The Data Asset

How Smart Companies Govern Their Data for Business Success

Tony Fisher

• Publisher: Wiley
• Year: 2009
• Print ISBN:
  9780470462263
• Edition: 1
• eBook ISBN:
  9780470508022
• Topic: Business
• Subtopic: Business Strategy
• Index: Business

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.

PART ONE

Building the Business Case for 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 1.1 Why 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.

The third reason—and one I think companies have yet to address sufficiently—is revenue optimization. Only 14 percent of respondents in the Information Age survey ranked revenue opti...

Table of contents

• Wiley & SAS Business Series
• Title Page
• Copyright Page
• Foreword
• Acknowledgements
• Introduction
• PART ONE - Building the Business Case for Data Governance
• PART TWO - The Data Governance Maturity Model
• PART THREE - Utilizing People and Processes to Achieve a Quality Culture
• PART FOUR - Closing the Loop: Selecting the Right Technology for Your Organization
• Glossary
• Index