What is Data Governance and Why It Matters for Modern AI Strategies

Artificial Intelligence has moved from experimentation to everyday business operation at a lightning speed. Organisations across multiple sectors are now expected to deliver measurable value from AI, whilst simultaneously maintaining high standards of security transparency and compliance. At the same time, data teams continue to battle challenges such as inconsistent data quality, fragmented platforms and unclear ownership. These issues make it difficult to implement AI safely and confidently.  
 
The solution? Strong data governance, supported by robust data security posture management (DSPM). Together, they provide the structure needed to manage these risks. Governance defines what data exists and who is responsible for it, whilst DSPM ensures that data is actively protected and used in line with legal and regulatory requirements. This blog explains what data governance is, how it differs from DSPM, why both matter for AI readiness, and how organisations can apply them in the real world. 

What is Data Governance?

Data governance is the framework that defines how data is owned, managed, protected and used across your organisation. It brings together policies, processes, roles and technology to ensure data remains accurate, consistent and secure throughout it’s lifecycle.

Data governance helps your organisation answer key questions about specific data, like:

• Who owns this data?
• Can this data be trusted?
• Who is allowed to access it?
• How is it being used?

In modern data platforms, governance is increasingly supported by tools that provide visibility, cataloguing and lineage. However, this does not happen automatically. Data teams must actively configure classifications, ownership models, policies and quality rules. Technology enables governance, but people and processes are still essential to make it effective.

If your organisation is using AI, proper governance ensures that models are built on reliable, well-understood data and that the origin and transformation of that data can be traced. This increases confidence in AI driven decisions and reduces operational and regulatory risk.

Another key objective of a data governance framework is to help organisations extract the maximum value from their data estate. That value may be financial, such as improving revenue, reducing costs or optimising funding, or efficiency-based, such as reducing manual work, improving service delivery or accelerating decision-making. 

Data Governance and DSPM: Strategy and Safeguards

In a Microsoft-centric data environment, governance alone is not enough. Organisations also need a DSPM approach to ensure that data is actively safeguarded. 

Data governance defines what data exists, who owns that data and how it should be used. While DSPM focuses on how sensitive data is encrypted, how access is restricted by role or context and how retention and deletion policies are enforced. 

For example, governance may identify that a dataset contains a “Date of Birth” field and assign an owner responsible for it. DSPM ensures that the same field is encrypted at rest, masked for certain users, and automatically deleted after a defined retention period in line with GDPR. Data governance provides the visibility and accountability. DSPM provides the legislative and technical enforcement. In highly regulated sectors such as healthcare, policing and local government, both are required to support AI safely and lawfully. 

Why Data Governance is Critical for AI Readiness

AI systems learn from the data provided to them. If that data is incomplete, inconsistent or poorly controlled, the results for your organisation will be unreliable and risky to use in decision-making. Data governance ensures that AI models are trained and operated using data that is trusted, well defined and properly protected. Without this foundation, organisations struggle to move beyond small pilots into business-critical AI solutions.  

Strong governance supports AI readiness in three keyways:

1- Data Quality and Consistency

AI depends on clean, standardised data. Data governance introduces validation rules, shared definitions and quality monitoring for your organisation that improve reliability across datasets and systems.

2- Security and Privacy Controls

AI often processes sensitive personal or commercial information. Data governance defines who can access data, how it can be used and how it is protected, helping your organisation meet regulatory and ethical requirements.

3- Transparency and Accountability

As AI becomes embedded into processes, your organisation must be able to explain how decisions are made and which data was used. Data governance provides traceability, audit trails and clear ownership, which are essential for trust and compliance.

Once these elements are in place, organisations can scale AI with confidence rather than constantly managing risk and remediation.

How Data Governance and DSPM Support Trust, Compliance and Scale

Together, governance and DSPM allow organisations to use data and AI with confidence. Here’s how data governance and DSPM introduce confidence, embed compliance and manage growth for your organisation:

Introducing Confidence in Shared Data

As more teams rely on dashboards, reports and AI driven insights, consistency has become critical. Data governance introduces shared definitions, certified datasets, lineage and quality indicators that help users understand whether data is suitable for reporting or AI models. This reduces conflicting reports and helps ensure that your decisions are based on consistent and understood data. Over time, this consistency improves collaboration between departments and supports wider adoption of analytics and AI across your organisation.

Embedding Compliance into Daily Workflows

Rather than treating compliance as a separate activity, data governance embeds controls directly into how data is stored, accessed and used. DSPM applies security and compliance controls directly to how data is stored, accessed and retained. Automated policies, combined with stewardship oversight, help ensure sensitive data is protected while still enabling legitimate use cases. Compliance becomes part of the data platform rather than a separate manual activity.

Managing Growth

As new data sources, systems and AI use cases are introduced, governance defines how data is onboarded, documented and approved, while DSPM ensures that security and retention controls are consistently applied. This prevents unmanaged growth, where systems expand faster than oversight, and allows analytics and AI initiatives to scale across teams safely.

Data Governance in the Real-World: Introducing Microsoft Purview

Microsoft Purview, formerly known as Azure Purview, supports both governance and DSPM by providing tools for data discovery, cataloguing, lineage, classification and policy enforcement across cloud, on-premises and SaaS environments. However, these capabilities still rely on active configuration and stewardship by data teams.

In a real-world scenario, consider a local authority working across housing and social care services. Each area operates its own systems, with sensitive personal data spread across the organisation. While this data is critical for planning and improving outcomes, it cannot be freely shared or analysed without strong controls in place.

Using Microsoft Purview, the council can scan data across these systems, with built in classification helping to identify personal information, which can then be reviewed and governed by data stewards before consistent access policies are applied. Approved datasets can then be documented in a shared catalogue with clear ownership, quality indicators and lineage, while DSPM controls ensure that access, encryption and retention policies are enforced across platforms.

This combination allows analytics and AI models to be developed using governed, compliant datasets while ensuring that data sovereignty is maintained. For public sector organisations, this includes ensuring data remains within approved UK regions and is not exposed to external or global AI services without appropriate safeguards.

In practice, Microsoft Purview allows organisations to move past legacy data governance and step into AI and advanced analytics, without increasing risk or losing control of their data.

Conclusion

Artificial Intelligence offers significant opportunity, but its success depends on more than just advanced algorithms. It depends on whether organisations can trust, protect and govern the data those models rely on.

Data governance defines ownership, quality and responsible use. DSPM ensures that security, privacy, retention and sovereignty are actively enforced. Together, supported by platforms such as Microsoft Purview and strong data stewardship, they provide the foundation needed for AI to move from experimentation to safe, scalable, enterprise-wide adoption. In short, strong data governance isn’t enough in 2026. Organisations need both governance and protection working together to unlock AI safely and sustainably.

How Simpson Associates can help you

As a Microsoft Solutions Partner and a Microsoft Partner of the year award winner for 2024, our talented team of data governance experts have what it takes to help you get started with data governance and implement Microsoft Purview. From initial discovery and Purview implementation to ongoing governance and optimisation, our team will ensure your data is secure, compliant, and ready to support your strategic goals.

Interested in data governance with Microsoft Purview? Have a look at our range of Microsoft Purview Consulting services, our guide to choosing the right purview partner or get in touch with us via email or live chat.