Early intervention prevents misery and saves money
Being able to predict the future would be rather useful.
While it’s certainly not time to completely abandon the crystal ball yet, the growing sophistication of predictive analytics is bringing the future into sharper focus for organisations who recognise that high quality customer/supplier/citizen data is the kingmaker for savings. These techniques can spot connections between vast quantities of data and predict what is likely to happen in different circumstances.
Predictive analytics have a number of unique applications within councils that are slightly different from commercial uses.
Take the situation in the London Borough of Croydon as an example. Just 30 troubled families in the borough account for £3m of council money spending on interventions. So you can just imagine the UK-wide figures. It’s a truly mind-boggling amount, but every council will have a similar list of problem families known to social workers, local police, and other council departments.
So how can the 30 families rack up such a mighty price tag? Imagine the case of Mary. She’s abused by her husband and taken to hospital. As a result, she misses another appointment with the council and her benefits are cut. She takes it out on the kids, who play truant at school and get involved in petty crime.
Regardless of the personal cost, the initial trigger of her husband’s violence (even though it probably wasn’t the first time) has a domino effect on other aspects of her life.
One of the problems with local government is that its services aren’t joined up. Yet the actions of one department can have ramifications for other departments in a butterfly effect. With budget cuts rife in councils, this situation is set to worsen. Cutting someone’s benefits, for example, will mean that they may be unable to pay their rent.
But what if councils were able to have all the information about Mary in one place, rather than held in individual department siloes? Then they could see that if one are of her life changes, how it is likely to have a knock-on effect in other areas of her life.
Several Universities in the ‘Northern Powerhouse’ are trialling analytics that can look out for these patterns. So for example, if someone has their benefits or funding cut off, they won’t be able to pay their housing bill or if you know that Mary has borrowed money then it’s an indication that she’s in trouble.
An intervention early on could help prevent the string of events that follow.
It sounds a bit Big Brother, but the benefits in terms of cost savings for the council and hopefully improved outcomes for the trouble families make it worth it.
The spanner in the works is the data protection lobbyists, who see this level of information held about people as too much of a personal intrusion. But to my mind it’s a quid pro quo: if you want councils to provide a fantastic service, then you need to give something back in return.
For me personally, I’d rather that the council could see I was in trouble and offer help rather than struggle on and do something stupid and the council would not delve into information that was truly private.
The tools and technology to predict these outcomes are becoming ever more commonplace, but councils need to know what kind of information about their citizens they need to capture and where do they want to capture it. The technology itself is secondary to that decision.