When the Troubled Families program was launched in 2011 by the UK coalition government, there were four key measures by which it would be judged
These were to get children back into school, reduce crime and anti-social behaviour, get adults into work, and reduce the costs associated with troubled families that hit the public purse.
Initially, report after report detailed the shortcomings of the Troubled Families programme – with a government-led study revealing that it had been “unable to find consistent evidence that the Troubled Families program had any significant or systematic impact” and there’s been little suggestion of significant progress.
Despite this, the intention behind Troubled Families remains good – and there are authorities delivering positive results due to it. Let’s remember that this is the government looking for proactive ways to improve the lives and life chances of thousands of people – and changing the way the public sector can support the households most in need. And with time still left before the programme ends in 2020, opportunities remain for local authorities to further access the central funding pot.
The key, however, will be looking at where Troubled Families initiatives are working, how they are working, and trying to emulate their success. For me, this is where solutions that efficiently collate, analyse and contextualise data can really make a difference.
Falling through the cracks
Let’s return to those government good intentions. Under Phase 2 of the Troubled Families programme, if a local authority can identify two of the six criteria that determine a troubled family, they can obtain funding support from a central budget of £720 million to an agreed level for each organisation.
Yet, while there have been numerous successes, there’s a clear issue that many experience when identifying families for inclusion in the program and accessing the funding. And from what I’ve seen and learned, it’s all about data.
Today, across the country, busy staff within local authorities are spending large amounts of time and effort trying to derive insight from information stored in numerous systems across their organisations. The inherent challenges this presents are often discussed in tech circles: siloed and fragmented data, inefficient processes, errors, time-consuming and manual work. And with only a limited number truly getting “full sight” of the data, building a holistic view is very difficult – making it really challenging to quickly identify those families who are most at need and demonstrating that their situation has improved. This lack of evidence can prevent local authorities from obtaining that essential central government funding from the Troubled Families programme.
The MHCLG document Supporting disadvantaged families: Annual Report of the Troubled Families Programme 2017-18 (published in March 2018) provides the status of claims to date for all organisations enrolled in the programme. Using that data, on the assumption that local authorities can claim 100% of what they are entitled to, there was a shortfall at March 2018 of £356 million still to be claimed. The programme finishes in March 2020, but the majority of families need to be identified by March 2019 in order for the funded intervention to realistically occur and significant, sustained improvement to be demonstrated.
Fortunately, now there is a better way to do things.
Making positive change
It’s true that there is a lot of data available to public sector organisations about families and households in their area. But, as we know, much of it is either inaccessible, hard to see in context with other sets, or difficult to analyse. And that’s what needs to change.
By fusing and combining data sets from multiple sources, it’s possible to build a single view of a family, with rules applied that can generate alerts that identify eligibility for funding in the first instance – then hopefully an improvement in circumstances further down the line.
Our work begins with that data-driven view of the individual/family, enabling early identification, real-time insight, and positive intervention. The only significant challenge is how many councils have – or rather, haven’t – invested in this kind of data-driven solution.
Unlike the private sector, public sector organisations have been slower off the mark to commit large efforts into the use of data as a strategic enabler. And others don’t see an easy start point or clear approach. Both things go against the idea of having a data-driven government.
The net result here is that problems with analysing, managing, and deriving value from data end up with authorities being unable to maximise their agreed share of the money on the table now – but will soon be off the table. Fortunately, many know they have a problem. They just need to get on to solving it.
Prevention beats cure
As with many societal issues, intervention at the right moment is far more effective than trying to deal with the consequences of a problem once it’s happened. That’s a core part of the reasoning behind the Troubled Families program. If local public services organizations can spot issues or patterns, they can make the move to resolve them and prevent compounding issues. But without the ability to accurately identify potentially at-risk families, getting the program resources to make a difference is going to be extremely hard work.
Remember – this government money is available right now. But it’s also a case of “use it or lose it.” The sooner a structural change around data interrogation in the public sector is made, the better for a lot of at-risk families.
Public Sector (EMEA North) – Industry & Innovation, SAP