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Enterprise Data Strategies for Life Sciences with Datavant’s Su Huang


In this episode of Real World Talk, Zoe Li welcomes Su Huang, the Head of Data Strategy at Datavant. Su and Zoe discuss the problem of siloed clinical research data at large life science companies and the efficiencies that can be gained by implementing an enterprise data strategy.



  • [00:57] Introduction — In this episode of Real World Talk, our host Zoe Li welcomes Su Huang, the Head of Data Strategy at Datavant, the leading health data connectivity platform for both compliant de-identified and identified data. 
  • [03:43] What is enterprise data? — According to Su, enterprise data is any data that can be used across various use cases to fulfill a specific company’s needs. For such use, it is essential to look at the flexibility, breadth, and representativeness of data, as well as proper consent and usage rights so that data can be utilized effectively. 
  • [09:12] What is data strategy? — As Su explains, data strategy should support a company’s overall business strategy. A company must look at both the internal and external data it generates, and the technical and analytical aspects of working with that data. 
  • [12:42] Teams using data should work together — Various teams in, for instance, life science companies use data. However, they rarely work together. The revealing moment for them occurs when a company such as Datavant comes talking about linking internal data, tokenization of data — all these processes that will allow different teams to use the data available for other use cases.
  • [16:12] Compliance and data governance are critical aspects of data strategies — Whether we talk about first-party or third-party data, once we decide to work with a specific data set, we need to ensure that either data is certified (third-party data) or that we have patient consent (first-party data). Whoever uses the data must ensure that the information available has depth and breadth but doesn’t allow for re-identification of the person. 
  • [19:38] Enterprise data helps achieve health equity — According to Su, enterprise data and strategy allow for companies to link patient data and demographic data through platforms such as Datavant, enabling them to identify underserved populations and, in the case of pharmaceutical companies, make a particular drug accessible to these groups.
  • [22:35] We can expect pharma companies to become more data-driven  —  Although companies rely on data to some extent, we still have a long way ahead of us before companies, especially those in life sciences, look at real-world data as the core of their business.

Key Points

  • Use first-party data like you use third-party data. Most companies rely on data vendors when conducting a specific study, with a focus on third-party data. However, as Su says, first-party data is as valuable as the external data, and organizations, including pharma companies, could benefit from combining these two data types. ”When we think about data, we think about externally available data from data vendors, such as COTA. But life science companies and all healthcare organizations have a ton of proprietary internal data, which we call first-party data. […] All of those datasets can oftentimes sit in silos, managed by different teams. And when I think about enterprise data, I think about how we can link and connect those disparate data sets so that the best insights can be unlocked, even from the internal data you’re generating.”
  • The data strategy should support your business strategy. Su suggests that companies should first define a business strategy and then build a data strategy to upgrade operations. ”Your enterprise data strategy should support your overall business strategy. And once you have your business strategy defined, you can gauge what data you will need to generate insights that support the execution of that business strategy. So, understanding the landscape of available third-party data assets out there and taking stock of the internal data assets you have [is important]. There needs to be a comprehensive approach to first-party and third-party data. And then you can think about the technical environment where you want to house all of these different data assets.”
  • The future lies in data. Su says a growing number of life science companies will become data-driven, employing data specialists and working with data to make their services more effective and affordable. ”I think that pharma companies will increasingly become data and analytics companies in the future. I foresee data as becoming more of a defined vertical at life science organizations. […] I think we can see clinical trials become faster and less costly through greater adoption of real-world evidence. We’ll know the safety and efficacy of newly launched therapeutics in real-time by following the clinical trial patients, even after the trial has ended, through their real-world data. I think we’ll have more pharma companies participate in value-based care discussions with payers because they will have access to data. Ultimately, this will bring about more efficiency and transparency to drug pricing, which will make drugs more accessible for everyone.”