by Emanuele Baldacci, Director of Digital Services at the European Commission's Directorate-General for Informatics
Public policies are increasingly focused on delivering value for money. This is, however, more challenging than ever in today’s ecosystem.
One the one hand, government budgets are tight and limited resources should be used well to yield expected benefits. On the other hand, demands for public policies to support the well-being of citizens and business growth are increasing.
Against this background, the policy intervention framework is challenged by several uncertainties: what is the context in which policies operate? How is this affecting their effects? Are behaviours by beneficiaries of public policies affected by both context and policies? Is this supporting or hampering the achievement of public policy goals?
In addition to these issues, demand for transparency and accountability of policy actions requires adequate instruments to help frame the dialogue with stakeholders on objectives, instruments and results of public actions.
While these challenges for policy making are important, in recent years advances in the availability of data from different sources and technologies to consume and interpret large amount of information offer a new perspective for policy design and execution.
There are three main priority areas where governments should invest to deliver better policy harnessing the power of data, information and knowledge to design, implement and assess their interventions. In all these areas, AI-powered data analytics solutions offer critical capabilities for the next generation of accountable evidence-based policies.
First priority is data integration. With multiple data sources increasingly available at low production cost (ranging from administrative data, to traces left by interaction of humans with Internet applications to sensor- and IOT-based data) data mash-ups can generate quality information through combination of different sources.
However, data integration is challenging as multiple sources also come with different formats and data models. Technologies based on machine learning algorithms can help integrate massive data volumes based on clustering, distance minimization and data profiling.
The development of open data spaces and libraries of data integration algorithms will help governments create the needed policy information capability.
Second, AI-based predictive analytics can be used to assess policy effects at design stage. Using integrated data systems and AI data analytics can help simulate at very low cost expected results of different policy interventions. This allows mapping winners and losers of planned actions and possible second-round effects of public measures. Simulating beneficiaries responses and behaviours with these technologies would make policy design more realistic and credible.
This can assist in optimizing the design of public measures and also help engage open communities in policy design and dialogue at the early stage of the government intervention cycle.
Third, assessing policy outcomes and intervention sustainability can benefit greatly from a data-driven approach. AI-powered solutions can help identify the additionality of these measures, by simulating different scenarios based on large volume integrated data. Advanced regression-based techniques can be used to conduct quasi-experimental analysis on data sets, which integrate administrative data about intervention beneficiaries with context data and information about target and control group characteristics.
These are three key areas where AI-based solutions coupled with large volume integrated data spaces can be a game changer for evidence-based policy making. Governments in Europe and beyond are now investing on these priorities. The European Commission has also launched a corporate initiative to harness data for better policy making services through the Data4policy initiative, in the context of the implementation of the Communication on Data, Information and Knowledge Management.
Unlocking the power of data analytics for evidence- based policies is now more possible than ever. Governments face similar challenges and opportunities and cooperation is essential to reuse generic solutions and progress faster to design, execute and assess public policies based on data-driven approaches.
- data integration accountable evidence-based policies multiple data sources predictive analytics
In reply to Thanks Emanuele for the by Marius Andreiana