Are all policymakers data scientists now? Data, data science and evidence in policymaking

In this piece we reflect on the state of affairs with regard to the adoption of data science methods in the public sphere. The analysis looks at the potential adoption these tools according to three dimensions: pace, penetration, and profundity.

Traditional government bureaucracies operated in a world of official statistics and filing systems, but without transactional data that might be used to inform policymaking, administrative design or the provision of public services. In a world powered by data science – there are many ways in which policymaking can be informed by data and ‘data-driven’. There has been a step change in the availability of data and methods of analysis. It is no longer acceptable to rely on custom-built statistics and consultants’ reports to inform high level policy discussions. We need to embed data science models into every stage of the policy process; understanding a population that the policy is to serve; running predictions on demand; elaborating detailed and sophisticated models for counter factual analysis and intervention; and simulating policy interventions to discover unintended consequences before policy interventions are introduced. These opportunities for policymaking place new demands on civil servants, and rapid updating of practices, standards and tools and the breaking down of boundaries between technical/analysis and non-technical/generalist teams.