# Regulatory assessment & approval-pipeline reporting

Suhas Narayan | Business Intelligence Engineer

Sanitized portfolio case study prepared for portfolio review.

## Business challenge

A regulatory assessment team manually assembled metrics across spreadsheets and a separate dashboard. Fragmented reporting and inconsistent source records made it difficult to reconcile results and track changes through the approval pipeline.

## My contribution

I owned the work from scoping through customer signoff: technical requirements, metric definitions, source investigation, reporting ETL, dashboard development, and validation. I also proposed an alternative data design when security restrictions prevented the requested use of ticket titles.

## Decisions & methods

- Resolved ambiguity before production development. Created a technical requirements document that captured metric definitions, filters, open questions, owners, and acceptance criteria. Secured customer agreement on all 10 metrics before implementing the production solution.
- Traced reporting errors to their source. Identified change records linked to multiple parents, inconsistent launch-date formats, and missing submission-batch start dates. Demonstrated their effects on counts and reporting periods, enabling source owners to correct the records.
- Preserved useful reporting within data-security constraints. Proposed a vetted, structured change-name field when storing unrestricted ticket titles was prohibited. Prepared ingestion and reporting so the approved-changes view worked once the customer populated the field.
- Combined throughput and bottleneck visibility. Brought approved changes, assessment timelines, submission volumes, hold reasons, and four pipeline-stage views into one daily reporting product.

## Outcome

Removed approximately three hours of weekly manual reporting by delivering one customer-validated dashboard covering 10 business metrics and four assessment-stage queues.

## Results & context

The product consolidated spreadsheet reporting and a separate approved-changes dashboard. Three source-data issues were corrected before launch, and the structured change-name approach supported useful identification while respecting data-storage restrictions.

## Methods & Tools

SQL, Python, AWS Glue, Amazon QuickSight, Requirements definition, Source-data investigation, Secure data design, Regulatory analytics