What set this problem in motion?
Sara is a Lab Technician who works at a Gene Therapy company. She is responsible for receiving
samples and their manifests. She inspects the samples that come in, their details, their
condition, and accepts or rejects the samples based on clearly defined criteria. Sara then tries
to reconcile any sample discrepancies and missing information, or sends a rejection notice to
the sample submitter, as needed.
- User Persona: Sara, (Lab Technician)
- Primary Task: Receives gene therapy samples and enters them into the system.
- User Goal: Inspect details against strict criteria and flags or rejects.
What wasn’t working and why?
Data integrity is the key to success at her work, but efficiency is also important when handling
massive amounts of data. She just entered 100s of rows of data into a table but apparently,
there’re some errors in the data and she can not continue saving the data. Without guided
resolution, a single cell error compromises the efficiency of the entire 100-row dataset.
- Invalid random data was entered in the table at row 25, 40, 72, 85 and 98 unintentionally.
- Until fixing the error, she is not able to save the data table.
What improved as a result?
The integrated Experiment Dashboard provides a high-fidelity interface for real-time data
capture. By implementing these safeguards, we expect to reduce data entry errors by 95% and
shorten the research-to-analysis lifecycle significantly.
- Automated validation with visual cues
- Instant error logging with fix suggestions
- Sticky table headers for large datasets
- Real-time error status & feedback