A pragmatic roadmap with five priorities for public sector data sharing

Governments increasingly recognize that administrative data are not just a by-product of public services. Used responsibly and effectively, they can improve service delivery, play a vital role in the national digital agenda, and inform policy against a global backdrop of declining response rates to statistical surveys. But the path from “data exists” to “data improves decisions” is rarely straightforward and often requires data sharing across institutions. Indeed, evidence shows that data sharing initiatives can generate US$5 in social returns for every dollar invested. Data sharing by mobile operators supported Nepal's 2015 earthquake relief; while in Brazil, the government has linked health and social data coming from different public agencies and given access to accredited researchers in order to study the impact of health and social policy on over 100 million citizens. The harder truth is that most governments do not fail at data sharing because they lack databases. They fail because the institutional conditions for safe, sustained, and useful sharing are weak. Public agencies often assume that another department has a reservoir of clean, well-documented, timely data, and all it takes is for the gatekeepers to switch the taps on. But in practice, administrative systems have usually evolved over decades and come with no instruction manuals. With each fiscal policy change, new tables appear in a legacy database. Decades later, understanding this data requires tacit knowledge spread across multiple individuals and teams. The data may be valuable, but it is rarely ready. Recent World Bank Group data governance assessments in the Indian state of Kerala, as well as in Mozambique, point to the same lesson from different contexts: data governance is “soft infrastructure” as much as technology. The state of Kerala had already developed sophisticated resilience-related platforms and applications, but the diagnostic found that many initiatives were not underpinned by a common set of standards, methods, and policies. Mozambique’s diagnostic similarly emphasized that a robust data governance framework is required to support data collection, management, and sharing within the national digital ecosystem. That is why public sector data sharing needs to be designed as an operating model, not a one-off transaction. Elements of technical architecture such as APIs, Trusted Research Environments, and interoperable standards are important, but not sufficient by themselves. Governments also need to establish a vision for data sharing, assign leadership, fund stewardship, build trust, and create incentives for agencies to participate. A pragmatic roadmap should begin with five priorities: 1. Start with a clear public purpose For data sharing to be sustainable, governments must define the benefits they aim to generate and ask themselves: Which specific government priority or transformation program will it support? Alignment with a strategic program creates accountability and urgency, as well as buy-in from agencies and the public whose data is being shared. This matters because vague data-sharing mandates quickly lose momentum. A request to “share more data” often sounds more like a new administrative burden than a mission to improve public services and transparency. Purpose also helps governments decide what not to do. Not every dataset should be linked. Not every data project deserves equal priority. A clear use case helps focus scarce capacity on the datasets, standards, safeguards, and partnerships that can generate the greatest public value. 2. Build governance around an anchor institution Cross-government data sharing requires a sustainable data ecosystem and a credible institutional anchor. For statistics and policy research, national statistical offices are the obvious place to start, while central digital authorities (the government entities responsible for digital standards and shared platforms) and/or digital service delivery units are best for operational data sharing. Health, tax, education, social protection, and civil registration agencies often bear the cost and risk of sharing data while others reap the analytical benefits. Expecting busy operational teams to curate, document, anonymize, negotiate, and support data users on the side is unrealistic. Dedicated gateway teams, with data engineers, metadata specialists, legal and privacy expertise, and domain knowledge, can make sharing safer and more predictable. Finally, governments should look beyond the public sector to the many successful examples of public-private partnerships on data sharing that can generate significant impact, for instance by integrating retailers’ price data in the production of inflation statistics. At a global level, the Development Data Partnership has shown how collaboration with the private sector can support projects to model the impact of transport policy choices on air quality in Southeast Asia, identify cost-effective solutions to closing internet connectivity gaps for schools in Jamaica, and monitor the cross-border mobility of AI talent at the global level. 3. Fix the incentive problem Data sharing stalls when data-rich departments bear all the political risk of data being used to highlight shortcomings in their own policies, as well as the costs of curating and sharing their data. The temptation, then, is to mandate data sharing. Unfortunately, this rarely works, as there are legitimate reasons (as well as convenient excuses) why a department might struggle to share data. The first challenge is political. This is why governments must align data sharing with clear government priorities. For example, sharing children’s health and education data across agencies works better within a broader reform program than as a standalone effort. The other challenge is linked to resources. A central agency is better positioned to coordinate a system of incentives than multiple departments acting separately. This could include grants to curate linked datasets that meet the needs of multiple departments, capacity building aimed at a cohort of smaller agencies, as well as core funding to ensure stability beyond time-bound programs. Incentives should reward reuse of data as well as sharing. A linked and anonymized dataset created for one analysis may be valuable for planning ministries, local governments, gender agencies, researchers, and service delivery teams. Treating reusable data assets as data infrastructure changes the investment logic. It makes the case for funding data stewardship before the urgent analytical request arrives. 4. Make trust visible Public trust is foundational to data sharing, not a communications add-on. Political support can unravel if governments do not explain intent, benefits, and safeguards to data holders' rights. Governments need to consult widely and genuinely with the public and commit to transparency standards, such as publishing which data are shared, for what purposes, and under which legal framework. This is especially important where data sharing involves personal or sensitive data. Transparency does not mean releasing confidential information. It means making governance legible. Citizens and data holders should be able to see that sharing is bounded by law, proportional to purpose, and subject to oversight. Trust also requires engagement with users outside government. Civil society, academia, firms, the media, and community organizations can help identify which datasets matter, where quality problems exist, and how data can be used for public value. Demand-side engagement prevents governments from building supply-driven portals and platforms that look impressive but remain underused. 5. Invest in people and practice not just platforms Usually, the most overlooked part of public sector data sharing is capacity. Data sharing teams need specialists who is able to manage relationships with diverse group of stakeholders and understands engineering, interoperability, metadata, anonymization, privacy, legal agreements, user support, and domain context. They need to translate between what researchers or policy teams want and what operational departments can safely and practically provide. Data sharing teams need to be staffed with specialists across the data lifecycle as well as across thematic areas. These teams need to combine functional expertise (engineering, data standards) with domain knowledge (e.g., education or welfare data) to act as a bridge between what researchers’ needs and what the origin department can provide. It is also important to invest in foundational work that often gets deprioritized, like creating metadata, data quality mapping, and uplift. Neglecting this is a false economy, as data quality problems compound across the lifecycle to render insights partial, if not misleading. Finally, a central agency can invest in linked and anonymized datasets to be reused for multiple purposes. For example, linking tax and education data enables fine-grained and gender-disaggregated analysis alongside existing surveys and this augmented dataset can be used by local government, planning ministries, and gender equality promotion agencies. These datasets need linkage “strategic teams” that bring thematic and data expertise across agencies through secondments that pay themselves back in terms of skill diffusion and more cohesion across the whole-of-government ecosystem. Conclusion: data sharing is a governance reform Governments that succeed at data sharing do so not by building perfect technical systems, but by establishing shared purpose, aligning incentives across agencies, anchoring governance in credible institutions, and investing in the people who make it work. Trust sits at the core of cross-government data sharing. Trust requires an ecosystem where a cadre of experts interact across organizational boundaries over time, not just for one-off, transactional data sharing negotiations that can turn into a zero-sum game. The investment in building and sustaining a well-calibrated data sharing ecosystem pales in comparison with the cost of digital transformation programs. But only when data sharing is central to digital reform, and not an afterthought, can public sector agencies really reap the benefits of their administrative data.