Improving Inter-Agency Data Sharing Through Linkage Spine Interoperability

Main Article Content

Nick von Sanden


Linkage of Federal Government data in Australia is conducted primarily through Accredited Integrating Authorities (AIAs). These agencies hold different dataset from Commonwealth and state/territory government agencies. Historically, linkage projects involving data held by different AIAs has been inefficient, requiring the transfer of identifiable data between agencies, and relinking data that have already been linked by another agency.

Objectives and Approach
Two AIAs (the AIHW and ABS) have developed a system of interoperable linkage spines to address this issue. By using common datasets as a base, the agencies have improved the efficiency and security of linkage projects.

This process was developed through an analysis of spine datasets, and two test projects to share data between the agencies.

The two test projects were successfully able to link cross-portfolio and cross-jurisdictional data without the need to share additional identifying information between the AIAs. Preliminary results suggest a high linkage rate from this process, and work is underway to quantify the linkage quality compared to traditional linkage methodologies. The ABS and AIHW are also investigating the implications for linkage quality as more datasets are included in the agencies’ linkage spines.

Conclusion / Implications
The success of this project will increase the efficiency of cross-jurisdictional and cross-portfolio linkage in Australia. It will also allow specialised AIAs to work on datasets where they have specific expertise, and feed these into broader projects. This is expected to have an additional impact on public trust in the linkage system, by minimising the sharing of personally identifiable information while still maintaining high quality linkage.

Article Details

How to Cite
von Sanden, N. (2020) “Improving Inter-Agency Data Sharing Through Linkage Spine Interoperability”, International Journal of Population Data Science, 5(5). doi: 10.23889/ijpds.v5i5.1577.