Laboratory data management – from the measured value to a reliable basis for decisions

What is data management in the laboratory?

The trend is clear: laboratories at large companies are producing more and more data continuously. Spectrometers, hardness testers, titrators, balances, climate chambers, automated sample changers – every instrument delivers, second by second, values that in the best case are processed immediately and stored in a central system.

Even so, a well-known market study by Strategic Directions International of Los Angeles shows: around 70% of companies that own a LIMS still enter their data into the system by hand. In other words, even where the infrastructure is already in place, it is not being used to its full potential. The instrument measures – a person types the value in.

This gives rise to three central problems that professional data management has to address:

• Typing error rate of 1–3% – every manual transfer predictably produces wrong values
• Time delay – production waits for values that are already on the instrument but not yet in the system
• Lack of comparability – data from different instruments, shifts and sites lands in different formats

Good data management in the laboratory closes these three gaps. It ensures that measured values flow directly from the instrument into a central database, are stored there in a standardised form and are available at any time for evaluations, reports and audits.

Laboratory data management vs general data management

The term “data management” is used in the IT world for many different disciplines. Anyone placing the term in a laboratory context has to distinguish it clearly from related concepts:

The laboratory does not therefore stand alone. It is a data producer whose output flows into downstream systems – into the ERP for batch release, into the MES for production control, into BI tools for evaluation. If data management in the laboratory does not work, every downstream process suffers along with it.

The data lifecycle in the laboratory

Laboratory data goes through a clearly defined lifecycle – from first capture to tamper-resistant archiving. [FP]-LIMS maps these stages in four modules that follow the data lifecycle exactly:

In simple terms: Collect answers the question “Which values have we measured?”, Analyse clarifies “Are they in order?”, Archive preserves them in a tamper-resistant way, and Trace later answers any audit question in seconds instead of hours.

Typical challenges in laboratory data management

From over 30 years of practice with industrial laboratories, we see recurring challenges. They share a common denominator: data is produced faster than it can be processed – when the workflow is not right.

• Growing data volumes – spectrometers, automated sample changers and climate chambers produce thousands of values per shift. Manual processing does not scale with them.
• Distributed data sources – values sit on instrument PCs, in Excel files, in email attachments. Anyone searching is searching for a long time.
• Media breaks – values are noted from the instrument onto paper, later transferred into Excel, then copied into a test report. Every step is a source of error.
• Missing central data foundation – different departments work with different data versions. Discussions arise about whose Excel file is “the right one”.
• Compliance pressure – ISO 17025, ISO 9001, audit trail. Hardly achievable any more with manual data handling.
• Competitive pressure – those who can issue releases within minutes win contracts. Those who need hours lose them.

Anyone who recognises these six points also knows the requirements for professional laboratory data management: central, automated, standards-compliant, fast.

LIMS as the backbone of laboratory data management

In theory, laboratory data management can also be done with Excel macros and home-built databases. In practice, this approach breaks down by the time of the first audit – or earlier, when data volumes grow.

A modern LIMS such as [FP]-LIMS solves the requirements in an integrated solution. Concretely:

The LIMS is therefore not a sub-module of data management – it is the central data source to which all other building blocks connect: instruments deliver into it, ERPs read out of it, BI tools aggregate from it, auditors examine it.

What professional data management measurably delivers

Instead of marketing promises, the numbers we see with real customers:

1. 1
   Documentation effort reduced by up to 80% At STANNOL (soldering technology, since 2020) the documentation effort has dropped by 80% since the rollout of [FP]-LIMS. That capacity flows back into the actual analytics.
2. 2
   Typing error rate in data capture practically eliminated Where values used to be typed in by hand (1–3% error rate), today they flow directly from the instrument into the LIMS. The typing error rate for those values approaches zero.
3. 3
   Audit preparation from weeks to hours Anyone with an end-to-end audit trail in the LIMS answers auditor questions in minutes instead of hours – and shrinks audit preparation from a multi-week project to a routine task.
4. 4
   Trends visible before complaints come in Real-time evaluations and trend analyses surface drift in instruments or processes early. At COMPO EXPERT (fertilisers), users report exactly that: spotting trends early, uncovering dependencies, actively optimising processes.
5. 5
   Proven for more than 30 years At Siempelkamp Foundry, [FP]-LIMS was introduced together with a spectrometer as early as 1992 and manages all chemical analyses centrally to this day. At Buderus Guss, the LIMS has been running for over 20 years – statement: “I cannot imagine how our production would work without the LIMS.”

Practical example STANNOL – “all data is valuable”

STANNOL GmbH & Co. KG, a long-established soldering technology company with over 142 years of history, has been using [FP]-LIMS for data management since 2020. The central statement from Head of Innovation Ingo Lomp: “All data is valuable.“

That is not just philosophy, it is practice: at STANNOL, documentation effort has been reduced by 80% since rollout. Where measured values were previously copied from Excel files into test reports, most of this now runs automatically. Data flows directly from the instrument into the LIMS, is evaluated there against specifications and is immediately available for every further evaluation.

Comparable constellations can be found with other customers: COMPO EXPERT (fertilisers, around 700 employees, international sales network), AGOSI (precious metal processing, ISO/IEC 17025-accredited since 2012), Siempelkamp Foundry (LIMS since 1992). They all share one thing in common: they have not treated their data management as a software project, but as a continuous investment in their competitiveness.

Typical pitfalls in laboratory data management

The most frequent mistakes we see when building up professional data management in the laboratory:

• Manual transcription despite having a LIMS – according to Strategic Directions International, 70% of companies with a LIMS do exactly that. Instrument integration is the biggest lever – use it consistently.
• Excel as a parallel system – every Excel silo that is not replaced becomes a permanent weak spot. Goal: replace, not supplement.
• Local data storage on instrument PCs – when the PC fails or an employee leaves, the data is gone. A central database solves this.
• Missing audit trail – without complete change documentation, any serious audit will fail. More in the ISO 17025 article.
• No ERP integration – duplicate master data entry, missing batch linkage. A certified SAP® interface solves this.
• Outdated method versions – if it is not clear which method version was valid at which measurement time, data integrity is gone. Versioning in the LIMS solves this.
• Data security as an afterthought – anyone planning data management without ISO 27001-compliant infrastructure builds up risks that become expensive later.

Frequently asked questions on laboratory data management