What Is Data Governance?

Data governance is the comprehensive framework of strategies, policies, and rules designed to ensure the security, availability, integrity, and compliance of enterprise data assets. Effective data governance ensures that data remains usable, accessible, and protected and isn’t misused, altered, or stolen. Many organisations base their data governance infrastructure and controls on compliance regulations. Good governance reduces the risk of a compromise and avoids data privacy violations, fines, and reputational damage.

The primary purpose of data governance is to cultivate data integrity, compliance, and business value. Data integrity is necessary for consistency, business productivity, and revenue. Without data governance to oversee data integrity, an organisation could have inconsistent data across databases, platforms, and departments. Suppose sales, customer service, and shipping have different addresses for the same customer. The package is delivered to the wrong address after a sale, but when the customer contacts the company to complain, customer service has the correct address on file. This issue could interfere with sales, revenue, customer satisfaction, and daily operations.

Another reason for data governance is compliance. Compliance regulations typically require data integrity infrastructure to ensure customer data is properly monitored and maintained. Major compliance regulations overseeing consumer data integrity include the European Union’s GDPR, the California Consumer Privacy Act (CCPA), and emerging privacy regulations worldwide. Organisations face potential fines of up to 4% of global revenue for serious violations—potentially running into millions of dollars.

• Enable better decision-making for data storage, authorised access, and management.
• Reduce integrity issues by ensuring data is consistent across all storage locations.
• Protect the interests of data stakeholders.
• Train employees, vendors, and stakeholders on data security best practices and compliance requirements.
• Establish data management standards so that strategies can be successfully repeated.

• Optimise operational efficiency while reducing costs.
• Create transparent processes.
• Enable data-driven innovation while maintaining security and compliance.
• Support digital transformation initiatives through reliable data management.

• Fewer inconsistencies across reports and applications reliant on data.
• Fewer data entry errors and changes to data.
• Consistency between performance metrics used to determine future performance strategies.
• Better monitoring and oversight of sensitive organisational and consumer data.

• Improved data quality and accessibility across the organisation.
• Enhanced data value through better quality, accessibility, and usability.
• Reduced risk of data breaches and compliance violations.
• Improved decision-making through trusted data sources.

Data governance consists of several interconnected components that form a comprehensive framework for managing and protecting organisational data assets.

Data Strategy and Framework

A well-defined governance framework establishes the foundation through clear objectives, guiding principles, and measurable goals that align with organisational strategy. This includes developing mission statements and specific metrics to evaluate success.

Roles and Responsibilities

A clear definition of data ownership and accountability is essential, with specific roles including Data Owners, Data Stewards, and Data Custodians. A Data Governance Council, typically comprising cross-functional leadership, oversees strategy implementation and policy decisions.

Policies and Standards

Organisations must establish comprehensive policies that guide data management, including data quality standards, security protocols, and compliance requirements. These policies create a standardised approach to data handling across the enterprise.

Data Quality Management

Robust processes for monitoring, measuring, and improving data quality ensure the accuracy and reliability of organisational data assets. This includes implementing validation procedures and data cleansing techniques to maintain high-quality standards.

Security and Privacy

Protection mechanisms must safeguard sensitive information through proper classification, access controls, and risk management procedures. This component ensures compliance with regulatory requirements while maintaining data accessibility for authorised users.

Data Catalogue and Metadata Management

A centralised data catalogue documents and tracks data assets, their relationships, and associated metadata. This enables a better understanding of data lineage, technical specifications, and the business context of information resources.

Performance Measurement

Organisations should implement key performance indicators (KPIs) and metrics to evaluate the effectiveness of their data governance programme. These measurements help track progress and identify areas for improvement in the governance framework.

In an age where one organisation could store millions of consumer records, data governance helps with the privacy and integrity of these records. Data governance benefits consumers and the organisation while ensuring that data procedures are compliant. Every organisation should have a data governance strategy, but certain industries benefit more due to the type of data stored.

• Medical: HIPAA highly regulates patient information. Prescriptions, images, contact information, and sensitive services must be protected from misuse and unauthorised access while enabling secure data sharing across healthcare providers.
• Risk management: Big data in risk management analysis must be protected and properly managed to ensure the accuracy of results so that consultants can make effective decisions and maintain regulatory compliance.
• Banking: Errors in financial data could affect consumer livelihood and close down banks. Data governance ensures that transactions and balances are correct across all platforms and consumer information is protected in accordance with financial regulations.
• Agriculture: Many agriculture organisations use legacy systems that do not adequately protect or govern data. An information governance plan protects current and legacy systems that store data.
• Cloud services: Organisations increasingly rely on cloud infrastructure, requiring robust governance frameworks to manage data across hybrid and multi-cloud environments while maintaining security and compliance.

Organisations typically establish a data governance leadership structure, often led by a Chief Data Officer (CDO) or Chief Information Officer (CIO), to oversee strategic data initiatives and ensure compliance. The CDO works with a data governance manager to oversee a team that plans procedures, develops automation, and determines policies.

Other parties might be involved with data governance. For example, a committee might determine standards and policies by voting on any changes to these procedures. Staff members carry out the committee’s regulations and are responsible for ensuring that standards are followed.

• Consistency across all data views while allowing organisations to update and add data.
• A plan that highlights all the policies and maintains consistent procedures.
• A “single point of truth” that covers every question and helps staff determine the proper way to handle particular challenges.
• Standardised methodologies for data quality management and validation.

• Role-based access controls and authentication protocols to ensure appropriate data accessibility.
• Integration with existing security and compliance frameworks.
• Clear procedures for data life cycle management.

Planning and implementing a data governance strategy usually happens in phases. How data governance is implemented depends on your organisation’s internal infrastructure, industry, internal procedures, technology, and location of data.

• Phase 1: Assess your organisation’s data governance maturity and regulatory requirements. If you don’t have someone on staff who understands data governance, consider help from outside consultants.
• Phase 2: With the help of consultants or internal staff, audit data for its location, usability, availability, and access permissions across both on-premises and cloud environments.
• Phase 3: Identify data ownership and determine roles and responsibilities for governance, including data stewards and custodians.
• Phase 4: Develop data definitions and determine if data is stored and maintained in the best location, considering security, compliance, and accessibility requirements.
• Phase 5: Implement training programmes for users and stakeholders on new standards, policies, and the importance of data governance.
• Phase 6: Monitor data and review metrics to determine if standards should be modified and improved using automated tools and dashboards.

• Start small and design achievable goals to continuously improve.
• Designate ownership of procedures so that everyone can be a part of the process to success.
• Assign roles and responsibilities to each data owner and manager.
• Implement ongoing training programmes for data governance awareness.
• Map tools and infrastructure with data to get a clear picture of where it’s used.
• Focus on the most critical data first to ensure changes significantly impact information governance maturity.

• Develop control procedures and policies that are available to those who need them.
• Use metrics to identify weaknesses and improvement opportunities.
• Communicate frequently with all individuals involved in data governance.
• Regularly review and update policies to align with evolving regulations.
• Implement automated data discovery and classification tools.

As with any new initiative, implementing a data governance strategy has its challenges. Proper solutions can overcome some scenarios in-house, while others may require outside help from consultants. Before you start your data governance journey, consider these common challenges:

• Limited resources: Small-to-midsize organisations struggle with finding on-site staff with the knowledge and skills to implement a data governance plan. Current administrators may be already overworked and may not have the bandwidth to take on another responsibility. While automation and AI tools can help, organisations still need skilled personnel, so many organisations need outside help to get started.
• Data complexity: Organisations face challenges with the addition of technology, communication barriers, cloud migrations, and hybrid environments, creating scattered data across multiple platforms.
• No leadership: Even staff familiar with data governance needs direction and leadership to deploy it. An effective leader will educate users and implement a data governance strategy from start to finish.
• Defined business requirements: The first step to defining data policies is to understand business requirements. This requires creating use cases and understanding how data is used throughout the organisation.
• Data quality: Poor quality data compromises data integrity and obfuscates data ownership. It may be necessary to organise and improve the data before creating a data governance plan.
• Data sprawl: Business growth may result in data that is mismanaged and scattered throughout the organisation, especially across cloud services and third-party applications. Data sprawl compromises control of all data, potentially resulting in missed data during an audit.

Data governance and data management serve distinct yet complementary roles in an organisation’s data strategy. Understanding their differences helps teams operate more effectively while maintaining data security and compliance.

Strategic vs. Tactical Focus

Data governance creates the strategic framework—defining the rules, policies, and standards—for how data should be handled across the organisation. Data management focuses on tactically executing the day-to-day processes of storing, organising, and maintaining data according to these guidelines.

Process and People

Data governance dictates the decision-making framework for data usage, including quality standards, access policies, and compliance requirements. It typically involves business stakeholders and domain experts who set strategic direction. Data management handles the practical execution through technical teams that implement storage solutions, security measures, and data integration processes.

Working Together

Think of data governance as the blueprint and data management as the construction process. Governance establishes who can take what actions with data, under what circumstances, and for what purposes. Management puts these decisions into action through technical implementation and daily operations. Both components must work in harmony to create an effective data strategy that protects sensitive information while enabling business objectives.

Technology and Tools

While governance focuses on policy management and documentation tools, management employs technical solutions for data storage, processing, and security implementation. This comprehensive approach enables governance to guide the overall strategy while management handles the technical execution.

Data governance is built on pillars that are critical to a successful strategy. When designing a data governance strategy, include the following pillars:

• People and culture: The people who take ownership of data make governance successful. A successful strategy requires that everyone on board understands the importance of information governance and what they can do to ensure the protection and integrity of corporate data through a data-driven culture.
• Processes: Take every action necessary to ensure data governance and integrity are effective and thoroughly tested. Processes should be standardised, documented, and automated where possible.
• Expertise: Subject matter experts and data stewards provide crucial guidance and oversight. They ensure that processes are effective and pass down effective procedures.
• Technology: Organisations need an effective infrastructure, including scalable and integrated solutions to monitor and implement policies. Modern data governance platforms should support automation, compliance monitoring, and real-time analytics while integrating with existing systems.

Compliance with multiple regulations can be difficult to achieve for an organisation without on-site expertise. Outside consultants proficient in specific compliance regulations can start an organisation on a journey towards effective data governance planning and practices.

Modern data governance platforms offer integrated solutions for data discovery, classification, monitoring, and compliance reporting. These tools can help automate routine tasks, provide real-time insights, and scale with organisational needs. Vendors can also provide expertise in implementation, training, and ongoing support to ensure the successful adoption of data governance practices.

Proofpoint provides comprehensive enterprise data protection solutions that combine advanced technology with expert guidance to help organisations strengthen their data governance programmes, from archiving solutions to data discovery. We offer integrated tools that enable administrators to properly oversee and manage information governance across the enterprise.

Through our Data Protection platform, Proofpoint helps organisations implement end-to-end data governance by providing user awareness training, automated compliance controls, and advanced threat protection so that they remain productive while following compliance standards to protect data across all channels and environments. To learn more, contact Proofpoint.