Across many social areas, from policing to identifying young people at risk of becoming NEET, predictive data modelling is increasingly being used to identify people at risk and track their progress against the support they receive. This approach offers huge opportunity to create targeted awareness channels, measure impact and make the case for further investment. An accompanying service design wraparound maximises the return on the investment.

This blog highlights how Policy in Practice and innovative service design company Uscreates are using predictive data within service design work and makes the case for a service design approach to data-led projects. Together we’re helping Luton Council develop a data-led early intervention homelessness prevention service.

Policy in Practice and Uscreates worked with Luton’s frontline staff to tackle homelessness using data and service design

Data and design can tackle homelessness

Luton Council already has a homelessness prevention service which supports clients up to 56 days (previously 28 days) before eviction, as required by the new Homelessness Reduction Act. Using data to predict who might be at risk before those 56 days will help people to take more action themselves, either through self-service online tools or community-led support, meaning the council can prioritise its resources for those who are most in crisis. This is really exciting.

The original Policy Lab and MHCLG project that informed the Trailblazer set out predictive data modelling as one of the referral routes into redesigned services. Some councils have been exploring this and found it challenging, so it is great to have the chance to work with one, Luton, that was ambitious in taking this pioneering approach.

Policy in Practice had already been commissioned to provide a predictive data tool to proactively identify people at risk of homelessness and we could see the value that Uscreates can provide by designing the subsequent support service and building the capability of frontline staff to use the data to continuously iterate the service.

Uscreates took a user-centred, design-led approach. They conducted staff observations, ethnographically informed interviews with customers, mapped community assets, co-designed an earlier outreach service and prototyped elements such as the contact text or letter, online self-help wireframes. At the same time, Policy in Practice created the data dashboard, and over the summer Luton staff are testing the prototypes with a first cohort of customers.

Four interesting learnings so far

Some of the biggest insights, challenges and immediate takeaways from this project so far are:

1. Thinking through the ethics of using data to identify individuals at risk: making sure the data used has the correct consent, making clear to those identified why they were being contacted, building in verification of their situation and providing choice over the service they receive. The initial touchpoint between the council and identified household was crucial. Even though it might have been more appropriate for a community-led service to contact those at early risk, legally it had to be the council as they were the data owners. We prototyped different channels and language to explain the contact and to collect other data, for example personal resilience / support networks, which determine the type of support they received.

“As long as they’re going to help me I don’t care if they own my data! If they are willing to help, I would be willing to tell them anything they need.” Customer interview

2. Early intervention and the role of the council. Providing an early intervention service went beyond the council’s statutory duty, which is to support those at risk of homelessness 56 days before an eviction, but not before. We had to provide an earlier intervention service which also manages expectations and supports residents too.

“Anything, any issues that is over a month, I will try and solve it myself. For anything that needs to be done in under a month, I’d go to the council.” Customer interview

3. Getting the buy-in of frontline staff in delivering a data-driven services. Frontline staff could have felt concerned about the potential of data automating their service, or ill-equipped to understand what the data was telling them about how their service was working. Rather than simply introducing the data dashboard to staff and training them in how to use it, we involved the staff throughout. We did this by interviewing them during the discovery period to understand how they currently used data, what their data needs were and what they thought, through their tacit knowledge, was the most important data to use in the model. We introduced predictive data examples from other sectors and co-designed the data-led service with them.

4. Even though we had a data-led solution in mind, not introducing it too early meant we could stay open to insights created in the discovery period. We wanted to be able apply the data opportunities to the areas that Luton needed most, framing it correctly so that staff could see it as adding value to their work, rather than being ‘yet another computer system’. We also needed to understand user needs so develop a support service that would help prevent early homelessness.

The future: data analysis and service design go hand in hand

Over the coming years, predictive data analysis and machine learning will become increasingly dominant. We need to make sure we can humanise this technology, using it in ways that are ethical, sensitive and understood. Data is the lifeblood of services and can help identify those who might benefit from them, track how people are using them so they can iteratively improve, and measure impact to make the case for them to scale. Service designers need to be data designers.

Further reading

, , , , ,

Leave a Reply

Your email address will not be published.

Fill out this field
Fill out this field
Please enter a valid email address.

Register for an upcoming webinar

TitleDateStart TimeDurationRegister
How to identify and support Just About Managing households using data The government has said it wants to make life easier for the 'squeezed middle' or people who are just about managing. These are the families who are not rich and they are also not those on the lowest incomes. Despite most being in work, they are struggling to meet their cost of living and it is no wonder.

The cost of living hit a 30-year high in February with inflation running at 6.2% and outpacing wage growth. Electricity bills were up nearly 20% in the year to January 2022, and gas bills by 28%, with further rises expected. Private rental prices across the UK went up by 2% in the year to January, the highest rate for five years; in the East Midlands that figure was 3.6%.

We know that one in five UK adults (10.3 million people) have less than £100 in savings, one in ten have no savings at all and more than a quarter have less than £500. Many are one broken appliance away from slipping into debt.

Local authorities want to help families who are struggling now to avoid a crisis down the line yet they have little or no visibility over people who are not already claiming benefits. Now though, analysis of other datasets can be used to get a clearer picture of families who are just about managing.

Join this webinar to learn:

- Who is just about managing now but at risk in the future due to the rising cost of living
- Which datasets can be used to identify families in danger of debt
- How local authorities can target support to avert crisis
29/6/202210:30 BST1.3 hours
Skip to content
%d bloggers like this: