Power BI Adoption at Red Eléctrica
Led the BI capability rollout across the planning department — from zero dashboards to a self-service analytics culture adopted by non-technical stakeholders.
Context
The planning department operated without a shared analytics platform. Decision-relevant data lived in Excel models, individual inboxes, and bespoke reports that took days to produce. Leadership had visibility into historical outcomes but no real-time or self-service access to operational KPIs.
Role
Internal champion and technical lead for Power BI adoption. Responsible for building the initial dashboards, defining the data model, training colleagues, and driving the cultural shift toward data-driven decision-making.
Approach
Adoption required solving both a technical problem and an organisational one:
Technical:
- Designed a shared data model connecting planning data sources (project registers, grid data, budget tracking) in Power BI’s semantic layer.
- Built DAX measures for KPIs that had previously required manual Excel calculation.
- Implemented incremental refresh to keep dashboards current without performance degradation.
- Standardised a report template so new dashboards could be built consistently across teams.
Organisational:
- Ran hands-on workshops for non-technical department members focused on reading and filtering dashboards, not on building them.
- Identified “data champions” in each sub-team who became the first line of support.
- Established a governance process for publishing reports to avoid the sprawl of unversioned dashboards.
Outcome
- Full planning department adopted Power BI as the primary reporting tool within one year.
- Monthly reporting cycle reduced from multi-day Excel assembly to automated refresh.
- Senior stakeholders gained self-service access to live KPIs for the first time.
- The data model and governance process have been maintained and extended by the team without central intervention.
What I Learned
Technology adoption is fundamentally a trust problem. The dashboards only mattered after stakeholders trusted that the numbers were correct. The investment in data validation and transparent lineage was what unlocked adoption — not the feature set of the tool.