Understanding current and future needs of residents

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In my previous blog post, I outlined the challenges faced by local authorities across the country to consistently deliver resident services amidst increasingly stringent budgetary cuts. In this post, I will propose some suggestions. Whilst it certainly will not undo the financial tight spot authorities find themselves in, I believe it will help alleviate funding pressures and, over time, bring costs down to a manageable level.

Firstly, it is imperative to understand not just current resident needs, but future needs. Secondly, authorities collect data in abundance, and it is time to start using this data smartly. The introduction of analytics, machine learning and AI will equip authorities to find previously undetected insight from their data to improve decision making, in particular on which services to focus their attention.

Hidden Insights: Using Machine Learning to improve local services for residents

Using machine learning to improve local services for residents.

Understanding current and future needs of residents

Further cost pressures are likely to mean inevitable cuts or downsizing of services for authorities. The previous approach taken by councils has been to maintain those services with statutory responsibility, often at the expense of those that are not core. The National Audit Office (NAO) concluded that spending on social care services for adults and children, a statutory service, has been relatively stable. Conversely, spending has fallen in real terms for planning and development (52.8%), housing (45.6%), highways and transport (37.1%), and cultural and related services (34.9%).

There is absolutely no debate that adult and child social care are vital services and rightfully so, being a statutory service. However, nonstatutory services, many of which have been cut, bring a plethora of benefits. On the surface, they may not appear to be core. But in fact, they serve as a preventive measure to the use of statutory services. I believe that the key lies in first understanding the resident needs at a local level, then planning services accordingly.

One size does not fit all

Let us examine this in more detail. There is not a generic demographic in all boroughs. For example, one cannot say with certainty that all boroughs have x% of elderly, y% of adults requiring social care and z% of looked-after children. Some areas have a mix of age groups and needs, whilst others may have a higher than average proportion of elderly. Thus a one-size-fits-all approach will certainly not yield the best outcome. By understanding the specific needs of residents, authorities can target services accordingly.

Once authorities understand current needs, they can turn their attention to planning for future needs. For example, if there is a predicted increase in young families moving to a new area, housing will be needed. Additionally, all the support services for this new cohort of incoming young families will need upgrading or increasing: schools, health facilities, leisure clubs, etc. If there is a predicted increase in older people, then concomitant services for their needs, albeit of a wide range, will be required.

Using data smartly

How can authorities both understand current needs and predict future ones? The answer involves using data smartly. Councils currently collect a lot of data about their residents and the services they use. Predominantly they tend to collect and use it in isolation, mainly for the purpose of business intelligence. That is reporting internally and to central government and the various regulatory bodies. What I am proposing is not just using the data to report on what has happened, but to use all the data available to predict and forecast what will happen. I believe this approach will equip authorities to make the best possible decisions for their residents and the council itself.

This, in my opinion, will require a two-phase approach. Firstly, councils need to break down the silos in data from each department. This will allow them to build a true picture of the current situation. This will not be an easy feat. Though in their 2018 briefing paper, SOCITM found that authorities have started on this journey, stating, “There is a huge opportunity to exploit the growing volume of data to better target resources and activities, in the interests of both citizen needs and demands.”

Secondly, once that data is available, councils can apply analytics and intelligence to the abundance of data goodness to help make better decisions and develop smarter strategies that improve the delivery of services to the public. The applications (and subsequent benefits) of analytics in local government are vast.

A few examples to illustrate this include:

  • Improving resident services by analysing data from the resident contact centre to understand common complaints and identify vulnerable residents.
  • Modelling how services are used to maximise the benefit to residents, for example, libraries and leisure facilities.
  • Detecting fraud by building a more sophisticated view of trends and patterns in benefit transaction.
  • Improving health and social care integration.
  • Looking at previous school results to predict future results and working with Ofsted to improve outcomes where needed.

After all, the elected members use predictive modelling in their political campaigns for better use of resources at election time. Why not apply predictive modelling to council services and resources, much like supermarkets do with their customers?

#AI and #MachineLearning can be applied to the abundance of data goodness to help make better decisions and develop smarter strategies that improve the delivery of services to the public. Click To Tweet

It certainly feels like local authorities across the country are wedged between Scylla and Charybdis. And indeed, times are tough with looming cuts on the horizon. But I believe all is not lost yet. By evolving and making that investment in using data smartly and applying analytics to the vast array of services, councils will be able to ride out this tempestuous storm. In part 3 of this blog series, I propose an entry point for councils into the world of analytics by using text analytics on their resident contact centre data to understand what their residents are saying, see causes of dissatisfaction and identify vulnerable residents. Find out more here.

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About Author

Vanessa Hurhangee

Vanessa Hurhangee is an Analytics professional within the SAS UK Customer Advisory team. She has spent over 8 years at SAS, starting as trainer in the Education Team covering a range of SAS products. She has worked across a number of verticals and is currently focussed on the retail sector, enabling customers to gain greater value from their analytical ecosystem to support business decisions. She has a keen interest in SAS Analytics and Intelligent Decisioning and has written a number of articles looking at how it can be used to help customers deliver informed and personalised decisions swiftly. Prior to SAS she worked in a number of public sector organisations in a variety of roles, and continues to passionately engage in this area and her local community through her role as a local councillor.

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