From Data Management to Data Excellence

Life Sciences organizations have, for many years, considered the ability to generate insights from high quality data as a source of competitive advantage that allows them to bring drugs to market efficiently and safely. However, this has, traditionally, been a very expensive and time-consuming endeavour.

Large Language Models’ abilities to interpret and summarize large datasets is game-changing not only for consumers of data, but also for data providers.  Regardless of the analytical methods, the foundational blocks remain the same: high quality, authoritative data from credible sources is key to credible, actionable insights that lead to reproducible results in an observable way. Building analytical solutions on unreliable data increases the risk of reaching inaccurate conclusions and being misled to make wrong or unfounded decisions.

In this whitepaper, we discuss a framework for Data Excellence, built on four key principles that must underpin a wider strategic and operational model:

• Robust sourcing practices to ensure that data is obtained from credible sources and that it maintains a verifiable lineage across its value chain
• Syntactic and semantic harmonization to ensure that analytical solutions make consistent use of the entire corpus
• Openness that allows all stakeholders to discover and use data throughout your organization to maximize reach and value creation
• Lifecycle management practices to ensure timely access to the most up-to-date data and removal of obsolete or inaccurate datasets, while maintaining version history and auditability