Framework by capability
This guide outlines the 26 data capability areas defined in the APS Data Capability Framework. Each has capability indicators that span across three proficiency levels of foundation, intermediate and advanced
Proficiency level definitions
Advanced |
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Intermediate |
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Foundation |
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1. Value organisational data as assets
Understanding the value and use of data and treating organisational data accordingly. This includes drawing insights from data for evidence-based decisions and recommendations.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
2. Data communication
Effectively communicating with data or about data with a range of audiences.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
3. Improvement and innovation - Data processes/systems and tools/products
Identifying and implementing change to create efficiencies and new opportunities by making existing processes, systems, tools and products better and/or creating new ones.
4. Data governance
Developing and/or implementing a collection of practices and processes, which help ensure the formal management of data assets within an organisation.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
5. Data availability
Identifying existing and new data sources that can be accessed and used.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
6. Data access
Obtaining or retrieving data.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
7. Sourcing and use of administrative data
Obtaining and using information which is collected by government departments, businesses and other organisations for a range of reasons such as registrations, sales and record keeping.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
8. Data collection
Gathering and measuring data on variables of interest, in an established and systematic fashion.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
9. Subject matter expertise
Applying knowledge and expertise in a specific subject, area, or program.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
10. Identify research questions
Determining questions that enable a topic of interest to be investigated through qualitative and quantitative research.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
11. Data outputs, products, or services
Delivering data-related useable items and services.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
12. Data collection methodology
The methods and standards relating to the collection of data.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
13. Data integrity and quality assurance
Applying measures and practices to ensure that data is fit for purpose. Includes data validation as well as ensuring data is not unintentionally changed along its lifecycle.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
14. Statistical concepts and methodologies
Understanding and/or applying the methods and terms relating to statistical techniques.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
15. Data and information management
Gathering data and then analysing, categorising, contextualising, and maintaining it (and in some cases, destroying) as an organisational resource.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
16. Data classification
Grouping a set of related categories in a meaningful, systematic, and standard format, for example, country or region.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
17. Integrate data
Combining multiple datasets together to form a larger dataset, aiming to maximise the value of the data.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
18. Data editing
Checking data for consistency, errors and outliers, and correcting errors.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
19. Metadata - describe and summarise data
Defining and describing data to effectively manage and accurately interpret it. Includes information about data, such as its size or creation date.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
20. Data use and re-use
Enabling data to be used for an immediate purpose, as well as appropriately re-used for alternative purposes.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
21. Data processing methodology
Understanding and/or applying statistical procedures used to deal with intermediate data and statistical outputs, for example, weighting schemes, statistical adjustment, or methods for imputing missing values or source data.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
22. Exploratory data analysis
Analysing datasets to describe their main characteristics, for example, the distribution of variables.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
23. Visualise data
Translating data into a visual context, including maps, charts and graphs, making data easier to interpret.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
24. Statistical data analysis
Analysing data using statistical measures and methods to produce informative statistics.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
25. Specialist data analysis
Analysing data in specialist areas such as geospatial analysis and timeseries forecasting.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |
26. Business intelligence data analysis
Analysing data from business operations that inform the organisation's strategic and operational business decisions.
Advanced |
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Intermediate |
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Foundation |
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Associated Categories |