Define a Clear Data Strategy

Data allows clinical teams to understand the safety and efficacy of important new therapies. Yet in 25 years, data review, analysis and submission preparation have not changed. In fact, these processes are even more complex, as decentralized trials become more widely adopted and as the volume of disparate data that is collected continues to grow.

By championing a data-centric technology approach, biopharmaceutical organizations can simplify data acquisition and analyses. This approach begins with a clear data strategy that defines data requirements and how that information will be presented. Central to an effective data strategy is the right clinical data platform. Importantly, you should look for automated solutions that are interoperable and provide full data lineage and governance.

Once implemented, the platform can streamline all data into a single source of truth, which directly impacts these critical trial metrics and areas in trial management: • Cycle-time reduction: Clinical data technologies have helped combat an upward trend in the last two years, in which trial cycle times have increased by 40 percent. • Improved internal resource utilization: An automated data pipeline reduces the time spent on data cleaning and implementing standardizations. • Data governance, quality and compliance assurances: Defined data management strategies enable quality assessments and ensure data governance and compliance.

The implementation of a data strategy and data-focused technology that centralizes all insights sets the stage for new opportunities to apply AI and machine learning to enhance data review processes. From enabling data managers and medical reviewers to work 4050 percent faster, to highlighting data trends and flagging potential risks, AI models that are supported by a clinical data cloud will help the biopharmaceutical industry take the next leap forward in optimizing clinical trials. How to prepare for a successful clinical data strategy Get the paper