Implementing a Data Governance Framework

Having an effective data governance framework in place is of utmost importance for businesses. These days, businesses are dealing with more data than ever before, meaning that a clear set of guidelines and policies regarding how that data is accessed, managed, and maintained is essential.

A well-designed data governance framework can provide numerous benefits for businesses, including:

• Help ensure data accuracy
• Increase transparency and accountability
• Improve decision-making

Additionally, a data governance framework can help businesses comply with regulatory requirements, which is particularly important in industries such as healthcare and finance.

In this article, we explore the benefits of a data governance framework, its relationship to regulatory compliance, and how to create a framework that works for your business. We’ll also discuss the role of master data governance frameworks, best practices for integrating data governance frameworks, and the common areas where companies’ data governance regulations fail.

What Is Data Governance Framework?

A data governance framework is a set of guidelines, policies, and procedures that define how an organization’s data should be managed, controlled, and protected. It covers all aspects of data management, including data quality, architecture, security, exchange, and privacy.

The framework also provides a mechanism for decision-making, monitoring, and enforcement to ensure that organizational data is managed in a consistent, efficient, and effective manner.

In short, the purpose of a data governance framework is to establish a structured approach to managing an organization’s data assets, enabling the organization to derive maximum value from its data while minimizing risks and ensuring compliance with relevant laws and regulations.

The Next Chapter in Data Governance: Data Enablement

Discover how to make data work for you; download Revelate's whitepaper on the evolution of data governance.

Download the whitepaper

Why Do You Need a Data Framework for Data Governance?

A well-designed data governance framework is essential for any organization looking to manage its data effectively. Here are several reasons why implementing a data governance framework is necessary.

Better decision support

A robust data governance framework ensures that the right data is available at the right time to support informed business decisions. By providing a clear understanding of data quality, lineage, and ownership, organizations can make better decisions based on accurate, reliable data.

Risk reduction

Data breaches and privacy violations can lead to significant financial and reputational damage. A data governance framework can help identify and mitigate risks associated with data management, ensuring that sensitive data is protected and compliance requirements are met.

Improved scalability

As organizations grow, so does the data within it. A data governance framework ensures that data management processes can scale with the organization’s growth, ensuring that data remains accessible, accurate, and relevant.

Improved efficiencies

By eliminating redundancies and inefficiencies in data management processes, organizations can enjoy  significant cost savings and increased productivity. A data governance framework can help identify areas where improvements can be made and assist with streamlining data management processes.

Stronger collaboration

A data governance framework promotes collaboration between different departments and stakeholders by providing a common understanding of data definitions, processes, and ownership. By improving communication and alignment around data, organizations can achieve better outcomes and make more informed decisions.

How Are Regulatory Compliance and Data Governance Frameworks Related?

• Regulatory compliance and data governance frameworks are two sides of the same coin. In today’s data-driven world, businesses collect vast amounts of information about their customers, employees, and operations. However, with the increasing threat of cyber-attacks and data breaches, regulatory compliance and data governance have become critical issues for organizations of all sizes and industries.
• Regulatory compliance refers to the adherence to specific laws, regulations, and standards that govern how organizations handle and protect data. These regulations vary depending on the industry, type of data, and geographic location of the business. For instance, healthcare organizations are subject to HIPAA regulations that require them to safeguard patient health information, while financial institutions must comply with SOX regulations that ensure the accuracy and integrity of financial reporting.
• Data governance refers to the policies, procedures, and controls organizations put in place to manage their data throughout its lifecycle. A robust data governance framework includes data quality management, data privacy, security, access controls, and data retention and disposal policies. A well-designed data governance framework ensures that data is consistent, accurate, and reliable, and available to authorized users when needed.

Regulatory compliance and data governance are interrelated because many regulations require organizations to have a comprehensive data governance framework to ensure compliance. For example, the EU’s General Data Protection Regulation (GDPR) mandates that organizations implement appropriate technical and organizational measures to protect personal data, including data governance and management practices. Similarly, the California Consumer Privacy Act (CCPA) requires organizations to have data privacy policies, data management processes, and data access controls to protect consumer data.

Failure to comply with these regulations can severely affect organizations, including financial penalties, legal liabilities, and reputational damage. In addition, data breaches and cyber-attacks can result in loss of revenue, customer trust, and intellectual property leading to legal action and regulatory fines.

Here’s a short table outlining the relationship between Regulatory Compliance and Data Governance Frameworks:

Ensuring that your data governance framework is applied consistently and accurately for data set requests can be done through Revelate’s data marketplace solution. In partnership with Immuta, a leading security organization, Revelate is able to offer centralized data security management for data transfer, even if your organization utilizes multi-cloud systems. Because Revelate is platform agnostic, organizations are able to securely extract, prepare, package, and transfer data from any source to any target.

Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started

How to Create a Data Governance Framework

Creating a robust and effective data governance framework may seem daunting, but with the right steps in place, it can be a smooth and rewarding process. Here are some steps to follow:

1. Revisit Your Definition of Data Governance

It is crucial to establish a clear understanding of what data governance means for your organization before creating a framework. This will help ensure everyone involved is on the same page and working towards the same objectives.

Establishing an enterprise data governance definition should include setting standards for your data. This includes a centrally-managed data language consisting of data identifier taxonomy (unique identifiers assigned to data for verification purposes), information fields that can be referenced, powered lists (maps submitted data to a list), and data science models that organize these elements and determine how they work together.

2. Identify and Define Data Domains

Take stock of the different types of data your organization handles and categorize them into domains. Each domain should have a clear purpose and definition.

3. Identify Domain Data Owners and Consumers

It’s important to identify who is responsible for each domain and who needs to access the data within it. This will help ensure that the right people are making decisions about the data. Supervisors and department leaders would be the primary data owners for most organizations.

4. Validate and Document Everything About the Data

Once you have identified your data domains, document everything about them. This includes data definitions, data lineage, and data quality.  Using data catalog tools to classify and tag data is essential so that data can be organized and searchable. Assigning metadata and specific business terminology tags is part of this process. This will help you better understand your data and ensure everyone is working with the same information during data exchange.

5. Conduct Data Security and Risk Assessments for Each Domain

Each data domain will have its unique set of risks and security concerns. It’s important to assess these risks and develop a mitigation plan. This will help ensure that your data is secure and protected.

By following these steps, you can create a data governance framework that is tailored to your organization’s needs and helps you achieve your objectives.

What Role Does a Master Data Governance Framework Play in Managing Your Data?

A master data governance framework is a set of policies and procedures that define how an organization’s critical data assets are managed. It provides a centralized approach to managing data across the organization, ensuring consistency, accuracy, and completeness. A master data governance framework is essential for managing critical data assets, such as customer, product, and financial data.

Without a centralized approach, master data can become scattered and siloed, leading to inconsistencies and errors that can impact important business decisions. Even with a data mesh approach to data management, certain data, such as customer information, should still be approached as singular, master data. With a well-designed master data governance framework, organizations can ensure that certain data remains as “one source of truth” and that there is always a fallback for determining the accuracy of certain data.

To achieve this level of consistency, it’s important to follow best practices when integrating your data governance framework. Here are some tips to help you get started:

Involve stakeholders from across the organization

Data governance is a cross-functional effort requiring input and collaboration from stakeholders. By involving stakeholders early on, you can ensure that your framework meets the needs of all teams and departments.

Define clear roles and responsibilities

Clearly defining roles and responsibilities is essential to ensure everyone understands their role in the data governance process. This includes identifying data owners, stewards, and custodians responsible for different aspects of the data.

Develop a data dictionary

A data dictionary is a central repository of data definitions and metadata that provides a shared understanding of the organization’s data assets. It should include definitions for all critical data elements, attributes, data types, and business rules.

Establish data quality metrics

Data quality metrics are used to measure data accuracy, completeness, and consistency. By establishing these metrics early on, you can monitor data quality over time and identify areas for improvement.

Implement a data governance tool

Data governance tools can help automate many aspects of the data governance process, such as data profiling, metadata management, and data lineage tracking. By implementing a tool, you can streamline your data governance efforts and ensure consistency across the organization.

Data Governance Framework Integration Best Practices

Data governance framework integration best practices are essential for ensuring an organization’s data is properly managed, controlled, and secured. Here are some best practices for integrating data governance into your organization:

1. Set format standards for your data

It is important to define standard formats for data to ensure consistency and accuracy across different departments and systems. This includes standards for data types, naming conventions, and data structures.

2. Account for unmanaged data

Organizations often have unmanaged data not covered by existing policies and procedures. It is important to identify and classify this data to ensure it is properly managed and secured. Storing this data in a lakehouse is one effective solution that helps maintain security.

3. Map your business goals for governance

A data governance framework should align with your organization’s business goals and objectives. It is important to identify your organization’s specific data needs and goals to ensure that your data governance framework is effective.

 4. Focus on simplicity in most areas

A data governance framework should be simple and easy to understand for all stakeholders. Avoid overcomplicating processes and procedures to ensure they are effective and easy to implement.

 5. Establish governance team roles

Establishing roles and responsibilities for the governance team can help ensure that everyone is aware of their specific duties and responsibilities. This can include roles such as data stewards, custodians, and owners.

6. Classify and tag all of your data

Data classification and tagging can help ensure that data is properly secured, managed, and used according to specific policies and procedures. This can help to reduce the risk of data breaches and ensure compliance with regulatory requirements.

7. Measure progress with multiple metrics

Measuring progress and success in data governance can help identify improvement areas. It is important to use multiple metrics to assess the effectiveness of the governance framework, such as data quality, compliance, and risk management.

8. Automate as much as possible

Automation can help reduce manual effort and improve the accuracy and consistency of data governance processes. This can include automating tasks such as data classification, data quality checks, and data access controls.

Data governance is crucial for managing, controlling, and securing an organization’s data effectively. By following these best practices for integrating data governance into your organization, you can ensure that your data governance framework aligns with your business goals and objectives, is simple and easy to understand, and includes the necessary roles and responsibilities for your governance team. Additionally, implementing automation and measuring progress with multiple metrics can help improve the effectiveness of your data governance framework and reduce the risk of data breaches and compliance issues.

Data Management and Governance Framework: Is There a Difference?

Yes, there is a difference between data management and data governance, although they are related and often work together to ensure the proper management and use of an organization’s data.

• Data management refers to the activities involved in managing an organization’s data throughout its lifecycle, including collection, storage, processing, and dissemination. It involves technical tasks such as data modeling, database design, data quality assurance, data integration, and data security.
• Data governance refers to the processes and policies that govern an organization’s data management activities. It involves defining the roles and responsibilities of those who manage and use the data, as well as setting standards for data quality, security, privacy, and compliance. Data governance also includes establishing processes for data classification, data retention, and data sharing, as well as ensuring that data is used ethically and legally.

To help you understand the difference between data management and data governance, we’ve created a comparison table below:

Where a Company’s Data Governance Regulations Commonly Fail

Data governance is a critical component of any modern business strategy. It involves the establishment of policies and procedures that ensure the accuracy, consistency, security, and privacy of a company’s data. Effective data governance enables organizations to better manage their data assets, improve decision-making, and reduce the risk of data breaches and regulatory violations.

However, despite the importance of data governance, many companies still struggle with its implementation. Here are some of the areas where a company’s data governance regulations commonly fail:

1. Integrating Data Governance With IT Policy

One of the most common areas where data governance regulations fail is in their integration with IT policy. Data governance should be integrated with the broader IT policy to ensure that data is managed effectively and in compliance with all relevant regulations. This requires a coordinated effort between the IT department and the data governance team to ensure that policies and procedures are properly aligned.

2. Employee and Executive Program Buy-in

Another area where data governance regulations commonly fail is in obtaining buy-in from employees and executives. Without support from key stakeholders, data governance policies and procedures are unlikely to be effective. To overcome this challenge, companies need to educate employees and executives on the benefits of data governance and the risks associated with poor data management practices.

3. Governance Flexibility and Simplicity

Many data governance regulations fail because they are too complex or inflexible. Data governance policies and procedures should be designed to be flexible and simple to ensure that they can be easily implemented and adapted to changing business needs. This requires a focus on developing policies and procedures that are easy to understand and follow.

4. Selecting Governance Technology Tools

Finally, the selection of the right technology tools to support data governance is critical to its success. Many companies fail to select the right technology tools, which can make it difficult to manage data effectively. To overcome this challenge, companies need to invest in the right technology tools that can support their data governance policies and procedures.

Conclusion

In conclusion, the data governance framework is a critical component for any organization that aims to ensure the accuracy, integrity, and security of its data. It helps businesses to establish rules, policies, and processes for data management, access, and usage. By implementing a robust data governance framework, organizations can improve their decision-making processes, mitigate risks, and comply with regulations.

At Revelate, we understand the importance of data governance. Our data marketplace solution automates your data governance framework for every transaction, ensuring consistent and effective data movement throughout your organization.

Discover our data marketplace solution today. Book a Demo.

Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started