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. Required fields are marked *

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

Register for an upcoming webinar

TitleDateStart TimeDurationRegister
Reducing barriers to work using data led campaigns In September 2023 the UK experienced an economic inactivity rate of 21.3% and an estimated unemployment rate of 4.3%, both of which have increased compared to previous data. Economic inactivity has surpassed pre-pandemic levels, prompting government efforts to integrate this group into the workforce.

Historically, policies under Universal Credit and legacy benefits emphasised pushing people into employment through conditionality and short term measures. Today, both major political parties are exploring ways to facilitate return to work and eliminate barriers to employment. However, the government is also extending conditionality and adopting a tougher stance on sanctions for a broader range of people.

Haringey is home to a young, ethnically diverse population. In June 2023, almost one fifth of those between 16 to 65 were on Universal Credit. Nearly 7% of residents over 16 were claiming unemployment related benefits, a figure above the London average of 4.7% and the 3rd highest rate of all UK councils

Haringey Council wanted to find ways to overcome barriers to employment for young people and families with children and has used data to achieve success with its employment support programmes.

Join this webinar to learn:

- The new carrot and stick policy changes designed to break down barriers to work and reduce economic inactivity
- What Haringey Council did to increase take up of free childcare for two year olds to 70%
- How Haringey Council successfully helped 95 NEETs on their employment journey

With guest speakers from Haringey Council
29/11/202310:30 GMT1.3 hours
Policy review of 2023 and what 2024 may hold Join our last webinar of 2023 to hear our policy analysts review 2023's policy changes and big issues, from the ongoing cost of living and energy crises to the funding of local government and the Autumn Statement.

We will highlight our policy findings from the year including our work that revealed that millions of households across the UK are missing out on £19 billion of support each year.

We'll look at the role that data is playing in helping leading organisations to tackle these issues.

Through case studies of different types of households we'll look at what the changes mean for families now, and what 2024 has in store.

Along the way we'll share the positive impact that organisations we work with ​are having, and give practical solutions that others can adopt.
6/12/202310:30 GMT1.3 hours
How the debt sector is connecting people to support31/1/202410:30 GMT1.3 hours
Skip to content
%d bloggers like this: