he phrase “predictive analytics” holds a lot of promises — about solving problems before they occur, better service outcomes, targeting scarce resources, supporting decisions and improving responses in time critical situations.

With so much opportunity available, how can government start to consider the risks and benefits associated with this technology?

The first step is to make sure that everyone is on the same page with a universal definition of what this complex and difficult term means. Gartner defines predictive analytics as:

“A form of advanced analytics which examines data or content to answer the question “What is going to happen?” or more precisely, “What is likely to happen?”, and is characterized by techniques such as regression analysis, forecasting, multivariate statistics, pattern matching, predictive modeling, and forecasting.”

The scope of predictive analytics to develop an understanding of hidden needs and future issues is huge — it can accelerate decision-making and reserve precious budgets.

1. The benefits of predictive analytics in local government

The benefits of predictive analytics are widely recognised in the private sector. However, practical implementation in the public sector is more difficult with complex stakeholder groups and public perception playing a large role in adoption. Clarity on the benefits is central to gaining broader buy-in.

The Local Government Association (LGA) services identifies the core benefits as:

• Better outcomes — You can avoid committing resources to areas where they are not

required to refocus on those most in need.

• Targeting scarce resources — Risk stratification means that you can target the allocation of

resources, identify adverse events and monitor the impact of interventions.

• Decision support — Predictive analytics cannot replace human decision-making (that kind of

complexity is years’ away), but it can flag patterns before they emerge to enable individuals

to apply their contextual analysis to the data.

• Improving responses through time-critical alerts and decisions — Real-time data means

that analytics can identify high-risk individuals such as those vulnerable to homelessness,

students at risk of becoming NEET and medical emergencies.

The benefits to both local and central government are clear, but the budgetary pressures and citizen needs in central and local government are different and determine the key use cases and applications that you should consider. So, it’s no wonder that the LGA has set up the Advanced and Predictive Analytics Network in Local Government — this is a complex field that requires careful navigation.

2. Predictive analytics in local government & councils

Innovation tends to start where pressure is the greatest, and local councils have pioneered the use of predictive analytics to manage increasing demand and target limited resources. The Local Government Association (LGA) 2020 report “predictive analytics in local government” identified “a small number of genuinely ground-breaking applications which suggest this type of technology is on the brink of real take-off.” In UK councils, these include:

• Predictive case notes for children’s services to support social workers in identifying where

preventative steps may be needed to protect a child.

• Internet of Things devices in the homes of social care clients to predict health problems at an

early stage.

• Determining which households are at risk of becoming homeless before it happens and then

linking that data with text alerts to offer appointments to vulnerable groups.

These innovations help local councils to manage the explosion in data by distinguishing between key trends and bad data to improve outcomes, identify needs and reduce costly emergency interventions.

3. Predictive analytics in central government

The role of Artificial Intelligence, the Internet of Things, and Predictive Analytics in central government is focused on citizen services at a national level.

Key use cases include:

• Preventing accidents by using predictive analytics to predict which categories of drivers are at higher risk of accidents to drive personalised intervention.

• Detecting tax fraud and increasing tax collection through targeted programmes.

Predictive analytics in central government can drive the strategic evolution of citizen services across the public sector. It is a core strategic asset that improves citizen service, meets citizens’ expectations for relevancy and builds core capacity and efficiency.

Final thoughts

The HEFCE-funded Catalyst project at the University of Essex found that the successful use of predictive analytics in local government requires a readiness for organisational transformation and an investigation of ethical concerns.

But the journey doesn’t stop there — as with every solution in the public sector, the spectrum of risks and issues spans from technical design to policy goals and operational use. Any project stakeholders would be wise to heed the words of Dr Theo Gazos, founder of the Predictive Analytics Group, when he says,

“Government and the public sector generally is becoming more and more interested in the data analytics and predictive analytics. Once you show somebody a solution and they get the intuition behind it, they are sold.”

So, do the groundwork, rigorously test before scaling and then, once your solution is demonstrated, it can be adopted and expanded.

Note: This blog was originally published in Mastek here is the source link https://blog.mastek.com/how-to-use-predictive-analytics-in-local-and-central-government-to-identify-risks-and-target-resources