Developing A Data Strategy For Clinical Trials

By Hugh Donovan, Managing Expert, Advarra Clinical Center of Excellence

Data is the most important asset that a pharmaceutical or biotechnology company has, and the number and complexity of sources of data in clinical trials is increasing all the time; at the same time, there is increasing pressure to deliver high quality data with accelerated timelines. Therefore, it is critical to have a central, cross-functional data strategy that:

• Facilitates data standardization, leading to increased efficiency throughout clinical trial execution, from setup to reporting
• Documents policies in a central location to ensure consistent and rapid decision making
• Documents how regulations are interpreted to ensure a consistent approach to compliance
• Identifies approved vendors and the process for approving new vendors to facilitate data integration
• Establishes a mechanism for evaluating and implementing new processes and technologies, ensuring a standard and cost-effective approach with appropriate oversight and cross-functional input
• Measures the effectiveness of the data management processes and systems through key performance indicators and establishes productivity and quality targets

Many, if not all, of these elements may exist within an organization but their location and knowledge of their existence may not be known, leading to inefficiencies, inconsistencies, and in some cases, duplication of effort.

According to a recent Tufts survey1, only one-third of such companies have a formal data strategy, whereas 40% of companies are considering or planning to have such a strategy. So, the industry recognizes the importance of having a data strategy, but implementation is lagging. One of the reasons is that there is no “recipe book” for developing a strategy covering how clinical trial data is to be collected, stored and managed. Developing a definitive, one-size-fits-all plan is not possible, given the large differences in each situation and set of requirements across biopharmaceutical companies.

This white paper describes a standardized approach to developing a data strategy that can be customized to a company’s current needs and can be adjusted as those needs change.