Administrative Data Quality Assurance Toolkit

1 February 2015
Last updated:
15 December 2022

Quality management actions

Assessors will look for evidence that producers have considered the following quality management actions: Investigate; Manage; Communicate. They represent three types of actions for assuring the suitability of the administrative data and in documenting the findings:

  • Investigate: Statistics producers should investigate, for example, the types of checks carried out by data collectors and suppliers, as well as the operational circumstances in which the data are produced. They should identify any coverage issues and potential sources of bias in the data collection and supply process.
  • Manage: Producers should also manage their relationships with suppliers by establishing clear processes for data provision and for managing change. They should also maintain regular quality assurance checks of the data and use other data sources where possible to corroborate their findings.
  • Communicate: Producers should communicate effectively with their data suppliers and others to ensure users are provided with clear explanations of the strengths and limitations of the data. Producers should work closely with other statistical producers using the administrative data to ensure a common understanding of any quality issues.

As illustrated in the diagram below, these practices are continuous and iterative, reflecting the ongoing use of the data and the dynamic nature of operational environments.

This review of administrative data should not be regarded as a one-off event, but is rather a process that requires repeated evaluation to understand the implications of changes and allow for the ongoing monitoring of the data quality.

Assessors will identify evidence showing the ongoing review of the administrative data by statistics producers.

Quality management actions


Such as:

  •  Data suppliers’ own QA arrangements
  •  Results of external audit of the admin data
  •  Areas of uncertainty and bias
  •  Distortive effects of targets and performance management regimes


Such as:

  • Cooperative relationship with suppliers, IT and operational, and policy officials
  • Guidance information on data requirements
  • QA checks and corroboration against other sources


Such as:

  • Description of data collection process
  • Regular dialogue with suppliers and providers
  • Document quality guidelines for each set of statistics
  • Description of errors and biases and their effects on the statistics
  • Communicate with users
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