 Welcome to today's session of theCUBE's presentation of the AWS Startup Showcase, the next big thing in AI, security and life sciences. Today we're joined by Abacus Insights for the Life Sciences Track. I'm your host, Natalie Erlich. Now we're going to be speaking about creating an innovation enabling data environment to accelerate your healthcare analytics journey. And we're now joined by our guests, Krishna Kottapoli, Chief Commercial Officer, as well as Sumanth Rao, Chief Product Officer, both working at Abacus Insights. Thank you very much for joining us. Thank you for having us. Well, let's kick off with our theme, Krishna. How can we create innovation enabling data environments in order to facilitate the healthcare analytics? Yeah, so I think if you sort of think about this, there is a lot of data proliferating inside the healthcare system, and whether it's through the internal sources, external sources, devices, patient monitoring platforms and so on. And all of these essentially carry, have useful data and intelligence, right? And essentially the users are looking to get insights out of it to solve problems. And we're also seeing that the journey that our clients are going through is actually a transformation journey. So they are thinking about how do we seamlessly interact with our stakeholders, with their stakeholders being members and providers so that they don't get frustrated and feel like they're interacting with multiple parts of the health plan, right? Typically when you call into a health plan, you feel like you're calling five different departments. So they want to have a seamless experience. And finally, I think the whole, the data being in the ecosystem between the patients, payers and providers, being able to operate and interact has intelligence. So what we think about this is, how do we take all of this and help our clients digitize their path forward and create a way to deliver, enable them to do meaningful analytics? Well, Sumat, when you think about your customers, what are the key benefits that advocates is providing? So that's a good question. So primarily speaking, we approach this as a framework that drives innovation that enables data and analytics. I mean, that's really what we're trying to do here. What advocates does though is, this is slightly different is how we think about this. So we firmly believe that data and analytics is not a linear journey. I mean, you cannot say that, oh, I'll build my data foundation first and then have the data and then they shall come. That's not how it works. So for us, the way advocates approaches this is, we focus really heavily on the data foundation part of it first. But along the way in the process, a big part of our value statement is, we engage and make sure we're driving business value throughout this piece. So general message is, make sure innovation for the sake of innovation data is not how you're approaching this, but think about your business users, get them engaged, have it small, milestone-driven progress that you make along the way. So generally speaking, we're not trying to be just a platform who moves bits and bytes of information. The way we think about this is, we'll help you along this journey. There's steps that happen that take you there. And because of veg, the message to most of our customers is you focus on your core confidence. You know your business, you have nuances in the data. You have nuances on needs that your customers need. You focus on that. The scale that Abacus brings, because this is what we do day in, day out, is more a long area of reusability. So if within our customers, they've got data assets, how do we reuse some of that? How does Abacus reuse the fact that because of what we do, we actually have data assets that we can bring data to life quickly. So general guidelines, right? So first is don't innovate for the sake of innovating. I mean, that's not gonna get you far. Respect the process that this is not a linear path. There's always value that's happening throughout the process. And that's, you know, Abacus will work closely with you to make sure you recognize that value. The second part is within your organization, you have assets. There's like major data assets, there's IP, there's things that can leverage that Abacus will do. And because we are a platform, what we focus on is configurability. We've done this for, I mean, a lot of us on the Abacus team come from healthcare space. We have got big bear DNA. We get this. And what we also know is data rules change. I mean, you know, it's really hard when you build a system that's tightly built and you cannot change and you cannot adapt as data rules change. So we've made that part of it easier. We have, we understand data governance. So we work closely with our peers, data governance teams to make sure that part of it happens. And I think the last part of this, which is really important in the context of this conversation is all of this is good stuff. I mean, you've got massive data foundation. You've got healthcare expertise flowing in. You've got partnerships with data governance. All that is great. If you don't have best in class infrastructure supporting all of that, then you will really have issues scaling. I mean, that's just the way it works. And this is why, you know, we're built on the AWS stack which kind of helps us and also helps our clients along with their cloud journey. So it's kind of an interesting set of events in terms of, you know, again, I'm going to repeat this because it's important that don't innovate for the sake of innovating, reuse your assets, leverage your existing IP, make things configurable, data changes and then leverage best in class infrastructure. So Avicus tries to address this across those four dimensions. I mean, that's an excellent point about healthcare data being really nuanced. And, you know, Krishna, I would love to get your insights on what you see are the biggest opportunities in healthcare analytics now. Yeah. So the biggest opportunities are, you know, there are two, we think about it in two dimensions, right? One is really around sort of the analytics use cases. And second is around the operational use cases, right? So if you think about a pair, they're trying to solve both and we see because of rather, you know, our, the way we think about data which is close to near real time, we are able to essentially serve up our clients with, you know, helping them solve both the use cases. So think of this that you are a patient, you go to, you know, you go to a CVS to do something and then you go to your doctor's office through something, right? To be able to take a test. If all of these are known to your pair, care management team, if you will, in close to near real time, they know, right? Where you've been, what you can do, how to be able to sort of intervene and so on and so forth. So from a next best action and operational use cases, we see a lot of them emerging, you know, thanks to the cloud as well as thanks to infrastructure which can do sort of near real time. So that's our own sort of operational use cases, if you will. In, when you think about the analytics, right? So, you know, every, all pairs struggle with this, which is you have limited dollars to be able to intervene with, you know, a large set of population, right? So every piece of data that you know about your patient, about the specific provider, so on and so forth, is able to actually, you know, give you analytics to be able to intervene or engage, if you will, with the patient in a very one to one manner. And what we find is at the end of the day, if the patient is not engaged in this, that the member or the patient is not engaged, you know, in the healthcare, you know, value chain, if you will, then your dollars go to the waste. And we feel that essentially both of these type of use cases can be sort of really well with the unified data platform, as well as with upstack analytics. And now, Siman, I'd love to hear from you, you know, you're really involved with the product. How do you see the competitive landscape? How do you make sure that your product is the best out there? So I think a lot of that is we think about ourselves across three vectors. Talk about it as core platform, which is at a very minimal level of description, it's really moving bits and bytes from point A to point B. That's one part of it, right? And I think there's a, it's a pretty crowded space. There's a whole bunch of folks out there trying to, you know, demonstrate that they can successfully land data from one point to the other. We do that too. We do that at scale. Where you'd start differentiating and pulling away from the pack is the second vector, which is enrichment. Now, this is where, again, it's you have to understand healthcare data to build a level of respect for how messy it can get. And you have to understand it and build it in a way where it's easy to keep up with the changes. We spend a lot of time, you know, building out our platform to do that so that we can implement data quickly. I mean, you know, for advocates to bring a data source to life in less than 45 days is pretty straightforward. And it's, you were talking on an average six to 12 months across the rest, because we get this, we've got a library of rules, we understand how to bring this piece. So we start pulling away from the competitors, if you may, more along the enrichment vector, because that's where we think getting high quality rules, getting these reused, all of this is part of it. But then we bring another level of enrichment where we have, you know, we use public data sets. We use reference data sets. We tie this, we fill in the blanks in the data. All of this is the end state. Let's make the data shall be ready for analytics. So we do all of that along the way. So now applying our expertise, cleansing data, making sure it's the gaps are all filled out and getting this ready. And then comes the next part where we tie this data out, because it's one thing to bring in multiple sources quickly at scale, high speed and all that good stuff, which is hard work, but you know, it's expected now. At the same time, how do you put all that together in a meaningful manner with which we can actually, you know, land it and keep it ready. So that's two parts. So first is the platform, the nuts and bolts, the pipes, all that is good stuff. The second is the enrichment. The third side, which is really where we start differentiating this distribution. We have a philosophy that, you know, the mission of the whole company was to get data available to solve use cases like the one Krishna just talked about. So rather than make this a massive change management program that takes five years to implement and really scares your end users away, our philosophy is like, let's have incremental use case all along the way, but let's talk to the users. Let them interact with the data as easy as they can. So we built our partnerships on our distribution hub, which makes us easy. So an example is if you have someone in the marketing team who really wants to analyze a particular population to reach out to them, and all they know is Tableau, this is great. It should be as simple as saying, look, what's this liver of data you need to get your job done? How do you interact? So we've, our distribution hub is really the part where users come in, interact with the data with, you know, we will meet you where you are, is the underlying principle. And that's how it operates. So, so I think on the first level on platform, yeah, crowded space, everyone's fighting for that piece. The second part of it is enrichment where we really start pulling away using expertise. And then at the end of it, you've got the distribution part where, you know, we just want to make it available to users. And a lot of work's gone and getting this done, but that's how we work. And if I could add a couple more things, so the other thing is security, right? So the reason that healthcare, healthcare players have not gone to the cloud until about three, four years back is the whole concern about security. So we have invested a ton of resources and money to make sure that a platform is running the most secure manner and giving confidence to our clients. And it's an expensive process, right? Even though you're on AWS, you have to have your own certification that. So that gives us a huge, you know, differentiator. And the last but not least is how we actually approach the whole data management deployment process, which is our clients think about as in two dimensions, total cost of ownership, but typically 50 to 60% of what it would cost internally and secondly, time to value. That you can't have an infinitely long deployment cycle. So we think about those two and actually put our skin in the game and tie our success to total cost of ownership and time to value. Well, just really quick and one to two sentences would love to get your insight on Abix's defining contribution to the future of cloud scale. Go next one. So as I see it, I think, so part of it is we've got some of our clients who appears and we've got them along their cloud journey, trusting one of their key assets, which is data and letting us drive it. And this is really driven by domain expertise, a good understanding of data governance and a great understanding of security. I mean, combining all of this, we've actually got our clients sitting and operating on, you know, pretty significant cloud infrastructure successfully day in, day out. So I think we've done our part as far as, you know, helping folks along that journey. Yeah, and just to close it out, I would say it is a speed, right? It is speed to deployment. You don't have to wait. We have set up the infrastructure, set up the cloud and the ability to get things up and running is literally we think about it in weeks and not months. Terrific. Well, thank you both very much for insights. Fantastic to have you on the show. Really fascinating to hear about how Abicus is leveraging healthcare data expertise on its platform to drive robust analytics. And of course here we were joined by Abicus Insights Krishna Kotapalli, the Chief Commercial Officer, as well as Sumant Rao, the Chief Product Officer. Thank you again very much for your insights on this program and this session of the AWS Startup Showcase.