Working in people-centric industries means you are always trying to understand your audience; what do they have, what are they missing, what could have a positive impact on their lives? The problem is, humans are complex, and insights within one facet of behaviour will only get you so far. Luckily for us, we live in the age of data, where we are literally swimming in information that can help us make more informed decisions.
In fact, a report by the New Local Government Network suggests that if councils fail to understand and harness the power of data they risk making themselves irrelevant to their citizens. But Local Authorities are facing a grand challenge, how can they filter through the noise and convert vast amounts of data into usable and implementable outcomes?
Taking on this challenge, we set out to create a framework which we could use to assess the ‘health’ of communities across London on a range of features from economics to health/wellbeing. We built up a catalogue of information ranging from the number of quarterly home sales (which would give us an idea of how stable communities are) to access to green space measures. All together, we were able to obtain data across 22 measures for each of the 633 wards within London (see example here).
One of the starkest findings from our audit was that even wards close to each other scored very differently. For example, South Twickenham is the highest scoring ward within London, with Syon, only 4km away being the lowest scoring. But the picture is not that simple. For example, both wards have average performing social housing quality, but if you reside in South Twickenham, you are likely to live on average 9 years longer than in Syon. This would suggest that one of the priorities for Syon is to implement interventions to reduce this health inequality.
This data is good at identifying overarching issues, but another layer is necessary to extract more impactful insights. For example, in Syon, we know heath inequality is an issue, but what interventions are needed to reduce this? Here you would need to dig deeper, surveying residents to identify what specific health needs are not being met. The combination of big and localised data allows investments to be extremely targeted, which for cash strapped local authorities is ideal to achieve the most efficient and effective positive outcomes.
Unfortunately, the infrastructure surrounding open data needs to be regularly maintained for these exercises to continue and remain relevant. For example, the Community Life Survey, focusing on social action and community empowerment, was last undertaken in 2016. As we all know, a lot has happened in that time which has impacted communities across the UK, so this data has well and truly gone past its ‘sell by date’ and is therefore less suitable for use in decision-making. The success of certain schemes, such as the London Datastore, now in it’s 10th year, is testament to the power big data can have on society.
Big data tools like that one we developed are critical, as they allow KPI benchmarks to be created across different groups, demographics, or in this case communities. In doing so, we can build up an accurate and objective picture of what the data is telling us. In our case, we designed the tool to be combined with our proprietary localised data to identify community pain points for local authorities, allowing for more directed interventions, to help craft and heal communities which are better suited to cater to the needs of residents.