This page belongs to: Data strategy 2021–2024

2. Governance – optimise our data governance and management

Data governance means having authority and control over data assets, including planning, monitoring and following best-practice. It guides all other data management functions including metadata management, data quality and data security.

Our Data Governance Framework sets out the rules, processes and roles that help us ensure robust data management and governance practices. Good data governance minimises risk, establishes rules for using data, helps us meet compliance requirements, improves communication, increases the value of data, and reduces the cost of managing data.

Our data management and governance is already mature. To date we have focused on compliance with our data standards, and identifying and managing data for analytics. We have also established a Data Governance Board made up of departmental executives to champion the effective, safe and ethical use of data.

However, the amount of data we are responsible for is growing. This creates new challenges for employees to navigate, analyse and interpret data appropriately. Given this, we need to improve our data governance to:

  • ensure the security of our data holdings
  • improve evidence-based advice
  • use automation to minimise manual intervention and human error
  • continue considering the whole data lifecycle
  • prioritise our most important data assets
  • be more dynamic.

Key initiatives and activities

Make data assets more discoverable and accessible

  • Ensure the department collects key metadata in line with our data standards.
  • Improve data cataloguing. This includes being able to search for datasets and access metadata and documentation related to data assets.  
  • Make data easier to find and access on DataHub, our internal data repository. This will help employees use the right data for the right purpose.  

Make data governance more efficient 

  • Proactively develop our current and emerging priority data assets to better connect activities with business and economic outcomes.
  • Use automation to reduce the administrative burden of governing and managing our assets.
  • Update our data governance framework and implement a self-service governance model for data stewards and custodians.

Ensure data is governed, well managed and secure across the whole data lifecycle

  • Ensure good governance for data systems we design and develop. This includes data security and risk management approaches in line with the Digital Transformation Agency’s whole-of-government Hosting Strategy and the department’s Digital Strategy 2021–23. 
  • Update our data collection standards, including incorporating relevant national and international standards. 
  • Review and streamline data management practices, including certifying data and collecting core data items, to ensure they are fit for purpose. 
  • Finalise and implement our Data Quality Framework so that data quality issues are managed consistently across the department.  

Data governance in practice

Sarah wanted to include data about support for small-to-medium businesses in a policy proposal.

She used our dataset register to access information about the ‘Entrepreneurs’ Programme – Accelerating Commercialisation Grants’ internal dataset. She contacted the data steward listed on the dataset, who checked and cleared the data in Sarah’s proposal.

Sarah could then finalise the briefing, confident she is using accurate data that can be shared.