ADG 3: How to craft a compelling Data Stategy

Alex Leigh

September 15 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 3 - Successful Data Strategies

Overview:

Formulating a data strategy that supports cost savings.

Key Points:

•  Aligning data strategy with cost-saving initiatives.

• Key components of a data strategy.

Turning a data strategy into action: A practical guide for universities

This webinar moved on from the data vision and into the development of a strategy designed for implementation. In this blog, we discuss why you absolutely should put time and effort into crafting a compelling strategy, what needs to go in it, and how to make it relevant to all parts of your institution.

Why do you need a data strategy?

Universities are swimming – often drowning – in data. Student, curriculum, research, staff, finance and estates data are some of the “big beasts”, but multiple islands of data similarly exist in systems, personal drives and cloud storage to name but a few.

Managing this fragmented landscape often feels more like a burden than a benefit. The first call of any strategy development is to choose- in the case of data this means accepting and agreeing that some data is more important than others.

We can’t fix everything, but we can fix something. Aligning our data strategy to what the university really cares about – both internally and externally is the start of a representative strategy that means something to everyone who reads it.

If our goal is to turn our data into a valuable university asset, it all starts with an authentic and ambitious data strategy.  We summarise this work by quoting “If you don’t know where you are going, you’ll end up somewhere else!”

Step 1: Start with a clear vision.

Every good strategy begins with a vision—a statement of where your institution wants to be with its data. This vision surfaces the choices of what is agreed to be most important so setting the direction and helping everyone understand the “why are we doing this when we have so many other things to do”

Consider the vision as making the case for change. The strategy then bridges the gap between where you are now and where that vision takes you. That strategy must be tailored to your institution’s priorities, culture, and goals. There’s no one-size-fits-all approach. It’s why you cannot just take another data strategy and try to make it your own, or copy a genetic strategy. That’s more one-size-fits-none!

Most importantly, your data strategy must be compelling when tested with the question “how does this support your university’s wider goals?” It’s not just about being better at managing data—it’s about using that data to help measurably support strategic priorities.

Step 2: Put People first

Implementing data strategies is an institution wide piece of change management. We are using the strategy as a jumping off point to change everyone’s relationship with data. So, it should be obvious that we start with people, not processes or technology or even data management. If we can’t make the case to “live in a better data world”, we’ve failed before we’ve started.

This is hard. Data is rarely considered a shared institutional asset. Most institutions struggle with siloed data practices leading a lack of shared understanding of how and where else data is used. That’s not because people don’t care—it’s often because they’re busy, and sometimes because data feels like it should be someone else’s job.

To change this, you need to bring people along. Help staff understand how their work connects to the bigger picture. Offer training, support, and clear communication. Recognise the skills they already have and give them a platform to contribute.

Whether someone has a data heavy role or thinks they “don’t do data”, they should all feel understood, confident and supported. Data strategies create a strong data culture when engagement and trust are pillars from the start. Don’t tell people what they need to do, show them how they could be better off if they work with you.

Step 3: Landing the strategy

A strategy without a practical implementation plan is just a document nobody is going to read!  So, while your strategy may make the compelling case for change, it must also be “doable” and that means seeding the ground for where it’s going to “land”.

Core to that is a Data Operating Model- think of this as the handbook for how we’re going to operate in this new data environment. Central to this is defining accountability through roles, responsibilities and governance structures.

This isn’t about burdening people with more work. Often, it’s about formalising what they are already doing and ensuring it’s recognised and supported. This is sometimes a tough sell, so you need to be realistic about scope. Data touches everything but trying to fix it all at once won’t work. Focus on areas where you can make a real impact—like the HESA student return or support for a new system rollout—and build from there.

You mustn’t become a creature of these initiatives though. Instead align your strategy with other high-profile projects and change across the university. That way data becomes part of the bigger picture, not an isolated one off effort for a single project. There’s a lot to think about when landing the strategy and you won’t get it right all the time. Don’t be disappointed though- implementing through an iterative approach, consistently engaging with people, and learning from your mistakes is absolutely the right way to go, compared to some kind of big bang.

Data doesn’t stop because you have a new strategy. It’s better to think of this phase as “re-tooling an aeroplane in flight” not building one from scratch.

Step 4: Use Technology Wisely

Of course, technology is a key part of any data strategy—but it should follow the strategy, not lead it.

You’ll need tools like data glossaries, dashboards, and issue logs to support governance and data quality.  It won’t be long before solutions supporting the strategy implementation will turn to data integration and data quality tooling.

These are important discussions, but we’ve seen too many strategies as thinly disguised investment cases for new tech!

IT colleagues should be important stakeholders in the development of your data strategy. Their understanding of how data moves and rests at the institution is vital when considering both the benefits of implementing the strategy, and the types of resources and funding needed to land it.

Also, look for opportunities to embed data strategy into planned system upgrades and new implementations—eg. upgrading a student records platform or moving systems to the cloud. These moments are perfect for improving how data is managed and used.

Step 5: Selling the sizzle!

We need our strategy to make that compelling case for change. Anyone – and we mean ANYONE not just senior leaders – needs to read it and think “I can see what that means to me and I can see what it means to my university AND I see why we need to do it now”. That’s a big ask!

So don’t think it has to be some 100 page monolith. It can be a document, a website, or even an interactive tool. What matters is that it’s concise (no one is reading 100 pages!) clear, engaging, aspirational but realistic and written in your institutions tone of voice.

As we said, there isn’t a generic template, but any well-crafted strategy should include:

• Vision, goals and benefits aligned to institutional priorities.

• Scope and initial focus areas with clear rationale for these choices

• Roles and responsibilities for the operating model AND for all staff

• Technology and tools needed to support the implementation

• A plan for that implementation with all the costs up front

• Communication and training plans for launch and implementation

Once the strategy is approved (and how to do that is a whole new blog we will write soon!), the next step is planning for successful implementation. That’s where we focus on the pillars for success—things like leadership, communication, skills, and structure. That’s all coming in a future blog.

Final Thoughts: It’s a destination not a checklist.

We have talked before about well managed and trusted data being a catalyst and a strategic enabler. The strategy is where this talk needs to turn to action! So, it must be authentic, aligned, ambitious but ultimately practical and doable.

Therefore, creating this data strategy isn’t a one-time task—it’s a journey to an agreed destination. As such, it takes time, collaboration, and a willingness to adapt when things don’t go to plan. And trust us, they won’t always go to plan!

There is so much more we could write about developing and implementing a data strategy, but we’ve run out of room! Do take a look at the webinar video and supporting material as there is lots of good advice in there. If you don’t get your answer there, drop us a line and we’ll be happy to help.