 Hi, this is Yoastaklim Bharti and welcome to another episode of T3M or topic of this month. And the topic of this month is data. And today we have with us once again, Shapsina, co-founder and CEO of Integral Shapsis. Good to have you on the show. Yeah, thank you for having me. Looking forward to chatting again. Yeah, last time we talked about the company and how you are helping some of the industries, especially the health industry, with all the regulations there. But today the focus is going to be on data. And what I want to ask you is that if you look at data from the early days of like, it's not early days, we still have data centers and we are going to be around for a long time. And today's word, a lot of things are cloud centric, Kubernetes centric. How do you have seen the evolution of this data and not the general data, but the data which has to comply with a lot of compliance and there are a lot of regulations around them. Happy to answer that. And just a quick recap of what Integral does. Integral makes it easy to integrate healthcare data where we maximize patient privacy and maximize analytic value because what's the point of analyzing the data if you can't get high quality insights and make high quality decisions. And so to that point, we provide a more flexible solution that still maintains the highest privacy of that which is standard in the industry today. But it also gives you the data flexibility to understand and experiment with different combinations of data sets such that you can truly see, which data sets do I want to combine? What outcomes am I optimizing for? How do I make sure the end patient or the end person has the most positive outcome here? I mean, when I was comparing with the old time versus today, I mean, most of us, we have smartphones and if you just look at Apple or it doesn't matter Android, whatever it is, if you open apps, a lot of times you'll see that apps also have your health access to your healthcare data and these devices are collecting a lot of data. So I also want to understand the evolution data from that perspective where it's not just for certain industries who needed the data and they were complying with all those regulations like HIPAA. But today, I mean, even the Facebook thread app, they have access to my health data and I don't know why. Yeah, and we're seeing that from the perspective of companies wanting to connect all these different data sets, but just more broadly speaking, the reason companies can have that desire and act on it by buying data sets and connecting them is because I think in the past five, 10, even 15 years or so, data is the new oil has become a cliche phrase, but it's very accurate in the sense that there's been massive data collection, maybe even without a necessary reason to. But because the technology was created like sensors that are in your Apple Watch or that in your phone or survey data online that you fill out and that is then aggregated somewhere, all of this data collection was seen as a viable business strategy just to accrue these large pools of data and use that as a moat for some sort of end business decision making. But what we're seeing this on our side with integral is that the culmination is connect all of these publicly available or at least commercially available if you buy the licenses and whatnot. Connect all these different data sets in order to have a holistic analysis of people like you and me such that we can be served better ads or such that we can find more medications available such that we can even be a recipient of like a study coming up. And so all this to say, I've noticed that data collection was done for the sake of itself for a long time with some sort of business value potentially attached to it, but now a lot of the business value is coming from these large pools of data that are naturally available because of the past 10, 15 years or so. Companies are becoming a lot wiser to saying, hey, we should connect all of this because it's already here and it doesn't cost that much to buy. What are the risks that you see are there when almost everybody is accessing these data? So let's talk about the risks and then we'll talk about how integral is kind of helping mitigate at least some of those. Yeah, yeah, for sure. Some of the risks that we see are one, just privacy invasion at a level that is uncomfortable such as seeing a first name and then seeing a potential disease that that person has right next to that first name. I mean, that's as raw and as naked of information as you can get. But even one level above that, there's plenty of companies who focus on data obfuscation today. And that's a good effort in the right direction of protecting privacy. The problem is when you're obfuscating data, you tend to think, oh, I'm obfuscating the data. I can include more and more of the data because I'm going to somehow mask it. But take a look at me, for example, I live in New York and Manhattan specifically in Chelsea and I would say I have a relatively unique name. I work in a certain area, I live in a certain area, I go to certain bars, I go to certain restaurants, I have certain medications. If you obfuscate some of my data, that will help. But if you line up enough of these attributes next to each other, you can still violate my privacy, even though you don't read, like Shub has diabetes, for example, you may not read that in the data set. But if you get enough data points about me, you can have this kind of like secondary analysis that leads to privacy invasion and multiply that on the scale of hundreds of millions because that's what these tech platforms or these just overall technology platforms can offer that level of analysis, even though it's an effort in the right direction. And so all this to say, you can have like, you know, the most raw privacy breaches, you can also have secondary privacy breaches where information points can add up to people at scale, which may not be as obvious, but it's still possible that technology today. And so to the second point you mentioned, integral, integral one, like our highest priority is to ensure that no naked or raw information gets transmitted over the wire. But even more so than that, we acknowledge that companies have good intentions in wanting to use data for analysis. And then we also realize companies are today, they're unfortunately saddled with solutions that make privacy a secondary priority, even though it's a first priority for them, like they have to wait for consultants to say, like in six weeks, they have to wait for consultants to say, hey, this is okay to use, whereas six weeks is a lot in the business world, right? And so all this to say like how integral is balancing all this or helping balance all of this is we provide real time privacy feasibility next to your data analysis goals such that if you're a data scientist, you don't have to wait six weeks to find out if you can act on the insight in your head and have to sacrifice speed or business value. We match them together in a tech platform such that you can see very clearly, hey, is this query going to reveal too many sensitive populations? If so, in a number of minutes, I can understand this rather than having to wait weeks. What kind of adoption are you seeing of integral and what kind of industries we can talk about? What has been a priority for us from day one is to specialize in the healthcare industry, particularly because of my experience. And I also think data analysis and healthcare is more than just give them the right ad. If data analysis is done right, it leads to a medication being delivered in a way to the person who needs it the most. And so we're seeing adoption across technology companies and pharma companies specifically today. And so large top 20 pharma companies as well as public tech companies and all the way down to small startups. And what we're noticing is that everybody is using us to definitely make sure the compliance is up to par. But they're using us for what I mentioned earlier where they're balancing speed, accuracy, and privacy together. And what's I think another gem of what we've discovered is that oftentimes these companies that we're working with separately are also working together to analyze data and engage in joint workflows. So a pharma company might work with a tech company and then they engage together using integral. And so we're able to power that data collaboration at scale through privacy automation through compliance automation. And so all this to say, when it comes to producing the right data set as quickly as possible in the most privacy compliant way possible, the rails of doing that are powered by integral. And we're noticing cross industry collaboration there where that's I think what excites me the most about integral. When you work with, you know, some of these players there, do you feel that as you're saying that they have to wait for the consultant, they do they have any processes where they get things audited? Is it like later stage or early stage where it's more or less like as you're talking about in the cloud world that, hey, security is no longer an afterthought security is no longer someone else's problem. It is kind of moving a developer's pipeline. You start writing the application with security in mind. When it comes to data, where are you seeing how further we are when it comes to ensuring once again, integrity, privacy, security of the data, or is once again, hey, let's roll the app. We'll talk about those things later on. And some cases if you're not, you don't have to comply with some regulations. You don't even care. What do you see there? Yeah, and I like to work top down in that sense. So one, just as a whole at the US policy level where at some level what integral is doing is automating policy enforcement to a degree. US policy is taking a lot, a lot bigger, I would say spotlight to privacy and specifically patient privacy and healthcare patient privacy in the financial industry, etc. And so that is like a just a trend that has emerged in the past 10 to 15 years. And I think it was catalyzed by COVID, especially because of all the digital care being delivered. And as a result of all the information being transmitted over the wire. And so then you work one level down to these companies that we're working with. So top 20 pharma all the way down to a small startup. In the healthcare industry, I think compliance and privacy go hand in hand with data. And that I think has always been true, but the and there's always been tension there because compliance wants to move slowly and meticulously, whereas data analysis rewards experimentation and iteration. And so you have some natural tension there. But I think that tension was blown up massively by all the adoption of all these cloud technologies and all these different players coming up like Smith, Flake, AWS and whatnot because you then get the ability and the flexibility to work with data however you want whenever you want and share data however you want and whenever you want. But again, that compliance still is like the leading force of can I do this? When can I do this? How can I do this? And so all this to say what I'm noticing is that if you kind of treat data or if you treat compliance as an afterthought, you don't get much done in the healthcare industry. And so they have to go hand in hand. And the status quo has been working with HIPAA consultants who maybe more so prioritize the slow and meticulous nature of what I just mentioned. But with integral, what I like to think is that you don't have to sacrifice meticulous. We're still meticulous. We have the best privacy algorithms by some of the best privacy experts in healthcare. And we put that into a very easily deployable and usable software. And so from that perspective, we don't make security an afterthought. We more so make it as like a co-pilot in a way or compliance becomes a co-pilot where it's writing in lockstep or shotgun with your data analysis. And so all this to say, I think data wants to move at the speed of light compliance today cannot, but we make sure that compliance can reach parity with the speed that data analysis occurs at. Because today there is a delta and it's a point of significant tension, but we aim to reduce that tension to zero. And I think that's where the best outcomes happen, where you're allowed to do the thing you do. And then you get good learnings or good insights from the thing you wanted to do. Shubh, thank you so much for joining me again today. And of course, talk about this topic. And I would love to chat with you again. Thank you. Yeah. Thank you for having me. I really enjoyed the discussion.