Contents

Summary

Data quality is a crucial aspect of data management and plays a significant role in various business operations, decision-making processes, and planning strategies. It’s not just about having accurate data, but also about ensuring that the data is complete, valid, consistent, unique, timely, and fit for purpose.

Categories

The categories applicable to this standard are:

Information and Data Management
  • Data Governance
  • Data Lifecycle

Purpose

Poor data quality can have significant business implications, hence the importance of maintaining high data quality standards.

Data Quality is ensuring data is fit for its purpose and good enough to support the outcomes it is being used for. Data quality is mandated as part of the National Data Strategy and the Government Data Quality Framework must be followed.

Accountability for data quality across its lifecycle, including ownership and resolution of any identified risks, issues and non-conformities sits with the Information Asset and /or Data Owner

How to meet this standard

To be compliant, you need to ensure the Government Data Quality Framework standards are met.

What Information Asset Owners must do

  1. Identify and rank critical data quality items.
  2. Ensure relevant KPI and assurance reporting is created. As a minimum KPI's are to be reported on critical data sets.
  3. Ensure data quality remains within agreed tolerance range(s).
  4. Report any data quality risk or issue to the relevant board / forum and escalate appropriately.
  5. Assign data quality measures to projects, programs, and initiatives that are relevant to the various stages of project development.
  6. Ensure data quality is consistently managed with clear accountability and defined roles.

What Data Stewards must do

  1. Identify appropriate data quality measures to data collected, held, used or shared.
  2. Apply data quality rules in a timely manner to all data held in their business area.
  3. Carry out scans, profiling and audits to agreed timescales.
  4. Carry out root cause analysis on any non-conformities.
  5. Ensure data quality issues are not remediated without identifying and addressing the root cause.
  6. Ensure remediation work is carried out and reported in accordance with any pre-defined change control process.
  7. Ensure remediation carried out is updated in the agreed data quality issues log.
  8. Report to relevant owner(s) where remediation is not feasible, including the issue, impact, interim mitigation measures and resolution plan(s).
  9. Ensure data quality training and support is available to educate data owners and other stakeholders on the best data quality practices.

Declaring conformance with this standard

Conformance with the standard must be recorded every 12 months.

Owner and contacts

Standard owner
Saheel Sankriwala
Chief Technology and Data Officer
Other point of contact
DDT Standards
Team