Fifth in the “Tools for Data Governance” series.
In earlier posts in this series, we explored three key tools that underpin effective data governance:
- Data dictionaries – the technical foundation, describing fields and structures
- Business glossaries – the shared language that brings clarity to definitions
- Data catalogues – the enterprise view that connects everything together
Each of these tools delivers value on its own. But their true power emerges when they’re connected through metadata management – the practice of collecting, organising, and maintaining information about data across the organisation.
What Is Metadata Management?
Put simply, metadata is “data about data.” It’s the information that describes where data comes from, what it means, how it’s used, and who is responsible for it.
Metadata management is the process of bringing this information together in a structured, usable way – ensuring that everyone can find, understand, and trust the data they work with.
For a university, this might include metadata such as:
- The source system of record for student enrolment data
- When a dataset was last refreshed
- Which HESA field or regulatory definition it aligns with
- Who owns it and who can access it
- The transformations it undergoes before appearing in reports
Managing this metadata consistently turns a complex data landscape into an understandable, navigable ecosystem.
Why It Matters
Modern universities operate dozens of systems that each hold partial pieces of the data puzzle. Without effective metadata management, data becomes fragmented, duplicated, and hard to trace.
A strong metadata framework helps to:
- Maintain data lineage, so you can track how a figure moves from source to dashboard
- Provide context, so users know what a field represents before using it
- Ensure accountability, by linking datasets to owners and stewards
- Support governance and compliance, by identifying where personal or regulated data appears
In short, it allows an institution to manage data as a strategic asset rather than as scattered information.
Connecting the Tools
The data dictionary, business glossary, and data catalogue each manage different types of metadata. The goal of metadata management is to link them together into a single, coherent ecosystem.
| Tool | Primary Focus | Metadata Type | Typical Users |
|---|---|---|---|
| Data Dictionary | Technical detail about fields and tables | Technical metadata | Data engineers, developers |
| Business Glossary | Agreed business meanings and terminology | Business metadata | Analysts, data stewards, managers |
| Data Catalogue | Enterprise-wide view and relationships | Combined metadata (technical + business) | Everyone |
When integrated, these tools form a metadata chain:
- The data dictionary captures technical definitions from each source system.
- The business glossary provides the human-readable meaning and connects to the corresponding technical fields.
- The data catalogue unites both layers, showing where data lives, what it means, who owns it, and how it flows.
This creates a “single pane of glass” for understanding data across the university.
Practical Steps for Implementation
Many institutions start with pockets of metadata – a dictionary here, a glossary there – and gradually bring them together. Here’s a roadmap for doing it effectively:
- Start with what you have.
Gather existing documentation, spreadsheets, and system notes. Even imperfect metadata is valuable as a foundation. - Choose a central repository.
A metadata management platform (such as Microsoft Purview, Collibra, or Alation) can pull metadata from multiple systems automatically. For smaller institutions, SharePoint or Power BI dataflows can serve as an interim solution. - Define ownership and workflows.
Assign data stewards responsible for approving and maintaining definitions. Build review cycles into governance processes. - Integrate your tools.
Connect data dictionaries, glossaries, and catalogs so that changes in one are reflected across all. For example, if a glossary term is updated, linked catalog entries should refresh automatically. - Automate metadata harvesting.
Use connectors and APIs to keep metadata current rather than relying on manual updates. This ensures accuracy and sustainability. - Make it visible.
Publish your metadata tools through an internal data portal or intranet. Encourage staff to explore and use them as part of daily data work. - Measure adoption and impact.
Track how often the catalog or glossary is used, what searches are most common, and where users need more clarity. This helps refine content and training.
Metadata in Action: A Higher Education Example
Imagine a university preparing its annual HESA Student return.
- The data dictionary lists every relevant field in the student record system, such as
EntryQualificationLevelorModeOfStudy. - The business glossary defines these terms in plain language, referencing HESA’s official specifications.
- The data catalog links these fields across systems (SITS, data warehouse, Power BI dashboards) and shows data lineage from collection to submission.
When an auditor asks how “first-time full-time student” is determined, the institution can trace the logic, definitions, and transformations instantly – providing both transparency and confidence in the data.
Benefits of Integrated Metadata Management
By connecting your metadata tools, you achieve:
- Efficiency: Fewer duplicated definitions and faster onboarding for new staff
- Clarity: Shared understanding of data meaning and context
- Trust: Confidence in reports and analytics outputs
- Compliance: Easier demonstration of GDPR and HESA alignment
- Sustainability: Reduced reliance on key individuals and manual documentation
Ultimately, integrated metadata management is what turns data governance from a static policy into a living practice.
The Takeaway
Data dictionaries, glossaries, and catalogues each play a vital role – but metadata management brings them together into a single, dynamic framework.
It’s the connective tissue of data governance, ensuring that your organisation doesn’t just collect data, but understands and manages it intelligently. In higher education, where compliance, collaboration, and credibility depend on reliable information, that understanding is invaluable.
Coming Up Next
In the next post in the Data Governance Tools series, we’ll focus on data stewardship — the human side of metadata management. We’ll explore how to define stewardship roles, embed them into governance processes, and build a culture of accountability around data.