As artificial intelligence reshapes government and society, open data has emerged as a critical foundation for digital transformation. Governments worldwide have embraced open data initiatives, yet many approach them incorrectly—viewing them primarily as publishing exercises designed to boost rankings rather than as sustained data-management operations. The difficulty lies not in releasing data once, but in maintaining it as a dependable public asset over time.
The core problem in government open data, from a data management lens, centres on preserving quality, consistency and accountability throughout a dataset's entire lifecycle. A dataset released and then abandoned serves no one. The more sophisticated path forward requires treating each significant dataset as a managed product: appointing responsible stewards, setting quality benchmarks, adopting uniform standards and metadata practices, scheduling automated refreshes, tracking performance, and gathering user input. Ukraine's advancement in the European Open Data Maturity assessment stemmed from adopting this comprehensive strategy, weaving open data into broader digital transformation efforts that linked policy, institutional structures, quality assurance, practical reuse and public confidence.
Four lessons of open data management
Mykhailo Kornieiev's work involved bridging multiple domains: crafting policy frameworks, coordinating across government agencies, building civil servant capacity, engaging data users, measuring outcomes and harmonising with European standards. This experience yielded several insights applicable to data stewards elsewhere.
First, government bodies must recognise that data—both open and protected—constitutes essential digital public infrastructure. Moving past simple file publication requires establishing processes for naming data custodians, creating publication roadmaps, implementing transparent licensing and machine-readable formats, building channels for user input, and tracking evidence of data's tangible benefits.
Second, treat open data as a strategic portfolio rather than a scattered collection. Prioritising high-impact datasets enables support for corruption prevention, commercial services, scholarly work, investigative reporting and fact-based governance.
Third, quality assurance must be continuous. Thorough metadata, stable APIs, frequent updates and adherence to standards like DCAT-AP determine whether data can be discovered, interpreted and put to use.
Fourth, demonstrating and recording open data's real-world impact matters. Tracking whether businesses, civic-tech developers, journalists and government agencies actually draw on the data reveals whether the effort succeeds.
Ukraine's approach: risk-based data governance
When Russia launched its full-scale invasion, security emerged as a paramount concern. Ukraine faced the need to limit access to certain datasets, recognising that information benign in peacetime could pose genuine dangers during armed conflict. Rather than retreating from transparency, the country adopted risk-based data governance: evaluating each dataset individually, safeguarding information that could harm Ukraine's interests, permitting secure reuse of non-personal information and explaining restrictions openly. Confidence erodes both when authorities conceal information without justification and when they release it without proper protections.
This framework enabled Ukraine to climb from 17th position in the European Open Data Maturity assessment in 2020 to a leading position, reaching second place in 2022. The country sustained this standing despite ongoing warfare and continues to set the pace across Europe in this domain.
Data management challenges and lessons learned
The e-Governance Academy encounters comparable data governance obstacles across multiple nations: Ecuador, Germany, the Netherlands, Croatia and others. Ecuador's experience demonstrates that interoperability and data administration must serve tangible public services, not remain isolated technical endeavours. Efforts to deploy the Data Governance Act in Germany, the Netherlands, Estonia and Croatia reveal that Europe still lacks established procedures for the controlled reuse of sensitive public-sector information.
Laws provide necessary frameworks, yet legislation alone proves insufficient; governments require concrete processes for putting rules into practice. Work on AI readiness with Riga and Rotterdam reinforces this principle from a different angle: preparing for artificial intelligence begins with data governance—cataloguing assets, clarifying roles, ensuring quality, addressing ethics and building expertise.
Ukraine's data governance work forms part of its broader alignment with the European digital space, including conformity with the Data Governance Act, Data Act, Interoperable Europe Act, eIDAS 2.0 and AI Act.
For a data governance specialist, the fundamental insight is straightforward: "an open data ranking is not the goal; it is a mirror." Climbing the rankings holds little meaning if datasets grow stale, resist reuse or disconnect from genuine public requirements.
What counts is the substance beneath surface improvements: sharper accountability, superior data quality, stronger interoperability, proof of actual reuse and heightened trust. When these dimensions align, open data transforms into genuine state digital infrastructure, enabling superior public services, economic growth, knowledge creation, governmental accountability and decisions grounded in evidence.
