Part 3 will discuss Data Handling.

Data Handling

The separation of data into raw analysis and summary analysis is a best practice at the project level. This distinction lets a researcher thoroughly review raw data to verify outcomes or perform a new analysis without the destruction of original data.

Prior to the implementation of an AI application, it is also advisable to de-duplicate files. Many organizations are realizing that storage space is not unlimited. Thus, they are now curating data instead of paying for additional storage. Curation involves the retention of only useful data and the destruction of data that is no longer needed.

Labs that have implemented a LIMS without historic data should consider its addition as a potential project. The inclusion of legacy data, especially from hard copy sources, will require time and effort. However, there might be invaluable data from the past that may shine light on the problems and questions of today.

Best practice would be harmonizing all data during the initial stages of a LIM’s implementation throughout an organization. The standardization of naming conventions and terms should also be done in conjunction. It is reasonable to expect a drift in standards over time and changes in personnel. Thus, it would be worthwhile for a laboratory’s management to ensure standards are regularly updated to maintain their relevance.

Obtaining valuable insights and actionable results from AI analyses will only be possible if the data an AI application accesses is robust. Ensuring the harmonization of your LIMS will enable AI analyses that are deep and comprehensive. Allocating the resources for tidying data before implementing a LIMS and making sure it stays clean will benefit your organization in the long term.

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