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Use casesData governance that works in practice
A practical governance framework that turns platform investment into a usable, sustainable data capability.
Context
The starting point
The client was investing in a modern data platform with Microsoft Fabric and Purview at the centre of the technical setup. The platform foundation was being built, but the organisation also needed the structure, ownership and ways of working that would help teams trust and use data consistently.
Challenge
What made this hard
The challenge was not just technical delivery. The client needed to define clear ownership, improve data quality, align governance with banking regulation, and create enough internal support for the approach to last.
- Constraints: regulatory requirements, cross-department alignment, change adoption, and the need to make governance practical.
- Success needed to look like clear roles, trusted data, consistent follow-up, and stronger collaboration between business, IT and compliance.
Approach
How we worked
Agoya worked side by side with the client to design a practical data governance framework that could work in the real organisation.
- Discovery & alignment
- Stakeholder alignment, kick-off, and RACI workshops per domain.
- Design choices
- Data owners and data stewards, an Enterprise Information Model, and a governance model tailored to the banking context.
- Implementation
- Iterative work per domain, combining role definition, data profiling and entity modelling.
- Change enablement
- Awareness, targeted training, and a shared language around data quality and ownership.
Solution
What we delivered
A governance approach that connected business ownership to data quality improvement and long-term adoption.
- Key components
- Governance framework, RACI matrices, role model, TIRE prioritisation model, and quality metric categories.
- Integrations
- Aligned with the Microsoft Fabric and Purview environment being implemented.
- Data model
- Structured via an Enterprise Information Model that links business language to data structures.
- Governance
- Clear ownership, responsibilities, review rhythms and alignment with compliance.
Outcome
What changed
A stronger foundation for trusted, usable data across the organisation. The client gained a clearer ownership model, a practical structure for improving data quality, and a governance approach designed to support adoption over time.
- Business impact
- Clearer accountability and better conditions for data-driven decision-making.
- Operational impact
- Better prioritisation, clearer follow-up and more consistent quality improvement.
- Data & AI readiness
- A more reliable basis for reporting, governance and future data initiatives.
- Now possible
- Turning platform investment into a more usable, governed and sustainable data capability.
“A modern data platform creates value faster when ownership, quality and governance are built alongside it.”
Agoya perspective
Getting your data to work
Innovation people can work with
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