Challenge:
Waternet, Amsterdam's regional water authority, needed a structured, organization-wide data governance framework to manage the increasing complexity of its data landscape. The challenge was fragmented data ownership, unclear responsibilities, and inconsistent validation procedures—all of which hindered data-driven decision-making and sustainable AI adoption.
Our approach:
As Data Management Specialists, we began by analyzing the existing data landscape and identifying systemic bottlenecks. Based on this analysis, we designed a comprehensive data governance framework with clear roles, responsibilities, and procedures for long-term data quality, ownership, and traceability. Simultaneously, we addressed day-to-day data management issues by acting as a sparring partner for various stakeholders within the organization. We translated complex data challenges into pragmatic, actionable solutions, promoting clarity and alignment in decision-making. Knowledge transfer was a key component—teams were supported through documentation, coaching, and strategic advice to increase their data maturity. Additionally, I contributed to Waternet's AI strategy by developing implementation plans for Microsoft Copilot AI and supporting the design of a future-proof AI governance structure.
Results:
This approach has laid the foundation for sustainable and structured data use throughout the organization. Collaboration has improved, roles and responsibilities are clear, and governance principles are embedded in daily operations. The AI governance plans position Waternet to scale AI-responsibly within a secure and transparent framework.
Future plans:
The next steps are to embed the data governance framework across all departments and launch a pilot for Copilot AI. We also want to implement metrics to measure data maturity improvements and ensure governance practices evolve with changing business and technological needs.
Team Expertise:
The approach was implemented in close collaboration between business stakeholders and data professionals. My expertise in Power BI, Microsoft Fabric, Databricks, DBT, and governance frameworks played a central role in the technical implementation, while cross-functional collaboration ensured that the solutions were aligned with operational realities and strategic goals.
FAQ
How does Power BiSon help in setting up Data Governance and Data Quality?
We combine DAMA-DMBOK3 best practices with our own Data Governance and Data Quality Playbook, including the Metro model for clear ownership and roles. This gives your organization a clear framework, measurable quality agreements and a basis for data-driven decision-making and AI adoption.
Schedule a Quick Scan and discover how we tailor this to your organization.
What does the Data Governance Playbook actually deliver?
Our Playbook gives you:
- Clear roles & responsibilities
- Templates and processes for data quality and validation
- Step-by-step guidance from quick scan to implementation
- A future-proof AI governance framework for Microsoft Copilot 365, among others
At Waternet this concept has led to visible collaboration, transparency and sustainable data governance.
Are there also Data Management training and coaching available?
Yes. We offer kick-offs, workshops and guided implementationsTeams get access to:
- Online training courses for yourself, together or with guidance
- Studio recordings and podcasts for knowledge sharing
- Coaching programs to increase data maturity step by step
This way your organization not only grows in tools, but also in data culture and adoption.
