The Data Asset E-Book

Tony Fisher

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: SAS Institute Inc
• Sprache: Englisch

Seitenzahl: 324

Veröffentlichungsjahr: 2009

Beschreibung

An indispensable guide that shows companies how to treat data as a strategic asset

Organizations set their business strategy and direction based on information that is available to executives. The Data Asset provides guidance for not only building the business case for data quality and data governance, but also for developing methodologies and processes that will enable your organization to better treat its data as a strategic asset.

Part of Wiley's SAS Business Series, this book looks at Business Case Building; Maturity Model and Organization Capabilities; 7-Step Programmatic Approach for Success; and Technologies Required for Effective Data Quality and Data Governance and, within these areas, covers

• Risk mitigation
• Cost control
• Revenue optimization
• Undisciplined and reactive organizations
• Proactive organizations
• Analysis, improvement, and control technology

Whether you're a business manager or an IT professional, The Data Asset reveals the methodology and technology needed to approach successful data quality and data governance initiatives on an enterprise scale.

Leseprobe

Table of Contents

Wiley & SAS Business Series

Title Page

Copyright Page

Foreword

Acknowledgements

Introduction

PART ONE - Building the Business Case for Data Governance

CHAPTER 1 - Making the Case for Better Data

BUILDING THE BUSINESS CASE

RISK MITIGATION : ONE VERSION OF THE TRUTH HELPS RETAIL BANKERS MANAGE RISK

COST CONTROL: MANUFACTURING COSTS RISE WHEN DATA ISN’T INTEGRATED

REVENUE OPTIMIZATION: HOW A LEADING RETAILER REVOLUTIONIZED INVENTORY ...

INTEGRATED, QUALITY DATA MEANS BETTER BUSINESS

INFORMATION FOR EVERY MEMBER OF THE EXECUTIVE TEAM

CHAPTER 2 - Risk Mitigation: How Quality Data Keeps Your Company Out of Trouble

MANAGING AND MITIGATING RISK EFFECTIVELY

ACHIEVING TRANSPARENCY WITH STRONG DATA GOVERNANCE

BEYOND GOVERNMENT COMPLIANCE: PROACTIVELY AVOIDING RISK

FIGHTING FRAUD WITH ACCURATE DATA

REDUCING THE RISK IN MERGERS AND ACQUISITIONS

CHAPTER 3 - Controlling Costs with Accurate and Reliable Data

HOW ACCURATE DATA PLAYS A ROLE IN CONTROLLING COSTS

MANAGING YOUR SUPPLY CHAIN BY UNDERSTANDING WHAT YOU ARE BUYING

USING CLEAN DATA TO BETTER CONTROL INVENTORY COSTS

WHY REDUCINGYOUR APPLICATIONS WILL NOT MAKE YOUR DATA PROBLEMS GO AWAY

PUTTING DATA AT THE FOREFRONT OF YOUR COST CONTAINMENT EFFORTS

CHAPTER 4 - Optimizing Revenue with Quality Data

SMALL FIXES EQUAL BIG REWARDS

ACHIEVING A 360° VIEW OF THE CUSTOMER

BETTING YOUR BUSINESS ON SOUND DATA

OPTIMIZING REVENUE THROUGH EFFICIENCY

MAKING REVENUE MANAGEMENT FUNCTION EFFECTIVELY

SO MANY SOLUTIONS, SO MUCH POTENTIAL FOR FAILURE

PART TWO - The Data Governance Maturity Model

CHAPTER 5 - Governing Your Data

THE BUSINESS OF DATA QUALITY

IMPACT OF HIGH QUALITY DATA

REACHING BEYOND IT: WHY BUSINESS USERS MUST BE INVOLVED

CHAPTER 6 - Undisciplined Organizations: Disasters Waiting to Happen

WHEN NO ONE IS ON THE SAME PAGE

NON EXISTENT TECHNOLOGIES AND PROCESSES

MAKING THE MOVE TO THE NEXT LEVEL

CHAPTER 7 - Reactive Organizations: Trying to Get Beyond Crisis Mode

DEFINING THE REACTIVE ORGANIZATION

MAKING THE MOVE TO THE PROACTIVE STAGE

CHAPTER 8 - Proactive Organizations: Reducing Risk, Avoiding Uncertainty

WHEN DATA GOVERNANCE BECOMES IMPORTANT

MASTER DATA MANAGEMENT AND SERVICE-ORIENTED ARCHITECTURE’S ROLE IN DATA GOVERNANCE

THE CUSTOMER PARADOX: WHY BUSINESS RULES ARE IMPORTANT

ADVANCING TO THE NEXT STAGE

CHAPTER 9 - Governed Organizations: Trust in Data Pays Multiple Benefits

PROFILE OF A GOVERNED ORGANIZATION

THE IMPORTANT PEOPLE OF DATA GOVERNANCE

WHAT DOES A GOVERNED ORGANIZATION LOOK LIKE?

PART THREE - Utilizing People and Processes to Achieve a Quality Culture

CHAPTER 10 - The Quality Culture

QUALITY AND DATA: MORE THAN JUST GETTING THE NUMBERS RIGHT

WHAT IS THE QUALITY CULTURE?

WHY QUALITY SHOULD BE FORWARD-THINKING

QUALITY CULTURE: ROADMAP TO DATA GOVERNANCE

CHAPTER 11 - People

EXECUTIVE SPONSORSHIP

THE RIGHT TEAM

A LOOK AT THE KEY PLAYERS

EXECUTIVE SPONSORS: IMPROVEMENT STARTS AT THE TOP

STAKEHOLDERS: WHERE THE RUBBER MEETS THE ROAD

BUSINESS EXPERTS: STEERING FOR SUCCESS

DATA STEWARDS : THE RENAISSANCE MEN AND WOMEN OF YOUR ORGANIZATION

IT EXPERTS: MAKING IT ALL WORK

CHAPTER 12 - Processes

DISCOVER

DESIGN

ENABLE

MAINTAIN

ARCHIVE

PART FOUR - Closing the Loop: Selecting the Right Technology for Your Organization

CHAPTER 13 - Undisciplined Organizations: Discovering the Value of Data ...

THE RAPID PACE OF TECHNOLOGY

COPING AND THRIVING AS AN UNDISCIPLINED ORGANIZATION

DATA PROFILING

DATA QUALITY

IDENTITY RESOLUTION

CHAPTER 14 - Reactive Organizations: Choose the Technology That Gets the Most ...

METADATA ANALYSIS

BUSINESS RULES

DATA RECONCILIATION

FINDING DUPLICATE CUSTOMER RECORDS

CHAPTER 15 - Proactive Organizations: Bridging the Chasm and Becoming Proactive

MASTER DATA MANAGEMENT: THE FOUNDATION OF THE PROACTIVE ORGANIZATION

SERVICE-ORIENTED ARCHITECTURE AND BUSINESS DATA SERVICES

CHAPTER 16 - Governed Organizations: Moving Beyond Data to Business Process Automation

BUSINESS BENEFITS OF BECOMING A GOVERNED ORGANIZATION

THE PATH TO GOVERNED

USING TECHNOLOGY TO MOVE ALONG THE GOVERNANCE PATH

CONCLUSION

Glossary

Index

Wiley & SAS Business Series

The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.

Titles in the Wiley and SAS Business Series include:

Business Intelligence Competency Centers: A Team Approach to Maximizing Competitive Advantage, by Gloria J. Miller, Dagmar Brautigam, and Stefanie Gerlach

Case Studies in Performance Management: A Guide from the Experts, by Tony C. Adkins

CIO Best Practices: Enabling Strategic Value with Information Technology, by Joe Stenzel

Credit Risk Assessment: The New Lending System for Borrowers, Lenders, and Investors, by Clark Abrahams and Mingyuan Zhang

Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring, by Naeem Siddiqi

Customer Data Integration: Reaching a Single Version of the Truth, by Jill Dyché and Evan Levy

Enterprise Risk Management: A Methodology for Achieving Strategic Objectives, by Gregory Monahan

Fair Lending Compliance: Intelligence and Implications for Credit Risk Management, by Clark R. Abrahams and Mingyuan Zhang

Information Revolution: Using the Information Evolution Model to Grow Your Business, by Jim Davis, Gloria J. Miller, and Allan Russell

Marketing Automation: Practical Steps to More Effective Direct Marketing, by Jeff LeSueur

Performance Management: Finding the Missing Pieces (to Close the Intelligence Gap), by Gary Cokins

Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics, by Gary Cokins

For more information on any of the above titles, please visit www.wiley.com.

Copyright © 2009 by SAS Institute, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400, fax [phone], or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, 201-748-6011, fax [phone], or online at http://www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

For general information on our other products and services, or technical support, please contact our Customer Care Department within the United States at 800-762-2974, outside the United States at 317- 572-3993 or fax [phone].

Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books.

For more information about Wiley products, visit our Web site at http://www.wiley.com.

Library of Congress Cataloging-in-Publication Data:

Fisher, Tony (Anthony), 1958-

The data asset : govern your data for business success / Tony Fisher. p. cm.—(Wiley & SAS business series)

Includes index.

eISBN : 978-0-470-50802-2

1. Database management. 2. Business intelligence. 3. Corporate governance. I. Title. QA76.9.D3F.74—dc22

2009010884

Fore! word

No, this is not a book about golf, but knowing the author as I do, I can confidently state that golf and data quality are two driving forces of his life. Data quality is a great deal like golf—only the truly experienced understand the complexity. Fortunately for all of us, Tony Fisher has unparalleled experience that he is willing to share.

Unfortunately, in some organizations, data quality and data governance seem to have taken a back seat to enterprise projects involving business intelligence and data warehousing. One of the main reasons for this situation is that data governance requires coordination and communication between the business and IT. That has never been easy, but organizations are increasingly recognizing the importance of this kind of cooperation. Additionally, they are now realizing that data management involves more than entering data and running reports. It requires enterprisewide data management or, more precisely, data governance. They are also becoming keenly aware of the ramifications of poor data quality: dissatisfied customers, confused employees, unhappy stakeholders, and poor bottom-line results.

There was a time—prior to the information explosion—when organizations were easily able to identify any anomalies in their data and adjust accordingly. Data was not considered a corporate asset. It was just one part of running the business, and most executives did not realize—and certainly were not concerned about—data quality.

Today there is a completely different situation. The amount of data each company produces has skyrocketed and shows no signs of decreasing. Coupled with pressures to outperform the competition, comply with a multitude of regulations, and, of course, improve the bottom line, this situation demands that every organization take steps to realize the full value of the data they have. Without data quality and data governance, this cannot be accomplished.

Due to the number of sources, the vast amounts of data and the multiplicity of data types, information quality must be automated. It can no longer be done manually. More importantly, attention to information quality is an ongoing process, and it has to be an integral and continuous part of the entire data governance process.

But let’s get back to golf. Playing a good game of golf doesn’t just happen. In order to play golf, you need to know the rules of the game. In this book, Tony Fisher outlines the rules of data governance, explaining how to improve data quality and how to plan and implement an effective data governance program.

Two of the key aspects of golf are the course where the game is played and the equipment that is used. Both can either positively or negatively affect the outcome of the game, just as the quality of an organization’s data can improve its performance or, if the data quality is poor, result in increased operational costs, poor decisions, and reconciliation problems.

Great golfers appreciate great golf courses. Creating courses that are not only scenic but challenging has been accomplished by notable golf course architects such as A.W. Tillinghast, Donald Ross, and Robert Trent Jones. When playing on a challenging course, the value of a knowledgeable caddy is immeasurable. Filled with the knowledge gleaned from experience, this book is the caddy for your data governance course.

A good golfer also appreciates playing on a course where the greens are well maintained. Putting on a well-maintained green is like putting on carpet because there are no inconsistencies, and the ball runs true. By contrast, putting on greens that are not maintained very well may increase your score (not a good thing in golf) by 6 to 10 strokes. When a corporation does not treat data as an asset (poorly maintained greens), the ball does not run true (inaccurate data produces incorrect and misleading results), and the corporation suffers increased inconsistencies, errors, and even compliance failure.

Golf equipment—drivers, wedges, putters, gloves, shoes—definitely makes a difference in each golfer’s game. A stick with a rock tied to the end does not send a drive down the fairway like a Callaway FT-iQ Tour Driver. Other tools used by every self-respecting golfer include subscriptions to several online or print golf periodicals that provide helpful advice on the latest innovations in golf equipment and methodologies for lowering one’s handicap. Similarly, to make a difference in their corporations, executives need to become knowledgeable about the methodologies and best practices (the equipment) for data quality and data governance.

As Tony points out in this book, the three major benefits to improving an organization’s data are risk mitigation, cost control, and revenue optimization. These benefits are clearly described using real-world examples. This book doesn’t present theoretical concepts, but actual problems and solutions from companies you’ll surely recognize.

Tony Fisher has definitely scored a hole in one that will benefit all those on the course in their quest for data quality and effective data governance.

Ron Powell Cofounder and Editorial Director BeyeNETWORK

Acknowledgments

Author Barbara Grizzuti Harrison said, “There are no original ideas. There are only original people.” In writing this book, it has become clear to me just how true these words are. Fortunately for me, and for you as the reader, I have had the privilege of working with and learning from a lot of original people. This book is not necessarily filled with new ideas as much as it is a compilation of ideas—from many people—combined to provide a practical approach to how to improve your business by improving your data. The individuals listed here shared ideas and experiences, and I am indebted to each one of them. Given that there are no original ideas just original people, the most important element becomes who you talk to. I’d like to acknowledge some of the folks who have helped me formulate ideas.

My many colleagues and experts in the data management community have provided a wealth of ideas and inspiration. These friends have been practitioners in helping organizations understand the value that data can bring. They have shared thoughts and information with me and with data professionals around the world. There is a very long list of industry experts, but these select few are ones that, time and time again, provide the rest of us with valuable insight. A special thanks to Jill Dyché and Evan Levy of Baseline Consulting. Few people have the business acumen, technology skills, and just plain common sense that Jill and Evan possess. Special thanks go out to these two good friends for continually providing fresh perspective and improving on the way things are done.

There are many more experts in the field that, both explicitly and implicitly, provided ideas and concepts that have been developed in this book. Fortunately, there are a lot of individuals that provide benefit to the rest of us based on their years of experience, and any attempt to list them will undoubtedly lead to omissions. My apologies to those that provided input that I have not listed here. I did, however, borrow ideas and concepts from Gwen Thomas of The Data Governance Institute, Mike Ferguson of Intelligent Business Strategies, and David Loshin of Knowledge Integrity. I would like to thank these three for the input they have provided me and the input they provide all of us endeavoring to improve our data.

I am thrilled that my good friend Ron Powell was gracious enough to write the foreword for this book. I have known Ron for years, and during that time I have been fortunate to benefit from the many colleagues he has introduced me to. Ron knows so many people that have helped me formulate the concepts in this book, and he has always been instrumental in providing introductions and advice through the years of our relationship.

So, there are no original ideas. There is, however, occasional inspiration. That brings up another of my favorite quotes, this one from Thomas Edison. Edison said, “Genius is one percent inspiration, ninety-nine percent perspiration.” That turns out to be true for writing a book as well. Fortunately, the perspiration was a burden shared by a number of my colleagues at DataFlux. I would like to thank all my colleagues at DataFlux for the years of collaboration that have led to the techniques found in this book. Special thanks to Lucia Riley and Daniel Teachey for the continuous suggestions about how to improve the book and make the book more appealing for readers. Scott Batchelor provided a great service in suggesting content and making sure the book was transformed from mere words to the finished product you hold in your hands now. Thanks also to Gail Baker for her outstanding work with our customers to receive permission to showcase their compelling stories in the book.

Catherine L. Traugot provided copious energy into the writing of this book. Her experience and advice from other similar engagements were essential in moving this book from a theoretical exercise to a practical project. Many thanks to her for the months of advice and guidance.

Amongst all of my friends and colleagues at DataFlux, Katie Fabiszak deserves special recognition. Katie is primarily responsible for the idea of writing this book, and she provided considerable input to the content as well as motivation to see the book through to completion. Katie’s most beneficial contributions to this book are her knowledge of the subject matter and her uncanny ability to take difficult concepts and break them down into manageable, coherent thoughts.

There is one last group of people to whom I owe special thanks—the biggest thanks go to my family. Although they have yet to fully understand why I would spend time writing a book about data management, I would really like to thank Linda, Andy, and Laura for their patience as I worked long hours on this project. My daughter Laura even asked to read the book (what better endorsement is there?). I hope you get as much enjoyment out of reading this book as I did in writing it.

Introduction

Over the past couple of decades, I’ve been a real advocate of encouraging organizations to understand the potential value of their data. At times, it has been frustrating to try to convince organizations that data can be the difference between business success and business failure. More recently, though, the value of data has begun to be better understood and more effectively utilized. If we look back 20 years ago, we were producers of data. For example, we used data for taking and processing orders. And, we produced copious amounts of transaction data. Companies spent a great deal of time inputting data, but very few resources were allocated to doing anything constructive with that data. As a result, the data largely sat unused after a transaction was completed. Data was a necessary part of doing business, but was not being utilized to its full potential.

As technologies and products began to emerge that could facilitate faster data entry, more and more organizations began to view data as a key piece of the business that could be leveraged to improve operations—through sales or cost-reduction or inventory management. But they still lacked the tools to see the data as more than fields in a database. It was as if the data that drove and supported their companies was stored inside a glass case—untouchable and out of reach.

Today, all companies have data. It is an integral part of day-to-day operations. Yet few companies treat data as a strategic asset. It reminds me of the seafaring poem by Samuel Taylor Coleridge:

Day after day, day after day, We stuck, nor breath nor motion; As idle as a painted ship Upon a painted ocean. Water, water, everywhere, And all the boards did shrink; Water, water, everywhere, Nor any drop to drink.

—“The Rime of the Ancient Mariner”

While Coleridge most certainly did not have data management in mind when he penned his masterpiece, many companies are in the same boat as the mariner in the poem—stuck in idle day after day, surrounded by data, with no idea how to utilize it to improve their companies.

The pressures on organizations today are ever-increasing: pressures to comply with regulatory and industry standards, pressures to achieve profitability and meet shareholder expectations, pressures to compete in an uncertain and constantly changing economy. To be able to combat these pressures, organizations must rely on consistent, accurate, and reliable data to govern their businesses, regardless of their industries.

In this book, I will use three terms over and over: data quality, data governance, and data management. Data quality examines whether an organization’s data is reliable, consistent, up to date, free of duplication, and fit for its purposes. Data governance encompasses the process created to maintain high standards of data quality across the enterprise. Data governance addresses how data enters the organization and who is accountable for it. Using people, process, and technology, your data achieves a quality standard that allows for complete transparency within your organization. Data management refers to a consistent methodology that ensures the deployment of timely and trusted data across the organization.

The demand for data quality and data governance to support critical business initiatives is skyrocketing—and with it the confusion. Executives and shareholders are beginning to realize that data is a strategic asset—and with that, there are mandates issued to ensure that proper data management practices are put in place. Without a sound data strategy and roadmap, even the most experienced executives can lose their way. Organizations often develop business strategies and set directions based on information that is available to executives, but—as I will discuss in this book—that information is often wrong or hopelessly out of date. From the threat of fines for not identifying and reporting terrorist financing to millions of dollars lost because customer data is riddled with errors and duplications, organizations risk not only money but their reputation when they make decisions based on data that cannot be trusted.

Now, tabulate your score and find the appropriate category below to see if your company is ready for a data governance program.

2-3 points: Your company is ready and prepared. Chances are that you have already seen the impact that good data can have on your organization. You are making important data decisions across the enterprise, but may still need some help achieving all your data goals. To maximize the effectiveness of this book, you may want to focus on the chapters discussing the stages of data governance maturity, to find out where your company falls and to make plans to take the next step.

• Undisciplined (Chapter 6)

• Reactive (Chapter 7)

• Proactive (Chapter 8)

• Governed (Chapter 9)

0-1 points: Even though you may not have all the pieces in place for a data governance program, you can easily identify the areas for improvement. With a few modifications and key personnel additions, you can quickly begin your data governance journey. Chapter 1 will show you how to build the business case for data governance. An effective program involves executive sponsorship, and a strong business case can help achieve this.

Less than zero points: If you fall into this category, don’t feel discouraged. As you will read later in this book, the majority of companies are here with you. All it means is that you have some work to do before you have a high level of data governance. But the fact you are reading this book means that you are interested in finding out how your data can become an asset to your company. You have to start somewhere, and following the instructions in this book is a great first step.

Internally, many organizations mistakenly view data, its accuracy, and its collection, as an “IT problem.” Past efforts to solve “IT problems” have often engulfed the organization in expensive, multiyear projects that have not seemed to pay the dividends promised, and the projects have frequently failed. Executives know they want trusted data; they just don’t know how to effectively reach that point. When they have been burned by approving expensive IT projects that never delivered the intended results and promised return on investment (ROI), executives can be reluctant to invest in additional programs.

In this book I will outline how to get your data to work for you without breaking the bank or scrapping your current solutions. I will first discuss the business case for ridding your organization of unreliable data and the opportunities that exist when you treat data as a strategic asset. Regardless of the industry that your company is in or the business issues that you face, I will tell you how managing your data is strategic to your goals.

Next I will build the case for creating a data quality and data governance framework. This framework will allow you to improve your data in incremental steps. This is important because too many organizations have been sold an application with a “this will solve all your problems” pitch. But your business is not static. New applications will emerge; existing ones will be improved; and old, legacy applications will be retired. All of these applications and solutions need to be viewed through the lens of trusted data. This book will help organizations determine their capacity for data governance by measuring their data maturity, and it will provide a methodical step-by-step program for successful data quality and data governance initiatives.

As I have worked with different organizations over the years, I have concluded that every company falls into one of four maturity stages, based on their IT and business practices. Organizations are either undisciplined, reactive, proactive, or governed with respect to the way they manage their data. I will provide an in-depth look at the technology adoption and business capabilities that are required at each stage to move an organization to the next stage. In the final part, I will lay out a methodology that encompasses the involvement of both business and IT professionals for collaborating on the establishment of data standards as well as the processes and technologies required for successful data quality and data governance.

Along the way, I will use real-world examples to illustrate how actual companies are using data quality and data governance strategies to better their businesses (an icon will be placed in the margins so that you can easily identify where these examples are). I have been fortunate to work with some amazing companies over the years. Undoubtedly, you are in a similar situation to many of them. They are good, solid companies, but have not been able to keep up with the vast amounts of data that reside within their organizations. Often, a small change in the way they approach data makes a significant difference in their ability to optimize revenue, manage costs, and mitigate risk.

Data is not “IT’s problem.” It is every employee’s problem. It is every executive’s problem. And seeking a way to constructively and economically address data issues is paramount to the success of your organization.

There are two mantras I repeat time and time again throughout this book. These are important truths to remember as you embark on your journey to data governance. First, data quality and data governance should never be considered a one-time project. A quality culture must be established, and it is an ongoing, continuous process. Second, no organization can tackle enterprise-wide data quality and data governance all at once. To be successful, your journey must be an evolutionary one. Start small and take achievable steps that can be measured along the way.

PART ONE

Building the Business Casefor Data Governance

CHAPTER 1

Making the Case for Better Data

The whole is more than the sum of its parts.

—ARISTOTLE (384-322 B.C.), PHILOSOPHER

EXECUTIVE OVERVIEW

One of the biggest mistakes that organizations make is to approach data as a technology asset. It is not. It is a corporate asset and needs to be treated and funded as a corporate asset. Justification for data management projects lies in the ability to create a business plan based on the benefit to an organization. Executives want to know how a data management initiative will enhance the business. To do this, any attempt to improve your organization must emphasize these benefits:

• Risk mitigation

• Revenue optimization

• Cost control

Building the business case is the first and most important step.

REMEMBER

1. Data quality and data governance should never be considered a one-time project. A quality culture must be established as an ongoing, continuous process.

2. No organization can tackle enterprisewide data quality and data governance all at once. To be successful, your journey must be evolutionary. Start small and take achievable steps that can be measured along the way.

Many organizations find that they cannot rely on the information that serves as the very foundation of their business. Unreliable data—whether about customers, products, or suppliers—hinders understanding and hurts the bottom line. It seems a rather simple concept: Better data leads to better decisions, which ultimately leads to better business. So why don’t executives take data quality and data governance more seriously? In my experience, this lack of attention to data severely and negatively impacts numerous organizations—some of which will be highlighted in this book. We all need to understand that we are seeing a shift in the way that we think about and treat data. Successful organizations are moving from a focus on producing data to a focus on consuming data.

For most organizations, this journey is just beginning. And for most organizations, this journey begins with education. Part of my reason for writing this book is to help organizations establish a solid data foundation as they embark on this journey.

This is what happens in organizations today. Data is typically somebody else’s problem—until something bad happens. The CEO of a plumbing manufacturer learned this the hard way a few years ago. One of his major manufacturing plants burned to the ground, and the CEO was eager to immediately inform customers of the situation. He asked for a list of products that were expected to be manufactured in the destroyed plant and for a list of customers that were expecting delivery.

This CEO, like any chief executive, undoubtedly believed that this information was a readily available corporate asset. In the era of business applications like enterprise resource planning (ERP), customer relationship management (CRM), and data warehouses, it should have been a simple request. It wasn’t. The finance department provided a list of everybody who had bought something, but that department didn’t know the product delivery schedule. The sales office knew who every customer was and what they had purchased, but not where the products would be manufactured. The manufacturing plant had a delivery list of what to produce, but not a full inventory of what was in the production pipeline.

Of course, the closest thing to what the CEO needed—the delivery list—was destroyed in the fire. Eventually, the IT department cobbled together an incomplete list and presented this to the CEO. Predictably, the CEO became frustrated (“How can you not know who our customers are?”). In the end, the CEO decided data wasn’t such a dull topic at all. It was integral to his business.

The CEO—and this entire organization—realized Aristotle’s message. The sum of the data in the individual systems did not accurately depict the whole of the business. Aristotle was one of the greatest of the ancient Greek philosophers and is still considered one of the most visionary thinkers of all time. As a pioneer in the field of study of metaphysics, Aristotle sought to develop a way of reasoning by which it would be possible to learn as much as possible about an entity.

While most discussions about data do not start with philosophical references, it is important to note that the crux of Aristotle’s philosophy is applicable to most enterprises. Exhaustive efforts at studying, cataloging, and accessing information led Aristotle to the observation that the whole is more than the sum of its parts. Like Aristotle’s quest to know and understand, data management is about learning everything there is to know about your organization—and more specifically, learning everything there is to know about the data that is required to run your organization.

The quality, accessibility, and usability of data have an impact on every organization, but the issue rarely captures the attention of executives. Mergers and acquisitions, creative marketing campaigns, and outsourcing are much hotter topics that can create the sales spikes or cost cutting that shareholders like to see.

Yet most of these high-profile initiatives fail or underperform if the data cannot be trusted. That creative marketing campaign may cost too much per sale if the customer list is riddled with redundant or inaccurate customer records. Buying another company to gain new customers is an expensive mistake if the purchased company turns out to share the same customer base. The cost savings of outsourcing are erased if the business cannot gather and measure customer complaints that emerge if the outsourced help desk isn’t doing its job. Inconsistent, inaccurate, and unreliable data has a huge impact on organizations. According to Gartner, a leading technology firm, “Through 2011, 75 percent of organizations will experience significantly reduced revenue growth potential and increased costs due to the failure to introduce data quality assurance and coordinate it with their data integration and metadata management strategies (0.7 probability).”1

High-quality, trusted data serves another purpose—one that executives wish they didn’t have to address. It keeps them out of trouble. Any financial services company must report potentially laundered money to a regulatory agency to avoid fines—or even jail time. An oil company needs to know which state-owned pipelines it uses to stay current with local regulations. Across the compliance arena, quality data can make the difference between spending money on fines or investing in the business.

New compliance regulations have illuminated a pressing need that has always been a critical part of running a successful business. Twenty-five years ago, it was common for a publicly-traded company to remain in the dark about profits and revenue until days before the quarter ended. Financial planning has now grown sophisticated enough that CEOs of publicly-traded companies are expected to project revenue and income and alert shareholders if the company is falling short. The quality of the data is critical—and more than one CEO has been shown the door when the company failed to get it right.

Even with the millions and billions of dollars invested in sophisticated information management systems and applications, CEOs are still getting hopelessly burned by incomplete, poorly managed, and inaccessible data. In early 2008, the French bank Sociéetée Géenéerale (SG) took $7 billion in losses after a rogue trader made unauthorized trades for many months—this loss represented almost all the profits SG had made in the past few years. The trader apparently covered his tracks by manipulating the way the company’s computer systems worked, but better data control and consistent monitoring would have uncovered the illegal trades—well before $7 billion evaporated.

Having money launderers as customers, overpaying for pipeline rights, rogue derivatives trading—these all seem to have very little in common. But there is one major commonality: These types of risk can all be minimized with better management of data.

Dwelling on the negatives is easy when it comes to data because disasters in data quality make the headlines. I have been on the phone with enough panicked executives to collect scare stories that could keep a CEO from ever sleeping again. But there is another side to data quality—how properly managed information turns to gold and creates the aha! moment that drives productivity and innovation. It does not always come with a precise return on investment (ROI)—since companies so often do not have a benchmark for how much errant data is costing them. The value of good data comes instead with what one business executive described as “leveraging maximum value from our investments.”

BUILDING THE BUSINESS CASE

In business today, it is impossible to get executive sponsorship or funding for any initiative without a clear and compelling business justification. How is spending this money going to help us increase revenue? How can this program improve the business? Can we afford to fund this initiative at this time? To make an investment in your data—and to ensure that it becomes a strategic corporate asset—you must first build the business case. The reason to better manage data is to improve your business. When it comes to building the business case, you have to document the potential benefits for your organization. As I have already indicated, there are three major benefits to improving your company’s data that are front-of-mind with executives in every organization: risk mitigation, cost control, and revenue optimization.

Risk mitigation is the most likely reason a company focuses on data quality, according to an Information Age survey of 279 companies.2 Almost one-third of companies said risk management (which encompasses compliance and regulatory issues) was a key driver of data quality (see Figure 1.1).

FIGURE 1.1Why do companies focus on data quality?

A few years ago, I worked with a company that had just completed a difficult and time-consuming acquisition. On the surface, the acquisition looked great. The two companies had some complementary products, but there was a fair amount of competitive products. The idea was to streamline the product offerings and reduce costs by combining redundant functions. Since the company that was acquired generated 60 percent as much revenue as the acquiring company, the merger would create a company with substantially more income. In reality, though, the results were not so satisfactory.

The reason for this underperformance was a lack of knowledge about the new company. One of the things the new parent company never discovered during the due diligence process was that almost half of the acquired company’s customers were already customers of the acquiring company. The amount of revenue that the merged company generated was substantially less than anticipated. This was a huge risk that could have been mitigated with better data management. By understanding who the customers really were, the new parent company would have been able to identify the scores of duplicate customers and would have had that information at the ready during the due diligence process.

According to 30 percent of the respondents in the Information Age survey, cost control is the second-most-likely reason companies look at data quality or data governance,. Properly managed data can help companies unearth numerous areas where money is leaking out of the organization. And, with a diligent data quality approach, you can deliver significant gains for your organization.

A global chemical manufacturer wanted to control costs when it purchased items. More than 600 people worldwide had purchasing authority, and they were inconsistent in the way they coded items at the time of purchase. These item codes were intended to provide a way to aggregate and sort products purchased, providing a better view of the organization’s spending habits. Unfortunately, the inconsistent product code entries did little to help with spend analysis. The company didn’t know what it was buying, nor did it have an understanding of what it was buying from its different suppliers. This prevented it from attaining any sort of bulk purchasing discounts. By incorporating product data rules, automating the classification, analyzing the results, and making changes to its purchasing process, the company now estimates that it will save up to 5 percent on its annual indirect spend of $3 billion. That’s a number that would excite any executive.