Moving beyond good intentions: Microsoft Purview Data Governance

Data governance often follows a natural progression. First, organisations need visibility into what data exists. Then they need confidence that the information they have found can be trusted. The next challenge is ensuring that governance standards continue to be applied consistently without relying on manual oversight. This article explores how Microsoft Purview supports that transition through Data Quality and Data Policy capabilities.

Many organisations can describe what good governance looks like. They have agreed definitions for important business terms. Data owners have been identified. Critical datasets have been documented. Governance policies exist, often supported by workshops, steering groups and carefully drafted frameworks.

Yet when governance struggles, it is rarely because organisations lack policies.

The difficulty comes from ensuring those policies continue to be applied consistently as data volumes grow, new systems are introduced and different teams begin using information in new ways.

This is the point where governance often reaches a crossroads.

One path relies primarily on process. People are expected to remember standards, maintain definitions, monitor quality and manually review access requests. The other path introduces automation, allowing governance principles to be embedded directly within the platforms through which data is managed and consumed.

The difference between the two approaches becomes increasingly significant as organisations expand their use of data.

In the previous articles in this series, I explored how Microsoft Purview Unified Catalog helps organisations discover data and how capabilities such as Business Glossary and Data Lineage help establish trust. These capabilities create visibility and understanding, but governance cannot rely entirely on visibility and understanding alone. At some stage organisations need mechanisms that help ensure information continues to meet agreed standards and that appropriate controls are applied consistently.

This is where governance begins to move from passive governance to active governance.

Why Governance cannot depend entirely on documentation

One of the common misconceptions about governance is that documentation creates control.

Documentation certainly creates clarity. It helps people understand expectations and provides a reference point for decision-making. However, documentation alone does not guarantee that data remains accurate, complete or appropriately managed.

A policy may define how customer information should be maintained, but that policy does not automatically identify records with missing values. A business glossary may establish a common definition, but it does not ensure every dataset uses that definition correctly. A governance framework may define who should have access to information, but it does not control permissions across multiple platforms and environments.

As organisations become more data-driven, these gaps become increasingly difficult to manage through manual oversight. Governance teams simply cannot review every dataset, monitor every change or manually approve every access request.

The challenge is not understanding governance. The challenge is operationalising governance.

Data Quality: Turning Trust into something measurable

Trust is often discussed in subjective terms. People may describe a dataset as trusted, reliable or authoritative, but these descriptions rarely explain why.

One of the advantages of modern governance platforms is the ability to make trust more measurable.

Within Microsoft Purview, data quality capabilities help organisations assess information against defined expectations. Rather than relying solely on anecdotal confidence, data assets can be evaluated using rules that assess characteristics such as completeness, consistency and validity.

Consider a customer dataset where email addresses are expected to be populated for active records. A quality rule can measure the percentage of records meeting that expectation. Similar assessments can be applied to mandatory fields, valid value ranges, duplicate records and a variety of other quality dimensions.

The value of these measurements is not simply that they produce scores. Their importance lies in making quality visible.

A dataset that appears trustworthy may, in reality, contain significant issues that impact reporting, analytics or AI outcomes. Conversely, a dataset that users have historically questioned may score consistently well against defined quality expectations. By establishing objective measures, governance becomes less dependent on assumption and more grounded in evidence.

For governance teams, quality scoring also provides a practical way of prioritising effort. Not every data asset requires the same level of scrutiny. Critical data elements that support regulatory reporting, financial processes or strategic decision-making often warrant much greater attention than less significant assets. Quality rules help focus attention where it has the greatest organisational impact.

Why Data Quality is becoming more important

Historically, many data quality issues remained hidden because they only affected specific teams or processes. Today that is changing.

Self-service analytics, data products and generative AI all increase the number of people consuming information and the speed at which information is used. Data quality issues that might previously have remained isolated within individual departments can now become visible across the organisation.

This is one reason governance discussions increasingly return to quality. The challenge is not simply reducing errors. The challenge is maintaining confidence as information is consumed at greater scale.

Trust established through cataloguing, curation and lineage becomes significantly more valuable when supported by measurable evidence of quality.

Data Policy: Turning Governance into action

While data quality focuses on whether information meets expected standards, data policy focuses on how information should be accessed and governed.

Most organisations already have policies governing access to information. They understand that not everyone should have access to every dataset and that decisions around sensitive information should be controlled appropriately.

The difficulty is applying those policies consistently across a complex technology landscape.

Manual access management often creates friction for both users and governance teams. Requests are submitted, approvals are sought and permissions are applied manually. As environments expand, the administrative burden increases.

Microsoft Purview Data Policy helps address this challenge by providing a centralised approach to defining and managing access policies. Rather than treating governance as a series of disconnected approval processes, organisations can establish rules that determine who can access specific categories of information and under what conditions.

This is particularly valuable when governance has already established trusted, curated assets through the catalog, glossary and lineage capabilities discussed in earlier articles. Once the organisation understands what a dataset represents and who owns it, policies can be applied more consistently and transparently.

The result is that governance becomes embedded within day-to-day operations rather than operating as a separate administrative activity.

Supporting Self-Service without losing control

One of the recurring themes within modern governance is the desire to support self-service while maintaining appropriate oversight. These objectives are often presented as competing priorities, when in practice they are closely connected.

Users want rapid access to trusted information. Governance teams want confidence that information is being used appropriately. Effective governance seeks to achieve both outcomes simultaneously.

This is where automation becomes particularly important. Well-implemented governance should reduce unnecessary friction rather than increase it. When quality standards are visible, ownership is clear and access policies are defined consistently, organisations are better positioned to enable self-service access to trusted information without compromising governance requirements.

In many respects, this represents the transition from governance as documentation to governance as an operational capability.

Governance that scales

The first article in this series focused on helping organisations understand what information exists. The second explored the role of trust and context in helping users understand whether information could be relied upon.

This third stage builds upon both. Discovery helps people find information. Curation, glossaries and lineage help them understand it. Data quality and policy introduce mechanisms that help governance operate consistently as the organisation grows.

The objective is not greater control for its own sake. The objective is creating an environment where people can access trusted information confidently, while governance standards are applied consistently and transparently.

Ultimately, active governance is about reducing reliance on memory, local knowledge and manual intervention. The more governance depends on individuals remembering what should happen, the harder it becomes to scale. The more governance can be embedded within the way information is managed and consumed, the more sustainable it becomes as the organisation continues to evolve.