ADG 2: The what, why and how of a Data Vision.

Alex Leigh

September 01 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 2 - Creating a Data Vision

Overview:

Establishing a clear data vision aligned with institutional goals.

Key Points:

• – Importance of a data vision.

• – Steps to create a data vision.

What is a data vision, and why do I need one

Part two set out the case for developing a compelling institutional data vision. While it is not essential, it does set the groundwork for developing and implementing a practical data strategy. Which will be the focus of our next blog.

Today though, let’s summarise what, why and how of a data vision.

Why start with a data vision?

It’s not too contentious to say that some institutions still treat their data as a byproduct of operations rather than a strategic asset. A data vision can help nudge the data culture and shift that mindset. It’s not about a technology roadmap or specific (often transformation) projects—rather it’s a collaborative attempt to articulate how data can enable the university to achieve its broader goals.

A vision is different from a strategy. Think of the vision as the destination—a high-level, aspirational statement of what success looks like. The strategy is the roadmap—the steps you’ll take to get there.

Not every institution needs both, but a well crafted vision can be a powerful tool for aligning staff from across the whole institution, especially senior leadership who may not see the day-to-day data challenges, but are but are vital to making the kinds of changes in culture and management we need to meet any vision.

How to build a vision that gets attention.

Creating a data vision involves two key steps:

1. Anchor the “As-Is” state

   Identify current pain points and inefficiencies. Don’t over-analyse—focus on a few strong examples that pass the “so what?” test. These should be issues that clearly demand change and are misaligned with institutional values or goals.

2. Define the “To-Be” state

   Paint a picture of what good looks like. This future state should be compelling, realistic, and clearly linked to strategic objectives. Use language that resonates—terms like trusted data, quick and impactful, and data as an asset tend to land well.

It’s really important not to get bogged down with quantitative metrics. A vision should describe how it “feels” both in the as-is and to-be state. A vision paints a picture of a data world everyone wants to live it.

Interviewing key data consumers—such as academic deans or heads of professional services—can help shape a vision that reflects real needs and aspirations. But you need to balance collaborative development with keeping the vision actionable not some unfulfilled wish list!

Avoiding common pitfalls

It’s easy to get stuck in the weeds of the current state. Because data touches every part of the institution, conversations about what’s broken can become endless. The key is to prioritise themes that matter most to senior leadership and align with institutional strategy.

Another common trap is creating a vision that’s too ambitious or vague.

A good vision should be:

• Aspirational but achievable – Think 3–5 years ahead, not 10.

• Succinct and powerful – A short statement that clearly communicates the desired future.

• Signposting benefits – answering what’s in it for me and why should I care

• Rooted in pragmatism – If you can’t act on it, it’s just words.

And most importantly, be ready to answer the question: What’s next?

From vision to strategy

Once your vision is in place, the next step is to develop a strategy—a practical plan for how to get there. This will be the focus of the next blog.  Sneak peak, we’ll be exploring developing a strategy through four lenses:

• People – What skills and roles must be develop

• Culture – How do we shift mindsets and behaviours?

• Scope – What’s realistic to tackle now vs. later?

• Technology – What tools and platforms are needed?

Notice technology comes last!

The strategy doesn’t have to be a huge undertaking. It just needs to be consultative, clear, actionable, and aligned with the vision.

Final thought: A data vision isn’t just a statement—it’s a catalyst.  We make no apology for repeatedly categorising data governance as a catalyst. It’s not a project or a technology roadmap and some kind of stealth investment proposal.  It should be considered as genuinely transformational if done properly.

When done this way, it can unify all staff regardless of grade or role, create some real change, and lay the foundation for a more data-aware, agile institution.