Generating more data does not make a lab more data-driven. If those data require extensive cleanup and manual intervention before they can be used, then increasing their volume will do little to improve analysis or decision-making.
Becoming data-driven depends on what happens to information after it is generated. Data must retain enough context to be interpreted and move through the lab in a form that other systems or analytical tools can use.
This Lab Manager resource guide can help you evaluate whether current data practices support present work and future growth. Use its frameworks and checklists to identify gaps and take steps to strengthen your lab’s data foundation.
Download the Digital Automation, Analytical Software, & Data Management Resource Guide to:
- Evaluate data integrity practices and your lab’s FAIR data maturity
- Determine whether existing data can support automation and AI-enabled analysis
- Examine how connected lab and enterprise systems can reduce manual handoffs
- Use a checklist to plan and implement reliable analytical workflows
