Data Governance Frameworks for Securing Your Data and Maximizing Its Value

Data governance frameworks provide companies with a structured approach to managing the complexities of enterprise data. They consist of policies, procedures, roles and technologies that allow organizations to maximize the value of their data while maintaining security and compliance. Teams may choose to use one of the frameworks available from today’s industry-leading organizations, adapt one of those frameworks or create a custom framework. Let’s look at the elements that make up a data governance framework and several popular frameworks.

What Is a Data Governance Framework?

A data governance framework is a unified system that an enterprise uses to manage its data assets. The framework regulates how data is collected, stored and protected to ensure consistency, quality and compliance with organizational standards, as well as industry and government regulations. Data governance frameworks are essential for data-driven decision-making, ensuring the data used to make those decisions remains accurate, consistent, secure and accessible. Business leaders rely on their data to provide critical insights into operations, customers and market trends, using these to build more efficient, profitable and competitive organizations. As the value of enterprise data increases, robust data governance has become a central focus for data-driven businesses across industries.

Essential Elements of a Data Governance Framework

There are many data governance frameworks, and most organizations adapt existing frameworks to suit their needs and requirements. However, every effective framework has the following in common.

Established organizational policies and standards

Data governance frameworks rest on a foundation of policies and standards. These inform all other components of the framework, providing consistency in how data is managed and secured across the organization’s entire data ecosystem. Organizational policies and standards provide clear guidelines that reduce risks, improve data quality and increase operational efficiency. Communicating these policies and standards to relevant stakeholders is vital to fostering a culture of data responsibility and accountability so data practices align with regulatory requirements and business objectives.  

Defined roles and responsibilities

Data governance roles and responsibilities must be clearly delineated. This is especially true in larger organizations with multiple departments and complex systems. Clearly defined roles and responsibilities prevent confusion, eliminate duplicate efforts, enhance collaboration and ensure that critical data governance activities are managed effectively.

Data analytics and AI management

For organizations to rely on data analytics and AI to inform decision-making, it’s crucial to ensure data accuracy and reliability. Data governance frameworks help ensure that the data feeding analytics and AI applications will produce trustworthy outcomes. Data governance frameworks also provide essential safeguards that dictate how AI technologies are developed and used — ethically, fairly and in alignment with organizational standards.

Data security and privacy

Data governance frameworks help protect sensitive information from unauthorized access, breaches and misuse. Robust data security and privacy practices protect not only information, but also the trust of customers, partners and employees — enhancing the organization’s reputation and competitive advantage.

Compliance and auditing

Compliance and auditing processes allow organizations to verify adherence to internal policies, industry standards and regulatory requirements. With this capability, organizations can proactively identify and address gaps, inconsistencies or violations in their current data management practices.  Data governance frameworks provide structure and consistency to compliance and auditing activities, supporting a culture of accountability and continuous improvement.

Five Leading Data Governance Frameworks

Data governance is a complex, multifaceted practice. For this reason, many organizations choose to use an established framework or adapt a framework. Here are five of the most commonly used data governance frameworks.

DGI data governance framework

The Data Governance Institute (DGI) offers a framework that provides organizations with a granular, structured approach to managing their data assets effectively. This framework includes 10 universal components organized using why, what, who and how questions. The questions guide organizations as they define the required structures, policies, processes, people, technologies and controls needed to ensure data quality, security, compliance and value creation across the business. 

PwC enterprise data governance framework

The PwC enterprise data governance framework is a forward-looking framework designed to help organizations meet the governance challenges presented by today’s fast-changing data landscapes. Less focused on granular details, this data governance framework consists of five broad components: data governance strategy, enterprise data management, data lifecycle management, data stewardship and data governance enablers. This data governance framework is ideally suited for organizations with established data governance programs seeking to modernize and future-proof their policies, processes and standards. 

COBIT data governance framework

COBIT (Control Objectives for Information and Related Technologies) is primarily designed for IT governance, but also encompasses elements of data governance as part of its overall framework. It focuses on translating stakeholder needs into actionable strategy and objectives, and contains 40 governance and management processes that define responsibilities and tasks.

The DAMA Guide to DMBOK 

The Data Management Association (DAMA) Guide to the Data Management Body of Knowledge (DMBOK) involves a wheel-like infographic that’s used to organize data governance visually. With data governance at the center, nine essential knowledge areas radiate out from it. Each knowledge area represents one core component of data governance, providing a blueprint for effectively managing enterprise data assets throughout the data lifecycle.

Organization-specific data governance framework

Some organizations design their own data governance framework from scratch or combine components from several of the established frameworks discussed above, tailoring the framework to meet the unique needs of their business, industry-related requirements and organizational culture. This approach affords greater flexibility and may lead to better adoption and more effective implementation, compared to adopting a generic framework without modification.

Snowflake Horizon: Govern Your Enterprise Data 

Snowflake Horizon delivers a unified set of compliance, security, privacy, interoperability and access capabilities built into the AI Data Cloud. With Snowflake Horizon, data governors and stewards can provide the right people with access to the right content, while safeguarding sensitive and/or personally identifiable information. Accelerate data-driven growth with advanced features that data teams and others can use to search, discover, access and share data, apps and models — including data assets from inside and outside the organization.