Data governance framework: definition, importance and examples

A data governance framework is a structured approach to managing and leveraging data in an organization. They include policies, procedures and standards that guide how data is collected, stored, managed and used. These frameworks help with data quality, data integration, data privacy and security, and effective data architecture.

Organizations are collecting and storing more data than ever before. This data can be used to improve business processes, but it can also become a burden if not handled properly. As a result, data governance frameworks have become a valuable way to protect customer privacy and comply with the latest privacy laws. However, organizations must implement a data governance framework that goes beyond basic data quality and management.

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To effectively manage data, organizations need a clear understanding of their data environment. They need to know where the data comes from, who owns it, how it is used and where it is stored. Gathering this information to build a data governance framework requires close collaboration between different departments and business units.

How a data governance framework works

In data governance, it’s not just about complying with laws or managing risks, but about effectively harnessing the power of data to drive decision-making, innovation and competitive advantage.

The main purpose of implementing a data governance framework is to come up with a systematic approach to managing the entire life cycle of data. This means developing internal standards and policies, defining ownership and management rights, and establishing quality management, security, compliance and privacy processes. The ultimate goal is to create an internal culture that views data as a critical business asset.

Data governance framework example

Here is a list of some commonly cited data governance frameworks:

Each of these frameworks has its own advantages and disadvantages. Organizations should choose the data governance framework that best suits their unique needs and goals.

Learn about the Eckerson data governance framework.Image: Exxon

Types of data governance frameworks

There are two opposing philosophies for creating a data governance framework, which offer different advantages and disadvantages depending on the specific goals of the organization.

bottom up philosophy

Popular as the big data movement continues to grow, a bottom-up approach to data governance starts with raw data. The data is first ingested and then a structure or schema is built on top of the data after reading it. At this point, governance rules, policies, and quality controls are also added to the data set.

The advantage of this approach is its scalability. However, maintaining consistent quality control over large amounts of data can be difficult.

top down philosophy

In a top-down approach, data modeling and governance takes precedence and is the first step in developing a data governance framework. The process starts with data professionals applying clearly defined methodologies and best practices to the data. The advantage of this method is its focus on quality control.

Components of a data governance framework

There are four main components of a data governance framework:

Data management: Data stewards ensure that an organization’s data assets are accurate, consistent, and compliant with all relevant regulations, especially during company projects.
Data quality management: Data quality management includes the processes and procedures used to ensure that an organization’s data assets are free of errors and inaccuracies, as well as the methods for identifying and correcting any errors or inaccuracies.
Data management: Data management processes define how an organization’s data assets are created, stored, accessed, and used, and establish the rules for how these assets are shared with internal and external stakeholders.
Technical infrastructure: This refers to the hardware and software systems used to collect, store and manage data. These include databases, enterprise resource planning systems, data warehouses and network connections that facilitate the exchange of information among stakeholders.

Pillars of a data governance framework

The data governance framework is built on four key pillars to ensure the effective management and use of data across the organization. These pillars ensure that data is accurate, can be effectively combined from disparate sources, is protected and used in compliance with laws and regulations, and is stored and managed in a way that meets the needs of the organization.

1. Data quality

Data quality is the cornerstone of any data governance framework. It ensures that the data used in the decision-making process is accurate, consistent and reliable. Additionally, data quality management involves establishing policies and procedures for data validation, data cleaning, and data analysis.

See: Explore these top data quality tools and software.

2. Data integration

Data integration involves using various tools to combine data from disparate sources to provide a unified view. This pillar ensures that data from various departments, business units or external partners can be effectively combined and used for analysis and decision-making.

3. Data Privacy and Security

Data privacy and security are critical in today’s digital age. This pillar involves implementing policies and procedures to protect sensitive data and complying with data protection laws and regulations. It includes data encryption, access control and data anonymization technologies.

4. Data architecture

The fourth pillar is data architecture, which refers to the design and structure of data systems. It involves the planning and design of data systems to ensure that they meet the needs of the organization. This includes the design of databases, data warehouses, and data lakes.

Why do you need a data governance framework?

We can identify three main reasons:

Maintain a standard set of policies and procedures: Without such a framework, critical data assets risk fragmentation, inaccuracies and non-compliance with relevant regulations.
Efforts to prevent rudderlessness: A lack of governance can lead to confusion and duplication of effort as different departments or individual users try to manage data in their own ways.
Regulatory Compliance: A data governance framework ensures compliance with laws and regulations ranging from HIPAA to GDPR.

Best practices for creating a data governance framework

The first step in creating an effective data governance framework is to have a clear understanding of the organization’s goals and the role that appropriate data management plays in achieving those goals.

When developing a data governance framework, it is also recommended to focus on simplicity and flexibility. Many unnecessary rules can hinder adoption, while flexibility (without compromising security and compliance) ensures rapid adaptation to changing business or regulatory environments.

Engage stakeholders in ongoing conversations to refine data governance practices as they become outdated. Finally, you must invest in the right data governance tools to optimize your operations.