Recent insights from TetraScience chromatography experts Tony Edge and George Van Den Driessche in R&D World illuminate a critical challenge facing the industry: transforming scientific data into the foundation for deep analytics and Scientific AI.
Chromatography generates roughly half of all analytical data in biopharmaceutical organizations. Yet despite this terabyte-scale data generation, most organizations struggle to extract meaningful insights beyond individual method results. As Anthony Edge, Ph.D., former president of the UK Chromatographic Society and scientific business analyst at TetraScience, observes, chromatography data systems excel at displaying immediate data, but they are "not so brilliant at looking across lots of different systems."
At TetraScience, we've calculated the true cost of chromatography data silos. Organizations waste approximately $20 million per year per 200 scientists on data preparation tasks instead of scientific analysis, with scientists spending 50% of their time preparing data rather than generating insights. An additional $4.8 million annually per 200 scientists disappears through time spent searching for data across fragmented systems.
The operational costs can be even more dramatic. Tony recently encountered a customer where "an entire fermenter sat idle for almost a week after the central QC lab sent back suspect chromatograms." The root cause? A minor settings error that could have been spotted immediately with automated retention-time monitoring across sites, rather than requiring personnel to "laboriously pull out data" from numerous injections in Excel to prove the lab was "dishing out duff data."
George Van Den Driessche, Ph.D., a scientific data specialist at TetraScience, highlights another hidden cost of data fragmentation: unnecessary equipment redundancy. A full LC/HPLC stack "can range from a couple hundred thousand up to a million dollars," he notes, yet because column-performance and instrument-utilization data sit in separate CDS silos, managers often resort to buying extra rigs as insurance.
This leads to what George describes as "backups to backups to backups that are sitting there collecting dust." Without advanced analytics to track usage, labs cannot easily determine which systems are busy and which sit idle—resulting in massive capital inefficiency across enterprise analytical operations.
Engineering Scientific Intelligence
The challenge extends far beyond technical interoperability. As George explains: "chromatography data systems have proprietary formats for how they're extracting and storing data, and you can't get that data out of the system." This forces scientists to manually curate data in separate tools like ELNs or Excel, creating what he calls a fundamental architecture problem.
TetraScience’s product and engineering teams, as well as our Sciborgs, scientific data experts embedded with customer organizations to achieve breakthrough business outcomes, identified the solution: transforming raw chromatographic traces into what Van Den Driessche describes as a "highly engineered data table"—an analytics-optimized, "AI-ready data set" that preserves scientific context while enabling computational analysis.
The approach involves extracting raw chromatograms from every major CDS and mapping them to a single, vendor-agnostic schema. This isn't merely data conversion; it's comprehensive re-engineering that adds crucial context the instruments never knew, such as project codes from ELNs, batch IDs from LIMS, and reagent lots from ERP systems.
When asked if this was anything like creating a "check engine light" for chromatography systems, Tony embraced the concept enthusiastically. He envisions AI-driven warnings based on harmonized data that could automatically detect when a column was starting to fail and preserve data integrity by halting analysis on compromised columns. Rather than treating columns as "throwaway technology" discarded after a set number of injections, labs could use them to their full potential while safeguarding accuracy.
This vision is already becoming reality through our Chromatography Insights app, which has delivered documented outcomes including up to 75% reduction in out-of-specification events and 80% reduction in SOP violations by automatically flagging repeat injections and manual processing errors.
From Implementation to Impact
The technical implementation begins with lightweight software “agents” installed next to every chromatography data system. These agents capture raw files, converting traces, metadata, and audit logs into standardized formats. A second pass adds crucial scientific context, creating analytics-ready tables that span sites and instruments.
This data engineering approach enables what George calls predictive modeling opportunities: "You can use all of that to build out a predictive model that tells you molecules targeting this protein will have X or Y binding potency so that you can increase your R&D efficiency of picking molecules with historical data backing their activities."
The regulatory benefits are equally compelling. Tony argues that comprehensive audit trails can shorten inspections because "you can very quickly and simply load that up and then get to see it" when auditors raise questions about specific data.
The Broader Scientific Opportunity
While operational improvements deliver immediate value, the larger opportunity lies in what Tony calls unlocking the "masses and masses of data" that chromatography generates. Despite accounting for "well over half of the dollars spent on analytical science," most chromatographic traces sit in "different silos," forcing scientists to "look at little snippets of information, rather than the whole big picture."
Edge argues that unifying those traces and flowing them into comprehensive scientific use cases and workflows would "start to unravel some of the mysteries we've presented ourselves." This represents the fundamental promise of Scientific AI: transforming isolated analytical outputs into interconnected intelligence that accelerates discovery and development.
At TetraScience, we're building the infrastructure to make this vision real through our comprehensive platform that combines purpose-built technology with deep scientific expertise. The path forward isn't primarily about acquiring new hardware, as Tony notes, but "fundamentally improving how data is handled, ensuring it can be effectively moved, aligned, and understood across systems."
The bottom line, as the R&D World article concludes: "Chromatography data has been whispering for decades; giving it a common language might finally let the lab hear it."
Learn more about our proven approach to chromatography data transformation through our case studies and technical resources.