What is Data Governance? Complete Guide

Data governance is a set of practices that help ensure data is safe and effective. It also helps your organization maintain data quality, critical for informed decision-making.

Data governance vs data management

Data governance and data management are related but separate concepts. Though they’re interconnected and often work together, they focus on different aspects of data within an organization.

Data governance focuses on establishing a framework for making decisions about data, defining roles and responsibilities, and enforcing policies and standards related to data management.

On the other hand, data management focuses on implementing the strategies defined by data governance. It covers the technical and operational aspects of data handling, such as how data is acquired, organized, stored, integrated, and used.

Data governance provides the frameworks, while data management encompasses the practical execution. They work hand-in-hand to establish effective data use and decision making and ensure data quality and compliance.

Master data management vs data governance

Master data management (MDM) is a subset of data governance. It focuses on the most essential parts of your organization’s data, which is master data.

Depending on your business, master data might include information on customers, products, suppliers, employees, and other critical reference data that your organization shares across multiple systems and applications.

While data governance focuses on managing and controlling data assets, MDM is concerned only with core business entities. It aims to establish a single, authoritative, and consistent version of master data across your entire enterprise.

MDM usually focuses on:

• Data standardization
• Data integration
• Data governance alignment
• Data quality management
• Data access and distribution

Data governance extends beyond MDM, encompassing the broader framework and practices for managing data, including master data. It also addresses more aspects, such as data strategy, stewardship, policies, and compliance.

Having a solid data governance strategy offers organizations several advantages. From improving trust to boosting your business’s bottom line, thorough data governance pays off.

Strong data ethics

Data governance policies establish the rules for handling data legally, securely, and ethically. This builds trust in your organization’s data practices.

High-quality data

Consistency standards and monitoring mechanisms ensure data is accurate, reliable, and complete. That means you can count on it to support your analytics and decision-making.

Effective collaboration and communication

With clearly defined roles and issue escalation processes, your stakeholders can get together to resolve problems faster while working towards shared data goals.

Supports data protection compliance and regulations

Documented policies and quality guidelines help demonstrate compliance with data regulations, including GDPR and CCPA.

Improved decision-making

Reliable data leads to better forecasting, targeting, and policy changes as it reflects facts rather than faults.

Opportunity for increased revenue

Governance enables customer insights, streamlined operations, and minimized risk and can directly boost your business’s top and bottom lines.

Once you implement data governance, a framework is critical to rolling it out. It creates structure and accountability to ensure data is trustworthy and valuable.

For most companies, core data governance framework components include:

• Policies: These are straightforward rules for data handling that support broader business goals. These provide black-and-white guidelines for your teams to follow.

• Standards: Define what makes data suitable, like accuracy, consistency, and completeness, giving teams clear benchmarks.

• Roles: Making stakeholders responsible for governing policies or standards related to their expertise. This brings clarity to the data management process.

• Monitoring: Spot checks to catch when data issues arise, such as errors or misuse. Early alerts lead to a faster response.

• Fixing issues: Step-by-step processes to address data problems.

You can secure and use data effectively by connecting high-level goals with daily tasks. The proper framework makes governing data efficient and approachable.

Data governance principles can help your organization come together to address data-related needs and conflicts. They’re essentially your business's goals and objectives, used to create a guiding set of principles.

Depending on your organization, you may establish tailored data governance principles that meet specific business needs. However, most companies will follow eight universal governance principles—the Data Governance Guiding Principles from The Data Governance Institute.

These principles focus on the most common data governance objectives and guide effective practices.

Integrity

Data governance should ensure accuracy, consistency, and reliability throughout its lifecycle. It should implement processes and controls to maintain data integrity, prevent data corruption, and address data quality issues promptly.

Transparency

Practices should foster transparency, ensuring processes, policies, and decisions are well-documented, communicated, and understood by stakeholders. They should provide visibility into data sources, flows, and usage.

Auditability

Any decisions, controls, and processes should undergo auditing to ensure they are traceable and accountable. Data governance requires comprehensive documentation to accompany these activities and meet compliance requirements.

Stewardship

Data frameworks should establish clear roles, responsibilities, and accountability for data management. It should define data ownership and assign stewards responsible for data quality, integrity, and proper usage.

Checks and balances

This principle establishes mechanisms that verify and balance the actions and decisions in data management processes. It should prevent conflicts of interest or ensure that nobody has unchecked process control.

Standardization

Data governance should establish and enforce consistent data definitions, formats, and standards across your organization. The framework ensures data is used and interpreted consistently across different systems and departments.

Change management

This framework establishes how and when data should change, which is especially important for master data and metadata. Data governance recognizes that some data types change frequently, while others should only be altered after considerable review and impact analysis.

Ineffective data governance can lead to security breaches or disruptive data loss, which can have devastating consequences for businesses—including costly penalties and loss of customer faith. Data governance is closely related to data security, as data governance is what enables data security policies to remain effective.

Data governance helps your organization:

• Ensure compliance with laws and regulations surrounding data.
• Increase the quality of data used to make data-driven decisions, such as product data management.
• Prevent costly data breaches and data loss by keeping data protection standards up to date.

However, you aren’t just dodging fines and penalties with your data governance strategy—you’re protecting the data most valuable to your business.

Data governance can help your organization by:

• Taking a more systematic approach to collecting, organizing, and analyzing data so you can make better decisions rooted in reliable information.
• Reducing costs by streamlining processes and improving efficiency and productivity metrics.
• Building customer trust, confidence, and satisfaction by ensuring the organization handles customer data securely and responsibly.

Getting data governance right is essential but can be tricky. Obstacles can derail your progress if not adequately planned for—setting it up involves clearing these common hurdles.

Showcasing the value of data governance

Failing to demonstrate the benefits of governance can lead to a lack of leadership buy-in. You risk wasting time and resources elsewhere if it’s not prioritized correctly. Keep track of your governance “quick wins,” including where it supports business goals and growth, to help pull in commitment.

Defining roles and responsibilities

Confusion over who handles what can hinder your governance attempts and allow crucial tasks to fall between the cracks. Outline precisely what data-related tasks each team or person owns in a central playbook available to all.

Limited resources

Budgets and staff bandwidth may restrict what’s possible, potentially leaving high-exposure data unmanaged. Consider adopting a gradual rollout, focusing on the riskiest data first. Automating your monitoring (where possible) also helps.

Poor quality data

Existing flaws in your data can undermine future governance and risk bad analytics, decisions, and compliance issues. Invest in assessments, clean-up, and prevention controls tailored to your data ecosystem.

Maintaining data security and privacy

Data breaches can violate trust, leading to irreparable operational and reputational damage. Keep laws and regulations in mind when creating policies and ensure vulnerability testing. Limit who has access to data to prevent leaks.

Scalability

Growth in data volume and new types can outpace governance systems, leading to overlooked data. Address future surges and sources by creating adaptable frameworks with embedded change processes.

Regulation

Failing to adapt to legislative changes can risk compliance and heavy fines. Stay on top of regulation shifts and realign your policy accordingly.

Implementing a successful data governance strategy requires careful planning, collaboration, and execution—in addition to the right tools.

The following best practices make it easier for organizations to create efficient, secure data governance strategies.

• Establishing roles and responsibilities: Establishing roles and responsibilities will restrict data to those who need it. Clearly define the roles and responsibilities of those managing data with least-privilege access or even zero trust. Meanwhile, elect data stewards and identify key stakeholders to ensure swift action if a threat occurs.

• Training: Training employees on data governance policies and procedures is essential for maintaining compliance. Data breaches often occur when people make mistakes—and anyone can make a mistake. However, training will help your team build better overall data governance, protection, and security habits.

• Auditing: Regularly auditing the system helps identify potential data risks and ensures that the company follows the data governance framework. Over time, data security and governance can drift. Proper auditing reduces this drift and helps the organization adapt to shifting needs.

• Monitoring: Routinely monitoring data usage helps organizations quickly identify suspicious or unauthorized activities. Today, advanced monitoring systems driven by AI technology can identify suspicious activity, even internal.

As data security concerns grow, the need for robust data governance policies and processes will only increase; however, the nature of data will also change. Preparing for data governance in the future means investing in tools and partnerships that will adapt to evolving technologies and regulations.

New regulations for data governance

Governments and regulatory bodies worldwide are introducing new regulations to ensure businesses comply with data protection laws. Some examples include:

• General Data Protection Regulation (GDPR): The GDPR is a set of European Union regulations designed to protect the personal data of EU citizens. Other countries are now looking at this as an example.

• California Consumer Privacy Act (CCPA): The CCPA is a California law that gives consumers more control over how businesses use their personal information. Other states may follow similar regulations regarding data catalogs.

Because AI technology is becoming more advanced and readily available, new regulations will likely emerge regarding how AI/ML can utilize collected data. Anyone using AI/ML for their organization—from marketing to analytics—should consider this.

Tools and technologies for managing data governance

Data governance is complex and changing all the time. However, you can leverage new tools and technologies to stay compliant and improve your data governance. Some notable data stewardship tools include:

• Machine learning (ML): Machine learning can help automate the process of monitoring data and flagging suspicious activities while digging deeper into the meaning behind the data collected.

• Automation: Automating specific processes, such as setting up access controls and tracking changes, can help you save time while improving accuracy and security.

• Analytics: With analytics tools, you can monitor your data usage in real time and gain valuable insights into your data usage. From there, you can optimize even big data and improve your business intelligence strategies and data governance processes.

• Data governance platforms: A comprehensive data governance platform can help your business follow data governance best practices and manage data more efficiently while ensuring regulatory compliance.

As data becomes an increasingly important asset, building data governance into your organization’s strategy is essential. Amplitude can help your data governance initiatives.

With Amplitude Analytics, you can:

• Govern your data sources and data modeling in one all-in-one, out-of-the-box solution.
• Restrict data access to only those who need it.
• Use machine learning and at-a-glance insights to get actionable information.

At Amplitude, we take data security seriously—to take the burden off you. Rather than worrying about data standards and storage, you can concentrate on the benefits of data analytics and digital transformation.

Sign up for free to get started on your data governance journey.