This page belongs to: Data strategy 2021–2024

3. Analytics – best-practice, fit-for-purpose analytics

Analytics lets us turn data into useable information, which is critical for developing and delivering evidence-based policy. The Australian Government and community expect us to use data to develop, implement and evaluate policies and programs.

We significantly improved our analytical capability under the previous data strategy. This included creating our Data Analytics Framework – a guide to best-practice data analytics in the department. 

Under this strategy we will:

  • embed analytics and visualisation capability at all levels of the department
  • modernise our data analysis processes to ensure they are fit-for-purpose and serve our strategic goals
  • give employees the tools and support they need for data analytics and visualisation that provide robust evidence using the best available data
  • continue to foster an analytics-rich culture
  • improve our analytical capabilities and make advanced analytics tools available to all employees. 

Key initiatives and activities

Embed best-practice analytics across the department

  • Identify the analytics needs and data sources that support each business area’s priorities.
  • Contribute to the new energy technologies 2020–21 Budget measure to improve energy and emissions data analytics, tools and reporting in alignment with the department’s Digital Strategy 2021–23.
  • Encourage employees to use data visualisation when communicating policy.
  • Provide learning and development opportunities that help employees use data analytics and visualisation to construct arguments.  
  • Promote the Data Analytics Framework and what it means for employees.
  • Develop and promote case studies that highlight important analytics work and global best practice. 

Expand self-service analytics and visualisation offerings

  • Make Power BI more accessible to content users and analysts.  
  • Explore more advanced analytics in Power BI and improve compatibility with other applications. 
  • Promote examples of best-practice self-service analytics and visualisation tools.

Provide accessible, ready-to-use analytics and support for decisions 

  • Provide more automated data products and reports, and make them more accessible in alignment with the department’s Digital Strategy 2021–23.
  • Explore options for automated data-cleaning in the department.
  • Support the use of advanced analytics to improve outcome monitoring and evaluation.

Improve data science tools and capability

  • Assess our data science tools and capability and identify improvements.
  • Enhance secure, enterprise-grade data tools for advanced analytics in alignment with the department’s Digital Strategy 2021–23.

Increase our understanding, management and use of qualitative data

  • Review and improve our use of qualitative data sources and analysis tools.
  • Improve metadata for our qualitative data, as well as our ability to use qualitative data from our CRM and other sources.

Data analytics in practice

Our Fraud and Audit team had to manually check large numbers of grant applications for fraud indicators. 

Wanting to improve the process, they developed a proof-of-concept system that used advanced artificial intelligence and machine learning techniques to check applications. 

Drawing on existing departmental datasets, the system showed that we can use data science techniques to automatically identify potential fraud indicators in grant applications.