Over the last few months, I’ve been asked to talk about data and design a lot. Last week I returned to my old uni, London College of Communication; last month I shared my thoughts with South Americans at the e-xperience conference in Medellin; and in September I was privileged to kick off TedX Westminster at the Soho Theatre. This is an emerging intersection and each time, the opportunity of the talk gives me a chance to reflect on where the practice is going.

In Policy Lab, we talk about the three Ds of policymaking: data, design and digital. We think about how we can combine them: designing the other Government levers (e.g. regulations or nudges) that sit around or as well as digital services; combining big data from large datasets with ‘thick’ data from human observations; and getting non-traditional data from digital interactions to have a better, real-time sense of what is happening.

3Ds of Policymaking

Over the past two years, we have been using data and design in what appears to be three main ways: designing data, designing policy with data and designing data-driven public services.

Designing data

Civil servants live in the world of words and numbers. But these are often extremely complex and time-consuming to interpret. In the Home Office Strategy Team, I remember wading through enormous slide packs filled to the brim with charts, graphs and tables. Design can make data much more accessible and easy to interpret, allowing us to open up policymaking and invite others into generate ideas. In our future of ageing project for GO-Science, we worked with Data Design to turn a 200,000 word review of the academic literature into data cards, which we used  in a workshop activity to help policymakers to explore the data relating to specific challenges. We also used the evidence to create personas which Hugo Yoshikawa illustrated for us. This was an important moment in the workshop. It really humanised the evidence and the policymakers could really start to imagine what people’s experiences would be like in 2040. GO-Science have been making these tools available for other non-Government organisations, in the UK and internationally to use, and we’re embarking on a new project – this time on waste with Sophie Thomas who led the Great Recovery design project at the RSA.

Visualising data well can not only help policymakers, stakeholders and frontline staff make better decisions, but it can also engage the public. The Office for National Statistics has started creating topical and playful data visualisations, such as this one about babies born on Halloween or this quiz about teenage pregnancy (I was hopelessly wrong).

Data made open and accessible can also be a policy in its own right, nudging people’s behaviour or prompting people to advocate for change. The red/yellow/green food labels allow us to decide what food to eat to stay healthy. Opening up cycle accident data helps people to plot safer routes to work. Crime maps help citizens hold their local police to account – and in the more commercial space, Anna Powell-Smith’s excellent ‘What size am I?’ allows people to see what dress sizes high street shops stock, and complain if they are too limited.

Designing policy with data

The civil service naturally places a lot of emphasis on evidence-based policy, so using quantitative data to develop a policy is nothing new. But the type of data and ways that computers can analyse it are. The Government Data Science programme has been set up to promote the use of data science across departments, and we have been combining it with more qualitative evidence in our projects.

Our health and work project with the joint Work & Health Unit (written up fully on page 36 here) has been looking at how we can support people to manage their health conditions and stay in work. We used data science techniques on the Understanding Society survey to understand the risk and protective factors for people who report to be on a health-related benefit. It confirmed many of the risk factors we knew about, but also revealed some new insights for example that bereavement was a risk factor and that women with clinical depression are less at risk than men with the same condition. But on its own, it was not enough. We needed to combine it with graphic design and design ethnography to get the full value.

The Sankey diagram was a great way of showing that once people flow onto benefits, they do not get off. But the visual output of the k-means clustering (grouping those on health related benefits into 5 groups based on known risk factors) was more difficult to interpret. The full insight became apparent when Laura Malan from Uscreates placed the the segments on an axis of good-poor health and high-low previous salary. Then it became crystal clear how different groups were, and how they would have different motivations and needs for returning to work, and therefore different policy interventions.

Original source – Policy Lab

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