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In today’s digital world, data is the lifeblood of every laboratory business. However, it’s not just the data itself that matters. It’s how that data is stored, tracked, and managed over time as electronic records. While they’re often discussed in the context of regulatory compliance, electronic records are fundamental to running an efficient, high-quality lab, whether the lab is regulated or not.
The United States Food and Drug Administration (FDA) defines an electronic record in 21 CFR Part 11 as:
“Any combination of text, graphics, data, audio, pictorial, or other information representation in digital form that is created, modified, maintained, archived, retrieved, or distributed by a computer system.”
In simple terms, an electronic record is any information your laboratory informatics system manages digitally, whether it’s a sample registration, test result, calibration log, or chain-of-custody signature.
Under this definition, every digitally captured lab activity becomes a record. That record must be stored and managed in a manner that protects its integrity, authenticity, and traceability.
Managing data in this manner is crucial for compliance in labs within regulated environments. But the principles behind it are just as important for non-regulated labs.
Consider the following scenarios:
In each case, well-structured electronic records support transparency, consistency, and collaboration. All of these factors are essential for any modern scientific team. Furthermore, they help labs reduce human error, increase data reuse, and enable reproducible science. In short, good electronic records are both good science and good business.
To fully take advantage of the power of electronic records, lab informatics systems — such as laboratory information management systems (LIMS), laboratory information systems (LIS), electronic lab notebooks (ELN), and laboratory execution systems (LES) — should be organized around the idea that every key workflow event is a change to an electronic record.
For example, logging a new sample creates a record. Moving the sample to a new storage location modifies the record. Approving a result means another modification. And if you’re archiving an old method, that’s yet another change.
When your informatics system is designed around these transitions, it becomes much easier to track status, manage permissions, and create a full audit trail. More importantly, it mirrors how work actually happens in the lab.
Organizing your workflows around electronic records isn’t just good data hygiene; it’s a smart strategy for lab excellence.
Benefits include:
In summary, labs with well-structured electronic records run smoother, can share data more confidently, and are better positioned to handle internal or external scrutiny.
Many labs assume a traditional relational database can easily support this kind of record tracking, but that’s not always the case.
While relational databases are useful for structured, tabular data, they’re not so good for tracking relationships and changes over time. Modeling complex workflows means creating numerous tables, foreign keys, and joins. Versioning records or maintaining detailed histories adds another layer of complexity. As lab processes become more interconnected, the database structure can grow increasingly rigid and fragile.
Graph databases, on the other hand, are built to reflect relationships and changes natively. Each record is a node, each action or relationship (for example, “sample moved” or “result approved by technician”) is an edge, and the full history of any entity is stored as a chain of connected events.
This structure is naturally aligned with lab workflows, which are full of dependencies, handoffs, and branching paths. With graph databases, it’s easier to model the real-world flow of data, track provenance, and make complex queries, such as “show me all tests approved by this technician who used a reagent from this batch”. That’s why graph-based architectures offer a compelling advantage for FDA-compliant electronic records.
Electronic records are the foundation of a well-run lab, regardless of industry or regulatory oversight. By organizing workflows around changes to electronic records, labs can improve data integrity, support traceability, and enhance overall operational quality.
However, to do this effectively, your lab’s data infrastructure matters. Systems built on graph databases offer an agile way to manage electronic records. They also reflect the reality of scientific work.
If your current LIMS or other lab software struggles to adapt to evolving complexity, it may be time to rethink your informatics system. Electronic records aren’t just a technical concept; they reflect your lab’s truth. More importantly, they’re the foundation of your lab’s current and future business performance and growth.
Talk to us about how you can improve your lab data quality by modeling electronic records in your LIMS.
