For public sector organisations today, data has become a highly valuable commodity. According to the Centre for Data, Ethics and Innovation (CDEI), it’s “a powerful asset for local authorities”, helping them to make better decisions, allocate resources more efficiently and create greater transparency for citizens. The CDEI report cites the recent pandemic as having given a huge boost to the use of data, for example using information at local authority level to identify vulnerable residents or using population-level data to contain local outbreaks.
More recently, councils have been adopting machine learning (ML) and exploring artificial intelligence (AI) to improve and streamline how services are delivered. Already, a huge range of potential applications for this high-profile technology are being put to work. AI is being used to review and help manage planning applications, collect business rates more effectively, take 99% of the effort out of analysing clinical data, and to make low-cost preventive maintenance possible using internet of things (IOT) connected devices.
Large data sets can be hard to manage if not properly connected and organised. Organisations of all sizes will be familiar with the huge headache of working with data that’s held in different forms across different systems, for example on a mix of spreadsheets, paper documents and legacy on-premise systems. Our research of public sector professionals shows that 85% believe missing, incomplete or inaccurate data is negatively impacting customer experience. The administrative friction this creates has driven the trend towards migrating onto cloud systems – a move that requires clean and well-organised data to be successful. Effective master data management is the answer – it can be used to constantly cleanse and maintain data to ensure disparate systems can be joined up to provide a single view of the citizen.
It’s worth noting that AI and ML models can sometimes pick up on imperfections and biases in data sets, creating outcomes that are sometimes the opposite of what the service provider wanted. When exam boards had to predict grades based on past performance, for example, a predictive analytics model downgraded high-performing students from schools that had performed poorly, because those students tended not to do well. And in the Netherlands, an algorithm designed to identify benefits fraud was found to discriminate based on race and gender. Importantly, it did this even though it had deliberately not been given applicants’ race or gender. Instead, it had used other data, such as Dutch language proficiency, as a proxy.
Stories like these can make people nervous about sharing their data, just at a time when public sector organisations have an opportunity to build valuable new services on it. However, a recent Boston Consulting Group report shows that people largely support sharing their data to improve services, especially with the NHS. So, part of any successful data-driven strategy will involve being transparent about how data is used, and for what purposes.
So what can local authorities and other public sector organisations do to ensure that their data is more of an asset?
These focus areas can help make data intro a true asset – and the solid foundation for more efficient policy decision making and improved data-driven public services.
Originally posted here