Smarter Together

Aligning Data and AI Governance to Manage Risk

and Drive Change












Scroll to Learn More ↓

Smarter Together

Aligning Data and AI Governance to Manage Risk and Drive Change









Scroll to Learn More ↓

Smarter Together

Aligning Data and AI Governance to Manage Risk

and Drive Change









Scroll to Learn More ↓

Siân Sewell

Senior Data Governance Consultant

Meet Siân


Siân is a specialist in data and AI governance who helps organisations get the most value from their data and AI while managing risk and meeting regulatory and ethical obligations. With experience across the public and private sectors, Siân brings a practical, people-first approach to governance. She believes that successful data and AI governance starts with culture, clarity, and connection.

Siân Sewell

Senior Data Governance Consultant

Meet Siân


Siân is a specialist in data and AI governance who helps organisations get the most value from their data and AI while managing risk and meeting regulatory and ethical obligations. With experience across the public and private sectors, Siân brings a practical, people-first approach to governance. She believes that successful data and AI governance starts with culture, clarity, and connection.

Across all industries artificial intelligence is growing as an integral part of a modern business strategy. With this shift comes the need to manage the growing number of associated risks by ensuring that there is effective governance over both AI and the data that feeds into it.


By understanding the relationship between data and AI governance, how they differ, and how they complement each other, an organisation can minimise the impact of embedding two governance disciplines, while utilising data as an asset, and harnessing AI responsibly and effectively.

Across all industries artificial intelligence is growing as an integral part of a modern business strategy. With this shift comes the need to manage the growing number of associated risks by ensuring that there is effective governance over both AI and the data that feeds into it.


By understanding the relationship between data and AI governance, how they differ, and how they complement each other, an organisation can minimise the impact of embedding two governance disciplines, while utilising data as an asset, and harnessing AI responsibly and effectively.

Defining Disciplines

Data governance refers to the people, processes and technology, informed by overarching policy, that ensure data is fit for purpose, secure, and used properly across an organisation. It focuses on managing data assets throughout their lifecycle, from creation or capture, storage, sharing and disposal. Effective data governance ensures accountability for data quality, compliance, and accessibility so that data can support business operations and decision-making.


AI governance refers to the oversight of artificial intelligence systems, including how they are developed, deployed, and monitored. Effective AI governance ensures that algorithms operate ethically, transparently, and in alignment with organisational values and legal requirements. It extends the focus of data governance to include AI-specific issues such as model bias, explainability, and the need for human oversight.

Data In, AI Out

There is a natural intersection between data and AI governance as both disciplines emphasise quality - AI systems are only as good as the data that goes into them -  security, and compliance. For example, if an organisation’s data governance framework enforces accurate data classification and data lineage mapping, the origins of training data used in AI models can be traced back to source, enabling transparency when determining how an outcome is reached and how potential bias is addressed.



Data and AI governance also overlap in the areas of privacy and ethical use. Data governance frameworks enforces privacy laws through rules on consent, data collection and data use. AI governance frameworks build on this with guardrails ensuring that AI models only use sensitive information for required and intended purpose, as well as preventing discriminatory outcomes resulting from intentional or unintentional unethical use of data.



Different Focus, Same People

While data and AI governance share foundational principles, their different focuses show why there is a need for two disciplines:

Focus Data Governance AI Governance
Scope People, processes, and technology, ensuring that data is trustworthy, compliant, and strategically valuable. The entire AI ecosystem, including algorithms, models, and automated decision-making processes.
Risks Data governance frameworks aim to manage risks related to data quality, security breaches, and compliance violations. AI governance frameworks aim to manage risks related to algorithmic bias, lack of transparency, and unintended consequences of automation.
Metrics Data governance frameworks measure: Data Quality Data Compliance & Security Data Governance Process Metadata and Data Catalog Data Usage & Accessibility Data Risk and Issue AI governance frameworks measure: Ethical & Responsible AI Model Performance Transparency/Explainability Risk, Security, &Compliance Data Input Governance Environmental/Sustainability

Due to these differences, there are specific roles that support each framework (data governance: data owners and stewards, privacy officers, AI governance: data scientists, subject matter experts), however the successful implementation of both data and AI governance frameworks depends on a comparable set of stakeholders:


Executive leadership: With executive backing, governance gains the direction and support it needs to build trust and add value across the organisation.


All staff: The success of a data or AI governance program depends on how well staff actively support, apply, and uphold the organisation’s governance principles in their daily work. 


Supporting functions: Functions that are integral in supporting data and AI governance programs include: 

  • Compliance, risk
  • Ethics, legal, privacy
  • Training, change, and communications
  • Information technology, including info/cybersecurity


Different Focus, Same People

While data and AI governance share foundational principles, their different focuses show why there is a need for two disciplines:

Focus Data Governance AI Governance
Scope People, processes, and technology, ensuring that data is trustworthy, compliant, and strategically valuable. The entire AI ecosystem, including algorithms, models, and automated decision-making processes.
Risks Data governance frameworks aim to manage risks related to data quality, security breaches, and compliance violations. AI governance frameworks aim to manage risks related to algorithmic bias, lack of transparency, and unintended consequences of automation.
Metrics Data governance frameworks measure: Data Quality Data Compliance & Security Data Governance Process Metadata and Data Catalog Data Usage & Accessibility Data Risk and Issue AI governance frameworks measure: Ethical & Responsible AI Model Performance Transparency/Explainability Risk, Security, &Compliance Data Input Governance Environmental/Sustainability

Due to these differences, there are specific roles that support each framework (data governance: data owners and stewards, privacy officers, AI governance: data scientists, subject matter experts), however the successful implementation of both data and AI governance frameworks depends on a comparable set of stakeholders:


Executive leadership: With executive backing, governance gains the direction and support it needs to build trust and add value across the organisation.


All staff: The success of a data or AI governance program depends on how well staff actively support, apply, and uphold the organisation’s governance principles in their daily work. 


Supporting functions: Functions that are integral in supporting data and AI governance programs include: 

  • Compliance, risk
  • Ethics, legal, privacy
  • Training, change, and communications
  • Information technology, including info/cybersecurity


One Way Or The Other

AI governance builds upon a strong data governance foundation. Without trustworthy, well-managed, good quality data, responsible AI can not be achieved. The opposite is also true, as effective AI governance frameworks push organisations to mature their data governance practices by demanding higher standards of data quality, integrity and transparency.


Due to these dependencies, the most efficient way to balance resource constraints, change fatigue and ensure organisation-wide buy-in is to align or integrate the two disciplines. Such integration may be achieved by:



  • Establishing a governance framework that includes both data and AI policies and principles under a single oversight structure.
  • Creating cross-functional teams that bring together experts in data management, machine learning, compliance, and ethics.
  • Prioritising transparency and documentation across data and AI systems to build trust internally, as well as with regulators, partners, and customers.
  • Investing in scalable tooling that can support classification, PII identification, data lineage tracking, model monitoring, and bias detection into the future.


TLDR

Recently, there has been a shift away from overly complex, unachievable, and resource-heavy governance frameworks to frameworks designed to be simple, achievable, and embedded into existing processes.


This change has seen an increase in the adoption and sustainability of governance practices, and has provided the space for data and AI governance to become integral to building responsible, trustworthy, and high-performing digital ecosystems. Together, they form the foundation of ethical innovation, enabling organisations to leverage AI’s potential without sacrificing accountability or public trust.