Is Your Institution Data-Rich and Insight-Poor?

Author: Matt Miller, Director of Global Sales, LabArchives

Your science is moving. The data isn't.

Every week, your Shared Research Resources (SRRs) and CORE facilities run experiments, process samples, produce outputs, and generate files. Flow cytometers, mass spectrometers, genomics platforms, imaging systems, the instruments are busy. Grants are funding the work. Faculty are publishing. By every surface measure, the institution's research engine is running.

But ask a different question, “Can your institution produce, right now, a single structured report showing exactly which SRRs contributed to which grants, publications, and faculty outcomes over the last three years?” The answer in most institutions is: not without significant manual effort and time, and likely with gaps in data.

That gap between the volume of research activity and the institution's ability to document, attribute, and activate the value of this data is not an IT problem. It is not a storage problem. It’s an attribution problem and it’s a strategic and operational risk.

The chain that creates the problem

The challenge compounds in a predictable, self-reinforcing sequence that every research institution we speak with describes in strikingly similar terms:

• Disconnected files, formats, and systems: Instrument outputs land in silos, folders, shared drives, facility-specific systems and thumb drives. There is no common metadata framework, storage strategies, inconsistent structure, and no institutional record.
• Manual harmonization: Teams must standardize and reconcile data by hand. Highly specialized and valuable team scientist time that should be used to advance science becomes connective tissue between systems that were never designed to work together.
• Manual attribution: When a grant renewal comes due or a paper goes to publish, someone must manually reconstruct which instruments, which SRRs, and which facility resources contributed to the work. It is time-consuming, incomplete, and inherently error prone.
• Weakened data governance & operational readiness: Incomplete attribution means incomplete instrument grant reporting (i.e. S10, CCSG/P30, NHMRC, Tri-Agency, Horizon reports). It means FAIR and DMSP compliance is aspirational rather than operational. And it means every AI initiative starts from a broken data foundation, governed by no one, attributed to nothing, and owned in practice by the folders it was last saved to.

AI-readiness starts with well attributed and governed data foundation.

Six data gaps hiding the value of your research data

Your institution is data-rich. These six identified gaps hide the value of that data-rich environment and compound its cost:

Why the urgency is real, critical…and growing

Four compounding pressures are making this problem impossible to defer:

Funding pressure and SRR compliance: grant compliance (S10 and CCSG/P30) [MM1] requires structured evidence of SRR utilization, user diversity, and research impact. With federal budgets shrinking internationally and funding competition intensifying, the institution that cannot produce this evidence cleanly is at a material disadvantage. This is no longer a reporting task. It is a budget defense mechanism.

FAIR and DMSP requirements: Structured, reusable, findable metadata is now expected, by international funders (NIH, NSF, Tri-Agency, Horizon, NHMRC), by publishers, and by the journals that accept your faculty's work. 200+ journals now require RRIDs in methods sections. DMSP compliance requires demonstrating data stewardship at a level most institutions cannot currently demonstrate without significant manual reconstruction. While funders policies tighten controls and create additional burden.

AI ambitions without a governed foundation: Every institution we speak with wants AI-ready research and reports the same barrier: they lack the governed, attributed, institution-owned data foundation to build on. AI built on fragmented data is unreliable and hard to leverage. Using your data in someone else's LLM means your institution's IP is no longer yours, and you lose value. Your data governance foundation must come first.

Aging infrastructure, shrinking budgets, and growing operational deficits: Manual workflows scale poorly. As research volumes grow and staff turnover continues, the hidden cost of data wrangling, metadata repair, and attribution reconstruction compounds. An institution running research operations on spreadsheets and shared folders is running an operational deficit that most leadership cannot yet see, but will.

The build-it-versus-buy-it inflection point

When a problem is big enough, urgent enough, and unresolved by any available product, institutions begin to think about how to build their own solutions. That is exactly what we are seeing across research institutions today. Home-grown scripts, custom bridges, internal data pipelines, fragile, expensive, and dependent on the few staff who built them.

The build-it route creates technical debt, concentrated knowledge risk, and an operational overhead that compounds every year. The question is no longer whether these problems need to be solved. It is whether to keep absorbing the cost of building around these critical issues or to deploy infrastructure that was designed to solve them from the ground up.

Up next in this series: Article 2 — Introducing Luma Lab Connect: The Enterprise RDM Gateway Purpose-Built for Shared Research Resources. How a production-proven, low-code platform addresses every gap described in this article... without rip-and-replace.