How to Write a Data Governance Charter

About the Data Governance Charter

According to the DMBOK, a Data Governance Charter "[i]dentifies the business drivers, vision, mission, and principles for data governance, including readiness assessment, internal process discovery, and current issues or success criteria" (ch. 3, section 2.5 Develop Data Governance Strategy). Data Governance refers to "exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets" (ch. 3, section 1 Introduction). Overall, this document is a helpful resource for setting up many Data Strategy best practices.

Sample Document

Text	Notes
1. Executive Summary This strategy describes the challenge and the opportunity facing [ORGANIZATION NAME] in terms of its effective management of data as an asset. The Data Governance Charter establishes the vision, guiding principles, scope and goals, essential processes, and integral roles and responsibilities relative to Data Governance, a crucial domain that will enhance the effective management of data assets at [ORGANIZATION NAME]. The problem statement and scope, stated below, define our organization’s first order problem and to whom it applies. The vision statement captures the future state of data. We will achieve its vision based on the guiding principles, driven by our goals and informed by our responsibilities.	Not every Data Governance Charter will contain an Executive Summary. However, we think it's a good idea to provide the bottom line up front. This document includes a great example of an Executive Summary.
2. Introduction Data Governance is a key component of the data strategy at [ORGANIZATION NAME]. This discipline supports the business strategy by enhancing the effectiveness of operations and decision-making through well-considered policies and procedures to manage data as an asset. [ORGANIZATION NAME] leaders require data in order to achieve the mission of [BRIEF MISSION STATEMENT SUMMARY]. Team members at all levels require access to data that is accurate, timely, and secure. The Data Governance Charter is a supporting document that helps ensure data is treated as an asset to help achieve these goals. The capabilities required by [ORGANIZATION NAME] depend upon effective Data Management policies, standards, and practices. Achieving our mission will require that we integrate data from diverse sources, comply with all relevant regulatory standards, maintain the highest level of data security and privacy, coordinate data across the business, use analytic tools to achieve superior situational awareness, and leverage data to drive insights that allow the business to continue to avoid threats and grow. This document describes the problem and establishes the vision, guiding principles, scope and goals, essential processes, and integral roles and responsibilities relative to data. The problem statement and scope, stated below, define our organization’s first order problem and to whom it applies. The vision statement captures the future state of data. We will achieve its vision based on the guiding principles, driven by our goals, and informed by our responsibilities.	Designating your sections clearly with numbers or letters enables easy discussion. You could also consider using line numbers. You may choose to list some additional benefits of data strategy from this article about offensive and defensive capabilities.
2a. Problem Statement [ORGANIZATION NAME] must accelerate its progress toward becoming a data-centric organization. Enterprise-level Data Management will ensure that trusted, secured data is made available as appropriate throughout the organization. A failure to implement effective Data Management will place limits on our capability to take advantage of real time, usable, high quality data to support operations, data-driven decision-making, and otherwise add value to the organization. Moving forward, our software and hardware systems should be procured, designed, tested, operated, sustained, and upgraded with data interoperability as a key requirement. Currently, these gaps are bridged with unnecessary manual steps that introduce complexity, delay, and risk of error. Lack of interoperability constrains our ability to achieve our potential efficiency across all domains. We also must improve the organization's capabilities across all fields required for effective Data Management. [ORGANIZATION NAME] must broaden efforts to assess our current talent, recruit new data experts, and retain our workforce while establishing policies to ensure that data talent is cultivated. We must also increase data-related training and promote a culture of data literacy.	The problem statement sets the stage for the rest of the Data Governance Charter. Your strategic suggestions will address the issues outlined in this section. Make sure the Problem Statement is clear and actionable. In your organization, what specific challenges are you facing? What specific measures do you suggest taking to mitigate the problem?
2b. Scope This Data Governance document applies to the entirety of [ORGANIZATION NAME] and its data on whichever systems that information resides.	The scope of your Data Governance policies may vary; perhaps you’re developing a document that only applies to one department or to the whole organization. Be specific and descriptive about your intention.
3. Guiding Principles Transparency: it should be clear how and when decisions are made and processes are created Metrics-driven: we should measure and report on how we are doing against our goals Consistency: all decisions should be applied consistently Stewardship: while formal stewardship roles exist, it is everyone's responsibility to protect the privacy, security, and confidentiality of our data as required Accountability: decisions and processes should be audited Agility: all processes should be able to adapt when appropriate Change Management: new processes will require a concerted effort in managing change among teams	A set of guiding principles helps you and your audience remain grounded. This will ensure that if there are disagreements about the finer points in your Data Governance policy, you have established common ground from which to work. Additionally, including a variety of formats in your documents, like bulleted lists, breaks up long stretches of text, highlights important points, and helps your reader follow along.
4. Goals A core tenet of our Data Strategy is the understanding that data is not an IT asset, but an essential organ in our day-to-day operations. Data is ubiquitous. It is critical that data is of high quality, accurate, complete, timely, protected, and trustworthy. As such, [ORGANIZATION NAME] makes data one of our strategic assets by establishing the following goals. Data should be: Accessible: users can locate and retrieve the needed data Understandable: users can find descriptions of data to recognize the content, context, and applicability Trustworthy: users can be confident in all aspects of data for decision-making Interoperable: users and producers have a common representation and comprehension of data Secure: users know that data is protected from unauthorized use and manipulation	In determining your goals for your Data Governance project, what problems are you aiming to address? What problems are solvable through policy and behavior changes? What do you want to achieve as a data-savvy organization that this framework will enable? In this example, we have provided some common goals for an organization, but feel free to be creative in order to meet your organization’s specific needs. It's important to explicitly articulate actionable objectives whenever possible.
4a. Accessible The goal of making data accessible enables authorized users to discover the existence of data that is of particular interest and to obtain the data they need when they need it, including having data automatically pushed to interested and authorized users. Data Stewards, Custodians, and Trustees are all responsible and obligated to identify, register, and expose their data in a way that makes it easily discoverable and accessible across the enterprise, and to external partners as appropriate. Accessibility requires that protective mechanisms (e.g., security controls) are in place for credentialed users to ensure that access is permitted in accordance with laws, regulations, and policies. We will know we have made progress on data accessibility when: Metadata standards are implemented, including location and access methods for shared data Common platforms and services create, retrieve, share, utilize, and manage data Data access and sharing is controlled through reusable APIs	Data accessibility enhances decision-making and operational efficiency by ensuring that authorized users can quickly access necessary information. It promotes innovation and fosters collaboration by making data readily available across the organization.
4b. Understandable Understanding data is critical to enable enhanced, more accurate, and timely decision-making. The inability to aggregate, compare, and truly understand data adversely affects our teams’ abilities to react to change. Without a proper understanding of data, interpretation and analysis could be flawed, resulting in equally flawed outcomes. Bringing together business and technology and applying a data-centric approach enable massive amounts of data to be transformed into the insights needed to lead [ORGANIZATION NAME] more effectively and efficiently. We will know we have made progress on data understandability when: Data elements are aligned into a comprehensive data dictionary with a controlled, yet flexible, vocabulary and taxonomy We have established processes to create, align, implement, and manage business vocabularies, including enterprise standards All team members feel comfortable working with our databases and drawing conclusions from the information inside	Ensuring data is understandable helps team members to make well-informed decisions. A comprehensive data dictionary and standardized business vocabulary can enhance understandability and help team members effectively engage with data, fostering a more data-literate organization, and improving overall decision-making processes.
4c. Trustworthy It is vital for our team members and clients can trust the data we use to deliver value. Lacking confidence in the data may result in less timely decision-making or, consequently, no decision when one is warranted. We will know we have made progress on data trustworthiness when: Data has protection, lineage, and pedigree metadata throughout its lifecycle We execute Data Quality Management techniques to assess and enhance data quality We properly tag and maintain all appropriate data and records in accordance with established processes and policies	Establishing trust in data is essential for effective decision-making and the overall value that can be derived from data. Ensuring trustworthiness involves implementing robust data protection measures, maintaining thorough lineage information, and continuously enhancing data quality through structured management techniques.
4d. Interoperable Properly exchanging data between systems and maintaining semantic understanding are critical for successful decision-making and operations across departments. Achieving interoperability using common data formats and machine-to-machine communications accelerates advanced algorithm development and provides a strategic advantage. We will know we have made progress on data interoperability when: All data assets are machine-readable We are able to rapidly mediate different data standards and formats without serious loss of fidelity or accuracy We have developed a data-tagging strategy and subsequent implementation plan to enable data interoperability	Interoperability entails standardization of data formats and enablement of seamless machine-to-machine communication.
4e. Secure Ensuring the security of data is crucial to maintaining the trust of stakeholders and protecting the organization against data breaches and other security threats. Data Security involves both physical and digital measures to safeguard data from unauthorized access, use, disclosure, disruption, modification, or destruction. Effective Data Security is implemented through a comprehensive set of policies, procedures, and technological controls. This includes, but is not limited to, the use of encryption, secure data storage solutions, rigorous access controls, and continuous monitoring of security practices. We will know we have made progress on data security when: There is a significant reduction in data-related security incidents year-over-year All sensitive data is encrypted both at rest and in transit Regular security audits and penetration tests are conducted and result in actionable improvements Secure awareness training is completed annually by all employees	Establishing strong Data Security measures not only protects the organization's data assets but also reinforces the integrity and reliability of its Data Governance framework.
5. Implementation Implementing the Data Governance Charter involves a series of strategic actions designed to align the organization's Data Management practices with its overarching goals. This process starts with the establishment of a dedicated Data Governance team and the development of an operational plan that outlines specific tasks, timelines, and responsibilities. The implementation phase includes the integration of Data Governance policies into everyday business processes, training staff on new data procedures and standards, and setting up an oversight mechanism to ensure compliance and continuous improvement. Critical steps in the implementation phase are: Formalizing the Data Governance structure and defining roles and responsibilities within the organization Developing and disseminating Data Governance policies and procedures Conducting training sessions for all stakeholders to ensure understanding and adherence to Data Governance practices Setting up monitoring systems to track compliance and effectiveness of Data Governance initiatives Regularly reviewing and updating the Data Governance framework to keep it relevant as organizational needs evolve	Effective implementation of the Data Governance Charter is critical for ensuring that the guidelines and practices outlined in the document are brought to life and become part of the organizational culture. This not only enhances data quality and security but also supports better decision-making across the organization.
6. Operating Framework Clearly defined roles and responsibilities are fundamental to the effective execution of Data Governance policies. This section delineates the key roles involved in Data Governance and outlines their specific responsibilities. Data Governance Board: provides strategic direction, oversight, and high-level monitoring of Data Governance activities; approves Data Governance policies and resolves high-impact issues Data Stewards: responsible for the management and fitness of data elements—both content and metadata; ensure the quality, accessibility, and protection of data Data Custodians: handle the technical environment and are responsible for the safe custody, transport, and storage of data, as well as the implementation of business rules Data Trustees: senior-level executives who own the data assets in their functional areas; they are accountable for the data quality and integrity within their domains Data Users: recipients of data who rely on accurate, timely, and relevant data for decision-making; they must adhere to Data Governance policies and procedures Each role is vital for maintaining the lifecycle and integrity of the data within the organization, ensuring that data-related decisions support the overall strategic objectives and comply with relevant regulations and standards.	The assignment of clear roles and responsibilities within Data Governance ensures accountability and establishes a structure for maintaining the quality and security of data throughout its lifecycle. This clarity supports the organization's Data Management and enhances compliance and operational effectiveness. To read more about the various roles listed here, check out this article.
6a. Data Governance Steering Committee (DGSC) The Data Governance Steering Committee (DGSC) plays a crucial role in overseeing the Data Governance program. Composed of senior leaders from various departments, the DGSC ensures alignment between Data Governance initiatives and organizational strategies. Key responsibilities of the DGSC include: Setting priorities for Data Governance projects based on business needs and strategic goals Approving Data Governance policies and frameworks Resolving conflicts and issues that arise within Data Governance operations Monitoring the effectiveness of Data Governance practices and making recommendations for improvements Communicating the value and progress of Data Governance initiatives to stakeholders across the organization The DGSC meets regularly to review the status of Data Governance activities and to ensure that Data Governance efforts are productive and aligned with the overall business objectives.	The establishment of a DGSC is essential for the success of the Data Governance program, providing strategic leadership and ensuring that Data Governance aligns with and supports the broader goals of the organization.
6b. Data Governance Program Office (DGPO) The Data Governance Program Office (DGPO) is the operational arm of the Data Governance initiative. It provides the day-to-day management and support necessary to ensure the successful implementation of Data Governance policies and practices across the organization. Responsibilities of the DGPO include: Coordinating and supporting Data Governance activities and projects Maintaining and updating the Data Governance framework and documentation Facilitating communication between the Data Governance Steering Committee, Data Stewards, and other stakeholders Monitoring compliance with Data Governance policies and reporting on performance metrics Providing training and resources to enhance Data Governance understanding and practice within the organization The DGPO acts as a hub for all Data Governance-related issues, ensuring consistency and coherence in how Data Governance is applied throughout the organization.	By managing the operational aspects of Data Governance, the DGPO plays a key role in sustaining and enhancing the organization's Data Governance capabilities. It ensures that Data Governance practices are effectively integrated into organizational processes and that the Data Governance program continues to evolve in response to changing needs and challenges.
6c. Data Steward Council The Data Steward Council consists of Data Stewards from various departments within the organization. This council is a critical component of the Data Governance structure, tasked with ensuring consistent and effective management of data assets across different areas of the business. Key responsibilities of the Data Steward Council include: Developing and maintaining data policies, standards, and procedures to guide Data Management practices Ensuring data quality by implementing data quality standards and monitoring compliance across departments Facilitating data sharing and integration efforts to enhance interoperability and consistency of data use Resolving data-related issues and conflicts that arise within the organization Advocating for the importance of data accuracy, privacy, and security within their respective domains Collaborating with IT and business units to align data-related activities with organizational goals and strategies The council meets regularly to review data practices, discuss challenges, and share best practices to continually improve the management of the organization’s data.	The Data Steward Council plays a pivotal role in the operationalization of Data Governance, bridging the gap between policy and practice. Their collaborative efforts ensure that data assets are effectively managed and leveraged to support the organization's strategic objectives.
7. Metrics Metrics are crucial for assessing the effectiveness of the Data Governance framework and ensuring accountability across the organization. They provide quantifiable data points that help measure success and identify areas for improvement. Key metrics for monitoring Data Governance include: Data Quality: error rates, completeness, consistency, and reliability of data Data Utilization: frequency of data access and usage patterns among various departments Compliance: rate of adherence to data policies and standards, including privacy and security regulations Data Management Maturity: progress in achieving higher levels of data management maturity, as outlined in the Data Management Maturity Assessment User Satisfaction: feedback from users on the accessibility and usability of data Operational Efficiency: improvement in operational efficiency and reduction in operational risks due to better Data Management Regularly reporting these metrics allows the organization to track its progress, justify investments in Data Governance, and guide future policy decisions.	By continuously measuring and analyzing these metrics, the organization can ensure that its Data Governance initiatives are aligned with its strategic goals and are delivering tangible benefits. To read more about Data Management Maturity Assessments, please check out this comparison of various models.
8. Conclusion The Data Governance Charter serves as a foundational document that guides the organization in managing its data assets responsibly and effectively. It establishes a framework for Data Governance that aligns with the organization’s overall strategy and compliance requirements. By clearly defining the structure, roles, responsibilities, and metrics, the Charter ensures that all stakeholders understand their roles and the importance of proper Data Management. This alignment is critical for fostering a culture of data-driven decision making, which ultimately enhances operational efficiency, reduces risks, and drives innovation. As the organization evolves, so too should the Data Governance Charter. Regular reviews and updates will ensure that it remains relevant and continues to provide value in a changing business and technological landscape.	The successful implementation of this Charter will require ongoing commitment and collaboration from all parties involved. It is the responsibility of everyone within the organization to uphold the principles set forth in this document to ensure the integrity and value of the organization's data assets.

Text

Notes

1. Executive Summary

This strategy describes the challenge and the opportunity facing [ORGANIZATION NAME] in terms of its effective management of data as an asset. The Data Governance Charter establishes the vision, guiding principles, scope and goals, essential processes, and integral roles and responsibilities relative to Data Governance, a crucial domain that will enhance the effective management of data assets at [ORGANIZATION NAME]. The problem statement and scope, stated below, define our organization’s first order problem and to whom it applies. The vision statement captures the future state of data. We will achieve its vision based on the guiding principles, driven by our goals and informed by our responsibilities.

Not every Data Governance Charter will contain an Executive Summary. However, we think it's a good idea to provide the bottom line up front. This document includes a great example of an Executive Summary.

2. Introduction

Data Governance is a key component of the data strategy at [ORGANIZATION NAME]. This discipline supports the business strategy by enhancing the effectiveness of operations and decision-making through well-considered policies and procedures to manage data as an asset. [ORGANIZATION NAME] leaders require data in order to achieve the mission of [BRIEF MISSION STATEMENT SUMMARY]. Team members at all levels require access to data that is accurate, timely, and secure. The Data Governance Charter is a supporting document that helps ensure data is treated as an asset to help achieve these goals.

The capabilities required by [ORGANIZATION NAME] depend upon effective Data Management policies, standards, and practices. Achieving our mission will require that we integrate data from diverse sources, comply with all relevant regulatory standards, maintain the highest level of data security and privacy, coordinate data across the business, use analytic tools to achieve superior situational awareness, and leverage data to drive insights that allow the business to continue to avoid threats and grow.

This document describes the problem and establishes the vision, guiding principles, scope and goals, essential processes, and integral roles and responsibilities relative to data. The problem statement and scope, stated below, define our organization’s first order problem and to whom it applies. The vision statement captures the future state of data. We will achieve its vision based on the guiding principles, driven by our goals, and informed by our responsibilities.

Designating your sections clearly with numbers or letters enables easy discussion. You could also consider using line numbers.

You may choose to list some additional benefits of data strategy from this article about offensive and defensive capabilities.

2a. Problem Statement

[ORGANIZATION NAME] must accelerate its progress toward becoming a data-centric organization. Enterprise-level Data Management will ensure that trusted, secured data is made available as appropriate throughout the organization. A failure to implement effective Data Management will place limits on our capability to take advantage of real time, usable, high quality data to support operations, data-driven decision-making, and otherwise add value to the organization.

Moving forward, our software and hardware systems should be procured, designed, tested, operated, sustained, and upgraded with data interoperability as a key requirement. Currently, these gaps are bridged with unnecessary manual steps that introduce complexity, delay, and risk of error. Lack of interoperability constrains our ability to achieve our potential efficiency across all domains.

We also must improve the organization's capabilities across all fields required for effective Data Management. [ORGANIZATION NAME] must broaden efforts to assess our current talent, recruit new data experts, and retain our workforce while establishing policies to ensure that data talent is cultivated. We must also increase data-related training and promote a culture of data literacy.

The problem statement sets the stage for the rest of the Data Governance Charter. Your strategic suggestions will address the issues outlined in this section.

Make sure the Problem Statement is clear and actionable. In your organization, what specific challenges are you facing? What specific measures do you suggest taking to mitigate the problem?

2b. Scope

This Data Governance document applies to the entirety of [ORGANIZATION NAME] and its data on whichever systems that information resides.

The scope of your Data Governance policies may vary; perhaps you’re developing a document that only applies to one department or to the whole organization. Be specific and descriptive about your intention.

3. Guiding Principles

Transparency: it should be clear how and when decisions are made and processes are created
Metrics-driven: we should measure and report on how we are doing against our goals
Consistency: all decisions should be applied consistently
Stewardship: while formal stewardship roles exist, it is everyone's responsibility to protect the privacy, security, and confidentiality of our data as required
Accountability: decisions and processes should be audited
Agility: all processes should be able to adapt when appropriate
Change Management: new processes will require a concerted effort in managing change among teams

A set of guiding principles helps you and your audience remain grounded. This will ensure that if there are disagreements about the finer points in your Data Governance policy, you have established common ground from which to work.

Additionally, including a variety of formats in your documents, like bulleted lists, breaks up long stretches of text, highlights important points, and helps your reader follow along.

4. Goals

A core tenet of our Data Strategy is the understanding that data is not an IT asset, but an essential organ in our day-to-day operations. Data is ubiquitous. It is critical that data is of high quality, accurate, complete, timely, protected, and trustworthy. As such, [ORGANIZATION NAME] makes data one of our strategic assets by establishing the following goals.

Data should be:

Accessible: users can locate and retrieve the needed data
Understandable: users can find descriptions of data to recognize the content, context, and applicability
Trustworthy: users can be confident in all aspects of data for decision-making
Interoperable: users and producers have a common representation and comprehension of data
Secure: users know that data is protected from unauthorized use and manipulation

In determining your goals for your Data Governance project, what problems are you aiming to address? What problems are solvable through policy and behavior changes? What do you want to achieve as a data-savvy organization that this framework will enable?

In this example, we have provided some common goals for an organization, but feel free to be creative in order to meet your organization’s specific needs.

It's important to explicitly articulate actionable objectives whenever possible.

4a. Accessible

The goal of making data accessible enables authorized users to discover the existence of data that is of particular interest and to obtain the data they need when they need it, including having data automatically pushed to interested and authorized users. Data Stewards, Custodians, and Trustees are all responsible and obligated to identify, register, and expose their data in a way that makes it easily discoverable and accessible across the enterprise, and to external partners as appropriate. Accessibility requires that protective mechanisms (e.g., security controls) are in place for credentialed users to ensure that access is permitted in accordance with laws, regulations, and policies.

We will know we have made progress on data accessibility when:

Metadata standards are implemented, including location and access methods for shared data
Common platforms and services create, retrieve, share, utilize, and manage data
Data access and sharing is controlled through reusable APIs

Data accessibility enhances decision-making and operational efficiency by ensuring that authorized users can quickly access necessary information. It promotes innovation and fosters collaboration by making data readily available across the organization.

4b. Understandable

Understanding data is critical to enable enhanced, more accurate, and timely decision-making. The inability to aggregate, compare, and truly understand data adversely affects our teams’ abilities to react to change. Without a proper understanding of data, interpretation and analysis could be flawed, resulting in equally flawed outcomes. Bringing together business and technology and applying a data-centric approach enable massive amounts of data to be transformed into the insights needed to lead [ORGANIZATION NAME] more effectively and efficiently.

We will know we have made progress on data understandability when:

Data elements are aligned into a comprehensive data dictionary with a controlled, yet flexible, vocabulary and taxonomy
We have established processes to create, align, implement, and manage business vocabularies, including enterprise standards
All team members feel comfortable working with our databases and drawing conclusions from the information inside

Ensuring data is understandable helps team members to make well-informed decisions. A comprehensive data dictionary and standardized business vocabulary can enhance understandability and help team members effectively engage with data, fostering a more data-literate organization, and improving overall decision-making processes.

4c. Trustworthy

It is vital for our team members and clients can trust the data we use to deliver value. Lacking confidence in the data may result in less timely decision-making or, consequently, no decision when one is warranted.

We will know we have made progress on data trustworthiness when:

Data has protection, lineage, and pedigree metadata throughout its lifecycle
We execute Data Quality Management techniques to assess and enhance data quality
We properly tag and maintain all appropriate data and records in accordance with established processes and policies

Establishing trust in data is essential for effective decision-making and the overall value that can be derived from data. Ensuring trustworthiness involves implementing robust data protection measures, maintaining thorough lineage information, and continuously enhancing data quality through structured management techniques.

4d. Interoperable

Properly exchanging data between systems and maintaining semantic understanding are critical for successful decision-making and operations across departments. Achieving interoperability using common data formats and machine-to-machine communications accelerates advanced algorithm development and provides a strategic advantage.

We will know we have made progress on data interoperability when:

All data assets are machine-readable
We are able to rapidly mediate different data standards and formats without serious loss of fidelity or accuracy
We have developed a data-tagging strategy and subsequent implementation plan to enable data interoperability

Interoperability entails standardization of data formats and enablement of seamless machine-to-machine communication.

4e. Secure

Ensuring the security of data is crucial to maintaining the trust of stakeholders and protecting the organization against data breaches and other security threats. Data Security involves both physical and digital measures to safeguard data from unauthorized access, use, disclosure, disruption, modification, or destruction.

Effective Data Security is implemented through a comprehensive set of policies, procedures, and technological controls. This includes, but is not limited to, the use of encryption, secure data storage solutions, rigorous access controls, and continuous monitoring of security practices.

We will know we have made progress on data security when:

There is a significant reduction in data-related security incidents year-over-year
All sensitive data is encrypted both at rest and in transit
Regular security audits and penetration tests are conducted and result in actionable improvements
Secure awareness training is completed annually by all employees

Establishing strong Data Security measures not only protects the organization's data assets but also reinforces the integrity and reliability of its Data Governance framework.

5. Implementation

Implementing the Data Governance Charter involves a series of strategic actions designed to align the organization's Data Management practices with its overarching goals. This process starts with the establishment of a dedicated Data Governance team and the development of an operational plan that outlines specific tasks, timelines, and responsibilities.

The implementation phase includes the integration of Data Governance policies into everyday business processes, training staff on new data procedures and standards, and setting up an oversight mechanism to ensure compliance and continuous improvement.

Critical steps in the implementation phase are:

Formalizing the Data Governance structure and defining roles and responsibilities within the organization
Developing and disseminating Data Governance policies and procedures
Conducting training sessions for all stakeholders to ensure understanding and adherence to Data Governance practices
Setting up monitoring systems to track compliance and effectiveness of Data Governance initiatives
Regularly reviewing and updating the Data Governance framework to keep it relevant as organizational needs evolve

Effective implementation of the Data Governance Charter is critical for ensuring that the guidelines and practices outlined in the document are brought to life and become part of the organizational culture. This not only enhances data quality and security but also supports better decision-making across the organization.

6. Operating Framework

Clearly defined roles and responsibilities are fundamental to the effective execution of Data Governance policies. This section delineates the key roles involved in Data Governance and outlines their specific responsibilities.

Data Governance Board: provides strategic direction, oversight, and high-level monitoring of Data Governance activities; approves Data Governance policies and resolves high-impact issues
Data Stewards: responsible for the management and fitness of data elements—both content and metadata; ensure the quality, accessibility, and protection of data
Data Custodians: handle the technical environment and are responsible for the safe custody, transport, and storage of data, as well as the implementation of business rules
Data Trustees: senior-level executives who own the data assets in their functional areas; they are accountable for the data quality and integrity within their domains
Data Users: recipients of data who rely on accurate, timely, and relevant data for decision-making; they must adhere to Data Governance policies and procedures

Each role is vital for maintaining the lifecycle and integrity of the data within the organization, ensuring that data-related decisions support the overall strategic objectives and comply with relevant regulations and standards.

The assignment of clear roles and responsibilities within Data Governance ensures accountability and establishes a structure for maintaining the quality and security of data throughout its lifecycle. This clarity supports the organization's Data Management and enhances compliance and operational effectiveness.

To read more about the various roles listed here, check out this article.

6a. Data Governance Steering Committee (DGSC)

The Data Governance Steering Committee (DGSC) plays a crucial role in overseeing the Data Governance program. Composed of senior leaders from various departments, the DGSC ensures alignment between Data Governance initiatives and organizational strategies.

Key responsibilities of the DGSC include:

Setting priorities for Data Governance projects based on business needs and strategic goals
Approving Data Governance policies and frameworks
Resolving conflicts and issues that arise within Data Governance operations
Monitoring the effectiveness of Data Governance practices and making recommendations for improvements
Communicating the value and progress of Data Governance initiatives to stakeholders across the organization

The DGSC meets regularly to review the status of Data Governance activities and to ensure that Data Governance efforts are productive and aligned with the overall business objectives.

The establishment of a DGSC is essential for the success of the Data Governance program, providing strategic leadership and ensuring that Data Governance aligns with and supports the broader goals of the organization.

6b. Data Governance Program Office (DGPO)

The Data Governance Program Office (DGPO) is the operational arm of the Data Governance initiative. It provides the day-to-day management and support necessary to ensure the successful implementation of Data Governance policies and practices across the organization.

Responsibilities of the DGPO include:

Coordinating and supporting Data Governance activities and projects
Maintaining and updating the Data Governance framework and documentation
Facilitating communication between the Data Governance Steering Committee, Data Stewards, and other stakeholders
Monitoring compliance with Data Governance policies and reporting on performance metrics
Providing training and resources to enhance Data Governance understanding and practice within the organization

The DGPO acts as a hub for all Data Governance-related issues, ensuring consistency and coherence in how Data Governance is applied throughout the organization.

By managing the operational aspects of Data Governance, the DGPO plays a key role in sustaining and enhancing the organization's Data Governance capabilities. It ensures that Data Governance practices are effectively integrated into organizational processes and that the Data Governance program continues to evolve in response to changing needs and challenges.

6c. Data Steward Council

The Data Steward Council consists of Data Stewards from various departments within the organization. This council is a critical component of the Data Governance structure, tasked with ensuring consistent and effective management of data assets across different areas of the business.

Key responsibilities of the Data Steward Council include:

Developing and maintaining data policies, standards, and procedures to guide Data Management practices
Ensuring data quality by implementing data quality standards and monitoring compliance across departments
Facilitating data sharing and integration efforts to enhance interoperability and consistency of data use
Resolving data-related issues and conflicts that arise within the organization
Advocating for the importance of data accuracy, privacy, and security within their respective domains
Collaborating with IT and business units to align data-related activities with organizational goals and strategies

The council meets regularly to review data practices, discuss challenges, and share best practices to continually improve the management of the organization’s data.

The Data Steward Council plays a pivotal role in the operationalization of Data Governance, bridging the gap between policy and practice. Their collaborative efforts ensure that data assets are effectively managed and leveraged to support the organization's strategic objectives.

7. Metrics

Metrics are crucial for assessing the effectiveness of the Data Governance framework and ensuring accountability across the organization. They provide quantifiable data points that help measure success and identify areas for improvement.

Key metrics for monitoring Data Governance include:

Data Quality: error rates, completeness, consistency, and reliability of data
Data Utilization: frequency of data access and usage patterns among various departments
Compliance: rate of adherence to data policies and standards, including privacy and security regulations
Data Management Maturity: progress in achieving higher levels of data management maturity, as outlined in the Data Management Maturity Assessment
User Satisfaction: feedback from users on the accessibility and usability of data
Operational Efficiency: improvement in operational efficiency and reduction in operational risks due to better Data Management

Regularly reporting these metrics allows the organization to track its progress, justify investments in Data Governance, and guide future policy decisions.

By continuously measuring and analyzing these metrics, the organization can ensure that its Data Governance initiatives are aligned with its strategic goals and are delivering tangible benefits.

To read more about Data Management Maturity Assessments, please check out this comparison of various models.

8. Conclusion

The Data Governance Charter serves as a foundational document that guides the organization in managing its data assets responsibly and effectively. It establishes a framework for Data Governance that aligns with the organization’s overall strategy and compliance requirements.

By clearly defining the structure, roles, responsibilities, and metrics, the Charter ensures that all stakeholders understand their roles and the importance of proper Data Management. This alignment is critical for fostering a culture of data-driven decision making, which ultimately enhances operational efficiency, reduces risks, and drives innovation.

As the organization evolves, so too should the Data Governance Charter. Regular reviews and updates will ensure that it remains relevant and continues to provide value in a changing business and technological landscape.

The successful implementation of this Charter will require ongoing commitment and collaboration from all parties involved. It is the responsibility of everyone within the organization to uphold the principles set forth in this document to ensure the integrity and value of the organization's data assets.

Summary and Next Steps

The Data Governance Charter provides high-level information about Data Governance strategy in a single document.

At minimum, the Charter should contain the following sections:

Introduction with Mission Statement

Some form of summarization (e.g., an Executive Summary at the beginning, Key Takeaways at the end of each section, and/or a Conclusion at the end)

Vision, Scope, and Guiding Principles

Process for Implementation

Roles and Responsibilities

A strong Charter will also include information about business drivers, current issues, target future state, success criteria, business strategy alignment, and principles for Data Governance.

You may also wish to include the following sections:

Definitions

References

Considerations

Contributors

The Charter should be shared with the organization as part of training initiatives and through the Data Governance site on the organization’s intranet, along with the Implementation Roadmap, Data Policies and Standards, descriptions of Roles and Responsibilities, program announcements, trainings, contact information for program leaders, and other relevant information such as Data Governance wins and associated pizza parties 🍕

After developing an effective Data Governance Charter, an organization may choose to turn its attention to producing supporting documents, such as building out an Operating Framework, developing a Roadmap and Implementation Strategy, or creating an Operations Plan. On a more tactical level, it may be useful to create a Business Glossary, Data Governance Scorecard, or Communication Plan. These deliverables will help the organization prepare to implement the Data Governance strategy.

For more information about documents related to Data Governance, we recommend checking out the Document Checklist. If you need assistance developing these documents, Data Strategy Professionals can help! Check out our Document Drafting & Review service.

Nicole offers a proven track record of applying Data Strategy and related disciplines to solve clients' most pressing challenges. She has worked as a Data Scientist and Project Manager for federal and commercial consulting teams. Her business experience includes natural language processing, cloud computing, statistical testing, pricing analysis, ETL processes, and web and application development.