Technical considerations when implementing digital infrastructure for social policy. Issue 2 (8th October 2020)

• Record Type: Journal Article
• Authors: Gulliver, Robyn; Fahmi, Marco; Abramson, David
• Is Part Of: Australian journal of social issues. Volume 56:Issue 2(2021)
• Page Start: 269
• Page End: 287
• Publication Date: 2020-10-08
• Subjects: big data -- data analytics -- digital infrastructure -- evidence‐based policymaking -- public policy; Australia -- Social policy -- Periodicals; Social service -- Australia -- Periodicals; Australia -- Social conditions -- Periodicals; Social conditions; Social service; Social policy; Australia; Periodicals; 362.99405
• DOI: 10.1002/ajs4.135
• Languages: English
• ISSNs: 0157-6321

Abstract

Abstract: Big data and advanced computational methods are increasingly being used to inform decision making in social policy globally. As a result, there is a pressing need to identify best practice digital infrastructure design that allows policymakers and social sciences researchers to access, manipulate and use big data soundly and ethically, while identifying and resolving issues that can lead to unintended consequences and adverse social policy outcomes. However, building such digital infrastructure continues to be a technical challenge for users of big social and administrative data. This paper presents a model to evaluate and design best practice infrastructure for the use of big data in social policy. Our model identifies key technical infrastructure considerations for six stages of a data analysis pipeline, namely (1) data storage, (2) data integration, (3) data access, (4) data analysis, (5) data interpretation and (6) data operationalisation. We demonstrate the model via two applications: the E‐Verify online employment rights system and the Australian COVIDSafe app. The model provides a high‐level guide for social policymakers and researchers to consider systematically the relevant technical considerations when designing or upgrading digital infrastructure that uses analytical tools and big datasets from multiple sources.