What is data governance?

Data governance is a vital method of managing data throughout its existence, from collection to utilization to final disposition, in ways that support, benefit, and protect the ever-evolving enterprise. The ability of organizations to acquire vast quantities of disparate data means that they also require principles to maximize usefulness, mitigate threats, and minimize costs associated with that information.  The stakes associated with effective data governance continue to rise due to reliance on quality data by technologies like Machine Learning and Artificial Intelligence (AI), as well as evolving digital transformations, as enterprises grow and scale. 

Regardless of industry, data governance impacts whether and how data supports the enterprise’s strategic initiatives, organizational goals, and stakeholders in areas like security, compliance, supply chain management, finance, procurement, marketing, sales, and many more. But what, exactly, is data governance? 

Data governance defined

Data governance is a collection of activities the enterprise conducts to ensure data is reliable, confidential, accessible, and functional. It comprises the procedures organizations must undertake, the processes they must enable, and the technology that supports their efforts. For successful data governance, the enterprise must develop data policies, internal requirements that specify how data is collected, managed, stored, and eliminated, and establish metrics that assess the effectiveness of its data governance program. 

Data governance specifies the types of data that must be managed, and which people, applications, and technologies can access this data. These policies are informed by numerous external and internal factors, including regulatory compliance requirements, industry standards, government agencies, and organizational goals and objectives. To establish data governance, organizations must answer questions like: 

• What data can be acted upon? 
• Who can act on this data? 
• What actions can these entities (employees, third parties, applications, devices) take? 
• Under what circumstances can these entities take these actions on this data? 
• What methods can be utilized to take these actions? 

Ensuring integrity, privacy, and security while enabling quality, availability, and usability of structured and unstructured data requires an effective decision-making process at the core of the enterprise’s data governance program. The organization must make many choices in terms of investment prioritization, resource allocation, and key performance indicators (KPIs) to properly utilize data to advance business initiatives. 

 

The enterprise must consider the entire life cycle of its data when it comes to data governance, with attention to big data, digital transformation, and scaling business intelligence as key drivers.

Why does data governance matter to organizations?

Among the many reasons that data governance matters to organizations are the following.

• Data-driven decision making
  By ensuring data quality and reliability, a data governance program allows leaders to rely on it to support decision-making, which leads to operational efficiencies and improved strategic planning. By providing a single source of truth and ensuring data integrity, data governance supports data-driven strategies and operational decisions.
• Data quality and reliability
  Effective data governance practices ensure that data used and created across an organization is consistent, accurate, and reliable. 
• Data security and privacy
  Data governance includes policies and practices for safeguarding sensitive information protecting against unauthorized access, data breaches, and cyber threats. It defines roles and responsibilities for data access, establishes security protocols, and ensures that data handling practices uphold privacy standards.   
• Operational efficiency
  By standardizing data management practices and reducing redundancies, data governance streamlines operations and reduces costs. It improves resource allocation, eliminates inefficiencies in data management, and enhances the efficacy of data use across the organization.
• Regulatory compliance
  Data governance frameworks provide the structure, processes, and controls required to meet strict data management rules set forth in regulations such as GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), and CCPA (California Consumer Privacy Act).
• Risk management
  Data governance provides a structured approach for managing data-related risks. It ensures that data is handled securely, accurately, and in compliance with applicable regulations, supporting an organization’s overall risk management strategy.  
• Streamlined data integration and interoperability
  Data governance helps organizations seamlessly integrate and share data across platforms by maintaining consistency and accuracy.

Additional goals for data governance include: 

• Planning for and managing increasing volumes of data 
• Facilitating data access to support workflows 
• Reducing data silos to increase efficiencies 
• Implementing policies for data from new sources, such as devices, Application Programming Interfaces, and the Internet of Things (IoT) 
• Improving the audit process while streamlining compliance

The organization will benefit from increasingly valuable insights as it continues to enhance data quality, security, and accessibility with robust data governance. 

Benefits of data governance

Data governance enables centralized, reliable visibility into the enterprise’s data. Other benefits of data governance include: 

• Better-quality data, user confidence in that data, and a shared understanding of that data through data governance that generates a single source of truth throughout the organization 
• Enhanced quality of decisions and outcomes for the enterprise, in a timelier fashion, with stakeholders able to access the data they need to capitalize on opportunities and build better relationships 
• Enriched data analytics, with greater opportunities for business intelligence, machine learning, and other advanced data initiatives  
• Decreased costs as data resources are more efficiently managed, with less waste from conclusions drawn from flawed or obsolete data, along with fewer silos 
• Improved regulatory compliance, not only from avoiding penalties and risks associated with non-compliance, but with better data management, easier audits, and proactive intelligence about upcoming regulatory demands 
• Mitigated cyber risk from reduced exposure to unauthorized users, insider threats, and data breaches 
• A positive reputation for proper handling of sensitive data within the enterprise’s industry and amongst shareholders, customers, and suppliers 

The enterprise flourishes when it has precise, dependable, and trustworthy data, which can be realized with effective data governance. 

What are some common data governance challenges?

Despite its many benefits, organizations face hurdles with data governance programs. Several of the commonly cited challenges include the following. 

• Adapting to regulatory changes
  New and continuously changing regulatory requirements for data governance make it difficult for organizations to keep policies up to date. Keeping up with the changes requires ongoing surveillance and the ability to adapt quickly, which strains already stretched resources.
• Cultural change and user adoption
  To be successful, data governance requires a cultural shift and broad user adoption. As with most change, this presents a challenge to organizations as they initiate a data governance program.
• Data quality and consistency
  Ensuring consistent, high-quality data across an organization is a significant challenge because issues come from various sources, such as inconsistent data entry practices, legacy systems, and lack of standardization. Beyond the initial data quality issues, maintaining the context and understanding of data as it flows through systems can be challenging.
• Defining clear roles and responsibilities
  Establishing clear data governance roles and responsibilities within an organization can be difficult, especially in organizations where data management practices have been informal or siloed. 
• Establishing policies and procedures
  Developing comprehensive data governance policies and procedures that meet organizational objectives and regulatory requirements can be complex. These policies need to be flexible enough to adapt to changes in the business environment or regulations.
• Explaining the value of data governance
  Many organizations struggle to measure and communicate the value that a data governance program provides. This causes difficulties with funding and support at the user and executive levels.
• Limited resources
  Organizations often face challenges in funding and allocating sufficient budget and personnel to data governance efforts, impacting the effectiveness of these initiatives. 
• Scaling data governance
  As organizations grow and evolve, scaling data governance practices to keep pace with changes in data volume, variety, and the organization’s current needs is challenging. The volume of data created presents challenges related to management and storage. In addition, deriving value from large datasets while adhering to governance and compliance rules is difficult.
• Securing and maintaining executive support
  Securing sustained commitment and support from senior management can be a challenge with data governance programs, as they require resources, time, and changes to established processes. The absence of executive commitment to data governance can result in a lack of direction, prioritization, and coordination of efforts across the organization.
• Siloed data
  Data often resides in silos within different groups, departments, and business units, each with its management practices and standards. This fragmentation can lead to inconsistencies, duplication, and challenges in data integration and analysis.

Data governance in the cloud

As the enterprise continues to migrate to the cloud, questions surface about how data governance must evolve and scale. Considerations include: 

• Data migration: The migration itself requires a data governance process to enable efficiency and security 
• Data security: Safeguarding data from threat exposure, ensuring privacy and confidentiality obligations are satisfied, and communicating assurances to stakeholders 
• Data visibility: Enabling stakeholders to access and control data across the organization as needed at every level, from making strategic business decisions to streamlining workflows 
• Data sprawl: Benefiting from cloud flexibility while minimizing impact from fewer controls in a decentralized environment 
• Regulatory compliance: Ensuring that the cloud provider conforms to legal standards and requirements and supports the enterprise on specifications, such as where data must reside 

Why data governance is useful

For many reasons, data governance is necessary. But beyond meeting mandatory requirements, data governance is useful to the enterprise in several ways, including: 

• Insights from customer data for service, renewals, cross-selling, and upselling 
• Product data including user behavior and usage that supports product design updates 
• Sales and marketing information for enablement and optimization 
• Integrations that streamline workflows, enable efficiencies, and offer insights on how various functions interact with one another across the organization 
• Real-time data availability for quick tactical pivots when needed 
• A system for archiving and deleting data as appropriate that makes the best use of organizational resources

What data governance is not 

Since there are many processes, practices, and procedures associated with data governance, it’s easy to confuse it with other data-related concepts, such as data privacy, data quality, data storage, data stewardship, data management, and master data management.  

Data governance vs data privacy

Data privacy is a part of data governance, but governance encompasses more than privacy. Implementing processes around data privacy throughout the governance program enables the enterprise to identify what data they possess, where it’s kept, and how it is utilized. 

Data governance vs data quality

Data quality defines the extent to which data is correct, comprehensive, and reliable, based upon the requirements for the enterprise. Data governance is the practice of management, control, and policymaking over data resources.  

Data governance vs data storage

Organizations that use a central repository for data storage and establish mechanisms for retrieving and managing that data are on the path to effective data governance, though data storage by itself is not data governance.  

Data governance vs data stewardship

Data stewardship is the aspect of data governance that addresses procedures, but not strategy, roles, policies, or processes. Stewardship involves the interpretation and execution of policies and processes, as opposed to creating or developing them, to ensure that the data is accurate and accessible to the appropriate parties. Data governance enables the right people to be assigned the role of data stewards.  

Data governance vs data management

Data governance is the foundation of data management, which describes administration of the complete data life cycle requirements for the enterprise. Data management implements data governance policies to collect and utilize data to make business decisions.  

Data governance vs master data management

Although effective master data management (MDM) requires good data governance, MDM goes further than governance. It emphasizes identification of the enterprise’s key entities, such as suppliers, customers, and products, and increasing the value derived from that data. The data governance program describes each key entity and its associated data policies. 

Data governance tools

The enterprise has many things to consider when it comes to data governance tools that will drive the best data governance approach. They include: 

• Scalability that enables focus on strategy alignment and supports data governance initiatives across business units 
• Machine learning and artificial intelligence (AI) to enrich decision-making and hone application performance 
• Cloud-based platforms to avoid additional overhead for on-premises servers 
• Fast, economical integration into current platforms with data pipeline tracking 
• Discovery, reporting, and benchmarking features to collect and analyze organizational data 
• Data quality capabilities for verification, cleansing, and enhancement 
• Data control features for evaluation and maintenance 
• Data documentation including metadata development (source, date, type, tags) 
• Self-service data stewardship technology for data profiling and monitoring the execution of the organization’s data governance policies 
• Automated data retention, archiving, and deletion to manage risk and generate cost efficiencies 
• Content management capabilities to digitize documents and incorporate appropriate content into operations and systems 

Data governance goals

Data governance goals should be defined at all levels of the enterprise so every stakeholder and team member will understand how to do their part to achieve them. Goals might include: 

• Increased consistency in data utilized for decision-making 
• Maximized revenue generation and reduced costs  
• Established baselines to generate key metrics and support continuous improvement 
• Better data security and data quality via clear accountability 
• Clarity for all stakeholders on data distribution policies, as well as how data ownership impacts data value 
• Metadata management to enact control on the collection and use of data 
• Improved planning and efficacy for teams, with less friction and rework 
• Decreased risk of regulatory non-compliance and associated penalties and fines 

Data governance roles

As mentioned above, data governance involves the entire enterprise; however, as with any initiative, certain roles are critical to ensure proper execution, reporting, and refinement of the data governance program. Some of the most crucial roles include: 

• Master data governance managers: People in this role oversee the design, execution, and maintenance of master data control and governance throughout the enterprise. 
• Data stewards: Also known as data champions, data stewards ensure that data standards and policies are observed on a daily basis. They frequently serve as the recognized experts for data entities and attributes and recommend improvements to data governance processes.  
• Solution and data governance architects: These team members direct solution designs and applications. 
• Data custodians: Also known as data operators, data custodians enable the onboarding, maintenance, and sunsetting of data resources.  
• Data owners: Also known as data sponsors, data owners are empowered to make and implement decisions across the enterprise. They are ultimately responsible for data as a resource. 
• Data analysts: Team members in this role utilize analytics to identify trends and provide reporting. 
• Data strategists: These team members contribute by creating and implementing plans based on trends detected via analytics. 
• Compliance specialists: People serving in this role manage conformity to mandatory standards and regulations. 

A data governance committee that comprises some or all of these roles is often established for significant areas of the enterprise to manage standards and policies and address escalated concerns. 

Data governance frameworks

Data is a digital asset that determines the success of the enterprise, and appropriately utilizing this data depends on a proper data governance framework. The data governance framework must support the organization and its strategies, goals, and objectives, as well as compliance programs and industry protocols.  

The data governance framework should also monitor data standards, specify essential roles and responsibilities, and establish deliverables for all functions. The best operating model will enable the framework to be seamlessly integrated into daily business activities.  

The data governance framework should include: 

• A data model that defines data flow: inputs, storage considerations, and outputs 
• Guidelines, policies, practices, methods, and procedures that are applicable to the data model 
• The organizational structure and accountabilities  
• A full scope of the relevant data and expected outcomes once the data governance framework is enacted  
• A data categorization and distribution process with defined channels, especially for sensitive data 
• A measurement and reporting plan with metrics that generate useful insights that enable refinement of the data governance framework 

What are the components of a data governance framework?

Among the key components of a data governance framework are the following, which support the overarching structure required to manage and govern an organization’s data assets effectively. 

• Data governance vision, strategy, and program
  The data governance vision articulates the core objective of the program. It is used to flesh out the overarching goals and objectives that the data governance initiative aims to achieve.
  
  The data governance strategy aligns the vision with the organization’s broader strategy, ensuring that data management efforts support the desired outcomes across the various producers and users of data. All of this is operationalized in a data governance program that includes the people, processes, and policies.
  
  A master data governance manager usually oversees data governance programs. In some cases, a data governance committee, comprised of executive management representatives and key data stakeholders, holds ultimate responsibility for strategic data governance decisions.
• Data literacy and culture
  A characteristic of successful data governance efforts is that they are driven into the culture of an organization. Everyone understands the value of data and the role of data governance in maintaining its accessibility and quality to enable data-driven decision-making.
• Education and communication
  Education and communication are critical for an effective data governance program. All data users and creatives require education and ongoing communication about data governance policies, processes, and best practices. This includes developing training programs and communication plans to develop and support a data governance culture within the organization.
• Master data management (MDM)
  MDM focuses on creating a single, accurate, and authoritative source of master data for an organization. It includes data identification, linking, consolidation, cleansing, enrichment, and synchronization to create a consistent, shared, and accurate set of master data that can be used across all users, systems, and applications.
• Metrics and monitoring
  Metrics and monitoring must be established to measure the performance and effectiveness of a data governance program. This includes monitoring compliance with data policies, measuring data quality, and assessing the impact of data governance on strategic objectives and day-to-day operations.
• Technology  
  Data governance programs require technology solutions and infrastructure to support the various components. Types of technology commonly leveraged for data governance tools include those for:
  • Compliance management
  • Data catalogs
  • Data dictionary management
  • Data integration and ETL (extract, transform, load)
  • Data lake management 
  • Data quality management 
  • Data privacy and security 
  • Data stewardship and workflow
  • Master data management 
  • Metadata management 

How is data governance implemented?

The following are the key steps for implementing a data governance program.

1. Define data strategy, objectives, and scope.
   Clearly define the data strategy and align it with the organization’s overall objectives. Establish specific, measurable data governance goals, such as improving data quality, ensuring regulatory compliance, or enhancing decision-making.
   
   Define the scope of the data governance program by determining which data assets, departments, and processes will be included. In many cases, data governance programs are rolled out in tiers to make the process more manageable and to incorporate learnings from initial launches into subsequent deployments and the overall program.
2. Secure executive support.
   It is crucial to gain the backing of the executive team when launching a data governance program. Executive support ensures the necessary resources are allocated and helps drive organization-wide engagement.
3. Engage stakeholders. 
   Identify and involve key stakeholders from across the organization at the beginning of the data governance program development. Key stakeholders include data owners, users, and IT staff. Their early engagement provides critical guidance on needs and technical requirements and ensures ongoing support for the program.
4. Create a data governance framework.
   Establish a framework for the data governance program that details the structure, related policies, standards, procedures, roles, and responsibilities. It is used to guide the management of data assets in alignment with strategic objectives.
   
   Detailed data governance policies and standards should cover data quality, privacy, security, compliance, and lifecycle management. These should provide clear guidelines for how data is to be managed, used, and protected across the organization.
5. Assign responsibilities.
   Clearly define roles within the data governance framework, such as data stewards, data owners, and the structure of a data governance council. 
6. Implement processes and tools.
   Establish processes for monitoring, reporting, and improving data quality, managing data access, and ensuring compliance with policies. Leverage technology solutions that support these processes and provide automation where possible to minimize human error.
7. Conduct training and awareness programs.
   Educate users about data governance policies, their roles in the program, and the importance of following data management best practices. Ongoing training and awareness are critical for fostering a data-centric culture and ensuring compliance with policies and standards. 
8. Define data metrics and quality indicators.
   Create and monitor data metrics, including quality indicators, to document the efficacy of data governance efforts and identify opportunities for improvement. 
9. Monitor and enforce compliance.
   Use audits, reviews of data quality metrics, and assessments of data management practices to confirm adherence to data governance policies.

Data governance best practices

Data governance best practices include: 

• Develop a business case for data governance that can be shared to generate support for the program; explain the need (including regulatory compliance), benefits, and required resources, as well as the risks and potential costs associated with failing to implement the program.  
• Select an executive sponsor to evangelize for the data governance program and keep it on track; this individual will demonstrate the preferred approach to the program, manage team member responsibilities, and respond to procedural questions.  
• Avoid treating data governance as a project; it is an ever-evolving practice that requires the executive sponsor and ongoing resources to keep pace with rapidly changing market conditions and regulations.  
• As with any initiative, establish baselines and then set specific, measurable, actionable goals; share reporting with relevant stakeholders and strive for iterative, ongoing improvements.  
• Ensure documentation includes standardized terminology and socialize these definitions throughout the enterprise, so everyone knows and uses the same language around data governance. 
• Keep the lines of communication open – not only with internal team members, but with partners, third-party vendors, suppliers, and customers. Stakeholders are more likely to conform to data governance requirements when they understand and appreciate the program and have a resource for getting questions answered. 

Data governance FAQ 

Frequently asked questions about data governance include the following.

What are the three pillars of data governance?

The three pillars of data governance refer to the foundational elements required to establish and maintain a strong data governance framework and strategy. These pillars ensure that data across an organization is accurate, accessible, secure, and used in compliance with policies and regulations. The three main pillars typically referenced are the following. However, some data governance frameworks include additional, more granular pillars, such as data architecture and integration, metadata management, and data lifecycle management.

1. Data management
   Also referred to as data ownership and stewardship, data management encompasses the processes, policies, and standards involved in managing data lifecycle needs, including data architecture, modeling, storage, and retrieval. Effective data management ensures that data is accessible, usable, and stored efficiently to enable easy sharing and collaboration across the organization.
   
   Data management also involves defining the roles and responsibilities of data owners and data stewards related to data management and governance. Data owners are typically responsible for the accuracy, privacy, and security of the data, while data stewards typically handle day-to-day management and ensure data governance policies are followed.
2. Data quality
   The data quality pillar includes the systems and functions necessary to ensure that data is accurate, complete, and reliable. Data quality involves processes and procedures for continuously cleaning, maintaining, and improving data. This pillar emphasizes the importance of maintaining high-quality data throughout its lifecycle to be sure that it can be used to support informed decision-making and maintain trust in data systems.
3. Data security
   The data security and privacy pillar focuses on safeguarding data against unauthorized access and data breaches. This involves establishing strong access management, employing encryption techniques, and conducting periodic reviews of security protocols to mitigate the risk of data compromise, theft, or improper use. Data security and privacy measures are necessary to protect sensitive data in accordance with legal and industry frameworks, such as GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), and PCI-DSS (Payment Card Industry Data Security Standard), as well as to maintain trust. 

What are the three key roles in data governance?

Data governance involves various stakeholders with distinct roles in establishing and maintaining an effective data governance program. Three main roles collaborate closely to ensure that data governance goals are met.

1. Master data governance managers
   Master data governance managers oversee all aspects of a data governance program. A master data governance manager develops and implements a data governance strategy that aligns with the overall objectives of the organization. This data governance role, usually supported by a team, is also responsible for creating policies and procedures that govern data use, quality, and protection.
   
   Additionally, the master data governance manager’s role involves coordinating with various stakeholders across the organization to ensure buy-in and compliance with the data governance framework.
2. Data stewards
   Data stewards ensure data quality and integrity within the organization. They have operational responsibility for managing, implementing, and monitoring data governance policies and procedures at the departmental or functional level.
   
   Data stewards are expected to understand both the operational context and the technical aspects of the data they manage, including its meaning, lineage, and interdependencies. Within a data governance program, data stewards are responsible for making sure that data is accurate, accessible, consistent, and protected. Data stewards also play a key role in data classification, data quality improvement initiatives, compliance with data-related regulations, and facilitating communication between technical and operational teams.
3. Data owners
   In the context of data governance, data owners are usually senior-level managers or executives who have overall accountability for the data assets in their group. Data owners are responsible for defining the data’s purpose and usage within the organization as well as ensuring that it meets the legal, regulatory, and policy requirements. Data owners make decisions about who can access and modify data, delegate day-to-day management tasks to data stewards, and are ultimately responsible for the data’s accuracy, privacy, and security throughout the data lifecycle.

What are the five areas of data governance?

Data governance encompasses a broad range of disciplines designed to ensure effective management, usage, and protection of data within an organization. Five key areas that are commonly focused on within data governance frameworks include the following.

1. Metadata management
   As part of data governance, metadata management requires defining, organizing, and maintaining data descriptions and lineage information to make data easy to find, understand, and use. This includes managing data dictionaries, catalogs, and lineage information that explain data’s source, usage, and transformations. 
2. Data compliance and regulatory requirements
   This area focuses on ensuring that an organization’s data usage complies with all relevant laws and regulations. Adhering to data compliance and regulatory requirements includes staying on top of changing rules, assessing the impact of these changes on the organization’s data practices, and implementing necessary adjustments to policies and procedures to maintain compliance.
3. Data lifecycle management
   Data governance relies on policies and processes associated with managing the flow of data throughout its lifecycle, from creation and collection to usage, archiving, and disposal. Data lifecycle management ensures that data is accessible when needed and that it is archived or destroyed when it becomes obsolete to ensure compliance with data governance policies and regulations.
4. Data standards and policies
   Establishing data standards and policies is a critical part of data governance. This area involves developing and enforcing guidelines around data formats, naming conventions, data entry standards, and overall data policies that govern how data is collected, stored, processed, and shared. Standards and policies ensure consistency and facilitate interoperability across different systems and departments within the organization.
5. Data strategy and architecture
   This encompasses the strategic planning and design of data governance programs. It involves aligning data governance with organizational objectives, defining the roles and responsibilities regarding data within the organization, and establishing a framework for data integration, processing, and analysis. A well-defined data strategy and architecture ensure that data governance efforts support the organization’s overall strategy and facilitate data-driven decision-making.

What are some examples of data governance use cases?

Several widely adopted use cases for data governance include:

• Business intelligence and analytics
• Collaborative analytics
• Data discovery
• Data literacy enhancement
• Data product development
• Data quality improvement
• Data security and privacy
• Ethical data use
• Master data management
• Regulatory compliance
• Risk management

Data governance accelerates growth of the enterprise 

The enterprise possesses enormous quantities of data about customers, clients, suppliers, team members, third-party vendors and contractors, and more. The importance of this information is equally vast when it is utilized to enhance knowledge of the organization, its industry and market, and its customers and prospects. Data governance enables this ability while ensuring the quality, reliability, access, privacy, and security of the data. 

Aligning data with its proper purpose gives the enterprise greater confidence in the quality of its business decisions. Being able to trust its data while meeting regulatory compliance requirements and minimizing data risk is an enormous asset to any organization in an environment where many of their competitors are struggling to do so. 

Learn more about how optimizing and leveraging data will support your enterprise in amplifying business value. Schedule a demo today! 

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