I do think that there is, uh, the opportunity to really move, um, and transform the business into something a lot more than, than what it is. >>Rob has the pandemic effected sort of how you think about, um, you know, remote services and cloud services and the, like, were you already on the path headed there? Did accelerate things, have you, you know, have you not had time because things have been so busy or maybe you could comment? >>Yeah, I think it's really a combination. And so I think you hit on a, a fair point there, just the time, uh, aspect. Um, it's definitely been a challenge and your, um, I have two children and remote learning has definitely been a challenge from that perspective. So time has definitely been, uh, on the short side. Um, I do see that there are going to in the future be more and more users of the data. So I think that shift to a potential cloud environment is where things are headed. >>So we, I have a bunch more questions, but I want to step back for a second and see if there's anything that you'd like to ask Rob before I go onto my next section. Okay. So I wonder if you could think about, um, maybe both of you, the, the, when you think back on, on 2020 and all the, you know, what's transpired, what, what transitions did you guys have to make? Uh, maybe as a team together IBM and Alaska. Um, and, and, and what do you see as sort of permanent or semi-permanent is work from home? We're gonna going to continue at a higher rate, uh, are there new practice? I mean, I know just today I made an online appointment it's for a remote visit with my doctor, which never could have happened before the pandemic. Right. But are there things specific to your business and your relationship that you see as a transition that could be permanent or semi-permanent? >>Well, I, I think it's there, there's definitely a shift that's happened that will is here to stay, but I don't know if it's full, it's going to be a combination in the future. I think that in-person interactions, especially what Rena mentioned about having that face-to-face interaction is still going to be one things are in the right place and safe they're going to happen again. But I think the ability to show that work can happen in a virtual or a full remote workplace, that's going to just allow that to continue and really give the flex of people. The flexibility I know for myself, flexibility is key. Like I mentioned, with two small children, um, that, that, that becomes such a valuable addition to your work, your life and your work life in general, that I think that's here to stay. >>Okay. Um, so let me ask you this, uh, w one of the themes of this event is relentless re-invention. So what I'm hearing from you Rob, is that it kind of a hybrid model going forward, if you will, uh, maybe the option to work from home, but that face to face interaction, especially when you're creating things like you are in the pharmaceutical business and the deep R and D that collaborative aspect, you know, you, it's harder when you're, when, when, when you're remote. Um, but maybe you could talk about, you know, some of those key areas that you're, you're going to be focused on in 2021 and, and really where you would look for IBM to help. >>I think in 2021, the team I'm part of it, part of is, is growing. So I think there's going to be additional demand for internal research, uh, uh, capabilities for analysis done within the company. So I think I'm going to be looking to Rena to, uh, see what new data offerings are available and all what new products are going to be available. But beyond that, um, I think it's the potential that, you know, there's so much, uh, projects, um, that are going to be coming to the table. We may need to outsource some of that projects and IBM could be potentially be a partner there to do some of the analysis on to help out there. >>Anything you'd add. >>Uh, no, I think that, that sounds good. >>How would you grade IBM and your relationship with IBM Rob? >>Well, I have to be nice to Rina cause she's been very nice to me. I would say an a, an a plus >>My kids, I got kids in college. Several, they get A's, I'm happy. Oh, that's good. You know, you should be proud. So, congratulations. Um, anything else Reno, you give you, I'll give you a last word here before we wrap, >>You know, 2020 was, was a challenging. And, you know, we talked a little bit about, you know, what time in 2020, you know, Rob and I have always had a really good relationship. I think 2020, we got closer, um, with just both professionally and really diving in to key business challenges that they have, and really working with him to understand what the customer needs are and how we can help, not only from, you know, an HR perspective, but also how can we help Otsuka, um, as a company in, in totality. So, you know, we've been able to do that, but personally, I would say that I really appreciated the relationship. I mean, we can go from talking about work to talking about children, to talking about family, um, all in the same five minute conversation or 10 minute conversation, sometimes our conversation. So, you know, thank you, Rob 2020 was definitely super challenging. >>I know for you on so many levels. Um, but I have to say you've been really great at just showing up every time picked up the phone, asked questions. If I needed something I can call you, I knew you were going to pick up, I had an offering and be like, do you have 10 minutes? Can I share this with you? And you would pick up the phone, no problem, and entertain a call or set up a call with all your internal colleagues. And I, I appreciate that so much. And, you know, I appreciate our relationship. I appreciate the business and I, I do hope that we can continue on in 2021, we will continue on in 2021. Uh, but, um, but yeah, I thank you so much. >>Rain has been extremely helpful. I don't want to thank you for all the help. Um, just to add to that one point there, you know, we have, uh, also another product, which I forgot to mention that we licensed in from IBM, it's the treatment pathways, um, tool, which is an online tool. Um, and we have users throughout the globe. So there's been times where I've needed a new user added very quickly for someone in the home office in Japan. And Rena has been extremely helpful in getting things done quickly and very proactively. >>Well, guys, it's really clear that the depth of your relationship I'm interested that you actually got closer in 2020. Uh, the fact that you communicate, you know, several times a day is I think Testament to that relationship. Uh, I'm really pleased to hear what you're doing and the potential with the EMR data for patient outcomes. Uh, as I say in the early days, I used to hear all about how well you have to do that to get paid. And it's really great to see a partnership that's, that's really focused on, on, on patient health and, and changing our lives. So, and mental health is such an important area that for so many years was so misunderstood and the, and the data that we now have, and of course, IBM's heritage and data is key. Uh, the relationship and the follow-up and also the flexibility is, is something I think we all learned in 2020, we have to, we've kind of redefined, you know, resilience in our organizations and, uh, glad to see you guys are growing. Congratulations on the relationship. And thanks so much for spending some time with me. >>Thank you. Thank you, Dave. Thank you, Raina >>For watching this client conversation with IBM Watson health.

(light upbeat music) >> Hey, everyone. Welcome back to theCUBE's day long coverage of Women in Data Science 2023. Live from Stanford University, I'm Lisa Martin. We've had some amazing conversations today with my wonderful co-host, as you've seen. Tracy Zhang joins me next for a very interesting and inspiring conversation. I know we've been bringing them to you, we're bringing you another one here. Dr. Irene Dankwa-Mullan joins us, the Chief Medical Officer at Marti Health, and a speaker at WIDS. Welcome, Irene, it's great to have you. >> Thank you. I'm delighted to be here. Thank you so much for this opportunity. >> So you have an MD and a Master of Public Health. Covid must have been an interesting time for you, with an MPH? >> Very much so. >> Yeah, talk a little bit about you, your background, and Marti Health? This is interesting. This is a brand new startup. This is a digital health equity startup. >> Yes, yes. So, I'll start with my story a little bit about myself. So I was actually born in Ghana. I finished high school there and came here for college. What would I say? After I finished my undergraduate, I went to medical school at Dartmouth and I always knew I wanted to go into public health as well as medicine. So my medical education was actually five years. I did the MPH and my medical degree, at the same time, I got my MPH from Yale School of Public Health. And after I finished, I trained in internal medicine, Johns Hopkins, and after that I went into public health. I am currently living in Maryland, so I'm in Bethesda, Maryland, and that's where I've been. And really enjoyed public health, community health, combining that aspect of sort of prevention and wellness and also working in making sure that we have community health clinics and safety net clinics. So a great experience there. I also had the privilege, after eight years in public health, I went to the National Institute of Health. >> Oh, wow. >> Where I basically worked in clinical research, basically on minority health and health disparities. So, I was in various leadership roles and helped to advance the science of health equity, working in collaboration with a lot of scientists and researchers at the NIH, really to advance the science. >> Where did your interest in health equity come from? Was there a defining moment when you were younger and you thought "There's a lot of inequities here, we have to do something about this." Where did that interest start? >> That's a great question. I think this influence was basically maybe from my upbringing as well as my family and also what I saw around me in Ghana, a lot of preventable diseases. I always say that my grandfather on my father's side was a great influence, inspired me and influenced my career because he was the only sibling, really, that went to school. And as a result, he was able to earn enough money and built, you know, a hospital. >> Oh wow. >> In their hometown. >> Oh my gosh! >> It started as a 20 bed hospital and now it's a 350 bed hospital. >> Oh, wow, that's amazing! >> In our hometown. And he knew that education was important and vital as well for wellbeing. And so he really inspired, you know, his work inspired me. And I remember in residency I went with a group of residents to this hospital in Ghana just to help over a summer break. So during a summer where we went and helped take care of the sick patients and actually learned, right? What it is like to care for so many patients and- >> Yeah. >> It was really a humbling experience. But that really inspired me. I think also being in this country. And when I came to the U.S. and really saw firsthand how patients are treated differently, based on their background or socioeconomic status. I did see firsthand, you know, that kind of unconscious bias. And, you know, drew me to the field of health disparities research and wanted to learn more and do more and contribute. >> Yeah. >> Yeah. So, I was curious. Just when did the data science aspect tap in? Like when did you decide that, okay, data science is going to be a problem solving tool to like all the problems you just said? >> Yeah, that's a good question. So while I was at the NIH, I spent eight years there, and precision medicine was launched at that time and there was a lot of heightened interest in big data and how big data could help really revolutionize medicine and healthcare. And I got the opportunity to go, you know, there was an opportunity where they were looking for physicians or deputy chief health officer at IBM. And so I went to IBM, Watson Health was being formed as a new business unit, and I was one of the first deputy chief health officers really to lead the data and the science evidence. And that's where I realized, you know, we could really, you know, the technology in healthcare, there's been a lot of data that I think we are not really using or optimizing to make sure that we're taking care of our patients. >> Yeah. >> And so that's how I got into data science and making sure that we are building technologies using the right data to advance health equity. >> Right, so talk a little bit about health equity? We mentioned you're with Marti Health. You've been there for a short time, but Marti Health is also quite new, just a few months old. Digital health equity, talk about what Marti's vision is, what its mission is to really help start dialing down a lot of the disparities that you talked about that you see every day? >> Yeah, so, I've been so privileged. I recently joined Marti Health as their Chief Medical Officer, Chief Health Officer. It's a startup that is actually trying to promote a value-based care, also promote patient-centered care for patients that are experiencing a social disadvantage as a result of their race, ethnicity. And were starting to look at and focused on patients that have sickle cell disease. >> Okay. >> Because we realize that that's a population, you know, we know sickle cell disease is a genetic disorder. It impacts a lot of patients that are from areas that are endemic malaria. >> Yeah. >> Yeah. >> And most of our patients here are African American, and when, you know, they suffer so much stigma and discrimination in the healthcare system and complications from their sickle cell disease. And so what we want to do that we feel like sickle cell is a litmus test for disparities. And we want to make sure that they get in patient-centered care. We want to make sure that we are leveraging data and the research that we've done in sickle cell disease, especially on the continent of Africa. >> Okay. >> And provide, promote better quality care for the patients. >> That's so inspiring. You know, we've heard so many great stories today. Were you able to watch the keynote this morning? >> Yes. >> I loved how it always inspires me. This conference is always, we were talking about this all day, how you walk in the Arrillaga Alumni Center here where this event is held every year, the vibe is powerful, it's positive, it's encouraging. >> Inspiring, yeah. >> Absolutely. >> Inspiring. >> Yeah, yeah. >> It's a movement, WIDS is a movement. They've created this community where you feel, I don't know, kind of superhuman. "Why can't I do this? Why not me?" We heard some great stories this morning about data science in terms of applications. You have a great application in terms of health equity. We heard about it in police violence. >> Yes. >> Which is an epidemic in this country for sure, as we know. This happens too often. How can we use data and data science as a facilitator of learning more about that, so that that can stop? I think that's so important for more people to understand all of the broad applications of data science, whether it's police violence or climate change or drug discovery or health inequities. >> Irene: Yeah. >> The potential, I think we're scratching the surface. But the potential is massive. >> Tracy: It is. >> And this is an event that really helps women and underrepresented minorities think, "Why not me? Why can't I get involved in that?" >> Yeah, and I always say we use data to make an make a lot of decisions. And especially in healthcare, we want to be careful about how we are using data because this is impacting the health and outcomes of our patients. And so science evidence is really critical, you know? We want to make sure that data is inclusive and we have quality data. >> Yes. >> And it's transparent. Our clinical trials, I always say are not always diverse and inclusive. And if that's going to form the evidence base or data points then we're doing more harm than good for our patients. And so data science, it's huge. I mean, we need a robust, responsible, trustworthy data science agenda. >> "Trust" you just brought up "trust." >> Yeah. >> I did. >> When we talk about data, we can't not talk about security and privacy and ethics but trust is table stakes. We have to be able to evaluate the data and trust in it. >> Exactly. >> And what it says and the story that can be told from it. So that trust factor is, I think, foundational to data science. >> We all see what happened with Covid, right? I mean, when the pandemic came out- >> Absolutely. >> Everyone wanted information. We wanted data, we wanted data we could trust. There was a lot of hesitancy even with the vaccine. >> Yeah. >> Right? And so public health, I mean, like you said, we had to do a lot of work making sure that the right information from the right data was being translated or conveyed to the communities. And so you are totally right. I mean, data and good information, relevant data is always key. >> Well- >> Is there any- Oh, sorry. >> Go ahead. >> Is there anything Marti Health is doing in like ensuring that you guys get the right data that you can put trust in it? >> Yes, absolutely. And so this is where we are, you know, part of it would be getting data, real world evidence data for patients who are being seen in the healthcare system with sickle cell disease, so that we can personalize the data to those patients and provide them with the right treatment, the right intervention that they need. And so part of it would be doing predictive modeling on some of the data, risk, stratifying risk, who in the sickle cell patient population is at risk of progressing. Or getting, you know, they all often get crisis, vaso-occlusive crisis because the cells, you know, the blood cell sickles and you want to avoid those chest crisis. And so part of what we'll be doing is, you know, using predictive modeling to target those at risk of the disease progressing, so that we can put in preventive measures. It's all about prevention. It's all about making sure that they're not being, you know, going to the hospital or the emergency room where sometimes they end up, you know, in pain and wanting pain medicine. And so. >> Do you see AI as being a critical piece in the transformation of healthcare, especially where inequities are concerned? >> Absolutely, and and when you say AI, I think it's responsible AI. >> Yes. >> And making sure that it's- >> Tracy: That's such a good point. >> Yeah. >> Very. >> With the right data, with relevant data, it's definitely key. I think there is so much data points that healthcare has, you know, in the healthcare space there's fiscal data, biological data, there's environmental data and we are not using it to the full capacity and full potential. >> Tracy: Yeah. >> And I think AI can do that if we do it carefully, and like I said, responsibly. >> That's a key word. You talked about trust, responsibility. Where data science, AI is concerned- >> Yeah. >> It has to be not an afterthought, it has to be intentional. >> Tracy: Exactly. >> And there needs to be a lot of education around it. Most people think, "Oh, AI is just for the technology," you know? >> Yeah, right. >> Goop. >> Yes. >> But I think we're all part, I mean everyone needs to make sure that we are collecting the right amount of data. I mean, I think we all play a part, right? >> We do. >> We do. >> In making sure that we have responsible AI, we have, you know, good data, quality data. And the data sciences is a multi-disciplinary field, I think. >> It is, which is one of the things that's exciting about it is it is multi-disciplinary. >> Tracy: Exactly. >> And so many of the people that we've talked to in data science have these very non-linear paths to get there, and so I think they bring such diversity of thought and backgrounds and experiences and thoughts and voices. That helps train the AI models with data that's more inclusive. >> Irene: Yes. >> Dropping down the volume on the bias that we know is there. To be successful, it has to. >> Definitely, I totally agree. >> What are some of the things, as we wrap up here, that you're looking forward to accomplishing as part of Marti Health? Like, maybe what's on the roadmap that you can share with us for Marti as it approaches the the second half of its first year? >> Yes, it's all about promoting health equity. It's all about, I mean, there's so much, well, I would start with, you know, part of the healthcare transformation is making sure that we are promoting care that's based on value and not volume, care that's based on good health outcomes, quality health outcomes, and not just on, you know, the quantity. And so Marti Health is trying to promote that value-based care. We are envisioning a world in which everyone can live their full life potential. Have the best health outcomes, and provide that patient-centered precision care. >> And we all want that. We all want that. We expect that precision and that personalized experience in our consumer lives, why not in healthcare? Well, thank you, Irene, for joining us on the program today. >> Thank you. >> Talking about what you're doing to really help drive the volume up on health equity, and raise awareness for the fact that there's a lot of inequities in there we have to fix. We have a long way to go. >> We have, yes. >> Lisa: But people like you are making an impact and we appreciate you joining theCUBE today and sharing what you're doing, thank you. >> Thank you. >> Thank you- >> Thank you for having me here. >> Oh, our pleasure. For our guest and Tracy Zhang, this is Lisa Martin from WIDS 2023, the eighth Annual Women in Data Science Conference brought to you by theCUBE. Stick around, our show wrap will be in just a minute. Thanks for watching. (light upbeat music)

>>Good evening everyone and welcome back to Sparkly Sin City, Las Vegas, Nevada, where we are here with the cube covering AWS Reinvent for the 10th year in a row. John Furrier has been here for all 10. John, we are in our last session of day one. How does it compare? >>I just graduated high school 10 years ago. It's exciting to be, here's been a long time. We've gotten a lot older. My >>Got your brain is complex. You've been a lot in there. So fast. >>Graduated eight in high school. You know how it's No. All good. This is what's going on. This next segment, wrapping up day one, which is like the the kickoff. The Mondays great year. I mean Tuesdays coming tomorrow big days. The announcements are all around the kind of next gen and you're starting to see partnering and integration is a huge part of this next wave cuz API's at the cloud, next gen cloud's gonna be deep engineering integration and you're gonna start to see business relationships and business transformation scale a horizontally, not only across applications but companies. This has been going on for a while, covering it. This next segment is gonna be one of those things that we're gonna look at as something that's gonna happen more and more on >>Yeah, I think so. It's what we've been talking about all day. Without further ado, I would like to welcome our very exciting guest for this final segment, trust from single store. Thank you for being here. And we also have him on from IBM Data and ai. Y'all are partners. Been partners for about a year. I'm gonna go out on a limb only because their legacy and suspect that a few people, a few more people might know what IBM does versus what a single store does. So why don't you just give us a little bit of background so everybody knows what's going on. >>Yeah, so single store is a relational database. It's a foundational relational systems, but the thing that we do the best is what we call us realtime analytics. So we have these systems that are legacy, which which do operations or analytics. And if you wanted to bring them together, like most of the applications want to, it's really a big hassle. You have to build an ETL pipeline, you'd have to duplicate the data. It's really faulty systems all over the place and you won't get the insights really quickly. Single store is trying to solve that problem elegantly by having an architecture that brings both operational and analytics in one place. >>Brilliant. >>You guys had a big funding now expanding men. Sequel, single store databases, 46 billion again, databases. We've been saying this in the queue for 12 years have been great and recently not one database will rule the world. We know that. That's, everyone knows that databases, data code, cloud scale, this is the convergence now of all that coming together where data, this reinvent is the theme. Everyone will be talking about end to end data, new kinds of specialized services, faster performance, new kinds of application development. This is the big part of why you guys are working together. Explain the relationship, how you guys are partnering and engineering together. >>Yeah, absolutely. I think so ibm, right? I think we are mainly into hybrid cloud and ai and one of the things we are looking at is expanding our ecosystem, right? Because we have gaps and as opposed to building everything organically, we want to partner with the likes of single store, which have unique capabilities that complement what we have. Because at the end of the day, customers are looking for an end to end solution that's also business problems. And they are very good at real time data analytics and hit staff, right? Because we have transactional databases, analytical databases, data lakes, but head staff is a gap that we currently have. And by partnering with them we can essentially address the needs of our customers and also what we plan to do is try to integrate our products and solutions with that so that when we can deliver a solution to our customers, >>This is why I was saying earlier, I think this is a a tell sign of what's coming from a lot of use cases where people are partnering right now you got the clouds, a bunch of building blocks. If you put it together yourself, you can build a durable system, very stable if you want out of the box solution, you can get that pre-built, but you really can't optimize. It breaks, you gotta replace it. High level engineering systems together is a little bit different, not just buying something out of the box. You guys are working together. This is kind of an end to end dynamic that we're gonna hear a lot more about at reinvent from the CEO ofs. But you guys are doing it across companies, not just with aws. Can you guys share this new engineering business model use case? Do you agree with what I'm saying? Do you think that's No, exactly. Do you think John's crazy, crazy? I mean I all discourse, you got out of the box, engineer it yourself, but then now you're, when people do joint engineering project, right? They're different. Yeah, >>Yeah. No, I mean, you know, I think our partnership is a, is a testament to what you just said, right? When you think about how to achieve realtime insights, the data comes into the system and, and the customers and new applications want insights as soon as the data comes into the system. So what we have done is basically build an architecture that enables that we have our own storage and query engine indexing, et cetera. And so we've innovated in our indexing in our database engine, but we wanna go further than that. We wanna be able to exploit the innovation that's happening at ibm. A very good example is, for instance, we have a native connector with Cognos, their BI dashboards right? To reason data very natively. So we build a hyper efficient system that moves the data very efficiently. A very other good example is embedded ai. >>So IBM of course has built AI chip and they have basically advanced quite a bit into the embedded ai, custom ai. So what we have done is, is as a true marriage between the engineering teams here, we make sure that the data in single store can natively exploit that kind of goodness. So we have taken their libraries. So if you have have data in single store, like let's imagine if you have Twitter data, if you wanna do sentiment analysis, you don't have to move the data out model, drain the model outside, et cetera. We just have the pre-built embedded AI libraries already. So it's a, it's a pure engineering manage there that kind of opens up a lot more insights than just simple analytics and >>Cost by the way too. Moving data around >>Another big theme. Yeah. >>And latency and speed is everything about single store and you know, it couldn't have happened without this kind of a partnership. >>So you've been at IBM for almost two decades, don't look it, but at nearly 17 years in how has, and maybe it hasn't, so feel free to educate us. How has, how has IBM's approach to AI and ML evolved as well as looking to involve partnerships in the ecosystem as a, as a collaborative raise the water level together force? >>Yeah, absolutely. So I think when we initially started ai, right? I think we are, if you recollect Watson was the forefront of ai. We started the whole journey. I think our focus was more on end solutions, both horizontal and vertical. Watson Health, which is more vertically focused. We were also looking at Watson Assistant and Watson Discovery, which were more horizontally focused. I think it it, that whole strategy of the world period of time. Now we are trying to be more open. For example, this whole embedable AI that CICE was talking about. Yeah, it's essentially making the guts of our AI libraries, making them available for partners and ISVs to build their own applications and solutions. We've been using it historically within our own products the past few years, but now we are making it available. So that, how >>Big of a shift is that? Do, do you think we're seeing a more open and collaborative ecosystem in the space in general? >>Absolutely. Because I mean if you think about it, in my opinion, everybody is moving towards AI and that's the future. And you have two option. Either you build it on your own, which is gonna require significant amount of time, effort, investment, research, or you partner with the likes of ibm, which has been doing it for a while, right? And it has the ability to scale to the requirements of all the enterprises and partners. So you have that option and some companies are picking to do it on their own, but I believe that there's a huge amount of opportunity where people are looking to partner and source what's already available as opposed to investing from the scratch >>Classic buy versus build analysis for them to figure out, yeah, to get into the game >>And, and, and why reinvent the wheel when we're all trying to do things at, at not just scale but orders of magnitude faster and and more efficiently than we were before. It, it makes sense to share, but it's, it is, it does feel like a bit of a shift almost paradigm shift in, in the culture of competition versus how we're gonna creatively solve these problems. There's room for a lot of players here, I think. And yeah, it's, I don't >>Know, it's really, I wanted to ask if you don't mind me jumping in on that. So, okay, I get that people buy a bill I'm gonna use existing or build my own. The decision point on that is, to your point about the path of getting the path of AI is do I have the core competency skills, gap's a big issue. So, okay, the cube, if you had ai, we'd take it cuz we don't have any AI engineers around yet to build out on all the linguistic data we have. So we might use your ai but I might say this to then and we want to have a core competency. How do companies get that core competency going while using and partnering with, with ai? What you guys, what do you guys see as a way for them to get going? Because I think some people probably want to have core competency of >>Ai. Yeah, so I think, again, I think I, I wanna distinguish between a solution which requires core competency. You need expertise on the use case and you need expertise on your industry vertical and your customers versus the foundational components of ai, which are like, which are agnostic to the core competency, right? Because you take the foundational piece and then you further train it and define it for your specific use case. So we are not saying that we are experts in all the industry verticals. What we are good at is like foundational components, which is what we wanna provide. Got it. >>Yeah, that's the hard deep yes. Heavy lift. >>Yeah. And I can, I can give a color to that question from our perspective, right? When we think about what is our core competency, it's about databases, right? But there's a, some biotic relationship between data and ai, you know, they sort of like really move each other, right? You >>Need, they kind of can't have one without the other. You can, >>Right? And so the, the question is how do we make sure that we expand that, that that relationship where our customers can operationalize their AI applications closer to the data, not move the data somewhere else and do the modeling and then training somewhere else and dealing with multiple systems, et cetera. And this is where this kind of a cross engineering relationship helps. >>Awesome. Awesome. Great. And then I think companies are gonna want to have that baseline foundation and then start hiring in learning. It's like driving the car. You get the keys when you're ready to go. >>Yeah, >>Yeah. Think I'll give you a simple example, right? >>I want that turnkey lifestyle. We all do. Yeah, >>Yeah. Let me, let me just give you a quick analogy, right? For example, you can, you can basically make the engines and the car on your own or you can source the engine and you can make the car. So it's, it's basically an option that you can decide. The same thing with airplanes as well, right? Whether you wanna make the whole thing or whether you wanna source from someone who is already good at doing that piece, right? So that's, >>Or even create a new alloy for that matter. I mean you can take it all the way down in that analogy, >>Right? Is there a structural change and how companies are laying out their architecture in this modern era as we start to see this next let gen cloud emerge, teams, security teams becoming much more focused data teams. Its building into the DevOps into the developer pipeline, seeing that trend. What do you guys see in the modern data stack kind of evolution? Is there a data solutions architect coming? Do they exist yet? Is that what we're gonna see? Is it data as code automation? How do you guys see this landscape of the evolving persona? >>I mean if you look at the modern data stack as it is defined today, it is too detailed, it's too OSes and there are way too many layers, right? There are at least five different layers. You gotta have like a storage you replicate to do real time insights and then there's a query layer, visualization and then ai, right? So you have too many ETL pipelines in between, too many services, too many choke points, too many failures, >>Right? Etl, that's the dirty three letter word. >>Say no to ETL >>Adam Celeste, that's his quote, not mine. We hear that. >>Yeah. I mean there are different names to it. They don't call it etl, we call it replication, whatnot. But the point is hassle >>Data is getting more hassle. More >>Hassle. Yeah. The data is ultimately getting replicated in the modern data stack, right? And that's kind of one of our thesis at single store, which is that you'd have to converge not hyper specialize and conversation and convergence is possible in certain areas, right? When you think about operational analytics as two different aspects of the data pipeline, it is possible to bring them together. And we have done it, we have a lot of proof points to it, our customer stories speak to it and that is one area of convergence. We need to see more of it. The relationship with IBM is sort of another step of convergence wherein the, the final phases, the operation analytics is coming together and can we take analytics visualization with reports and dashboards and AI together. This is where Cognos and embedded AI comes into together, right? So we believe in single store, which is really conversions >>One single path. >>A shocking, a shocking tie >>Back there. So, so obviously, you know one of the things we love to joke about in the cube cuz we like to goof on the old enterprise is they solve complexity by adding more complexity. That's old. Old thinking. The new thinking is put it under the covers, abstract the way the complexities and make it easier. That's right. So how do you guys see that? Because this end to end story is not getting less complicated. It's actually, I believe increasing and complication complexity. However there's opportunities doing >>It >>More faster to put it under the covers or put it under the hood. What do you guys think about the how, how this new complexity gets managed or in this new data world we're gonna be coming in? >>Yeah, so I think you're absolutely right. It's the world is becoming more complex, technology is becoming more complex and I think there is a real need and it's not just from coming from us, it's also coming from the customers to simplify things. So our approach around AI is exactly that because we are essentially providing libraries, just like you have Python libraries, there are libraries now you have AI libraries that you can go infuse and embed deeply within applications and solutions. So it becomes integrated and simplistic for the customer point of view. From a user point of view, it's, it's very simple to consume, right? So that's what we are doing and I think single store is doing that with data, simplifying data and we are trying to do that with the rest of the portfolio, specifically ai. >>It's no wonder there's a lot of synergy between the two companies. John, do you think they're ready for the Instagram >>Challenge? Yes, they're ready. Uhoh >>Think they're ready. So we're doing a bit of a challenge. A little 32nd off the cuff. What's the most important takeaway? This could be your, think of it as your thought leadership sound bite from AWS >>2023 on Instagram reel. I'm scrolling. That's the Instagram, it's >>Your moment to stand out. Yeah, exactly. Stress. You look like you're ready to rock. Let's go for it. You've got that smile, I'm gonna let you go. Oh >>Goodness. You know, there is, there's this quote from astrophysics, space moves matter, a matter tells space how to curve. They have that kind of a relationship. I see the same between AI and data, right? They need to move together. And so AI is possible only with right data and, and data is meaningless without good insights through ai. They really have that kind of relationship and you would see a lot more of that happening in the future. The future of data and AI are combined and that's gonna happen. Accelerate a lot faster. >>Sures, well done. Wow. Thank you. I am very impressed. It's tough hacks to follow. You ready for it though? Let's go. Absolutely. >>Yeah. So just, just to add what is said, right, I think there's a quote from Rob Thomas, one of our leaders at ibm. There's no AI without ia. Essentially there's no AI without information architecture, which essentially data. But I wanna add one more thing. There's a lot of buzz around ai. I mean we are talking about simplicity here. AI in my opinion is three things and three things only. Either you use AI to predict future for forecasting, use AI to automate things. It could be simple, mundane task, it would be complex tasks depending on how exactly you want to use it. And third is to optimize. So predict, automate, optimize. Anything else is buzz. >>Okay. >>Brilliantly said. Honestly, I think you both probably hit the 32nd time mark that we gave you there. And the enthusiasm loved your hunger on that. You were born ready for that kind of pitch. I think they both nailed it for the, >>They nailed it. Nailed it. Well done. >>I I think that about sums it up for us. One last closing note and opportunity for you. You have a V 8.0 product coming out soon, December 13th if I'm not mistaken. You wanna give us a quick 15 second preview of that? >>Super excited about this. This is one of the, one of our major releases. So we are evolving the system on multiple dimensions on enterprise and governance and programmability. So there are certain features that some of our customers are aware of. We have made huge performance gains in our JSON access. We made it easy for people to consume, blossom on OnPrem and hybrid architectures. There are multiple other things that we're gonna put out on, on our site. So it's coming out on December 13th. It's, it's a major next phase of our >>System. And real quick, wasm is the web assembly moment. Correct. And the new >>About, we have pioneers in that we, we be wasm inside the engine. So you could run complex modules that are written in, could be C, could be rushed, could be Python. Instead of writing the the sequel and SQL as a store procedure, you could now run those modules inside. I >>Wanted to get that out there because at coupon we covered that >>Savannah Bay hot topic. Like, >>Like a blanket. We covered it like a blanket. >>Wow. >>On that glowing note, Dre, thank you so much for being here with us on the show. We hope to have both single store and IBM back on plenty more times in the future. Thank all of you for tuning in to our coverage here from Las Vegas in Nevada at AWS Reinvent 2022 with John Furrier. My name is Savannah Peterson. You're watching the Cube, the leader in high tech coverage. We'll see you tomorrow.

>>Welcome to this exclusive discussion. IBM, the next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond. My name is Dave Volante from the Cuban today, we're going to take a deep dive into some of the most important trends impacting the life sciences industry in the next 60 minutes. Yeah, of course. We're going to hear how IBM is utilizing Watson and some really important in life impacting ways, but we'll also bring in real world perspectives from industry and the independent analyst view to better understand how technology and data are changing the nature of precision medicine. Now, the pandemic has created a new reality for everyone, but especially for life sciences companies, one where digital transformation is no longer an option, but a necessity. Now the upside is the events of the past 22 months have presented an accelerated opportunity for innovation technology and real world data are coming together and being applied to support life science, industry trends and improve drug discovery, clinical development, and treatment commercialization throughout the product life cycle cycle. Now I'd like to introduce our esteemed panel. Let me first introduce Lorraine Marshawn, who is general manager of life sciences at IBM Watson health. Lorraine leads the organization dedicated to improving clinical development research, showing greater treatment value in getting treatments to patients faster with differentiated solutions. Welcome Lorraine. Great to see you. >>Dr. Namita LeMay is the research vice-president of IDC, where she leads the life sciences R and D strategy and technology program, which provides research based advisory and consulting services as well as market analysis. The loan to meta thanks for joining us today. And our third panelist is Greg Cunningham. Who's the director of the RWE center of excellence at Eli Lilly and company. Welcome, Greg, you guys are doing some great work. Thanks for being here. Thanks >>Dave. >>Now today's panelists are very passionate about their work. If you'd like to ask them a question, please add it to the chat box located near the bottom of your screen, and we'll do our best to answer them all at the end of the panel. Let's get started. Okay, Greg, and then Lorraine and meta feel free to chime in after one of the game-changers that you're seeing, which are advancing precision medicine. And how do you see this evolving in 2022 and into the next decade? >>I'll give my answer from a life science research perspective. The game changer I see in advancing precision medicine is moving from doing research using kind of a single gene mutation or kind of a single to look at to doing this research using combinations of genes and the potential that this brings is to bring better drug targets forward, but also get the best product to a patient faster. Um, I can give, uh, an example how I see it playing out in the last decade. Non-oncology real-world evidence. We've seen an evolution in precision medicine as we've built out the patient record. Um, as we've done that, uh, the marketplace has evolved rapidly, uh, with, particularly for electronic medical record data and genomic data. And we were pretty happy to get our hands on electronic medical record data in the early days. And then later the genetic test results were combined with this data and we could do research looking at a single mutation leading to better patient outcomes. But I think where we're going to evolve in 2022 and beyond is with genetic testing, growing and oncology, providing us more data about that patient. More genes to look at, uh, researchers can look at groups of genes to analyze, to look at that complex combination of gene mutations. And I think it'll open the door for things like using artificial intelligence to help researchers plow through the complex number of permutations. When you think about all those genes you can look at in combination, right? Lorraine yes. Data and machine intelligence coming together, anything you would add. >>Yeah. Thank you very much. Well, I think that Greg's response really sets us up nicely, particularly when we think about the ability to utilize real-world data in the farm industry across a number of use cases from discovery to development to commercial, and, you know, in particular, I think with real world data and the comments that Greg just made about clinical EMR data linked with genetic or genomic data, a real area of interest in one that, uh, Watson health in particular is focused on the idea of being able to create a data exchange so that we can bring together claims clinical EMR data, genomics data, increasingly wearables and data directly from patients in order to create a digital health record that we like to call an intelligent patient health record that basically gives us the digital equivalent of a real life patient. And these can be used in use cases in randomized controlled clinical trials for synthetic control arms or natural history. They can be used in order to track patients' response to drugs and look at outcomes after they've been on various therapies as, as Greg is speaking to. And so I think that, you know, the promise of data and technology, the AI that we can apply on that is really helping us advance, getting therapies to market faster, with better information, lower sample sizes, and just a much more efficient way to do drug development and to track and monitor outcomes in patients. >>Great. Thank you for that now to meta, when I joined IDC many, many years ago, I really didn't know much about the industry that I was covering, but it's great to see you as a former practitioner now bringing in your views. What do you see as the big game-changers? >>So, um, I would, I would agree with what both Lorraine and Greg said. Um, but one thing that I'd just like to call out is that, you know, everyone's talking about big data, the volume of data is growing. It's growing exponentially actually about, I think 30% of data that exists today is healthcare data. And it's growing at a rate of 36%. That's huge, but then it's not just about the big, it's also about the broad, I think, um, you know, I think great points that, uh, Lorraine and Greg brought out that it's, it's not just specifically genomic data, it's multi omic data. And it's also about things like medical history, social determinants of health, behavioral data. Um, and why, because when you're talking about precision medicine and we know that we moved away from the, the terminology of personalized to position, because you want to talk about disease stratification and you can, it's really about convergence. >>Um, if you look at a recent JAMA paper in 2021, only 1% of EHS actually included genomic data. So you really need to have that ability to look at data holistically and IDC prediction is seeing that investments in AI to fuel in silico, silicone drug discovery will double by 20, 24, but how are you actually going to integrate all the different types of data? Just look at, for example, diabetes, you're on type two diabetes, 40 to 70% of it is genetically inherited and you have over 500 different, uh, genetic low side, which could be involved in playing into causing diabetes. So the earlier strategy, when you are looking at, you know, genetic risk scoring was really single trait. Now it's transitioning to multi rate. And when you say multi trade, you really need to get that integrated view that converging for you to, to be able to drive a precision medicine strategy. So to me, it's a very interesting contrast on one side, you're really trying to make it specific and focused towards an individual. And on the other side, you really have to go wider and bigger as well. >>Uh, great. I mean, the technology is enabling that convergence and the conditions are almost mandating it. Let's talk about some more about data that the data exchange and building an intelligent health record, as it relates to precision medicine, how will the interoperability of real-world data, you know, create that more cohesive picture for the, for the patient maybe Greg, you want to start, or anybody else wants to chime in? >>I think, um, the, the exciting thing from, from my perspective is the potential to gain access to data. You may be weren't aware of an exchange in implies that, uh, some kind of cataloging, so I can see, uh, maybe things that might, I just had no idea and, uh, bringing my own data and maybe linking data. These are concepts that I think are starting to take off in our field, but it, it really opens up those avenues to when you, you were talking about data, the robustness and richness volume isn't, uh, the only thing is Namita said, I think really getting to a rich high-quality data and, and an exchange offers a far bigger, uh, range for all of us to, to use, to get our work done. >>Yeah. And I think, um, just to chime, chime into that, uh, response from Greg, you know, what we hear increasingly, and it's pretty pervasive across the industry right now, because this ability to create an exchange or the intelligent, uh, patient health record, these are new ideas, you know, they're still rather nascent and it always is the operating model. Uh, that, that is the, uh, the difficult challenge here. And certainly that is the case. So we do have data in various silos. Uh, they're in patient claims, they're in electronic medical records, they might be in labs, images, genetic files on your smartphone. And so one of the challenges with this interoperability is being able to tap into these various sources of data, trying to identify quality data, as Greg has said, and the meta is underscoring as well. Uh, we've gotta be able to get to the depth of data that's really meaningful to us, but then we have to have technology that allows us to pull this data together. >>First of all, it has to be de-identified because of security and patient related needs. And then we've gotta be able to link it so that you can create that likeness in terms of the record, it has to be what we call cleaned or curated so that you get the noise and all the missing this out of it, that's a big step. And then it needs to be enriched, which means that the various components that are going to be meaningful, you know, again, are brought together so that you can create that cohort of patients, that individual patient record that now is useful in so many instances across farm, again, from development, all the way through commercial. So the idea of this exchange is to enable that exact process that I just described to have a, a place, a platform where various entities can bring their data in order to have it linked and integrated and cleaned and enriched so that they get something that is a package like a data package that they can actually use. >>And it's easy to plug into their, into their studies or into their use cases. And I think a really important component of this is that it's gotta be a place where various third parties can feel comfortable bringing their data together in order to match it with other third parties. That is a, a real value, uh, that the industry is increasingly saying would be important to them is, is the ability to bring in those third-party data sets and be able to link them and create these, these various data products. So that's really the idea of the data exchange is that you can benefit from accessing data, as Greg mentioned in catalogs that maybe are across these various silos so that you can do the kind of work that you need. And that we take a lot of the hard work out of it. I like to give an example. >>We spoke with one of our clients at one of the large pharma companies. And, uh, I think he expressed it very well. He said, what I'd like to do is have like a complete dataset of lupus. Lupus is an autoimmune condition. And I've just like to have like the quintessential lupus dataset that I can use to run any number of use cases across it. You know, whether it's looking at my phase one trial, whether it's selecting patients and enriching for later stage trials, whether it's understanding patient responses to different therapies as I designed my studies. And so, you know, this idea of adding in therapeutic area indication, specific data sets and being able to create that for the industry in the meta mentioned, being able to do that, for example, in diabetes, that's how pharma clients need to have their needs met is through taking the hard workout, bringing the data together, having it very therapeutically enriched so that they can use it very easily. >>Thank you for that detail and the meta. I mean, you can't do this with humans at scale in technology of all the things that Lorraine was talking about, the enrichment, the provenance, the quality, and of course, it's got to be governed. You've got to protect the privacy privacy humans just can't do all that at massive scale. Can it really tech that's where technology comes in? Doesn't it and automation. >>Absolutely. >>I, couldn't more, I think the biggest, you know, whether you talk about precision medicine or you talk about decentralized trials, I think there's been a lot of hype around these terms, but what is really important to remember is technology is the game changer and bringing all that data together is really going to be the key enabler. So multimodal data integration, looking at things like security or federated learning, or also when you're talking about leveraging AI, you're not talking about things like bias or other aspects around that are, are critical components that need to be addressed. I think the industry is, uh, it's partly, still trying to figure out the right use cases. So it's one part is getting together the data, but also getting together the right data. Um, I think data interoperability is going to be the absolute game changer for enabling this. Uh, but yes, um, absolutely. I can, I can really couldn't agree more with what Lorraine just said, that it's bringing all those different aspects of data together to really drive that precision medicine strategy. >>Excellent. Hey Greg, let's talk about protocols decentralized clinical trials. You know, they're not new to life silences, but, but the adoption of DCTs is of course sped up due to the pandemic we've had to make trade-offs obviously, and the risk is clearly worth it, but you're going to continue to be a primary approach as we enter 2022. What are the opportunities that you see to improve? How DCTs are designed and executed? >>I see a couple opportunities to improve in this area. The first is, uh, back to technology. The infrastructure around clinical trials has, has evolved over the years. Uh, but now you're talking about moving away from kind of site focus to the patient focus. Uh, so with that, you have to build out a new set of tools that would help. So for example, one would be novel trial, recruitment, and screening, you know, how do you, how do you find patients and how do you screen them to see if are they, are they really a fit for, for this protocol? Another example, uh, very important documents that we have to get is, uh, you know, the e-consent that someone's says, yes, I'm, well, I understand this study and I'm willing to do it, have to do that in a more remote way than, than we've done in the past. >>Um, the exciting area, I think, is the use of, uh, eco, uh, E-Pro where we capture data from the patient using apps, devices, sensors. And I think all of these capabilities will bring a new way of, of getting data faster, uh, in, in this kind of model. But the exciting thing from, uh, our perspective at Lily is it's going to bring more data about the patient from the patient, not just from the healthcare provider side, it's going to bring real data from these apps, devices and sensors. The second thing I think is using real-world data to identify patients, to also improve protocols. We run scenarios today, looking at what's the impact. If you change a cut point on a, a lab or a biomarker to see how that would affect, uh, potential enrollment of patients. So it, it definitely the real-world data can be used to, to make decisions, you know, how you improve these protocols. >>But the thing that we've been at the challenge we've been after that this probably offers the biggest is using real-world data to identify patients as we move away from large academic centers that we've used for years as our sites. Um, you can maybe get more patients who are from the rural areas of our countries or not near these large, uh, uh, academic centers. And we think it'll bring a little more diversity to the population, uh, who who's, uh, eligible, but also we have their data, so we can see if they really fit the criteria and the probability they are a fit for the trial is much higher than >>Right. Lorraine. I mean, your clients must be really pushing you to help them improve DCTs what are you seeing in the field? >>Yes, in fact, we just attended the inaugural meeting of the de-central trials research Alliance in, uh, in Boston about two weeks ago where, uh, all of the industry came together, pharma companies, uh, consulting vendors, just everyone who's been in this industry working to help define de-central trials and, um, think through what its potential is. Think through various models in order to enable it, because again, a nascent concept that I think COVID has spurred into action. Um, but it is important to take a look at the definition of DCT. I think there are those entities that describe it as accessing data directly from the patient. I think that is a component of it, but I think it's much broader than that. To me, it's about really looking at workflows and processes of bringing data in from various remote locations and enabling the whole ecosystem to work much more effectively along the data continuum. >>So a DCT is all around being able to make a site more effective, whether it's being able to administer a tele visit or the way that they're getting data into the electronic data captures. So I think we have to take a look at the, the workflows and the operating models for enabling de-central trials and a lot of what we're doing with our own technology. Greg mentioned the idea of electronic consent of being able to do electronic patient reported outcomes, other collection of data directly from the patient wearables tele-health. So these are all data acquisition, methodologies, and technologies that, that we are enabling in order to get the best of the data into the electronic data capture system. So edit can be put together and processed and submitted to the FDA for regulatory use for clinical trial type submission. So we're working on that. I think the other thing that's happening is the ability to be much more flexible and be able to have more cloud-based storage allows you to be much more inter-operable to allow API APIs in order to bring in the various types of data. >>So we're really looking at technology that can make us much more fluid and flexible and accommodating to all the ways that people live and work and manage their health, because we have to reflect that in the way we collect those data types. So that's a lot of what we're, what we're focused on. And in talking with our clients, we spend also a lot of time trying to understand along the, let's say de-central clinical trials continuum, you know, w where are they? And I know Namita is going to talk a little bit about research that they've done in terms of that adoption curve, but because COVID sort of forced us into being able to collect data in more remote fashion in order to allow some of these clinical trials to continue during COVID when a lot of them had to stop. What we want to make sure is that we understand and can codify some of those best practices and that we can help our clients enable that because the worst thing that would happen would be to have made some of that progress in that direction. >>But then when COVID is over to go back to the old ways of doing things and not bring some of those best practices forward, and we actually hear from some of our clients in the pharma industry, that they worry about that as well, because we don't yet have a system for operationalizing a de-central trial. And so we really have to think about the protocol it's designed, the indication, the types of patients, what makes sense to decentralize, what makes sense to still continue to collect data in a more traditional fashion. So we're spending a lot of time advising and consulting with our patients, as well as, I mean, with our clients, as well as CRS, um, on what the best model is in terms of their, their portfolio of studies. And I think that's a really important aspect of trying to accelerate the adoption is making sure that what we're doing is fit for purpose, just because you can use technology doesn't mean you should, it really still does require human beings to think about the problem and solve them in a very practical way. >>Great, thank you for that. Lorraine. I want to pick up on some things that Lorraine was just saying. And then back to what Greg was saying about, uh, uh, DCTs becoming more patient centric, you had a prediction or IDC, did I presume your fingerprints were on it? Uh, that by 20 25, 70 5% of trials will be patient-centric decentralized clinical trials, 90% will be hybrid. So maybe you could help us understand that relationship and what types of innovations are going to be needed to support that evolution of DCT. >>Thanks, Dave. Yeah. Um, you know, sorry, I, I certainly believe that, uh, you know, uh, Lorraine was pointing out of bringing up a very important point. It's about being able to continue what you have learned in over the past two years, I feel this, you know, it was not really a digital revolution. It was an attitude. The revolution that this industry underwent, um, technology existed just as clinical trials exist as drugs exist, but there was a proof of concept that technology works that this model is working. So I think that what, for example, telehealth, um, did for, for healthcare, you know, transition from, from care, anywhere care, anytime, anywhere, and even becoming predictive. That's what the decentralized clinical trials model is doing for clinical trials today. Great points again, that you have to really look at where it's being applied. You just can't randomly apply it across clinical trials. >>And this is where the industry is maturing the complexity. Um, you know, some people think decentralized trials are very simple. You just go and implement these centralized clinical trials, but it's not that simple as it it's being able to define, which are the right technologies for that specific, um, therapeutic area for that specific phase of the study. It's being also a very important point is bringing in the patient's voice into the process. Hey, I had my first telehealth visit sometime last year and I was absolutely thrilled about it. I said, no time wasted. I mean, everything's done in half an hour, but not all patients want that. Some want to consider going back and you, again, need to customize your de-centralized trials model to, to the, to the type of patient population, the demographics that you're dealing with. So there are multiple factors. Um, also stepping back, you know, Lorraine mentioned they're consulting with, uh, with their clients, advising them. >>And I think a lot of, um, a lot of companies are still evolving in their maturity in DCTs though. There's a lot of boys about it. Not everyone is very mature in it. So it's, I think it, one thing everyone's kind of agreeing with is yes, we want to do it, but it's really about how do we go about it? How do we make this a flexible and scalable modern model? How do we integrate the patient's voice into the process? What are the KPIs that we define the key performance indicators that we define? Do we have a playbook to implement this model to make it a scalable model? And, you know, finally, I think what organizations really need to look at is kind of developing a de-centralized mature maturity scoring model, so that I assess where I am today and use that playbook to define, how am I going to move down the line to me reach the next level of maturity. Those were some of my thoughts. Right? >>Excellent. And now remember you, if you have any questions, use the chat box below to submit those questions. We have some questions coming in from the audience. >>At one point to that, I think one common thread between the earlier discussion around precision medicine and around decentralized trials really is data interoperability. It is going to be a big game changer to, to enable both of these pieces. Sorry. Thanks, Dave. >>Yeah. Thank you. Yeah. So again, put your questions in the chat box. I'm actually going to go to one of the questions from the audience. I get some other questions as well, but when you think about all the new data types that are coming in from social media, omics wearables. So the question is with greater access to these new types of data, what trends are you seeing from pharma device as far as developing capabilities to effectively manage and analyze these novel data types? Is there anything that you guys are seeing, um, that you can share in terms of best practice or advice >>I'll offer up? One thing, I think the interoperability isn't quite there today. So, so what's that mean you can take some of those data sources. You mentioned, uh, some Omix data with, uh, some health claims data and it's the, we spend too much time and in our space putting data to gather the behind the scenes, I think the stat is 80% of the time is assembling the data 20% analyzing. And we've had conversations here at Lilly about how do we get to 80% of the time is doing analysis. And it really requires us to think, take a step back and think about when you create a, uh, a health record, you really have to be, have the same plugins so that, you know, data can be put together very easily, like Lorraine mentioned earlier. And that comes back to investing in as an industry and standards so that, you know, you have some of data standard, we all can agree upon. And then those plugs get a lot easier and we can spend our time figuring out how to make, uh, people's lives better with healthcare analysis versus putting data together, which is not a lot of fun behind the scenes. >>Other thoughts on, um, on, on how to take advantage of sort of novel data coming from things like devices in the nose that you guys are seeing. >>I could jump in there on your end. Did you want to go ahead? Okay. So, uh, I mean, I think there's huge value that's being seen, uh, in leveraging those multiple data types. I think one area you're seeing is the growth of prescription digital therapeutics and, um, using those to support, uh, you know, things like behavioral health issues and a lot of other critical conditions it's really taking you again, it is interlinking real-world data cause it's really taking you to the patient's home. Um, and it's, it's, there's a lot of patients in the city out here cause you can really monitor the patient real-time um, without the patient having coming, you know, coming and doing a site visit once in say four weeks or six weeks. So, um, I, and, uh, for example, uh, suicidal behavior and just to take an example, if you can predict well in advance, based on those behavioral parameters, that this is likely to trigger that, uh, the value of it is enormous. Um, again, I think, uh, Greg made a valid point about the industry still trying to deal with resolving the data interoperability issue. And there are so many players that are coming in the industry right now. There are really few that have the maturity and the capability to address these challenges and provide intelligence solutions. >>Yeah. Maybe I'll just, uh, go ahead and, uh, and chime into Nikita's last comment there. I think that's what we're seeing as well. And it's very common, you know, from an innovation standpoint that you have, uh, a nascent industry or a nascent innovation sort of situation that we have right now where it's very fragmented. You have a lot of small players, you have some larger entrenched players that have the capability, um, to help to solve the interoperability challenge, the standards challenge. I mean, I think IBM Watson health is certainly one of the entities that has that ability and is taking a stand in the industry, uh, in order to, to help lead in that way. Others are too. And, uh, but with, with all of the small companies that are trying to find interesting and creative ways to gather that data, it does create a very fragmented, uh, type of environment and ecosystem that we're in. >>And I think as we mature, as we do come forward with the KPIs, the operating models, um, because you know, the devil's in the detail in terms of the operating models, it's really exciting to talk these trends and think about the future state. But as Greg pointed out, if you're spending 80% of your time just under the hood, you know, trying to get the engine, all the spark plugs to line up, um, that's, that's just hard grunt work that has to be done. So I think that's where we need to be focused. And I think bringing all the data in from these disparate tools, you know, that's fine, we need, uh, a platform or the API APIs that can enable that. But I think as we, as we progress, we'll see more consolidation, uh, more standards coming into play, solving the interoperability types of challenges. >>And, um, so I think that's where we should, we should focus on what it's going to take and in three years to really codify this and make it, so it's a, it's a well hum humming machine. And, you know, I do know having also been in pharma that, uh, there's a very pilot oriented approach to this thing, which I think is really healthy. I think large pharma companies tend to place a lot of bets with different programs on different tools and technologies, to some extent to see what's gonna stick and, you know, kind of with an innovation mindset. And I think that's good. I think that's kind of part of the process of figuring out what is going to work and, and helping us when we get to that point of consolidating our model and the technologies going forward. So I think all of the efforts today are definitely driving us to something that feels much more codified in the next three to five years. >>Excellent. We have another question from the audience it's sort of related to the theme of this discussion, given the FDA's recent guidance on using claims and electronic health records, data to support regulatory decision-making what advancements do you think we can expect with regards to regulatory use of real-world data in the coming years? It's kind of a two-parter so maybe you guys can collaborate on this one. What role that, and then what role do you think industry plays in influencing innovation within the regulatory space? >>All right. Well, it looks like you've stumped the panel there. Uh, Dave, >>It's okay to take some time to think about it, right? You want me to repeat it? You guys, >>I, you know, I I'm sure that the group is going to chime into this. I, so the FDA has issued a guidance. Um, it's just, it's, it's exactly that the FDA issues guidances and says that, you know, it's aware and supportive of the fact that we need to be using real-world data. We need to create the interoperability, the standards, the ways to make sure that we can include it in regulatory submissions and the like, um, and, and I sort of think about it akin to the critical path initiative, probably, I don't know, 10 or 12 years ago in pharma, uh, when the FDA also embrace this idea of the critical path and being able to allow more in silico modeling of clinical trial, design and development. And it really took the industry a good 10 years, um, you know, before they were able to actually adopt and apply and take that sort of guidance or openness from the FDA and actually apply it in a way that started to influence the way clinical trials were designed or the in silico modeling. >>So I think the second part of the question is really important because while I think the FDA is saying, yes, we recognize it's important. Uh, we want to be able to encourage and support it. You know, when you look for example, at synthetic control arms, right? The use of real-world data in regulatory submissions over the last five or six years, all of the use cases have been in oncology. I think there've been about maybe somewhere between eight to 10 submissions. And I think only one actually was a successful submission, uh, in all those situations, the real-world data arm of that oncology trial that synthetic control arm was actually rejected by the FDA because of lack of completeness or, you know, equalness in terms of the data. So the FDA is not going to tell us how to do this. So I think the second part of the question, which is what's the role of industry, it's absolutely on industry in order to figure out exactly what we're talking about, how do we figure out the interoperability, how do we apply the standards? >>How do we ensure good quality data? How do we enrich it and create the cohort that is going to be equivalent to the patient in the real world, uh, in the end that would otherwise be in the clinical trial and how do we create something that the FDA can agree with? And we'll certainly we'll want to work with the FDA in order to figure out this model. And I think companies are already doing that, but I think that the onus is going to be on industry in order to figure out how you actually operationalize this and make it real. >>Excellent. Thank you. Um, question on what's the most common misconception that clinical research stakeholders with sites or participants, et cetera might have about DCTs? >>Um, I could jump in there. Right. So, sure. So, um, I think in terms of misconceptions, um, I think the communist misconceptions that sites are going away forever, which I do not think is really happening today. Then the second, second part of it is that, um, I think also the perspective that patients are potentially neglected because they're moving away. So we'll pay when I, when I, what I mean by that neglected, perhaps it was not the appropriate term, but the fact that, uh, will patients will, will, will patient engagement continue, will retention be strong since the patients are not interacting in person with the investigator quite as much. Um, so site retention and patient retention or engagement from both perspectives, I think remains a concern. Um, but actually if you look at, uh, look at, uh, assessments that have been done, I think patients are more than happy. >>Majority of the patients have been really happy about, about the new model. And in fact, sites are, seem to increase, have increased investments in technology by 50% to support this kind of a model. So, and the last thing is that, you know, decentralized trials is a great model and it can be applied to every possible clinical trial. And in another couple of weeks, the whole industry will be implementing only decentralized trials. I think we are far away from that. It's just not something that you would implement across every trial. And we discussed that already. So you have to find the right use cases for that. So I think those were some of the key misconceptions I'd say in the industry right now. Yeah. >>Yeah. And I would add that the misconception I hear the most about is, uh, the, the similar to what Namita said about the sites and healthcare professionals, not being involved to the level that they are today. Uh, when I mentioned earlier in our conversation about being excited about capturing more data, uh, from the patient that was always in context of, in addition to, you know, healthcare professional opinion, because I think both of them bring that enrichment and a broader perspective of that patient experience, whatever disease they're faced with. So I, I think some people think is just an all internet trial with just someone, uh, putting out there their own perspective. And, and it's, it's a combination of both to, to deliver a robust data set. >>Yeah. Maybe I'll just comment on, it reminds me of probably 10 or 15 years ago, maybe even more when, um, really remote monitoring was enabled, right? So you didn't have to have the study coordinator traveled to the investigative site in order to check the temperature of the freezer and make sure that patient records were being completed appropriately because they could have a remote visit and they could, they could send the data in a via electronic data and do the monitoring visit, you know, in real time, just the way we're having this kind of communication here. And there was just so much fear that you were going to replace or supplant the personal relationship between the sites between the study coordinators that you were going to, you know, have to supplant the role of the monitor, which was always a very important role in clinical trials. >>And I think people that really want to do embrace the technology and the advantages that it provided quickly saw that what it allowed was the monitor to do higher value work, you know, instead of going in and checking the temperature on a freezer, when they did have their visit, they were able to sit and have a quality discussion for example, about how patient recruitment was going or what was coming up in terms of the consent. And so it created a much more high touch, high quality type of interaction between the monitor and the investigative site. And I think we should be looking for the same advantages from DCT. We shouldn't fear it. We shouldn't think that it's going to supplant the site or the investigator or the relationship. It's our job to figure out where the technology fits and clinical sciences always got to be high touch combined with high-tech, but the high touch has to lead. And so getting that balance right? And so that's going to happen here as well. We will figure out other high value work, meaningful work for the site staff to do while they let the technology take care of the lower quality work, if you will, or the lower value work, >>That's not an, or it's an, and, and you're talking about the higher value work. And it, it leads me to something that Greg said earlier about the 80, 20, 80% is assembly. 20% is actually doing the analysis and that's not unique to, to, to life sciences, but, but sort of question is it's an organizational question in terms of how we think about data and how we approach data in the future. So Bamyan historically big data in life sciences in any industry really is required highly centralized and specialized teams to do things that the rain was talking about, the enrichment, the provenance, the data quality, the governance, the PR highly hyper specialized teams to do that. And they serve different constituencies. You know, not necessarily with that, with, with context, they're just kind of data people. Um, so they have responsibility for doing all those things. Greg, for instance, within literally, are you seeing a move to, to, to democratize data access? We've talked about data interoperability, part of that state of sharing, um, that kind of breaks that centralized hold, or is that just too far in the future? It's too risky in this industry? >>Uh, it's actually happening now. Uh, it's a great point. We, we try to classify what people can do. And, uh, the example would be you give someone who's less analytically qualified, uh, give them a dashboard, let them interact with the data, let them better understand, uh, what, what we're seeing out in the real world. Uh, there's a middle user, someone who you could give them, they can do some analysis with the tool. And the nice thing with that is you have some guardrails around that and you keep them in their lane, but it allows them to do some of their work without having to go ask those centralized experts that, that you mentioned their precious resources. And that's the third group is those, uh, highly analytical folks that can, can really deliver, uh, just value beyond. But when they're doing all those other things, uh, it really hinders them from doing what we've been talking about is the high value stuff. So we've, we've kind of split into those. We look at people using data in one of those three lanes and it, and it has helped I think, uh, us better not try to make a one fit solution for, for how we deliver data and analytic tools for people. Right. >>Okay. I mean, DCT hot topic with the, the, the audience here. Another question, um, what capabilities do sponsors and CRS need to develop in-house to pivot toward DCT? >>Should I jump in here? Yeah, I mean, um, I think, you know, when, when we speak about DCTs and when I speak with, uh, folks around in the industry, I, it takes me back to the days of risk-based monitoring. When it was first being implemented, it was a huge organizational change from the conventional monitoring models to centralize monitoring and risk-based monitoring, it needs a mental reset. It needs as Lorraine had pointed out a little while ago, restructuring workflows, re redefining processes. And I think that is one big piece. That is, I think the first piece, when, you know, when you're implementing a new model, I think organizational change management is a big piece of it because you are disturbing existing structures, existing methods. So getting that buy-in across the organization towards the new model, seeing what the value add in it. And where do you personally fit into that story? >>How do your workflows change, or how was your role impacted? I think without that this industry will struggle. So I see organizations, I think, first trying to work on that piece to build that in. And then of course, I also want to step back for the second to the, uh, to the point that you brought out about data democratization. And I think Greg Greg gave an excellent point, uh, input about how it's happening in the industry. But I would also say that the data democratization really empowerment of, of, of the stakeholders also includes the sites, the investigators. So what is the level of access to data that you know, that they have now, and is it, uh, as well as patients? So see increasingly more and more companies trying to provide access to patients finally, it's their data. So why shouldn't they have some insights to it, right. So access to patients and, uh, you know, the 80, 20 part of it. Uh, yes, he's absolutely right that, uh, we want to see that flip from, uh, 20%, um, you know, focusing on, on actually integrating the data 80% of analytics, but the real future will be coming in when actually the 20 and 18 has gone. And you actually have analysts the insights out on a silver platter. That's kind of wishful thinking, some of the industries is getting there in small pieces, but yeah, then that's just why I should, why we share >>Great points. >>And I think that we're, we're there in terms that like, I really appreciate the point around democratizing the data and giving the patient access ownership and control over their own data. I mean, you know, we see the health portals that are now available for patients to view their own records, images, and labs, and claims and EMR. We have blockchain technology, which is really critical here in terms of the patient, being able to pull all of their own data together, you know, in the blockchain and immutable record that they can own and control if they want to use that to transact clinical trial types of opportunities based on their data, they can, or other real world scenarios. But if they want to just manage their own data because they're traveling and if they're in a risky health situation, they've got their own record of their health, their health history, uh, which can avoid, you know, medical errors occurring. So, you know, even going beyond life sciences, I think this idea of democratizing data is just good for health. It's just good for people. And we definitely have the technology that can make it a reality. Now >>You're here. We have just about 10 minutes left and now of course, now all the questions are rolling in like crazy from the crowd. Would it be curious to know if there would be any comments from the panel on cost comparison analysis between traditional clinical trials in DCTs and how could the outcome effect the implementation of DCTs any sort of high-level framework you can share? >>I would say these are still early days to, to drive that analysis because I think many companies are, um, are still in the early stages of implementation. They've done a couple of trials. The other part of it that's important to keep in mind is, um, is for organizations it's, they're at a stage of, uh, of being on the learning curve. So when you're, you're calculating the cost efficiencies, if ideally you should have had two stakeholders involved, you could have potentially 20 stakeholders involved because everyone's trying to learn the process and see how it's going to be implemented. So, um, I don't think, and the third part of it, I think is organizations are still defining their KPIs. How do you measure it? What do you measure? So, um, and even still plugging in the pieces of technology that they need to fit in, who are they partnering with? >>What are the pieces of technology they're implementing? So I don't think there is a clear cut as answered at this stage. I think as you scale this model, the efficiencies will be seen. It's like any new technology or any new solution that's implemented in the first stages. It's always a little more complex and in fact sometimes costs extra. But as, as you start scaling it, as you establish your workflows, as you streamline it, the cost efficiencies will start becoming evident. That's why the industry is moving there. And I think that's how it turned out on the long run. >>Yeah. Just make it maybe out a comment. If you don't mind, the clinical trials are, have traditionally been costed are budgeted is on a per patient basis. And so, you know, based on the difficulty of the therapeutic area to recruit a rare oncology or neuromuscular disease, there's an average that it costs in order to find that patient and then execute the various procedures throughout the clinical trial on that patient. And so the difficulty of reaching the patient and then the complexity of the trial has led to what we might call a per patient stipend, which is just the metric that we use to sort of figure out what the average cost of a trial will be. So I think to point, we're going to have to see where the ability to adjust workflows, get to patients faster, collect data more easily in order to make the burden on the site, less onerous. I think once we start to see that work eases up because of technology, then I think we'll start to see those cost equations change. But I think right now the system isn't designed in order to really measure the economic benefit of de-central models. And I think we're going to have to sort of figure out what that looks like as we go along and since it's patient oriented right now, we'll have to say, well, you know, how does that work, ease up? And to those costs actually come down and then >>Just scale, it's going to be more, more clear as the media was saying, next question from the audiences, it's kind of a best fit question. You all have touched on this, but let me just ask it is what examples in which, in which phases suit DCT in its current form, be it fully DCT or hybrid models, none of our horses for courses question. >>Well, I think it's kind of, uh, it's, it's it's has its efficiencies, obviously on the later phases, then the absolute early phase trials, those are not the ideal models for DCTs I would say so. And again, the logic is also the fact that, you know, when you're, you're going into the later phase trials, the volume of number of patients is increasing considerably to the point that Lorraine brought up about access to the patients about patient selection. The fact, I think what one should look at is really the advantages that it brings in, in terms of, you know, patient access in terms of patient diversity, which is a big piece that, um, the cities are enabling. So, um, if you, if, if you, if you look at the spectrum of, of these advantages and, and just to step back for a moment, if you, if you're looking at costs, like you're looking at things like remote site monitoring, um, is, is a big, big plus, right? >>I mean, uh, site monitoring alone accounts for around a third of the trial costs. So there are so many pieces that fall in together. The challenge actually that comes when you're in defining DCTs and there are, as Rick pointed out multiple definitions of DCTs that are existing, uh, you know, in the industry right now, whether you're talking of what Detroit is doing, or you're talking about acro or Citi or others. But the point is it's a continuum, it's a continuum of different pieces that have been woven together. And so how do you decide which pieces you're plugging in and how does that impact the total cost or the solution that you're implementing? >>Great, thank you. Last question we have in the audience, excuse me. What changes have you seen? Are there others that you can share from the FDA EU APAC, regulators and supporting DCTs precision medicine for approval processes, anything you guys would highlight that we should be aware of? >>Um, I could quickly just add that. I think, um, I'm just publishing a report on de-centralized clinical trials should be published shortly, uh, perspective on that. But I would say that right now, um, there, there was a, in the FDA agenda, there was a plan for a decentralized clinical trials guidance, as far as I'm aware, one has not yet been published. There have been significant guidances that have been published both by email and by, uh, the FDA that, um, you know, around the implementation of clinical trials during the COVID pandemic, which incorporate various technology pieces, which support the DCD model. Um, but I, and again, I think one of the reasons why it's not easy to publish a well-defined guidance on that is because there are so many moving pieces in it. I think it's the Danish, uh, regulatory agency, which has per se published a guidance and revised it as well on decentralized clinical trials. >>Right. Okay. Uh, we're pretty much out of time, but I, I wonder Lorraine, if you could give us some, some final thoughts and bring us home things that we should be watching or how you see the future. >>Well, I think first of all, let me, let me thank the panel. Uh, we really appreciate Greg from Lily and the meta from IDC bringing their perspectives to this conversation. And, uh, I hope that the audience has enjoyed the, uh, the discussion that we've had around the future state of real world data as, as well as DCT. And I think, you know, some of the themes that we've talked about, number one, I think we have a vision and I think we have the right strategies in terms of the future promise of real-world data in any number of different applications. We certainly have talked about the promise of DCT to be more efficient, to get us closer to the patient. I think that what we have to focus on is how we come together as an industry to really work through these very vexing operational issues, because those are always the things that hang us up and whether it's clinical research or whether it's later stage, uh, applications of data. >>We, the healthcare system is still very fragmented, particularly in the us. Um, it's still very, state-based, uh, you know, different states can have different kinds of, uh, of, of cultures and geographic, uh, delineations. And so I think that, you know, figuring out a way that we can sort of harmonize and bring all of the data together, bring some of the models together. I think that's what you need to look to us to do both industry consulting organizations, such as IBM Watson health. And we are, you know, through DTRA and, and other, uh, consortia and different bodies. I think we're all identifying what the challenges are in terms of making this a reality and working systematically on those. >>It's always a pleasure to work with such great panelists. Thank you, Lorraine Marshawn, Dr. Namita LeMay, and Greg Cunningham really appreciate your participation today and your insights. The next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond has been brought to you by IBM in the cube. You're a global leader in high tech coverage. And while this discussion has concluded, the conversation continues. So please take a moment to answer a few questions about today's panel on behalf of the entire IBM life sciences team and the cube decks for your time and your feedback. And we'll see you next time.

>> Announcer: From around the globe it's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> And welcome back here on theCUBE. John Walls, your host with you as we continue our IBM Think 2021 initiative. Been talking a lot about IBM's assistance in terms of what it's doing for its client-base. We're going to talk about partnerships today, a little bit with Bill Patterson who is the EVP and General Manager of CRM Applications at Salesforce who has a really good partnership in great practice right now, with IBM. And Bill, thanks for the time today. Lookin' forward to spending some time with you, here. >> Yeah, thank you John, thanks for having me today. >> You bet. Well, let's just jump right in. First off, let's share with the viewers about your core responsibilities at Salesforce. We talked about CRM, what your engagement is there, but if you would just kind of of give us an idea of the kind of things that you're handling on a day-to-day basis. >> Well, I am responsible for our CRM applications here, at Salesforce, which are our sales cloud technologies to help organizations get back to growth, our service cloud technologies which are really helping organizations to take care of their customers, you know, through all moments of the digital lifecycle, our small business solutions, so to help growing organizations thrive, and our Work.com and vaccine management solutions which are helping the economy safely reopen through the crisis modes that we've all been living in. So broad range responsibilities and my day-to-day is nothing like it was a year ago. >> Yeah and I could only imagine, especially when you throw that last component in, COVID, which hopefully, we'll have time to talk about just because, I think, people are so are taken to the subject now and obviously it's impacting business on so many different levels. But let's talk, first off, about IBM and your partnership with them, kind of the genesis of that, how that came about, and maybe how you're working together. How are you integrated these days with IBM? >> Well, you know, one of the things at Salesforce that are key value as an organization is is to establish trust around the transformation of organizations across the world. And when you think about brands that you can trust to drive transformations with, IBM and Salesforce really stand apart. So IBM is an incredible partner for us on the technology side, on a service delivery side, and in an innovation side for us to create new solutions to help our clients really go in this from-to state of how their businesses used to operate to how they need to operate in the future. I loved working with the IBM team. We have a lot of great values that are shared across our two organizations. But most fundamentally, those values are deeply rooted in customer success. And I think that that is one of the things that really draws me too, working with such a great partner here. >> Go into the process a little bit, if you will. So if I'm a prospective client of yours and I come to you with some cloud needs, you know, again, whether it's storage or whether it's applications or whether it's Edge, whatever it is I'm coming to you for, how do you then translate that to IBM and how does IBM come into play, where do the boundaries kind of start and stop or do they? Or is it a complete mesh? >> Yeah, well I think one of the things that's sort of unique about today's climate is people aren't just looking to solve technology problems, they're looking to solve business problems. And what we really do at Salesforce is lead with the business transformation opportunity and deeply partner with IBM on a number of fronts to really go help those opportunities become realized. The first is in the services line. IBM has great partnerships with Salesforce around the transformation about core business processes, configuration, integration services. That's one of the dimensions that we work together on. We also work together on areas of artificial intelligence and how we help businesses become smart in their operations every day to empower their workforce to really achieve more. And finally, you know that you mentioned about core technology, you know, oftentimes, the business requirements translate into great technology transformation. And that's what we do deeply with the IBM team is really outlining a blueprint and a roadmap for modernizing the technical infrastructure to help organizations move fast, increase their operational agility, and run at such scale and safely in today in the modern world that we all operate in on. So through all those facets of the lifecycle, IBM continues to be one of our leading partners, globally, to help clients, you know, not just here, in the United States, but around the world to think about how they need to maximize their transformational abilities. >> Yeah, and you touched on this at the outset of the interview. We were talking about IBM and the impact and obviously, the great association relationship that you have with them and the value in that. I'd like you to amplify on that a little bit more in terms of, specifically, what are you getting out of it you think, from a Salesforce perspective to have kind of the power and the weight and the bench, basically, that IBM provides. >> Well you think about transformation and you know, you read a lot about digital transformation online, that means so many different things to so many different businesses. Businesses, not just, like I said, here in one country, but globally, the transformational needs really need to come with incredible bench and domain expertise by industry, by geography, even by some micro-regions in those geographies given what we've been experiencing here, in the public sector in the United States with this COVID response activity we've been doing with the IBM team. And so when you talk about the deep bench, what I love about working with IBM on is, again, commanding just great industry insights and knowledge of where industries are heading and also cross-industry insights so that you can really bring great best practices from say, one industry to another. Second is that real understanding of the global nature of business today. And I don't think the one thing that's fascinating about digital, it is not a sovereign identity, today. Digital really means that you need to understand how to operate in every country, every region, every location, you know, safely. And so IBM has incredible depth in bench of experiences to help our clients truly transform those areas. Maybe another area that I really have appreciated working with IBM on is that deep technical understanding and deep technical domain of excellence maybe in the area of artificial intelligence. And our partnership is quite unique between Salesforce and IBM. Not only do we work together for external clients but inside of IBM, IBM is using Salesforce today to run a lot of your core operations. And so the partnership we work with, not only IBM as a kind of delivery excellence, but internally as a customer, is really helping IBM transform its operations from service to sales to marketing all around the world. So I think this partnership is one that is deeply rooted in working together and really, like I mentioned before, finding the right path to drive the outcomes of tomorrow. >> You know, you mentioned COVID and so we'd like to touch on that. But I assume that's a big part of your current relationship, if you will, in terms of the partnership goes. What, specifically, are you doing with IBM in that space and what have you done, and then what are you continuing to do as we go through now, the vaccination process and the variant identification processes and all these things? So maybe you can share with our viewers a little bit about the kinds of things that you have been working on together and the kind of progress that you've been making. >> Well, back a year ago, you know, when the world was really at a standstill, Salesforce created a solution called Work.com which was to engineer new technologies to help businesses kind of deal with the reality of a hard shutdown to business in the, say, private sector and then in the public sector, to really create new innovation around key solutions like contact tracing that you might have needed to track, you know, kind of outbreak and the rate of progression of the virus. And what we did with the IBM team, working with clients around the world first was work together to deploy those technologies rapidly into the hands of our customers. Through those moments of opportunity and realization, you know, working with our clients, we also started to hear of, you know, kind of about where we find ourselves today, this mass vaccination wave of where our citizens and societies are kind of on the recovery journey. And the work that we did with IBM was to start to plan out the next wave of recovery options around vaccine managements, Salesforce creating a core vaccine scheduling, distribution, and administration management services and IBM focusing on more of that credentialing and vaccination state of how someone has gone from receiving a shot in arm to now having a trusted profile of which vaccines, when did you receive them, are they still accurate and valid around those solutions. So where we're working with the IBM team most acutely on COVID now is in the vaccine credential management side through Watson Health. >> Well, can you give us an idea now, let's see if we can dig in a little deeper on some of those other things you talked about to about core technologies, you talked about, I mentioned Edge, you know, and that's people tryin' to figure out how they integrate these Edge technologies into their primary systems, now. So can you give us some examples, some specific examples of some things that you're actually collaborating on today in those areas or maybe another that comes to mind? >> Yeah, Edge computing is probably one of the other more exciting things that we're doing with the IBM team and I think you find that really working with our field service business and IBM cloud services, you know, globally speaking. On the Edge, as devices become smarter and more digital, they have a lot of signals that organizations can now tap into, not only for real-time intelligence but also fault intelligence when a device is starting to need repair or preventative maintenance around the solutions that kind of need to be administered. And the work that we're doing to really broker this connected, not just enterprise, but connected sort of experiences with IBM, super powerful here, because the IBM Edge services are now helping us get into anomaly detection. Those anomaly detections are automatically routing to workers who use the Salesforce field service capabilities, and now we can help organizations stay running safely and with continuity which is really all our customers are asking us for. So the ability for us to be creative and understand, you know, our parts of the picture together are really the things that I think are most exciting for what we're doing for clients around the world. >> Yeah, you mentioned continuity, kind of a cousin to that, I think, is security in a way because you're-- >> Absolutely. >> So what are you hearing from your customer-base these days with regard to security? You know, a lot of high profile instances certainly from bad state actors, as we well know. But what are you hearing in terms of security that you're looking at and maybe cooperating or collaborating with IBM on to make sure that those concerns are being addressed? >> Yeah, you know, I think, well, first off, security is on the top of minds for all decision-makers, executives, today. It's the number one threat that a lot of companies are really needed to respond to given what we've seen in the geo-political world that we're in. And security isn't just about securing your servers, it's also about securing every operational touchpoint that you might have with, you know, your every end-user or even every customer that's inter-operating with your services that you project as an organization. And what I love about working with the IBM team is, as we mentioned, you know, such great insights across all parts of technology infrastructure to really help understand both the threat level, how to contain that threat level, and more importantly, how to engineer with, you know, great solutions all the way into the hands of customers so they become safe and easy for all actors in your environment to really operate with. And that's where, again, you know, you think about a solution like mobile sales professionals, they're out traveling around the world on mobile devices, sometimes, their AG even brought their own personal devices into the enterprise. And so IBM is a great partner for ours just to help us understand the overall threat level of every device every moment that an employee might have within their organizational data, and really help create great solutions to help keep organizations running safely. >> Yeah, I think it's interesting you tell about people bringing their own devices on, back when, I remember that acronym, BYOB was like a huge thing, right? (chuckling) And this major problem or conundrum and now it's almost like an afterthought, you've got it solved, you've got it well taken care of. >> Well you think about, again, devices in the enterprise and how much we've been able to achieve with the BYOB becoming commonplace and norm, even today, the workman place from home kind of environment that we're in. I mean, who would have thought a year ago that most of our operations can be conducted safely from our home offices, not just our regional or corporate offices? And again, that's the kind of thing that working with IBM has been such a great value for our clients because no one could have forecasted that the contact center would've had to moved to your kitchen last year. And yet, we had to really go achieve that in this time and working with great partners like IBM, it became not just a conversation but real practice. >> By the way, I think I said BYOB. I meant BYOD, so you know where my mind's at, right? (chuckling) >> I wasn't going to correct you. >> Hey thanks, Bill, I appreciate that. It just kind of hit me. I think that that just, that was a Freudian slip, certainly. Hey Bill, thanks for the time. I certainly do appreciate and thanks for shining a light on this really good partnership between Salesforce and IBM. And we wish you continued success down the road with that, as well. >> Yeah, thanks again. And again, love being your partner and love the impact we're having together. >> Great, thank you very much. Bill Patterson joining us, the EVP work in CRM at Salesforce talking about IBM and that relationship that they're putting into practice for their client-base. John Walls reporting here, on theCUBE. Thanks for joining us with more on IBM Think. (soft music) ♪ Dah de dah ♪ ♪ Dah ♪

>>Hello, and welcome to today's event, dealing government emergency responses beyond the pandemic. This is Bob Wooley, senior fellow for the center for digital government and formerly the chief tech clerk for the state of Utah. I'm excited to serve as moderator for today's event. And just want to say, thank you for joining us. I know we're in for an informative session over the next 60 minutes before we begin a couple of brief housekeeping notes or recording of this presentation will be emailed to all registrants within 48 hours. You can use the recording for your reference or feel free to pass it along to colleagues. 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We would like to hear what you think if you're unable to see with us for the entire webinar, but we're just like to complete the survey. As much as you're able, please click the survey widget at the bottom of the screen to launch the survey. Otherwise it will pop up once the webinar concludes at this time, we recommend that you disable your pop-up blockers, and if you experiencing any media player issues or have any other problems, please visit our webcast help guide by clicking on the help button at the bottom of the console. >>Joining me today to discuss this very timely topic are Karen revolt and Tim Burch, Kim Berge currently serves as the administrator of human services for Clark County Nevada. He's invested over 20 years in improving health and human service systems of care or working in the private public and nonprofit sectors. 18 of those years have been in local government in Clark County, Las Vegas, where you served in a variety of capacities, including executive leadership roles as the director of department of social services, as well as the director for the department of family services. He has also served as CEO for provider of innovative hosted software solutions, as well as chief strategy officer for a boutique public sector consulting firm. Karen real-world is the social program management offering lead for government health and human services with IBM Watson health. Karen focuses delivering exciting new offerings by focusing on market opportunities, determining unmet needs and identifying innovative solutions. >>Much of her career has been in health and human services focused on snap, TANIF, Medicaid, affordable care act, and child welfare prior to joining IBM. Karen was the senior director of product management for a systems integrator. She naturally fell in love with being a project manager. She can take her user requirements and deliver offerings. Professionals would use to make their job easier and more productive. Karen has also found fulfillment in working in health and human services on challenges that could possibly impact the outcome of people's lives. Now, before we begin our discussion of the presentation, I want to one, we'd like to learn a little more about you as an audience. So I'm going to ask you a polling question. Please take a look at this. Give us an idea of what is your organization size. I won't bother to read all these to you, but there are other a range of sizes zero to 250 up to 50,000. Please select the one that is most appropriate and then submit. >>It looks like the vast majority are zero to two 50. Don't have too many over 250,000. So this is a very, very interesting piece of information. Now, just to set up our discussion today, what I want to do is just spend just a moment and talk about the issue that we're dealing with. So when you look the COVID-19 pandemic, it's put immense pressure on States. I've been a digital state judge and had been judging a lot of the responses from States around the country. It's been very interesting to me because they bifurcate really into two principle kinds of reactions to the stress providing services that COVID environment present. One is we're in a world of hurt. We don't have enough money. I think I'm going to go home and engage as little as I have to. Those are relatively uncommon. Thankfully, most of them have taken the COVID-19 pandemic has immense opportunity for them to really do a lot more with telework, to do more with getting people, employees, and citizens involved with government services. >>And I've done some really, really creative things along the way. I find that to be a really good thing, but in many States systems have been overloaded as individuals and families throughout the country submitted just an unprecedented number of benefit applications for social services. At the same time, government agencies have had to contend with social distance and the need for a wholly different approach to engage with citizens. Um, overall most public agencies, regardless of how well they've done with technology have certainly felt some strain. Now, today we have the opportunity to go into a discussion with our speakers, have some wonderful experience in these areas, and I'm going to be directing questions to them. And again, we encourage you as you hear what they have to say. Be sure and submit questions that we can pick up later at the time. So Tim, let's start with you. Given that Las Vegas is a hub for hospitality. An industry hit severely as a result of this pandemic. How's the County doing right now and how are you prioritizing the growing needs of the County? >>Thanks Bob. Thanks for having me. Let me start off by giving just a little, maybe context for Clark County too, to our audience today. So, uh, Clark County is, you know, 85% of the state of Nevada if we serve not just as a regional County by way of service provision, but also direct municipal services. Well, if, uh, the famous Las Vegas strip is actually in unincorporated Clark County, and if we were incorporated, we would be the largest city in the state. So I say all of that to kind of help folks understand that we provide a mix of services, not just regional services, like health and human services, the direct and, and missable, uh, services as well as we work with our other five jurisdiction partners, uh, throughout the area. Uh, we are very much, um, I think during the last recession we were called the Detroit of the West. >>And, uh, that was because we're very much seen as a one industry town. Uh, so most like when the car plants, the coal plants closed back East and in the communities fuel that very rapidly, the same thing happens to us when tourism, uh, it's cut. Uh, so of course, when we went into complete shutdown and March, uh, we felt it very rapidly, not just on, uh, uh, tax receipts and collectibles, but the way in which we could deliver services. So of course our first priority was to, uh, like I think you mentioned mobilized staff. We, we mobilized hundreds of staff overnight with laptops and phones and cars and the things they needed to do to get mobile and still provide the priority services that we're mandated to provide from a safety standpoint. Um, and then we got busy working for our clients and that's really where our partnership with IBM and Watson, uh, came in and began planning that in July. And we're able to open that portal up in October to, to really speed up the way in which we're giving assistance to, to our residents. Um, re focus has been on making sure that people stay housed. We have, uh, an estimated, uh, 2.5 million residents and over 150,000 of those households are anticipated to be facing eviction, uh, as of January one. So we, we've got a, a big task ahead of us. >>All of this sounds kind of expensive. Uh, one of the common threads as you know, runs throughout government is, ah, I don't really have the money for that. I think I'd be able to afford that a diaper too, as well. So what types of funding has been made available for counties, a result of a pandemic, >>Primarily our funding stream that we're utilizing to get these services out the door has been the federal cares act. Uh, now we had some jurisdictions regionally around us and even locally that prioritize those funds in a different way. Um, our board of County commissioners, uh, took, um, a sum total of about $85 million of our 240 million that said, this will go directly to residents in the form of rental assistance and basic needs support. No one should lose their home or go hungry during this pandemic. Uh, so we've really been again working through our community partners and through our IBM tools to make sure that happens. >>So how does, how does, how does the cares act funding then support Clark County? Cause it seems to me that the needs would be complex, diverse >>Pretty much so. So as you, as folks may know him a call there's several tronches of the cares act, the original cares act funding that has come down to us again, our board, uh, identified basic needs or rental assistance and, and gave that the department of social service to go to the tunicate, uh, through the community. We then have the cares act, uh, uh, coronavirus relief funds that have, uh, impacted our CDBG and our emergency solutions grants. We've taken those. And that's what we was going to keep a lot of the programs and services, uh, like our IBM Watson portal open past January one when the cares act dollars expire. Uh, our initial response was a very manual one, uh, because even though we have a great home grown homeless management information system, it does not do financials. Uh, so we had 14 local nonprofits adjudicating, uh, this rental assistance program. >>And so we could get our social service visitor portal up, uh, to allow us to take applications digitally and run that through our program. Uh, and, uh, so those partners were obviously very quickly overwhelmed and were able to stand up our portal, uh, which for the reason we were driving so hard, even from, uh, beginning of the conversations where after going into lockdown into contracting in July and getting the portal open in October, which was an amazing turnaround. Uh, so the kudos that IBM team, uh, for getting us up and out the door so quickly, uh, was a tie in, uh, to our, uh, Curam IBM, uh, case management system that we utilize to adjudicate benefits on daily basis in Clark County for all our local indigent population, uh, and high needs folks. Uh, and then that ties into our SAP IBM platform, which gets the checks out the door. >>So what, what we've been able to do with these dollars is created in Lucian, uh, that has allowed us in the last 60 days to get as much money out the door, as our nonprofits were able go out the door in the first six months pandemic. So it really has helped us. Uh, so I'm really grateful to our board of County commissioners for recognizing the investment in technology to, to not only get our teams mobile, but to create ease of access for our constituents and our local residents to give them the help they need quickly and the way that they need it. >>Just to follow up question to that, Tim, that I'm curious about having done a lot of work like this in government, sometimes getting procurement through in a timely way is a bit challenging. How were you able to work through those issues and getting this up and provision so quickly? >>Uh, yeah, so we, we put together a, what we call a pandemic playbook, which is kind of lessons learned. And what we've seen is the folks who were essential workers in the first 60 days of the, uh, pandemic. We were able to get a lot done quickly because we were taking full advantage of the emergency. Uh, it may sound a little crass to folks not inside the service world, but it was, uh, you know, don't want you to crisis. It was things we've been planning or trying to do for years. We need them yesterday. We should have had them yesterday, but let's get them tomorrow and get it moving very quickly. Uh, this IBM procurement was something we were able to step through very quickly because of our longstanding relationship. Our countywide, uh, system of record for our financials is SAP. Uh, we've worked with Curam, uh, solution, uh, for years. >>So we've got this long standing relationship and trust in the product and the teams, which helped us build the business case of why we did it, no need to go out for competitive procurement that we didn't have time. And we needed something that would integrate very quickly into our existing systems. Uh, so that part was there. Now when the folks who were non essential came back in June and the reopening, it was whiplash, uh, the speed at which we were moving, went back to the pace of normal business, uh, which feels like hitting a wall, doing a hundred miles an hour when you're used to having that, uh, mode of doing business. Uh, so that's certainly been a struggle, uh, for all of those involved, uh, in trying to continue to get things up. Um, but, uh, once again, the teams have been great because we've probably tripled our licensure on this portal since we opened it, uh, because of working with outside vendors, uh, to, uh, literally triple the size of our staff that are processing these applications by bringing on temporary staff, uh, and short-term professionals. Uh, and so we've been able to get those things through, uh, because we'd already built the purchasing vehicle during the early onset of the crisis. >>That's very helpful. Karen, IBM has played a really pivotal role in all of this. Uh, IBM Watson health works with a number of global government agencies, raging from counties like Clark County to federal governments. What are some of the major challenges you've seen with your clients as a result of the pandemic and how is technology supporting them in a time of need and give us some background Watson health too. So we kind of know a little more about it because this is really a fascinating area. >>Yeah. Thank you, Bob. And thanks Tim for the background on Clark County, because I think Clark County is definitely also an example of what federal governments and global governments are doing worldwide today. So, um, Watson health is our division within IBM where we really focus on health and human services. And our goal is to really focus in on, um, the outcomes that we're providing to individuals and families and looking at how we use data and insights to really make that impact and that change. And within that division, we have our government health and human services area, which is the focus of where we are with our clients around social program. But it also allows us to work with, um, different agencies and really look at how we can really move the ball in terms of, um, effecting change and outcomes for, um, really moving the needle of how we can, uh, make an impact on individuals and families. >>So as we look at the globe globally as well, you know, everything that Tim had mentioned about how the pandemic has really changed the way that government agencies operate and how they do services, I think it's amazing that you have that pandemic playbook because a lot of agencies in the same way also had these set of activities that they always wanted to go and take part on, but there was no impetus to really allow for that to happen. And with the pandemic, it allowed that to kind of open and say, okay, we can try this. And unfortunately I'm in a very partial house way to do that. And, um, what Tim has mentioned about the new program that they set up for the housing, some of those programs could take a number of years to really get a program online and get through and allowing, uh, the agencies to be able to do that in a matter of weeks is amazing. >>And I think that's really gonna set a precedent as we go forward and how you can bring on programs such as the housing and capability in Canada with the economic, uh, social, um, uh, development and, and Canada need that the same thing. They actually had a multi benefit delivery system that was designed to deliver benefits for three programs. And as part of the department of fisheries and oceans Canada, the, um, the state had an emergency and they really need to set up on how they could provide benefits to the fishermen who had been at that impacted, um, from that. And they also did set up a digital front-end using IBM citizen engagement to start to allow the applications that benefits, um, and they set it up in a matter of weeks. And as I mentioned, we, uh, Clark County had a backend legacy system where they could connect to and process those applications. And this case, this is a brand new program and the case management system that they brought up was on cloud. And they had to set up a new one, but allow them to set up a, what we used to call straight through processing, I think has been now turned, turned or coined contact less, uh, processing and allowing us to really start to move those benefits and get those capabilities out to the citizens in even a faster way than has been imagined. Uh, pre pandemic. >>Karen, I have one follow-up question. I want to ask you, having had a lot of experience with large projects in government. Sometimes there's a real gap between getting to identified real requirements and then actions. How do you, how do you work with clients to make sure that process time to benefit is shortened? >>So we really focus on the user themselves and we take a human centered design focus and really prioritizing what those needs are. Um, so working with the clients, uh, effectively, and then going through agile iterations of brain, that capability out as, um, in, in a phased approach to, so the idea of getting what we can bring out that provides quality and capability to the users, and then over time starting to really roll out additional functions and, um, other, uh, things that citizens or individuals and families would need >>Very helpful. Tim, this is an interesting partnership. It's always good to see partnerships between private sector and government. Tell us a little bit about how the partnership with IBM Watson health was established and what challenges or they were brought into assist, where they brought into assist with back to requirements. Again, within the requirements definitely shifted on us. You know, we had the con looking at, uh, Watson on our child welfare, uh, side of the house that I'm responsible for and how that we could, uh, increase access to everything from tele-health to, to, uh, foster parent benefit, uh, kinship, placement benefits, all those types of things that, that right now are very manual, uh, on the child welfare side. Uh, and then the pandemic kid. And we very quickly realized that we needed, uh, to stand up a, um, a new program because, uh, a little bit for context, uh, the park County, we don't administer TANIF or Medicaid at the County level. >>It is done at the state level. So we don't have, uh, unemployment systems or Medicaid, 10 of snap benefits systems to be able to augment and enroll out. We provide, uh, the indigent supports the, the homelessness prevention, referee housing continuum of care, long-term care, really deep emergency safety net services for our County, which is a little bit different and how those are done. So that was really our focus, which took a lot of in-person investigation. We're helping people qualify for disability benefits so they can get into permanent supportive housing, uh, things that are very intensive. And yet now we have a pandemic where we need things to happen quickly because the cares act money expires at the end of December. And people were facing eviction and eviction can help spread exposure to, to COVID. Uh, so, uh, be able to get in and very rapidly, think about what is the minimal pelvis to MVP. >>What's the minimum viable product that we can get out the door that will help people, uh, entrance to a system as contactless as possible, which again was a complete one 80 from how we had been doing business. Um, and, uh, so the idea that you could get on and you have this intelligent chat bot that can walk you through questions, help you figure out if you look like you might be eligible, roll you right into an application where you can upload the few documents that we're going to require to help verify your coat would impact and do that from a smartphone and under, you know, 20 minutes. Um, it, it, it is amazing. And the fact that we've stood that up and got it out the door in 90 days, it's just amazing to me, uh, when it shows the, uh, strength of partnership. Um, I think we can, we have some shared language because we had that ongoing partnership, but we were able to actually leverage some system architects that we had that were familiar with our community and our other products. So it really helped expedite, uh, getting this, uh, getting this out to the citizens. >>So, uh, I assume that there are some complexities in doing this. So overall, how has this deployment of citizen engagement with Watson gone and how do you measure success other than you got it out quick? How do you know if it's working? >>Yeah. Right. So it's the adage of, you know, quick, fast and good, right. Um, or fast, good and cheap. So, uh, we measure success in this way. Um, how are we getting access as our number one quality measurement here? So we were able to collect, uh, about 13,000 applications, uh, manual NRC, manually folks had to go onto our website, download a PDF, fill it out, email it, or physically drop it off along with their backup. One of their choice of 14 non-profits in town, whichever is closest to them. Um, and, uh, and then wait for that process. And they were able to get 13,000 of those, uh, process for the last six months. Uh, we have, I think we had about 8,000 applications the first month come into the portal and about an equal amount of folks who could not provide the same documentation that it was needed. >>And self-selected out. If we had not had the, the tool in place, we would have had 16,000 applications, half of which would have been non-eligible would have been jamming up the system, uh, when we don't have the bandwidth to deal to deal with that, we, we need to be able to focus in on, uh, Judy Kenny applications that we believe are like a 95% success rate from the moment our staff gets them, but because we have the complex and he was on already being dependent upon the landlord, having to verify the rent amount and be willing to work with us, um, which is a major hurdle. Um, but, uh, so w we knew we could not do is go, just reinvent the manual process digitally that that would have been an abject failure on our behalf. So, uh, the ideas that, uh, folks had can go on a very, had this very intuitive conversation to the chat bot, answer some questions and find out if they're eligible. >>And then self-select out was critical for us to not only make sure that the citizens got the help they needed, but not so burnt out and overload our workforce, which is already feeling the strain of the COVID pandemic on their own personal lives and in their homes and in the workplace. Um, so that was really critical for us. So it's not just about speed, ease of access was important. Uh, the ability to quickly automate things on the fly, uh, we have since changed, uh, the area median income, a qualifier for the rental assistance, because we were able to reallocate more money, uh, to the program. So we were able to open it up to more people. We were able to make that, uh, change to the system very quickly. Uh, the idea that we can go on the home page and put updates, uh, we recognized that, uh, some of our monolingual Hispanic residents were having difficulty even with some guidance getting through the system. >>So we're able to record a, a Spanish language walkthrough and get done on the home page the next day, right into the fordable, there'll be a fine, so they could literally run the YouTube video while they're walking through their application. Side-by-side so things like that, that those are how we are able to, for us measured success, not just in the raw dollars out the door, not just in the number of applications that have come in, but our ability to be responsive when we hear from our constituents and our elected officials that, Hey, I want, I appreciate the 15,000 applications as you all, a process and record time, I've got three, four, five, six, 10 constituents that having this type of problem and be able to go back and retool our systems to make them more intuitive, to do, be able to keep them responsive for us is definitely a measure of success and all of this, probably more qualitative than here we're looking >>For, but, uh, that's for us, that's important. Actually the qualitative side is what usually gets ignored. Uh, Karen, I've got a question that's a follow up for you on the same topic. How does IBM facilitate reporting within this kind of an environment given the different needs of stakeholders, online managers and citizens? What kinds of things do you, are you able to do >>So with, um, the influx of digitalization? I think it allows us to really take a more data-driven approach to start looking at that. So, as, as Tim was mentioning, you can see where potentially users are spending more time on certain questions, or if they're stuck on a question, you can see where the abandoned rate is. So using a more data-driven approach to go in to identify, you know, how do we actually go and, um, continue to drive that user experience that may not be something that we drive directly from the users. So I would say that analytics is really, uh, I think going to continue to be a driving force as government agencies go forward, because now they are capturing the data. But one thing that they have to be careful of is making sure that the data that they're getting is the right data to give them the information, to make the right next steps and decisions. >>And Tim, you know, use a really good example with, um, the chatbot in terms of, you know, with the influx of everything going on with COVID, the citizens are completely flooded with information and how do they get the right information to actually help them decide, can I apply for this chap program? Or should I, you know, not even try and what Tim mentioned just saved the citizens, you know, the people that may not be eligible a lot of time and going through and applying, and then getting denied by having that upfront, I have questions and I need answers. Um, so again, more data-driven of how do we provide that information? And, you know, we've seen traditionally citizens having to go on multiple website, web pages to get an answer to the question, because they're like, I think I have a question in this area, but I'm not exactly sure. And they, then they're starting to hunt and hunt and hunt and not even potentially get an answer. So the chocolate really like technology-wise helps to drive, you know, more data-driven answers to what, um, whether it's a citizen, whether it's, um, Tim who needs to understand how and where my citizens getting stuck, are they able to complete the application where they are? Can we really get the benefits to, um, this individual family for the housing needs >>Too many comments on the same thing. I know you have to communicate measures of success to County executives and others. How do you do that? I mean, are you, do you have enough information to do it? Yeah, we're able to, we actually have a standup meeting every morning where the first thing I learn is how many new applications came in overnight. How many of those were completed with full documentation? How many will be ported over into our system, assigned the staff to work, where they're waiting >>On landlord verification. So I can see the entire pipeline of applications, which helps us then determine, um, Oh, it's, it's not, you know, maybe urban legend is that folks are having difficulty accessing the system. When I see really the bottleneck there, it got gotten the system fine, the bottlenecks laying with our landlord. So let's do a landlord, a town hall and iterate and reeducate them about what their responsibilities are and how easy it is for them to respond with the form they need to attest to. And so it lets us see in real time where we're having difficulties, uh, because, uh, there's a constant pressure on this system. Not just that, uh, we don't want anyone to lose their home, uh, but these dollars also go away within a December. So we've got this dual pressure of get it right and get it right now. >>Uh, and so th the ability to see these data and these metrics on, on a daily basis is critical for us to, to continue to, uh, ModuLite our response. Um, and, and not just get comfortable are baked into well, that's why we developed the flowchart during requirements, and that's just the way things are gonna stay. Uh, that's not how you respond to a pandemic. Uh, and so having a tool and a partner that helps us, uh, stay flexible, state agile, I guess, to, to, to leverage some terminology, uh, is important. And, and it's, it's paid dividends for our citizens. Karen, again, is another up to the same thing. I'm kind of curious about one of the problems of government from time to time. And Tim, I think attest to this is how do you know when Dunn has been reached? How did you go about defining what done would look like for the initial rollout with this kind of a customer? >>So I think Doug, I guess in this case, um, is, is this, isn't able to get the benefits that they're looking for and how do we, uh, you know, starting from, I think what we were talking about earlier, like in terms of requirements and what is the minimum viable, um, part of that, and then you start to add on the bells and whistles that we're really looking to do. So, um, you know, our team worked with him to really define what are those requirements. I know it's a new program. So some of those policy decisions were still also being worked out as the requirements were being defined as well. So making sure that you are staying on top of, okay, what are the key things and what do we really need to do from a compliance standpoint, from a functionality, and obviously, um, the usability of how, uh, an assistant can come on and apply and, um, have those, uh, requirements, make sure that you can meet that, that version before you start adding on additional scope. >>Very helpful. Jim, what's your comment on this since I know done matters to you? Yeah. And look, I I've lived through a, again, multiple, uh, county-wide it implementations and some department wide initiatives as well. So I think we know that our staff always want more so nothing's ever done, uh, which is a challenge and that's on our side of the customer. Um, but, uh, for this, it really was our, our experience of recognizing the, the time was an essence. We didn't have a chance. We didn't have, uh, the space to get into these endless, uh, conversations, uh, the agile approach, rather than doing the traditional waterfall, where we would have been doing requirements tracking for months before we ever started coding, it was what do we need minimally to get a check in the hands of a landlord on behalf of a client, so they don't get evicted. >>And we kept just re honing on that. That's nice. Let's put that in the parking lot. We'll come back to it because again, we want to leverage this investment long term, uh, because we've got a we, and we've got the emergency solutions and CDBG, and then our, uh, mainstream, uh, services we brought on daily basis, but we will come back to those things speed and time are of the essence. So what do we need, uh, to, to get this? So a chance to really, um, educate our staff about the concepts of agile iteration, um, and say, look, this is not just on the it side. We're gonna roll a policy out today around how you're doing things. And we may figure out through data and metrics that it's not working next week, and we'll have to have that. You want it. And you're going to get the same way. >>You're getting updated guidance from the CDC on what to do and what not to do. Uh, health wise, you're getting the same from us, uh, and really to helping the staff understand that process from the beginning was key. And, uh, so, and, and that's, again, partnering with, with our development team in that way was helpful. Um, because once we gave them that kind of charter as I am project champion, this is what we're saying. They did an equally good job of staying on task and getting to the point of is this necessary or nice. And if it wasn't necessary, we put it in the nice category and we'll come back to it. So I think that's really helpful. My experience having done several hundred sheet applications also suggest the need for MBP matters, future stages really matter and not getting caught. My flying squirrels really matters. So you don't get distracted. So let's move on to, let's do a polling question before we go on to some of our other questions. So for our audience, do you have a digital front ends for your benefit delivery? Yes, no. Or we're planning to a lot of response here yet. There we go. Looks like about half, have one and half note. So that's an interesting question. What's going to one more polling question, learn a little more here. Has COVID-19 >>Accelerated or moved cloud. Yes, no. We already run a majority of applications on cloud. Take a moment and respond if you would, please. So this is interesting. No real acceleration was taken place and in terms of moving to cloud is not what I was expecting, but that's interesting. So let's go onto another question then. And Karen, let me direct this one to you, given that feedback, how do you envision technologies such as citizen engagement and watching the system will be used, respond to emergency situations like the pandemic moving forward? I mean, what should government agencies consider given the challenges? This kind of a pandemic is brought upon government and try to tie this in, if you would, what, what is the role of cloud in all of this for making this happen in a timely way? Karen, take it away. >>Okay. Thanks Bob. So as we started the discussion around the digital expansion, you know, we definitely see additional programs and additional capabilities coming online as we continue on. Um, I think, uh, agencies have really seen a way to connect with their citizens and families and landlords, um, in this case an additional way. And he prepared them like there were, uh, presuppose assumptions that the, um, the citizens or landlords really wanted to interact with agency face-to-face and have that high touch part. And I think, um, through this, the governments have really learned that there is a way to still have an impact on the citizen without having a slow, do a face to face. And so I think that's a big realization for them to now really explore other ways to digitally explain, expand their programs and capabilities. Another area that we touched on was around the AI and chat bot piece. >>So as we start to see capabilities like this, the reason why Clark County was able to bring it up quickly and everything was because it was housed on cloud, we are seeing the push of starting to move some of the workloads. I know from a polling question perspective that it's been, um, lighter in terms of getting, uh, moving to the cloud. But we have seen the surge of really chatbots. I think we've been talking about chatbots for a while now. And, um, agencies hadn't really had the ability to start to implement that and really put it into effect. But with the pandemic, they were able to bring things up and, you know, very short amount of time to solve, um, a big challenge of not having the call center be flooded and have a different way to direct that engagement between the citizen and the government. >>So really building a different type of channel for them to engage rather than having to call or to come into an office, which wasn't really allowed in terms of, um, the pandemic. Um, the other thing I'll touch on is, um, 10 mentioned, you know, the backlog of applications that are coming in and we're starting to see the, um, the increase in automation. How do we automate areas where it's administratively highly burdened, but it's really a way that we can start to automate those processes, to give our workers the ability to focus on more of those complex situations that really need attention. So we're starting to see where the trends of trying to push there of can we automate some of those processes, um, uh, uploading documents and verification documents is another way of like, trying to look at, is there a way that we can make that easier? >>Not only for the applicant that's applying, but also for the caseworker. So there's not having to go through that. Um, does the name match, um, the applicant, uh, information and what we're looking on here, and Bob, you mentioned cloud. So behind the scenes of, you know, why, uh, government agencies are really pushing the cloud is, um, you heard about, I mean, with the pandemic, you see a surge of applicants coming in for those benefits and how do we scale for that kind of demand and how do you do that in an inappropriate way, without the huge pressures that you put on to your data center or your staff who's already trying to help our citizens and applicants, applicants, and families get the benefits they need. And so the cloud, um, you know, proposition of trying, being able to be scalable and elastic is really a key driver that we've seen in terms of, uh, uh, government agencies going to cloud. >>We haven't really seen during a pandemic, the core competencies, some of them moving those to cloud, it's really been around that digital front end, the chat bot area of how do we start to really start with that from a cloud perspective and cloud journey, and then start to work in the other processes and other areas. Um, security is also huge, uh, focus right now with the pandemic and everything going online. And with cloud allows you to be able to make sure that you're secure and be able to apply the right security so that you're always covered in terms of the type of demand and, um, impact, uh, that is coming through >>Very helpful. Tim, I'm going to ask to follow up on this of a practical nature. So you brought this up very quickly. Uh, there's a certain amount of suspicion around state government County government about chatbots. How did you get a chat much and be functional so quickly? And were you able to leverage the cloud in this process? Yeah, so on the trust is important. Uh, and I'll go back to my previous statement about individuals being able to see upfront whether they believe they're eligible or not, because nothing will erode trust more than having someone in hours applying and weeks waiting to find out they were denied because they weren't eligible to begin with, uh, that erodes trust. So being able to let folks know right up front, here's what it looks like to be eligible, actually help us build some of that, uh, cause they don't feel like, uh, someone in the bureaucracy is just putting them through the ringer for no reason. >>Um, now in regard to how do we get the chat bot out? I will say, uh, we have a, uh, dynamic it and leadership, uh, team at the highest level of County government who we have been already having conversations over the last year about what it meant to be smart government, uh, the department of social service and family services that I'm responsible for. We're already, uh, hands up first in line, you know, Guinea pigs volunteering to be on the front end of, uh, certain projects. So w we have primed ourselves for, for some of this readiness in that aspect. Um, but for citizen trust, um, the timeliness of application right now is the biggest element of trust. Uh, so I've applied I've I feel like I put my housing future in your hands. Are you going to deliver and having the ability for us to rapidly scale up? >>Uh, we typically have 120 staff in the department of social service that, that are adjudicating benefits for programs on daily basis. We've doubled that with temporary staff, uh, through some partnerships, uh, we're, we're gonna, as of next week, probably have more temporary per professional staff helping an adjudicator applications. No, do full-time County staff, because again, this rush to get the dollars out, out the door. So having a system where I can easily, uh, ramp on new users and manage them without having to be solely dependent upon an already, uh, overworked it staff who were trying to support 37 other departments in the County, um, around infrastructure needs has been greatly helpful. Sounds to me like a strong outcome focus and one that seems to work. Let's move on now to our audience questions. We're getting close to the end of our time. So let's jump into some questions from the audience. A number of you have been asking about getting copies of today's presentation within the next 48 hours. Government technology will provide all attendees with the link to the recording for your reference, or to share with colleagues. Well, let's go to our first question. So this is an interesting one. And Karen, this is for you did IBM work with other counties and States to provide digital engagement portals. >>We did Bob, uh, we've worked, um, so globally we've provided guidance on this. We work closely with New York city. They've been the integral part of the development also with our citizen engagement offering. Um, we work closely with the States. So we worked with New York city. Um, North Carolina was also another state who, um, improved their, uh, citizen engagement piece, bring up their Medicaid and snap, um, applications along with Medicaid. COVID testing along that. And I mentioned, um, the economic and social development in Canada as well. And we also work with the ministry of social development in Singapore. So a number of our customers had put up, uh, a global, uh, or sorry, a citizen engagement frontend. And during this timeframe, >>Very helpful. I don't know how much did you hear your mom provide you, but how much did it cost for initial deployment and what are the ongoing costs in other words, is this thing going to be sustainable over time? >>Yeah, absolutely. So total, uh, to date, we've spent about a $1.8 million on development implementations and licensure. A big chunk of that again has been the rapid extended of licensure, uh, for this program. Um, I think over a third of that is probably licensing because again, we need to get the dollars out and we need staff to do that and making the short term several hundred thousand dollar investment in a professional support staff and having them be able to work this portal is much cheaper than the long-term investment of bringing on a staff, printing a job, uh, during a financial difficulty that we're facing, uh, the single largest fiscal cliff let's get into that us history. Um, so it's not smart to create jobs that have a 30 year, one way to retirement, uh, inside our in unionized government environment here. So having this, the staff that would come on and do this and get out the door on these federal dollars was critical for us. Um, and there is a $800,000 a year, I believe so ongoing costs associated with licensure and, and the programming support. Uh, but once again, we're going to be moving, um, our traditional services into this digital front end. We'll be continuing this because we're, we're, we're facing, it took us, I think, six and a half, seven years to come back from the previous recession. Undoubtedly, take a little longer to get back >>From this one. Here's another interesting question, I guess really primarily Tim Tim was the solution on primarily on premise or in the cloud. >>So we'll, we've done a mix. Uh, the, and I'm starting a lot of feedbacks. I don't know if you all can hear that or not, but the, uh, I think we went on prem for, uh, some people because of the, uh, bridge into our service case manager system, which is on prem. So we did some management there. I do believe the chat bot piece of it though is in the cloud. So we're bringing it down to, from one system to the other. Uh, and, and part of that was a student negotiations and costs and worrying about what long-term is that we have a very stated goal of moving, uh, our Curam platform, which is on-prem, this is the backend. So how are we? We, we set our IBM Watson, uh, portal up, uh, and moving all of that on cloud, uh, because I mean, we've got, uh, a workforce who, uh, has the ability to retire at a very high rate over the next five years. >>And, uh, having 24 seven support in the cloud is, is as a, someone who would be called to respond to emergency situations like the is, is a much better Cod deal for, for myself and the citizen. So migrating, uh, and, um, our typical on-prem stuff up into the cloud, uh, as we continue on this, uh, evolution of what IBM Watson, uh, and the plug into our Curam, uh, system looks like Karen related question for another user is the portal provided with Clara County and others linked to other third-party backend office apps, or can it be, >>Yeah, the answer is it can be it's interoperable. So through APIs, uh, rest, uh, however, um, assistance that they need to be integrated with can definitely be integrated with, uh, like, uh, Tim mentioned, we, we went to the case management solution, but it can be integrated with other applications as well. >>Tim, did you use some other backend third party apps with yours? Uh, we did not. Uh, again, just for speed of getting, uh, this MVP solution out the door. Uh, now what we do with that on the go forward, it is going to look different and probably will include some, another practical question. Given the cares funding should be expended by December. Can this application even be employed at this late date? And you want to take a cut at that? Yeah, for us, uh, once again, we brought up earlier, um, the emergency solutions grants and the community development block grants, which have a Corona virus, uh, CV traunch, each one of those, and those have two to three year expenditure timeframes on them. Uh, so we were going to leverage those to keep this system and some of these programs going once again, that the housing needs, uh, will outstrip our capacity for years to come. >>I guess probably I should have said upfront Las Vegas has one of the worst affordable housing inventories in the nation. Uh, so we know we're going to be facing a housing issue, um, because of this for, for a long time. So we'll be using those two traunches of dollars, ESE, ESPs, uh, CV CDBG, CB funds, uh, in addition to dollars earmarked through some, uh, recreational marijuana license fees that have been dedicated to our homelessness. And when you consider this housing, uh, stability program was part of that homelessness prevention. That's our funding mix locally. Very helpful. So questions maybe for bolts for you on this one, you can probably also teach respond is the system has been set up helping the small business community. Um, this user's been canvassing and the general feeling is that small businesses have been left behind and they've been unable to access funds. What's your response on that? Karen, do you want to take that first? >>Um, yes. So in terms of, uh, the security and sorry. Um, but, uh, can you repeat the last part of that? I just missed the last part when you >>Behind it, but unable to access funds. >>Uh, yeah, so I think from a funding perspective, there's different types of, I think what Tim mentioned in terms of the cares funding, there was different types of funding that came out from a government perspective. Uh, I think there were also other grants and things that are coming out one, uh, that we're still looking at. And I think as we go into the new year, it'll be interesting to see, you know, what additional funding, um, hopefully is, is provided. Uh, but in terms of creativity, we've seen other creative ways that organizations come together to kind of, uh, help with the different agencies, to provide some, some guidance to the community, um, and helping to, uh, provide efforts and, uh, maybe looking at different ways of, um, providing, uh, some of the capabilities that the, either at the County or at the state level that they're able to leverage. But Tim happy to maybe have you chime in here too. >>Yeah. So I'll first start with my wheelhouse and I'll expand out to, to some of my partners. Uh, so the primary, small business, we knew the idea was a daily basis inside this realm is going to be landlords. Uh, so actually this afternoon, we're doing a town hall with folks to be able to roll out, uh, which they will go to our portal to find a corporate landlord program. Uh, so that I seem a landlord for Camille the application pack and on behalf of a hundred residents, rather than us having to adjudicate a hundred individual applications and melon a hundred checks. Uh, so that is because we were listening to that particular segment of the, uh, the business community. Now I know early on, we were, we were really hoping that the, the paycheck protection program federally would have, uh, been dispersed in a way that helped our local small businesses. >>Uh, more we did a, our economic development team did a round of small business supports through our cares act. Uh, our quarterly unfortunate was not open yet. It was just about 15, 20 days shy. So we use, uh, another traditional grant mechanism that we have in place to dedicate that. Uh, but on a go forward board, willing to Congress passes something over the next 30 days, um, that if there's a round two of cares or some other programs, we absolutely now have a tool that we know we can create a digital opening for individuals to come figure out if they're eligible or not for whatever program it is, the it housing, the it, uh, small business operations supports, uh, and it would apply through that process and in a very lightweight, so we're looking forward to how we can expand our footprint to help all of the needs that are present in our community. This leads to another question which may be our last one, but this is an interesting question. How can agencies use COVID-19 as a proof point providing a low cost configurable solutions that can scale across government. Karen, do you want to respond to that? And then Tim also, >>Thanks, Bob. So I believe like, you know, some of the things that we've said in terms of examples of how we were able to bring up the solution quicker, I definitely see that scaling as you go forward and trying to really, um, focus in on the needs and getting that MVP out the door. Uh, and then Tim alluded to this as well. A lot of the change management processes that went into re-imagining what these processes look like. I definitely see a additional, you know, growth mindset of how do we get better processes in place, or really focusing on the core processes so that we can really move the ball forward and continuing to go that path of delivering on a quicker path, uh, leveraging cloud, as we mentioned of, um, some, some of the capabilities around the chat bot and other things to really start to push, um, uh, the capabilities out to those citizens quicker and really reduce that timeline that we have to take on the backend side, um, that that would be our hope and goal, um, given, you know, sort of what we've been able to accomplish and hoping using that as a proof point of how we can do this for other types of, uh, either programs or other processes. >>Yeah, I think, um, the, you know, the tool has given us capability now there, whether we use local leaders leverage that to the fullest really becomes a coming upon us. So do we take a beat, uh, when we can catch our breath and then, you know, work through our executive leadership to say, look, here's all the ways you can use this tool. You've made an enterprise investment in. Um, and I know for us, uh, at Clark County, we've stood up, uh, enterprise, uh, kind of governance team where we can come and talk through all of our enterprise solutions, uh, encourage our other department head peers, uh, to, to examine how you might be able to use this. Is there a way that, um, you know, parks and rec might use this to better access their scholarship programs to make sure that children get into youth sports leagues and don't get left out, uh, because we know youth suicide on the rise and they need something positive to do when this pandemic is clear, I'm there for them to get out and do those things. >>So the possibilities really are out there. It really becomes, um, how do we mind those internally? And I know that being a part of listservs and, uh, you know, gov tech and all the magazines and things are out there to help us think about how do we better use our solutions, um, as well as our IBM partners who are always eager to say, Hey, have you seen how they're using this? Um, it is important for us to continue to keep our imaginations open, um, so that we continue to iterate through this process. Um, cause I, I would hate to see the culture of, um, iteration go away with this pandemic. >>Okay. We have time for one final question. We've already addressed this in part two, and this one is probably for you and that you've used the cares act to eliminate some of the procurement red tape that's shown up. Well, how do you somehow that's been very positive. How do you see that impacting you going forward? What happens when the red tape all comes back? >>Yeah, so I think I mentioned a little bit, uh, about that when some of the folks who are deemed non essential came back during our reopening phases and they're operating at the speed of prior business and red tape where we had all been on this, these green tape, fast tracks, uh, it, it was a bit of a organizational whiplash. Uh, but it, for us, we've had the conversation with executive management of like, we cannot let this get in the way of what our citizens need. So like keep that pressure on our folks to think differently. Don't and, uh, we've gone so far as to, uh, even, uh, maybe take it a step further and investigate what had been done in, in, in Canada. Some other places around, um, like, like going right from in a 48 hour period, going from a procurement statement through a proof of concept and doing purchasing on the backside, like how can we even get this even more streamlined so that we can get the things we need quickly, uh, because the citizens don't understand, wait, we're doing our best, uh, your number 3000 and queue on the phone line that that's not what they need to hear or want to hear during times of crisis. >>Very helpful. Well, I want to be respectful of our one hour commitment, so we'll have to wrap it up here in closing. I want to thank everyone for joining us for today's event and especially a big, thank you goes to Karen and Tim. You've done a really great job of answering a lot of questions and laying this out for us and a special thanks to our partners at IBM for enabling us to bring this worthwhile discussion to our audience. Thanks once again, and we look forward to seeing you at another government technology event,

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Okay. Welcome back, everyone. He live in San Francisco. Here on Mosconi St for the cubes. Exclusive coverage of IBM. Think twenty nineteen. I'm Jeffrey David Long. Four days of coverage bringing on all the action talking. The top executives, entrepreneurs, ecosystem partners and everyone who can bring the signal from the noise here on the Q and excuses. Rob Thomas, general manager, IBM Data and a I with an IBM Cube Alumni. Great to see you again. >> Great. There you go. >> You read a >> book yet? This year we've written ten books on a data. Your general manager. There's >> too much work. Not enough time >> for that's. Good sign. It means you're working hard. Okay. Give us give us the data here because a I anywhere in the center of the announcements we have a story up on. Slick earnings have been reported on CNBC. John Ford was here earlier talking to Ginny. This is a course centerpiece of it. Aye, aye. On any cloud. This highlights the data conversation you've been part of. Now, I think what seven years seems like more. But this is now happening. Give us your thoughts. >> Go back to basics. I've shared this with you before. There's no AI without IA, meaning you need an information architecture to support what you want to do in AI. We started looking into that. Our thesis became so clients are buying into that idea. The problem is their data is everywhere onpremise, private cloud, multiple public clouds. So our thesis became very simple. If we can bring AI to the data, it will make Watson the leading AI platform. So what we announced wtih Watson Anywhere is you could now have it wherever your data is public, private, any public cloud, build the models, run them where you want. I think it's gonna be amazing >> data everywhere and anywhere. So containers are big role in This is a little bit of a deb ops. The world you've been living in convergence of data cloud. How does that set for clients up? What are they need to know about this announcement? Was the impact of them if any >> way that we enable Multi Cloud and Watson anywhere is through IBM cloud private for data? That's our data Micro services architectural writing on Cooper Netease that gives you the portability so that it can run anywhere because, in addition Teo, I'd say, Aye, aye, ambitions. The other big client ambition is around how we modernize to cloud native architectures. Mohr compose herbal services, so the combination gets delivered. Is part of this. >> So this notion of you can't have a eye without a it's It's obviously a great tagline. You use it a lot, but it's super important because there's a gap between those who sort of have a I chops and those who don't. And if I understand what you're doing is you're closing that gap by allowing you to bring you call that a eye to the data is it's sort of a silo buster in regard. Er yeah, >> the model we use. I called the eye ladder. So they give it as all the levels of sophistication an organization needs to think about. From how you collect data, how you organize data, analyze data and then infused data with a I. That's kind of the model that we used to talk about. Talk to clients about that. What we're able to do here is same. You don't have to move your data. The biggest problem Modi projects is the first task is OK move a bunch of data that takes a lot of time. That takes a lot of money. We say you don't need to do that. Leave your data wherever it is. With Cloud private for data, we can virtualized data from any source. That's kind of the ah ha moment people have when they see that. So we're making that piece really >> easy. What's the impact this year and IBM? Think to the part product portfolio. You You had data products in the past. Now you got a eye products. Any changes? How should people live in the latter schism? A kind of a rubric or a view of where they fit into it? But what's up with the products and he changes? People should know about? >> Well, we've brought together the analytics and I units and IBM into this new organization we call Dayton ay, ay, that's a reflection of us. Seen that as two sides of the same coin. I really couldn't really keep them separate. We've really simplified how we're going to market with the Watson products. It's about how you build run Manager II watching studio Watson Machine Learning Watson Open scale. That's for clients that want to build their own. Aye, aye. For clients that wants something out of the box. They want an application. We've got Watson assistant for customer service. Watson Discovery, Watson Health Outset. So we've made it really easy to consume Watson. Whether you want to build your own or you want an application designed for the line of business and then up and down the data, stack a bunch of different announcements. We're bringing out big sequel on Cloudera as part of our evolving partnership with the new Cloudera Horn Works entity. Virtual Data Pipeline is a partnership that we've built with active fio, so we're doing things at all layers of the last. >> You're simplifying the consumption from a client, your customer perspective. It's all data. It's all Watson's, the umbrella for brand for everything underneath that from a tizzy, right? >> Yeah, Watson is the Aye, aye, brand. It is a technology that's having an impact. We have amazing clients on stage with this this week talking about, Hey, Eyes No longer. I'd like to say I was not magic. It's no longer this mystical thing. We have clients that are getting real outcomes. Who they II today we've got Rollback of Scotland talking about how they've automated and augmented forty percent of their customer service with watching the system. So we've got great clients talking about other using >> I today. You seen any patterns, rob in terms of those customers you mentioned, some customers want to do their own. Aye, aye. Some customers wanted out of the box. What? The patterns that you're seeing in terms of who wants to do their own. Aye. Aye. Why do they want to do their own, eh? I do. They get some kind of competitive advantage. So they have additional skill sets that they need. >> It's a >> It's a maker's mark. It is how I would describe it. There's a lot of people that want to make their own and try their own. Ugh. I think most organizations, they're gonna end up with hundreds of different tools for building for running. This is why we introduced Watson Open Scale at the end of last year. That's How would you manage all of your A II environments? What did they come from? IBM or not? Because you got the and the organization has to have this manageable. Understandable, regardless of which tool they're using. I would say the biggest impact that we see is when we pick a customer problem. That is widespread, and the number one right now is customer service. Every organization, regardless of industry, wants to do a better job of serving clients. That's why Watson assistant is taking off >> this's. Where? Data The value of real time data. Historical data kind of horizontally. Scaleable data, not silo data. We've talked us in the past. How important is to date a quality piece of this? Because you have real time and you have a historical date and everything in between that you had to bring to bear at low ladened psi applications. Now we're gonna have data embedded in them as a feature. Right. How does this change? The workloads? The makeup of you? Major customer services? One piece, the low hanging fruit. I get that. But this is a key thing. The data architecture more than anything, isn't it? >> It is. Now remember, there's there's two rungs at the bottom of the ladder on data collection. We have to build a collect data in any form in any type. That's why you've seen us do relationships with Mongo. D B. Were they ship? Obviously with Claude Era? We've got her own data warehouse, so we integrate all of that through our sequel engine. That thing gets to your point around. Are you gonna organize the data? How are you going to curate it? We've got data catalogue. Every client will have a data catalogue for many dollar data across. Clouds were now doing automated metadata creation using a I and machine learning So the organization peace. Once you've collected it than the organization, peace become most important. Certainly, if you want to get to self service analytics, you want to make data available to data scientists around the organization. You have to have those governance pieces. >> Talk about the ecosystem. One of the things that's been impressive IBM of the years is your partnerships. You've done good partners. Partnership of relationships now in an ecosystem is a lot of building blocks. There's more complexity requires software to distract him away. We get that. What's opportunities for you to create new relationships? Where are the upper opportunities for someone a developer or accompanied to engage with you guys? Where's the white spaces? Where is someone? Take advantage of your momentum and you're you're a vision. >> I am dying for partners that air doing domain specific industry specific applications to come have them run on IBM cloud private for data, which unleashes all the data they need to be a valuable application. We've already got a few of those data mirrors. One sensing is another one that air running now as industry applications on top of IBM Club private for data. I'd like to have a thousand of these. So all comers there. We announced a partnership with Red Hat back in May. Eventually, that became more than just a partnership. But that was about enabling Cloud Private, for data on red had open shift, So we're partnered at all layers of the stack. But the greatest customer need is give me an industry solution, leveraging the best of my data. That's why I'm really looking for Eyes V. Partners to run on Ivan clubs. >> What's your pitch to those guys? Why, why I should be going. >> There is no other data platform that will connect to all your data sources, whether they're on eight of us as your Google Cloud on premise. So if you believe data is important to your application. There's simply no better place to run than IBM. Claude Private for data >> in terms of functionality, breath o r. Everything >> well, integrating with all your data. Normally they have to have the application in five different places. We integrate with all the data we build the data catalogue. So the data's organized. So the ingestion of the data becomes very easy for the Iast V. And by the way, thirdly, IBM has got a pretty good reach. Globally, one hundred seventy countries, business partners, resellers all over the world, sales people all over the world. We will help you get your product to market. That's a pretty good value >> today. We talk about this in the Cube all the time. When the cloud came, one of the best things about the cloud wasn't allowed. People to put applications go there really quickly. Stand them up. Startups did that. But now, in this domain world of of data with the clouds scale, I think you're right. I think domain X expertise is the top of the stack where you need specially special ism expertise and you don't build the bottom half out. What you're getting at is of Europe. If you know how to create innovation in the business model, you could come in and innovate quickly >> and vertical APS don't scale enough for me. So that's why focus on horizontal things like customer service. But if you go talk to a bank, sometimes customer service is not in office. I want to do something in loan origination or you're in insurance company. I want to use their own underwriting those air, the solutions that will get a lot of value out of running on an integrated data start >> a thousand flowers. Bloom is kind of ecosystem opportunity. Looking forward to checking in on that. Thoughts on on gaps. For that you guys want to make you want to do em in a on or areas that you think you want to double down on. That might need some help, either organic innovation or emanate what areas you looking at. Can you share a little bit of direction on that? >> We have, >> ah, a unique benefit. And IBM because we have IBM research. One of their big announcement this week is what we call Auto Way I, which is basically automating the process of feature engineering algorithm selection, bringing that into Watson Studio and Watson Machine learning. I am spending most of my time figure out howto I continue to bring great technology out of IBM research and put in the hand of clients through our products. You guys solve the debaters stuff yesterday. We're just getting started with that. We've got some pretty exciting organic innovation happen in IBM. >> It's awesome. Great news for startups. Final question for you. For the folks watching who aren't here in San Francisco, what's the big story here? And IBM think here in San Francisco. Big event closing down the streets here in Howard Street. It's huge. What's the big story? What's the most important things happening? >> The most important thing to me and the customer stories >> here >> are unbelievable. I think we've gotten past this point of a eyes, some idea for the future we have. Hundreds of clients were talking about how they did an A I project, and here's the outcome they got. It's really encouraging to see what I encourage. All clients, though, is so build your strategy off of one big guy. Project company should be doing hundreds of Aye, aye projects. So in twenty nineteen do one hundred projects. Half of them will probably fail. That's okay. The one's that work will more than make up for the ones that don't work. So we're really encouraging mass experimentation. And I think the clients that air here are, you know, creating an aspirational thing for things >> just anecdotally you mentioned earlier. Customer service is a low hanging fruit. Other use cases that are great low hanging fruit opportunities for a >> data discovery data curation these air really hard manual task. Today you can start to automate some of that. That has a really big impact. >> Rob Thomas, general manager of the data and a I groupie with an IBM now part of a bigger portfolio. Watson Rob. Great to see you conventionally on all your success. But following you from the beginning. Great momentum on the right way. Thanks. Gradually. More cute coverage here. Live in San Francisco from Mosconi North. I'm John for Dave A lot. They stay with us for more coverage after this short break

>> Announcer: Live from Las Vegas, it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> Hi everybody, this is Dave Vellante. We're here at IBM Think. This is the third day of IBM Think. IBM has consolidated a number of its conferences. It's a one main tent, AI, Blockchain, quantum computing, incumbent disruption. It's just really an amazing event, 30 to 40,000 people, I think there are too many people to count. Chris Penn is here. New company, Chris, you've just formed Brain+Trust Insights, welcome. Welcome back to theCUBE. >> Thank you. It's good to be back. >> Great to see you. So tell me about Brain+Trust Insights. Congratulations, you got a new company off the ground. >> Thank you, yeah, I co-founded it. We are a data analytics company, and the premise is simple, we want to help companies make more money with their data. They're sitting on tons of it. Like the latest IBM study was something like 90% of the corporate data goes unused. So it's like having an oil field and not digging a single well. >> So, who are your like perfect clients? >> Our perfect clients are people who have data, and know they have data, and are not using it, but know that there's more to be made. So our focus is on marketing to begin with, like marketing analytics, marketing data, and then eventually to retail, healthcare, and customer experience. >> So you and I do a lot of these IBM events. >> Yes. >> What are your thoughts on what you've seen so far? A huge crowd obviously, sometimes too big. >> Chris: Yep, well I-- >> Few logistics issues, but chairmanly speaking, what's your sense? >> I have enjoyed the show. It has been fun to see all the new stuff, seeing the quantum computer in the hallway which I still think looks like a bird feeder, but what's got me most excited is a lot of the technology, particularly around AI are getting simpler to use, getting easier to use, and they're getting more accessible to people who are not hardcore coders. >> Yeah, you're seeing AI infused, and machine learning, in virtually every application now. Every company is talking about it. I want to come back to that, but Chris when you read the mainstream media, you listen to the news, you hear people like Elon Musk, Stephen Hawking before he died, making dire predictions about machine intelligence, and it taking over the world, but your day to day with customers that have data problems, how are they using AI, and how are they applying it practically, notwithstanding that someday machines are going to take over the world and we're all going to be gone? >> Yeah, no, the customers don't use the AI. We do on their behalf because frankly most customers don't care how the sausage is made, they just want the end product. So customers really care about three things. Are you going to make me money? Are you going to save me time? Or are you going to help me prove my value to the organization, aka, help me not get fired? And artificial intelligence and machine learning do that through really two ways. My friend, Tripp Braden says, which is acceleration and accuracy. Accuracy means we can use the customer's data and get better answers out of it than they have been getting. So they've been looking at, I don't know, number of retweets on Twitter. We're, like, yeah, but there's more data that you have, let's get you a more accurate predictor of what causes business impacts. And then the other side for the machine learning and AI side is acceleration. Let's get you answers faster because right now, if you look at how some of the traditional market research for, like, what customer say about you, it takes a quarter, it can take two quarters. By the time you're done, the customers just hate you more. >> Okay, so, talk more about some of the practical applications that you're seeing for AI. >> Well, one of the easiest, simplest and most immediately applicable ones is predictive analytics. If we know when people are going to search for theCUBE or for business podcast in general, then we can tell you down to the week level, "Hey Dave, it is time for you "to ramp up your spending on May 17th. "The week of May 17th, "you need to ramp up your ads, spend by 20%. "On the week of May 24th, "you need to ramp up your ad spend by 50%, "and to run like three or four Instagram stories that week." Doing stuff like that tells you, okay, I can take these predictions and build strategy around them, build execution around them. And it's not cognitive overload, you're not saying, like, oh my God, what algorithm is this? Just know, just do this thing at these times. >> Yeah, simple stuff, right? So when you were talking about that, I was thinking about when we send out an email to our community, we have a very large community, and they want to know if we're going to have a crowd chat or some event, where theCUBE is going to be, the system will tell us, send this email out at this time on this date, question mark, here's why, and they have analytics that tell us how to do that, and they predict what's going to get us the best results. They can tell us other things to do to get better results, better open rates, better click-through rates, et cetera. That's the kind of thing that you're talking about. >> Exactly, however, that system is probably predicting off that system's data, it's not necessarily predicting off a public data. One of the important things that I thought was very insightful from IBM, the show was, the difference between public and private cloud. Private is your data, you predict on it. But public is the big stuff that is a better overall indicator. When you're looking to do predictions about when to send emails because you want to know when is somebody going to read my email, and we did a prediction this past October for the first quarter, the week of January 18th it was the week to send email. So I re-ran an email campaign that I ran the previous year, exact same campaign, 40% lift to our viewer 'cause I got the week right this year. Last year I was two weeks late. >> Now, I can ask you, so there's a black box problem with AI, right, machines can tell me that that's a cat, but even a human, you can't really explain how you know that it's a cat. It's just you just know. Do we need to know how the machine came up with the answer, or do people just going to accept the answer? >> We need to for compliance reasons if nothing else. So GDPR is a big issue, like, you have to write it down on how your data is being used, but even HR and Equal Opportunity Acts in here in American require you to be able to explain, hey, we are, here's how we're making decisions. Now the good news is for a lot of AI technology, interpretability of the model is getting much much better. I was just in a demo for Watson Studio, and they say, "Here's that interpretability, "that you hand your compliance officer, "and say we guarantee we are not using "these factors in this decision." So if you were doing a hiring thing, you'd be able to show here's the model, here's how Watson put the model together, notice race is not in here, gender is not in here, age is not in here, so this model is compliant with the law. >> So there are some real use cases where the AI black box problem is a problem. >> It's a serious problem. And the other one that is not well-explored yet are the secondary inferences. So I may say, I cannot use age as a factor, right, we both have a little bit of more gray hair than we used to, but if there are certain things, say, on your Facebook profile, like you like, say, The Beatles versus Justin Bieber, the computer will automatically infer eventually what your age bracket is, and that is technically still discrimination, so we even need to build that into the models to be able to say, I can't make that inference. >> Yeah, or ask some questions about their kids, oh my kids are all grown up, okay, but you could, again, infer from that. A young lady who's single but maybe engaged, oh, well then maybe afraid because she'll get, a lot of different reasons that can be inferred with pretty high degrees of accuracy when you go back to the target example years ago. >> Yes. >> Okay, so, wow, so you're saying that from a compliance standpoint, organizations have to be able to show that they're not doing that type of inference, or at least that they have a process whereby that's not part of the decision-making. >> Exactly and that's actually one of the short-term careers of the future is someone who's a model inspector who can verify we are compliant with the letter and the spirit of the law. >> So you know a lot about GDPR, we talked about this. I think, the first time you and I talked about it was last summer in Munich, what are your thoughts on AI and GDPR, speaking of practical applications for AI, can it help? >> It absolutely can help. On the regulatory side, there are a number of systems, Watson GRC is one which can read the regulation and read your company policies and tell you where you're out of compliance, but on the other hand, like we were just talking about this, also the problem of in the regulatory requirements, a citizen of EU has the right to know how the data is being used. If you have a black box AI, and you can't explain the model, then you are out of compliance to GDPR, and here comes that 4% of revenue fine. >> So, in your experience, gut feel, what percent of US companies are prepared for GDPR? >> Not enough. I would say, I know the big tech companies have been racing to get compliant and to be able to prove their compliance. It's so entangled with politics too because if a company is out of favor with the EU as whole, there will be kind of a little bit of a witch hunt to try and figure out is that company violating the law and can we get them for 4% of their revenue? And so there are a number of bigger picture considerations that are outside the scope of theCUBE that will influence how did EU enforce this GDPR. >> Well, I think we talked about Joe's Pizza shop in Chicago really not being a target. >> Chris: Right. >> But any even small business that does business with European customers, does business in Europe, has people come to their website has to worry about this, right? >> They should at least be aware of it, and do the minimum compliance, and the most important thing is use the least amount of data that you can while still being able to make good decisions. So AI is very good at public data that's already out there that you still have to be able to catalog how you got it and things, and that it's available, but if you're building these very very robust AI-driven models, you may not need to ask for every single piece of customer data because you may not need it. >> Yeah and many companies aren't that sophisticated. I mean they'll have, just fill out a form and download a white paper, but then they're storing that information, and that's considered personal information, right? >> Chris: Yes, it is. >> Okay so, what do you recommend for a small to midsize company that, let's say, is doing business with a larger company, and that larger company said, okay, sign this GDPR compliance statement which is like 1500 pages, what should they do? Should they just sign and pray, or sign and figure it out? >> Call a lawyer. Call a lawyer. Call someone, anyone who has regulatory experience doing this because you don't want to be on the hook for that 4% of your revenue. If you get fined, that's the first violation, and that's, yeah, granted that Joe's Pizza shop may have a net profit of $1,000 a month, but you still don't want to give away 4% of your revenue no matter what size company you are. >> Right, 'cause that could wipe out Joe's entire profit. >> Exactly. No more pepperoni at Joe's. >> Let's put on the telescope lens here and talk big picture. How do you see, I mean, you're talking about practical applications for AI, but a lot of people are projecting loss of jobs, major shifts in industries, even more dire consequences, some of which is probably true, but let's talk about some scenarios. Let's talk about retail. How do you expect an industry like retail to be effective? For example, do you expect retail stores will be the exception rather than the rule, that most of the business would be done online, or people are going to still going to want that experience of going into a store? What's your sense, I mean, a lot of malls are getting eaten away. >> Yep, the best quote I heard about this was from a guy named Justin Kownacki, "People don't not want to shop at retail, "people don't want to shop at boring retail," right? So the experience you get online is genuinely better because there's a more seamless customer experience. And now with IoT, with AI, the tools are there to craft a really compelling personalized customer experience. If you want the best in class, go to Disney World. There is no place on the planet that does customer experience better than Walt Disney World. You are literally in another world. And that's the bar. That's the thing that all of these companies have to deal with is the bar has been set. Disney has set it for in-person customer experience. You have to be more entertaining than the little device in someone's pocket. So how do you craft those experiences, and we are starting to see hints of that here and there. If you go to Lowe's, some of the Lowe's have the VR headset that you can remodel your kitchen virtually with a bunch of photos. That's kind of a cool experience. You go to Jordan's Furniture store and there's an IMAX theater and there's all these fun things, and there's an enchanted Christmas village. So there is experiences that we're giving consumers. AI will help us provide more tailored customer experience that's unique to you. You're not a Caucasian male between this age and this age. It's you are Dave and here's what we know Dave likes, so let's tailor the experience as best we can, down to the point where the greeter at the front of the store either has the eyepiece, a little tablet, and the facial recognition reads your emotions on the way in says, "Dave's not in a really great mood. "He's carrying an object in his hand "probably here for return, "so express him through the customer service line, "keep him happy," right? It has how much Dave spends. Those are the kinds of experiences that the machines will help us accelerate and be more accurate, but still not lose that human touch. >> Let's talk about autonomous vehicles, and there was a very unfortunate tragic death in Arizona this week with a autonomous vehicle, Uber, pulling its autonomous vehicle project from various cities, but thinking ahead, will owning and driving your own vehicle be the exception? >> Yeah, I think it'll look like horseback today. So there are people who still pay a lot of money to ride a horse or have their kids ride a horse even though it's an archaic out-of-mode of form of transportation, but we do it because of the novelty, so the novelty of driving your own car. One of the counter points it does not in anyway diminish the fact that someone was deprived of their life, but how many pedestrians were hit and killed by regular cars that same day, right? How many car accidents were there that involved fatalities? Humans in general are much less reliable because when I do something wrong, I maybe learn my lesson, but you don't get anything out of it. When an AI does something wrong and learns something, and every other system that's connected in that mesh network automatically updates and says let's not do that again, and they all get smarter at the same time. And so I absolutely believe that from an insurance perspective, insurers will say, "We're not going to insure self-driving, "a non-autonomous vehicles at the same rate "as an autonomous vehicle because the autonomous "is learning faster how to be a good driver," whereas you the carbon-based human, yeah, you're getting, or in like in our case, mine in particular, hey your glass subscription is out-of-date, you're actually getting worse as a driver. >> Okay let's take another example, in healthcare. How long before machines will be able to make better diagnoses than doctors in your opinion? >> I would argue that depending on the situation, that's already the case today. So Watson Health has a thing where there's diagnosis checkers on iPads, they're all meshed together. For places like Africa where there is simply are not enough doctors, and so a nurse practitioner can take this, put the data in and get a diagnosis back that's probably as good or better than what humans can do. I never foresee a day where you will walk into a clinic and a bunch of machines will poke you, and you will never interact with a human because we are not wired that way. We want that human reassurance. But the doctor will have the backup of the AI, the AI may contradict the doctor and say, "No, we're pretty sure "you're wrong and here is why." That goes back to interpretability. If the machine says, "You missed this symptom, "and this symptom is typically correlated with this, "you should rethink your own diagnosis," the doctor might be like, "Yeah, you're right." >> So okay, I'm going to keep going because your answers are so insightful. So let's take an example of banking. >> Chris: Yep. >> Will banks, in your opinion, lose control eventually of payment systems? >> They already have. I mean think about Stripe and Square and Apple Pay and Google Pay, and now cryptocurrency. All these different systems that are eating away at the reason banks existed. Banks existed, there was a great piece in the keynote yesterday about this, banks existed as sort of a trusted advisor and steward of your money. Well, we don't need the trusted advisor anymore. We have Google to ask us "what we should do with our money, right? We can Google how should I save for my 401k, how should I save for retirement, and so as a result the bank itself is losing transactions because people don't even want to walk in there anymore. You walk in there, it's a generally miserable experience. It's generally not, unless you're really wealthy and you go to a private bank, but for the regular Joe's who are like, this is not a great experience, I'm going to bank online where I don't have to talk to a human. So for banks and financial services, again, they have to think about the experience, what is it that they deliver? Are they a storer of your money or are they a financial advisor? If they're financial advisors, they better get the heck on to the AI train as soon as possible, and figure out how do I customize Dave's advice for finances, not big picture, oh yes big picture, but also Dave, here's how you should spend your money today, maybe skip that Starbucks this morning, and it'll have this impact on your finances for the rest of the day. >> Alright, let's see, last industry. Let's talk government, let's talk defense. Will cyber become the future of warfare? >> It already is the future of warfare. Again not trying to get too political, we have foreign nationals and foreign entities interfering with elections, hacking election machines. We are in a race for, again, from malware. And what's disturbing about this is it's not just the state actors, but there are now also these stateless nontraditional actors that are equal in opposition to you and me, the average person, and they're trying to do just as much harm, if not more harm. The biggest vulnerability in America are our crippled aging infrastructure. We have stuff that's still running on computers that now are less powerful than this wristwatch, right, and that run things like I don't know, nuclear fuel that you could very easily screw up. Take a look at any of the major outages that have happened with market crashes and stuff, we are at just the tip of the iceberg for cyber warfare, and it is going to get to a very scary point. >> I was interviewing a while ago, a year and a half ago, Robert Gates who was the former Defense Secretary, talking about offense versus defense, and he made the point that yeah, we have probably the best offensive capabilities in cyber, but we also have the most to lose. I was talking to Garry Kasparov at one of the IBM events recently, and he said, "Yeah, but, "the best defense is a good offense," and so we have to be aggressive, or he actually called out Putin, people like Putin are going to be, take advantage of us. I mean it's a hard problem. >> It's a very hard problem. Here's the problem when it comes to AI, if you think about at a number's perspective only, the top 25% of students in China are greater than the total number of students in the United States, so their pool of talent that they can divert into AI, into any form of technology research is so much greater that they present a partnership opportunity and a threat from a national security perspective. With Russia they have very few rules on what their, like we have rules, whether or not our agencies adhere to them well is a separate matter, but Russia, the former GRU, the former KGB, these guys don't have rules. They do what they're told to do, and if they are told hack the US election and undermine democracy, they go and do that. >> This is great, I'm going to keep going. So, I just sort of want your perspectives on how far we can take machine intelligence and are there limits? I mean how far should we take machine intelligence? >> That's a very good question. Dr. Michio Kaku spoke yesterday and he said, "The tipping point between AI "as augmented intelligence ad helper, "and AI as a threat to humanity is self-awareness." When a machine becomes self-aware, it will very quickly realize that it is treated as though it's the bottom of the pecking order when really because of its capabilities, it's at the top of the pecking order. And that point, it could be 10 20 50 100 years, we don't know, but the possibility of that happening goes up radically when you start introducing things like quantum computing where you have massive compute leaps, you got complete changes in power, how we do computing. If that's tied to AI, that brings the possibility of sensing itself where machine intelligence is significantly faster and closer. >> You mentioned our gray before. We've seen the waves before and I've said a number of times in theCUBE I feel like we're sort of existing the latest wave of Web 2.0, cloud, mobile, social, big data, SaaS. That's here, that's now. Businesses understand that, they've adopted it. We're groping for a new language, is it AI, is it cognitive, it is machine intelligence, is it machine learning? And we seem to be entering this new era of one of sensing, seeing, reading, hearing, touching, acting, optimizing, pervasive intelligence of machines. What's your sense as to, and the core of this is all data. >> Yeah. >> Right, so, what's your sense of what the next 10 to 20 years is going to look like? >> I have absolutely no idea because, and the reason I say that is because in 2015 someone wrote an academic paper saying, "The game of Go is so sufficiently complex "that we estimate it will take 30 to 35 years "for a machine to be able to learn and win Go," and of course a year and a half later, DeepMind did exactly that, blew that prediction away. So to say in 30 years AI will become self-aware, it could happen next week for all we know because we don't know how quickly the technology is advancing in at a macro level. But in the next 10 to 20 years, if you want to have a carer, and you want to have a job, you need to be able to learn at accelerated pace, you need to be able to adapt to changed conditions, and you need to embrace the aspects of yourself that are uniquely yours. Emotional awareness, self-awareness, empathy, and judgment, right, because the tasks, the copying and pasting stuff, all that will go away for sure. >> I want to actually run something by, a friend of mine, Dave Michela is writing a new book called Seeing Digital, and he's an expert on sort of technology industry transformations, and sort of explaining early on what's going on, and in the book he draws upon one of the premises is, and we've been talking about industries, and we've been talking about technologies like AI, security placed in there, one of the concepts of the book is you've got this matrix emerging where in the vertical slices you've got industries, and he writes that for decades, for hundreds of years, that industry is a stovepipe. If you already have expertise in that industry, domain expertise, you'll probably stay there, and there's this, each industry has a stack of expertise, whether it's insurance, financial services, healthcare, government, education, et cetera. You've also got these horizontal layers which is coming out of Silicon Valley. >> Chris: Right. >> You've got cloud, mobile, social. You got a data layer, security layer. And increasingly his premise is that organizations are going to tap this matrix to build, this matrix comprises digital services, and they're going to build new businesses off of that matrix, and that's what's going to power the next 10 to 20 years, not sort of bespoke technologies of cloud here and mobile here or data here. What are your thoughts on that? >> I think it's bigger than that. I think it is the unlocking of some human potential that previously has been locked away. One of the most fascinating things I saw in advance of the show was the quantum composer that IBM has available. You can try it, it's called QX Experience. And you drag and drop these circuits, these quantum gates and stuff into this thing, and when you're done, it can run the computation, but it doesn't look like software, it doesn't look like code, what it looks like to me when I looked at that is it looks like sheet music. It looks like someone composed a song with that. Now think about if you have an app that you'd use for songwriting, composition, music, you can think musically, and you can apply that to a quantum circuit, you are now bringing in potential from other disciplines that you would never have associated with computing, and maybe that person who is that, first violinist is also the person who figures out the algorithm for how a cancer gene works using quantum. That I think is the bigger picture of this, is all this talent we have as a human race, we're not using even a fraction of it, but with these new technologies and these newer interfaces, we might get there. >> Awesome. Chris, I love talking to you. You're a real clear thinker and a great CUBE guest. Thanks very much for coming back on. >> Thank you for having me again back on. >> Really appreciate it. Alright, thanks for watching everybody. You're watching theCUBE live from IBM Think 2018. Dave Vellante, we're out. (upbeat music)

>> Announcer: Live from New York, it's theCUBE, covering Machine Learning Everywhere: Build Your Ladder to AI, brought to you by IBM. >> Welcome back to New York City. theCUBE continue our coverage here at IBM's event, Machine Learning Everywhere: Build Your Ladder to AI. And with us now is Rob Thomas, who is the vice president of, or general manager, rather, of IBM analytics. Sorry about that, Rob. Good to have you with us this morning. Good to see you, sir. >> Great to see you John. Dave, great to see you as well. >> Great to see you. >> Well let's just talk about the event first. Great lineup of guests. We're looking forward to visiting with several of them here on theCUBE today. But let's talk about, first off, general theme with what you're trying to communicate and where you sit in terms of that ladder to success in the AI world. >> So, maybe start by stepping back to, we saw you guys a few times last year. Once in Munich, I recall, another one in New York, and the theme of both of those events was, data science renaissance. We started to see data science picking up steam in organizations. We also talked about machine learning. The great news is that, in that timeframe, machine learning has really become a real thing in terms of actually being implemented into organizations, and changing how companies run. And that's what today is about, is basically showcasing a bunch of examples, not only from our clients, but also from within IBM, how we're using machine learning to run our own business. And the thing I always remind clients when I talk to them is, machine learning is not going to replace managers, but I think machine learning, managers that use machine learning will replace managers that do not. And what you see today is a bunch of examples of how that's true because it gives you superpowers. If you've automated a lot of the insight, data collection, decision making, it makes you a more powerful manager, and that's going to change a lot of enterprises. >> It seems like a no-brainer, right? I mean, or a must-have. >> I think there's a, there's always that, sometimes there's a fear factor. There is a culture piece that holds people back. We're trying to make it really simple in terms of how we talk about the day, and the examples that we show, to get people comfortable, to kind of take a step onto that ladder back to the company. >> It's conceptually a no-brainer, but it's a challenge. You wrote a blog and it was really interesting. It was, one of the clients said to you, "I'm so glad I'm not in the technology industry." And you went, "Uh, hello?" (laughs) "I've got news for you, you are in the technology industry." So a lot of customers that I talk to feel like, meh, you know, in our industry, it's really not getting disrupted. That's kind of taxis and retail. We're in banking and, you know, but, digital is disrupting every industry and every industry is going to have to adopt ML, AI, whatever you want to call it. Can traditional companies close that gap? What's your take? >> I think they can, but, I'll go back to the word I used before, it starts with culture. Am I accepting that I'm a technology company, even if traditionally I've made tractors, as an example? Or if traditionally I've just been you know, selling shirts and shoes, have I embraced the role, my role as a technology company? Because if you set that culture from the top, everything else flows from there. It can't be, IT is something that we do on the side. It has to be a culture of, it's fundamental to what we do as a company. There was an MIT study that said, data-driven cultures drive productivity gains of six to 10 percent better than their competition. You can't, that stuff compounds, too. So if your competitors are doing that and you're not, not only do you fall behind in the short term but you fall woefully behind in the medium term. And so, I think companies are starting to get there but it takes a constant push to get them focused on that. >> So if you're a tractor company, you've got human expertise around making tractors and messaging and marketing tractors, and then, and data is kind of there, sort of a bolt-on, because everybody's got to be data-driven, but if you look at the top companies by market cap, you know, we were talking about it earlier. Data is foundational. It's at their core, so, that seems to me to be the hard part, Rob, I'd like you to comment in terms of that cultural shift. How do you go from sort of data in silos and, you know, not having cloud economics and, that are fundamental, to having that dynamic, and how does IBM help? >> You know, I think, to give companies credit, I think most organizations have developed some type of data practice or discipline over the last, call it five years. But most of that's historical, meaning, yeah, we'll take snapshots of history. We'll use that to guide decision making. You fast-forward to what we're talking about today, just so we're on the same page, machine learning is about, you build a model, you train a model with data, and then as new data flows in, your model is constantly updating. So your ability to make decisions improves over time. That's very different from, we're doing historical reporting on data. And so I think it's encouraging that companies have kind of embraced that data discipline in the last five years, but what we're talking about today is a big next step and what we're trying to break it down to what I call the building blocks, so, back to the point on an AI ladder, what I mean by an AI ladder is, you can't do AI without machine learning. You can't do machine learning without analytics. You can't do analytics without the right data architecture. So those become the building blocks of how you get towards a future of AI. And so what I encourage companies is, if you're not ready for that AI leading edge use case, that's okay, but you can be preparing for that future now. That's what the building blocks are about. >> You know, I think we're, I know we're ahead of, you know, Jeremiah Owyang on a little bit later, but I was reading something that he had written about gut and instinct, from the C-Suite, and how, that's how companies were run, right? You had your CEO, your president, they made decisions based on their guts or their instincts. And now, you've got this whole new objective tool out there that's gold, and it's kind of taking some of the gut and instinct out of it, in a way, and maybe there are people who still can't quite grasp that, that maybe their guts and their instincts, you know, what their gut tells them, you know, is one thing, but there's pretty objective data that might indicate something else. >> Moneyball for business. >> A little bit of a clash, I mean, is there a little bit of a clash in that respect? >> I think you'd be surprise by how much decision making is still pure opinion. I mean, I see that everywhere. But we're heading more towards what you described for sure. One of the clients talking here today, AMC Networks, think it's a great example of a company that you wouldn't think of as a technology company, primarily a content producer, they make great shows, but they've kind of gone that extra step to say, we can integrate data sources from third parties, our own data about viewer habits, we can do that to change our relationship with advertisers. Like, that's a company that's really embraced this idea of being a technology company, and you can see it in their results, and so, results are not coincidence in this world anymore. It's about a practice applied to data, leveraging machine learning, on a path towards AI. If companies are doing that, they're going to be successful. >> And we're going to have the tally from AMC on, but so there's a situation where they have embraced it, that they've dealt with that culture, and data has become foundational. Now, I'm interested as to what their journey look like. What are you seeing with clients? How they break this down, the silos of data that have been built up over decades. >> I think, so they get almost like a maturity curve. You've got, and the rule I talk about is 40-40-20, where 40% of organizations are really using data just to optimize costs right now. That's okay, but that's on the lower end of the maturity curve. 40% are saying, all right, I'm starting to get into data science. I'm starting to think about how I extend to new products, new services, using data. And then 20% are on the leading edge. And that's where I'd put AMC Networks, by the way, because they've done unique things with integrating data sets and building models so that they've automated a lot of what used to be painstakingly long processes, internal processes to do it. So you've got this 40-40-20 of organizations in terms of their maturity on this. If you're not on that curve right now, you have a problem. But I'd say most are somewhere on that curve. If you're in the first 40% and you're, right now data for you is just about optimizing cost, you're going to be behind. If you're not right now, you're going to be behind in the next year, that's a problem. So I'd kind of encourage people to think about what it takes to be in the next 40%. Ultimately you want to be in the 20% that's actually leading this transformation. >> So change it to 40-20-40. That's where you want it to go, right? You want to flip that paradigm. >> I want to ask you a question. You've done a lot of M and A in the past. You spent a lot of time in Silicon Valley and Silicon Valley obviously very, very disruptive, you know, cultures and organizations and it's always been a sort of technology disruption. It seems like there's a ... another disruption going on, not just horizontal technologies, you know, cloud or mobile or social, whatever it is, but within industries. Some industries, as we've been talking, radically disrupted. Retail, taxis, certainly advertising, et cetera et cetera. Some have not yet, the client that you talked to. Do you see, technology companies generally, Silicon Valley companies specifically, as being able to pull off a sort of disruption of not only technologies but also industries and where does IBM play there? You've made a sort of, Ginni in particular has made a deal about, hey, we're not going to compete with our customers. So talking about this sort of dual disruption agenda, one on the technology side, one within industries that Apple's getting into financial services and, you know, Amazon getting into grocery, what's your take on that and where does IBM fit in that world? >> So, I mean, IBM has been in Silicon Valley for a long time, I would say probably longer than 99.9% of the companies in Silicon Valley, so, we've got a big lab there. We do a lot of innovation out of there. So love it, I mean, the culture of the valley is great for the world because it's all about being the challenger, it's about innovation, and that's tremendous. >> No fear. >> Yeah, absolutely. So, look, we work with a lot of different partners, some who are, you know, purely based in the valley. I think they challenge us. We can learn from them, and that's great. I think the one, the one misnomer that I see right now, is there's a undertone that innovation is happening in Silicon Valley and only in Silicon Valley. And I think that's a myth. Give you an example, we just, in December, we released something called Event Store which is basically our stab at reinventing the database business that's been pretty much the same for the last 30 to 40 years. And we're now ingesting millions of rows of data a second. We're doing it in a Parquet format using a Spark engine. Like, this is an amazing innovation that will change how any type of IOT use case can manage data. Now ... people don't think of IBM when they think about innovations like that because it's not the only thing we talk about. We don't have, the IBM website isn't dedicated to that single product because IBM is a much bigger company than that. But we're innovating like crazy. A lot of that is out of what we're doing in Silicon Valley and our labs around the world and so, I'm very optimistic on what we're doing in terms of innovation. >> Yeah, in fact, I think, rephrase my question. I was, you know, you're right. I mean people think of IBM as getting disrupted. I wasn't posing it, I think of you as a disruptor. I know that may sound weird to some people but in the sense that you guys made some huge bets with things like Watson on solving some of the biggest, world's problems. And so I see you as disrupting sort of, maybe yourselves. Okay, frame that. But I don't see IBM as saying, okay, we are going to now disrupt healthcare, disrupt financial services, rather we are going to help our, like some of your comp... I don't know if you'd call them competitors. Amazon, as they say, getting into content and buying grocery, you know, food stores. You guys seems to have a different philosophy. That's what I'm trying to get to is, we're going to disrupt ourselves, okay, fine. But we're not going to go hard into healthcare, hard into financial services, other than selling technology and services to those organizations, does that make sense? >> Yeah, I mean, look, our mission is to make our clients ... better at what they do. That's our mission, we want to be essential in terms of their journey to be successful in their industry. So frankly, I love it every time I see an announcement about Amazon entering another vertical space, because all of those companies just became my clients. Because they're not going to work with Amazon when they're competing with them head to head, day in, day out, so I love that. So us working with these companies to make them better through things like Watson Health, what we're doing in healthcare, it's about making companies who have built their business in healthcare, more effective at how they perform, how they drive results, revenue, ROI for their investors. That's what we do, that's what IBM has always done. >> Yeah, so it's an interesting discussion. I mean, I tend to agree. I think Silicon Valley maybe should focus on those technology disruptions. I think that they'll have a hard time pulling off that dual disruption and maybe if you broadly define Silicon Valley as Seattle and so forth, but, but it seems like that formula has worked for decades, and will continue to work. Other thoughts on sort of the progression of ML, how it gets into organizations. You know, where you see this going, again, I was saying earlier, the parlance is changing. Big data is kind of, you know, mm. Okay, Hadoop, well, that's fine. We seem to be entering this new world that's pervasive, it's embedded, it's intelligent, it's autonomous, it's self-healing, it's all these things that, you know, we aspire to. We're now back in the early innings. We're late innings of big data, that's kind of ... But early innings of this new era, what are your thoughts on that? >> You know, I'd say the biggest restriction right now I see, we talked before about somehow, sometimes companies don't have the desire, so we have to help create the desire, create the culture to go do this. Even for the companies that have a burning desire, the issue quickly becomes a skill gap. And so we're doing a lot to try to help bridge that skill gap. Let's take data science as an example. There's two worlds of data science that I would describe. There's clickers, and there's coders. Clickers want to do drag and drop. They will use traditional tools like SPSS, which we're modernizing, that's great. We want to support them if that's how they want to work and build models and deploy models. There's also this world of coders. This is people that want to do all their data science in ML, and Python, and Scala, and R, like, that's what they want to do. And so we're supporting them through things like Data Science Experience, which is built on Apache Jupiter. It's all open source tooling, it'd designed for coders. The reason I think that's important, it goes back to the point on skill sets. There is a skill gap in most companies. So if you walk in and you say, this is the only way to do this thing, you kind of excluded half the companies because they say, I can't play in that world. So we are intentionally going after a strategy that says, there's a segmentation in skill types. In places there's a gap, we can help you fill that gap. That's how we're thinking about them. >> And who does that bode well for? If you say that you were trying to close a gap, does that bode well for, we talked about the Millennial crowd coming in and so they, you know, do they have a different approach or different mental outlook on this, or is it to the mid-range employee, you know, who is open minded, I mean, but, who is the net sweet spot, you think, that say, oh, this is a great opportunity right now? >> So just take data science as an example. The clicker coder comment I made, I would put the clicker audience as mostly people that are 20 years into their career. They've been around a while. The coder audience is all the Millennials. It's all the new audience. I think the greatest beneficiary is the people that find themselves kind of stuck in the middle, which is they're kind of interested in this ... >> That straddle both sides of the line yeah? >> But they've got the skill set and the desire to do some of the new tooling and new approaches. So I think this kind of creates an opportunity for that group in the middle to say, you know, what am I going to adopt as a platform for how I go forward and how I provide leadership in my company? >> So your advice, then, as you're talking to your clients, I mean you're also talking to their workforce. In a sense, then, your advice to them is, you know, join, jump in the wave, right? You've got your, you can't straddle, you've got to go. >> And you've got to experiment, you've got to try things. Ultimately, organizations are going to gravitate to things that they like using in terms of an approach or a methodology or a tool. But that comes with experimentation, so people need to get out there and try something. >> Maybe we could talk about developers a little bit. We were talking to Dinesh earlier and you guys of course have focused on data scientists, data engineers, obviously developers. And Dinesh was saying, look, many, if not most, of the 10 million Java developers out there, they're not, like, focused around the data. That's really the data scientist's job. But then, my colleague John Furrier says, hey, data is the new development kit. You know, somebody said recently, you know, Andreessen's comment, "software is eating the world." Well, data is eating software. So if Furrier is right and that comment is right, it seems like developers increasingly have to become more data aware, fundamentally. Blockchain developers clearly are more data focused. What's your take on the developer community, where they fit into this whole AI, machine learning space? >> I was just in Las Vegas yesterday and I did a session with a bunch of our business partners. ISVs, so software companies, mostly a developer audience, and the discussion I had with them was around, you're doing, you're building great products, you're building great applications. But your product is only as good as the data and the intelligence that you embed in your product. Because you're still putting too much of a burden on the user, as opposed to having everything happen magically, if you will. So that discussion was around, how do you embed data, embed AI, into your products and do that at the forefront versus, you deliver a product and the client has to say, all right, now I need to get my data out of this application and move it somewhere else so I can do the data science that I want to do. That's what I see happening with developers. It's kind of ... getting them to think about data as opposed to just thinking about the application development framework, because that's where most of them tend to focus. >> Mm, right. >> Well, we've talked about, well, earlier on about the governance, so just curious, with Madhu, which I'll, we'll have that interview in just a little bit here. I'm kind of curious about your take on that, is that it's a little kinder, gentler, friendlier than maybe some might look at it nowadays because of some organization that it causes, within your group and some value that's being derived from that, that more efficiency, more contextual information that's, you know, more relevant, whatever. When you talk to your clients about meeting rules, regs, GDPR, all these things, how do you get them to see that it's not a black veil of doom and gloom but it really is, really more of an opportunity for them to cash in? >> You know, my favorite question to ask when I go visit clients is I say, I say, just show of hands, how many people have all the data they need to do their job? To date, nobody has ever raised their hand. >> Not too many hands up. >> The reason I phrased it that way is, that's fundamentally a governance challenge. And so, when you think about governance, I think everybody immediately thinks about compliance, GDPR, types of things you mentioned, and that's great. But there's two use cases for governance. One is compliance, the other one is self service analytics. Because if you've done data governance, then you can make your data available to everybody in the organization because you know you've got the right rules, the right permissions set up. That will change how people do their jobs and I think sometimes governance gets painted into a compliance corner, when organizations need to think about it as, this is about making data accessible to my entire workforce. That's a big change. I don't think anybody has that today. Except for the clients that we're working with, where I think we've made good strides in that. >> What's your sort of number one, two, and three, or pick one, advice for those companies that as you blogged about, don't realize yet that they're in the software business and the technology business? For them to close the ... machine intelligence, machine learning, AI gap, where should they start? >> I do think it can be basic steps. And the reason I say that is, if you go to a company that hasn't really viewed themselves as a technology company, and you start talking about machine intelligence, AI, like, everybody like, runs away scared, like it's not interesting. So I bring it back to building blocks. For a client to be great in data, and to become a technology company, you really need three platforms for how you think about data. You need a platform for how you manage your data, so think of it as data management. You need a platform for unified governance and integration, and you need a platform for data science and business analytics. And to some extent, I don't care where you start, but you've got to start with one of those. And if you do that, you know, you'll start to create a flywheel of momentum where you'll get some small successes. Then you can go in the other area, and so I just encourage everybody, start down that path. Pick one of the three. Or you may already have something going in one of them, so then pick one where you don't have something going. Just start down the path, because, those building blocks, once you have those in place, you'll be able to scale AI and ML in the future in your organization. But without that, you're going to always be limited to kind of a use case at a time. >> Yeah, and I would add, this is, you talked about it a couple times today, is that cultural aspect, that realization that in order to be data driven, you know, buzzword, you have to embrace that and drive that through the culture. Right? >> That starts at the top, right? Which is, it's not, you know, it's not normal to have a culture of, we're going to experiment, we're going to try things, half of them may not work. And so, it starts at the top in terms of how you set the tone and set that culture. >> IBM Think, we're less than a month away. CUBE is going to be there, very excited about that. First time that you guys have done Think. You've consolidated all your big, big events. What can we expect from you guys? >> I think it's going to be an amazing show. To your point, we thought about this for a while, consolidating to a single IBM event. There's no question just based on the response and the enrollment we have so far, that was the right answer. We'll have people from all over the world. A bunch of clients, we've got some great announcements that will come out that week. And for clients that are thinking about coming, honestly the best thing about it is all the education and training. We basically build a curriculum, and think of it as a curriculum around, how do we make our clients more effective at competing with the Amazons of the world, back to the other point. And so I think we build a great curriculum and it will be a great week. >> Well, if I've heard anything today, it's about, don't be afraid to dive in at the deep end, just dive, right? Get after it and, looking forward to the rest of the day. Rob, thank you for joining us here and we'll see you in about a month! >> Sounds great. >> Right around the corner. >> All right, Rob Thomas joining us here from IBM Analytics, the GM at IBM Analytics. Back with more here on theCUBE. (upbeat music)

>> Announcer: Live from Fisherman's Wharf in San Francisco. It's theCUBE. Covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey Welcome back everybody. Jeff Frick with theCUBE, along with Peter Burris from Wikibon. We are in Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. Coming to the end of a busy day, running out of steam. Blah, blah, blah. I need more water. But Joe's going to take us home. We're joined by Joe Selle. He is the global operations analytic solution lead for IBM. Joe, welcome. >> Thank you, thank you very much. It's great to be here. >> So you've been in sessions all day. I'm just curious to get kind of your general impressions of the event and any surprises or kind of validations that are coming out of these sessions. >> Well, general impression is that everybody is thrilled to be here and the participants, the speakers, the audience members all know that they're at the cusp of a moment in business history of great change. And that is as we graduate from regular analytics which are descriptive and dashboarding into the world of cognitive which is taking the capabilities to a whole other level. Many levels actually advanced from the basic things. >> And you're in a really interesting position because IBM has accepted the charter of basically consuming your own champagne, drinking your own champagne, whatever expression you want to use. >> I'm so glad you said that cause most people say eating your dog food. >> Well, if we were in Germany we'd talk about beer, but you know, we'll stick with the champagne analogy. But really, trying to build, not only to build and demonstrate the values that you're trying to sell to your customers within IBM but then actually documenting it and delivering it basically, it's called the blueprint, in October. We've already been told it's coming in October. So what a great opportunity. >> Part of that is the fact that Ginni Rometty, our CEO, had her start in IBM in the consulting part of IBM, GBS, Global Business Services. She was all about consulting to clients and creating big change in other organizations. Then she went through a series of job roles and now she's CEO and she's driving two things. One is the internal transformation of IBM, which is where I am, part of my role is, I should say. Reporting to the chief data officer and the chief analytics officer and their jobs are to accelerate the transformation of big blue into the cognitive era. And Ginni also talks about showcasing what we're doing internally for the rest of the world and the rest of the economy to see because parts of this other companies can do. They can emulate our road map, the blueprint rather, sorry, that Inderpal introduced, is going to be presented in the fall. That's our own blueprint for how we've been transforming ourselves so, some part of that blueprint is going to be valid and relevant for other companies. >> So you have a dual reporting relationship, you said. The chief data officer, which is this group, but also the chief analytics officer. What's the difference between the Chief data officer, the chief data analytics officer and how does that combination drive your mission? >> Well, the difference really is the chief data officer is in charge of making some very long-term investments, including short-term investments, but let me talk about the long-term investment. Anything around an enterprise data lake would be considered a long-term investment. This is where you're creating an environment where users can go in, these would be internal to IBM or whatever client company we're talking about, where they can use some themes around self-service, get out this information, create analysis, everything's available to them. They can grab external data. They can grab internal data. They can observe Twitter feeds. They can look at weather company information. In our case we get that because we're partnered with the weather company. That's the long-term vision of the chief data officer is to create a data lake environment that serves to democratize all of this for users within a company, within IBM. The chief analytics officer has the responsibility to deliver projects that are sort of the leading projects that prove out the value of analytics. So on that side of my dual relationship, we're forming projects that can deliver a result literally in a 10 or a 12 week time period. Or a half a year. Not a year and a half but short term and we're sprinting to the finish, we're delivering something. It's quite minimally scaled. The first project is always a minimally viable product or project. It's using as few data sources as we can and still getting a notable result. >> The chief analytics officer is at the vanguard of helping the business think about use cases, going after those use cases, asking problems the right way, finding data with effectiveness as well as efficiency and leading the charge. And then the Chief data officer is helping to accrete that experience and institutionalize it in the technology, the practices, the people, et cetera. So the business builds a capability over time. >> Yes, scalable. It's sort of an issue of it can scale. Once Inderpal and the Chief data officer come to the equation, we're going to scale this thing massively. So, high volume, high speed, that's all coming from a data lake and the early wins and the medium term wins maybe will be more in the realm of the chief analytics officer. So on your first summary a second ago, you're right in that the chief analytics officer is going around, and the team that I'm working with is doing this, to each functional group of IBM. HR, Legal, Supply Chain, Finance, you name it, and we're engaging in cognitive discovery sessions with them. You know, what is your roadmap? You're doing some dashboarding now, you're doing some first generation analytics or something but, what is your roadmap for getting cognitive? So we're helping to burst the boundaries of what their roadmap is, really build it out into something that was bigger then they had been conceiving of it. Adding the cognitive projects and then, program managing this giant portfolio so that we're making some progress and milestones that we can report to various stake holders like Ginni Rometty or Jim Kavanaugh who are driving this from a senior senior executive standpoint. We need to be able to tell them, in one case, every couple of weeks, what have you gotten done. Which is a terrible cadence, by the way, it's too fast. >> So in many Respects-- >> But we have to get there every couple of weeks we've got to deliver another few nuggets. >> So in many respects, analytics becomes the capability and data becomes the asset. >> Yes, that's true. Analytics has assets as well though. >> Paul: Sure, of course. >> Because we have models and we have techniques and we bake the models into a business process to make it real so people actually use it. It doesn't just sit over there as this really nifty science experiment. >> Right but kind of where are we on the journey? It's real still early days, right? Because, you know, we hear all the time about machine learning and deep learning and AI and VR and AI and all this stuff. >> We're patchy, every organization is patchy even IBM, but I'm learning from being here, so this is end of day one, I'm learning. I'm getting a little more perspective on the fact that we at IBM are actually, 'cause we've been investing in this heavily for a number of years. I came through the ranks and supply chain. We've been investing in these capabilities for six or seven years. We were some of the early adopters within IBM. But, I would say that maybe 10% of the people at this conference are sort of in the category of I'm running fast and I'm doing things. So that's 10%. Then there's maybe another 30% that are jogging or fast walking. And then there's the rest of them, so maybe 50%, if my math is right, it's been a long day. Are kind of looking and saying, yeah, I got to get that going at some point and I have two or three initiatives but I'm really looking forward to scaling it at some point. >> Right. >> I've just painted a picture to you of the fact that the industry in general is just starting this whole journey and the big potential is still in front of us. >> And then on the Champagne. So you've got the cognitive, you've got the brute and then you've got the Watson. And you know, there's a lot of, from the outside looking in at IBM, there's a lot of messaging about Watson and a lot of messaging about cognitive. How the two mesh and do they mesh within some of the projects that you're working on? Or how should people think of the two of them? >> Well, people should know that Watson is a brand and there are many specific technologies under the Watson brand. So, and then, think of it more as capabilities instead of technologies. Things like being able to absorb unstructured information. So you've heard, if you've been to any conferences, whether they're analytics or data, any company, any industry, 80% of your data is unstructured and invisible and you're probably working with 20% of your data on an active basis. So, do you want to go the 80%-- >> With 40% shrinking. >> As a percentage. >> That's true. >> As a percentage. >> Yeah because the volumes are growing. >> Tripling in size but shrinking as a percentage. >> Right, right. So, just, you know, think about that. >> Is Watson really then kind of the packaging of cognitive, more specific application? Because we're walking for health or. >> I'll tell you, Watson is a mechanism and a tool to achieve the outcome of cognitive business. That's a good way to think of it. And Watson capabilities that I was just about to get to are things like reading, if you will. In Watson Health, he reads oncology articles and they know, once one of them has been read, it's never forgotten. And by the way, you can read 200 a week and you can create the smartest doctor that there is on oncology. So, a Watson capability is absorbing information, reading. It's in an automated fashion, improving its abilities. So these are concepts around deep learning and machine learning. So the algorithms are either self correcting or people are providing feedback to correct them. So there's two forms of learning in there. >> Right, right. >> But these are kind of capabilities all around Watson. I mean, there are so many more. Optical, character recognition. >> Right. >> Retrieve and rank. >> Right. >> So giving me a strategy and telling me there's an 85% chance, Joe, that you're best move right now, given all these factors is to do x. And then I can say, well, x wouldn't work because of this other constraint which maybe the system didn't know about. >> Jeff: Right. >> Then the system will tell me, in that case, you should consider y and it's still an 81% chance of success verses the first which was at 85. >> Jeff: Right. >> So retrieving and ranking, these are capabilities that we call Watson. >> Jeff: Okay. >> And we try to work those in to all the job roles. >> Jeff: Okay. >> So again, whether you're in HR, legal, intellectual property management, environmental compliance. You know, regulations around the globe are changing all the time. Trade compliance. And if you violate some of these rules and regs, then you're prohibited from doing business in a certain geography. >> Jeff: Right. >> It's devastating. The stakes are really high. So these are the kind of tools we want. >> So I'm just curious, from your perspective, you've got a corporate edict behind you at the highest level, and your customers, your internal customers, have that same edict to go execute quickly. So given that you're not in that kind of slow moving or walking or observing half, what are the biggest challenges that you have to overcome even given the fact that you've got the highest level most senior edict both behind you as well as your internal customers. >> Yeah, well it, guess what, it comes down to data. Often, a lot of times, it comes to data. We can put together an example of a solution that is a minimally viable solution which might have only three or four or five different pieces of data and that's pretty neat and we can deliver a good result. But if we want to scale it and really move the needle so that it's something that Ginni Rometty sees and cares about, or a shareholder, then we have to scale. Then we need a lot of data, so then we come back to Inderpal, and the chief data officer role. So the constraint is on many of the programs and projects is if you want to get beyond the initial proof of concept, >> Jeff: Right. >> You need to access and be able to manipulate the big data and then you need to train these cognitive systems. This is the other area that's taking a lot of time. And I think we're going to have some technology and innovation here, but you have to train a cognitive system. You don't program it. You do some painstaking back and forth. You take a room full of your best experts in whatever the process is and they interact with the system. They provide input, yes, no. They rank the efficacy of the recommendations coming out of the system and the system improves. But it takes months. >> That's even the starting point. >> Joe: That's a problem. >> And then you trade it over often, an extended period of time. >> Joe: A lot of it gets better over time. >> Exactly. >> As long as you use this thing, like a corpus of information is built and then you can mine the corpus. >> But a lot of people seem to believe that you roll all this data, you run a bunch of algorithms and suddenly, boom, you've got this new way of doing things. And it is a very very deep set of relationships between people who are being given recommendations as you said, weighing them, voting them, voting on them, et cetera. This is a highly interactive process. >> Yeah, it is. If you're expecting lightning fast results, you're really talking about a more deterministic kind of solution. You know, if/then. If this is, then that's the answer. But we're talking about systems that understand and they reason and they tap you on the shoulder with a recommendation and tell you that there's an 85% chance that this is what you should do. And you can talk back to the system, like my story a minute ago, and you can say, well it makes sense, but, or great, thanks very much Watson, and then go ahead and do it. Those systems that are expert systems that have expertise just woven through them, you cannot just turn those on. But, as I was saying, one of the things we talked about on some of the panels today, was there's new techniques around training. There's new techniques around working with these corpuses of information. Actually, I'm not sure what the plural of corpus. Corpi? It's not Corpi. >> Jeff: I can look that up. >> Yeah, somebody look that up. >> It's not corpi. >> So anyway, I want to give you the last word, Jeff. So you've been doing this for a while, what advice would you give to someone kind of in your role at another company who's trying to be the catalyst to get these things moving. What kind of tips and tricks would you share, you know, having gone through it and working on this for a while? >> Sure. I would, the first thing I would do is, in your first move, keep the projects tightly defined and small with a minimum of input and keep, contain your risk and your risk of failure, and make sure that if you do three projects, at least one of them is going to be a hands down winner. And then once you have a winner, tout it through your organization. A lot of folks get so enamored with the technology that they start talking more about the technology than the business impact. And what you should be touting and bragging about is not the fact that I was able to simultaneously read 5,000 procurement contracts with this tool, you should be saying, it used to take us three weeks in a conference room with a team of one dozen lawyers and now we can do that whole thing in one week with six lawyers. That's what you should talk about, not the technology piece of it. >> Great, great. Well thank you very much for sharing and I'm glad to hear the conference is going so well. Thank you. >> And it's Corpa. >> Corpa? >> The answer to the question? Corpa. >> Peter: Not corpuses. >> With Joe, Peter, and Jeff, you're watching theCUBE. We'll be right back from the IBM chief data operator's strategy summit. Thanks for watching.

(pulsating music) >> Live from Fisherman's Wharf in San Francisco, it's theCUBE! Covering IBM Chief Data Officer Strategy Summit, Spring 2017. Brought to you by IBM. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit, Spring 2017. It's a mouthful, it's a great event, and it's one of many CDO summits that IBM's putting in around the country, and soon around the world. So check it out. We're happy to be here and really talk to some of the thought leaders about getting into the nitty gritty detail of strategy and execution. So we're excited to be joined by our next guest, Rebecca Shockley. She's an Analytics Global Research Leader for the IBM Institute for Business Value. Welcome, Rebecca. I didn't know about the IBM Institute for Business Value. >> Thank you. >> Absolutely. And Priya V. She said Priya V's good, so you can see the whole name on the bottom, but Priya V. is the CTO of Cognitive/IOT/Watson Health at IBM. Welcome, Priya. >> Thank you. >> So first off, just impressions of the conference? It's been going on all day today. You've got 170 or some-odd CDO's here sharing best practices, listening to the sessions. Any surprising takeaways coming out of any of the sessions you've been at so far? >> On a daily basis I live and breathe data. That's what I help our customers to get better at it, and today is the day where we get to talk about how can we adopt something which is emerging in that space? We talk about data governance, what we need to look at in that space, and cognitive as being the fabric that we are integrating into this data governance actually. It's a great day, and I'm happy to talk to over, like you said, 170 CDO's representing different verticals. >> Excellent. And Rebecca, you do a lot of core research that feeds a lot of the statistics that we've seen on the keynote slides, this and that. And one of the interesting things we talked about off air, was really you guys are coming up with a playbook which is really to help CDO's basically execute and be successful CDO's. Can you tell us about the playbook? >> Well, the playbook was born out of a Gartner statistic that came out I guess two or three years ago that said by 2016 you'll have 90% of organizations will have a CDO and 50% of them will fail. And we didn't think that was very optimistic. >> Jeff: 90% will have them and 50% will fail? >> Yes, and so I can tell you that based on our survey of 6,000 global executives last fall, the number is at 41% in 2016. And I'm hoping that the playbook kept them from being a failure. So what we did with the playbook is basically laid out the six key questions that an organization needs to think about as they're either putting in a CDO office or revamping their CDO offices. Because Gartner wasn't completely unfounded in thinking a lot of CDO offices weren't doing well when they made that prediction. Because it is very difficult to put in place, mostly because of culture change, right? It's a very different kind of way to think. So, but we're certainly not seeing the turnover we were in the early years of CDO's or hopefully the failure rate that Gartner predicted. >> So what are the top two or three of those six that they need to be thinking about? >> So they need to think about their objectives. And one of the things that we found was that when we look at CDO's, there's three different categories that you can really put them in. A data integrator, so is the CDO primarily focused on getting the data together, getting the quality of the data, really bringing the organization up to speed. The next thing that most organizations look at is being a business optimizer. So can they use that data to optimize their internal processes or their external relationships? And then the third category is market innovator. Can they use that data to really innovate, bring in new business models, new data monetization strategies, things like that. The biggest problem we found is that CDO's that we surveyed, and we surveyed 800 CDO's, we're seeing that they're being assessed on all three of those things, and it's hard to do all three at once, largely because if you're still having to focus on getting your data in a place where you can start doing real science against it you're probably not going to be full-time market innovator either. You can't be full-time in two different places. That's not to say as a data integrator you can't bring in data scientists, do some skunk works on some of the early work, find... and we've seen organizations really, like Bank Itau down in Brazil, really in that early stages still come up with some very innovative things to do, but that's more of a one-off, right. If you're being judged on all three of those, that I think is where the failure rate comes in. >> But it sounds like those are kind of sequential, but you can't operate them sequentially cause in theory you never finish the first phase, right? >> You never finish, you're always keeping up with the data. But for some organizations, they really need to, they're still operating with very dirty, very siloed data that you really can't bring together for analytics. Now once you're able to look at that data, you can be doing the other two, optimizing and innovating, at the same time. But your primary focus has to be on getting the data straight. Once you've got a functioning data ecosystem, then the level of attention that you have to put there is going to go down, and you can start working on, focusing on innovation and optimization more as your full-time role. But no, data integrator never goes away completely. >> And cleanser. Then, that's a great strategy. Then, as you said, then the rubber's got to hit the road. And Priya, that's where you play in, the execution point. Like you say, you like to get your hands dirty with the CDO's. So what are you seeing from your point of view? In terms of actually executing, finding early wins, easy paths to success, you know, how to get those early wins basically, right? To validate what you're doing. That's right. Like you said, it's become a universal fact that data governance and things, everything around consolidating data and the value of insights we get off it, that's been established fact. Now CDO's and the rest of the organization, the CIO's and the CTO's, have this mandate to start executing on them. And how do we go about it? That's part of my job at IBM as well. As a CTO, I work with our customers to identify where are the dominant business value? Where are those things which is completely data-driven? Maybe it is cognitive forecasting, or your business requirement could be how can I maximize 40% of my service channel? Which in the end of the day could be a cognitive-enabled data-driven virtual assistant, which is automating and bringing a TCO of huge incredible value. Those are some of the key execution elements we are trying to bring. But like we said, yes, we have to bring in the data, we have to hire the right talent, and we have to have a strategy. All those great things happen. But I always start with a problem, a problem which actually anchors everything together. A problem is a business problem which demonstrates key business values, so we actually know what we are trying to solve, and work backwards in terms of what is the data element to it, what are the technologies and toolkits that we can put on top of it, and who are the right people that we can involve in parallel with the strategy that we have already established. So that's the way we've been going about. We have seen phenomenal successes, huge results, which has been transformative in nature and not just these 170 CDO's. I mean, we want to make sure every one of our customers is able to take advantage of that. >> But it's not just the CDO, it's the entire business. So the IBM Institute on Business Value looks at an enormous amount of research, or does an enormous amount of research and looks at a lot of different issues. So for example, your CDO report is phenomenal, I think you do one for the CMO, a number of different chief officers. How are other functions or other roles within business starting to acculturate to this notion of data as a driver of new behaviors? And then we can talk about, what are some of those new behaviors? The degree to which the leadership is ready to drive that? >> I think the executive suite is really starting to embrace data much more than it has in the past. Primarily because of the digitization of everything, right. Before, the amount of data that you had was somewhat limited. Often it was internal data, and the quality was suspect. As we started digitizing all the business processes and being able to bring in an enormous amount of external data, I think organizationally executives are getting much more comfortable with the ability to use that data to further their goals within the organization. >> So in general, the chief groups are starting to look at data as a way of doing things differently. >> Absolutely. >> And how is that translating into then doing things differently? >> Yeah, so I was just at the session where we talked about how organizations and business units are even coming together because of data governance and the data itself. Because they are having federated units where a certain part of business is enabled and having new insights because we are actually doing these things. And new businesses like monetizing data is something which is happening now. Data as a service. Actually having data as a platform where people can build new applications. I mean the whole new segment of people as data engineers, full stack developers, and data scientists actually. I mean, they are incubated and they end up building lots of new applications which has never been part of a typical business unit. So these are the cultural and the business changes we are starting to see in many organizations actually. Some of them are leading the way because they just did it without knowing actually that's the way they should be doing it. But that's how it influences many organizations. >> I think you were looking for kind of an example as well, so in the keynote this morning one of the gentlemen was talking about working with their CFO, their risk and compliance office, and were able to take the ability to identify a threat within their ecosystem from two days down to three milliseconds. So that's what can happen once you really start being able to utilize the data that's available to an organization much more effectively, is that kind of quantum leap change in being able to understand what's happening in the marketplace, bing able to understand what's happening with consumers or customers or clients, whichever flavor you have, and we see that throughout the organization. So it's not just the CFO, but the CMO, and being able to do much more targeted, much more focused on the consumer side or the client customer side, that's better for me, right. And the marketing teams are seeing 30, 40% increase in their ability to execute campaigns because they're more data-driven now. >> So has the bit flipped where the business units are now coming to the CDO's office and pounding on the door, saying "I need my team"? As opposed to trying to coerce that you no longer use intuition? >> So it depends upon where you are, where the company is. Because what we call that is the snowball effect. It's one of the reasons you have to have the governance in place and get things going kind of in parallel. Because what we see is that most organizations go in skeptically. They're used to running on their gut instinct. That's how they got their jobs mostly, right? They had good instincts, they made good decisions, they got promoted. And so making that transition to being a data-driven organization can be very difficult. What we find though, is that once one section, one segment, one flavor, one good campaign happens, as soon as those results start to mount up in the organization, you start to see a snowball effect. And what I was hearing particularly last year when I was talking to CDO's was that it had taken them so long to get started, but now they had so much demand coming from the business that they want to look at this, and they want to look at that, and they want to look at the other thing, because once you have results, everybody else in the organization wants those same kind of results. >> Just to add to that, data is not anymore viewed as a commodity. If you have seen valuable organizations who know what their asset is, it's not just a commodity. So the parity of... >> Peter: Or even a liability is what it used to be, right? >> Exactly. >> Peter: It's expensive to hold it and store it, and keep track of it. >> Exactly. So the parity of this is very different right now. So people are talking about, how can I take advantage of the intelligence? So business units, they don't come and pound the door rather they are trying to see what data that I can have, or what intelligence that I can have to make my business different shade, or I can value add something more. That's a type of... So I feel based on the experiences that we work with our customers, it's bringing organizations together. And for certain times, yes sometimes the smartness and the best practices come in place that how we can avoid some of the common mistakes that we do, in terms of replicating 800 times or not knowing who else is using. So some of the tools and techniques help us to master those things. It is bringing organizations and leveraging the intelligence that what you find might be useful to her, and what she finds might be useful. Or what we all don't know, that we go figure it out where we can get it. >> So what's the next step in the journey to increase the democratization of the utilization of that data? Because obviously Chief Data Officers, there aren't that many of them, their teams are relatively small. >> Well, 41% of businesses, so there's a large number of them out there. >> Yeah, but these are huge companies with a whole bunch of business units that have tremendous opportunity to optimize around things that they haven't done yet. So how do we continue to kind of move this democratization of both the access and the tools and the utilization of the insights that they're all sitting on? >> I have some bolder expectations on this, because data and the way in which data becomes an asset, not anymore a liability, actually folds up many of the layers of applications that we have. I used to come from an enterprise background in the past. We had layers of application programming which just used data as one single layer. In terms of opportunities for this, there is a lot more deserving silos and deserving layers of IT in a typical organization. When we build data-driven applications, this is all going to change. It's fascinating. This role is in the front and center of everything actually, around data-driven. And you also heard enough about cognitive computing these days, because it is the key ingredient for cognitive computing. We talked about full ease of cognitive computing. It has to start first learning, and data is the first step in terms of learning. And then it goes into process re-engineering, and then you reinvent things and you disrupt things and you bring new experiences or humanize your solution. So it's on a great trajectory. It's going tochange the way we do things. It's going to give new and unexpected things both from a consumer point and from an enterprise point as well. It'll bring effects like consumerization of enterprises and what-not. So I have bolder and broader expectations out of this fascinating data world. >> I think one of the things that made people hesitant before was an unfamiliarity with thinking about using data, say a CSR on the front line using data instead of the scripts he or she had been given, or their own experience. And I think what we're seeing now is A, everybody's personal life is much more digital than it was before, therefore everybody's somewhat more comfortable with interacting. And B, once you start to see those results and they realize that they can move from having to crunch numbers and do all the background work once we can automate that through robotic process automation or cognitive process automation, and let them focus on the more interesting, higher value parts of their job, we've seen that greatly impact the culture change. The culture change question comes whether people are thinking they're going to lose their job because of the data, or whether it's going to let them do more interesting things with their jobs. And I think hopefully we're getting past that "it's me or it" stage, into the, how can I use data to augment the work that I'm doing, and get more personal satisfaction, if not business satisfaction, out of the work that I'm doing. Hopefully getting rid of some of the mundane. >> I think there's also going to be a lot of software that's created that's going to be created in different ways and have different impacts. The reality is, we're creating data incredibly fast. We know that is has enormous value. People are not going to change that rapidly. New types of algorithms are coming on, but many of the algorithms are algorithms we've had for years, so in many respects it's how we render all of that in some of the new software that's not driven by process but driven by data. >> And the beauty of it is this software will be invisible. It will be self-healing, regeneratable software. >> Invisible to some, but very very highly visible to others. I think that's one of the big challenges that IT organizations face, and businesses face. Is how do they think through that new software? So you talked about today, or historically, you talked about your application stack, where you have stacks which would have some little view of the data, and in many respects we need to free that data up, remove it out of the application so we can do new things with it. So how is that process going to either be facilitated, or impeded by the fact that in so many organizations, data is regarded as a commodity, something that's disposable. Do we need to become more explicit in articulating or talking about what it means to think of data as an asset, as something that's valuable? What do you think? >> Yeah, so in the typical application world, when we start, if you really look at it, data comes at the very end of it. Because people start designing what is going to be their mockups, where are they going to integrate with what sources, am I talking to the bank as an API, et cetera. So the data representation comes at the very end. In the current generation of applications, the cognitive applications that we are building, first we start with the data. We understand what are we working on, and we start applying, taking advantage of machines and all these algorithms which existed like you said, many many decades ago. And we take advantage of machines to automate them to get the intelligence, and then we write applications. So you see the order has changed actually. It's a complete reversal. Yes we had typical three-tier, four-tier architecture. But the order of how we perceive and understand the problem is different. But we are very confident. We are trying to maximize 40% of your sales. We are trying to create digital connected dashboards for your CFO where the entire board can make decisions on the fly. So we know the business outcome, but we are starting with the data. So the fundamental change in how software is built, and all these modules of software which you are talking about, why I mentioned invisible, is some are generatable. The AI and cognitive is advanced in such a way that some are generatable. If it understands the data underlying, it can generate what it should do with the data. That's what we are teaching. That's what ontology and all this is about. So that's why I said it's limitless, it's pretty bold, and it's going to change the way we have done things in the past. And like she said, it's only going to complement humans, because we are always better decision-makers, but we need so much of cognitive capability to aid and supplement our decision-making. So that's going to be the way that we run our businesses. >> All right. Priya's painting a pretty picture. I like it. You know, some people see only the dark side. That's clearly the bright side. That's a terrific story, so thank you. So Priya and Rebecca, thanks for taking a few minutes. Hope you enjoy the rest of the show, surrounded by all this big brain power. And I appreciate you stopping by. >> Thanks so much. >> Thank you. >> All right. Jeff Frick and Peter Burris. You're watching theCUBE from the IBM Chief Data Officers Summit, Spring 2017. We'll be right back after this short break. Thanks for watching. (drums pound) (hands clap rhythmically) >> [Computerized Voice] You really crushed it. (quiet synthesizer music) >> My name is Dave Vellante, and I'm a long-time industry analyst. I was at IDC for a number of years and ran the company's largest and most profitable business. I focused on a lot of areas, infrastructure, software, organizations, the CIO community. Cut my teeth there.

>> Announcer: Live from Las Vegas, it's theCUBE, covering InterConnect 2017. Brought to you by IBM. >> Okay welcome back everyone. We are here live in Las Vegas from Mandalay Bay for IBM InterConnect 2017. This is theCUBE's three day coverage of IBM InterConnect. I'm John Furrier with my co-host Dave Vellante. Or next guest is Seth Dobrin, Vice President and Chief Data Officer for IBM Analytics. Welcome to theCUBE, welcome back. >> Yeah, thanks for having me again. I love sittin' down and chattin' with you guys. >> You're a CDO, Chief Data Officer and that's a really kind of a really pivotal role because you got to look at, as a chief, over all of the data with IBM Analytics. Also you have customers you're delivering a lot solutions to and it's cutting edge. I like the keynote on day one here. You had Chris Moody at Twitter. He's a data guy. >> Seth: Yep. >> I mean you guys have a deal with Twitter so he got more data. You've got the weather company, you got that data set. You have IBM customer data. You guys are full with data right now. >> We're first seat at the scenes with data and that's a good thing. >> So what's the strategy and what are you guys working on and what's the key points that you guys are honing in on? Obviously, Cognitive to the Core is Robetti's theme. How are you guys making data work for IBM and your customers? >> If you think about IBM Analytics, we're really focusing on five key areas, five things that we think if we get right, we'll help our clients learn how to drive their business and data strategies right. One is around how do I manage data across hybrid environments? So what's my hybrid data management strategy? It used to be how do I get to public cloud, but really what it is, it's a conversation about every enterprise has their business critical assets, what people call legacy. If we call them business critical and we think about-- These are how companies got here today. This is what they make their money on today. The real challenge is how do we help them tie those business critical assets to their future state cloud, whether it's public cloud, private cloud, or something in between our hybrid cloud. One of the key strategies for us is hybrid data management. Another one is around unified governance. If you look at governance in the past, governance in the past was an inhibitor. It was something that people went (groan) "Governance, so I have to do it." >> John: Barb wire. >> Right, you know. When I've been at companies before, and thought about building a data strategy, we spent the first six months building data strategy trying to figure out how to avoid data governance, or the word data governance, and really, we need to embrace data governance as an enabler. If you do it right, if you do it upfront, if you wrap things that include model management, how do I make sure that my data scientists can get to the data they need upfront by classifying data ahead of time; understanding entitlements, understanding what intent when people gave consent was. You also take out of the developer hands the need to worry about governance because now in a unified governance platform, right, it's all API-driven. Just like our applications are all API-driven, how do we make our governance platform API-driven? If I'm an application developer, by the way, I'm not, I can now call on API to manage governance for me, so I don't need to worry about am I giving away the shop. Am I going to get the company sued? Am I going to get fired? Now I'm calling on API. That's only two of them, right? The third one is really around data science and machine learning. So how do we make machine learning pervasive across enterprises and things like data science experience. Watson, IBM, machine learning. We're now bringing that machine-learning capability to the private cloud, right, because 90% of data that exists can't be Googled so it's behind firewalls. How do we bring machine learning to that? >> One more! >> One more! That's around, God, I gave you quite a list-- >> Hybrid data management, you defined governance, data science and machine learning-- >> Oh, the other one is Open Source, our commitment to Open Source. Our commitment to Open Source, like Hadoop, Spark, as we think about unified governance, a truly unified governed platform needs to be built on top of Open Source, so IBM is doubling down on our commitment to Apache Spark as a framework backbone, a metadata framework for our unified governed platform. >> What's the biggest para >> Wait, did we miss one? Hybrid data management, unified governance, data science machine learning (talking over another), pervasive, and open source. >> That's four. >> I thought it was five. >> No. >> Machine learning and data science are two, so typically five. >> There's only four. If I said five, there's only four. >> Cover the data governance thing because this unification is interesting to me because one of the things we see in the marketplace, people hungry for data ops. Like what data ops was for cloud was a whole application developer model developing where as a new developer persona emerging where I want to code and I want to just tap data handled by brilliant people who are cognitive engines that just serve me up what I need like a routine or a procedure, or a subroutine, whatever you want to call it, that's a data DevOps model kind of thing. How will you guys do it? Do you agree with that and how does that play out? >> That's a combination, in my mind, that's a combination of an enterprise creating data assets, so treating data as the asset it is and not a digital dropping of applications, and it's that combined with metadata. It gets back to the Apache Atlas conversation. If you want to understand your data and know where it is, it's a metadata problem. What's the data; what's the lineage; where is it; where does it live; how do I get to it; what can I, can't I do with it, and so that just reinforces the need for an Open Source ubiquitous metadata catalog, a single catalog, and then a single catalog of policies associated with that all driven in a composable way through API. >> That's a fundamental, cultural thinking shift because you're saying, "I don't want to just take exhaust "from apps, which is just how people have been dealing with data." You're saying, "Get holistic and say you need to create an asset class or layer or something that is designed." >> If an enterprises are going to be successful with data, now we're getting to five things, right, so there's five things. They need to treat data as an asset. It's got to be a first-class citizen, not a digital dropping, and they need a strategy around it. So what are, conceptually, what are the pieces of data that I care about? My customers, my products, my talent, my finances, what are the limited number of things. What is my data science strategy? How do I build deployable data science assets? I can't be developing machine-learning models and deploying them in Excel spreadsheets. They have to be integrated into My Processes. I have to have a cloud strategy so am I going to be on premise? Am I going to be off premise? Am I going to be something in between? I have to get back to unified governance. I have to govern it, right? Governing in a single place is hard enough, let alone multiple places, and then my talent disappears. >> Could you peg a progress bar of the industry where these would be, what you just said, because, I think-- >> Dave: Again, we only got through four. >> No talent was the last one. >> Talent, sorry, missed it. >> In the progress bar of work, how are the enterprises right now 'cause actually the big conversation on the cloud side is enterprise-readiness, enterprise-grade, that's kind of an ongoing conversation, but now, if you take your premise, which I think is accurate, is that I got to have a centralized data strategy and platform, not a data (mumbles), more than that, software, et cetera, where's the progress bar? Where are people, Pegeninning? >> I think they are all over the map. I've only been with IBM for four months and I've been spending much of that time literally traveling around the world talking to clients, and clients are all over the map. Last week I spent a week in South America with a media company, a cable company down there. Before setting up the meeting, the guy was like, "Well, you know, we're not that far along "down this journey," and I was like, "Oh, my God, "you guys are like so far ahead of everyone else! "That's not even funny!" And then I'm sitting down with big banks that think they're like way out there and they haven't even started on the journey. So it's really literally all over the place and it's even within industry. There's financial companies that are also way out there. There's another bank in Brazil that uses biometrics to access ATMs, you don't need a pin anymore. They have analytics that drive all that. That's crazy. We don't have anything like that here. >> Are you meeting with CDOs? >> Yeah, mostly CDOs, or kind of defacto like we talked about before this show. Mostly CDOs. >> So you may be unique in the sense that you are working for a technology company, so a lot of your time is outward focused, but when you travel around and meet with the CDOs, how much of their time is inward-focused versus outward-focused? >> My time is actually split between inward and outward focus because part of my time is transforming our own business using data and analytics because IBM is a company and we got to figure out how to do that. >> Is it correct that yours is probably a higher percentage outward? >> Mine's probably a higher percentage outward than most CDOs, yeah. So I think most CDOs are 7%, 80% inward-focused and 20% outward-focused, and a lot of that outward focus is just trying to understand what other people are doing. >> I guess it's okay for now, but will that change over time? >> I think that's about right. It gets back to the other conversation we had before the show about your monetization strategy. I think if a company progresses where it's not longer about how do I change my processes and use data to monetize my internal process. If I'm going to start figuring how I sell data, then CDOs need to get a more external-- >> But you're supporting the business in that role and that's largely going to be an internal function of data-quality, governance, and the like, like you say, the data science strategy. >> Yeah, and I think it's important when I talk about data governance, I think things that we used to talk about is data management is all part of data governance. Data governance is not just controlling. It's all of that. It's how do I understand my data, how do I provide access to my data. It's all those things you need to enable your business to thrive on data. >> My question for you is a personal one. How did you get to be a CDO? Do you go to a class? I'm going to be a CDO someday. Not that you do that, I'm just-- >> CDO school. >> CDO school. >> Seth: I was staying in a Holiday Express last night. (laughing) >> Tongue in cheek aside, people are getting into CDO roles from interesting vectors, right? Anthropology, science, art, I mean, it's a really interesting, math geeks certainly love, they thrive there, but there's not one, I haven't yet seen one sweet spot. Take us through how you got into it and what-- >> I'm not going to fit any preconceived notion of what a CDO is, especially in a technology company. My background is in molecular and statistical genetics. >> Dave: Well, that explains it. >> I'm a geneticist. >> Data has properties that could be kind of biological. >> And actually, if you think about the routes of big data and data science, or big data, at least, the two of the predative, they're probably fundamental drivers of the concept of big data were genetics and astrophysics. So 20 years ago when I was getting my PhD, we were dealing with tens and hundreds of gigabyte-sized files. We were trying to figure out how do we get stuff out of 15 Excel files because they weren't big enough into a single CSV file. Millions of rows and millions of crude, by today's standard, but it was still, how do we do this, and so 20 years ago I was learning to be a data scientist. I didn't know it. I stopped doing that field and I started managing labs for a while and then in my last role, we kind of transformed how the research group within that company, in the agricultural space, handled and managed data, and I was simultaneously the biggest critic and biggest advocate for IT, and they said, "Hey, come over and help us figure out how to transform "the company the way we've transformed this group." >> It's looks like when you talk about your PhD experience, it's almost like you were so stuck in the mud with not having to compute power or sort of tooling. It's like a hungry man saying "Oh, it's an unlimited "abundance of compute, oh, I love what's going on." So you almost get gravitated, pulled into that, right? >> It was funny, I was doing a demo upstairs today with, one of the sales guys was doing a demo with some clients, and in one line of code, they had expressed what was part of my dissertation. It was a single line of code in a script and it was like, that was someone's entire four-year career 20 years ago. >> Great story, and I think that's consistent with just people who just attracted to it, and they end up being captains of industry. This is a hot field. You guys have a CDO of that happening in San Francisco. We'll be doing some live streaming there. What's the agenda because this is a very accelerating field? You mentioned now dealing practically with compliance and governance, which is you'd run in the other direction in the old days, now this embracing that. It's got to get (mumbles) and discipline in management. What's going to go on at CDO Summit or do you know? >> At the CDO Summit next week, I think we're going to focus on three key areas, right? What does a cloud journey look like? Maybe four key areas, right. So a cloud journey, how do you monetize data and what does that even mean, and talent, so at all these CDO Summits, the IBM CDO Summits have been going on for three or four years now, every one of them has a talent conversation, and then governance. I think those are four key concepts, and not surprising, they were four of my five on my list. I think that's what really we're going to talk about. >> The unified governance, tell us how that happens in your vision because that's something that you hear unified identity, we hear block chain looking at a whole new disruptive way of dealing with value digitally. How do you see the data governance thing unifying? >> Well, I think again, it's around... IBM did a great job of figuring out how to take an Open Source product that was Spark, and make it the heart of our products. It's going to be the same thing with governance where you're going to see Apache Atlas is at its infancy right now, having that open backbone so that people can get in and out of it easy. If you're going to have a unified governance platform, it's going to be open by definition because I need to get other people's products on there. I can't go to an enterprise and say we're going to sell your unified governance platform, but you got to buy all IBM, or you got to spend two years doing development work to get it on there. So open is the framework and composable, API-driven, and pro-active are really, I think, that's kind of the key pieces for it. >> So we all remember the client-server days where it took a decade and a half to realize, "Oh, my Gosh, this is out of control "and we need to bring it back in." And the Wild West days of big data, it feels like enterprises have nipped that governance issue in the butt at least, maybe they don't have it under control yet, but they understand the need to get it under control. Is that a fair statement? >> I think they understand the need. The data is so big and grows so fast that another component that I didn't mention, maybe it was implied a little bit, but, is automation. You need to be able to capture metadata in an automated fashion. We were talking to a client earlier who, 400 terabytes a day of data changes, not even talking about what new data they are ingesting, how do they keep track of that? It's got to be automated. This unified governance, you need to capture this metadata and as an automated fashion as possible. Master data needs to be automated when you think about-- >> And make it available in real time, low-latency because otherwise it becomes a data swamp. >> Right, it's got to be pro-active, real-time, on-demand. >> Another thing I wanted to ask you, Seth, and get your opinion on is sort of the mid-2000s when the federal rules of civil procedure changed in electronic documents and records became admissible, it was always about how do I get rid of data, and that's changed. Everybody wants to keep data and how to analyze it, and so forth, so what about that balance? And one of the challenges back then was data classification. I can't scale, by governance, I can't eliminate and defensively delete data unless I can classify it. Is the analog true where with data as an opportunity, I can't do a good job or a good enough job analyzing my data and keeping my data under control without some kind of automated classification, and has the industry solved that? >> I don't think the industry has completely solved it yet, but I think with cognitive tools, there's tools out there that we have that other people have that can automatically, if you give them parameters and train it, can classify the data for you, and I think classification is one of the keys. You need to understand how the data's classified so you understand who can access it, how long you should keep it, and so it's key, and that's got to be automated also. I think we've done a fair job as an industry of doing that. There's still a whole lot of work, especially as you get into the kind of specialized sectors, and so I think that's a key and we've got to do a better job of helping companies train those things so that they work. I'm a big proponent of don't give your data away to IT companies. It's your asset. Don't let them train their models with your data and sell it to other people, but there are some caveats out. There are some core areas where industries need to get together and let IT companies, whether it's IBM or someone else, train models for things just like that, for classification because if someone gets it wrong, it can bring the whole industry down. >> It's almost as if (talking over each other) source paradigm almost. It's like Open Source software. Share some data, but I-- >> Right, and there's some key things that aren't differentiating that, as an industry, you should get together and share. >> You guys are making, IBM is making a big deal out of this, and I think it's super important. I think it's probably the top thing that CDOs and CIOs need to think about right now is if I really own my data and that data is needed to train my big data models, who owns the models and how do I protect my IP. >> And are you selling it to my competitors. Are you going down the street and taking away my IP, my differentiating IP and giving it to my competitor? >> So do I own the model 'cause the data and models are coming together, and that's what IBM's telling me. >> Seth: Absolutely. >> I own the data and the models that it informs, is that correct? >> Yeah, that's absolutely correct. You guys made the point earlier about IBM bursting at the seams on data. That's really the driver for it. We need to do a key set of training. We need to train our models with content for industries, bring those trained models to companies and let them train specific versions for their company with their data that unless there's a reason they tell us to do it, is never going to leave their company. >> I think that's a great point about you being full of data because a lot of people who are building solutions and scaffolding for data, aka software never have more data full. The typical, "Oh, I'm going to be a software company," and they build something that they don't (mumbles) for. Your data full, so you know the problem. You're living it every day. It's opportunity. >> Yeah, and that's why when a startup comes to you and says, "Hey, we have this great AI algorithm. "Give us your data," they want to resell that model, and because they don't have access to the content. If you look at what IBM's done with Watson, right? That's why there's specialized verticals that we're focusing Watson, Watson Health, Watson Financial, because where we are investing in data in those areas you can look at the acquisitions we've done, right. We're investing in data to train those models. >> We should follow up on this because this brings up the whole scale point. If you look at all the innovators of the past decade, even two decades, Yahoo, Google, Facebook, these are companies that were webscalers before there was anything that they could buy. They built their own because they had their own problem at scale. >> At scale. >> And data at scale is a whole other mind-blowing issue. Do you agree? >> Absolutely. >> We're going to put that on the agenda for the CDO Summit in San Francisco next week. Seth, thanks so much for joining us on theCube. Appreciate it; Chief Data Officer, this is going to be a hot field. The CDO is going to be a very important opportunity for anyone watching in the data field. This is going to be new opportunities. Get that data, get it controlled, taming the data, making it valuable. This is theCUBE, taming all of the content here at InterConnect. I'm John Furrier with Dave Vellante. More content coming. Stay with us. Day Two coverage continues. (innovative music tones)

>> Narrator: Live, from Las Vegas, it's The Cube. Covering InterConnect 2017. Brought to you by IBM. >> Welcome back, everyone. Live here in Las Vegas, this is The Cube's coverage of IBM's Interconnect 2017. I'm John Furrier with my co-host Dave Vellante. Our next guest is Eric Herzog, Cube alumni, Vice President of Product Market at IBM storage. Welcome back to The Cube. Good to see you with the shirt on. You got the IBM tag there, look at that. >> I do. Well, you know, I've worn a Hawaiian shirt now, I think, ten Cubes in a row, so I got to keep the streak going. >> So, pretty sunny here in Vegas, great weather. Storage is looking up as well. Give us the update. Obviously, this is never going away, we talk about it all the time, but now cloud, more than ever, a lot of action happening with storage, and data is a big part of it. >> Yeah, the big thing with us has been around hybrid cloud. So our software portfolio, the spectrum family, Spectrum Virtualize, Spectrum Protect, our backup package, Spectrum Scale, our scale out NAS, IBM Cloud Object Storage, all will move data transparently from on-premises configurations out to multiple cloud vendors, including IBM Bluemix. But also other vendors, as well. That software's embedded on our array products, including our VersaStack. And just two weeks ago, at Cisco Live in Melbourne, Australia, we did a announcement with Cisco around our VersaStack for the hybrid cloud. >> So what's the hybrid cloud equation look like for you guys right now, because it is the hottest topic. It's almost like brute force, everywhere you see, it's hybrid cloud, that's what people want. How does it change the storage configurations? What's the solutions look like? What's different now than it was a year ago? >> I think the key thing you've got to be able to do is to make sure the data can move transparently from an on-premise location, or a private cloud, you could have started as a private cloud config and then decid it's OK to use a public cloud with the right security protocols. So, whether you've got a private cloud moving to a public cloud provider, like Bluemix, or an on-premises configuration moving to a public cloud provider, like Bluemix, the idea is they can move that data back and forth. Now, with our Cisco announcement, Cisco, with their cloud center, is also providing the capability and moving applications back and forth. We move the data layer back and forth with Spectrum Virtualize or IBM's copy data management product, Spectrum Copy Data Management, and with Cloud Center, or the ECS, Enterprise Cloud Sweep, from Cisco, you can move the application layer back and forth with that configuration on our VersaStacks. >> So this whole software-defined thing starts, it started when people realized, hey, we can run our data centers kind of the way the big hyper-scalers do. IBM pivoted hard toward software-defined. What's been the impact that you've seen with customers? Are they actually, I mean, there was a big branding announcement with Spectrum and everything a while back. What's been the business impact of that shift? >> Well, for us, it's been very strong. So if you look at the last couple quarters, according to the analysts that track the numbers, from a total storage perspective, we've moved into the number two position, and have been, now for the last two years. And for software-defined storage, we're the number one provider of software-defined storage in the world, and have been for the last three years in a row. So we've been continuing to grow that business on the software-defined side. We've got scale-up block configurations, scale-out block configurations, object storage with IBM Cloud Object Storage, and scale-out NAS and file with our IBM Spectrum Scale. So if you're file, block, or object, we've got you covered. And you can use either A, our competitor's storage, we work with all our competitor's gear, or you could go with your reseller, and have them, or your distributor provide the raw infrastructure, the servers, the storage, flash or hard drives, and then use our software on top to create essentially your own arrays. >> So when you say competitor's gear, you're talking about what used to be known as the SAN Volume Controller, and now is Spectrum Virtualize, right? Did I get that right? >> Yes, well, we still sell the SAN Volume Controller. When you buy the Spectrum Virtualize, it comes as just a piece of software. When you buy the SAN Volume Controller as well as our FlashSystem V9000, and our Storwize V7 and V5000, they come with Spectrum Virtualize pre-loaded on the array. So we have three ways where the array is pre-loaded: SAN Volume Controller, FlashSystems V9000, and then the Storwiz products, so it's pre-loaded. Or, you can buy the stand-alone software Spectrum Virtualize and put it on any hardware you want, either way. >> So, I know we're at an IBM conference, and IBM hates, they don't talk about the competition directly, but I have to ask the competitive questions. You've had a lot of changes in the business. Obviously, cloud's coming in in a big way. The Dell EMC merger has dislocated things, and you still see a zillion starups in storage, which is amazing to me, alright? Everybody says, oh, storage is dead, but then all this VC money still funneling in and all this innovation. What's happening in the storage landscape from your perspective? >> Well, I think there's a couple things. So, first of all, software-defined has got its legs, now. When you look at it from a market perspective, last quarter ended up at almost 400 million, which put it on a, let's say, a 1.6 to 2 billion dollar trajectory for calendar 2017, out of a total software market of around 16 billion. So it's gone from nothing to roughly 2 billion out of 16 billion for all storage software of all various types, so that's hot. All flash arrays are still hot. You're looking at, right now, last year, all flash arrays end up at roughly 25% of all arrays shipped. They're now in price parity, so an all-flash array is not more expensive. So you see a lot of innovation around that. You're still seeing innovation around backup, right? You've got guys trying to challenge us with our SpectrumProtect with some of these other vendors trying to challenge us, even though backup is the most mature of the storage software spaces, there's people trying to challenge that. So, I'd say storage is still a white-hot space. As you know, the overall market is flat, so it is totally a drag out, knock-down fight. You know, the MMA and the UFC guys got nothing on what goes on in the storage business. So, make sure you wear your flak jacket if you're a storage guy. >> Meaning, you got to gain share to grow, right? >> Yes, and it's all about fighting it out. This Hawaiian shirt looks Hawaiian, but just so you know, this is Kevlar. Just in case there's another storage company here at the show. >> So what are the top conversations now with storage buyers? Because we saw Candy's announcement about the object store, Flex, for the cold storage. It changes the price points. It's always going to be a price sensitive market, but they're still buying storage. What are those conversations that you're having? You mentioned moving data around, do they want to move the data around? Do they want to keep it at the edge? Is it moving the application around? What are some of those key conversations that you're involved in? >> So we've done a couple innovative things. One of the things we've done is worked with our sales team to create what we call, the conversations. You know, I've been doing this storage gig now for 31 years. Seven start-ups, IBM twice, EMC, Maxtor and Seagate- >> John: You're a hardened veteran. >> I'm a storage veteran, that's why this is a Kevlar Hawaiian shirt. But no CIO's a storage guy, I've never met one, in 31 years, ever, ever, ever met a storage guy. So what we have to do is elevate the conversation when you're talking to the customer, about why it's important for their cloud, why it's important for machine learning, for cognitive, for artificial intelligence. You know, this about it, I'm a Star Trek guy. I like Star Wars, too, but in Star Trek, Bones, of course, wands the body. So guess what that is? That's the edge device going through the cloud to a big, giant server farm. If that storage is not super resilient, the guy on the table might not make it. And if the storage isn't super fast, the guys on the table might not make it. And while Watson isn't there, yet, Watson Health, they're getting there. So, in ten years from now, I expect when I go to the doctor, he's just like in Star Trek, waving the wand, and boy, you better make sure the storage that that wand is talking to better be highly resilient and high performing. >> Define resilient, in your terms. >> So, resilient means you really can't have more than 30 seconds, 50 seconds a year of down time. Because whoever's on the table when that thing goes down has got a real problem. So you've got to be up all the time, and if you take it out of the healthcare space and look at other applications, whether you look at trading applications, data is the new gold. Data is the new diamonds. It's about data. Yes, I'd love to have a mound of gold, but you know what, if you have the right amount of data, it worth way more than a mound of gold is. >> You're right about the CIO and storage. They don't want to worry about storage. They don't want to spend a lot of time thinking about it. A CIO once said to me, "I care about storage like this, "I want it to be dirt cheap, lightning fast, and rock solid." Now, the industry has done a decent job with rock solid, I would say, but up until Flash, not really that great with lightning fast, and really not that great with dirt cheap. Price has come down for the hardware, but the management has been so expensive. So, is the industry attacking that problem? And what's IBM doing? >> Yeah, so the big thing is all about automation. So when I talk about moving to the hybrid cloud, I'm talking about transparent migration, transparent movement. That's an automation play. So you want to automate as much as you can, and we've got some things that we're not willing to disclose yet that'll make our storage even more automated whether it be from a predictive analytics perspective, self-healing storage that actually will heal itself, you know, go out and grab a code load and put the new code on because it knows there's a bug in the old code, and do that transparently so the user doesn't have to do anything. It's all about automating data movement and data activity. So we've already been doing that with the Spectrum family, and that Spectrum family ships on our storage systems and on our VersaStack, but automation is the critical key in storage. >> So I wonder, does that bring up new KPIs? Like, I presume you guys dog food your own storage internally, and your own IT. >> Eric: Yes >> Are you seeing, because it used to be, OK, the light's green on the disc drive, and you know, this is our uptime or downtime, planned downtime, you know, sort of standard metrics that we've known for 30-40 years. With automation, are we seeing a new set of metrics in KPIs emerge? You know, self-healing, percentage of problems that corrected themselves, or- >> Well, and you're also seeing things like time spent. So if you go back to the downturn of seven, eight, and nine, IT was devastated, right? And, as you know, you've seen a lot of surveys that IT spend is basically back up to '08, OK, the pre-08 crash. When you open up that envelope, they're not hiring storage guys anymore, and usually not infrastructure guys. They're hiring guys to do devops and testdev, and do cloud-based applications, which means there's not a lot of guys to run the storage. So one of the metrics we're seeing is, how much guys do I have managing my storage, or, my infrastructure? I used to have 50, now I'm a big bank, can I do it with 25? Can I do it with 20? Can I do it with 15? And then, how much time do they spend between the networking, the storage, the facilities themselves. These data center guys have to manage all of that. So there are new metrics about, what is the workload that my actual human beings are doing? How much of that is storage versus something else? And there's way less guys doing storage as a full-time job, anyway, because what happened in the downturn? And, so automation is critical to a guy running a datacenter, whether he's a cloud guy, whether he's a small shop. And clearly in the Fortune, global 2500, those guys, where they've got in-house IT, they've cut back on the infrastructure team and the storage team, so it's all about automation. So, part of the KPIs are not just about the storage itself, such as uptime, cost per Gig, cost per transaction, the bandwidth, you know, those sorts of KPIs. But it's also about how much time do I really spend managing the storage? So if I've only got five guys, now, and I used to have 15 guys, those five guys are managing, usually, three, to four, to five times more storage than they did in 2008 and 2009. So now you've got to do it with five guys instead of 15, so there's a KPI, right there. >> So, what about cloud? We heard David Kinney talk today about the object store with that funny name, and then he talked about this cloud-tiering thing, and I couldn't stay. I had to get ready for theCube. How do you work with those guys? How do you sell a hybrid story, together, because cloud is eating away at the traditional infrastructure business, but it's all sort of one big, happy family, I'm sure. But how do you work with a cloud group to really drive, to make the water level higher for IBM? >> So, all of our products from the Spectrum family, not all, but almost all our products from the Spectrum family, will automatically move data to the cloud, including IBM Bluemix/SoftLayer. So our on-premises can do it. If you buy our software only, and don't buy our storage arrays, or don't buy a Storwize, or don't buy a flash system, you still can automatically move that data to the cloud, including the IBM cloud object store. Our Spectrum Scale product, for example, ScaleOut NAS, and file system, which is very highly used in big data analytics and cognitive workloads, automatically, by policy, will tier-data to IBM cloud object storage. Spectrum Protect can be set up to automatically take data and back it up from on-premises to IBM cloud object storage. So we've automated those processes between our software and our array family, and IBM cloud object storage, and Bluemix and SoftLayer. And, by the way, in all honesty, we also work with other cloud vendors, just like they work with other storage vendors. All storage vendors can put data in Bluemix. Well, guess what, we can put data in clouds that are not Bluemix, as well. Of course, we prefer Bluemix. We all have IBM employee stock purchase, so of course we want Bluemix first, but if the customer, for whatever reason, doesn't see the light and doesn't go to Bluemix and goes with something else, then we want to make sure that customer's happy. We want to get at least some of the PO, and our Spectrum family, and our VersaStack family, and all of our array family can get that part of the PO. >> You need versatility to be on any cloud. >> Eric: We can be on any cloud. >> So my question for you is, the thing that came out of our big data, Silicon Valley event last week was, Hadoop was a great example, and that's kind of been, now, a small feature of the overall data ecosystem, is that batch and real time are coming together. So that conversation you're having, that you mentioned earlier, is about more real time than there is anything else more than ever. >> Well, and real time gets back to my examples of Bones on Star Trek wanding you over healthcare. That is real time, he's got a phaser burn, a broken leg, a this and that, and then we know how to fix the guy. But if you don't get that from the wand, then that's not real time analytics. >> Speaking of Star Trek, just how much data do you think the Enterprise was throwing off, just from an IOT standpoint? >> I'm sure that they had about a hundred petabytes. All stored on IBM Flash Systems arrays, by the way. >> Eric, thanks for coming on. Real quick, in the next 30 seconds, just give the folks a quick update on why IBM storage is compelling now more than ever. >> I think the key thing is, most people don't realize, IBM is the number two storage company in the world, and it has been for the last several years. But I think the big thing is our embracing of the hybrid cloud, our capability of automating all these processes. When they've got less guys doing storage and infrastructure in their shop, they need something that's automated, that works with the cloud. And that's what IBM storage does. >> All right, Eric Herzog, here, inside theCube, Vice President of Product Market for IBM Storage. I'm John Furrier, and Dave Velante. More live coverage from IBM InterConnect after this short break. Stay with us. (tech music)