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What is data governance?

It's important to always know who's in control of your data—learn about data governance and how your teams can manage and secure data resources across your data estate.

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Data governance definition

The definition of data governance includes the collection of processes, policies, roles, metrics, and standards that ensures an effective and efficient use of information. This also helps establish data management processes that keep your data secured, private, accurate, and usable throughout the data life cycle.

A robust data governance strategy is crucial for any organization that uses data to drive business growth, make improved decision-making, and ensure successful outcomes in a competitive market. When collecting vast amounts of internal and external data, you'll need to have a strategy that manages risks, reduces costs, and executes business objectives effectively.

The benefits of data governance

A big part of data governance is building a program that breaks down data silos through a collaborative process with stakeholders from disconnected business units. Your data governance program will need to do the heavy lifting to ensure that organized data is appropriately used and accurately entered into systems. Implementing a robust data governance strategy helps ensure that your information is:

  • Cleanly audited
  • Evaluated
  • Documented
  • Managed
  • Protected
  • Trustworthy
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But first, your IT team needs to make sure you can provide reliable data. The benefits of having accessible, accurate data are:

Having a single source of truth. All decision-makers work from the same data sets, terminology, and view, giving more opportunities for internal flexibility.

Improved data quality. Your team can rest assured that all the available data is safe to use, complete, and consistent.

Improved data management. Helping establish a code of conduct and best practices to ensure your team addresses organizational needs and concerns immediately and consistently.

Faster, consistent compliance. Having clean data management throughout your governance process means procedures correctly generate, handle, and protect your data to keep it in compliance.

Reduced costs and a better profit margin. Eliminating decisions based on outdated information results in efficient day-to-day operations, easier audits, and reduced waste.

A stellar organizational reputation. When your business is steadfast and reliable, you position your business as a leader in your marketplace.

While adding a data governance strategy to your organization has many benefits, a few challenges might arise if your team isn't prepared for its organizational implementation.


Challenges of data governance

Though the rewards are great, creating a data governance solution may feel difficult. Some of those challenges include:

Company-wide acceptance. Since data spans across multiple departments, there needs to be clear leadership from the top down as well as cross-functional collaboration.

Poor data management. If your data management is structured from an incomplete data governance program, the data will be unsecured and siloed as well as having undisciplined processes—possibly leading to massive data breaches and non-compliance.

Standardization. Organizations need to find the right balance between governance standards and flexibility.

Aligning stakeholders. You'll need to work hard to convince stakeholders of the value of your data—providing transparency to stakeholders will persuade them to invest in your organization's governance and securities budgets.

Assignment of responsibilities. There might be struggles with deciding who and who shouldn't have access to particular segments of data. Creating a system of who sees what and when will help you and your team eliminate potential issues.

Your data governance strategy —both the technical and business aspects—needs to be accepted by everyone in the company. And to ensure you have a successful strategy, you'll need to implement best practices and principles into your data governance program.

Data governance principles and best practices

When creating the framework needed for your data governance, you'll need to create one that fits the objectives of your organization. Some things you'll need to think about are how to use your data properly, improve data security, create and enforce data distribution policies, and keep in compliance with all regulatory requirements.

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To guarantee a successful implementation, focus on these five data governance principles:


Across the organization, you'll need team members to take control of your data—if no one takes that responsibility, then there's no data governance. You and your IT team must implement ownership and responsibility. Establish a data governance team with representatives from other departments to ensure cross-organization accountability.

Rules and regulations

You'll need standardized rules and regulations for everyone to follow—developed by your data governance team to implement and create criteria for all data usage.

Data administration

Selecting a dedicated data administrator, also known as a data steward, is key to enacting and ensuring the proper protection of your data governance. As a data steward, this person's responsibility is to report to the data governance team and enforce data rules and regulations, ensuring they're followed regularly.

Data quality

You'll need high-quality, clean, and reliable data to make informed business decisions. To do this, your data steward will create a shared set of standards to improve data quality.


All data governance processes need to be transparent as possible. Maintaining permanent records of all functions and steps ensures any future audits can determine data usage, what data was used, how you handled the data, and why your team used it.

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Data governance definition

As your business expands and follows these five data governance principles, you’ll also need to adapt to the latest data governance practices and ensure you stay up to date on the newest technology.

The first five best practices for data governance are:

1. Think big but start small

Document your high-level goals but keep in mind your project objectives and milestones.

2. Appoint an executive sponsor

This person will advocate your data governance strategy to your high-level executives, as well as the broader organization.

3. Build your case

Create the business case you'll need to justify why you need to implement a successful data governance plan as soon as possible.

4. Develop the right metrics

Too many or too few metrics will make it difficult to understand if you're reaching your goals. The users, operators, and teams will need to quickly determine which metrics are and aren't necessary as you meet their objectives.

5. Keep communicating with all levels

Stay open to this new process, especially encouraging those adverse to change. You'll need to provide context and transparency to many who might not understand your process and its importance.

Cloud data governance framework

Moving your data to cloud storage will be crucial in your growth. Your cloud data governance framework serves as a blueprint and lays the foundation for how your data strategy is stored in the cloud. Products like Microsoft Purview helps your team explore data flows—the ins and outs—while your governance integrates your rules, responsibilities, procedures, and processes on how those data flows are managed and controlled safely within cloud storage.

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A global industry council, the EDM Council, created Cloud Data Management Capabilities (CDMC) that prescribe a framework for what data governance should encapsulate. This includes:

Data cataloging and discovery—The automatic identification and physical record of data assets in a unified manner to enable logical search, description, and discovery of an organization's data.

Data classification—Tagging data with appropriate information, privacy, or other sensitivity classifications to secure onward use and protection.

Data ownership—Ensuring data is owned for protection, description, access, and quality by accountable and empowered agents within the organization.

Data security—Ensuring data is encrypted, obfuscated, tokenized, or has other appropriate security measures applied in line with its classification. Includes capturing evidence of security application and management of data loss prevention.

Data sovereignty and cross-border data sharing—Ensuring data is being stored, accessed, and processed according to jurisdictional rules and prohibitions.

Data quality—Ensuring data is fit for purpose according to the core measures of data quality—accuracy, completeness, consistency, validity, relevance, and timeliness.

Data lifecycle management—Ensuring data is sourced, stored, processed, accessed, and disposed of in line with its legal, regulatory, and privacy lifecycle requirements, which are often defined in a retention schedule.

Data entitlements and access tracking—Data must only be accessible to those that are intending to access it. Auditing this access is an important part of evidencing and ensuring control.

Data lineage—Ensuring it is possible to identify where data has originated, the steps it has undertaken, and where it is being used at a granularity and frequency that is relevant.

Data privacy—Define a framework for the protection of the privacy of data subjects that reflects the regulatory and privacy laws governing your organization. Ensure processes and technology are employed to ensure the privacy framework is actively applied.

Trusted source management and data contracts—Large organizations may have similar data originating from or processed through a number of sources. Identifying and managing trusted sources and defining consumption data contracts is important to ensure data is being sourced from an agreed source of truth and the overall data architecture is being managed effectively.

Ethical use and purpose—Increasingly, the ethical use of data is being questioned beyond privacy laws and data subject rights. As the use of AI and machine learning increases, it is important to ensure data is being processed in a way that customers would expect according to your company's code of ethics.

Master data management—Master data is the most commonly used and duplicated data within an organization. It is often the data that describes the core operational aspects of a company (for example, product, customer, employees, and company structure). Ensuring there is a single consistent view of this data is fundamental to accurate and reliable data usage.

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Data governance definition

It's important to realize that data governance is not satisfied by technology solutions alone, but in an increasing hybrid and multicloud world, an integrated data governance architecture is becoming a more important part of any solution.

Data governance tools, software, and resources

While there is no single data governance strategy that will work for every business, a cloud-based, scalable solution, such as Azure, will help organizations adapt to future needs while being more cost-effective. Plus, whatever data governance tool you select, make sure it helps:

  • Improve the quality of data with validation, cleansing, and enrichment.
  • Capture and understand data through discovery, profiling, and benchmarking.
  • Manage your data to track data integration with end-to-end lineage.
  • Document data to increase relevance, searchability, accessibility, and more.
  • Actively review and monitor data at all times.
  • Empower your people to know that data best.

Additionally, look for software capabilities that include AI, machine learning, information lifecycle and content management, and enterprise metadata management (EMM).

Azure governance and management is a perfect example of a management and governance cloud solution that features advanced capabilities to help manage your data throughout its entire IT lifecycle. With this data governance, you're able to track data flows from end-to-end of your organization, ensuring the right people all have access to reliable, accurate data they need, whenever they need it.

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