 Live from the MIT campus in Cambridge, Massachusetts, it's theCUBE covering the 12th annual MIT Chief Data Officer and Information Quality Symposium, brought to you by SiliconANGLE Media. Welcome back to theCUBE's coverage of MIT CDOIQ here in Cambridge, Massachusetts. I'm your host, Rebecca Knight, along with my co-host, Peter Burris. We're welcoming back Dr. Santagari, who is the Vice President and Chief Data Officer of ERT. Thanks for coming back on the program. Thank you very much. So in our first interview, we talked about the why and the what, and now we're really going to focus on how, the how. How, what are the kinds of imperatives that ERT needs to build into its platform to accomplish the goals that we talked about earlier? Yeah, that's a great question. So that's where our data and technology pieces come in. We are, as we were talking about in our first session, that the complexity of clinical trials. So in our platform, we are just drowning in data, because the data is coming from everywhere. There are real-time data, there is unstructured data, there is binary data, such as image data, and they normally don't fit in one data store. They're like different types of data. So what we have come up with is a unique way to really gather that data real-time in a data lake. And we implemented that platform on Amazon Web Services Cloud, and that has the ability to ingest, as well as integrate data of any volume, of any type coming to us at any velocity. So it's a unique platform, and it is already live. Press release came out early part of June, and we are very excited about that. And you know, it is commercial right now. So, yeah. But you're more than just a platform, you're providing services on top of that platform. One might say that the services, in many respects, are what you're really providing to the customers, the services that the platform provides. Have I got that right? Yes, yes. So platform, like you build different kinds of services, we call it data products on top of that platform. So one of the data products is business intelligence, where you do real-time decisioning. Another product is RBM, the risk-based monitoring, where you come up with all the risks that a clinical trial may be facing, and really expose those risks preemptively. So give us some examples. Examples will be like patient visit, for example. Patient may be non-compliant with the protocol. So if that happens, then FDA is not going to like it. So before they get there, our platform almost warns the sponsors that, hey, there's something going on. Can you take preemptive actions? Instead of just waiting for the 11th hour and only to find out that you have really missed out on some major things. There's just one example. Another could be data quality issues, right? So let's say there's a gap in data or inconsistent data, or the data is not statistically significant. So you have to raise some of these with the sponsors so that they can start gathering data that makes sense. Because at the end of the day, data quality is vital for the approval of the drug. If the quality of the data that you are collecting is not good, then what good is the drug? So that also suggests that data governance has got to be a major feature of some of the services associated with the platform. Yes, data governance is key. Because that's where you get to know who owns which data. How do you really maintain the quality of data over time? So we use both tools, technologies, and processes to really govern the data. And as I was telling you in our session one, that we have the custodian of this data. So we have fiduciary responsibility in some sense to really make sure that the data is ingested properly, gathered properly, integrated properly. And then we make it available real time for real time decision making. So that our customers can really make the right decisions based on the right information. So data governance is key. One of the things that I believe about medical profession is that it's always been at the vanguard of ethics, social ethics, and increasingly, well, there's always been a correspondence between social ethics and business ethics. Ideally, they're very closely aligned. Are you finding that the medical ethics, social medical ethics of privacy, how you handle data, are starting to inform a broader understanding of the issues of privacy, ethical use of data? And how are you guys pushing that envelope if you think that that is an important feature? Yeah, that's a great question. We use all these, but we have data security in place in our platform, right? And the data security in our case plays at multiple level. We don't like commingle one sponsors data with others. So they're always like verticalized. We partition the data in technical sense. And then we have permissions and roles. So they will see what they're supposed to be seeing, not like, you know, depending on the roles. So yeah, data security is very critical to what we do. We also de-anonymize the data. We don't really store the PII, like personally identifiable information as well, like email address or first name or last name, you know, or social security number for that matter. We don't, when you do analysis, we de-identify the data. Are you working with, say, European pharmaceuticals as well, Bayer and others? Yeah, we have, like, as I said, like... So you have GDPR issues that you have done as well? We have GDPR issues. We have, like, HIPAA issues. So you name it. So data privacy, data security, data protection, they're all a part of what we do. And that's why technology is one piece that we do very well. Another pieces are the compliance, science, because you need all of those three in order to be really, you know, trustworthy to your ultimate customers. And in our case, they are pharmaceutical companies, medical device companies, and biotechnology companies. Where there are lives at stake. Exactly. So I know you have worked, Santi, in a number of different industries. I'd love to get your thoughts on what differentiates ERT from your competitors, and then more broadly, what will separate the winners from the losers in this area? Yeah, yeah, obviously. Before joining ERT, I was the head of data engineering at eBay. And who? So that's the, you know, the bidding platform. And so obviously we were dealing with consumer data, right? So we were applying artificial intelligence, machine learning, and predictive analytics, all kinds of things to drive the business. In this case, while we are still doing predictive analytics, but the idea of predictive analytics is very different. Because in our case, here at ERT, we can't recommend anything, because they are all like, we can't say, hey, don't take aspirin, take Tylenol, we can't do that. It needs to be driven by doctors. Whereas at eBay, we were just talking to the end consumers here, and we would just predict. Again, that different ethical considerations. Exactly. But in our domain primarily, like ERT, ERT is the best of breed in terms of what we do, driving clinical trials, and helping our customers. And the things that we do best are those three areas, like data collection, obviously the data custodiancy, that includes privacy, security, you name it. Another thing we do very well is real time decision. So with that allow our customers, in this case, pharmaceutical companies, who will have this integrated data set in one place, almost like a cockpit, where they can see which data is where, where the risks are, how to mitigate those risks. Because remember that these trials are happening globally. So your sites, some sites are here, some sites are in India, who knows where. So the mission control is so critical. Critical, time critical. And as well as cost effective as well, because if you can mitigate those risks before they become problems, you save not only cost, but you shorten the timeline of the study itself. So your time to market, you know? You reduce that time to market so that you can go to market faster. And you mentioned that it's can be as long as, that could be, the process can be a $3 billion process. So reducing time to market could be a billion dollars of cost and a few billion dollars of revenue because you get your product out before anybody else. Exactly. Plus you are helping your end goals, which is to help the ultimate patients. Right? Because if you could bring the drug five years earlier, then what you had ended for, then you know, you would save lots of, lots of life there. So the one question I have is, we've talked a lot about these various elements. We haven't once mentioned master data management. Yes. So give us a little sense of the role that master data management plays within ERT and how you see it changing. Because it used to be a very metadata, technical oriented thing and it's becoming much more something that is almost a reflection of the degree to which an institution has taken up the role that data plays within decision making and operations. Exactly, great question. The master data management has like people, process and technology, all three, they co-mingle each other to drive master data management. So it's not just about technology. So in our case, our master data is for example, site or customers or vendors or study. They're master data because they live in each system. Now, definition of those entities and semantics of those entities are different in each system. Now, in our platform, when we bring data together from disparate systems, somehow we need to harmonize these master entities. That's where master data management has to be. While complying with regulatory and ethical requirements. Exactly. So customers for example, no birdies let's say or pick any other name can be spared 20 different ways in 20 different systems. But when we are bringing the data together into our core platform, we want no birdies to be spared only one way. So that's how you maintain the data quality of those master entities. And then obviously we have the technology side of things. We have master data management tools. We have data governance that is allowing data qualities to be established over time. And then that is also allowing us to really help our ultimate customers who are also seeing the high quality data set. Because that's the end goal where they can trust the number. And that's the main purpose of our integrated platform that we have just launched on AWS. Trust is just, it's been such a recurring theme in our conversation. The immense trust that the pharmaceutical companies are putting in you, the trust that the patients are putting in the pharmaceutical companies to build and manufacture these drugs. How do you build trust, particularly in this environment? I mean, we've talked on the main stage, they were talking this morning about how just this very notion of data as an asset, it really requires buy-in, but also trust in that fact. Yeah. Yeah, it's a trust is a two-way street, right? Because it has always been. So our customers trust us, we trust them. And the way you build the trust is through showing, not through talking, right? So as I said, in 2017 alone, 60% of the FDA approval went through our platform. So that says something. So customers are seeing the results. So they're seeing their drugs are getting approved. We are helping them with compliance, with audits, with science, obviously with tools and technologies. So that's how you build trust over time. And we have been around since 1977. That helps as well because it's a true and tried methods. We know the procedures. We know the water, as they say. And obviously, folks like us, we know the modern tools and technologies to expedite the clinical trials to really gain efficiency within the process itself. I'll just add one thing to that. Yeah. And trust and test you on this. Trust is a social asset. Yeah. At the end of the day, it's a social asset. And I think we're a lot of people in the technology industry, continuously forget, is that they think that trust is about your hardware, or it's about something in your infrastructure, or even your applications. You can say you have a trusted asset, but if your customer says you don't, or a partner says you don't, or some group of your employees say you don't, you don't have a trusted asset. Exactly. It's, trust is where the technological, the process, and the people really come together, that's the test of whether or not you've really got something that people want. Yes. And your results will show that, right? Because at the end of the day, your ultimate test is the results, right? And because that everything hinges on that. And then the experience helps as you're experienced with tools and technologies, science, regulators, because it's a multidimensional Venn diagram almost. And we're very good at that. And we have been for the past 50 years. Right. Thank you so much for coming on the program again. It was really fun talking to you. Thank you. I'm Rebecca Knight for Peter Boris. We will have more from MIT CDO IQ in just a little bit.