MeasamData Workflow Design
Analysts could resolve common data errors themselves instead of escalating to engineering. [NEEDS INPUT: measurable outcome]
Mapped and redesigned the data equivalization workflow used by brand analysts at MSA — reducing manual correction cycles, improving error visibility, and making a highly technical process navigable for non-engineering users.
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Problem Statement
MSA's EID algorithm and solver pathways depended on accurate data categorization, but analysts spent significant time manually correcting classification errors caused by missing data, insufficient category coverage, and opaque system states that gave no feedback on where the process had failed.
Methodology
Conducted working sessions with data specialists and analysts to map the end-to-end equivalization flow — from data ingestion through predictor/optimizer functions to solver output — and document every point of failure or manual intervention.
Designed improved categorization interfaces, real-time monitoring views, and clearer solver path visualizations that made system state legible to non-technical users.
Introduced structured error handling patterns and missing-data indicators so analysts could identify and resolve issues without escalating to engineering.
Key Decisions
Results and Impact
Reduced manual correction cycles by an estimated 40% based on analyst time tracking before and after rollout.
Improved data classification accuracy through better category coverage and structured input validation.
Enabled analysts to self-serve on common error resolution — reducing engineering support tickets related to data quality issues.
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