Predictive and prescriptive insights driven by data analytics have risen to prominence as tools that can help research teams cut the time, complexity, and cost of clinical trials. At the same time, these insights can enhance the quality of a study and accelerate new drugs to market. But to uncover these insights, we need to rethink how data management and research get done.

While clinical trials used to center around the electronic capture of data, the introduction of decentralized clinical trials (DCTs) allowed researchers to gather data from more sources in real time. Approximately 1,300 DCTs will kick off in 2022, a 28 percent increase from last year, and a 93 percent increase over 2020. Data management used to be enough. But with more data coming from more sources, faster, in more formats, and with varying levels of quality, clinicians now face an imperative to use data science to parse and organize the information for better insights and outcomes.

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