What Communities Cannot See, They Cannot Change
Why the lack of place-specific data leaves rural and island areas on the back foot
While I love writing on Substack, it’s been a challenge recently. My time has been devoted to income generating activities. Right now, this orientates around applying to a range of grant funding bodies to secure funds to undertake research into some of the bigger mobility related challenges facing the UK’s rural and island communities.
The process of writing these funding applications reminded me that in spite of all of the information we have at our fingertips, there is so much we don’t know in detail about rural and island areas and the implications for big ideas related to community empowerment.
When governments talk about empowering communities, they usually mean more consultation, more local participation, and a little more devolved decision-making. That all sounds fine. But empowerment without timely local data is mostly theatre.
Across rural and island Britain, communities are too often expected to shape their future using blunt instruments: annual datasets, regional averages, and evidence that sits in offices far away from the places it describes. For a village, a small town, or an island, that is not good enough. Decisions about housing, transport, energy, local services, and business survival can turn on changes that happen over months, not years.
If local empowerment is to mean anything, communities need access to reliable, place-specific data that gives them a near real-time picture of local conditions. In practical terms, that could mean a dashboard bringing together indicators such as traffic volumes, energy demand, empty homes, business openings and closures, school rolls, public transport reliability, population change, and local employment trends. Not every community will need every indicator. But every community should be able to see what is changing around it before problems become crises.
Why this matters
The problem today is not simply that data is missing. It is also that the people who can most easily access, interpret, and use it are often not the people living with the consequences. The superusers of local data are usually central government, local authorities, consultants, or researchers. Their work can be valuable, but they are not the ones trying to keep a village economy functioning week by week.
That matters because communities are then forced into a reactive position. A service closes. A building is sold. A business owner retires. A bus route disappears. A garage shuts. Only then does the scramble begin: volunteers assemble, emergency meetings are held, funding applications are drafted, and everyone wonders why nobody saw it coming. Very often, the warning signs were there. They just were not visible in a timely or usable form to the people who needed to act.
Independent rural and island garages are a good example of this wider problem. We know enough to say they face serious pressure. Costs are rising, skills needs are changing, and the transition to EVs is beginning to disrupt business models built around servicing petrol and diesel vehicles. But once you move from the national level to the local one, the fog thickens fast.
In most places, we do not have a clear, timely picture of how many independent garages are closing, where the pressure is greatest, which services are still being offered, how many EVs they are seeing, what the age profile of owners looks like, or what social value those businesses contribute beyond straightforward vehicle repair. And that matters, because a rural garage is often more than a garage. It can be a source of fuel, advice, informal welfare checking, delivery support, and local resilience.
The danger is that communities assume these businesses will always be there because they have always been there. But that is exactly what people once thought about village pubs, post offices, and local banks. By the time closure becomes obvious, the community is already on the back foot.
Two places that show what data can do
There are already examples that point to a better way.
On Fair Isle, the community did not stumble into its local energy model by accident. Before the island’s renewable electricity system was developed, local people spent years recording and analysing meteorological conditions, especially wind. That evidence helped make the case that the island could generate more of its own electricity and reduce dependence on imported diesel. In other words, data changed who held power. It gave local people the confidence and evidence to argue for a system designed around their own circumstances rather than one imposed from outside.
Or take Orkney. The ReFLEX Orkney project has shown what becomes possible when a place begins to connect data across electricity, heat, and transport. The project has linked distributed renewable generation with flexible demand using technologies such as battery storage, smart meters, smart chargers, EVs, an electric car club, and electric buses. You do not need to agree with every detail of the model to see the broader lesson: communities become more capable when they can see how local systems interact in real time and respond accordingly.
Neither Fair Isle nor Orkney is a copy-and-paste model for every rural place. That is precisely the point. Good local data should not force every place into the same template. It should help each place make better decisions based on its own geography, economy, and community priorities.
Signs of progress, but also a long way to go
To be fair, there are signs that policymakers are starting to understand this. In Scotland, consultation feedback on the National Islands Plan review explicitly said that communities should have access to island-scale data so they can evidence priorities and monitor progress locally. The Scottish Islands Data Dashboard is a useful step forward. It brings together island-level indicators in one public-facing place and has improved as more island-specific survey and official statistical data have become available.
But island-level or regional data is only part of the answer. It still leaves major blind spots at settlement, neighbourhood, and business-sector level. It may tell you something useful about a wider island group, but not whether one particular village is losing young families faster than another, whether one stretch of road is creating a freight bottleneck, or whether the last independent garage in a fragile local economy is months away from closure.
That is where the next stage of empowerment needs to go. Not just more data, but more usable local data. Not just better collection, but better community access, interpretation, and action. And not just polished dashboards for officials, but tools that local development trusts, community councils, anchor organisations, and volunteer groups can actually use.
What real empowerment would look like
Real empowerment would mean three things.
First, communities would have access to timely, relevant, place-based data that is presented clearly enough to support local decision-making.
Second, local people would have the capability to use it. That means training, support, and trusted intermediaries, not a lazy assumption that publishing a spreadsheet counts as empowerment.
Third, communities would have access to tools that help them turn information into action. That is where newer digital tools, including AI, could become genuinely useful. Not as some grand replacement for local judgement, but as practical support: helping non-specialists analyse trends, model scenarios, identify risks earlier, and build stronger evidence-based cases for funding or intervention.
So the question I would leave you with is this: what data about your own community do you wish existed, or was easier to access, before the next local crisis arrives?
For some places it will be transport reliability. For others it will be energy costs, business fragility, vacant housing, demographic change, or access to care. For many rural and island communities, it will be all of the above.
If governments are serious about community empowerment, they need to stop treating data as something that is extracted from places and interpreted elsewhere. They need to start treating it as local infrastructure: something communities can use to understand themselves, anticipate change, and shape their own future.