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This article discusses data governance frameworks and how they cover the policies, processes and standards to ensure data accuracy, security and compliance across the data lifecycle from collection to deletion. It covers the unique features and strengths of a data governance framework, criteria to consider and five steps to implement one. Implementing the right framework is key to optimising data quality and value while mitigating risks. However, there are several different data governance frameworks you can choose. You need to ensure the one you pick aligns with your organisational goals.
Data is essential to better decision-making, but only if it’s accurate. That sounds obvious, but many enterprises struggle to ensure data accuracy and sync across their organization. Years of legacy data stuck in siloed platforms and an explosion of new data generated daily make it challenging to manage.
Robust data governance is essential to organise data assets throughout their lifecycle and ensure high-quality, accurate data. Data governance maintains the integrity and security of the data. Choosing the right data governance framework — and ensuring strict adherence — are mission-critical for effective data management and compliance.
What is a data governance framework? In simple terms, it is the steps you take to ensure data is secure, private, accurate and available. This includes actions that users must take, the processes they must follow and the technology needed to support the data lifecycle.
Data governance frameworks set standards and policies for how data is:
Your data governance policy should also cover what kinds of data you collect, who has access to it, and compliance with internal and external requirements.
With strong policies in place, organisations benefit from:
Despite understanding the importance of data governance, many companies have yet to implement one. A Deloitte study showed that just 19% of companies reported a “strong governance structure in place.” About half say they are working toward a policy but lack a cohesive plan.
Yet, a comprehensive plan is crucial. Essential components of a data governance policy should include:
Data governance frameworks outline the reason they are being put in place and what they will cover. They outline the principles, responsibilities and guidelines for effective data governance.
It must also define the roles and responsibilities of those involved in managing and processing data.
For example:
Emerging privacy and compliance regulations have tight controls over data collection, storage and processing. Your policies need to define:
Data governance frameworks establish standards for ensuring reliable and error-free data and maintaining uniformity across systems. It eliminates duplicated or missing data and explains processes for data validation and cleanup.
Detail your data security and privacy guidelines to keep sensitive data secure. Besides noncompliance and potential fines for unauthorised exposure, companies pay a heavy price for data breaches.
The average cost of a data breach worldwide reached a record-high $4.45 million in 2023 — an increase of 15% over the past three years. Guidelines should address items like encryption at rest and in transit, access controls for sensitive data, and anonymisation to de-identify personal information.
Policies must also adhere to all compliance requirements, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Regularly review policies to ensure they align with emerging data protection laws in the U.S. and globally.
Include processes for regular assessments and audits. This helps ensure adherence to the data governance policy and its effectiveness.
Employees should also understand the consequences of failing to follow data governance policies. This may include a warning, retraining, termination and potential legal action in extreme cases.
Up-to-date technology systems and software will help teams follow policies and procedures and ensure optimal security.
Your data governance policy should also explain the importance of training and ensure employees have a proper foundation to comply.
There are several data governance frameworks that enterprises can deploy. There are unique features, strengths, and limitations to each and which data governance model you choose will depend on your specific needs and goals.
Check out this comparison of the different attributes you should consider:
DAMA-DMBOK (Data Management Body of Knowledge)
CMMI's DMM (Data Management Maturity Model)
McKinsey Framework
PwC Enterprise Data Governance Framework
DGI Governance Framework (Data Governance Institute)
Eckerson Framework
So, which data governance framework is right for your organisation and how do you choose? You need to evaluate them against the criteria that align most closely with your needs and objectives.
Some of the key factors to consider include:
The right framework will be flexible enough to adapt to changing business requirements, evolving technologies and growing data volumes. It should be scalable to accommodate future growth and expansion without requiring a complete overhaul.
Depending on your industry and location, you may need to comply with specific regulations and standards, such as the GDPR, CCPA, Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI DSS), Sarbanes–Oxley Act (SOX) and more. Ensure the framework provides the mechanisms to support compliance efforts and meet your obligations.
A robust data governance framework should incorporate tools and processes to maintain data quality, ensure consistency and address data quality issues proactively.
Your framework should also provide guidance and best practices for managing data throughout the entire lifecycle, including creation and acquisition, storage, access, processing, archiving and disposal.
Other factors to consider include:
The data governance framework you choose will play a significant role in the steps you take to build and deploy your policies. In general, these five steps will form the foundation for implementation.
While objectives and scope will vary depending on your organisation, start by defining what you want the framework to achieve. Common goals include:
You need to understand your current data landscape. Develop a data catalog or inventory of data sources, formats and locations. As part of the assessment, also identify individuals or teams responsible for managing or accessing specific data sets (data stewards, data domain owners, data custodians).
You also need to evaluate your organisation’s attack surface to mitigate threats. As businesses migrate data to the cloud and accumulate more data, new vulnerabilities arise.
With your objectives, scope and evaluation in hand, you can create the specific policies and procedures that will make up your data governance policies. Your guidelines should cover items such as:
For your data governance framework to be effective, everyone involved needs to understand it and know their responsibilities. This includes employees, contractors, suppliers, or partners. This is especially important in today’s hyper-connected environment, where 98% of organisations have integrations with at least one third-party vendor that has experienced a breach.
Communication and training are key to educating stakeholders on the importance of governance policies and creating a shared responsibility for data stewardship.
Data governance policies should track back against the goals established in the first step, so it’s important to ensure the objectives are met. However, data governance is an ongoing process that requires continuous monitoring and refinement to ensure effectiveness.
As new technology is introduced or new government regulations are enacted, policies must be updated to comply. For example, the emergence of AI tools must be considered in the rapidly evolving landscape.
Implementing a data governance framework comes with a few learning curves.
Installing a data governance framework is a fairly complex task. Integrating the data governance framework with existing technologies and ensuring interoperability can also present a significant technical challenge.
Another major challenge is organisational resistance. People are used to doing things a certain way. Your framework may affect workflow, authorization access and behaviours. As such, you need to account for the human factor when rolling out your framework. This requires a focus on change management. Even though employees understand the importance of data governance, there may be pushback when policies change how people work. Proper training and education are part of managing the change and consistent communication throughout the process is key.
Enforcing compliance with data governance policies and standards across different teams and systems can also be complicated, especially when roles, responsibilities and accountabilities are unclear. Clear definitions are crucial. This is especially important in organisations where people have multiple roles or transition from one position to another. For example, shifts in access and use may occur as employees transition into new roles.
Throughout this article, we’ve discussed some of the challenges and strategies to ensure the effective implementation of a data governance framework. Other best practices include:
Data governance requires universal application across an organisation. Leadership from top management will be necessary to drive change and allocate resources to accomplish your goals. Without support from executive leadership, any change initiative can fall short.
Adoption is accelerating when employees recognise data as a strategic asset. When people understand the true value of data, they are more likely to comply with governance policies.
Poor data quality undermines the entire framework and can lead to poor decision-making. Your data governance framework needs a robust quality control process for data profiling, cleansing and standardisation. You also need regular monitoring and checks to ensure accuracy and consistency across the enterprise.
The right tools and automation can streamline the data governance process. Automation follows defined rules, which aids in the consistent application of your guidelines.
Implementing a robust data governance framework is essential for organisations to unlock the full potential of their data assets while maintaining compliance and mitigating risks. When you adapt to industry-recognised frameworks that align with your goals and unique environment, you can achieve the consistent, accurate and high-quality data you need to make better, more informed, data-driven decisions.
Data lineage is all about tracking the journey of data from its source to its final form, including all the changes it undergoes along the way. In a data governance framework, understanding data lineage is crucial because it helps ensure the accuracy and reliability of data. It makes it easier to pinpoint where errors might have been introduced into the data and to assess the impact of data changes on business decisions.
Data governance tools are essential for automating and managing the various tasks associated with a data governance framework, such as data quality control, metadata management and compliance monitoring. These tools help teams follow the framework's policies and procedures efficiently, ensuring data is secure, high-quality and compliant with regulations.
Master data represents the core business entities of an organisation, such as customers, products and suppliers. In a data governance framework, managing master data is key to maintaining a consistent and accurate view of business-critical information. It helps in creating a single source of truth, ensuring all departments work with the same data and make informed decisions based on accurate information.
Metadata management involves organising and understanding data through its descriptions, making it easier for everyone in the organisation to find, use and manage data effectively. In a data governance framework, metadata management is crucial because it supports data quality, helps meet compliance requirements and enhances data lineage understanding, contributing to better data accuracy and decision-making.
Data architecture outlines how data is structured, stored and integrated across an organisation. It's a key component of a data governance framework because it provides the blueprint for managing data in a way that supports the framework's goals. Good data architecture ensures that data flows efficiently between systems, is stored securely and is accessible to authorised users, aligning with the overall data governance policies and standards
This article discusses data governance frameworks and how they cover the policies, processes and standards to ensure data accuracy, security and compliance across the data lifecycle from collection to deletion. It covers the unique features and strengths of a data governance framework, criteria to consider and five steps to implement one. Implementing the right framework is key to optimising data quality and value while mitigating risks. However, there are several different data governance frameworks you can choose. You need to ensure the one you pick aligns with your organisational goals.
Data is essential to better decision-making, but only if it’s accurate. That sounds obvious, but many enterprises struggle to ensure data accuracy and sync across their organization. Years of legacy data stuck in siloed platforms and an explosion of new data generated daily make it challenging to manage.
Robust data governance is essential to organise data assets throughout their lifecycle and ensure high-quality, accurate data. Data governance maintains the integrity and security of the data. Choosing the right data governance framework — and ensuring strict adherence — are mission-critical for effective data management and compliance.
What is a data governance framework? In simple terms, it is the steps you take to ensure data is secure, private, accurate and available. This includes actions that users must take, the processes they must follow and the technology needed to support the data lifecycle.
Data governance frameworks set standards and policies for how data is:
Your data governance policy should also cover what kinds of data you collect, who has access to it, and compliance with internal and external requirements.
With strong policies in place, organisations benefit from:
Despite understanding the importance of data governance, many companies have yet to implement one. A Deloitte study showed that just 19% of companies reported a “strong governance structure in place.” About half say they are working toward a policy but lack a cohesive plan.
Yet, a comprehensive plan is crucial. Essential components of a data governance policy should include:
Data governance frameworks outline the reason they are being put in place and what they will cover. They outline the principles, responsibilities and guidelines for effective data governance.
It must also define the roles and responsibilities of those involved in managing and processing data.
For example:
Emerging privacy and compliance regulations have tight controls over data collection, storage and processing. Your policies need to define:
Data governance frameworks establish standards for ensuring reliable and error-free data and maintaining uniformity across systems. It eliminates duplicated or missing data and explains processes for data validation and cleanup.
Detail your data security and privacy guidelines to keep sensitive data secure. Besides noncompliance and potential fines for unauthorised exposure, companies pay a heavy price for data breaches.
The average cost of a data breach worldwide reached a record-high $4.45 million in 2023 — an increase of 15% over the past three years. Guidelines should address items like encryption at rest and in transit, access controls for sensitive data, and anonymisation to de-identify personal information.
Policies must also adhere to all compliance requirements, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Regularly review policies to ensure they align with emerging data protection laws in the U.S. and globally.
Include processes for regular assessments and audits. This helps ensure adherence to the data governance policy and its effectiveness.
Employees should also understand the consequences of failing to follow data governance policies. This may include a warning, retraining, termination and potential legal action in extreme cases.
Up-to-date technology systems and software will help teams follow policies and procedures and ensure optimal security.
Your data governance policy should also explain the importance of training and ensure employees have a proper foundation to comply.
There are several data governance frameworks that enterprises can deploy. There are unique features, strengths, and limitations to each and which data governance model you choose will depend on your specific needs and goals.
Check out this comparison of the different attributes you should consider:
DAMA-DMBOK (Data Management Body of Knowledge)
CMMI's DMM (Data Management Maturity Model)
McKinsey Framework
PwC Enterprise Data Governance Framework
DGI Governance Framework (Data Governance Institute)
Eckerson Framework
So, which data governance framework is right for your organisation and how do you choose? You need to evaluate them against the criteria that align most closely with your needs and objectives.
Some of the key factors to consider include:
The right framework will be flexible enough to adapt to changing business requirements, evolving technologies and growing data volumes. It should be scalable to accommodate future growth and expansion without requiring a complete overhaul.
Depending on your industry and location, you may need to comply with specific regulations and standards, such as the GDPR, CCPA, Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI DSS), Sarbanes–Oxley Act (SOX) and more. Ensure the framework provides the mechanisms to support compliance efforts and meet your obligations.
A robust data governance framework should incorporate tools and processes to maintain data quality, ensure consistency and address data quality issues proactively.
Your framework should also provide guidance and best practices for managing data throughout the entire lifecycle, including creation and acquisition, storage, access, processing, archiving and disposal.
Other factors to consider include:
The data governance framework you choose will play a significant role in the steps you take to build and deploy your policies. In general, these five steps will form the foundation for implementation.
While objectives and scope will vary depending on your organisation, start by defining what you want the framework to achieve. Common goals include:
You need to understand your current data landscape. Develop a data catalog or inventory of data sources, formats and locations. As part of the assessment, also identify individuals or teams responsible for managing or accessing specific data sets (data stewards, data domain owners, data custodians).
You also need to evaluate your organisation’s attack surface to mitigate threats. As businesses migrate data to the cloud and accumulate more data, new vulnerabilities arise.
With your objectives, scope and evaluation in hand, you can create the specific policies and procedures that will make up your data governance policies. Your guidelines should cover items such as:
For your data governance framework to be effective, everyone involved needs to understand it and know their responsibilities. This includes employees, contractors, suppliers, or partners. This is especially important in today’s hyper-connected environment, where 98% of organisations have integrations with at least one third-party vendor that has experienced a breach.
Communication and training are key to educating stakeholders on the importance of governance policies and creating a shared responsibility for data stewardship.
Data governance policies should track back against the goals established in the first step, so it’s important to ensure the objectives are met. However, data governance is an ongoing process that requires continuous monitoring and refinement to ensure effectiveness.
As new technology is introduced or new government regulations are enacted, policies must be updated to comply. For example, the emergence of AI tools must be considered in the rapidly evolving landscape.
Implementing a data governance framework comes with a few learning curves.
Installing a data governance framework is a fairly complex task. Integrating the data governance framework with existing technologies and ensuring interoperability can also present a significant technical challenge.
Another major challenge is organisational resistance. People are used to doing things a certain way. Your framework may affect workflow, authorization access and behaviours. As such, you need to account for the human factor when rolling out your framework. This requires a focus on change management. Even though employees understand the importance of data governance, there may be pushback when policies change how people work. Proper training and education are part of managing the change and consistent communication throughout the process is key.
Enforcing compliance with data governance policies and standards across different teams and systems can also be complicated, especially when roles, responsibilities and accountabilities are unclear. Clear definitions are crucial. This is especially important in organisations where people have multiple roles or transition from one position to another. For example, shifts in access and use may occur as employees transition into new roles.
Throughout this article, we’ve discussed some of the challenges and strategies to ensure the effective implementation of a data governance framework. Other best practices include:
Data governance requires universal application across an organisation. Leadership from top management will be necessary to drive change and allocate resources to accomplish your goals. Without support from executive leadership, any change initiative can fall short.
Adoption is accelerating when employees recognise data as a strategic asset. When people understand the true value of data, they are more likely to comply with governance policies.
Poor data quality undermines the entire framework and can lead to poor decision-making. Your data governance framework needs a robust quality control process for data profiling, cleansing and standardisation. You also need regular monitoring and checks to ensure accuracy and consistency across the enterprise.
The right tools and automation can streamline the data governance process. Automation follows defined rules, which aids in the consistent application of your guidelines.
Implementing a robust data governance framework is essential for organisations to unlock the full potential of their data assets while maintaining compliance and mitigating risks. When you adapt to industry-recognised frameworks that align with your goals and unique environment, you can achieve the consistent, accurate and high-quality data you need to make better, more informed, data-driven decisions.
Data lineage is all about tracking the journey of data from its source to its final form, including all the changes it undergoes along the way. In a data governance framework, understanding data lineage is crucial because it helps ensure the accuracy and reliability of data. It makes it easier to pinpoint where errors might have been introduced into the data and to assess the impact of data changes on business decisions.
Data governance tools are essential for automating and managing the various tasks associated with a data governance framework, such as data quality control, metadata management and compliance monitoring. These tools help teams follow the framework's policies and procedures efficiently, ensuring data is secure, high-quality and compliant with regulations.
Master data represents the core business entities of an organisation, such as customers, products and suppliers. In a data governance framework, managing master data is key to maintaining a consistent and accurate view of business-critical information. It helps in creating a single source of truth, ensuring all departments work with the same data and make informed decisions based on accurate information.
Metadata management involves organising and understanding data through its descriptions, making it easier for everyone in the organisation to find, use and manage data effectively. In a data governance framework, metadata management is crucial because it supports data quality, helps meet compliance requirements and enhances data lineage understanding, contributing to better data accuracy and decision-making.
Data architecture outlines how data is structured, stored and integrated across an organisation. It's a key component of a data governance framework because it provides the blueprint for managing data in a way that supports the framework's goals. Good data architecture ensures that data flows efficiently between systems, is stored securely and is accessible to authorised users, aligning with the overall data governance policies and standards
