 From the CUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hi everyone, welcome to this CUBE Conversation here in the Palo Alto CUBE Studios. I'm John Furrier, your host of theCUBE. We've got a great guest here, Jean-Pierre Vigneshwaran, who's with the Office of Strategic Programs in the Center for Drug Evaluation and Research within the U.S. Food and Drug Administration. FDA is the Informatica Intelligent Disruptor of the Year Award. Congratulations, something welcome to this CUBE Conversation, thanks for joining me. Thank you for having me. Congratulations on being the Informatica Intelligent Disruptor of the Year Award. Tell us more about the organization. Obviously, the FDA, everyone's probably concerned these days, making sure everything's going faster and faster, more complex, more things are happening. Tell us about your organization and what you work on. FDA is huge. Our organization is Center for Drug Evaluation Research and its core mission is to promote public health by ensuring the availability of safety and effective drugs. For example, any drugs you go into, go and buy it in the pharmacy today, our organization helps in trying to approve them and make sure it's, so it's having a quality and integrity of the marketed products in the industry. My office is specifically Office of Strategic Programs whose mission is to transform the drug regulatory operations with the customer focus through analytics and informatics. They work towards the advancement for the CDER's public health mission. What are some of the objectives that you guys have? What are some of the things you guys have as your core top objectives of the CDER, the drug research group? The core objectives is like, we wanted to make sure like we are promoting a safe use of the marketed drugs. We want to make sure there's the availability of the drugs that are going to the patients or effective and also the quality of the drugs that are being marketed are able to protect the public health. What are some of the challenges that you guys have to take in managing the pharmaceutical safety because I can only imagine, certainly now the supply chains, tracing, monitoring, drug efficacy, safety, all these things are happening. What are some of the challenges in doing all this? In our office, there are like challenges in three different areas. One is the drug regulation challenges because as drugs are being more advanced and like as there are more increasingly complex products and there are like challenging in the development area of the drugs, we wanted to make sure like we have a regulation that supports the innovation advancement in science and technology. The other thing is also like Congress is actually given like new authorities and roles for the FDA to act. For example, the Drug Quality and Security Act, which means any drug that's like, they want to trace track and trace all the drugs that goes to the public is like, they know like who are the distributors, who are the manufacturers. Then you have the 21st Century Cures Act and also the CARES Act package, which was recently assigned, which also has a lot of the OTC drug regulatory modernization. Then there's also the area of globalization because just as disease don't have any borders, product safety and quality are no longer like on one country. It's basically a lot of the drugs that are being manufactured are overseas. And as a result, we wanted to make sure like there are like 300 US ports and we want to make sure like the FDA-regulated shipments are coming through correctly through proper venues and everything is done correctly. Those are like some of the challenges we have to deal with. So much going on, a lot of moving parts, as people say, there's always drug shortages, always demand, knowing that and tracking it. I can only imagine the world you're living in because you got to be innovative, you got to be fast, got to be cutting edge, got to get the quality right. Data is super critical. And can you share, take a minute to explain some of the data challenges you have to address and how you did that? Because I mean, I can almost just, my mind's blown just thinking about it. You live it every day. Can you just share some of those challenges you had to address and how did you do that? Some of the key challenges we actually see is like we have roughly like 170,000 regulatory submissions per year. There are like roughly 80,000 firm registration and product listing that comes to us and then there are more than 2 million adverse event reports. So with all these data submissions and organization such as us, we need it. We have multiple systems where these data is acquired and each has its own criteria for validating the data. Adding to it our internal and external stakeholders also want certain rules in the way the data is being identified. So we wanted to make sure like there's robust MDM framework to make sure to cleanse and enrich and standardize the data so that it basically makes sure like the trust and the availability and the consistent of the data is being supplied to our publish to the CETA regulatory data users. You guys are dealing with this. It's almost to give them a 360 degree view of the drug development lifecycle through each of the different phases, both pre-market, which is like before the drug hits the market and then after it hits the market, we still want to make sure the data we receive still supports the regulatory review and decision-making process. Yeah, and you got to deliver a consumer product to get people at the right time. All these things have to happen. And you can see it clearly in the impacts every day life. I got to ask you that the database question because you know, the database geek inside of me is just going, okay, I can only imagine the silos and the different systems and the codes because you know, data silos, it's been documented. We've been reporting on this on theCUBE for a long time around making data available, automation. All these things have to happen if there's data availability. Can you just take one more minute to talk about some of the challenges there because you got to break down the silos at the same time, you really can't replace them. That's true. But what we did was we did a little bit more of a, I mean, step back like seven years ago when we did the data management, we had like a lot of silos systems as well. And we wanted to look at like, we wanted to establish a, we knew we wanted to establish a master data management. So we took a little bit more of strategic vision. And so what we ended up saying is like, identifying what are the key areas of the domain that will give us some kind of a relationship. What are the key areas that will give us the 360 degree lifecycle? So that's what we did. We identified the domains. And then we took a step back and said like, then we looked at like, what is the first domain we wanted to tackle? Because we know what are these domains are going to be. And then we were like, okay, let's take a step back and say, which is the domain we do it first that will give us the most return on investment which will make people actually look at it and say like, hey, this makes sense. This data is good. So that's what we ended up looking at. And both ends, one is from a end user perspectives, like which is the one they get the benefit out of and also from a data silo perspective, which is the one data domains that are common, like where there's duplication that we can consolidate. So that's good. You did the work upfront. That's critical knowing what you want to do and get out of it. What were some of the benefits you guys got out of it from an IT standpoint? How does that translate to the business benefits? And what was achieved? I think the benefits we got from the IT standpoint was a lot of the deduplication was not theirs, which basically means like a lot of the legacy systems and all of the manual data quality work we had to do, we automated it. We had bots. We also had like other automation process that we actually put into work with Informatica that actually helped us to make sure the cost of it actually went for us considerably. For example, like it used to take us three days to process submissions. Now it takes us less than like 24 hours to do it for the users to see the data. So it was kind of like a little bit more, we saw the, we wanted to look at like what are the low hanging fruits where it's kind of like labor intensive and how can we improve it? That's how we tackled it. What are some of the things that you're experiencing? I mean, like when you look back at what it was before kind of where it is now, is it more agility? Are you more responsive to the changes? Was it an aspirin? Was it a complete transformation? Was some pain reduced? Can you share just kind of just some color commentary on kind of before the way it was before and then kind of what you're experiencing now. So for us, I think before we didn't know where the, for us, I mean, I wouldn't say we didn't know it when we had the data. We looked at product and it was just product. We looked at manufactured, they were all in separate silos. But when we did the MDM domain, we were able to look at the relationship. And it was very interesting to see the relationship because we now are able to say is like, for example, if there is a drug shortage during due to hurricane, with the data we have, we can narrow down and say like, hey, this area is going to be affected, which means these are the manufacturing facilities in that area that are going to be not be able to function or impacted by it. We can get to the place where the hurricane tracks. We use the national weather service data, but it helps us to narrow down some of the challenges and we can able to predict where the next risk is going to be. And before the old model, there was either a blind spot or you kind of were ad hoc probably, right? Probably didn't have that with you. Yeah, before you were either blind spot or you're doing it more of a reactionary, not proactively. Now we are able to do a little bit more proactively. And even with drugs, I mean drug shortages and drug supply chain are the biggest benefit we saw with this model because for us, the drug supply chain means like linking the pre and post market spaces that kind of lets us know, like if there's a trigger in the adverse events, we actually can go back to the pre market side and see where the traceability is. Who's touched that drug? What are all the different things that was going on? You know, this is one of the common threads I see in innovation where people look at the business model and data and look at it as a competitive advantage. In this case, proactivity on using data to make decisions before things happen, less reactivity. So that increases time. I mean, that would probably, you mean you're saying it, you get there faster, if you can see it, understand it and impact the workflows involved. This is a major part of the data innovation that's going on. You're starting to see new kinds of data warehouses come out. So again, they're starting to see a real new change over to scaling up this kind of concept almost foundationally. What's your thoughts? Just as someone a practitioner in the industry, as you start to get this kind of feelings and seeing the benefits, what's next? What do you see happening? Because you having success, how do you scale it? What do you, how do you guys look at that? I think our next is like, we have the domains and we actually have the practices that we work. We look at it as a, it's basically data always changes. So we look at it as like, what are some of the ways that we can improve the data? How can we take it to the next level? Because now they talk about cloud, they are also data warehouse data lakes. So we want to see is like, how can we take these domains and get that relationship or get that linkages when there's a bigger set of data that's available for us? What can we use that? And it actually, we think there are other use cases we wanted to explore and see what is the benefit that we can get a little bit more on the predictability to do like post-market surveillance or like to look at like safety signals and other things to see what are the quick things that we can use for the business operations. It's really a lot more fun. You're in there using the data, you're seeing the benefits in real. This is what cloud's all about. The data cloud's here, it's scaling. Super fun to talk about and exciting when you see the impacts in real time, not waiting for it later. So congratulations. You guys have been selected and you receive recognition from Informatica as the 2020 intelligent disruptor of the year. Congratulations. What does that mean for your organization? I think we were super excited about it. But one thing I can say is like, when we embarked on this work like seven years ago, our sole problem was like we were trying to identify and develop new scientific methods to improve the quality of our drags to get that 360 degree view of the drug development lifecycle. The program today enables FDA CDER to capture all the granular details of data we need for the regulatory data. It kind of helps us to support the informed decisions that we have to make in real time sometimes or like and also to make sure like when there's an emergency we are able to respond with a quick look at the data to say like, Hey, this is what we need to do. It also, it's kind of like helps the teams recognize it's all the hard work and the hours we put into establishing the program, it helped to build the awareness within FDA and also with the industry of how critical master data management is. It's a great reward to see the fruits of the labor and good decision making. I'm sure it was a lot of hard work. For folks out there that are watching who are also kind of grinding away in some cases, some cases moving faster, you guys are epitome of a supply chain that's super critical and speed is critical, quality is critical, a lot of data is critical. A lot of businesses are starting to feel this as part of an integrated data strategy and I'm a big proponent. I think you guys have a great example of this. What advice would you have for other practitioners because you've got data scientists but you have data engineers now who are trying to architect and create scale and programmability and automation and you got the scientists in the front lines kind of coming together and they all feed into applications. So it's kind of a new things going on. Your advice to folks out there on how to do this, how to do it right, learnings, share. I think the key thing, at least for my learning experience was it's not like within one year you're going to have to accomplish it. It's kind of, we have to be very patient and it's a long road. If you make mistakes, you will have to go back and reassess, even with us, with all the work we did. We almost went back to a couple of the domains because we thought like, hey, there are additional use cases how this can be helpful. There are additional, for example, we went with the supply chain but then now we go back and look at it and say like, hey, there may be other things that we can use with the supply chain not just with this data, can we expand it? How can we look at the study data or other information? So that's what we try to do. It's not like you're done with MDM and that said your domain is complete. It's almost like you look at it and it creates a web and you need to look at each domain and you want to come back to it and see how it is you have to go. But the starting point is you need to establish what are your key domains? That will actually drive your vision for the next four or five years. You can't just do bottom up. It's more of like a top-down approach. That's great insight. And again, it's never done. I mean, it's data's coming. It's not going away. It's going to be integrated. It's going to be shared. You got to scale it up. A lot of hard work. Yeah. Shanti, thank you so much for the insight. Congratulations on your receiving the Disruptor of the Year Award winner for Informatica. Congratulations. Intelligent Disruptor. Yeah, thank you very much for having me. Thank you. Thank you for sharing. Shanti Vigneshwaran is here, Office of Strategic Programs at the Center for Drug Evaluation and Research with the US FDA. Thanks for joining us. I'm John Furrier with theCUBE. Thanks for watching.