How Governments Can Go Beyond Open Data to Enable Smarter Sharing

Open data initiatives have long been a hallmark of digital governance, promising transparency, innovation, and civic engagement. But as our digital landscape evolves and becomes increasingly more complex, the open-by-default model is no longer enough. Governments are being called to lead a broader shift: from publishing datasets to enabling secure, responsible, and collaborative data sharing across institutions and sectors. 

Going beyond open data doesn't mean abandoning transparency. It means building the legal, technical, and ethical data infrastructure to enable smarter use, especially when the data is too sensitive or complex to be fully public.  

In fact, public sector agencies actually have the opportunity to lead the charge in building data-sharing ecosystems that are secure, inclusive, and rooted in public value. 

Why Open Data Alone Isn't Enough 

While open data has enabled valuable civic innovation (e.g. fueling new apps, academic research, etc.), it often excludes the most impactful datasets. Health records, education outcomes, social service usage, and criminal justice data are examples where privacy, security, or commercial sensitivities make full openness impractical. 

While transparency is still essential, so is respecting and protecting the privacy of citizens. As effective as open data initiatives may sound on paper, strategic sharing is more beneficial for both governments as well as the public they serve. Governments must go beyond publishing static datasets. They must design systems that allow controlled access to sensitive data by trusted partners under defined conditions. 

The Evolving Role of Government in the Data Economy 

The Canadian government has identified data as a strategic asset essential to public service delivery, economic growth, and innovation. But to fully leverage data, governments must take on new roles: 

• Policy Makers: Governments must define the rules for ethical and legal data use. This includes crafting governance frameworks, privacy laws, and clear data-sharing protocols. These policies set the foundation for responsible innovation and inter-agency collaboration. 

• Infrastructure Providers: Secure data sharing needs robust digital infrastructure. Governments should invest in platforms, APIs, identity verification systems, and encryption technologies that enable seamless but protected access to data. Without this backbone, data initiatives stall before they start. 

• Capacity Builders: Public servants need more than tools; they need the skills to use them wisely. That means training in data literacy, privacy regulation, AI ethics, and digital risk management. Building internal capacity for digital readiness ensures that policies are applied effectively and ethically. 

• Trust Stewards: Trust is the currency of any data-sharing initiative. Governments must be transparent about how data is collected, used, and shared, and establish clear accountability mechanisms. When citizens trust the process, they are more willing to participate in data-driven services. 

This shift repositions government from a gatekeeper of data to an enabler of responsible use. 

What Responsible Data Sharing Looks Like 

To move beyond open data and build a sustainable, citizen-focused data ecosystem, governments must embrace a more mature model of data governance. According to McKinsey, a healthy data-sharing environment is built on three critical components: interoperability, legal and ethical guidelines, and scalable infrastructure. Each one plays a distinct role in enabling access, protecting citizens, and unlocking public value. 

Interoperability 

One of the biggest roadblocks to effective data sharing is fragmentation. Valuable data often sits in silos — within departments, across jurisdictions, or in incompatible systems. This leads to duplication, inefficiencies, and missed opportunities for collaboration. 

Governments must make interoperability a core priority by: 

• Enforcing common standards and formats: Adopting consistent data schemas, taxonomies, and technical standards ensures that data can be used across systems and institutions without the need for extensive rework or translation. 

• Mandating metadata and documentation: Every dataset should come with contextual information about its origin, structure, and limitations. Standardized metadata practices help users understand and evaluate data quality and relevance. 

• Promoting integrated platforms across agencies: Governments can create cross-departmental platforms or data warehouses that bring information together under shared protocols, reducing fragmentation and encouraging collaboration. 

• Fostering open APIs and digital interfaces: Well-designed APIs allow authorized users and systems to access datasets programmatically, enabling real-time insights and automation. 

By making interoperability the default, governments can ensure that data flows easily, but securely, between stakeholders. 

Legal and Ethical Guidelines 

Responsible data sharing hinges on strong legal frameworks and ethical practices that protect individual rights and foster trust. These practices and principles include: 

• Data protection laws: Regulations like the GDPR (EU) and PIPEDA (Canada) outline clear obligations for collecting, storing, and sharing personal information. These laws help prevent abuse and empower citizens with greater control over their data. 

• Consent management and tiered access: Not all data users need access to raw or personally identifiable information. Tiered access models allow data to be shared in different formats or levels of detail based on user credentials and use cases. Consent tools — whether opt-in, anonymized, or dynamic — give individuals a say in how their data is used. 

• Oversight and accountability: Governments should establish ethics boards, audit mechanisms, or algorithmic transparency requirements to monitor how shared data is used, especially when it informs automated decision-making. Public reporting can further enhance transparency. 

• Clarity in terms of use: Data sharing agreements and memoranda of understanding should clearly outline rights, responsibilities, permissible uses, and penalties for misuse. 

Ethical frameworks ensure that public data sharing is not just legal, but also fair, inclusive, and aligned with public values. 

Scalable Infrastructure 

Even with the right policies and permissions, data sharing is only as effective as the infrastructure that supports it. Scalable, secure, and privacy-conscious technology enables institutions to collaborate on data use without exposing sensitive information or increasing risk. 

Government infrastructure strategies should consider: 

• Federated learning and data mesh models: These approaches allow institutions to analyze and derive insights from decentralized datasets, without ever moving or centralizing the data. This protects sensitive information while still generating value. 

• Secure data environments: Also known as "data clean rooms," these environments allow multiple stakeholders to perform joint analysis or run algorithms on shared data while ensuring raw data remains protected. 

• Cloud-native platforms: Cloud infrastructure offers scalability, agility, and cost efficiency, especially when paired with strong cybersecurity protocols and identity management tools. 

• Real-time monitoring and access logs: To maintain control over data usage, infrastructure should include robust tracking systems that log who accessed what data, when, and for what purpose. 

• Backup and disaster recovery systems: Sharing data also requires confidence that it won't be lost or corrupted in the process. Redundancy systems and fail-safes are key to protecting data assets. 

By investing in modern, secure infrastructure, governments can support not just current data-sharing needs but also future use cases in AI, machine learning, and predictive analytics. 

Building Trust Through Data Intermediaries 

Not every government agency has the resources or neutrality to manage sensitive data exchanges directly. That's where data intermediaries come in. These trusted third parties can act as stewards of shared data, ensuring secure, ethical, and efficient use. 

There are several types of intermediaries that governments can leverage: 

• Data Trusts: These are legal structures that manage data on behalf of a group of beneficiaries. A data trust can set rules about who accesses the data, how it's used, and how risks are mitigated — creating a layer of governance that's both flexible and protective. 

• Clean Rooms: Also called secure data environments, "clean rooms" allow multiple organizations to run joint analysis or train models on pooled datasets without exposing sensitive or personal data. The raw data never leaves the environment, which helps mitigate privacy risks. 

• Statistical and Research Services: Entities like Statistics Canada or the UK's Office for National Statistics offer controlled access to anonymized datasets through secure research environments. They often require vetting and training for researchers before access is granted. 

Intermediaries are essential for unlocking complex data-sharing opportunities; especially when trust, privacy, and compliance are paramount. Benefits of using intermediaries include: 

• Reducing risk by enforcing data governance rules and consent policies. 

• Enhancing trust among participants by serving as neutral entities. 

• Lowering transaction costs and legal complexity in multi-stakeholder data collaborations. 

Open Data and Beyond: Real Examples from Across the Globe 

Across the globe, governments are demonstrating that responsible data sharing is not only feasible; it's already delivering results. Below are a few illustrative examples: 

Canada's Pandemic Data Collaboration 

During COVID-19, Canadian federal and provincial governments coordinated to develop shared datasets on testing, hospitalizations, and vaccine distribution. These efforts enabled real-time decision-making, better resource allocation, and transparent public reporting, all while respecting health data confidentiality. 

New Zealand's Integrated Data Infrastructure (IDI) 

The IDI combines de-identified data from a wide range of government departments, including health, education, and social development. Researchers can access the data in secure settings to evaluate policy effectiveness and understand long-term social outcomes. It's a model for how data can be linked responsibly to support evidence-based policymaking. 

United Kingdom's Secure Research Service 

The UK's Office for National Statistics operates a Secure Research Service where approved researchers can access sensitive government data under strict controls. Only accredited projects with public value objectives are granted access, and all analysis happens in a secure, monitored environment. 

These initiatives reflect how governments can go beyond static open data portals to create structured, purposeful data-sharing ecosystems — ones that prioritize public benefit and ethical integrity. 

Common Barriers to Responsible & Strategic Data Sharing 

Despite increasing interest in data sharing, many governments face persistent challenges. These barriers are not just technical — they're cultural, regulatory, and organizational. Below are some of the most common roadblocks and strategies to overcome them: 

Cultural Resistance 

Many agencies operate under a "need-to-know" mindset, wary of losing control or being blamed for data misuse. This fear-based culture can stall even the most well-intentioned initiatives. 

To overcome this, governments can build internal champions and incentivize collaboration and highlight success stories to show the tangible benefits of data sharing. 

Talent and Skills Gaps 

Not every public agency has the expertise to manage legal, technical, and ethical aspects of data sharing. Upskilling public sector employees through data governance and AI ethics training is essential ot bridge this talent and skills gap.  

In addition, governments can also bring in multidisciplinary teams that include technologists, policy experts, and legal advisors, and partner with institutions for capacity-building programs. 

Funding Limitations 

Data sharing initiatives often lack stable funding, leading to half-built systems or pilot projects that don't scale. By treating data infrastructure as a strategic investment and building multi-year funding plans that include maintenance and capacity-building, governments can address these challenges and move from isolated efforts to a coherent, scalable data-sharing strategy. 

Centering the Public as Primary Beneficiaries of Data  

Responsible data sharing must be guided by a singular purpose: serving the public good. That means ensuring that citizens — not corporations — are the primary beneficiaries of data use.  

Governments can embed public value into data strategies by: 

• Centering equity and inclusion: Data-sharing initiatives must avoid reinforcing systemic inequalities. This means investing in disaggregated data, engaging underrepresented communities, and applying an equity lens to every dataset. 

• Protecting privacy and autonomy: Citizens should have clear, understandable ways to opt in, opt out, or revoke consent. Transparent communication about how data is used helps build long-term trust. 

• Creating feedback loops: Governments should inform citizens about how their data contributes to improved policies, programs, or services. Public dashboards, regular updates, and participatory governance mechanisms can help close the loop. 

• Evaluating impact beyond efficiency: Success should be measured not just in terms of faster services or cost savings, but also in improved well-being, reduced disparities, and increased civic trust. 

Rethinking Open Data as a Foundation for Better Service Delivery 

To truly harness the potential of data for public good, governments must evolve their approach towards open data and crafting policies that enable collaboration, building infrastructure that ensures security, and leading with transparency and trust. 

By going beyond open data, governments can unlock a new era of digital transformation, one where data is not only open but also shared wisely, safely, and in the service of all citizens.