The Path to Becoming a Data-Driven Public Sector by OECD

Author:OECD
Language: eng
Format: epub
Tags: science/governance
Publisher: OECD Publishing
Published: 2019-11-28T00:00:00+00:00

Policy evaluation

The first area in which the “evaluation and monitoring” of data can generate public value is in the process of evaluating the success, or otherwise, of policy interventions. In a well-functioning democratic society, the implementation of policy is scrutinised by a variety of actors. There are those who wish to ensure government resources have been effectively managed, those motivated by a desire to understand the impact of the intervention and those who may be looking for political opportunity to exploit. These actors have competing priorities, but the reporting of progress, particularly if done in public, offers an evidence-generating activity that can hold the tension of the spectrum between politically motivated and ideological claims at one end, and theoretically unbiased and rational evidence-based policy making at the other.

In this sense, the evaluation of policy after its implementation complements the use of evidence in the initial design and development of a policy intervention. The insights generated from evaluating policy are critical for iterating and developing new policy approaches and it is in responding to the evaluation of what has been done that governments can generate public value. Nevertheless, beyond the direct application of data in shaping government activity, “evaluation and monitoring” data serve an important role when shared in being used, and reused, to inform and equip politicians, journalists, academics and the wider public.

Increasing the amount of data associated with the outcome of a given policy allows for agile policy adjustments in the short term, but more importantly will generate better insights into the policy process in terms of accountability and learning in the mid- to long term. Those responsible for a given policy can establish whether their policies have had the desired effect or not and, if those data are published as OGD, so can other stakeholders. As a result, policy evaluation can turn into an open, inclusive and ongoing process rather than an internal, snapshot moment. The ability to reduce the lag between the design of a policy, its implementation and insights into its performance should not just have theoretical and conceptual value, but should provide the basis for rapidly informing “delivery” activity and remedying any unintended adverse effects (Höchtl, Parycek and Schöllhammer, 2016[47]). While the monitoring of performance might be prompted by a top-down desire for oversight and reporting on delivery, a DDPS is interested in how those insights can be analysed and, crucially, applied in improving performance based on a deeper understanding of the needs of the organisation and its users.

Carrying out retrospective evaluation and analysis is helpful in keeping an open mind as it encourages an ongoing learning from experience and stimulates efforts to adapt future policy as a result. Putting in place mechanisms to gather, and apply, new insights set an expectation that lessons will be learnt and new insights gained. Taking this approach enhances the ongoing quality of outcomes. The OECD/Bloomberg (2019[49]) report on Enhancing Innovation Capacity in Cities establishes that “those cities which evaluate … are better positioned to scale up innovative projects

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