Data-centric public services as potential source of policy knowledge. Can "design for policy" help?

被引:1
作者
Leoni, Francesco [1 ]
Carraro, Martina [1 ]
McAuliffe, Erin [1 ]
Maffei, Stefano [1 ]
机构
[1] Politecn Milan, Dept Design, Milan, Italy
关键词
Data-driven innovation; Analytical policy capacity; Data for policy; Data-centric public services; Design for policy; BIG DATA; DATA SCIENCE; BENEFITS; CYCLE; IF;
D O I
10.1108/TG-06-2022-0088
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
PurposeThe purpose of this paper is three-fold. Firstly, through selected case studies, to provide an overview of how non-traditional data from digital public services were used as a source of knowledge for policymaking. Secondly, to argue for a design for policy approach to support the successful integration of non-traditional data into policymaking practice, thus supporting data-driven innovation for policymaking. Thirdly, to encourage a vision of the relation between data-driven innovation and public policy that considers policymaking outside the authoritative instrumental logic perspective. Design/methodology/approachA qualitative small-N case study analysis based on desk research data was developed to provide an overview of how data-centric public services could become a source of knowledge for policymaking. The analysis was based on an original theoretical-conceptual framework that merges the policy cycle model and the policy capacity framework. FindingsThis paper identifies three potential areas of contribution of a design for policy approach in a scenario of data-driven innovation for policymaking practice: the development of sensemaking and prefiguring activities to shape a shared rationale behind intra-/inter-organisational data sharing and data collaboratives; the realisation of collaborative experimentations for enhancing the systemic policy analytical capacity of a governing body, e.g. by integrating non-traditional data into new and trusted indicators for policy evaluation; and service design as approach for data-centric public services that connects policy decisions to the socio-technical context in which data are collected. Research limitations/implicationsThe small-N sample (four cases) selected is not representative of a broader population but isolates exemplary initiatives. Moreover, the analysis was based on secondary sources, limiting the assessment quality of the real use of non-traditional data for policymaking. This level of empirical understanding is considered sufficient for an explorative analysis that supports the original perspective proposed here. Future research will need to collect primary data about the potential and dynamics of how data from data-centric public services can inform policymaking and substantiate the proposed areas of a design for policy contribution with practical experimentations and cases. Originality/valueThis paper proposes a convergence, yet largely underexplored, between the two emerging perspectives on innovation in policymaking: data for policy and design for policy. This convergence helps to address the designing of data-driven innovations for policymaking, while considering pragmatic indications of socially acceptable practices in this space for practitioners.
引用
收藏
页码:399 / 411
页数:13
相关论文
共 66 条
  • [1] AlgorithmWatch, 2020, Automating society report 2020
  • [2] [Anonymous], 2019, Government at a glance 2019, DOI [10.1787/8ccf5c38-en, DOI 10.1787/8CCF5C38-EN]
  • [3] [Anonymous], 2015, DATA DRIVEN INNOVATI, DOI DOI 10.1787/9789264229358-EN
  • [4] [Anonymous], 2017, The Global Innovation Index 2017-Innovation Feeding the World
  • [5] Ansell C., 2014, Public innovation through collaboration and design, DOI DOI 10.4324/9780203795958
  • [6] The hidden potential of call detail records in The Gambia
    Arai, Ayumi
    Knippenberg, Erwin
    Meyer, Moritz
    Witayangkurn, Apichon
    [J]. DATA & POLICY, 2021, 3
  • [7] Data science in the design of public policies: dispelling the obscurity in matching policy demand and data offer
    Arnaboldi, Michela
    Azzone, Giovanni
    [J]. HELIYON, 2020, 6 (06)
  • [8] Beyond prediction: Using big data for policy problems
    Athey, Susan
    [J]. SCIENCE, 2017, 355 (6324) : 483 - 485
  • [9] Big data and public policies: Opportunities and challenges
    Azzone, Giovanni
    [J]. STATISTICS & PROBABILITY LETTERS, 2018, 136 : 116 - 120
  • [10] Bason C., 2014, Design for Policy, V1