KSS Energia clarifies data governance and connects data to the business
From fragmented data governance to a systematic model that connects data ownership to business processes - ensuring data is high quality, available and correctly assigned.
The challenge
Data ownership and responsibilities were undefined. Governance sat centrally in the data team without active business involvement, and decisions were made case-by-case.
Undefined ownership and responsibilities
At KSS Energia, data ownership and responsibilities were undefined. Data governance was centralized in the data team without active business involvement.
Case-by-case decisions, fragmented documentation
Decisions were made case-by-case in projects, and documentation was fragmented. There was no consistent way to manage data across the organization.
Technology-driven, not business-anchored
Data management was technology-driven and not anchored to business processes. This made it harder to utilize information in core processes.
What we built
A five-part data governance model that anchors governance to business processes rather than technology platforms - built collaboratively with three business areas.
- 01The Baseline
Stakeholder interviews
Conducted interviews with key stakeholders to build a shared picture of pain points, priorities and existing collaboration.
- 02Collaboration
Three workshops across business areas
Ran three workshops with over 17 representatives from energy markets, customer lifecycle, and grid operations.
- 03The Model
Five-part governance model
Designed a model covering roles, data domains, decision-making, tasks, and coordination - anchored to business processes, not technology platforms.
- 04Adoption
Integrated into the strategy cycle
The outcome connects data governance to the same strategy cycle as the business. The model integrates into existing business rhythms rather than operating as a separate process.
Results
Named data owners across seven data domains, a three-level decision-making model and a roadmap for systematic execution of data-driven initiatives.
Clear ownership across seven data domains and a decision-making model across three levels - governance integrated into the strategy cycle.
Roles, data domains, decision-making, tasks and coordination under one model.
Interviews and three workshops ensured business buy-in across all business areas.
The model doesn't run as a separate process - it moves in the same strategy cycle as the business.
Beyond the numbers
- Roadmap includes deploying a data catalog and implementing prioritized use cases
- Regular coordination forums create the basis for systematic execution
