Big-data analytics: the power of prediction

Rachel Willcox in Public Finance: “The ability to anticipate demands will improve planning and financial efficiency, and collecting and analysing data will enable the public sector to look ahead…

Hospitals around the country are well accustomed to huge annual rises in patient numbers as winter demand hits accident and emergency departments. But Wrightington, Wigan and Leigh NHS Foundation Trust (WWL) had to rethink service planning after unprecedented A&E demand during a sunny July 2014, which saw ambulances queuing outside the hospital. The trust now employs computer analysis to help predict and prepare for peaks in demand.

As public sector organisations grapple with ever-tighter savings targets, analysis of a broad range of historical data – big data analytics – offers an opportunity to pre-empt service requirements and so help the public sector manage demand more effectively and target scarce resources better. However, working with data to gain insight and save money is not without its challenges.

At WWL, a partnership with business support provider NHS Shared Business Services – a 50:50 joint venture between the Department of Health and technology firm Sopra Steria – resulted in a project that uses an analysis of historical data and complex algorithms to predict the most likely scenarios. In September, the partners launched HealthIntell, a suite of data reporting tools for A&E, procurement and finance.

The suite includes an application designed to help hospitals better cope with A&E pressures and meet waiting time targets. HealthIntell presents real-time data on attendances at A&E departments to doctors and other decision makers. It can predict demand on a daily and hourly basis, and allows trusts to use their own data to identify peaks and troughs – for example, the likely rise in attendances due to bad weather or major sporting events – to help deploy the right people with the right expertise at the right time….

Rikke Duus, a senior teaching fellow at University College London’s School of Management, agrees strongly that an evidence-based approach to providing services is key to efficiency gains, using data that is already available. Although the use of big data across the public sector is trailing well behind that in the private sector, pressure is mounting for it to catch up. Consumers’ experiences with private sector organisations – in particular the growing personalisation of services – is raising expectations about the sort of public services people expect to receive.

Transparency, openness and integration can benefit consumers, Duus says. “It’s about reinventing the business model to cut costs and improve efficiency. We have to use data to predict and prevent. The public-sector mindset is getting there and the huge repositories of data held across the public sector offer a great starting point, but often they don’t know how to get into it and skills are an issue,” Duus says.

Burgeoning demand for analytics expertise in retail, banking and finance has created a severe skills shortage that is allowing big-data professionals to command an average salary of £55,000 – 31% higher than the average IT position, according to a report published in November 2014 by the Tech Partnership employers’ network and business analytics company SAS. More than three quarters of posts were considered “fairly” or “very” difficult to fill, and the situation is unlikely to have eased in the interim.

Professor Robert Fildes, director of the Lancaster Centre for Forecasting, part of Lancaster University Management School, warns that public sector organisations are at a distinct disadvantage when it comes to competing for such sought-after skills.

The centre has worked on a number of public sector forecasting projects, including a Department of Health initiative to predict pay drift for its non-medical workforce and a scheme commissioned by NHS Blackpool to forecast patient activity.

“The other constraint is data,” Fildes observes. “People talk about data as if it is a uniform value. But the Department of Health doesn’t have any real data on the demand for, say, hip operations. They only have data on the operations they’ve done. The data required for analysis isn’t good enough,” he says….

Despite the challenges, projects are reaping rewards across a variety of public sector organisations. Since 2008, the London Fire Brigade (LFB) has been using software from SAS to prioritise the allocation of fire prevention resources, even pinpointing specific households most at risk of fire. The software brings together around 60 data inputs including demographic information, geographical locations, historical data, land use and deprivation levels to create lifestyle profiles for London households.

Deaths caused by fire in the capital fell by almost 50% between 2010 and 2015, according to the LFB. It attributes much of the reduction to better targeting of around 90,000 home visits the brigade carries out each year, to advise on fire safety….(More)”