Overview
Excelra helped a pharmaceutical client bring order to a sprawling LIMS dataset containing more than half a million analytical test entries recorded under inconsistent naming conventions. The client’s data spanned 25 unique analytical test types captured under more than 1,000 different test names, making it difficult to consistently identify, query, and trace results across release and stability testing programs. Through a structured data harmonization and taxonomy mapping methodology, Excelra consolidated seven naming variants of the “Moisture Content” test into a single standardized, traceable definition, validating an approach built for full-scale rollout across the remaining test types. This proof of concept demonstrates Excelra’s strength in CMC analytical data management, taxonomy design, and large-scale LIMS data curation, capabilities that help life sciences organizations turn fragmented lab data into consistent, regulatory-ready records. Explore more about Excelra’s Lab Informatics and Scientific Data Management capabilities, or connect with us to standardize your analytical data.
Our client
Our client is a pharmaceutical organization managing analytical testing data for critical intermediates and final materials across both release and stability testing programs. Their Laboratory Information Management System (LIMS) had accumulated more than half a million analytical data entries over time, with the same underlying tests recorded under a wide range of naming conventions. The client needed a reliable way to consolidate this data into standardized, traceable categories before it could support consistent reporting and downstream regulatory and quality workflows.
Client’s challenge
Because the same analytical test had been captured under multiple naming conventions, the client’s LIMS dataset was difficult to consistently identify, query, and track. Twenty-five unique analytical test types were spread across more than 1,000 different test names, spanning over half a million data entries covering both release and stability samples. This inconsistency undermined efficient reporting, created ambiguity across records, reduced the overall usability of the LIMS data, and increased the risk of incomplete or inconsistent analysis in downstream regulatory and quality workflows.
Client’s goals
The client wanted to consolidate more than 1,000 inconsistent test names into a defined set of 25 standardized analytical test types and apply a consistent taxonomy framework across all of them. Success meant ensuring that multiple test names mapped accurately to a single standardized test, that the taxonomy was applied consistently, that data remained fully traceable and interpretable, and that no critical information was lost in the process. The client wanted this approach proven on a representative test case before committing to a full-scale rollout across the entire dataset.
Our Approach
Excelra proposed a data harmonization and normalization methodology built around manual review and structured analysis of the client’s metadata, including sample type (release or stability), analysis code and description, test method, test date, time, analyst, and result value and unit. The “Moisture Content” test was selected as the representative case for this proof of concept, and the methodology followed four steps.
1.Identification of test variants
Excelra identified every test name in the dataset corresponding to Moisture Content, including:
- Moisture content
- Karl Fisher Water Content
- Moisture determination
- Water assay
- Residual water
- Water concentration
- Residual moisture
2. Method standardization
The underlying analytical methods behind these variants were identified as Karl Fischer Titration and Karl Fischer Volumetric, establishing the analytical basis for a single, unified test definition.
3. Data harmonization
All seven variants were consolidated under one standardized definition, with a consistent result name, display name, unit, and result type applied across every record, regardless of how the original test had been labeled in the source system.
4. Taxonomy mapping
Using a standardized taxonomy provided by the client, Excelra mapped the extracted analytical data to the appropriate taxonomy elements. This structured mapping ensured consistency across all records while establishing a machine-readable connection between raw LIMS data and the standardized taxonomy, with a clear governance model: where a taxonomy was not yet available, the client defined it, and any queries arising during taxonomy assignment were clarified and validated with the client before full data mapping began.
Our solution
- Consolidated seven inconsistent test name variants into a single standardized “Moisture Content” definition via data harmonization, with a consistent result name, display name, unit, and result type.
- Applied a client-defined taxonomy framework consistently across the harmonized test data, creating a machine-readable link between raw LIMS entries and standardized taxonomy elements.
- Delivered structured Excel outputs for the harmonized test data, giving the client a clear, traceable, and interpretable view of previously fragmented records.
- The PoC was completed in 3 days, whereas the overall project was completed over a period of 2–3 months.
The “Moisture Content” test case validated the feasibility of the approach end to end. A sample of the harmonized output is shown below.
Table 1: Sample of harmonized, taxonomy-mapped output for the standardized “Moisture Content” test.
Conclusion
This proof of concept confirms that analytical data harmonization combined with taxonomy mapping is a viable solution to the naming inconsistencies in the client’s LIMS data. Standardizing test names and applying a structured taxonomy measurably improves data traceability, consistency across records, and usability for downstream analysis and reporting. Building on this validated approach, Excelra will apply the same methodology to the remaining analytical test entries, complete taxonomy mapping for all 25 test types, perform end-to-end data consistency validation, and deliver the finalized structured datasets to the client within the agreed timeline.
