MAY NOT BE REPRODUCED WITHOUT PERMISSION
TL:DR
Data governance frameworks cover the policies, processes, and standards to ensure data accuracy, security, and compliance lifecycle from collection to deletion. Implementing the right framework is key to optimizing data value while mitigating risks, but there are several different data governance frameworks you can choose. You need to ensure the one you pick aligns with your organizational goals.
Data is essential to better decision-making, but only if it’s accurate. That sounds obvious, but many enterprises are struggling to ensure data accuracy and sync across their organization. Years of legacy data, stuck in siloed platforms, and an explosion of new data being generated every day are making it challenging to manage.
Robust data governance is essential to organize 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 requiring strict adherence — are mission-critical for effective data management and compliance.
Key Takeaways:
- Data governance frameworks include the policies, processes, and standards to ensure accuracy, security, and compliance across the entire data lifecycle.
- Many organizations struggle to enact strict governance policies, so defining goals, roles, and practices is crucial.
- There are multiple data governance frameworks. Choosing the right one will ensure alignment with organizational goals and meet your standards for compliance.
- Besides the technical framework, executive sponsorship, communication, and change management practices will be key to success.
Understanding Data Governance
What is a data governance framework? In simple terms, it is the steps you take to make sure data is secure, private, accurate, and available. This includes actions that must be taken by users, the processes that must be followed, and the technology needed to support the data lifecycle.
Data governance frameworks set standards and policies for how data is:
- Collected
- Stored
- Processed
- Disposed
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, organizations benefit from:
- Accurate and consistent data for better decision-making
- Improved cost control by eliminating data duplication
- Enhanced regulatory compliance
- Reduced risk exposure
- Protection and security of sensitive data
The Components of a Data Governance Framework
Despite understanding the importance of data governance, few companies have put them in place. 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:
Purpose and Scope
Data governance frameworks should outline the reason they are being put in place and what they will cover. It outlines the principles, responsibilities, and guidelines necessary to ensure effective data governance.
Data Governance Structure
It must also define the roles and responsibilities of those involved in managing and processing data. For example:
- Data Owners: Responsible for specific data domains. They define data requirements, access levels, and usage guidelines.
- Data Stewards: Manage day-to-day data operations, enforce policies, and maintain data quality.
- Data Custodians: Handle technical aspects such as data storage, access controls, and backups.
Data Access and Usage Rules
Emerging privacy and compliance regulations have tight controls over the collection, storage, and processing of data. Your policies need to define who has access to types of data, how it can be used, and any usage restrictions, including data sharing.
Data Quality Standards
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.
Data Security and Privacy Guidelines
You need to detail your data security and privacy guidelines to keep sensitive data secure. Besides noncompliance and potential fines for unauthorized 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 anonymization to de-identify personal information.
Legal Obligations
Policies must also adhere to all compliance requirements, such as the GDPR and CCPA. Policies should be reviewed regularly to align with emerging data protection laws in the U.S. and globally.
Review and Audit Procedures
Also included will be processes for regular assessments and audits to validate adherence to the data governance policy and its effectiveness.
Procedure for Policy Violations
Employees should also understand the consequences of failing to follow data governance policies from warning, retraining, termination, and potential legal action in extreme cases.
Training and Awareness
The data governance policy should also explain the importance of training and ensure employees have a proper foundation to comply.
Common Data Governance Frameworks
There are several data governance frameworks that enterprises can deploy. There are unique features, strengths, and limitations to each, and which data governance maturity model you choose will depend on your specific needs and goals.
This chart compares the different attributes you should consider:
| FRAMEWORK | UNIQUE FEATURES | STRENGTHS | LIMITATIONS |
| DAMA-DMBOK (Data Management Body of Knowledge LINK | Comprehensive and widely recognized. Covers 10 data management functions. | Widely accepted and offers details insights. Vendor-neutral and industry-agnostic. | Broad framework requires customization for specific organizational needs. |
| CMMI’s DMM (Data Management Maturity Model) LINK | Maturity model approach with five levels. Focused on process improvement and optimization. | Provides a roadmap for continuous improvement. Aligns with other CMMI models. | Primarily aimed at software development organizations. Adoption and certification can be resource-intensive, |
| McKinsey Framework LINK | Focuses on strategic alignment with business goals. | Covers data quality, integration, privacy, security, and architecture. | Designed for large organizations and may require dedicated resources. |
| PwC Enterprise Data Governance Framework LINK | Focuses on strategy, stewardship, enablers, and data management. | Aligns with legal requirements and provides actionable steps for implementation. | Can be complex, requiring organizational buy-in along with regular updates. |
| DGI Governance Framework (Data Goverannce Institute) LINK | Logical structure for classifying, organizing, and communicating with enterprise data. | Cover 10 core values for focused data governance activities and data usage. | Requires clear articulation of value statements to align with business goals. |
| Eckerson Framework LINK | Emphasizes clear roles within the organization and alignment with organizational goals. | Ensures data supports business decisions and encourages cross-functional use. | May not cover detailed technical aspects of implementation. |
Choosing a Data Governance Framework
So, which data governance framework is right for your organization 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 evaluate include:
Adaptability and Scalability
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.
Compliance and Regulatory Support
Depending on your industry and location, you may need to comply with specific regulations and standards, such as the GDPR, CCPA, HIPAA, PCI-DSS, SOX, and more. Ensure that the framework provides the mechanisms to support compliance efforts and meet your obligations.
Data Quality Management Capabilities
A robust data governance framework should incorporate tools and processes for maintaining data quality, ensuring consistency, and addressing data quality issues proactively.
Data Lifecycle Management
The framework should also provide guidance and best practices for managing data throughout the entire lifecycle from creation and acquisition, storage, access, processing, archiving, and disposal.
Other Factors to Consider
There are other factors to consider as well, including:
- Industry specifics: Certain industries have unique data governance needs and best practices. Choose frameworks tailored to your specific field, offering specialized features and capabilities.
- Existing data management: The new framework should integrate seamlessly with existing systems or provide a clear migration path.
- Organizational size and complexity: Large, complex organizations often require more robust and comprehensive data governance frameworks with advanced features.
- Budget and Resources: Different frameworks vary in costs for implementation, maintenance, and support. Ensure the chosen framework accomplishes your goals, but fits within any budget and resource constraints.
5 Steps to Implement a Data Governance Framework
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.
1. Define Objective and Scope
While objectives and scope will vary depending on your organization, you start by defining what you want the framework to achieve. Common goals include:
- Improving data quality and consistency across different systems and departments
- Ensuring compliance with industry-specific regulations
- Enhancing data security and access control measures
- Facilitating data integration and interoperability
- Optimizing data lifecycle management
2. Conduct a Comprehensive Data Assessment
You need to understand your current data landscape. Develop an inventory of data sources, formats, and locations. As part of the assessment, you should also identify individuals or teams responsible for managing or accessing specific data sets.
You also need to evaluate your organization’s attack surface to mitigate threats. As businesses migrate data to the cloud and accumulate more data, new vulnerabilities arise.
3. Establish Policies, Procedures, and Standards
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:
- Data quality control measures, including validation rules, and data cleansing
- Data retention and archiving policies for different data types
- Data security protocols
- Data integration and interoperability standards
- Data classification and labeling conventions
- Data lifecycle management standards
4. Communicate the Framework to All Stakeholders
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 hyperconnected environment where 98% of organizations have integrations with at least one third-party vendor that has experienced a breach.
Communication and training will be key to educating stakeholders on the importance of governance policies and creating a shared responsibility for data stewardship.
5. Monitor and Evaluate Effectiveness
Data governance policies should track back against the goals established in the first step, so it’s important to ensure the goals are met. However, data governance is an ongoing process that requires continuous monitoring and refinement.
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 taken into account when deploying. As such, data governance should be seen as an ongoing effort that requires an iterative process to ensure effectiveness.
Challenges in Implementing a Data Governance Framework
Installing a data governance framework is a fairly complex task. Integrating the data governance framework with existing technologies and ensuring interoperability is a significant technical challenge.
Another major challenge is organizational resistance. People are used to doing things a certain way. Your framework may affect workflow, authorization access, and behaviors. 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 the way people work. Proper training and education are part of managing the change, but 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 organizations where people have multiple roles or transition from one position to another. Shifts in access and use, for example, may occur as employees transition into new roles.
Best Practices for Successful Data Governance
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:
Executive Sponsorship
Data governance requires universal application across an organization. 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.
Establish a Data-Driven Mindset
Adoption is accelerating when employees recognize data as a strategic asset. When people understand the true value of data, they are more likely to comply with governance policies.
Robust Quality Control
Bad data 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 standardization. You also need regular monitoring and checks to ensure accuracy and consistency across the enterprise.
Automation and Technology
The right tools and automation can streamline the data governance process. Automation follows defined rules, which aids in the consistent application of your guidelines.
Conclusion
Implementing a robust data governance framework is essential for organizations to unlock the full potential of their data assets while maintaining compliance and mitigating risks. Adapting industry-recognized 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.