Pharmaceutical development generates huge amounts of analytical data and metadata, but volume is not the core problem. The real challenge is data fragmentation. The information needed for confident scientific decisions is spread across disconnected systems. Process details may sit in an electronic laboratory notebook (ELN), analytical results in multiple chromatography data systems (CDS), sample metadata in a laboratory information management system (LIMS), and critical supporting context in spreadsheets, slide decks, PDFs, and emails (Figure 1). By the time teams need clear answers on impurities, routes, or control strategies, the challenge is no longer limited to the science itself. They must also pull together the underlying data and context needed to evaluate it properly.
Excel – Where Data Loses Context & Connection
In many organisations, Microsoft Excel still serves as the practical bridge between disconnected systems. It is familiar, flexible, and easy to share. So, it becomes the place where reaction routes, impurity names, relative retention times, peak areas, registration numbers, and comments are pulled together into one working view. However, this convenience comes with drawbacks. Once data is copied into a spreadsheet, the connection to the original analytical record is lost entirely. If a peak is reprocessed, structure assignment changes, or a name is corrected, the spreadsheet must be manually updated by a project team member. The burden of maintaining accuracy and alignment falls on the scientist.
This challenge is more significant than it first appears. Drug development teams track more than a single compound, including starting materials, intermediates, products, degradants, elemental impurities, and process impurities across multistep pathways, often while also comparing process variants. The dataset grows quickly as does the number of hand-offs between process chemists, analytical chemists, and structure elucidation groups. In this environment, multiple synthetic steps, data sources, and contributors must all be brought together into a form that supports process understanding and better scientific decisions. Progress is slowed not by the lack of data, but by the manual effort required to assemble data and make it interpretable with context.
More Data Is Not the Answer: From Stored Data to Usable Knowledge
That challenge becomes even more significant when viewed through the broader lens of technical development. McKinsey’s perspective on the future of Pharmaceutical Development argues that chemistry, manufacturing and controls (CMC) have become more central to successful process and product development due to shrinking timelines, more complex products, rapid advancement of enabling technologies, and expanding data volumes. Drug substance and drug product teams are being asked to move faster, while also working across broader and more complex technical datasets.
