You are previewing Getting in Front on Data.
O'Reilly logo
Getting in Front on Data

Book Description

This is the single best book ever written on data quality. Clear, concise, and actionable. We all want to leverage our data resources to drive growth, but we too often ignore the fundamentals of data quality, which almost always inhibits our success. Tom lays out a clear path for each organization to holistically improve not only its data quality, but more importantly the performance of its business as a whole.
--Jeffrey G. McMillan, Chief Analytics and Data Officer, Morgan Stanley

This book lays out the roles everyone, up and down the organization chart, can and must play to ensure that data is up to the demands of its use, in day-in, day-out work, decision-making, planning, and analytics.

By now, everyone knows that bad data extorts an enormous toll, adding huge (though often hidden) costs, and making it more difficult to make good decisions and leverage advanced analyses. While the problems are pervasive and insidious, they are also solvable! As Tom Redman, "the Data Doc," explains in Getting in Front on Data, the secret lies in getting the right people in the right roles to "get in front" of the management and social issues that lead to bad data in the first place.

Everyone should see himself or herself in this book. We are all both data customers and data creators--Getting in Front on Data proposes new roles for data professionals as:
  • embedded data managers, in helping data customers and creators complete their work,
  • DQ team leads, in connecting customers and creators, pulling the entire program together, and training people on their new roles,
  • data maestros, in providing deep expertise on the really tough problems,
  • chief data architects, in establishing common data definitions, and
  • technologists, in increasing scale and decreasing unit cost.
Data quality has always been important. But now, in the growing digital economy where business transactions and customer experiences are automated and tailored, data quality is critical. This book comes just in time.
--Maria C. Villar, Global Vice President, SAP America, Inc.

Winning, and more importantly thriving, in the digital age requires more than stating "Data is a strategic corporate asset." Leaders and organizations need a plan of action to make the new vision a reality. Tom's latest book is a how-to for those seeking that reality.
--Bob Palermo, Vice President, Performance Excellence, Shell Unconventionals

Many, if not most, companies still struggle with their data. With his latest offering, Tom Redman sets out a path they can follow to Get in Front on Data. Based on his decades of experience working with many companies and individuals, this is the most practical guide around. A must read for data professionals, and especially data "provocateurs".
--Ken Self, President IAIDQ

This book offers a unique perspective on how to think about data and address Data Quality - offering practical guidance and useful instruction from the perspective of each stakeholder. The process--and processes--to go from business need to having the right quality data to address that need is no small task.
--John Nicodemo, Global Leader, Data Quality, Dun & Bradstreet

Getting in Front on Data is a clearly written survival handbook for the new data-driven economy. It is a "must read" for the employees of any organization expecting to remain relevant and competitive. The "Data Doc" has an extraordinary talent for explaining key concepts with simple examples and understandable analogies making it accessible to everyone in their organization regardless of their role.
--John R. Talburt, Director of the Information Quality Graduate Program University of Arkansas at Little Rock

Table of Contents

  1. FOREWORD By Thomas H. Davenport
  2. ACKNOWLEDGEMENTS
  3. INTRODUCTION
  4. CHAPTER 1: Data Quality: Why, How, Who, and When?
    1. Why?
    2. How and Who?
    3. When?
    4. In Summary
  5. CHAPTER 2: What’s in it for me?
    1. Do I Have a Data Quality Problem?
    2. The Wrong Reaction in the Face of Bad Data
    3. Use the Rule of Ten to Estimate Costs
    4. Identify “Hard-to-Quantify” Costs of Special Importance
    5. Think Longer-Term
    6. So What’s in It for Me?
    7. In Summary
  6. CHAPTER 3: Data Customers Must Speak Up
    1. Recognize That You Are a Data Customer
    2. Communicate Your Needs
    3. Innovate and Encourage Innovation
    4. Actively Manage Both Internal and External Suppliers
    5. Make Your Hidden Data Factories Explicit and Efficient
    6. Build Organizational Capability
    7. In Summary
  7. CHAPTER 4, Part I: Must-Dos for All Data Creators
    1. Recognize You Are a Data Creator and That Your Work Impacts Others
    2. Focus on the Most Important Needs of the Most Important Customers
    3. Measure Quality Against Those Needs, in the Eyes of the Customers
    4. Find and Eliminate Root Causes of Error
    5. Establish Control
    6. Innovate, Innovate, Innovate
    7. In Summary
  8. CHAPTER 4, Part II: Process Management Advances Data Creation
    1. Manage Data Creation as a Process
    2. Clarify Managerial Responsibilities
    3. Extend the Voice of the Customer
    4. Look for Improvement Opportunities on the Interfaces Between Tasks/Steps
    5. Build Organizational Capabilities
    6. Employ Embedded Data Managers
    7. The Fundamental Organization Unit for Data Quality
    8. Special Instructions for Creating Common Data Definitions
    9. In Summary
  9. CHAPTER 5: Provocateurs Disrupt Organizational Momentum
    1. Provocateurs Look to Improve Their Current Work
    2. Provocateurs Dig Deeper
    3. Provocateurs Achieve a Real Result
    4. Provocateurs Are Not Rabble Rousers
    5. Provocateurs Have Courage and Judgment
    6. In Summary
  10. CHAPTER 6: Building a Data Quality Team
    1. Pay Special Attention to Proprietary Data
    2. Focus the Effort
    3. Engage Senior Management
    4. Connect Data Creators and Data Customers
    5. Provide Common Functions Where It Makes Sense
    6. Own the Data Definition Processes
    7. Actively Manage Change
    8. Build Small but Powerful Core Data Quality Teams
    9. In Summary
  11. CHAPTER 7: Essential Roles of Senior Management in Getting to the Next Level
    1. Understand the Business Case
    2. Put the Right People and Structure in Place
    3. Insist that the Organization Get in Front on Data Quality
    4. Engage Visibly
    5. In Summary
  12. CHAPTER 8: Great Data Quality Programs Need Great Tech
    1. Store, Move, and Deliver Data Safely and Securely
    2. Automate Data Translation as Data Moves From System to System
    3. Contribute to the DQ Effort
    4. Don’t Take Overall Responsibility for Data Quality
    5. Use Business Data Definitions in Systems Development
    6. Reduce Data Translation by Simplifying the Data Architecture
    7. Put in Place a Powerful Team to Support Data Quality
    8. In Summary
  13. CHAPTER 9: Data Quality in Practice
    1. Improving Access Bill Quality at AT&T
    2. Data Definitions That Stand the Test of Time at Aera Energy
    3. In Summary
  14. CHAPTER 10: Advancing Data Quality
  15. APPENDIX A: Special Instructions for Decision Makers and Data Scientists
  16. APPENDIX B: Special Instructions for Automated Measurement, Connected Devices, and the Internet of Things
  17. GLOSSARY
  18. INDEX