4.1 Data and AI: what does good look like?

Estimated read time: 4 minutes

Data and AI have a symbiotic relationship and together can transform public services, improve policy-making, and achieve better outcomes for citizens. However, without strong governance, ethical practices, and robust security, they can also erode trust and harm public services. Trust is therefore the foundation of any data or AI initiative, and its preservation must be the starting point for transformation efforts. According to the OECD, a high-performing digital government should implement the following:

Clear strategic vision and leadership for data

Governments must treat data as a strategic asset, essential to digital transformation. A strong data strategy ensures alignment with national priorities and guides data use across the public sector.

Establishing a National Data Strategy: Articulate how data will be collected, managed, and used to support government priorities. Provide a comprehensive roadmap that addresses every stage of the data lifecycle, from collection and storage to sharing and disposal.
Empowering data leadership: Appoint a dedicated Chief Data Officer or equivalent role to lead a unified vision for data use. This leadership position promotes best practices, coordinates efforts across departments, and ensures consistency in data management.
Cultivating a data-driven culture: Task leaders to champion data literacy across all levels of government, ensuring that public servants integrate data into their roles. This cultural shift positions data as an essential asset that informs policy and service delivery.

High-quality data infrastructure and interoperability

Seamless, joined-up public services depend on data that can flow efficiently across departments and systems.

Developing centralised data platforms and APIs: Modern data infrastructure includes centralised platforms and standardised APIs that facilitate interoperability, allowing credentials and proofs to be verified and data to flow efficiently across government services. These systems support the “once-only” principle, so citizens provide data once, which is then securely shared across relevant services.
Establishing data quality standards: Effective data governance includes well-defined standards that ensure data accuracy, consistency, and suitability for decision-making. These standards are reinforced by robust governance mechanisms that uphold quality across departments.
Standardising data formats: Promote cross-departmental collaboration and reduce redundancy in mismatched data. Standards can improve the resilience of government services and simplify integration.

Ethical, transparent, and responsible use of data and AI

Trustworthy AI and data practices are non-negotiable for public confidence. Governments must implement ethical frameworks that prioritise transparency, privacy, and accountability.

Creating ethics boards and oversight: An ethics board or advisory body provides guidance on the responsible use of AI and data, ensuring that public values are upheld and that issues like fairness and accountability are prioritised.
Ensuring transparency in AI applications: Maintain transparency by publishing records of the algorithms in use, allowing citizens to learn how AI is used in decision-making processes. Publishing results of data audits and transparency reports strengthens accountability and fosters trust.
Implementing privacy and security safeguards: Embed privacy-by-design principles from the outset in all data and AI projects. Effective digital governments prioritise privacy, provide tools for data management, and adhere to strict data protection standards.

Data-driven decision-making and anticipatory services

Governments must embed data into decision-making processes and use it to create proactive, citizen-centred services.

Embedding data in policy design: Informs every stage of the policy cycle, from initial planning to monitoring outcomes, with data. By leveraging data analytics, governments can tailor services to meet citizen needs and adapt policies in real time based on impact assessments.
Using predictive analytics for proactive interventions: Identify individuals or communities that may benefit from targeted support, allowing governments to act before issues escalate. This proactive approach drives better outcomes for citizens and optimises resource allocation.
Fostering data literacy across the workforce: Equip public servants with the skills they need to interpret and apply data insights. These programmes ensure that all employees, not just data specialists, can leverage data in their roles to improve services.

Open Government: Data, Standards, and Storytelling

Open government practices are essential for building trust and enabling collaboration. By embracing openness, governments foster innovation, improve accountability, and create citizen-centred solutions.

Open data as a public good: Release high-value datasets by default, ensuring accessibility and usability. This drives innovation, transparency, and new services from civil society and the private sector.
APIs and integration by design: Recognise the critical role of open standards and robust APIs as the backbone of interoperability in government services. By adopting these practices, governments can create seamless user experiences while simplifying technical complexity. This approach ensures efficient collaboration across multiple technical partners and addresses the diverse, cross-cutting needs inherent in public sector operations.
Open policymaking and collaboration: Engage citizens and external stakeholders early and often in policymaking, enabling co-creation and aligning government actions with public needs.
Transparent storytelling: Use data and clear communication to narrate how policy decisions are being made and services designed and delivered, fostering trust and accountability.