>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a cube conversation. >> Everyone welcome to this cube conversation here in the Palo Alto cube studios. I'm John Furrier your host of theCUBE, with a great guest here, Shanthi Vigneshwaran, who is with the Office of Strategic programs in the Center for Drug Evaluation and Research within the US Food and Drug Administration, FDA, is the Informatica Intelligent Disrupter of the Year award. Congratulations, Shanthi welcome to this cube conversation. Thanks for joining me. >> Thank you for having me. >> Congratulations on being the Informatica Intelligent Disrupter of the year award. Tell us more about the organization. I see FDA everyone's probably concerned these days making sure things 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 and buy it in the pharmacy today, Our administration helps in trying to approve them and make sure it's so in term of 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 CDERs public health mission. >> What are some of the objectives that you guys have? What are some things you guys have as your core top objectives of the CDER, the drug research group? >> The core objectives is we wanted to make sure that 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 are effective. And also the quality of the drugs that are being marketed are able to protect 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 that 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 challenges in three different areas. One is the drug regulation challenges because as drugs are being more advanced and as there are more increasingly complex products, and there are challenging in the development area of the drugs, we wanted to make sure here we have a regulation that supports any advancement in science and technology. The other thing is also Congress is actually given new authorities and roles for the FDA to act. For example the Drug Quality and Security Act, which means any drug that's they want to track and trace all the drugs that goes to the public is they know 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 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 there are 300 US ports. And we want to make sure the FDA regulated shipments are coming through correctly to proper venues and everything is done correctly. Those are some the challenges we have to deal with. >> So much going on a lot of moving purchase 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, 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 could almost just my mind's blown just thinking about how you live it every day. Can you just share some of those challenges that you had to address and how did you do? >> Some of the key challenges we actually see is we have roughly 170,000 regulatory submissions per year. There are roughly 88,000 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 as such as us we need it, we have multiple systems where this data is acquired and each has its own criteria for validating the data. Adding to it are internal and external stakeholders also want certain rules and the way the data is being identified. So we wanted to make sure there is a robust MDM framework to make sure to cleanse and enrich and standardize the data. So that it basically make sure the trust and the availability and the consistent of the data, is being supplied to published to the CDER regulatory data users. >> You guys are dealing with- >> Otherwise like 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 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 a 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 the impacts everyday life. I got to ask you that the database question 'cause the database geek inside of me is just going okay. I can only imagine the silos and the different systems and the codes, because data silos is big document. 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 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. What we did was we did leave it more of us I mean, step back like seven years ago, when we did the data management. We had like a lot of silo systems as well. And we wanted to look at we wanted to establish a, we knew we wanted to establish a master data management. So we took a little bit more of a strategic vision. And so what we ended up saying is 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 and then we looked at 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, hey, this makes sense. This data is good. So that's what we ended up looking at. We looked at it as at both ends. One is from a end user perspective. 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, where there's duplication that we can consolidate. >> So that's good. You did the work up front. 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 other automation process that we actually put into work with Informatica, that actually helped us to make sure it's the cost of it actually went for us considerably. For example it used to take us three days to process submissions. Now it takes us less than 24 hours to do it, for the users to see the data. So it was a little bit more, we saw the, we wanted to look at what are the low hanging fruits where it's labor intensive and how can we improve it. That's how we acted there. >> What are some of the things that you're experiencing? I mean, like, we look back at what it was before, where it is now? Is it more agility, you more responsive to the changes? Was it an aspirin? Was it a complete transformation? Was some pain reduced? Can you share just some color commentary on kind of before the way it was before and then 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 have 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. for example, if there is a drug shortage during due to hurricane, with the data we have, we can narrow down and say, 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 then before the old model, there was either a blind spot or you were ad hoc, probably right? Probably didn't have that with you. >> Yeah, before you were either blind or you're doing in a more of a reactionary not proactively. Now we are able to do a little bit more proactively. And even with 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 linking the pre and post market phases that lets us know if there's a trigger and the adverse events, we actually can go back to the pre market side and see where the traceability is who's at that truck. What are all the different things that was going on. >> This is one of the common threats 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're saying, and 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 and you starting to see new kinds of data whereas has come out. So again, starting to see a real new changeover to scaling up this kind of concept almost foundationally. What's your thoughts just as someone who's 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 haven't success. How do you scale it? What how do you guys look at that? >> I think our next is we have the domains and we actually have the practices that we work. We look at it as it's basically data always just changes. So we look at is 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 power. They are also warehouse data lakes. So we want to see is how can we take these domains and get that relationship or get that linkages when there is 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 and real. This is what clouds all about the data clouds here. It's scaling. Super fun to talk about and excited. When you see the impacts in real time, not waiting for later. So congratulations. You guys have been selected and you receive recognition from Informatica as the 2020, Intelligent Disrupter 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 when we embarked on this work, like seven years ago, or so, problem was like we were trying to identify and develop new scientific methods to improve the quality of our drugs 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 helps us to support the informed decisions that we have to make in real time sometimes or and also to make sure 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 helps the teams. It recognizes all the hard work. And the hours we put into establishing the program and it helped to build the awareness within FDA and also with the industry of our political 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 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 days 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 have a great example of this. What advice would you have for other practitioners because you got data scientists, but yet data engineers now who are trying to architect and create scale, and programmability, and automation, and you got the scientists in the the front lines coming together and they all feed into applications. So it's kind of a new things go on. Your advice to folks out there, on how to do this, how to do it right, the learnings, share. >> I think the key thing I, at least for my learning experience was, it's not within one year you're going 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 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, hy, 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 is it. 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. That's great the insight. And again, it's never done. I mean, it's data is 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. >> Shanthi thank you so much for the insight. Congratulations on your receiving the Disrupter of the Year Award winner for Informatica. congratulations. Intelligence >> Yeah, thank you very much for having me. Thank you. >> Thank you for sharing, Shanthi Vigneshswaran 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 for theCUBE. Thanks for watching. (soft music)