Biotech companies typically operate with small teams using file-based mechanisms for managing their critical reagents and entities. They tend to use spreadsheets for their data. This practice can cause significant data management issues over time. Auditing of changes to data is a particular challenge in that context, as is record retention, such as version tracking and data traceability.
Most scientists follow a five-step data-management process during the life cycle of an experiment: plan, execute, analyze, process and report. Through this process, scientists interact with a variety of software tools. Some of these are highly valuable, automated solutions, while many are not. Generally, for every automated process, there is at least one manual process to copy, paste, transform and load data. Each time this is done there is a risk that a mistake is made, which can lead to inaccurate data reporting. To mitigate this risk, laboratories conduct a manual review of this process to ensure data quality.
To address the data management challenge, labs often deploy either an SDMS or LIMS system. However, neither of these provide context sensitivity. Bringing the entire experimental process into a single, integrated scientific workflow provides several significant advantages:
WEDNESDAY, July 15, 2020
Co-founder and COO Arxspan – A Bruker Company
Enterprise SaaS Sales Manager Arxspan – A Bruker Company