The Data Governance Imperative

Chapter 1: The Inherent Need for Data Governance

The book begins by establishing the core problem that data governance is designed to solve. Sarsfield argues that most companies arrive at a data crisis organically through the “sins of the past,” where departmental data silos and incompatible systems from mergers and acquisitions make it nearly impossible to get reliable answers to basic business questions. The author makes a persuasive case that this chaos leads to decisions based on “gut feel” rather than solid metrics and introduces key business drivers for governance, including regulatory compliance and supply chain efficiency.

Chapter 2: A Holistic View of Data Governance

This chapter defines data governance as a business strategy, not merely an IT function. The author explains its value from multiple perspectives: for a CEO, it’s about efficiency and risk reduction; for a business user, it’s about trusting the data in their applications; and for IT, it’s about the technical management of data assets. Sarsfield emphasizes that a key benefit of a formal program is its ability to bridge the disconnect between business and IT, creating a common language and shared goals to improve data quality.

Chapter 3: Defining and Measuring Success

The book provides a framework for defining what a successful data governance program looks like. The author breaks down success factors into two categories. Generic success factors, which apply to any organization, include fixing data anomalies, developing repeatable processes, and establishing clear data ownership. Specific success factors are tailored to an organization’s unique goals, such as improving marketing effectiveness or meeting specific compliance mandates. This chapter provides the “how” for setting clear, measurable objectives.

Chapter 4: A Practical Guide to Getting Funded

This chapter offers actionable strategies for getting a data governance initiative off the ground. The author introduces the critical role of the “data champion”-a leader who can bridge business and IT and sell the vision to stakeholders. The book outlines several practical methods for securing funding, such as calculating Return on Investment (ROI), leveraging a recent crisis caused by bad data, and quantifying the ongoing cost of the “do nothing” option.

Chapter 5: Building the Data Governance Team

Sarsfield asserts that people, not technology, are the core of a successful program. This chapter details the essential roles required, emphasizing that these are roles, not necessarily full-time job titles. The key players include an Executive Sponsor, a Project Manager (the Data Champion), Business Stakeholders, and Data Stewards. The book provides a clear model for how these roles collaborate, often formalizing into a Data Governance Council as the program matures.

Chapter 6: Planning for Success: The Scorecard

This chapter focuses on the planning and communication essential for any data governance project. The author stresses the need for a clear mission statement and a tailored communication strategy. The centerpiece of the chapter is the Data Quality Scorecard, a practical tool for measuring and communicating the health of corporate data. The book presents a five-level aggregation model for metrics, providing a clear “how-to” for translating low-level technical profiling results into a high-level “Go/No-Go” view for executives.

Chapter 7: A Methodology for Fixing Your Data

The book outlines a six-phase methodology for executing a data-intensive project with governance baked in. The phases-Project Preparation, Making the Blueprint, Implement, Rollout Preparation, Go Live, and Maintain-provide a structured, repeatable process. This chapter serves as a practical project plan, guiding the reader from initial scope definition and data profiling to user acceptance testing and post-launch maintenance, ensuring that business users are involved at every critical stage.

Chapter 8: The Enabling Technologies

Sarsfield stresses that technology is a tool to support the people and processes of governance, not a solution in itself. This chapter provides a useful categorization of the relevant technologies. These include preventative tools (e.g., real-time address verification), diagnostic tools (with data profiling being the most critical), infrastructure platforms (like ETL and MDM), and enrichment services from third-party data providers.

Chapter 9: The Audacity of Data Governance

This concluding chapter serves as a motivational call to action. The author frames data governance as a choice between chaos and order. He argues that in a data-centric world, treating data as a formal corporate asset is an imperative for survival and success. A successful program requires the “audacity” to challenge the status quo and build a culture that values information quality.

Chapter 10: A Real-World Case Study: BT

The book concludes with a powerful case study of British Telecommunications (BT). Facing massive data challenges from decades of mergers, BT started a small data governance program that grew over ten years to save the company over a billion dollars. The key lessons from BT’s success-starting small with high-ROI projects, always measuring financial impact, and making data quality everyone’s responsibility-provide a compelling, real-world example of the book’s principles in action.