Data Standards and Methods
The Data Standards and Methods Program provides critical conceptual and infrastructure support that underpins the ABS mission. The program is responsible for facilitating the comparability, integration, coherence and quality of ABS statistics, through the use of standard concepts, definitions, classifications, collection methods and procedures. The program aims to support the statistical collection process, mitigate statistical risk, enhance delivery capability, and provide statistical information management leadership to the external statistical community.
The program seeks to achieve this by:
- setting and reviewing ABS policies and principles for key statistical collection methods, standards and classifications, and for data and metadata management best practice
- planning and monitoring developments in the application of the corporate information architecture
- developing, maintaining and reviewing infrastructure used to hold key definitional metadata (e.g. classifications and standards) and to store statistical data from which ABS outputs are sourced
- providing leadership and expertise in the development and implementation of statistical and data management standards, practices and procedures within the ABS and National Statistical Service (NSS).
Key outputs from the program cover the following areas:
- development and maintenance of key national standards on statistical units, concepts, definitions, methods, questions and classifications that underpin population, social, economic and environmental statistics
- development and maintenance of key infrastructure and processes across ABS collections, including the Information Warehouse and Corporate Metadata Repository
- data management policies and best practice expertise to internal stakeholders
- data management and metadata guidelines and expertise for the NSS
- policy and technical research on emerging information architecture and related infrastructure issues
- autocoding facilities and computer assisted coding indexes
- training and assistance to subject matter areas in the use and/or development of statistical collection methods, standards and associated tools for their implementation
- design, develop and implement data and metadata management models, practices and policies for the NSS.
The main medium-term developments in the program are to:
- review the Australian and New Zealand Standard Classification of Occupations (ANZSCO) to ensure that it remains relevant by identifying emerging occupations – due June 2014
- develop the next generation of coding tools, processes and procedures – due June 2014
- improve the methodology for conduct and processing of Census of Population and Housing, for implementation in the 2016 Census – due June 2013
- implement updates to the 2006 Australian and New Zealand Standard Industrial Classification (ANZSIC 06) to ensure that it remains relevant by identifying emerging industries – due December 2014
- provide advice on standards for exchange of data within the ABS and NSS as part of the ABS 2017 Program – due June 2013
- establish international statistical standards and classifications development and maintenance processes as part of the IMTP – due June 2014
- promulgate relevant standards for population, social, economic and environmental statistics on the ABS intranet and the ABS website – ongoing
- undertake and advise pathfinder projects to evaluate the application of the Data Documentation Initiative and Statistical Data and Metadata Exchange standards to the ABS information environment – ongoing
- contribute to the development of the ABS enterprise architecture by developing information management policies and guidelines – ongoing
- develop and implement best practices and standards for collecting survey data by web-forms and other electronic modes – ongoing
- re-develop, align and integrate key data and metadata management infrastructure with mainstream input processing, output production and data archiving systems as part of the implementation of ABS 2017 – ongoing.
Data Standards and Methods Branch
This page last updated 8 August 2012