# Evidence quality & rework measurement

Suhas Narayan | Business Intelligence Engineer

Sanitized portfolio case study prepared for portfolio review.

## Business challenge

The organization needed to understand how often evidence satisfied external assessors the first time and where rework occurred. Inconsistent acceptance records and offline reviews made a straightforward acceptance metric unreliable.

## My contribution

I led the data investigation, worked directly with the assessor-portal product owner, designed the acceptance and rework classification, and delivered the acceptance dashboard through stakeholder review and customer validation. I partnered with a teammate on the supporting feedback analysis.

## Decisions & methods

- Challenged the original metric before implementation. Tested the proposed “passes until acceptance” measure against completed engagements and found that many requests never recorded acceptance. Demonstrated that relying on that status would omit a substantial population and misrepresent rework.
- Investigated the workflow behind the data. Worked with product and business owners to establish which status changes were automatic, which required assessor action, and how offline acceptance and subsequent edits affected the recorded history.
- Measured rework through observable feedback. Used chronological feedback transitions to identify rework, including requests that received feedback but were never formally marked accepted. Distinguished first-pass acceptance, rework, and requests without sufficient recorded activity.
- Made measurement coverage explicit. Kept requests without acceptance or feedback signals visible as excluded, documented the limitation with the customer, and presented the classification distribution alongside acceptance rates.

## Outcome

Gave managers their first automated view of evidence acceptance and rework by engagement, program, and completion month. Among classifiable requests, first-pass acceptance increased from 60% before launch to 76% afterward, providing visibility into process performance and opportunities to address recurring feedback.

## Results & context

The investigation covered 5,352 requests. The before-and-after comparison covered 1,320 classifiable requests before launch and 437 afterward. The dashboard also surfaced gaps in how acceptance was recorded, informing discussions with business and product owners.

## Methods & Tools

Workflow analysis, Metric design, Lifecycle modeling, Spark SQL, AWS Glue, Amazon QuickSight, Cross-functional investigation