FDA Tackles Training, Recruiting in Next Phase of Data Strategy

FDA Tackles Training, Recruiting in Next Phase of Data Strategy

CDO Ram Iyer noted FDA’s data progress preparing for emerging technologies since the launch of its roadmap in March 2021.

The Food and Drug Administration is breaking down silos, spearheading new data sharing efforts and upskilling its workforce under its data strategy to turn data into a “strategic asset” and drive new efficiencies.

“We need to address gaps in our data capabilities. We need to break the silos. We need to scale capabilities where there’s an opportunity to build economies of scale. We need to streamline operations. Then we need to coordinate emerging practices such as [artificial intelligence, machine learning] and blockchain,” said FDA Chief Data Officer Ram Iyer during AFCEA Bethesda’s webinar Tuesday. “Our goal is to act as a connective tissue and as an orchestrator.”

In March 2021, FDA released its Data Modernization Action Plan (DMAP), its strategic roadmap for data modernization that builds on its September 2019 Technology Modernization Action Plan (TMAP). 

As Iyer told GovernmentCIO Media & Research earlier this year, the TMAP provided a technological foundation for the development of FDA’s Data Strategy, and the DMAP will realize the strategy with immediate and longer-term actions.

Since March, FDA has continued to expand its data efforts to break down silos across its internal centers, Iyer said. With nine centers, FDA recognized that its structure created multiple individual data silos and “cross-dependency,” which was only emphasized with the COVID-19 pandemic. 

“We may be very good at tracking the ventilator supply chain, but if we don’t have the drugs that go with the lung medication along with the ventilator supply — which the data is siloed in two different centers — we won’t be able to really help the patients and public health,” Iyer said. 

From FDA’s structure and processes, data sharing was slower than it needed to be to respond to public health needs. To solve this challenge, FDA honed in on data management and modernization to drive new business value. 

FDA’s data strategy revolves around three pillars: driver projects, consistent and repeatable data practices, and fostering a talent network both within the agency and with external partners. These components will create new value, find gaps and drive talent. 

“We are not starting from zero. Some of the centers have done really great work. We’re going to look at what is working across the various centers, then we’re going to scale the one that seems most mature across the agency,” Iyer said. 

FDA is developing a robust talent strategy to attract, train and retain talent in the emerging technologies field. The agency is also creating a collaborative framework to bring in new internal and external partnerships that will build a data and analytics ecosystem. 

“All of these become critical components of how we set up the DMAP, and we have now just launched the group that will form the implementation work streams as well as a standing committee that was just formed in April 2021,” Iyer said. 

One of the ways FDA is fostering new relationships is by bringing in Presidential Innovation Fellows, or PIFs, from the General Services Administration to form an elite team that supports modernization priorities. From a talent perspective, Iyer said that new relationships will help break down silos and spark culture change. 

“Those will be the key parts for us to be successful. It’s not the technology, it’s not the raw data. It’s about bringing all of these things together,” Iyer added. 

FDA is also upskilling its workforce to support its data initiatives and create a data literate culture by using a 70-20-10 approach to training, Iyer said. This method deems that 70% of training will be project-based learning, 20% will be mentoring and 10% will come from classical learning. 

“If you just start with lots of training, it’s easy to roll out, but it really doesn’t provide a whole lot of tangible value,” Iyer said. “We’re trying to see if we can reverse that model.”

 
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