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Unlocking the power of good data governance

In a world of rapidly advancing technology, the need for robust data governance has never been more critical. In the past decade, we’ve witnessed the evolution of data governance from a niche concept to a cornerstone of organisational success. So it’s worth taking a closer look at data governance - and how far we’ve come - exploring its pillars, roles, levels, and why it's indispensable in the era of AI.

The four pillars of data governance

According to established principles, data governance has four foundational pillars: data quality, data security, data management, and data access. Each pillar is pivotal in ensuring an organisation's data is accurate, secure, and effectively utilised to drive business decisions.

  • Data quality: At the heart of effective data governance is data quality assurance. This pillar focuses on maintaining accurate, consistent, and reliable data throughout its lifecycle.
  • Data security: In an age where data breaches make daily headlines, securing sensitive information is paramount. Data governance ensures robust security measures are in place to protect against unauthorised access and potential breaches.
  • Data management: Efficient data management involves data organisation, storage, and retrieval. Data governance establishes protocols for data handling, making it easily accessible for authorised personnel while maintaining its integrity.
  • Data access: While ensuring security, data governance facilitates appropriate data access. It defines roles and permissions, ensuring individuals can access the data necessary for their responsibilities.

The three key roles of data governance

The key roles in data governance, as widely recognised in industry best practices, include:

  • Data owners: Individuals responsible for the overall management and quality of specific data domains.
  • Data stewards: Custodians of data, ensuring its quality, security, and adherence to governance policies.
  • Data users: Individuals who rely on data for decision-making, following governance protocols for responsible usage.

The five levels of data governance

It’s commonly acknowledged in discussions on data governance maturity models that data governance operates on a continuum with five key levels:

  • Ad hoc: Limited awareness and understanding of data governance.
  • Awareness: Recognition of the importance of data governance.
  • Defined: Formalized governance policies and procedures.
  • Managed: Ongoing monitoring and optimisation of governance processes.
  • Optimised: Continuous improvement and integration of data governance into organisational culture.

What is data governance, and what it's not!

Data governance is the strategic management of data to ensure high quality, security, and effective utilisation. It is not a one-time project but an ongoing commitment to excellence in data management. Often confused with data management and stewardship, data governance is a holistic approach encompassing these elements while providing strategic direction.

Data governance can also be called...

Data governance is often referred to as information governance or data management. These terms encapsulate the broader scope of activities involved in orchestrating the use and protection of data assets within an organisation.

Why data governance is important

The significance of data governance cannot be overstated. It builds trust in data, a crucial asset in today's data-enabled decision-making. Trustworthy data enhances decision accuracy, reduces risks, and fosters compliance with data protection regulations. To help organisations with this, the ODI has launched a framework of nine essential data practices to enable organisations to understand and build their trustworthiness with data.

Data governance and AI

In the symbiotic relationship between data governance and AI, the former acts as the guiding force. Effective data governance ensures the quality and reliability of data inputs, mitigating biases and inaccuracies in AI outputs. It also addresses ethical considerations in AI development and deployment.

Data governance vs. data security

While data governance focuses on the strategic management of data, data security is a subset concerned primarily with protecting data from unauthorised access. While distinct, they are complementary, working together to fortify an organisation's data ecosystem. Data security will be included as part of a broader data governance strategy.

Data governance vs. data stewardship

Data governance sets the overarching strategy, while data stewardship ensures data quality, security, and compliance. Together, they form a robust framework for responsible data management.

Data governance as a service

The concept of data governance as a service (DGaaS) is gaining traction. It involves outsourcing data governance processes to specialised service providers, allowing organisations to benefit from expert guidance without needing an in-house team. Contact the ODI to find out more about our expert consultancy services.

More about this topic

ODI courses to develop data governance expertise


Data governance is the linchpin of successful data management, ensuring the reliability, security, and strategic utilisation of an organisation's data. As the digital landscape continues to evolve, a commitment to robust data governance is not just a choice; it's a necessity. For more information on data governance services, explore the ODI's offerings on data stewardship, data sharing, our data Practices guidance, consultancy, and products.

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