Data Governance Roadmap for Ireland

Evolving Governance in the Face of Change

Leading responsible governance of data and artificial intelligence, supporting AI, data mastery and leveraging data value as the basis of a sustainable information-enabled and data-informed society through the development of data capabilities, the alignment of data initiatives, and the development and promotion of data literacy, contributing to benefits that span organisations, individuals and wider society
Download the Roadmap below:


To navigate the complexities of data governance and AI, it is imperative to have a clear set of recommendations. These guidelines provide a strategic framework for ethical, transparent, and efficient data use, ensuring that Ireland remains competitive and innovative in the digital era. Our recommendations cover critical areas such as leadership, skills, policy, and technology, addressing needs at individual, organizational, and national levels. Following is the latest list of our recommendations:


In the context of the Data Governance Framework, Leadership is defined as the ability to motivate individuals and groups towards a common strategic direction and value proposition, allowing for the establishment of processes and decision-making that will maximise the availability, use and security of data. To make sure that processes and decisions are implemented all the way down on an individual level, leadership also involves establishing appropriate decision-making rights, mechanisms and rules for accountability and oversight.

The Leadership Theme addresses issues such as:

  • Alignment of policy and legislation with implementation
  • Identification of best practice models for data governance and data management
  • Ensuring that changes to initiatives are evaluated against goals through a defined process.
  • Ensuring that the ethical issues of proposed data use, or proposed technology deployments are considered at all times, and particularly as part of the development of policy, scoping of initiatives, and execution of projects.
  • Ensuring clarity on roles, responsibilities, and relationships between organisations. In particular effective Data Governance leadership should ensure clarity on the relevant regulatory bodies prior to legislation coming into force which affects the governance of data and the execution of data processes.

Encourage continuous learning and development in data governance and leadership skills

  • Problem Solved: Insufficient leadership skills and understanding in data governance.
  • Explanation: To address a lack of leadership abilities and expertise in data governance, individuals should prioritise ongoing learning and development in these areas. Individuals who actively seek educational opportunities, such as workshops, online courses, and seminars, can improve their leadership and execution skills. This dedication to personal development will result in improved implementation of data governance objectives listed in the Canadian Data Governance Strategy Roadmap, supporting more effective and informed data management practices.

Develop Data Literacy and Acumen Development Programs 

  • Problem Solved: Insufficient data skills among government employees and in private sector.
  • Explanation: To overcome the lack of data skills among government personnel and the corporate sector, individuals should participate in Data Literacy and Acumen Development Programmes. These programmes, inspired by the New Zealand Government Data Strategy and Roadmap, provide training to help people understand and apply data more effectively. Individuals who participate can considerably enhance their data skills, resulting in more informed decision-making and better service delivery, as demonstrated by the success of the New Zealand public sector.

Establish clear accountability frameworks with defined roles and responsibilities for data governance 

  • Problem Solved: Ambiguity in data governance roles. 
  • Explanation: To address ambiguity in data governance roles, organisations should create explicit accountability frameworks that identify roles and duties. Following the NBC Roadmap to Digital Transition, these frameworks ensure that each team member understands their individual roles in data governance. This clarity promotes collaboration and efficiency, resulting in better data management practices and overall organisational success. Establishing these mechanisms is critical to creating a transparent and successful data governance environment.

Establish a Centralized Data Governance Body in Public Sector, extending scope and role of Data Governance Board 

  • Problem Solved: Lack of coordination and inconsistent data practices across Public Sector agencies. 
  • Explanation: To address the lack of coordination and inconsistent data practices among public sector agencies, organisations should form a Centralised Data Governance Body, which would expand the scope and responsibility of the Data Governance Board. Inspired by the New Zealand Government’s Data Strategy and Roadmap, this centralised organisation will improve data interoperability and collaboration. The success witnessed in New Zealand, with improved public services and decision-making, underlines the efficacy of this method in developing coherent and uniform data practices.  

Ensure political leadership and commitment to data governance by integrating it into national policy agendas and ensuring alignment of data-related policy initiatives from scoping to execution

  • Problem Solved: Lack of high-level commitment to data governance.
  • Explanation: To address the lack of high-level commitment to data governance, national policy agendas should incorporate data governance and coordinate data-related policy activities from scoping to implementation. As proposed by the Canadian Data Governance Strategy Roadmap, this provides governmental leadership and long-term commitment. Such integration promotes a consistent approach to data governance, resulting in national policies that better support effective data management and utilisation, ultimately improving overall governance and public service outcomes.

Develop national data strategies that include leadership commitments and outline the role of leadership in successful implementation

  • Problem Solved: Absence of comprehensive data strategies.
  • Explanation: To overcome the lack of comprehensive data plans, national data strategies should be developed, including leadership pledges and a clear outline of the role of leadership in effective execution. Such strategies, modelled after the United Nations Roadmap for Digital Cooperation, ensure high-level engagement and systematic guidance. Countries with these comprehensive policies have seen better coordination in their data governance initiatives, resulting in more efficient and effective data management practices across sectors.

Mandate Open Data Initiatives with improved standardisation of metadata and co-ordinated governance 

  • Problem Solved: Limited access to government data for public use and innovation.
  • Explanation: To overcome limited access to government data for public usage and innovation, require Open Data Initiatives with enhanced standardisation of metadata and coordinated governance. Inspired by the New Zealand Government Data Strategy and Roadmap, these efforts promote data accessibility and usability. Historical experience in New Zealand illustrates that such projects encourage innovation across multiple sectors, including health, transportation, and education, by providing trustworthy and standardized data for public and private sector usage.

Establish a National Data Steward Network for Public Sector 

  • Problem Solved: Fragmented data management and lack of coordination across government entities.
  • Explanation: To address fragmented data management and lack of coordination across government agencies, build a National Data Steward Network for the Public Sector. Inspired by Estonia’s data governance framework, which underlines the role of data stewards in boosting data quality and accessibility, this network will improve data management standards. Estonia’s successful adoption of a data steward network has led to better policy-making and more efficient data utilisation, illustrating the usefulness of this method.


The Skills Theme of the Data Governance Framework encompasses different aspects of skills development for the workforce but also the broader question of data literacy and data acumen development across society. Individuals equipped to critically evaluate information and understand data privacy rights become active participants in the data ecosystem. Businesses with a data-literate workforce can leverage data governance frameworks for informed decision-making and regulatory compliance.

Public data literacy empowers policymakers to craft effective policies that resonate with citizen needs. Investing in educational initiatives that integrate data literacy and launching targeted public awareness campaigns are crucial steps toward building a foundation for responsible data use and informed decision-making at individual, organisational, and national levels. This fosters trust in government data practices and unlocks the true potential of data governance policies.


Provide access to continuous professional development and training in data skills

  • Problem Solved: Outdated or insufficient data skills and low levels of understanding of data management principles and practices in a practical context.
  • Explanation: To address outdated or insufficient data skills and low awareness of data management concepts, provide access to continual professional development and training in data skills at the individual level. As proposed by the NBC Roadmap to Digital Transition, this effort improves data management skills. Individuals who have a deeper grasp of data’s function and impact in society are more likely to succeed, resulting in a more informed workforce capable of effectively navigating and using data in varied circumstances.

    Encourage individuals to participate in data literacy programs and education and skills development courses to develop data acumen at appropriate levels

    • Problem Solved: Low levels of data literacy and acumen
    • Explanations: To address low levels of data literacy and acumen, encourage people to take part in data literacy programmes and skill development courses suited to their specific needs. As indicated in the Canadian Data Governance Strategy Roadmap, the goal of this effort is to increase public awareness and competence in data-related skills and abilities. Success is visible when individuals acquire greater skill in navigating data, enabling them to make educated decisions and contribute effectively to a data-driven society.

    Support education and training in data and digital literacy and acumen through partnerships with educational institutions, professional bodies, and relevant specialist training providers

    • Problem Solved: Insufficient collaboration between industry and education and lack of awareness or availability of relevant management/staff education. 
    • Explanation: To address insufficient collaboration between industry and education, as well as a lack of awareness or availability of relevant education, organisations should encourage partnerships with educational institutions, professional bodies, and specialist training providers to provide data and digital literacy education and training. Successful collaborations, as evidenced by the GovLab’s mapping of data governance frameworks, lead to improved training programmes and better-prepared graduates. This method ensures that firms have access to a trained workforce armed with the essential data literacy and acumen to prosper in the digital age.  

    Introduce Data Management Certification Programs 

    • Problem Solved: Insufficient data management competencies to develop relevant core competencies in data management at all levels.
    • Explanation: To address insufficient data management competencies across all levels, introduce Data Management Certification Programs. Drawing inspiration from Estonia’s emphasis on promoting expertise and data literacy within the public sector, these programs aim to develop relevant core competencies in data management. Estonia’s historical success in focusing on data literacy and management training has resulted in a more competent public sector, capable of leveraging data effectively for improved service delivery and informed decision-making. 

    Implement national initiatives to improve data literacy and data competencies across various sectors

    • Problem Solved: Widespread skills gaps in data literacy. 
    • Explanation: To address significant skills shortages in data literacy, create national efforts focused at enhancing data competencies across multiple sectors. Referencing the Canadian Data Governance Strategy Roadmap, these measures enable a more proficient national workforce. Success is demonstrated by increased data literacy, acumen, and management abilities across the country. Such programmes build a workforce capable of efficiently using data, resulting in informed decision-making and promoting innovation in a variety of areas.  

    Promote inclusive and equitable access to relevant education and training interventions in data-related skills and competencies to bridge the skills gap at all levels in the workforce and society

      • Problem Solved: Inequities in access to data-related skills education.
      • Explanation: To address gaps in access to data-related skills education, promote inclusive and equitable access to relevant training interventions across all levels of the workforce and society. Referencing the UN Roadmap for Digital Cooperation, this recommendation attempts to bridge the skills gap and offer fair opportunities for skill development. Success is achieved by reducing gaps in digital skills, resulting in a more equitable distribution of abilities across areas and populations. This effort strives to create a more inclusive and empowered society in the digital era by making education and training more accessible. 


      Within a data governance framework, data governance policies act as the actionable guidelines that dictate how data is managed throughout its lifecycle. These policies translate the overarching goals of the framework into specific rules and procedures for data collection, storage, access, use, security, and privacy.

      Data governance policies can help achieve the following goals:

      • Clarity and Consistency: They provide clear instructions for all stakeholders, ensuring consistent data practices across the organisation.
      • Accountability: Policies define roles and responsibilities for data governance, fostering accountability for data quality and security.
      • Compliance: They ensure adherence to relevant data privacy regulations and industry standards.
      • Enforcement: Policies establish mechanisms for monitoring compliance and enforcing consequences for non-compliance.

      Educate individuals on their data rights and the importance of data privacy and security and other social and societal aspects of data

      • Problem Solved: Lack of public knowledge on data rights. 
      • Explanation: To address the public’s lack of information of data rights, educate them on their rights, emphasising the necessity of data privacy, security, and societal issues. This programme, based on the GovLab’s Mapping and Comparing Data Governance Frameworks, attempts to increase public knowledge and compliance with data privacy standards. Success is accomplished when individuals acquire a better understanding of their rights regarding data, leading to increased vigilance in preserving personal information and establishing a culture of responsible data use within society.

      Implement robust data privacy and security measures within organisations to protect data integrity

      • Problem Solved: Weak data privacy and security measures. 
      • Explanation: Explanation: To overcome inadequate data privacy and security safeguards, organisations should put in place strong data privacy and security measures. This guideline, based on the UN Roadmap for Digital Cooperation, intends to effectively protect data integrity. Success is demonstrated by increased data security within organisations and a decrease in data breach events. Prioritising these approaches allows organisations to increase stakeholder trust, reduce the risk of data breaches, and maintain regulatory compliance, ultimately strengthening overall data protection efforts.  

      Highlight importance of Data Quality Standards 

      • Problem Solved: Variability in data quality leading to unreliable data for decision-making. 
      • Explanation: Data Quality Standards are critical to managing variability in data quality and guaranteeing decision-making reliability. They set norms for accuracy, consistency, and completeness, drawing inspiration from New Zealand’s experience in improving data analytics and policy decisions through such standards. Adhering to these standards allows organisations to improve data integrity, reduce errors, and encourage trust in data-driven insights. Consistently high-quality data allows for informed decision-making, which supports effective plans and policies across sectors, ultimately leading to superior outcomes and performance. 

      Develop and implement regulatory frameworks for data governance that align with international best practices

      • Problem Solved: Lack of regulatory alignment. 
      • Explanation: To address the absence of regulatory alignment, states should create and execute data governance regulatory frameworks that are in line with international best practices. This recommendation, based on GovLab’s Mapping and Comparing Data Governance Frameworks, seeks to standardise regulations. Success is defined by increased international collaboration and trust, since these frameworks encourage uniformity and interoperability in data management techniques. Adopting such legislation allows countries to create a favourable atmosphere for global data sharing, innovation, and cooperation, benefiting both national and international interests.  

      Implement a Comprehensive Data Ethics Framework 

      • Problem Solved: Concerns over data privacy and ethical use of data. 
      • Explanation: Implement a Comprehensive Data Ethics Framework to address privacy concerns and ethical data use. This programme, which draws on the OECD Modern Policy Capability Framework, ensures ethical data management practices. Estonia’s effectiveness in implementing such principles has increased public trust and compliance with data privacy regulations. Establishing explicit ethical standards allows organisations to traverse complex data landscapes ethically, developing stakeholder trust and ensuring data is utilised in ways that protect privacy and uphold society values, eventually promoting transparency and accountability in data activities.


      Within a data governance framework, technology serves as the essential infrastructure. Data catalogues meticulously document assets, location, and ownership. Data quality tools automate processes for accurate and consistent data. Granular access controls define user permissions, safeguarding sensitive information. Data lineage tools track data movement for auditing, impact analysis, and robust regulatory compliance. Encryption safeguards data at rest and in transit. Technology’s significance lies in automating tasks, streamlining processes, and managing massive data volumes. It translates policies into functionalities, ensuring data quality, security, and regulatory adherence. By functioning as the engine of the framework, technology unlocks its full potential for effective data management.


      Encourage individuals to adopt and utilize appropriate technological tools and organisational controls for better data management

      • Problem Solved: Inefficient data management practices or inappropriate use of technologies. 
      • Explanation: To address inefficient data management practices and inappropriate technology use, encourage individuals to adopt and utilise appropriate technological tools and organisational controls. Referring to the Canadian Data Governance Strategy Roadmap, this recommendation aims for improved data management. Success is marked by individuals reporting increased efficiency and better data management practices. Organisations can empower individuals with the right tools and controls.  

      Provide training on how to use appropriate data-related technologies effectively 

      • Problem Solved: Lack of skills in using data governance technologies. 
      • Explanation: To address a shortage of expertise in data governance technologies, provide training on how to successfully use appropriate data-related technology. This recommendation, based on the Canadian Data Governance Strategy Roadmap, intends to improve technological proficiency. Success is measured by the increased use of appropriate data management and governance methods and technology. Organisations can optimise their data management processes, ensure better data governance practices, and leverage the full potential of technology to drive data management innovation and efficiency by providing personnel with the essential skills. 

      Adopt appropriate data management systems (people, processes, and technologies) to enhance and support data management and governance practices

      • Problem Solved: Outdated, improperly implemented or inefficient data management systems and associated technologies. 
      • Explanation: To address old, badly implemented, or inefficient data management systems and related technologies, organisations should implement adequate data management systems that include people, processes, and technologies. This recommendation, based on the UN Roadmap for Digital Cooperation, intends to improve data management and governance procedures. Organisations that implement new systems claim considerable gains in data handling and governance, indicating success. Businesses can improve overall performance and competitiveness by aligning systems with organisational goals and best practices, resulting in streamlined processes, data integrity, and more effective decision-making. 

      Promote the development and adoption of national data infrastructures to support data governance

      • Problem Solved: Lack of cohesive national data infrastructure.
      • Explanation: To address the lack of a unified national data infrastructure, encourage the creation and acceptance of national data infrastructures that will support data governance at the national level. This recommendation, based on the Canadian Data Governance Strategy Roadmap, intends to improve national data management capabilities. Success requires improved infrastructure, enabling more efficient data governance procedures, and, ultimately, enhancing service delivery across industries. Countries that have a strong national data infrastructure can improve data access, integration, and sharing, supporting innovation, informed decision-making, and economic prosperity. 

      Disclaimer: These recommendations do not necessarily reflect the views of the SFI Centres, SFI, or NSAI. It is solely a summary and reflection of the discussions held by the stakeholders and individuals who developed this roadmap.