By Hugh Donovan, Managing Expert, Advarra Clinical Center of Excellence
Data stands as the most invaluable asset for pharmaceutical and biotechnology firms. The landscape of clinical trials continually evolves, presenting an ever-increasing array of data sources and complexity. Concurrently, the industry faces mounting pressure to deliver high-quality data within condensed timelines. Thus, the imperative lies in establishing a centralized, cross-functional data strategy that accomplishes the following:
- 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
In many organizations, these elements may already exist, but their location and awareness might be lacking, leading to inefficiencies, inconsistencies, and even duplicated efforts. A recent Tufts survey reveals that only one-third of such companies have a formal data strategy, while 40% are contemplating or planning to implement one. While the industry recognizes the importance of a data strategy, implementation lags behind. A primary reason is the absence of a definitive "recipe book" for crafting a strategy that encompasses data collection, storage, and management in clinical trials. Given the substantial variations in requirements across biopharmaceutical companies, a one-size-fits-all approach isn't feasible.
This white paper outlines a standardized method for developing a data strategy that can be tailored to a company's current needs and adapted as those needs evolve.