Data Governance Frameworks: Definition, Importance, and Examples

Data governance frameworks are structured approaches to managing and utilizing 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 be a liability if mishandled. Data governance frameworks have, therefore, become an invaluable way to protect the privacy of their customers 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 govern data effectively, organizations need to have a clear understanding of their data landscape. They need to know where their data comes from, who owns it, how it’s being used and where it’s stored. Gathering this information to build a data governance framework requires close collaboration between different departments and business units.

How data governance frameworks work

In data governance, it is not just about following the law or managing risk, it is about effectively harnessing the power of data to drive decision-making, innovation and competitive advantage.

The primary purpose of implementing a data governance framework is to come up with a systematic approach to managing data across its entire lifecycle. This means setting internal standards and policies, defining ownership and stewardship, and formulating processes for quality management, security, compliance and privacy. The end goal is to create an in-house culture where data is treated like a critical business asset.

Examples of data governance frameworks

Below is a list of some commonly referenced data governance frameworks:

• McKinsey.
• Eckerson.
• PwC enterprise data governance framework.
• DAMA DMBOK.
• The SAS data governance framework.
• The DGI data governance framework.

Each of these frameworks has its own pros and cons. Organizations should select the data governance framework that best aligns with their unique needs and goals.

Types of data governance frameworks

There are two opposing philosophies to creating data governance frameworks that offer different pros and cons depending on an organization’s specific objectives.

Bottom-up philosophy

The bottom-up approach to data governance, popularized by the growing big data movement, begins with raw data. Data is first ingested, then structures or schemas are built on top of the data once it has been read. Governance rules, policies and quality controls are also added to the dataset at this time.

The advantage of this approach is its scalability. However, it can be difficult to maintain consistent quality control across a large volume of data.

Top-down philosophy

In the top-down approach, data modeling and governance take priority and are the first steps in developing a data governance framework. The process begins with data professionals applying well-defined methodologies and best practices to data. The advantage of this approach is its focus on quality control.

Components of a data governance framework

There are four primary components of a data governance framework:

• Data stewardship: Data stewards ensure an organization’s data assets are accurate, consistent and compliant with all relevant regulations, especially over the course of company projects.
• Data quality management: Data quality management includes processes and procedures used to ensure an organization’s data assets are free from errors and inaccuracies, as well as 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 they establish the rules for how those assets will be shared with internal and external stakeholders.
• Technology 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 between stakeholders.

Pillars of data governance frameworks

Data governance frameworks are built on four key pillars that ensure the effective management and use of data across an organization. These pillars ensure data is accurate, can be effectively combined from different 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 decision-making processes is accurate, consistent and reliable. Further, data quality management involves establishing policies and procedures for data validation, data cleansing and data profiling.

SEE: Explore these top data quality tools and software.

2. Data integration

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

3. Data privacy and security

Data privacy and security are crucial in today’s digital age. This pillar involves the implementation of policies and procedures to protect sensitive data and comply with data protection laws and regulations. It includes data encryption, access control and data anonymization techniques.

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 they meet the needs of the organization. This includes the design of databases, data warehouses and data lakes.

Why is a data governance framework necessary?

We can identify three main reasons:

1. Keep a standard set of policies and procedures: Without such a framework, critical data assets are at risk of becoming fragmented, inaccurate and non-compliant with relevant regulations.
2. Prevent rudderless effort: A lack of governance can lead to confusion and duplication of effort, as different departments or individual users try to manage data with their own methods.
3. Regulatory compliance: A data governance framework ensures compliance with various laws and regulations from HIPAA to GDPR.

Best practices for creating a data governance framework

The first step to creating an effective data governance framework begins with a clear understanding of the organization’s goals and the role proper data management plays in achieving them.

It is also advisable to focus on simplicity and flexibility when developing a data governance framework. Many unnecessary rules can hinder adoption, while flexibility (without compromising security and compliance) ensures quick adaptation to a changing business or regulatory environment.

Involve stakeholders in a continuous dialogue to refine data governance practices when they become outdated. Finally, you must invest in the right data governance tools to optimize your operations.

SEE: For more detailed information, check out our guide on data governance best practices.