3 Keys to Eliminating Data Breakdowns in Life Sciences

Across life sciences, experts boldly predict that data is the new currency—and that mobilizing data from clinical trials, wearable devices and even social media will ignite innovation in 2022. But leveraging data for innovation depends not just on data analytics, but also access to the right data and timely analysis. 

As the amount of data that life sciences experts have at their fingertips explodes, with leaders tapping into real-world evidence from smartphone apps, wearable devices and even Twitter for clinical discovery, more than 66% of health and life sciences leaders say their companies use data to drive innovation with data. Meanwhile, half of life sciences leaders report increased data investments.

Yet even as digital transformation and advanced analytics propel data-fueled discovery—with experts predicting biopharma innovation advanced 10 years during the first 10 months of the pandemic alone—life sciences companies still face challenges capturing and analyzing data in a timely fashion. These data breakdowns hamper research. They also slow down efforts to put new drugs and products into consumers’ hands.

How can life sciences companies eliminate the data breakdowns that stand in the way of more efficient research and discovery? Instead of building and deploying their data analytics platform, life sciences teams could strengthen their ability to compete by focusing on procuring the data they need for research and development. As the volume of data available for real-world analysis continues to surge, life sciences organizations need to know that their data platform can scale as demands for rapid analysis of large quantities of data become more intense. Today, there are software-as-a-service solutions that allow life sciences companies to pay only for the data analytic capabilities they need. For instance, some life sciences organizations rely on cloud-based analytics to automate data lake management. Advancements in data discovery in pharma and biotech are changing lives across the globe, but life sciences companies need an agile approach to data capture, data processing and data analytics to stay ahead of the curve.