Who are they?
Cleveland is a geographically small area of 230 square miles. It has a resident population of 560,000 in a mix of densely-populated areas, many of them with high levels of deprivation, although there are more affluent neighbourhoods and rural areas. Cleveland Police has a force of around 1,600 and uses a threat and risk-based approach to define policing and resource allocation.
What did they do?
Police data is often collected in difficult conditions or under time pressures, resulting in a high level of duplicate or incomplete records. Cleveland Police discovered that it had 1,867,000 people recorded in its system, three times the level of the region’s actual population. The impacts from this data quality problem were directly on the service’s ability to respond to the public because of the performance delays caused by searching through duplicates. Removing these would release resource and budget by reducing manual effort in linking them. In a first-of-a-kind initiative, the service adopted a “golden nominal” programme which was co-funded by the Home Office Innovation Fund. This police version of a single customer view involved managing data at scale and across multiple IT systems. Working with Experian, external reference data was used to verify records and remove duplicates. Intelligent reversible matching rules were adopted to ensure the highest possible number of credible matches based on a number of defined scenarios. The cleansed database is now available to call handlers alongside the full Electoral Roll, allowing them to look-up caller details, match to an existing record or create a new record instantly as required. These mean the data source continues to be accurate and comprehensive. One metric of success for the initiative was to reduce the level of exact duplicates at point of entry by 20% – in fact, the current figure is zero. Any records with a high probability of being duplicates are automatically merged within 24 hours, with a manual review and merge process for less probable ones. Growth in new records has fallen by 56% per week based on a 42-week comparison ofthe database’s size before and after the system implementation. Importantly, call handlers are now able to access existing records instantly and create new records with little or no data input, leading to quicker response times from reduced call handling. Costs estimated at over £250,000 from inputting, processing, reviewing and amending poor quality data have been removed. Cleveland Police has a decision model called THRIVE to identify the most appropriate response to emergency calls, such as ensuring police officers are on the scene as quickly as possible. The Golden Nominal has helped to improve the accuracy of this model, while also enhancing how resources are deployed within the service’s financial constraints and helping the service to safeguard vulnerable citizens better.
What did the judges say?
A remarkable solution for its scope and ambition which is now serving as a blueprint for a step change across the business globally.
