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.

中文翻译：

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为社会政策实施数字基础设施时的技术考虑

大数据和先进的计算方法越来越多地用于为全球社会政策的决策提供信息。因此，迫切需要确定最佳实践数字基础设施设计，使决策者和社会科学研究人员能够以合乎道德的方式访问、操纵和使用大数据，同时识别和解决可能导致意外后果和不利社会政策的问题结果。然而，对于大社会和行政数据的用户来说，构建这样的数字基础设施仍然是一个技术挑战。本文提出了一个模型来评估和设计在社会政策中使用大数据的最佳实践基础设施。我们的模型确定了数据分析管道六个阶段的关键技术基础设施考虑因素，即（1）数据存储，(2) 数据集成，(3) 数据访问，(4) 数据分析，(5) 数据解释和 (6) 数据操作化。我们通过两个应用程序演示该模型：E-Verify 在线就业权利系统和澳大利亚 COVIDSafe 应用程序。该模型为社会政策制定者和研究人员在设计或升级使用来自多个来源的分析工具和大数据集的数字基础设施时系统地考虑相关技术考虑因素提供了高级指南。