>>Machine intelligence is everywhere. AI is being embedded into our everyday lives, through applications, process automation, social media, ad tech, and it's permeating virtually every industry and touching everyone. Now, a major issue with machine learning and deep learning is trust in the outcome. That is the black box problem. What is that? Well, the black box issue arises when we can see the input and the output of the data, but we don't know what happens in the middle. Take a simple example of a picture of a cat or a hotdog for you. Silicon valley fans, the machine analyzes the picture and determines it's a cat, but we really don't know exactly how the machine determined that. Why is it a problem? Well, if it's a cat on social media, maybe it isn't so onerous, but what if it's a medical diagnosis facilitated by a machine? And what if that diagnosis is wrong? >>Or what if the machine is using deep learning to qualify an individual for a home loan and that person applying for the loan gets rejected. Was that decision based on bias? If the technology to produce that result is opaque. Well, you get the point. There are serious implications of not understanding how decisions are made with AI. So we're going to dig into the issue and the topic of how to make AI explainable and operationalize AI. And with me are two guests today, Shea speaky, who's the co-founder and COO of cognitive scale and long time friend of the cube and newly minted CEO of cognitive scale. Bob pitchy, Yano, gents. Welcome to the cube, Bob. Good to see you again. Welcome back on. >>Thanks for having us >>Say, let me start with you. Why did you start the company? I think you started the company in 2013. Give us a little history and the why behind cognitive scale. >>Sure. David. So, um, look, I spent some time, um, you know, through multiple startups, but I ended up at IBM, which is where I met Bob. And one of the things that we did was the commercialization of IBM Watson initially. And that led to, uh, uh, thinking about how do you operationalize this because of the, a lot of people thinking about data science and machine learning in isolation, building models, you know, trying to come up with better ways to deliver some kind of a prediction, but if you truly want to operationalize it, you need to think about scale that enterprises need. So, you know, we were in the early days, enamored by ways, I'm still in landed by ways. The application that takes me from point a to point B and our view is look as you go from point a to point B, but if you happen to be, um, let's say a patient or a financial services customer, imagine if you could have a raise like application giving you all the insights that you needed telling you at the right moment, you know, what was needed, the right explanation so that it could guide you through the journey. >>So that was really the sort of the thesis behind cognitive scale is how do you apply AI, uh, to solve problems like that in regulated industries like health care management services, but do it in a way that it's done at scale where you can get, bring the output of the data scientists, application developers, and then those insights that can be powered into those end applications like CRM systems, mobile applications, web applications, applications that consumers like us, whether it be in a healthcare setting or a financial services setting can get the benefit of those insights, but have the appropriate sort of evidence and transparency behind it. So that was the, that was the thesis for. >>Got it. Thank you for that. Now, Bob, I got to ask you, I knew you couldn't stay in the sidelines, my friend. So, uh, so what was it that you saw in the marketplace that Lord you back in to, to take on the CEO role? >>Yeah, so David is an exciting space and, uh, you're right. I couldn't stay on the sideline stuff. So look, I always felt that, uh, enterprise AI had a promise to keep. Um, and I don't think that many enterprises would say, you know, with their experience that yeah, we're getting the value that we wanted out of it. We're getting the scale that we wanted out of it. Um, and we're really satisfied with what it's delivered to us so far. So I felt there was a gap in keeping that promise and I saw cognitive scale as an important company and being able to fill that gap. And the reason that that gap exists is that, you know, enterprise AI, unlike AI, that relates to one particular conversational service or one particular small narrow domain application is really a team sport. You know, it involves all sorts of roles, um, and all sorts of aspects of a working enterprise. >>That's already scaled with systems of engagement, um, and, and systems of record. And we show up in the, with the ability to actually help put all of that together. It's a brown field, so to speak, not a Greenfield, um, and where Shea and Matt and Minosh and the team really focused was on what are the important last mile problems, uh, that an enterprise needs to address that aren't necessarily addressed with any one tool that might serve some members of that team? Because there are a lot of great tools out there in the space of AI or machine learning or deep learning, but they don't necessarily help come together to, to deliver the outcomes that an enterprise wants. So what are those important aspects? And then also, where do we apply AI inside of our platform and our capabilities to kind of take that operationalization to the next level, uh, with, you know, very specific insights and to take that journey and make it highly personalized while also making it more transparent and explainable. >>So what's the ICP, the ideal customer profile, is it, is it highly regulated industries? Is it, is it developers? Uh, maybe you could parse that a little bit. >>Yeah. So we do focus in healthcare and in financial services. And part of the reason for that is the problem is very difficult for them. You know, you're, you're working in a space where, you know, you have rules and regulations about when and how you need to engage with that client. So the bar for trust is very, very high and everything that we do is around trusted AI, which means, you know, thinking about using the data platforms and the model platforms in a way to create marketplaces, where being able to utilize that data is something that's provisioned in permission before we go out and do that assembly so that the target customer really is somebody who's driving digital transformation in those regulated industries. It might be a chief digital officer. It might be a chief client officer, customer officer, somebody who's really trying to understand. I have a very fragmented view of my member or of my patient or my client. And I want to be able to utilize AI to help that client get better outcomes or to make sure that they're not lost in the system by understanding and more holistically understanding them in a more personalized way, but while always maintaining, you know, that that chain of trust >>Got it. So can we get into the product like a little bit more about what the product is and maybe share, you can give us a census to kind of where you started and the evolution of the portfolio >>Look where we started there is, um, the application of AI, right? So look, the product and the platform was all being developed, but our biggest sort of view from the start had been, how do you get into the trenches and apply this to solve problems? And as well, pointed out, one of the areas we picked was healthcare because it is a tough industry. There's a lot of data, but there's a lot of regulation. And it's truly where you need the notion of being able to explain your decision at a really granular level, because those decisions have some serious consequences. So, you know, he started building a platform out and, um, a core product is called cortex. It's the, it's a software platform on top of this. These applications are built, but to our engagements over the last six, seven years, working with customers in healthcare, in financial services, some of the largest banks, the largest healthcare organizations, we have developed a software product to essentially help you scale enterprise AI, but it starts with how do you build these systems? >>Building the systems requires us to provide tooling that can help developers take models, data that exists within the enterprise, bring it together, rapidly, assemble this, orchestrate these different components, stand up. These systems, deploy these systems again in a very complex environment that includes, you know, on-prem systems as well as on the cloud, and then be able to done on APIs that can plug into an application. So we had to essentially think of this entire problem end to end, and that's poor cortex does, but extremely important part of cortex that didn't start off. Initially. We certainly had all the, you know, the, the makings of a trusted AI would be founded the industry wasn't quite ready over time. We've developed capabilities around explainability being able to detect bias. So not only are you building these end to end systems, assembling them and deploying them, you have as a first-class citizen built into this product, the notion of being able to understand bias, being able to detect whether there's the appropriate level of explainability to make a decision and all of that's embedded within the cortex platform. So that's what the platform does. And it's now in its sixth generation as we >>Speak. Yeah. So Dave, if you think about the platform, it really has three primary components. One is this, uh, uh, application development or assembly platform that fits between existing AI tools and models and data and systems of engagement. And that allows for those AI developers to rapidly visualize and orchestrate those aspects. And in that regard were tremendous partners with people like IBM, Microsoft H2O people that provide aspects that are helping develop the data platform, the data fabric, things like the, uh, data science tools to be able to then feed this platform. And then on the front end, really helping transform those systems of engagement into things that are more personalized with better recommendations in a more targeted space with explainable decisions. So that's one element that's called cortex fabric. There's another component called cortex certify. And that capability is largely around the model intelligence model introspection. >>It works, uh, across things that are of cost model driven, but other things that are based on deterministic algorithms, as well as rule-based algorithms to provide that explainability of decisions that are made upstream before they get to the black box model, because organizations are discovering that many times the data has, you know, aspects of dimensions to it and, and, and biases to it before it gets to the model. So they want to understand that entire chain of, of, uh, of decisioning before it gets there. And then there's the notion of some pew, preacher rated applications and blueprints to rapidly deliver outcomes in some key repeating areas like customer experience or like lead generation. Um, those elements where almost every customer we engage with, who is thinking about digital transformation wants to start by providing better client experience. They want to reduce costs. They want to have operational savings while driving up things like NPS and improving the outcomes for the people they're serving. So we have those sets of applications that we built over time that imagine that being that first use application, that starter set, that also trains the customer on how to you utilize this operational platform. And then they're off to the races building out those next use cases. So what we see as one typical insertion place play that returns value, and then they're scaling rapidly. Now I want to cover some secret sauce inside of the platform. >>Yeah. So before you do, I think, I just want to clarify, so the cortex fabric, cause that's really where I wanted to go next, but the cortex fabric, it seems like that's the way in which you're helping people operationalize inject use familiar tooling. It sounds like, am I correct? That the cortex certify is where you're kind of peeling the onion of that complicated, whether it's deep learning or neural networks, which is that's where the black box exists. Maybe you could tell us, you know, is that where the secret sauce lives, if not, where is it? And if >>It actually is in all places right though. So there's some really important, uh, introductions of capabilities, because like I mentioned, many times these, uh, regulated industries have been developed and highly fragmented pillars. Just think about the insurance companies between property casualty and personal lines. Um, many times they have grown through acquisition. So they have these systems of record that are, that are really delivering the operational aspects of the company's products, but the customers are sometimes lost in the scenes. And so they've built master data management capabilities and data warehouse capabilities to try to serve that. But they find that when they then go to apply AI across some of those curated data environments, it's still not sufficient. So we developed an element of being able to rapidly assemble what we call a profile of one. It's a very, very intimate profile around declared data sources, uh, that relate to a key business entity. >>In most cases, it's a person, it's a member, it's a patient, it's a client, but it can be a product for some of our clients. It's real estate. Uh, it's a listing. Um, you know, it can be someone who's enjoying a theme park. It can be someone who's a shopper in a grocery store. Um, it can be a region. So it's any key business entity. And one of the places where we applied our AI knowledge is by being able to extract key information out of these declared systems and then start to make longitudinal observations about those systems and to learn about them. And then line those up with prediction engines that both we supply as well as third parties and the customers themselves supply them. So in this theme of operationalization, they're constantly coming up with new innovations or a new model that they might want to interject into that engagement application. Our platform with this profile of one allows them to align that model directly into that profile, get the benefits of what we've already done, but then also continue to enhance, differentiate and provide even greater, uh, greater value to that client. IBM is providing aspects of those models that we can plug in. And many of our clients are that's really >>Well. That's interesting. So that profile of one is kind of the instantiation of that secret sauce, but you mentioned like master data management data warehouse, and, you know, as well as I do Bob we've we've we've decades of failures trying to get a 360 degree view for example of the customer. Uh, it's just, just not real time. It's not as current as we would want it to be. The quality is not necessarily there. It's a very asynchronous process. Things have changed the processing power. You and I have talked about this a lot. We have much more data now. So it's that, that, that profile one. So, but also you mentioned curated apps, customer experience, and lead gen. You mentioned those two, uh, and you've also talked about digital transformation. So it sounds like you're supporting, and maybe this is not necessarily the case, but I'm curious as to what's going on here, maybe supporting more revenue generation in the early phases than say privacy or compliance, or is it actually, do you have use cases for both? >>It's all, it's all of it. Um, and, and shake and, you know, really talk passionately about some of the things we've helped clients do, like for instance, uh, J money. Why don't you talk about the, the hospital, um, uh, uh, you know, discharge processes. >>Absolutely. So, so, you know, just to make this a bit more real, they, you know, when you talk about a profile on one, it's about understanding of patient, as I said earlier, but it's trying to bring this notion of not just the things that you know about the patient you call that declared information. You can find the system in, you can find this information in traditional EMR systems, right? But imagine bringing in, uh, observed information, things that you observed an interaction with the patient, uh, and then bring in inferences that you can then start drawing on top of that. So to bring this to a live example, imagine at the point of care, knowing when all the conditions are right for the patient to be discharged after surgery. And oftentimes as you know, those, if all the different evidence of the different elements that don't come together, you can make some really serious mistakes in terms of patient discharge, bad things can happen. >>Patient could be readmitted or even worse. That could be a serious outcome. Now, how do you bring that information at the point of care for the person making a decision, but not just looking at the information, you know, but also understanding not just the clinical information, but the social, the socioeconomic information, and then making sure that that decision has the appropriate evidence behind it. So then when you do make that decision, you have the appropriate sort of, uh, you know, the guidance behind it for audit reasons, but also for ensuring that you don't have a bad outcome. So that's the example Bob's talking about, where we have a flight this in real settings, in, in healthcare, but also in financial services and other industries where you can make these decisions based on the machine, telling you with a lot of detail behind it, whether this is the right decision to be made, we call this explainability and the evidence that's needed. >>You know, that's interesting. I, I, I'm imagining a use case in my mind where after a patient leaves, so often there's just a complete disconnect with the patient, unless that patient has problems and goes back, but that patient might have some problems, but they forget it's too much of a pain in the neck to go back, but, but the system can now track this and we could get much more accurate information and that could help in future diagnoses and, and also decision-making for a patient in terms of, of outcomes and probability of success. Um, question, what do you actually sell? So it's a middleware product. It's a, how do I license it? >>It's a, it's a, uh, it's a software platform. So we sell software, um, and it is deployed in the customer's cloud environment of choice. Uh, of course we support complete hybrid cloud capabilities. Um, we support native cloud deployments on top of Microsoft and Amazon and Google. And we support IBM's hybrid cloud initiative with red hat OpenShift as well, which also puts us in a position to both support those public cloud environments, as well as the customer's private cloud environments. So constructed with Kubernetes in that environment, um, which helps the customer also re you know, realize the value of that operational appar operationalization, because they can modify those applications and then redeploy them directly into their cloud environment and start to see those as struck to see those spaces. Now, I want to cover a couple of the other components of the secret sauce, if I could date to make sure that you've got a couple other elements where some real breakthroughs are occurring, uh, in these spaces. >>Um, so Dave, you and I, you know, we're passionate about the semiconductor industry, uh, and you know, we know what is, you know, happening with regard to innovation and broadening the people who are now siliconized their intellectual property and a lot of that's happening because those companies who have been able to figure out how to manufacture or how to design those semiconductors are operationalizing those platforms with our customers. So you have people like apple who are able to really break out of the scene and do things by utilizing utilities and macros their own knowledge about how things need to work. And it's just, it's very similar to what we're talking about doing here for enterprise AI, they're operationalizing that construction, but none of those companies would actually start creating the actual devices until they go through simulation and design. Correct. Well, when you think about most enterprises and how they develop software, they just immediately start to develop the code and they're going through AB testing, but they're all writing code. >>They're developing those assets. They're creating many, many models. You know, some organizations say 90% of the models they create. They never use some say 50, and they think that's good. But when you think about that in terms of, you know, the capital that's being deployed, both on the resources, as well as the infrastructure, that's potentially a lot of waste as well. So one of the breakthroughs is, uh, the creation of what we call synthetic data and simulations inside of our, of our operational platform. So cortex fabric allows someone to actually say, look, this is my data pattern. And because it's sensitive data, it might be, you know, PII. Um, we can help them by saying, okay, what is the pattern of that data? And then we can create synthetic data off of that pattern for someone to experiment with how a model might function or how that might work in the application context. >>And then to run that through a set of simulations, if they want to bring a new model into an application and say, what will the outcomes of this model be before I deployed into production, we allow them to drive simulations across millions or billions of interactions to understand what is that model going to be effective. Was it going to make a difference for that individual or for this application or for the cost savings goal and outcomes that I'm trying to drive? So just think about what that means in terms of that digital transformation officers, having the great idea, being in the C-suite and saying, I want to do this with my business. Oftentimes they have to turn around to the CIO or the chief data officer and say, when can you get me that data? And we all know the answer to that question. They go like this, like the, yeah, I've got a couple other things on the plate and I'll get to that as soon as I can. >>Now we're able to liberate that. Now we're able to say, look, you know, what's the concept that you're trying to develop. Let's create the synthetic data off of that environment. We have a Corpus of data that we have collected through various client directions that many times gets that bootstrapped and then drive that through simulation. So we're able to drive from imagination of what could be the outcome to really getting high confidence that this initiative is going to have a meaningful value for the enterprise. And then that stimulates the right kind of following and the right kind of endorsement, uh, throughout really driving that change to the enterprise and that aspect of the simulations, the ability to plan out what that looks like and develop those synthetic aspects is another important element that the secret sauce inside of cortex fabric, >>Back to the semiconductor innovation, I can do that very cheaply. I think, I think I I'm thinking AWS cloud, I could experiment using graviton or maybe do a little bit of training with some, you know, new processors and, and then containerize it, bring it back to my on-premise state and apply it. Uh, and so, uh, just a as you say, a much more agile environment, um, yeah, >>Speed efficiency, um, and the ability to validate the hypothesis that, that started the process. >>Guys, think about the Tam, the total available market. Can we have that discussion? How big is that? >>I mean, if you think about the spend across, uh, the healthcare space and financial services, we're talking about hundreds of billions, uh, in that, in terms of what the enterprise AI opportunity, as in just those spaces. And remember financial services is a broad spectrum. So one of the things that we're actually starting to roll out today in fact, is a SAS service that we developed. That's based on top of our offerings called trust star trust star.ai, and trust star is a set of personalized insights that get delivered directly to the loan officer inside of, uh, an institution who's trying to, uh, really match, uh, lending to someone who wants to buy a property. Um, and when you think about many of those organizations, they have very, very high demand. They've got a lot of information, they've got a lot of regulation they need to adhere to. >>But many times they're very analytically challenged in terms of the tools they have to be able to serve those needs. So what's happening with new listings, what's happening with my competitors, what's happening. As people move from high tax states, where they want to potentially leave into new, more attractive toxin and opportunity-based environments where they're not known to those lending institutions that maybe, you know, they're, they're trying to be married up with. So we've developed a set of insights that are, is, this is a subscription service trust r.ai, um, which goes directly to the loan officer. And then we use our platform behind the scenes to use things like the home disclosure act, data, MLS data, other data that is typically Isagenix to those sources and providing very customized insights to help that buyer journey. And of course, along the way, we can identify things like are some of the decisions more difficult to explain, are there potential biases that might be involved in that environment as people are applying for mortgages, and we can really drive growth through inclusion for those lending institutions, because they might just not understand that potential client well enough, that we can identify the kind of things that they can do to know them better. >>And the benefit is really to hold there, right? And shale, I'll let you jump in, but to me, it's twofold. There. One is, you know, you want to have accurate decisions. You want to have low risk decisions. And if you want to be able to explain that to an individual that may get rejected, here's why, um, and, and it wasn't because of bias. It was because of XYZ and you need to work on these things, but go ahead shape. >>Now, this is going to add that point here, Dave, which is a double-faced point on the dam. One of the things that, and the reason why, you know, industries like healthcare, financial services spending billions, it's not because they look at AI in isolation, they actually looking at the existing processes. So, you know, established disciplines like CRM or supply chain procurement, whether it is contact center and so on. And the examples that we gave you earlier, it's about infusing AI into those existing applications, existing systems. And that's, what's creating the left because what's been missing so far is the silos of data and you traditional traditional transaction systems, but this notion of intelligence that can be infused into the systems and that's, what's creating this massive market opportunity for us. >>Yeah. And I think, um, I think a lot of people just misunderstood in the, or in the early, early days of the AI, you know, new AI when we came out of the AI winter, if you will, people thought, okay, the incumbents are in big trouble now because they are not, they're not AI developers, but really what you guys are showing is it's not about building your own AI. It's about applying AI and having the tools to do so. The incumbents actually have a huge advantage because they've got the systems in place. They can, if they, if they're smart, they can infuse AI and then extract value out of that for their customers. >>And that's why, you know, companies like, uh, like IBM are an investor in a great partner in this space. Anthem is an investor, uh, you know, of the company, but also, you know, someone who can utilize the capabilities, Microsoft, uh, Intel, um, you know, we've been, we've been, uh, you know, really blessed with a great backing Norwest venture partners, um, obviously is, uh, an investor in us as well. So, you know, we've seen the ability to really help those organizations think about, um, you know, where that future lies. But one of the things that is also, you know, one of the gaps in the promises when a C-suite executive like a digital transformation officer, chief digital chief customer officer, they're having their idea, they want to be accountable to that idea. They're having that idea in the boardroom. And they're saying, look, I think I can improve my customer satisfaction and, uh, by 20 points and decrease the cost of my call center by 20 or 30 or 50 points. >>Um, but they need to be able to measure that. So one of the other things that, uh, we've done a cognitive scale is help them understand the progress that they're making across those business goals. Um, now when you think about this people like Andrew Nang, or just really talking about this aspect of goal oriented AI, don't start with the problem, start with what your business goal is, start with, what outcome you're trying to drive, and then think about how AI helps you along that goal. We're delivering this now in our product, our version six product. So while some people are saying, yeah, this is really the right way to potentially do it. We have those capabilities in the product. And what we do is we identify this notion of the campaign, an AI campaign. So when the case that I just gave you where the chief digital officer is saying, I want to drive customer satisfaction up. >>I want to have more explainable decisions, and I want to drive cost down. Maybe I want to drive, call avoidance. Um, you know, and I want to be able to reduce a handling time, um, to drive those costs down, that is a campaign. And then underneath that campaign, there's all sorts of missions that support that campaign. Some of them are very long running. Some of them are very ephemeral. Some of them are cyclical, and we have this notion of the campaign and then admission planner that supports the goals of that campaign, showing that a leader, how they're doing against that goal by measuring the outcomes of every interaction against that mission and all the missions against the campaign. So, you know, we think accountability is an important part of that process as well. And we've never engaged an executive that says, I want to do this, but I don't want to be accountable to the result, but they're having a hard time identifying I'm spending this money. >>How do I ensure that I'm getting the return? And so we've put our, you know, our secret sauce into that space as well. And that includes, you know, the information around the trustworthiness of those, uh, capabilities. Um, and I should mention as well, you know, when we think about that aspect of the responsible AI capabilities, it's really important. The partnerships that we're driving across that space, no one company is going to have the perfect model intelligence tool to be able to address an enterprise's needs. It's much like cybersecurity, right? People thought initially, well, I'll do it myself. I'll just turn up my firewall. You know, I'll make my applications, you know, uh, you know, roll access much more granular. I'll turn down the permissions on the database and I'll be safe from cybersecurity. And then they realized, no, that's not how it was going to work. >>And by the way, the threats already inside and there's, long-term persistent code running, and you have to be able to scan it, have intelligence around it. And there are different capabilities that are specialized for different components of that problem. The same is going to be turnaround responsible and trustworthy AI. So we're partnered with people like IBM, people like Microsoft and others to really understand how we take the best of what it is that they're doing partner with the best, uh, that they're doing and make those outcomes better for clients. And then there's also leaders like the responsible AI Institute, which is a non-profit independent organization who were thinking about a new rating systems for, um, the space of responsible and trusted AI, thinking about things like certifications for professionals that really drive that notion of education, which is an important component of addressing the problem. And we're providing the integration of our tools directly with those assessments and those certifications. So if someone gets started with our platform, they're already using an ecosystem that includes independent thinkers from across the entire industry, um, including public sector, as well as the private sector, to be able to be on the cutting edge of what it's going to take to really step up to the challenge in that space. >>Yeah. You guys got a lot going on. I mean, you're eight years in now and you've got now an executive to really drive the next scale. You mentioned Bob, some of your investors, uh, Anthem, IBM Norwest, uh, I it's Crunchbase, right? It says you've raised 40 million. Is that the right number? Where are you in fundraising? What can you tell? >>Um, they're a little behind where we are, but, uh, you know, we're staged B and, uh, you know, we're looking forward to now really driving that growth. We're past that startup phase, and now we're into the growth phase. Um, and we're seeing, you know, the focus that we've applied in the industries, um, really starting to pay off, you know, initially it would be a couple of months as a customer was starting to understand what to be able to do with our capabilities to address their challenges. Now we're seeing that happen in weeks. So now is the right time to be able to drive that scalability. So we'll be, you know, looking in the market of how we assemble that, uh, you know, necessary capability to grow. Um, Shay and I have worked, uh, in the past year of, uh, with the board support of building out our go to market around that space. >>Um, and in the first hundred days, it's all about alignment because when you're going to go through that growth phase growth phase, you really have to make sure that things were pointed in the right direction and pointed together in the right direction, simplifying what it is that we're doing for the market. So people could really understand, you know, how unique we are in this space, um, and what they can expect out of an engagement with us. Um, and then, you know, really driving that aspect of designing to go to market. Um, and then scaling that. >>Yeah, I think I, it sounds like you've got, you got, if you're, if you're in down to days or weeks in terms of the ROI, it sounds like you've got product market fit nailed. Now it's about sort of the next phase is you really driving your go to market and the science behind how your dimension and your, your sales productivity, and you can now codify what you've learned in that first phase. I like the approach. A lot of, a lot of times you see companies, of course, this comes out of the west coast, east coast guy, but you see the double, double, triple, triple grow, grow, grow, grow, grow, and then, and then churn becomes that silent killer of the S the software company. I think you guys, it sounds you've, you've taken a much, much more adult-like approach, and now you're ready to really drive that scale. I think it's the new formula really for success for hitting escape velocity. Guys, we got to go, but thanks so much. Uh, uh, Bob, I'll give you the last word, w w w what you mentioned some of your a hundred day priorities. Maybe you can summarize that and what should we be looking for as Martin? >>I mean, I, I think, I think the, you know, the, our measures of success are our clients measure success and the same for our partners. So we're not doing this alone, we're doing it with system integrator partners, and we're doing it with a great technology partners in the market as well. So this is a part about keeping that promise for enterprise AI. And one of the things that I'll say just in the last couple of minutes is, you know, this is not just a company with a great vision and great engineers to develop out this great portfolio, but it's a company with great values, great commitments to its employees and the marketplace and the communities we serve. So I was attracted to the culture of this company, as well as I was, uh, to the, uh, innovation and what they mean to the, to the space of a, >>And I said, I said, I'll give you last word. Actually, I got a question for Shea you Austin based, is that correct? >>But we have a global presence, obviously I'm operating out of Austin, other parts of the U S but, uh, offices in, in, uh, in the UK, as well as in India, >>You're not moving to tax-free Texas. Like everybody else. >>I've got to, I've got an important home, uh, and life in Connecticut cell. I'll be traveling back and forth between Connecticut and Austin, but keeping my home there. >>Thanks for coming on and best of luck, we want to follow your progress and really appreciate your time today. Good luck. >>Thank you, Dave. All right. >>Thank you for watching this cube conversation. This is Dave Volante. We'll see you next time.

>>from around the globe. It's the queue with digital coverage of Docker Con Live 2020 brought to you by Docker and its ecosystem partners. Welcome to the Special Cube coverage of Docker Con 2020. It's a virtual digital event co produced by Docker and the Cube. Thanks for joining us. We have great segment here. Precision cancer medicine really is evolving where the personalization of the data are really going to be important to personalize those treatments based upon unique characteristics of the tumors. This is something that's been a really hot topic, talking point and focus area in the industry. And technology is here to help with two great guests who are using technology. Docker Docker containers a variety of other things to help the process go further along. And we got here spring and who's the bioinformatics research assistant and Camille took Who's a student and in turn, you guys done some compelling work. Thanks for joining this docker con virtualized. Thanks for coming on. >>Thanks for having me. >>So first tell us about yourself and what you guys doing at the Children's Cancer Institute? That's where you're located. What's going on there? Tell us what you guys are doing there? >>Sure, So I built into Cancer Institute. As it sounds, we do a lot of research when it comes to specifically the Children's cancer, though Children a unique in the sense that a lot of the typical treatment we use for adult may or may not work or will have adverse side effects. So what we do is we do all kinds of research. But what lab and I love, which we call a dry love What we do research in silica, using computers at the develop pipelines in order to improve outcomes for Children. >>And what are some of the things you get some to deal with us on the tech side, but also there's the workflow of the patients survival rates, capacity, those constraints that you guys are dealing with. And what are some of the some of the things going on there that you have to deal with and you're trying to improve the outcomes? What specific outcomes were you trying to work through? >>Well, at the moment off of the past decade and all the work you've done in the past decade, we've made a substantial impact on the supply of ability off several high risk cancers in Pediatrics on and we've Got a certain Program, which spent I'll talk about in more depth called the Zero Childhood Cancer Program and essentially that aims to reduce childhood cancer in Children uh, zero. So that, in other words, with the previous five ability 100% on hopefully, no lives will be lost. But that's >>and what do you guys doing specifically? What's your your job? What's your focus? >>Yes, so part of our lab Old computational biology. Uh, we run a processing pipeline, the whole genome and our next guest that, given the sequencing information for the kids, though, we sequence the healthy cells and we sequence there. Two missiles. We analyze them together, and what we do is we find mutations that are causing the cancel that help us determine what treatment. So what? Clinical trials might be most effective for the kids and so specifically Allah books on that pipeline where we run a whole bunch of bioinformatics tools, that area buying thematic basically biology, informatics, and we use the data generated sequel thing in order to extract those mutations that will be the cancer driving mutations that hopefully we can target in order to treat the kids. >>You know, you hear about an attack and you hear Facebook personalization recommendation engines. What the click on you guys are really doing Really? Mawr personalization around treatment recommendations. These kinds of things come into it. Can you share a little bit about what goes on there and and tell us what's happening? >>Well, as you mentioned when you first, some brought us into this, which we're looking at, the the profile of the team itself and that allows us to specialize the medication on the young treatment for that patient on. Essentially, that lets us improve the efficiency and the effectiveness off the treatment, which in turn has an impact on this probability off. >>What are some of the technical things? How did you guys get involved with Docker with Docker fit into all this? >>Yeah, I'm sure Camille will have plenty to bring up on this as well. But, um, yes, it's been quite a project to the the pipeline that we have. Um, we have built on a specific platforms and is looking great. But as with most tools in a lot of things that you develop when your engineers eyes pretty easy for them to become platform specific. And then that kind of stuck there. And you have to re engineer the whole thing kind of of a black hole. That's such a pain to there. So, um, the project that Mikhail in my field working on was actually taking it to the individual's pools we used in the pipeline and Docker rising them individually containing them with the dependencies they need so that we could hook them up anyway. We want So we can configure the pipeline, not just customized based off of the data like we're on the same pipeline and every it even being able to change the pipeline of different things to different kids. Be able to do that easily, um, to be able to run it on different platforms. You know, the fact that we have the choice not only means that we could save money, but if there's a cloud instance that will run an app costal. If there's a platform that you know wanted to collaborate with us and they say, Oh, we have this wholesome data we'd love for you to analyze. It's over hell, like a lot of you know, >>use my tool. It's really great. >>Yeah. And so having portability is a big thing as well. And so I'm sure people can go on about, uh, some of the pain point you having to do authorize all of the different, But, you know, even though they Austin challenges associated with doing it, I think the payoff is massive. >>Dig into this because this is one of the things where you've got a problem statement. You got a real world example. Cancer patients, life or death gets a serious things going on here. You're a tech. You get in here. What's going on? You're like, Okay, this is going to be easy. Just wrangle the data. I throw some compute at it. It's over, right? You know what? How did you take us through the life? They're, you know, living >>right. So a supreme I mentioned before, first and foremost well, in the scale of several 100 terabytes worth of data for every single patient. So obviously we can start to understand just how beneficial it is to move the pipeline to the data, rather the other way around. Um, so much time would be saved. The money costs as well, in terms of actually Docker rising the but the programs that analyze the data, it was quite difficult. And I think Sabrina would agree mate would agree with me on this point. The primary issue was that almost all of the apps we encountered within the pipeline we're very, very heavily dependent on very specific versions off some dependencies, but that they were just build upon so many other different APS on and they were very heavily fined tuned. So docker rising. It was quite difficult because we have to preserve every single version of every single dependency in one instance just to ensure that that was working. And these apps get updated quite Simpson my regularly. So we have to ensure that our doctors would survive. >>So what does it really take? The doc arise your pipeline. >>I mean, it was a whole project. Well, um, myself, Camille, we had a whole bunch of, um, automatic guns doing us over the summer, which was fantastic as well. And we basically have a whole team of lost words like, Okay, here's another automatic pull in the pipeline. You get enterprise, you get to go for a special you get enterprise, they each who individually and then you've been days awake on it, depending on the app. Easier than others. Um, but particularly when it comes to things a lot by a dramatic pools, some of them are very memory hungry. Some of them are very finicky. Some of the, um ah, little stable than others. And so you could spend one day characterizing a tool. And it's done, you know, in a handful of Allah's old. Sometimes it could make a week, and he's just getting this one tool done. And the idea behind the whole team working on it was eventually use. Look through this process, and then you have, um, a docker file set up. Well, anyone to run it on any system. And we know we have an identical set up, which was not sure before, because I remember when I started and I was trying to get the pipeline running on my own machine. Ah, lot of things just didn't look like Oh, you don't have the very specific version of ah that this developer has. 00 that's not working because you don't have this specific girl file that actually has a bug fixes in it. Just for us like, Well, >>he had a lot of limitations before the doctor and doctor analyzing docker container izing it. It was tough. What was it like before and after? >>And we'll probably speak more people full. It was basically, uh, yeah, days or weeks trying to set up on in. Stole everything needed around the whole pipeline. Yeah, it took a long time. And even then, a lot of things, But how you got to set up this? You know, I think speculation of pipeline, all the units, these are the three of the different programs. Will you need this version of obligation? This new upgrade of the tools that work with that version of Oz The old, all kinds of issues that you run into when they schools depend on entirely different things and to install, like, four different versions of python. Three different versions of our or different versions of job on the one machine, you know, just to run it is a bit of >>what has. It's a hassle. Basically, it's a nightmare. And now, after you're >>probably familiar with that, >>Yeah. So what's it like after >>it's a zoo? It supports ridiculously efficient. Like it. It's It's incredible what Michael mentioned before, as soon as we did in stone. Those at the versions of the dependencies. Dhaka keeps them naturally, and we can specify the versions within a docker container. So we can. We can absolutely guarantee that that application will run successfully and effectively every single time. >>Share with me how complicated these pipelines are. Sounds like that's a key piece here for you guys. And you had all the hassles that you do. Your get Docker rised up and things work smoothly. Got that? But tell >>me about >>the pipelines. What's what's so complicated about them? >>Honestly, the biggest complication is all of the connection. It's not a simple as, um, run a from the sea, and then you don't That would be nice, but that know how these things work if you have a network of programs with the output of this, input for another, and you have to run this program before this little this one. But some of the output become input for multiple programs, and by the time you hook the whole thing up, it looks like a gigantic web of applications. The way all the connections, so it's a massive Well, it almost looks like a massive met when you look at it. But having each of the individual tools contained and working means that we can look them all up. And even though it looks complicated, it would be far more complicated if we had that entire pipeline. You know, in a single program like having to code, that whole thing in a single group would be an absolute nightmare. Where is being able to have each of the tools as individual doctors means we just have the link, the input on that book, which is the top. But once you've done that, it means that you know each of the individual pools will run. And if an individual fails, or whatever raised in memory or other issues run into, you can rerun that one individual school re hooks the output into whatever the next program is going without having one massive you know, program will file what it fails midway through, and there's nothing you can do. >>Yeah, you unpack. It really says, Basically, you get the goodness to the work up front, and a lot of goodness come out of it. So this lets comes to the future of health. What are the key takeaways that you guys have from this process? And how does it apply to things that might be helpful to you right around the corner? Or today, like deep learning as you get more tools out there with machine learning and deep learning? Um, we hope there's gonna be some cool things coming out. What do you guys see here? And the insights? >>Well, we have a section of how the computational biologist team that is looking into doing more predictive talks working out, um, basically the risk of people developing can't the risks of kids developing cancel. And that's something you can do when you have all of this data. But that requires a lot of analysis as well. And so one of the benefits of you know being able to have these very moveable pipelines and tools makes it easier to run them on. The cloud makes it easier to shale. You're processing with about researches to the hospitals, just making collaboration easier. Mainz that data sharing becomes a possibility or is before if you have three different organizations. But the daughter in three different places. Um, how do you share that with moving the daughter really feasible. Pascal, can you analyze it in a way that practical and so I don't want one of the benefits of Docker? Is all of these advanced tools coming out? You know, if there's some amazing predicted that comes out that uses some kind of regression little deep learning, whatever. If we wanted to add that being able to dock arise a complex school into a single docker ice makes it less complicated that highlighted the pipeline in the future, if that's something we'd like to do, >>Camille, any thoughts on your end on this? >>Actually, I was Sabrina in my mind for the last point. I was just thinking about scalability definitely is very. It's a huge point because the part about the girls as a technology does any kind of technology that we've got to inspect into the pipeline. As of now, it be significantly easier with the use of Docker. You could just docker rise that technology and then implant that straight into the pipeline. Minimal stress. >>So productivity agility doesn't come home for you guys. Is that resonate? >>Yeah, definitely. >>And you got the collaboration. So there's business benefits, the outcomes. Are there any proof points you could share on some results that you guys are seeing some fruit from the tree, if you will, from all this Goodness. >>Well, one of the things we've been working on is actually a collaboration with those Bio Commons and Katica. They built a platform, specifically the development pipelines. We wanted to go out, and they have support for Docker containers built into the platform, which makes it very easy to push a lot of containers of the platform, look them up and be able to collaborate with them not only to try a new platform without that, but also help them look like a platform to be able to shoot action access data that's been uploaded there as well. But a lot of people we wouldn't have been able to do that if we hadn't. Guys, they're up. It just wouldn't have. Actually, it wouldn't be possible. And now that we have, we've been able to collaborate with them in terms of improving the platform. But also to be able to share and run our pipelines on other data will just pretty good, >>awesome. Well, It's great to have you on the Cube here on Docker Con 2020 from down under. Great Internet connections get great Internet down. They're keeping us remote were sheltering in place here. Stay safe and you guys final question. Could you eat? Share in your own words from a developer? From a tech standpoint, as you're in this core role, super important role, the outcomes are significant and have real impact. What has the technology? What is docker ization done for you guys and for your work environment and for the business share in your own words what it means. A lot of other developers are watching What's your opinion? >>But yeah, I mean, the really practical point is we've massively increased capacity of the pipeline. One thing that been quite fantastic years. We've got a lot of increased. The Port zero child who can program, which means going into the schedule will actually be able to open a program. Every child in Australia that, uh, has cancel will be ableto add them to the program. Where is currently we're only able to enroll kids who are low survivability, right? So about 30% the lowest 30% of the viability we're able to roll over program currently, but having a pipeline where we can just double the memory like that double the amount of battle. Uh, and the fact that we can change the instance is really to just double the capacity trip. The capacity means that now that we have the support to be able to enroll potentially every kid, Mr Leo, um, once we've upgraded the whole pipeline, it means will actually be a code with the amount of Children being enrolled, whereas on the existing pipeline, we're currently that capacity. So doing the upgrade in a really practical way means that we're actually going to be a triple the number of kids in Australia. We can add onto the program which wouldn't have been possible otherwise >>unleashing the limitations and making it totally scalable. Your thoughts as developers watching you're in there, Your hand in your hands, dirty. You built it. It's showing some traction. What's what's your what's your take? What's your view? >>Well, I mean first and foremost locks events. It just feels fantastic knowing that what we're doing is as a substantial and quantify who impact on the on a subset of the population and we're literally saving lives. Analyze with the work that we're doing in terms off developing with With that technology, such a breeze especially compared Teoh I've had minimal contact with what it was like without docker and from the horror stories I've heard, it's It's It's a godsend. It's It's it's really improved The quality of developing. >>Well, you guys have a great mission. And congratulations on the success. Really impact right there. You guys are doing great work and it must feel great. I'm happy for you and great to connect with you guys and continue, you know, using technology to get the outcomes, not just using technology. So Fantastic story. Thank you for sharing. Appreciate >>you having me. >>Thank you. >>Okay, I'm John for we here for Docker Con 2020 Docker con virtual docker con digital. It's a digital event This year we were all shale three in place that we're in the Palo Alto studios for Docker con 2020. I'm John furrier. Stay with us for more coverage digitally go to docker con dot com from or check out all these different sessions And of course, stay with us for this feat. Thank you very much. Yeah, yeah, yeah, yeah, yeah, yeah

>>live from Las Vegas. It's the Q covering AWS executive. Something >>brought to you by Accenture. >>Welcome back, everyone. We are kicking off day two of the cubes. Live coverage of the ex center Executive Summit here at AWS. Reinvent, I'm your host, Rebecca Knight. We have three guests for this panel. We have some bad meta. He is the chief information officer at Putnam based in Boston. Where? Boston People together. Thank you so much for coming on the show. Nitin Gupta. He's the partner and solutions lead. Financial service is at AWS Welcomed and Shale Jane back again for more. Who leads the data business group in North America. Thanks >>so much the last time. >>Yes. We can't get enough of each other. So thank you so much for coming on the show. We're talking about the data data journey and financial service is so I'm gonna start with you, Sam. It tell us. Tell our viewers a little bit about Putnam. That your assets under management. Your employees? >>Sure. So you know, problem is a global firm. We are a leader in mutual funds in the mutual fund business. We're in 84 year old organization. We based in Boston on, and we are known for innovation. We've done a lot of firsts in our industry on our focus has always bean looking after the needs of our shareholders. So even as we launch digital transformation, we launch it with the lens off, making sure we're covering the needs of our shareholders. >>So what was the impetus? What was the driving force to it? To embark on this cloud journey? >>Sure, So you look recovered. The financial markets recover industries. We look at our own industry as well. Things are changing rather rapidly, right, if I may just turn it around a little bit. Last year's letter from our CEO Bob Reynolds, said That problem now has Maur increasingly Maur four and five star funds, according to Morningstar, then we've had it as a percent of total funds ever. Before we had inflows, when the rest of the industry were having outflows, we built a digital platform and we said digital technology at problem is how we gonna view the internal technology department who will help enable our company to go and provide the investment insights directly to our advisors and to our shareholders so that they can benefit from the performance that we're we're delivering, right? We can only do that through a change. What's really going on in our industry is that there's more choice that's now available to shareholders than ever before. So while we talk about where there's outflows in in in our world, there's actually a lot of flow happening, right, So So it's for us to figure out how. How are the tastes changing right? What are people buying would do advisors need? When do they need them and can reposition ourselves to service them at scale, and so that those are the things that are driving our business? For us to continue to serve the shareholders needs. We really need to be in tune with where the market is. So we're helping do that at Putnam through technology, >>so shale in it. And I mean, what he's just described is thin. This enormously changing landscape and financial service is disrupted by a lot of new entrance. A lot of financial text in tak, a lot of different kinds of technologies. A lot of industries are experiencing this rapid pace of change. How do u ex ensure in AWS work with Putnam amidst this tremendous change, and how do you sit down with the client and sort of work out? Where do we go from here? >>So you know, I want to touch upon a couple of things that made you said And Rebecca You said, So no one is the cloud of their journey. It's It's not a destination that you're trying to get to, And then the other thing that you talked about, it's change. So we had in the cycle right now. But there's a lot of change happening at an industry we had in the cycle Where you nothing, that $38 trillion or something, which is a generator, you know, they're just getting transferred from one generation to the other. I'm not getting any off it. Unfortunately, you know >>all of >>this change that is happening in the industry. What is really required is you need something up in terms of technology, a platform that allows you to move quickly on adapt really quickly to this change. And I think that's where cloud comes in when we talk about all the new generation technologies like data machine learning, artificial intelligence, how >>do you >>leverage all of those. How do you fail quickly? How do you test experiment? Run thousands of not millions of experiments and see what will work in what will not work and do that in a very cost effective way and cloud of a very easy. It's an effective way to do it. And the weight of Louis is helping our customers. Obviously. You know, we we announced a bunch of service is yes, today way have the widest and the deepest tack that is dead in the industry today. You know the strength of our partners. Accenture. So you know, Accenture has Bean one of our longest standing partners altar and financial firm on, you know, working with them, working with our partners to enable our customers. But then we're also investing very heavily in building our industry capabilities. Are accounting solution architects? Professional service is security professionals helping our customers answer all the questions that they would need to answer as they go in this journey with us. So it's, you know, we are in this with them for for the long haul on dhe, you know, super excited about parking trip. >>So from our perspective, I think where we view the world as at a point where we're post digital, where digital was to put a front end that made your engagement with the customers much better. But now we're talking about intelligent enterprise, which is to really digitize the company from the inside out. So not only you need cloud for agility and all the other benefits that cloud offers, but you also need to look at data is the vehicle that would actually not only transform the culture of the company but also be able to integrate with your partners. For example, Cement talked about, you know, getting mind share from the advisers. But if you can exchange data, integrate data much better, faster with them and serve data to them in shapes and speeds that they need, they'll be more amenable to put you on their roster as well. So I think we're seeing a change that's mostly driven by the fintech industry disruption. That's that's happening as well. And it is no better time than now with the cloud and data to really help transform companies like >>the's tons of innovation, right, it's We heard Andy Jassy talk about the Let's roll Sweet the Sweets that are available to us. Our job is to learn what they are and how does it apply to our business because at the end of the game you said it's about our shareholders. It's about the value that we can bring. But we want to harness the power off all of the innovation, and we can't even though we've Bean an innovator, we're not going to innovate alone, all right, so it's really helpful to have to surround yourself with partners who have done this before, to be learning from others and bringing in the right tools at the right time, so so we can turn things around quickly, right? This is way are obviously very conservative and risk averse when it comes to managing other people's money. So we have to be very, very careful. Having said that, you know, we want to learn about all the guardrails we can put in place so we can go faster. >>I want to actually do something about what Shayla brought up, and that is the cultural change within the organization, because change is hard and so many people are resistant, particularly when things are going relatively well and they say Why mess that up with the new technology? So how is hard? Maybe >>is the understatement of the week very hard, and as you guys know, you know where it's not. It's not hard because people don't just want changes. They are experts in things that they've been doing for the last 15 years. 20 years. They've bean at our firm for a really long time. They really know how everything works from front to back. What happens, though? Now, when we get a changing need from the market and people want to buy things differently and we want to sell different products and maybe wanna introduce new products to the market, we can create bottlenecks that slow things down if we're not careful. So this is where we want to learn about the two pizza teams and how you can do things faster. How can we apply that to our world? Which means business partners working with technology, co located in small teams, being completely empowered to deliver solutions, right, working with our risk and compliance people, making sure that everyone's doing things that there were supposed to be doing right? How do we put that to work in the financial service is industry. So where we're learning as we go, we're learning to break down the sidles in the organization, and it's hot all the way around because we're experts in our areas. We know what we've done really well. But fortunately we have a leader in our CEO who's basically said that Let's transform problem so that we become leaders in the digital era for financial service is so with his support waken. Get the executive team align, and as the executive team aligns, then you find that people in the organization they want to work in this model, right but way don't know yet what we don't know, right? It's so we know how to do things from yesterday. Now we're learning and working together. So you guys have come in and this is where we've said, Bring in the people who have done this before and let's hold a session with 40 50 people that Putnam and let's just learn about what that transformation looked like at other places, so we don't make the same mistakes. >>Well, that's what Andy Jassy said in his fireside chat this morning. He was talking about how he had surgery recently in the question you need to ask your surgeon is how many times have you done this surgery? Because that is the critical thing. And so having a trusted partner is so important. So how how does it work that we're working together, collaborating on this relationship? How are you ensuring that Putnam doesn't make mistakes and does do the right tool for the right job shell? >>So, um, earlier this year, we actually launched an offering. A devious lighthouse with eight of us and what it is is a is a collection off. All of our assets are thought, leadership and architectures that we have garnered over the years, having worked with plants like Putnam and have them through the journey. So we put them all together and we bring Bring that Fourth Putnam is one of the first clients actually take advantage of it Abuse Data Lighthouse and, for example, we have a methodology that is specially customized for doing data on on eight of us. So things like that is what we bring to the table to help eliminate the risk that they may encounter. >>And data is critical to us, right? It's we manage a set of data assets, and that's the engine off the organization. So when we look at cloud migration way, look at what's our data strategy? How are rebuilding the so called you guys introduce the terminology for confirmed data sets? And then can we gallon eyes the rest of the organization around it, from investment professionals to operational professionals who used that data every day. Manager governent Make sure that it is what it's supposed to be. And to do that in a cloud environment where their user experience becomes a lot simpler, a lot easier almost takes I t a little away from the day to day. We don't have to be in the report writing business because we can make them more self service right that will create efficiencies in our organization. Our clients are asking us to do things at a lower cost than ever before and introduce more products and more tools and more service is right, so >>I would just tie with Samantha, just said with your question about culture. So if you can make it easy for people, for example, making things self service and data that's discovered through a catalog, so you have a place where you can go and find all the data sets it available. What is the quality? What is the veracity of data and then be able to take a piece of that and try some experiments with it? I think that would enable the cultural change much faster >>because they are able to basically do their jobs better. >>Yes, yes, >>it is. A is a more productive implement. Will highly >>engaged employees, right? We don't want to be in a situation where we find a lot of those disengagement moving employees and the mission for company. We want high engagement. We own people committed to what they're doing. We want to remove hurdles, and technology is they can produce great efficiencies, but it's not done right. It can also be a big hurdle. So we want to learn how to deliver the right tools, the right products to make it easier for way like to say, bring delightful experiences for our clients and our employees. >>Delightful. Another were another Jeff Bezos favorite word of his Obviously Putnam is, is a real innovator and really on the vanguard of this new technology. What are you seeing in the greater financial service is landscape. I mean, how how what are the what is the corporate mind set when it comes to this kind of change? >>So you know, when we look across our financial service is customer base across banking, capital markets, insurance pretty much every customer today. The question is not, you know if we should move to the cloud or when should we move to the cloud? But I think every every CEO and see io is asking the question, How do I move too loud? And what applications do I move over? How do I start on this journey of transformation? Whether it's a digital or it's reducing costs are improving my risk. Posher whatever that end goal is on dhe, you know, when we look at use cases across the industry, risk and data is with one of the easiest use cases to get started with, say, on Ben Field. They were looking at Solvent E to calculations for 25 million other policy holders, and they reduce that time from 10 days to 10 minutes. That is a, you know, really good use case off getting moving to the cloud. You know, if Indra is a great example. They're very public customer analyzing 38 building over market records in the stock market and looking in on alive in all of the data. On it up with data and risk is one of the core use cases that companies start with but then >>has to >>get more as they learn more about the cloud. As they get more get a deeper understanding, they start looking at other things, like Transforming Corp core applications. Today we have core creating applications, scored insurance application score, banking applications that are running running on the cloud. And then they start looking and innovation. You know, how do we look at artificial intelligence? How do we look at machine learning? How do we look at the new technologies to really transform our business and one of the great use case? And we thought so. You know, a lot off insurance companies Liberty Mutual using Lexx as part of their there was a conversational agent for their customers. But one of the interesting examples I have is it's ah, it's a reinsurer in Denmark, Italy insurer in Denmark, and what they're doing is they're using image recognition from from Amazon to look at on accident in the field and then analyzing that, using the using our recognition service to see what that that actual damages and what the cost is and feeding that information to the underwriter really compressing the time that it takes two from a clean filing to processing and payment to a matter of a few few few hours on getting that payment to the to the customer. So really creating a very positive customer experience. >>So it speaking of customer experiences, what have you know? You said you thought you were in service to your shareholders. What have been some of the results that you've seen? >>So you have to look across the organization, right? So our advisers served the need on the retail side, so we were like a bee to be business, right? So we have to be cognizant of what's going on in their world. They're sitting down with clients and talking through the choices, and they have certain needs what they need to fulfill their obligations. They need to explain why they're doing what they're doing. If Putnam knows where each of the advisers are at in their journey with their clients, we can be more helpful to them in explaining why our funds are behaving the way they are right, that information can be had at the right time at the right moment when they need it. Need it, And that brings advisers closer to our our teams are retail distribution teams are marketing teams are investment teams are investment professionals, are using data and analytics to get information to. We're using technology to get information to them faster, so companies are doing releases. There's a ton of information out there these days. We're using technology to dig deeper into the press releases as well as the SEC filings, looking at the footnotes, really trying to understand what they're trying to say, what they said before and what are analysts should be focused on. And we can take a 70 page document, condense it to seven pages and pinpoint what the technology tools say's are really insights. And the analysts will take the time and read the whole thing. But they'll also look at the insides and they'll add it into their process. So technology's additive to the investment process and really making a change help and then that's helping Dr performance. So at the end of the day, we're living good performance on our funds through data analytics technology, you know, give you another example. Some off the were were very strong in the in the mortgage analytics business and on the fixed income side. Our team's very well known. They've been together for many, many years now. They're starting to use data at scale, and we found that being able to go to the cloud to do these analytics right in hours instead of days has really made a material difference in the number of iterations we can run. So now the questions are, when we do risk management, can we do that a little differently and run more reiterations and get more accuracy? So we're seeing all of that benefit. That's direct user experience, that people are seeing people seeing how technology is helping them do a better job with their thesis. >>Excellent. Thank you so much for coming on. The Cube seem ed knitting and shale. A pleasure having you on. >>Thank you for being here. >>I'm Rebecca night. Stay tuned for more of the cubes. Live coverage of the Ex Center Executive Summit coming up in just a little bit

>>Locke from Las Vegas. It's the cube covering KWS executive sub brought to you by extension. >>Welcome back everyone to the cubes live coverage of the Accenture executive summit here at AWS reinvent. I'm your host, Rebecca Knight. We have three guests for this segment. We have Dan Sheeran, the director of global accounts at AWS. Thank you so much for coming on the show. We have Ken Schwartz, vice president, enterprise analytics at health first. Welcome Ken and shale Jane lead data business group in North America. Accenture. Thank you so much. I am glad to have you all here. Good to be here. Yes. So we're talking today about driving digital transformation via data and analytics. I'm going to start with, you can tell us our viewers a little bit about health first as a business. >>Sure. Health first is the largest not-for-profit health plan in New York city. It's a 26 year old company. It's owned by 15 sponsor hospitals. So the business model is a little different than most health plans. The sponsor hospitals who own us, we actually share risk with the sponsor hospitals. So if our members obtain their medical services at sponsor hospitals, we have the same goal of keeping them out of the hospital essentially. And we, the revenue stays within the health healthcare delivery system. So it's a little bit different business model. We've been very successful. We're very local plan, so we have a big footprint in the communities, the very diverse communities in New York city. We're kind of part of the fabric of New York city and that's really very much part of our brand. >>So your patient population is mostly, I mean who, who, who are cuckoo prizes? >>1.4 million members, 1.4 million people mostly in New York city. So we like to say if you ride the subway in New York city, it's very likely that one in eight people are health first members, a one in three if you're in the Bronx, mostly underserved populations in a lot of cases. And people that really, like I said, sort of the, the real fabric of communities in the city. >>So what were the reasons that health works? Health first embarked on this data transformation. >>Really just again, a 26 year old company kind of outgrowing its infrastructure and really wanting to make sure that we can keep up with growth. We've been lucky to grow steadily over our entire history and at a certain point in time the legacy systems and legacy data systems don't support the new ways to do things. Prescriptive, predictive analytics, some of the great new capabilities that you can do in the cloud. So it became really important to get off the legacy hardware, get off the legacy approaches and big people change management to make that happen. I mean that's kind of what we've been living for about the last three years. >>So what were some of the goals? >>The goals are just to be able to do things at scale for in the legacy systems. I think we really didn't support analytics across our entire membership and our entire 30 million claims a year. 1.4 million members, 37,000 providers. So just being able initially just being able to query and do sort of business intelligence at scale across that, that much data, the old infrastructure just didn't support it from there. We've gone into launching our data science platform and things like that. So like I said, just, just being able to keep up with the times and provide more information, get to know everything we can possibly know about our members so that we can reach out to them in better and more effective ways. >>So shale, I want to bring you in here a little bit. How was, how did Accenture partner with health first and helping it achieve this goal? >>Yeah, so, um, we work with companies like health verse all the time and you almost have to embark on a journey that starts with a concept, almost the imagination, if you will. And then you take it into a test mode, the pilot mode in the scale up mode. And we were fortunate enough to actually to be involved in, in the journey that health first has had all throughout that, those stages, if you will. Um, and it's been, it's been a very rewarding experience because health first is one of those companies that actually took a very early lead on moving to the cloud, moving to the new data architectures and actually trying new technologies such as we recently finished a, uh, a knowledge graph project with them as well, which is relatively new in this space. So it's been a rewarding experience for us as well. >>So what are kind of, what are some of the challenges that you faced along this journey? Organization of lead technically and how did you overcome them? >>I think early on it's, it's whole new roles and new new technical paths that just didn't exist at the company. So Accenture being partner, good support from AWS really helped us. So we didn't have machine learning engineers and data engineers and cloud practitioners. So you don't grow that overnight. So having professionals come on graph as well. We oftentimes you start off with the use case and you have somebody just download things and get going. Right. And that's great, but that doesn't really land it. So getting professionals who have done things in the new environments on board to help us out was, was really key in the challenges side. I really think the people change management can be really hard. Again, if you're a sort of a brand new company or startup and you're just, you have to do your business on the cloud and it's dependent on that from day one. >>It's a lot different than we have a lot of people. Our company has been successful for 26 years. We have to look to the future to make these changes, but we've been doing pretty well sort of on our legacy platforms and things like that. So it's not always easy to just get people to change streams and say like, Hey, you really should be be doing this differently. So I think the people change management realizing you have to kind of sometimes lead with use cases, lead with pilots, lead people by the hands to get from point a to point B was kind of surprising. But we've, we've learned that that's true. >>So Dan, he you had a nice shout out from Ken here by giving you some prompts buddy in the U S and what you bring to the value you bring to the table. What do you, what do you make of what he said about the people change and how that is in a lot of ways the hardest >>couldn't agree more. In fact, that was the first point that Andy Jesse led off with this morning in his keynote that it's any of these projects, if you don't start with leadership that is both committed to the change and coordinated among themselves, then you've got no chance of success. Now that's, that's a necessary condition. It's not sufficient. You do need to drive that change through the organization and this, the scenario that Ken described is very common in what we see in that you start with enthusiasts typically that will, we often call builders who are going to be at a department who are playing around with tools because one of the advantages of course of AWS is it's all self-serve. You can get started very easily create your own account. But it is tricky to make sure that before that gets too far along that an enterprise wide architecture and strategy is agreed upon or else you can get sort of half pregnant with an approach that really is not going to serve the longterm objectives. And that's the reason why working with Accenture, getting the reference architecture for a data Lake really agreed on early on in this project was essential and that's what allowed once that foundation was in place. All these other benefits to accrue pretty quickly. >>So on a project like this, how closely are you all working together in teams to get the job done? I mean, and what is the collaboration, what is the process and what does it look like? >>Well, you know, I'm sure that each of us is going to have an answer to that, but our perspective on that at AWS is to always be customer led. We have some customers who themselves want to use a journey like this to become a builder organization. And one of their strategic objectives is that their developers are the ones who are really at the controls longterm building out a lot of new features. We have other customers who really want to be principally buyers. They'll have some enthusiasts here and there in their organization, but they really want to principally define the objectives, participate in the architecture, but then really lean on somebody like an Accenture to implement it >>and to also stand behind it afterwards. So in this case, Accenture played a central role, but we really think that the very first meeting needs to be sit down and listen to what the customer wants. Yeah. I'd say we're builders but with guidance that against them we want people who have, who have hit their heads on things and kind of learn from that and that's, that can be a force multiplier instead of having, and we definitely jumped into use cases that we wanted to just build. Like I said in a year later, we're a little bit spinning our wheels. It's not really hurting anything cause it's not necessarily anything anybody else's for anyway is standing up a graph database. It's just something we wanted to do. Right. So having these guys come in as force multiplier has been really useful. So we reach out to AWS, have really good support from AWS when we need it. AWS also has great online training, the loft in lower Manhattan or in Soho we go to things as well so we can help ourselves. And the next venture is just really been embedded with us too. We have seven or eight data engineers that have really walked pretty much every mile with us so far on this journey. So >>yeah, the only thing I would, I would add to it is that, you know, we have a very strong relationship with AWS and as such we become privy to a lot of the things that are coming down the pike, if you will. So that can add value. At the same time, we have very good access to some of the top technologists within AWS as well, so we can bring that to bear so that that all kind of works really well together. Having a partnership with AWS and then with our, we have different parts of the organization. They can also bring not just the technology skills but also domain skills as well. So we can add to some of the thinking behind the use cases as well. So that's another part of the collaboration that happens including in the security model. Right. And if we don't have that right from the beginning, then very true. Nothing else becomes possible. And there's a lot of domain expertise within Accenture. It helps us scale. >>One of the things that we, that I've heard a lot today at the Accenture executive summit is this idea of thinking differently about failure. And this is an idea that's in Silicon Valley, failed, fail better, fail happier, fail up all these things. Fail fast. Exactly. But all of them do. How do you, how but how does a co does a nonprofit in New York city, how does it embrace that? I mean, as we've talked about a lot here just now is the people are, are the hardest part that then that's a really different mindset in a really big change for an organization like health first. >>But the, the, the business model of working with AWS to is pay as you go and everything. It's like failing cheapest, very possible. You know, we're not putting out huge upfront costs to turn something on. We can turn it on for pennies sometimes and do a use case. So it really does support experimentation. We've been, one of our successes I think is we really just try a lot of things. So we've, we've had to learn how to do that and learn how to sort of either pull in more experienced people to help us or just just cut it off kind of in some cases. So yeah, the cloud patterns and AWS is business model just makes it really easy. >>And it's also key of course, to have some quick wins that are highly visible. So to my understanding that in the case of health first there was, you know, whether it's reimbursement claims or there's potential fraud that can be detected, that is a lot easier to start doing once you got your data into a common data Lake and you've got world-class analytics tools that are available directly to the business analysts. Instead of requiring lots of hand holding and passing datasets around, when you get those initial quick wins that builds the kind of enthusiasm that allows you to then take this from being a project that people are skeptical about to people really seeing the value >>and people get excited about it too. So talk about some of the benefits that your members have seen from this. >>Sure. So again, we have 1.4 million members. So just something pretty simple. Every health plan wants to prevent readmissions. So someone's been in the hospital and then they have to go right back with the same condition. That's bad for the member or bad for the plan. Bad for everybody, right? So just just being able to take a data science model on our own data, train it up for predicting readmissions. Again, we have large care management community. Many nurses go out in the field every day and meet members, but now that we can give them a list of the 500 most important members and it's also self-service, it's, it's in a dashboard that's running in red shift and people can go and just get their lists. I mean that's really profoundly satisfying and important to change our members health outcomes. You know, that's only one example. That was kind of the first model we've built, but we have models for people being adherent to their medication. Just a lot of things that we can do. Targeted interventions instead of kind of having a bunch of business rules. Kind of in your head of who you think you should reach out to. This is the data's telling us who's most at risk and sometimes empowering the call center personnel >>when you can give them access to data that allows them to really personalize that, that phone call experience with somebody. It's a, it's a relatively low cost way to surprise and delight the patient or the health plan member. And that then drives customer satisfaction scores, which are very important in the healthcare industry for all sorts of reasons related to accreditation are related to reimbursement. And also frankly just related to enrollment and retention. >>I speak from experience when I say the best, the companies are the ones with the good call centers that you just are happy and you get off the phone, you don't want to slam it down, you're, you're happy to talk to them. So final pieces of advice for companies that are, that are trying to drive change through data analytics. What, what is a best practice? Best piece of advice? Well, because you looked at me, I'll let you go first. >>Um, we always, it sounds obvious, but it's surprisingly often not the case. Once you get past the initial five minutes of a conversation, really stress are we actually focused on a real problem as opposed to something that sounds cool or fun to go experiment with. Because these tools, as Ken said, these are, it's fun to play with these self-service AI tools. You can predict all sorts of things. Isn't an actual pain point for either an internal customer or an external customer. >>Yeah, I think you hit it on the head as well. That's advice to starting this as get, get some wins, get some early wins and then don't be afraid to experiment and don't be afraid to think outside the box. I think I would say there are two pieces of advice. One is focused on strategy like Dan was talking about before, because with tools like AWS where you can literally use your credit card to get started, you can lose sight of the big picture. So have a data strategy that is directly tied to your business strategy is very important. And the second is instead of thinking about building a data pipeline for a specific use case, think about building a platform, a data platform that can serve the need of today and tomorrow as well in a, in an architecture that is, that is fit for purpose architecture like Andy Jesse talked about today. So don't go for a Swiss army knife approach. Go for fit for purpose platforms, products, models, if you will, that can allow you to build that platform that can serve the need of the future as well. >>Excellent. Thank you so much shale. Ken and Dan, thanks for coming on the cube. Thank you. Thanks. Thank you. I'm Rebecca Knight. Stay tuned for more of the cubes live coverage of the Accenture executive summit.