This is a case study for Principle Q3: Assured quality.
The Department for Work and Pensions (DWP) publishes statistics on new National Insurance number (NINo) registrations to adult overseas nationals, on a quarterly basis. In 2017, following an assessment by OSR, the statistics had its National Statistics designation suspended. Since then, DWP have implemented a range of improvements leading to re-designation of the statistics in November 2020. One of the areas of greatest improvement is their quality assurance processes and how they communicate these procedures with users.
The team have worked hard to review their quality assurance processes and apply guidance from OSR’s Quality Assurance of Administrative Data (QAAD) toolkit, as discussed in their Quality report. Applying guidance from QAAD supports good practice in monitoring the quality of data over time and in identifying data quality issues, whilst the Quality report itself demonstrates transparency about the quality assurance approach taken.
More recently, the production team have focused on harnessing aspects of Reproducible Analytical Pipelines (RAP). DWP have a separate dedicated team focused on introducing RAP across the department. Working closely with them, the production team – recognizing the benefits associated with quality assurance, streamlining and automating the production of the publication – have started to adopt many of the RAP principles. This has also involved team members upskilling and developing their programming skills. These are DWP’s first statistics to be produced using RAP and the team continues to develop and further integrate RAP principals into its production processes.
To further explain the quality assurance processes, the administrative data and the strengths and limitations of the statistics, an informative background and methodology document is available for users. This includes illustrative figures of the data journey – showing how different data sources fit together – and flow diagrams documenting the steps in the quality assurance process. The inclusion of these helpful diagrams, alongside descriptions and further detail within the text, offers users a clear explanation of the strengths and limitations of the data, and enables an understanding the underlying data and processes.
This case study shows how DWP has made improvements to how it assures the quality of its NINO statistics and how it communicates this assurance transparently to aid users in their appropriate interpretation. The enhanced quality assurance processes and their clear communication through new documentation, reflect a positive and engaged approach to enhancing the quality of the statistics and improvements to their overall public value.