ADG 10: What Success Looks Like

Alex Leigh

14 November 2025

4 min read

At the start of 2025, we ran the Agile Data Governance (ADG) webinar series.  In this collection of short articles, we’re going to look back at each one to summarise the most important takeaways. It’s been fun going back after a few months to see if there’s anything we missed or even forgotten!

ADG 10 - What Good Looks Like

Overview:

Data governance priorities and examples

Key Points:

• Increasing internal demand.

• Evolving regulatory landscape.

We’ve included two slides from the ADG#10 deck for this blog. As it’s important to acknowledge that what good feels like, AND what good looks like. Both are valuable and actionable measurements of your Data Governance capability.

The old mantra that “if you can’t measure it, you can’t manage it” doesn’t always apply here. DG is a strategic enabler. It not only makes doing the right thing the easy thing, but it also makes doing the right things easier. It’s the hidden accelerator that fuels almost everything the university cares about.

Our challenge is not to celebrate “making the data better” but to get others to acknowledge this capability has visible and sustainable value to the institution.  To do so we need to show that “living in this new data world” has both tangible benefits (what good looks like) and qualitative ones (what good feels like)

This blog is a bit longer than normal. We make no apology for that as there is lots we want to share. Let’s get into it.

What good looks like: what drives our DG capability.

Data governance (DG) in higher education isn’t really about policies and platforms; they matter of course, but nowhere near as much as people and their behaviours building trust in data across the institution. But we must align that to what makes our DG capability really “sing”.

1. Start with something, not everything.

In the first year of DG, the goal isn’t to transform everything overnight. Instead, it’s to build a capability that shows the value of doing things differently.  That means setting up the operating model, identifying critical data domains (like admissions or enrolment), assessing priorities and fixing something.

You may not see huge returns or those hard to find quick fixes, but you will make a difference. It is best to consider your first year as laying solid foundations doing enough to make the case to do more the following year.

2. Make It visible through a “shop window”.

One of the simplest but most powerful enablers is showing people there is a thing they can engage with. We’ve talked about “Fake it before you make it” in previous blogs.

A great place to start is a central hub or “shop window” for data governance; this is where staff can find data landscapes, owners and stewards contact details, simple tools, trusted data sources, best practice guidance and data signposting.

All these set out that DG is a serious institutional capability that isn’t going away, and it’s not just some back-office function. The clear message must be “it’s a resource for everyone”

This hub should connect with existing processes and systems, making it easy for users to find how DG can help in their day-to-day work. It’s also a great way to gain engagement, build awareness, and promote collaboration.

3. Embed DG into project initiation.

DG works best when it’s woven into the project lifecycle, not bolted on at the end where it is seen as a burden or a blocker.  Whether it’s a new student record system or a data-heavy transformation initiative, embedding DG principles and practices from the start ensures that data is considered as a strategic asset. This also helps raise the profile of DG across the institution and aligns it with broader change programmes.

4. Turn the tools outwards.

Two DG assets offer much both to support owners and stewards AND to create somewhere for the wider institution to engage.

• Data Quality Issues Log: Enables staff to report, track, and resolve data issues. It supports accountability and helps surface problems that might otherwise stay hidden. It shows the impact of these issues, and moves them from “too hard to fix” to “cross silo collaboration”

• Data Glossary: Standardises definitions, reduces confusion and increases utility. Ask three people what a “student” is, and you’ll likely get three different answers. A shared glossary helps everyone speak the same language and stops, or at least slows down, the proliferation of multiple unreconciled data sources.

These tools aren’t just operational, there a way for staff to engage with and improve the capability. They help shift the conversation from “we need more data” to “we need to better understand, improve and use the data we already have.”

5. Relationships first, frameworks second.

We’ve said it before, and we won’t apologise for saying it again: DG is a team sport with people at its heart. Meaning the success of your DG capability is predicated on building strong and sustainable relationships across functions and all levels. Thea relationship fosters collaboration and creates a community around data. You can never have too many friends as a DG manager!

When people know who to ask, feel confident in the answers they get, and trust the data they use, DG becomes part of the institutional fabric. It’s no longer a new thing; it’s part of how things get done in the right way.

6. Don’t focus on the top 5%

Trust in data shouldn’t be limited to senior leaders. They often live in a different data world that the rest of the university. DG engagement needs to be felt across the entire institution. Starting from those inputting or collating data to those making strategic decisions with that very same data, and everyone in between.

Often, the biggest improvements are felt by those closest to the data issues. Their experience is a powerful indicator of progress and a solid foundation for getting others on board. If someone like you is having a better data experience, then you’re far more likely to be interested.

I mean sure don’t forget about that 5%. It’s hard to push DG bottom up. But a good DG capability is institution wide and agenda free.

7. Measurement is important, but it’s not everything.

Another maxim: “Not everything valuable can be measured”. But that doesn’t mean you should have some metrics to track improvements. Some of the best indicators of DG success are felt:

• Are more people using the same data sources?

• Are meetings about data more aligned and productive?

• Are staff more confident in the data they use?

• Do people know who to ask if they aren’t.

There’s not a “standard” set of metrics though, you need to figure out what your institution cares about.  And when you’ve done that then think some more about other outcomes; collaboration, trust, understanding and just happier people!

We have a webinar about measurement coming up next year as it’s a big and important topic.

Summary is progress can be checked, and success can be measured. Just not always in the way that seems obvious.

8. Data skills make best use of trusted.

The outputs of a DG capability are mostly lost without people who “get” data. Organic learning has been our go to; it happens when new staff sit next to data-savvy colleagues. That’s just not enough.

We encourage universities to have both base level data skill competencies and role specific ones as well. These comes from intentional and sustained investment in improving those skills. We teach people to manage budgets, people and buildings. We need to do the same with data if it is to be an institutional asset.  

Final Thought: Data Governance as a Strategic Enabler

Data governance is not a one off cleansing exercise, a boring training programme or a litany of non-sustainable technical fixes. What it is is a strategic enabler. It builds trust, encourages collaboration, and empowers institutions to make save money, motivate staff and make better decisions.

Whether you’re just starting out or embedding DG into mature processes, the journey is long, often frustrating and absolutely worth it.

We hope this series has shown you that good DG is visible, practical, and people centred. It’s not about perfection, it’s about pragmatism, visibility and sustainable solutions. All that starts with the foundational behaviours, processes, and outputs that move data governance from theory to practice.

So, the next time someone asks if your data is an asset,  nod vigorously and tell them why that’s so important.

We hope you’ve found these blogs helpful. We’ve plans to run another series next year. Details coming soon. Until then, if there’s anything you’d like to know more about then do get in touch with us at hello@ed-connect.co.uk.