Steps to Implement Data Governance

Video Summary:

In this lesson, we explored the traditional and modern approaches to data governance frameworks, analyzing how to implement data governance effectively.

Traditional Approaches: Top-Down vs. Bottom-Up

• Top-Down Approach: The top-down method is a centralized approach to data governance that prioritizes data control. A small group of data professionals manage data quality, modeling, and governance before making it available to the broader organization for analytics. While it enforces high-quality control, it leads to scalability issues because it restricts control to a few data providers and limits flexibility for end-users.
• Bottom-Up Approach: The bottom-up method prioritizes data access and flexibility. It begins with raw data ingestion and allows the structure to be created afterward. This approach is more scalable and supports greater agility, especially with big data, but can lead to regulatory risks and higher management costs due to uncontrolled data access.

Modern Approach to Data Governance

The modern approach to data governance combines the best of both methods, balancing data control and access. It focuses on team collaboration and breaking down data silos to ensure that data is trustworthy and scalable. In this model, subject matter experts in each business unit become data stewards, maintaining high data quality for specific data sets. This strategy promotes both flexibility and control, making the governance process adaptable to an increasing number of data sources.

Steps for Implementing Data Governance:

• Define goals and objectives.
• Obtain executive support: Gather user input.
• Establish a data governance council.
• Define roles and responsibilities within the governance framework.
• Develop a data governance framework.
• Implement governance tools.
• Create a training program for data governance.
• Monitor and measure the effectiveness of the governance program.
• Foster a data-driven culture across the organization.
• Continuously improve the data governance program.

Real-Life Application

Data governance frameworks are integral to businesses across various industries. For instance, companies like Facebook faced public scrutiny and regulatory fines due to poor governance and lax control over data access. In 2018, Facebook was fined $5 billion by the Federal Trade Commission (FTC) for failing to protect user privacy. This could have been mitigated with a better data governance structure balancing control and access.

In contrast, Google adopted a data governance council across its business units, implementing data steward roles to ensure scalability and data quality. This has allowed the company to maintain its vast database infrastructure while complying with privacy laws such as GDPR.

Mnemonic Reviewer:

• T-D-A-B-S-C-T-P:
  • T: Top-Down Method
  • D: Data Control
  • A: Access (Bottom-Up)
  • B: Balancing Control and Access (Modern Approach)
  • S: Scalability
  • C: Council (Data Governance Council)
  • T: Tools (Governance Tools)
  • P: Program (Training Program)

This lesson emphasizes that modern data governance is about balancing access and control while ensuring that data is secure, reliable, and scalable across an organization. By following these steps and evolving from traditional to modern frameworks, organizations can maintain data quality, comply with regulations, and stay competitive in the digital era.