Rolled out governed enterprise dashboards and semantic models to power C-suite decisioning across multiple business units and regions. The program focused on building trusted metrics, repeatable dataset governance, and performant dashboards that executives actually use.
Context & Challenge
Large organizations often suffer from multiple versions of the truth: many teams build dashboards against different data extracts and transformation logic. This case targeted a global enterprise effort to replace ad-hoc reports with governed executive dashboards that provide a single source of truth and fast answers for leadership.
- Multiple BI tools in use (Power BI, QuickSight, legacy Excel-based reporting).
- Inconsistent KPIs across business units and regions.
- Long lead times for dashboard requests due to manual dataset preparation.
- Performance issues — some dashboards took minutes to render, harming adoption.
Approach
We used a three-layer approach: governance, semantic modeling, and performance engineering. The work was split into discovery, pilot, and enterprise roll-out phases with clear acceptance criteria at each stage.
- Discovery: aligned stakeholders on top 10 executive KPIs and source-of-truth datasets.
- Pilot: built canonical semantic models and a few high-impact dashboards with Power BI/QuickSight connectors.
- Roll-out: operationalised dataset certification, scheduling, and access controls; established change-control for KPI evolution.
Solution — What We Built
Delivered a repeatable pattern for enterprise dashboards that combined governed datasets (served from the data platform), semantic models, and optimized dashboard design.
- Certified datasets: curated, tested datasets in the data lakehouse (Iceberg/Parquet) exposed via semantic views.
- Semantic layer: reusable semantic models (calculated measures, hierarchies) that Power BI & QuickSight teams consumed.
- Performance layer: pre-aggregations and query acceleration (materialized views / serving tables) to ensure sub-second to few-second dashboard loads.
- Governance: dataset certification process, role-based access, a KPI registry, and a lightweight data contract process for downstream owners.
- Onboarding & docs: dashboard design patterns, performance checklists, and training for analysts and report authors.
Representative Tech Stack
Apache Iceberg / Parquet, Presto/Trino or Spark SQL, materialized serving tables (Redshift/ClickHouse/Postgres), Power BI, AWS QuickSight, dbt or Spark-based transforms, Airflow/GitHub Actions for orchestration, RBAC via IAM/ABAC, and monitoring via Prometheus/Grafana.
Results & Business Impact
The program shifted dashboards from being a cost center to a strategic asset — executives began using dashboards daily for decision making.
- Adoption: executive dashboard usage grew from ad-hoc (sporadic) to daily active use among leadership teams.
- Trust: certified datasets reduced KPI disputes by >80% in pilot units.
- Performance: majority of executive dashboards rendered within 2–5 seconds (where previously many took minutes).
- Delivery speed: average time to deliver a new executive dashboard dropped from weeks to days due to semantic model reuse.
Sample Metrics
- Trusted KPI ratio (certified KPIs / requested KPIs): increased to ~90% in rolled-out divisions.
- Dashboard render time: median reduced to 2–5s for executive views.
- Time-to-delivery: mean time to create a new certified dashboard reduced by ~60%.
How we ensured sustainability
To keep dashboards healthy after rollout, we introduced governance cadence (dataset owners, monthly KPI reviews), automated freshness alerts, and a lightweight SLA for dataset issues. Analysts were empowered with semantic models and a "pattern library" to avoid one-off reports.