This case study reflects the integration layer that transformed an executive-created reporting tool into a reliable, auto-populated system. While the reporting interface itself was owned elsewhere, I designed the underlying data logic that connected operational learning trackers to the executive-facing output.
Rather than rebuilding the tool, the solution respected ownership constraints while eliminating manual entry, reducing error risk, and restoring trust in the reported outcomes.
This system functioned as a translation and stabilization layer between operational learning data and an executive-controlled reporting interface. It was designed to work within tool ownership limitations while still enforcing consistency, accuracy, and defensible outputs.
The executive-owned reporting interface was treated as read-only to preserve stakeholder ownership.
Data inputs were normalized upstream to prevent formula drift and manual override risk.
Complex Excel logic was used to auto-populate required fields without altering the original tool structure.
Validation rules were embedded to surface incomplete or conflicting data before executive review.
Manual reconciliation steps were intentionally eliminated to reduce dependency on tribal knowledge.
Executives received timely readiness and completion views without manual follow-up.
Reporting conversations shifted away from data accuracy and toward program implications.
Learning operations could support leadership reporting without owning the executive tool.
Risk associated with manual entry and last-minute corrections was materially reduced.
Trust in the reporting output improved without forcing a tool redesign.