# Post-engagement snapshots & retrospective triggers

Suhas Narayan | Business Intelligence Engineer

Sanitized portfolio case study prepared for portfolio review.

## Business challenge

The organization had no standardized post-engagement snapshot dashboard or automated retrospective-eligibility mechanism. Teams needed a unified view of engagement performance and a consistent, data-driven way to identify which engagements warranted a retrospective.

## My contribution

I owned metric definitions, data modeling, ETL, dashboard delivery, and customer validation. I also conducted the cohort analysis, proposed the P75 effort rule, and implemented the eligibility flags consumed by the data-engineer-built notification system.

## Decisions & methods

- Derived retrospective eligibility from cohort analysis. Analyzed total engagement hours across complexity, product, and region cohorts and proposed a P75 effort threshold. Incorporated status-history and complexity criteria into reusable eligibility flags, providing consistent, data-driven inputs to the retrospective-triggering workflow.
- Established a consistent engagement-level reporting model. Preserved the complete engagement population and kept metrics with different levels of detail separate. Missing source data remained visible as unknown values, allowing meaningful comparisons without silently excluding engagements or treating missing information as zero activity.
- Grounded staffing and duration metrics in recorded activity. Counted contributors using actual logged effort and calculated phase durations from system-recorded status transitions. Left unsupported durations unpopulated, giving stakeholders defensible measurements of participation and elapsed time.
- Designed sensitive-download metrics for privacy and accurate counting. Kept sensitive processing within the restricted layer and used email only during filtering, excluding it from reporting outputs. Counted distinct request–artifact–user combinations after identifying that bulk downloads could share timestamps, preventing separate downloads from being collapsed into one.

## Outcome

Saved eight hours of manual work per reporting cycle while giving the organization its first unified view of post-engagement performance and its first automated mechanism for triggering retrospectives. Cohort analysis informed a P75 total-hours threshold, combined with status-history and complexity criteria, to identify qualifying engagements and feed the notification workflow.

## Results & context

Delivered 37 engagement-level metrics across five categories: personnel, evidence requests, shared artifacts, sensitive downloads, and cycle duration. Customer validation established the reporting definitions, while reusable eligibility flags connected the analytics product to the notification delivery system built by data engineers.

## Methods & Tools

Metric design, Cohort analysis, Data modeling, PySpark, AWS Glue, Amazon QuickSight, Privacy-aware analytics