The Business Intelligence Analytics Standard helps to ensure that entities conduct business intelligence analytics activities in a secure, ethical, effective, and efficient manner. Several technologies are available and widely used across Commonwealth entities. These can assist as the Australian Government commits to collecting and analysing data to assess whether policies and services are achieving their intended purpose and are being implemented in the best possible way. By harnessing emerging analytics tools such as machine learning, entities can predict service needs, improve user experience, support evidence-based decisions, and gain efficiencies in operations.
It is critical this standard be considered alongside those of complementary capabilities:
- Data Analytics
- Information Asset Security
- Information Asset Management
- Permissions
- Privacy
Comply with legislation
Entities must:
- comply with relevant Commonwealth legislation including (but not limited to):
- Archives Act 1983 (Cth)
- Data Availability and Transparency (DAT) Act 2022 (Cth)
- Privacy Act 1988 (Cth)
- comply with any other legislation applicable to specific functions and circumstances.
Entities should:
- make use of the Guide to Data Analytics and the Australian Privacy Principles, by the Office of the Australian Information Commissioner. This guide provides information about the Australian Privacy Principles (APPs) and how they apply to data analytics activities, which include (but are not limited to) big data, data mining and data integration.
Ensure privacy and security whenever business intelligence analytics are used
Entities must:
- make non-sensitive data open by default, in line with the public data policy.
Entities should:
- encrypt sensitive data in transit and at rest
- ensure the handling conditions, particularly access controls, attached to source data are respected when derivative use is made of that data, including its surfacing in analytics platforms
- consider the Guide to Data Analytics and the Australian Privacy Principles.
Establish and enforce data governance and quality standards
Entities should:
- prioritise the capture and storage of well considered, high-quality data that can be mapped and aligned for analytic purposes
- develop comprehensive data governance policies that define roles, responsibilities, and processes for managing data
- develop a data strategy in line with the Foundational Four
- design a robust business analytics architecture covering hardware, software, data flow, and storage options (on-premises or cloud), integrating internal and external data sources as well as centralised data repositories (data warehouses and/or data lakes).
Prioritise safe integration with data source(s)
Entities should:
- establish and enforce standards and protocols to ensure seamless connectivity between business intelligence analytics tools and data sources
- ensure that the provisioning of data for analytics does not negatively impact the functionality, including performance and stability, of the system hosting a data source
- develop a comprehensive data integration plan to ensure authorised users can easily and securely access data, and analytics tools can efficiently retrieve and process the information
- outline processes for data extraction and transformation
- provide guidelines for maintaining data quality and integrity during the integration of data sources with analytics tools.
Ensure the ethical use of data
Entities should:
- align business intelligence analytics activities with principles of integrity, respect, and transparency
- develop guidelines to identify and mitigate biases in data collection, analysis, and interpretation
- maintain transparency in data use and analytics processes by providing clear documentation and justification for analytical methods and decisions.
Monitor innovation and emerging trends in business data analytics
Entities should:
- stay informed about relevant emerging technologies such as artificial intelligence, machine-assisted decision-making, and big data analytics to enhance analytics capabilities
- evaluate and adopt new tools and methodologies to improve the effectiveness and efficiency of business intelligence analytics.
Prioritise sustainability and scalability
Entities should:
- develop long-term strategies for the sustainability and scalability of business intelligence analytics solutions, including planning for future growth and technological advancements
- ensure that the business intelligence architecture is scalable to accommodate increasing data volumes and complex analytics requirements.
Develop data and analytics capabilities of staff
Entities should:
- establish comprehensive training and development programs to enhance the data and analytics capabilities of staff
- consider providing opportunities for advanced training in areas such as machine learning, predictive analytics, and data visualisation.
The APS Data, Digital and Cyber Workforce Plan 2025-30 aligns with broader government strategies and provides a coordinated approach to attract, develop and retain people with skills in these areas of growing need.
The Foundational Four recommends a Senior Data Officer to oversee and coordinate data functions, including business intelligence analytics, and a Chief Data Officer for data analytics. As such, policy-focused entities may need to enhance their data capabilities to answer complex questions through advanced data analytics or data integration. This would require assessing and improving the quality of data assets and investing in staff data and analytics capabilities.
The Australian Public Service Commission’s Data Profession is open to all APS employees with an interest in learning about and working with data. The Profession caters to emerging and existing data specialists, sophisticated data users and producers through knowledge sharing, peer-based learning and career development opportunities.
Adhere to reuse principles
The Australian Government Architecture provides information for entities on Reuse.
Entities should:
- compare their requirements with those of other comparable entities and system functions, and seek to reuse learnings from preceding implementations
- consider specific functional and non-functional requirements prior to solution design or consideration of technology choice, including:
- volume and nature of information assets
- broader entity purpose
- performance and availability requirements
- privacy/sensitivity concerns
- meet the requirements of the Digital and ICT Reuse Policy.