Data Platform

Looker Studio Dashboard Usage Intelligence: Automated Metadata Sync via Colab Enterprise

How I engineered a report adoption monitoring system by sinking Data Access Audit logs and syncing report metadata via the Looker Studio API in Google Colab Enterprise.

#Looker Studio #GCP Cloud Logging #Looker Studio API #Google Colab Enterprise #BigQuery

Executive Summary

AstraPay maintains dozens of active Looker Studio dashboards to support product and business decision-making. However, there was no concrete visibility into dashboard adoption—which reports were actively utilized by leadership, who accessed them, and which dashboards were redundant and eligible for decommissioning.

I engineered an automated Looker Studio Usage Intelligence solution that tracks user interactions by integrating Cloud Logging Sinks, Looker Studio APIs, and Google Colab Enterprise Python workflows.

Solution Architecture & Engineering Steps

  1. Cloud Audit Log Ingestion: Enabled granular Data Access Audit logging for Looker Studio in the GCP console. Dashboard access events are routed in real-time via a Cloud Logging Sink directly into a centralized BigQuery table, logging timestamps, user identities, and unique Report IDs.

  2. Metadata Synchronization via Looker Studio API: Raw audit logs provide Report IDs as random hash strings. To translate hashes into human-readable metrics, I built an automated Python workflow inside Google Colab Enterprise:

    • Authorized secure API calls to the Looker Studio API to fetch report metadata (Title, Owner, Folder Category, Creation Date).
    • Scheduled the synchronization script as a daily job, storing compiled report definitions into a BigQuery dimensions table.

  3. Data Modeling & Looker Analytics: Joined the raw daily interaction access logs with the Report Metadata dimensions table in BigQuery, serving the consolidated intelligence back into a centralized dashboard adoption Looker report.

Business Impact & Success Metrics

  • Decommissioned Redundant Reports: Identified and deleted dozens of duplicate, obsolete, or unused dashboards, freeing up downstream query computing resources.
  • Data Platform Governance: Empowered the data platform team with concrete adoption metrics, highlighting the most valuable dashboard features and reports for stakeholders.