 From around the globe, it's theCUBE with digital coverage of smart data marketplaces brought to you by Io Tahoe. We're back, we're talking about smart data and have been for several weeks now. Really, it's all about injecting intelligence and automation into the data lifecycle and the data pipeline. And today we're drilling into smart data marketplaces, really trying to get to that self-serve, unified, trusted, secured and compliant data models. And this is not trivial. And with me to talk about some of the nuances involved in actually getting there and with folks that have experienced doing that. Vade Sen is here, he's the digital evangelist with Tata Consultancy Services, TCS. And A.J. Vahora is back, he's the CEO of Io Tahoe. Guys, great to see you. Thanks so much for coming on. Good to see you, Dave. Hi, Dave. Nice to meet you. A.J., let's start with you. Let's set up the sort of smart data concept. What's that all about? What's your perspective? Yeah, so, I mean, our way of thinking about this is you've got data, it has latent value. And it's really about discovering what the properties are of that data. Does it have value? Can you put that data to work? And the way we go about that with algorithms and machine learning to generate signals in that data identified patterns, that means we can start to discover how can we apply that data downstream? What value can we unlock for a customer and business? Well, so you've been on this, I mean, you're really like a laser. Why, I mean, why this issue? Did you see a gap in the marketplace in terms of talking to customers? And maybe you can help us understand the origin. Yeah, I think as the gap has always been there, there's become more apparent over recent times with big data. So the ability to manually work with volumes of data in petabytes is prohibitively complex and expensive. So you need a different route, you know, a different set of tools and methods to do that. Metadata, a data that you can understand about data. That's what we at Itaha focus on discovering and generating that metadata that really then allows you to automate those data ops processes. So the gap David is being felt by business enterprises in all sectors, healthcare, telecoms in putting their data to work. So, Vade, let's talk a little bit about your role. You work with a lot of customers. I see you as an individual who's a company who's really trying to transform what is a very challenging industry that's sort of ripe for transformation. But maybe you could give us your perspective on this. What kind of signals you're looking for from the data pipeline and we'll get into how you're helping transform healthcare. Thanks, David. You know, I think this year has been one of those years where we've all realized about this idea of unknown unknowns where something comes around the corner that you're completely not expecting. And that's really hard to plan for, obviously. And I think what we need is the ability to find the early signals and be able to act on things as soon as you can. Sometimes, and you know, the COVID-19 scenario, of course is hopefully once in a generation thing, but most businesses struggle with the idea that they may have the data there in their systems, but they still don't know which bit of that is really valuable and what other signals they should be watching for. And I think the interesting thing here is the ability for us to extract from a mass of data the most critical and important signals. And I think that's where we want to focus on. And so, talk a little bit about healthcare in particular and sort of your role there and maybe at a high level how Tata and your ecosystem are helping transform healthcare. So, if you look at healthcare, you've got the bit where people need active intervention from a medical professional. And then you've got this larger body of people, typically elderly people who aren't unwell, but they have frailties, they have underlying conditions and they're very vulnerable, especially in the world that we are in now in the post COVID-19 scenario. And what we are trying to look at is how do we keep people who are elderly, frail and vulnerable, how do we keep them safe in their own homes rather than move into care homes where there has been an incredibly high level of infection for things like COVID-19. So, the world works better if you can keep people safe in their own homes. If you can see the slide we've got, we're also talking about a world where care is expensive. In most Western countries, especially in Western Europe, the number of elderly people is increasing as a percentage of the population, quite significantly, and resources just are not keeping up. We don't have enough people, we don't have enough funding to look after them effectively. And the care industry that used to do that job has been struggling awfully. So, it's kind of a perfect storm for the need for technology intervention there. And in that space, what we're saying is the data signals that we want to receive are exactly what, as a relative or a son or daughter, you might want from a parent, to say, everything's okay. We know that today's been just like every other day, there are no anomalies in your daily living. If you could get the signals that might tell us that something's wrong, something's not quite right, we don't need very complex diagnostics, we just need to know something's not quite right, that my dad hasn't woken up as always at seven o'clock, but till nine o'clock there's no movement, maybe he's a bit unwell. It's that kind of signal that if we can generate, can make a dramatic difference to how we can look after these people, whether through professional carers or through family members. So, what we're looking to do is to sensor-enable homes of vulnerable people so that those data signals can come through to us in a curated manner, in a way that protects privacy and security of the individual, but gives the right people, which is carers or chosen family members, the access to the signals, which is alerts that might tell you there was too much movement at night or the front door's been left open, things like that, that would give you a reason to call in and check. Everybody I've spoken to in this always has an example of an uncle or a relative or a parent that they've looked after and all they're looking for is a signal. Even stories like my father's neighbor calls me when he doesn't open his curtain by 11 o'clock. That actually, if you think about it as a data signal, that something might not be all right. And I think what we're trying to do with technology is create those kinds of data signals because ultimately the healthcare system works much better if you can prevent rather than cure. So every dollar that you put into prevention saves maybe $3 to $5 downstream so that the economics of it also work in our favor. And those signals give family members the confidence to act. AJ, it's interesting to hear what Vade was talking about in terms of the unknowns because when you think about the early days of the computer industry, there were a lot of knowns. The processes were known. It was like the technology was the big mystery. Now I feel like it's flipped. We've certainly seen that with COVID. The technology is actually quite well understood and quite mature and reliable. One of the examples is automated data discovery which is something that you guys have been focused on at IOTAO. Why is automated data discovery such an important component of a smart data lifecycle? Yeah, I mean, if we look, David, at the schematic and this one moves from left to right where right at the outset with that latent data, the value is latent because you don't know. Does it have, can it be applied? Can that data be put to work or not? And the objective really is about driving some form of exchange or monetization of data. If you think about it in insurance or healthcare, you've got lots of different parties, providers, payers, patients, everybody's looking to make some kind of an exchange of information. The difficulties in all of those organizations, the data sits within its own system. So data discovery, if we drill into the focus of that, it's about understanding which data has value, classifying that data so that it can be applied and being able to tag it so it can then be put to use. It's the real enabler for data ops. So maybe talk a little bit more about this, we're trying to get to self-service. It's something that we hear a lot about. You mentioned putting data to work. It seems to me that if the business can have access to that data and serve themselves, that's a way to put data to work. Do you have thoughts on that? Yeah, I mean, thinking back in terms of what IT and the IT function in a business could provide, there have been limitations around infrastructure, around scaling, around compute. Now that we're in an economy that is digital driven by APIs, your infrastructure, your data, your business rules, your intelligence, your models, all of those are on the back of an API. So the options become limitless, how you can drive value and exchange that data. What that allows us to do is to be more creative if we can understand what data has value for what use case. Fade, let's talk a little bit about the US healthcare system. It's a good use case. I was recently at a chief data officer conference and listening to the CDO of Johns Hopkins talk about the multiple different formats that they had to ingest to create that COVID map. They even had some PDFs. They had different definitions and that sort of underscored to me the state of the US healthcare industry. I'm not as familiar with the UK and Europe generally, but I am familiar with the US healthcare system and the diversity that's there, the duplication of information and the like. Maybe you could sort of summarize your perspectives and give us kind of the before and your vision of the after, if you will. The US, of course, is particularly large and complex system. We all know that. We also know, I think there is some research that suggests that in the US, the per capita spend on healthcare is among the highest in the world. I think it's about in 17% and that compares to about just under 9% which is sort of a European, typical European figure. So it's almost double of that but the outcomes are still vastly poorer. And when Ajay and I were talking earlier, I think we believe that there is a concept of a data friction. When you've got multiple players in an ecosystem trying to provide a single service, as a patient, you are receiving a single healthcare service but there are probably a dozen up to 20 different organizations that have to collaborate to make sure you get that top of the line healthcare service that that kind of investment deserves. And what prevents it from happening very often is what we would call data friction which is the entity organizations to effectively share data. Something as simple as a healthcare record which says, this is Dave, this is Ajay and when we go to a hospital for anything, whatever happens, that healthcare record can capture all the information and tie to us as an individual. And if you go to a different hospital then that record will follow you. This is how you would expect that to be implemented. But I think we're still, on that journey, there are lots and lots of challenges. I've seen anecdotal data around people who suffered because they weren't carrying a card when they went into hospital because that card has the critical elements of data. But in today's world, should you need to carry a piece of paper or can that entire thing be a digital data flow that can easily be, can certainly navigate through lack of paper and those kinds of things. So the vision that I think we need to be looking at is an effective data exchange or marketplace backed with a kind of a backbone model where people agree and sign off for a data standard where each individual's data is always tied to the individual. So if you want to move states, if you want to move providers, change insurance companies, none of that would impact your medical history, your data and the ability of the care and medical professionals to access the data at the point of need and at the point of healthcare delivery. So I think that's the vision we're looking at. But as you rightly said, that there are enormous number of challenges, partly because of the history. Healthcare I think was technology enablement of healthcare started early, so there's a lot of legacy as well. So we shouldn't trivialize the challenges that the industry faces, but that I think is the way we want to go. Well, privacy is obviously a huge one and a lot of the processes are built around non-digital processes and what you're describing is a flip for digital first. I mean, as a consumer, as a patient, I want an app for that. So I can see my own data, I can see price, price transparency, give access to people that I think need it. And that is a daunting task, isn't it? Absolutely. And I think the implicit idea and what you just said, which is very powerful is also on the app, you want the control. Yes. And sometimes you want to be able to change a access of data at that point. Right now, I'm at the hospital, I would like you to access my data. And when I walk away or maybe three days that I want to revoke that access, it's that level of control. And absolutely, it is by no means a trivial problem, but I think that's where you need the data automation tools. If you try to do any of this manually, we'll be here for another decade trying to solve this. But that's where tools like IOTAO come in, because to do this, a lot of the heavy lifting behind the scenes has to be automated. There has to be a machine churning that and presenting the simpler options. And you were talking about it just a little while ago, Ajay. I was reminded of the example of how McDonald's are cooked when because the self-serve idea that you can go in and you can do your own ordering off a menu, or you can go in and select five different flavors from a Coke machine and choose your own particular blend of Coke. It's a very trivial example, but I think that's the word we want to get to with access of data as well. If it was that simple for consumers, for enterprise, business people, for doctors, then that's where we ultimately want to be able to arrive. But of course, to make something very simple for the end user, somebody has to solve for complexity behind the scenes. So Ajay, it seems to me, Ajay, there are two major outcomes here. One is of course, the most important, I guess, is patient outcomes. And the other is cost. I mean, they talked about the cost issues. We all in the US especially understand the concerns about rising cost of health care. My question is, how does a smart data marketplace fit into achieving those two very important outcomes? Well, we think about how automation is enabling that, where we've got different data formats, the manual tasks that are involved, duplication of information, the administrative overhead of that alone, and the work, the rework, and the cycles of work that generates. That's really what we're trying to help with data is to eliminate that wasted effort. And with that wasted effort comes time and money to employ people to work through those silo systems. So getting to the point where there is an exchange in a marketplace, just as they would be for banking or insurance, is really about automating the classification of data to make it available to a system that can pick it up through an API and to run a machine learning model and to manage a workflow, a process. Right, so you mentioned banking insurance, you're right. I mean, we've actually come a long way in just in terms of know the customer and applying that to know the patient would be, we're very powerful. I'm interested in what you guys are doing together just in terms of your vision. Are you going to market together? Kind of what you're seeing in terms of promoting or enabling this self-service, self-care. Maybe you could talk a little bit about Ayo Taho and Tata, the intersection at the customer. Sure, I think we've been really impressed with the TCS vision of 4.0, how they're reimagining traditional industries, whether it's insurance, banking, healthcare, and bringing together automation, agile processes, robotics, AI, and once those enablers, technology enablers are brought together to reimagine how those services can be delivered digitally. All of those are dependent on data. So we see that there's a really good fit here to enable understanding the legacy, the historic situation that is built up over time in an organization, a business, and to help shine a light on what's meaningful in there to migrate to the cloud, or to drive a digital twin data science project. Anything you can add to that? Sure, I mean, we do take the business 4.0 model quite seriously in terms of the lens with which to look at any industry. And what I talked about in healthcare was an example of that. For us, business 4.0 means a few very specific things. The technology that we use in today's work should be agile, automated, intelligent, and cloud-based. These have become kind of hygiene factors now. On top of that, the businesses we built should be mass customized, there should be risk embracing, they should engage ecosystems, and they should strive for exponential value, not 10% growth year on year, but doubling, tripling every three, four years, because that's the competition that most businesses are facing today. And within that, the data group itself is an extremely purpose-driven business. We really believe that we exist to serve communities, not just one specific set, i.e. shareholders, but the broader community in which we live and work. And I think this framework also allows us to apply that to things like healthcare, to education, and to a whole vast range of areas where, everybody has a vision of using data science or doing really clever stuff with algorithms. But what becomes clear is to do any of that, the first thing you need is a foundational piece. And if the foundation isn't right, then no matter how much you invest in the data science tools, you won't get the answers you want. And the work we're doing without our really, for me, is particularly exciting because it sorts out that foundational piece. And at the end of it, to make all of this, again, I will repeat that, to make it simple and easy to use for the end user, whoever that is. And I realize that I'm probably the first person to use fast food as a shiny example for healthcare in this discussion, but you can take a lot of different examples. And today, if you press a button and start a car, that's simplicity, but someone has solved for that. And that's what we want to do with data as well. I mean, that makes a lot of sense to me. I mean, we talk a lot about digital transformation and a digital business, and I would observe that a digital business puts data at the core. And you could certainly, but the best example is, of course Google is an all digital business, but take a company like Amazon, who's got a, obviously a massive physical component to its business, data is at the core. And that's exactly my takeaway from this discussion. It's both of you are talking about putting data at the core, simplifying it, making sure that it's compliant and healthcare, it's taking longer because it's such a high risk industry, but it's clearly happening. COVID, I guess was an accelerant. Guys, AJ, I'll start with you. Any final thoughts that you wanna leave the audience with? Yeah, we're really pleased to be working with TCS. We've been able to explore how we're able to put data to work in a range of different industries. Made as mentioned, healthcare, telecoms, banking and insurance are others. And the same impact needs to be true whenever we see the exciting digital transformations that are being planned. Being able to accelerate those, unlock the value from data is where we're having a purpose. And it's good that we can help patients in healthcare sector, consumers in banking, realize a better experience through having a more joined up marketplace for their data. And Vade, what excites me about this conversation is that as a patient or as a consumer of helping loved ones, I can go to the web and I can search and I can find a myriad of possibilities. What you're envisioning here is really personalizing that with real time data. And that to me is a game changer, your final thoughts. Thanks, David. I absolutely agree with you that the idea of data simplicity and simplicity are absolutely forefront. But I think if you were to design an organization today, you might design it very differently to how most companies today are structured. And maybe Google and Amazon are better examples of that because you almost have to think of a business as having a data engine room at its core. A lot of businesses are trying to get to that stage, whereas what we call digital natives are people who have started life with that premise. So I absolutely agree with you on that. But extending that a little bit, if you think of most industries as ecosystems that have to collaborate, then you've got multiple organizations who will also have to exchange data to achieve some shared outcomes, whether you look at supply chains of automobile manufacturers or insurance companies or healthcare as we've been talking about. So I think that's the next level of change we want to be able to make, which is to be able to do this at scale across organizations at industry level or in population scale for healthcare. Yeah, thank you for that. Go ahead, AJ. David, that's where it comes back to, again, the origination where we've come from in big data, that the volume of data combined with the specificity of individualizing, personalizing a service around an individual amongst that massive data from different providers is where it's exciting that we're able to have an impact. Well, and you know, AJ, I'm glad you brought that up because in the early days of big data, there were only a handful of companies of biggest financial institutions, obviously the internet giants who had all these engineers that were able to take advantage of it. But with companies like Io, Tahoe and others and the investments that the industry has made in terms of providing the tools and simplifying that, especially with machine intelligence and AI and machine learning, these are becoming embedded into the tooling so that everybody can have access to them, small, medium and large companies. That's really to me the exciting part of this new era that we're entering. Yeah, and we're pleased to also take it down to a level of not-for-profits and smaller businesses that want to innovate and leapfrog into growing their digital delivery of their service. And I know a lot of time but, Vade, what you were saying about TCS's responsibility to society, I think is really, really important. Large companies like yours, I believe, and you clearly do as well, have a responsibility to society more than just a profit. And I think, you know, big tech gets a bad rap in a lot of cases. But so thank you for that and thank you gentlemen for this great discussion. Really appreciate it. Thank you. Thank you. All right, keep it right there, but right back, right after this short break, this is Dave Vellante for theCUBE.