The contemporary technological advancements in information and communication technologies (ICT) enable the employment of non-traditional data sources (e.g. satellite data, sensors, cell phone networks data, social media, etc.) in different aspects of the public sphere. Datafication is changing the relationship between governments and citizens, and the way governments address policy problems. Nowadays, policy-makers are urged to harness data for policies and public service design, while answering at the same time the demand for citizen engagement; as a consequence, innovative government/governance models appeared to connect these two instances. Although it is not a new concept, the model of Anticipatory Governance is particularly worth considering in light of contemporary data availability. Predictive analytics based on data increasingly realizes predictions for public action, although it presents many controversial implications (e.g. the epistemology of data evidence, public trust and privacy). In this article, we address Anticipatory Governance models emerging from data used in futures thinking and policy-making. To understand this phenomenon, we will briefly retrace current paradigms of futures thinking and Anticipatory Governance concerning policy-making, specifying the contemporary perspective design has on these topics. Then, we identify the use of data in futures thinking practices through a systematic literature search. Finally, we will address the challenges and implications of designing datadriven Anticipatory Governance by portraying three scenarios supported by real cases of data for policy-making.

Purpose The 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/approach A 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. Findings This 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/implications The 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/value This 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.

No abstract