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What is data governance?

Data governance is key to managing and securing enterprise data. Learn about data governance core concepts and best practices.
Manage your data lifecycle

Data governance defined

Data governance is the system of internal policies that organizations use to manage, access, and secure enterprise data. While systems may vary in complexity from organization to organization, they always have some common features: internal processes, policies, defined roles, metrics, and compliance standards. The goal of the system is to help people efficiently and securely use the vast amounts of data generated by today’s enterprises.

The importance of data security cannot be overstated. To that end, well-designed data governance accounts for not only data protection but also management of sensitive data from intake to disposal.

Why data governance matters

Companies are increasingly developing, using, and adopting technology solutions to streamline business operations and deliver increased value to customers. This digital transformation generates increased volumes of data (from sources like internal processes and external metrics) and breaks down information silos within organizations. So how can organizations keep it all organized?

The answer is data governance. Secure data governance keeps data organized and consistent, defines who can and cannot access data, and helps organizations handle data—especially customer data— in compliance with relevant standards and regulations.

Well-designed data governance fits an organization’s needs—the data capacity, the permissions, the compliance requirements—both in the present and in the future. The most successful systems are designed to grow as an organization changes.

Benefits of data governance

Well-designed data governance provides organizations with a variety of benefits.

A single source of truth

Consistent data increases opportunities for internal flexibility when all decision-makers have access to the same information.

Improved data quality

Organizations make better informed decisions when their data is safe to use, complete, and consistent.

Improved data management

Organizations can address needs quickly and consistently with a data code of conduct and best practices.

Faster, more consistent compliance

A data governance strategy can be built with compliance considerations at every stage to help organizations with correct data handling and disposal.

Reduced costs and increased value

Consistent data facilitates operational effectiveness, future audits, and efficiency allocation of time and resources.

Challenges to data governance

While a new data governance strategy can yield big benefits for organizations, there are usually several challenges that need to be addressed. Some of these challenges include:

Organizational adoption. Well-governed data is only useful if an organization embraces its availability. Data governance is most successful when every team—executive leaders, business stakeholders, data experts—are empowered to use organizational data through skill-building training.

Defined roles and responsibilities. Related to adoption, defining data governance roles and responsibilities provides clarity on who manages which aspects of the process. Roles can include titles like chief data officer or data steward—each organization can decide for themselves what type of data governance team structure works best for their needs and available resources.

Siloed data. Internal divisions in data organization create silos—pillars of data that are isolated from other areas of the organization. This may lead to duplicate or inconsistent data once everything is unified. Cleaning and preparing data is an essential step of implementing a successful data governance strategy—for good data to come out, good data must go in.

How data governance works

A key part of data governance is managing data from intake to disposal, usually using data governance tools. Details may vary between organizations and use cases, but this management process includes at least these steps:
  • Intake. Companies manage not only the secure collection of new data (sources like Internet of Things devices and apps) but also existing data collected before implementing a data governance strategy.
  • Storage. Companies decide how and where to store all of the data they collect.
  • Classification. Data is classified so correct policies (like for personal identifiable information or sensitive data) can be applied.
  • Sharing. Once it’s organized, data is made available to the users who need it. Classification policies and defined data governance roles specify which internal and external users can access what types of data.
  • Archival. Some data is needed for only a limited period of time, but it might need to be referenced later. Organizations define what data needs to be kept and how to securely store it for as long as is needed.
  • Disposal. Some data does not need to be retained; in other cases, organizations may be required to dispose of it after a certain amount of time. In these instances, data is securely deleted—compliance regulations may require specific steps or proof of disposal. 
Throughout all of these steps, data must remain secure and compliant. A well-designed data governance strategy defines what steps and regulations are needed to maintain compliance and security. A data governance tool can also help to ensure that data is secure at every stage of its lifecycle.

Data governance framework

The data governance framework acts as a blueprint for your data strategy. It integrates your rules, responsibilities, procedures, and processes on how data flows are managed within cloud storage.

Information protection

Applying labels and encryption around sensitive data.

Data loss prevention

Identifying and helping prevent unsafe or inappropriate sharing, transfer, or use of sensitive data, especially in support of regulatory compliance. Learn more about data loss prevention.

Data cataloging and discovery

Automatically identifying and recording your organization’s data assets to enable search, description, and discovery.

Data classification

Tagging data with information, privacy, or other sensitivity classifications to ensure proper data protection and use in the future.

Data ownership

Ensuring that accountable groups or individuals within the organization have the ability to access, describe, protect, and control the quality of data.

Data security

Using cybersecurity capabilities to apply the right security measures (including encryption, obfuscation, and tokenization) per classification as well as managing data loss prevention in the event of a disaster or cyberattack.

Data sovereignty and cross-border data sharing

Establishing jurisdictional rules and prohibitions for data storage, access, and processing.

Data quality

Ensuring that data is fit for purpose in terms of accuracy, completeness, consistency, validity, relevance, and timeliness.

Data lifecycle management

Defining a data retention schedule with legal, regulatory, and privacy requirements so that data is sourced, stored, processed, accessed, and disposed of correctly.

Data entitlements and access tracking

Making data accessible only to those who need access as well as auditing access for evidencing and ensuring control.

Data lineage

Tracking data origins, processing, and usage.

Data privacy

Helping protect the privacy of data subjects with processes and technology that reflect the regulatory and privacy laws governing your organization.

Trusted source management and data contracts

Identifying trusted sources and defining consumption data contracts to ensure that data is being sourced from an agreed source of truth.

Ethical use and purpose

Ensuring that data is being processed (especially by AI and machine learning) in a way that customers expect according to your company’s code of ethics.

Master data management

Providing a single, consistent view of data that describes the core aspects of an organization (such as product, customer, or company structure) to ensure accurate and reliable data usage.

Data governance tools and technology

Organizations may choose to develop or identify the data governance tools and technologies that are right for both their current and future needs. Data governance tools can help to:
 
  • Improve data quality through validation, cleansing, and enrichment.
  • Capture and comprehend data through discovery, profiling, and benchmarking.
  • Manage and track data from intake to disposal.
  • Classify data for internal purposes, such as to increase relevance or searchability.
  • Audit data continuously and in real time.
  • Empower internal decision-makers to understand the data and how it’s used by the organization.
Scalable, cloud-based solutions like Microsoft Purview Data Lifecycle Management are designed for enterprise data governance. These solutions help organizations manage data from intake to disposal, enable access controls, and support quality control. 
 

Data governance pillars

Organizations can use these pillars as guides when designing their data governance systems.

Administration

Establishing a data team helps organizations administer the different processes and aspects of the data governance system. How the team is set up depends on the organization’s resources and data goals.

Standards

Data governance rules—often defined by the data team—help the system stay in line with both internal processes and any relevant compliance regulations.

Accountability

Defining roles and responsibilities helps clarify who owns which elements of governance.

Quality

Established quality standards for data help to inform business decisions.

Transparency

Tracking data is essential—not only the data itself but how it’s acquired, stored, handled, and disposed of, for future audits.

Five data governance best practices

Here are five best practices that organizations can follow as they develop and implement their data governance systems:
 
  1. Designate an executive data sponsor. Data governance adoption starts with leadership. The sponsor represents the system and advocates for its usage across the organization.
  2. Build a business case. Identify organizational goals and benefits to justify the time and resources required to create a data governance system.
  3. Think big, start small. Set data goals at a high level, and then design granular project objectives that build towards those goals.
  4. Define metrics. With so much reliable data accessible, be judicious in choosing what to measure so that organizations use resources most efficiently.
  5. Communicate constantly. Even the most thoughtfully designed data governance will grow and change with use and time. Create ways to provide feedback so that the data team can adjust the system to meet the goals of the organization.

Data governance solutions

Technology solutions can help facilitate and simplify the process of designing and implementing secure data governance. Organizations can use cloud-based tools like Microsoft Purview Data Lifecycle Management to unify, classify, manage, and dispose of data while maintaining regulatory compliance. By using a technology solution, organizations can automate or streamline certain data management tasks while freeing up data team resources to work on more important projects.



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FAQ

Frequently asked questions

  • Data governance keeps an organization’s data organized and consistent, defines who can and cannot access data, and remains compliant with how data is used. Data governance supports both the current and future needs of an organization.
  • A data governance framework integrates rules, responsibilities, procedures, and processes for how data flows are managed within cloud storage. It is the foundation of an organization’s data strategy. A framework includes a variety of elements, including data security, ownership, privacy, and classification.
  • Data governance tools are technology solutions that help with developing and implementing data governance. They should help organizations securely manage data from intake to disposal, enable access controls, and support quality control. For example, Microsoft Purview Data Lifecycle Management is a scalable, cloud-based solution for enterprise data governance.
  • Key data governance pillars include:
     
    • Administration—set up a dedicated data team.
    • Standards—establish rules and regulations for all aspects of data governance.
    • Accountability—define roles and responsibilities so it’s always clear who owns what parts and processes of the system.
    • Quality—set data quality standards for the organization.
    • Transparency—consistently track data including lifecycle and usage.
  • Once a data team is formed, specify the goals of the system. Identify key roles and responsibilities, necessary permissions, and regulatory compliance standards. Identify technology solutions and skill-building opportunities for the data team and stakeholders. Share the system standards, tools, and requirements with the organization—adjust as needed to grow and adapt.

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