Quality Assurance: Four key areas of practice

Data collection and operational context

Gain an understanding about the data sources and the nature of the collection processes.

  • What factors might risk the quality of the data?
  • Are targets or performance management regimes used?
  • How many data collector and supplier bodies are there?
  • What are the information governance arrangements?

Communication with supply partners

Effective relationships with suppliers stem from a common understanding.

  • Is there a service level agreement or memorandum of understanding?
  • How do they manage changes to processes and systems?
  • Are data needs thought about?
  • Do the partners meet up often?

Data suppliers’ quality assurance

Gain an understanding of the validation checks conducted by suppliers and other partners, and find out the results of the checks. For example:

  • Have there been operational inspections of the data records?
  • Are independent audits conducted?
  • What have checks shown?

Statistical producers’ own checks

Consistency and sense checks help show whether the data is meaningful.

  • Can you explain changes in trends and discontinuities?
  • Why are you confident of the data?
  • Do you explain to your users about any important implications stemming from data quality issues?