There is a common assumption in research and industry that data access is an administrative step, which is a formality that precedes the real work. In practice, it is one of the most time-consuming and unpredictable phases of any data-intensive project. A survey of researchers at a large hospital research institute found that the most frequently cited barrier to their work was not funding, expertise, or technology; instead it was the lengthy turnaround time before data became accessible, cited by 51% of respondents. Inconsistent and opaque access processes were cited by 46%, and the inability to link data across sources by 43%. Turnaround times for data access in that setting ranged from months to years.[1]
This is not an isolated finding. It reflects a structural reality of working with clinical data: the gap between knowing what data you need and being able to use it is wide, manual, and poorly governed in most institutions. Researchers and industry teams routinely spend weeks or months navigating data request processes, reconciling incompatible formats, and waiting for approvals before a single analysis has been run.
The Cost Is Not Abstract
In drug development, the financial weight of delay is well documented. A 2024 study by the Tufts Center for the Study of Drug Development, the most rigorous empirical analysis of its kind, found that each day of delay in clinical development costs approximately $500,000 in unrealised prescription drug sales and $40,000 in direct trial operating costs. For Phase III trials, the direct daily cost rises to over $55,000.[2] Across a trial that spans years, even small, recurring delays in data access compound into material losses in time and money.
The financial figure is significant, but it is not the whole picture. Drug development timelines are already long, clinical trials take on average six to seven years to assess safety and efficacy, and 85% of trials are delayed.[3] Every week spent waiting for data is a week added to an already stretched timeline. For patients, that translates directly into delayed access to treatments. For industry teams and research institutions, it translates into higher costs, slower pipelines, and compounding uncertainty.
Where the Time Actually Goes
The slowness of data access is not usually the result of a single large failure. It accumulates from a series of smaller frictions: data that lives in incompatible systems, access requests that require manual coordination across multiple stakeholders, governance processes that are inconsistently applied, and datasets that arrive in formats requiring significant preprocessing before they can be used. Each step adds time. Together, they add up to a process that is fundamentally misaligned with the pace at which research and development need to move.
The problem is particularly acute when data needs to be sourced across institutions or jurisdictions. Multi-site studies, cross-border research, and drug development pipelines that draw on real-world data from multiple countries face all of these frictions simultaneously, multiplied by the number of data sources involved. A team working across five institutions in three countries is not navigating one data access process; it is navigating fifteen, each with its own timelines, formats, and governance requirements.
What this means in practice is that a significant share of the effort invested in research and development goes not into the science, but into the overhead of getting to the science. That overhead is largely invisible in published timelines and budget summaries, but it is felt acutely by every team that works with clinical data at scale.
The Fix Is Upstream
Addressing this requires more than faster approvals or better tooling at the point of request. The root cause is structural: clinical data is fragmented across incompatible systems, governed inconsistently, and not designed to be discoverable or comparable without significant manual effort. Fixing that requires investment at the infrastructure level, in how data is catalogued, harmonised, and made accessible under clear, auditable governance frameworks before a request is ever made.
When the infrastructure is right, the time between a data need and a usable dataset collapses from months to days. Queries that previously required manual negotiation with data custodians can be expressed, refined, and routed through a governed workflow. Datasets from different institutions arrive pre-harmonised and comparable, not as raw material that needs weeks of preprocessing before it can be used.
This is the problem PAICON built PaiX Navigator to address. By combining a harmonised, multi-institutional disease data catalogue with an agent-assisted discovery layer and governed per-tenant delivery, the PaiX Navigator is designed to remove the manual overhead that currently sits between a research question and a usable dataset without compromising the auditability and governance that clinical and regulated industry workflows require.
The clock starts running long before the science does. The question is how much of that time is spent waiting, and how much of it could be spent working.
PaiX Navigator is now accepting beta registrations. Clinical and research data teams can apply for early access here.
References
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Ho HKK, Görges M, Portales-Casamar E. Data Access and Usage Practices Across a Cohort of Researchers at a Large Tertiary Pediatric Hospital: Qualitative Survey Study. JMIR Med Inform. 2018 May 14;6(2):e32. doi: 10.2196/medinform.8724. PMID: 29759958; PMCID: PMC5972187.
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Smith ZP, DiMasi JA, Getz KA. New estimates on the cost of a delay day in drug development. Ther Innov Regul Sci. 2024;58(5):855–862. doi:10.1007/s43441-024-00667-w
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PharmaSols. The cost of delaying a trial. 2021 [cited 2026 May 21]. Available from: https://pharmasols.com/news/april-2021/the-cost-of-delaying-a-trial/
