CDI: Harnessing the Value of Enterprise Data Part 2


Published in October 2004


The importance of data quality has undergone a major transformation over the last few years. Not long ago, it seems there were just a few proponents of this topic trying to convince a reluctant
industry that high quality customer data was a valuable corporate asset. Now when I speak at conferences, C-level executives assure me that data quality is a top priority at their organizations.

But in practice, the question remains: Is the message of the importance of data quality really reaching the executive suites and boardrooms? And as a pragmatic measurement of commitment, is the
apparent value of data quality reflected in budgets and staff?

In this article, I will review the current status of data quality’s perceived value, provide a questionnaire for determining your organization’s level of commitment to data quality, and offer
some suggestions for how to achieve C-level and boardroom buy-in.

Perception vs. Reality

The last few years have seen a dramatic increase in the data management industry’s coverage of the importance of data quality to Blue Chip and other major organizations. Analysts and trade
publications have publicized how poor data quality has contributed to disastrous CRM projects and expensive enterprise application implementations that failed to deliver the ROI and cost savings
they promised.

The impact of poor data quality on marketing expenditures has been well documented by industry analysts, data quality software providers, consultants, and industry associations. In its
well-received report on data quality, The Data Warehousing Institute (TDWI) estimated that poor data quality costs U.S. businesses approximately $600 billion a year in direct costs, and estimated
even higher costs due to missed opportunities. Similarly, research by META Group indicates that 90% of all business decisions are sub-optimal because of one or more data quality issues.

The message that finally seems to be making its way to executive suites is that poor data quality not only interferes with major, mission-critical enterprise initiatives, but it also drains
marketing budgets and leads to misinformed strategic decision-making. This would seem to be a compelling enough message to justify C-level commitment to funding enterprise-level data quality
initiatives. But despite solid evidence of the business value that data quality delivers and recognition by leading executives that it is essential to the success of their businesses, there remains
a disconnect between recognition and allocation of budget for enterprise data quality initiatives.

A few years back, Pricewatehouse Coopers (PWC) conducted a global data management survey that produced some very interesting results. Essentially, the study found many inconsistencies between word
and action. For instance, it concluded that although executives did agree that data quality was critical to their businesses, most companies were not discussing data management at the board level
and most did not consider measuring the value of data to be important. If you can’t measure it, how can you manage it?

We’ve seen similar trends among our own clients. A major UK financial services organization made a substantial investment and implemented many of the essential requirements to ensure an enterprise
cultural commitment to data quality, including an executive-level sponsor, a dedicated staff, and regularly-scheduled data quality audits. As a result of these steps, they achieved an exceptionally
high level of data quality. But when business slowed and money tightened, data quality was one of the first victims of severe budget cuts.

Another company invested $15 million in an enterprise CRM application, but the systems integrator failed to include any budget for data quality management. The additional cost to dramatically
improve the data quality and de-risk the data migration would have added only another $250,000 to $500,000 to the overall cost, but the integrator worried that it may delay the project start. The
integrator felt that the additional investment required for ensuring the quality of the new application’s data was not an important enough factor in the overall success of the project to justify a
delay, or at least not critical to the short-term project goals and deadlines for which they were responsible.

These are just a few examples of the inconsistencies that surround data quality. Even though there is an increasing awareness among business and technology executives alike that accurate data is
the necessary foundation for their corporate business strategies, it appears that the adoption of fundamental data quality practices on an enterprise level with true executive buy-in has not yet
reached broad enough adoption.

Bottom-line, even if the executives of your organization state that data quality is a corporate priority, if that commitment is not currently reflected in staff and budget, you will most likely
have to fight for it.

The Critical Factors in Ensuring C-Level Commitment to Data Quality

From my company’s experience working with a wide variety of clients worldwide, we have identified four factors that are critical to ensuring an enterprise cultural commitment to data quality. They

  • C-level sponsor for the data quality function
  • Dedicated data quality staff
  • Separate data quality budget
  • Regularly scheduled data quality audits – both internal and external

Without all these factors in place, the integrity of your organization’s data quality function is at the mercy of business fluctuations and management’s whims. And it is much too valuable a
corporate asset to be put at such risk.

How Important Is Data Quality to Your Organization?

Following is a brief questionnaire designed to help you determine whether or not your organization truly has a C-level commitment to data quality. Please take a few moments and see how you measure

1. Does data quality have executive-level sponsorship?

  1. Board level
  2. Other
  3. None

2. Do you have established accuracy definitions and thresholds?

  1. Enterprise basis
  2. Project basis
  3. None

3. Do you conduct regular data quality audits?

  1. Internal & external
  2. Internal only
  3. None

4. Do you have common data entry standards?

  1. Across enterprise
  2. Within business line
  3. None

5. Is data quality awareness part of new staff induction?

  1. All staff
  2. ‘Relevant’ staff only
  3. None

6. Do you have a dedicated data quality staff?

  1. Team
  2. An individual
  3. None

7. How is your data quality budget handled?

  1. Separate major budget line
  2. Separate line in each project
  3. Ad hoc funded from project


A = 3 points
B = 2 points
C = 1 point

Total __

How Does Your Organization’s Data Quality Commitment Stack Up?

If you scored 17 or higher, congratulations! You’re among the leaders in taking advantage of the strategic resource that your customer data represents.

If you scored between 10 and 16, your organization has taken important steps towards protecting the value of your customer data, but there remains some more work to be done. Your organization’s
marketing initiatives and strategic decision-making are probably at risk due to faulty data.

If you scored below 10, you should consider strategies for achieving executive buy-in to the importance of data quality. Without a significant change in how your organization views and manages its
customer data, you risk losing ground – and customers – to competitors.

What You Can Do To Achieve C-Level Buy-In

The truth is that although good data quality does cost money, it is probably not as much as poor data quality will cost your organization over time. To achieve a sustained cultural commitment to
data quality, you have to be able to justify the investment in real financial terms:

  • Hard ROI
  • Cost savings
  • Reduction in operational risk

As evidenced by the findings of industry research, data quality’s contributions to these areas are real and can be demonstrated. The key is to set up cost vs. revenue/savings studies to justify
the cost of data quality. By connecting the definition of quality and measurement scale to how the data is used, i.e., whether it’s for call center, marketing, risk management or management
information, you can identify or estimate the value of certain customer-based events, such as:

  • The cost of loyal customers who are lost because your call center didn’t recognize them or didn’t have the right information to properly service them
  • The cost of sending duplicate mailings to the same customer or household, including production and postage – and multiplying that over years of multiple mailings
  • The money that can be saved by preventing the over-extension of credit to a customer who deals with several different departments of your organization or makes purchases under different aliases
  • The value of accurate customer information in making real-time pricing decisions for customers who expect one-to-one personalization
  • The value of protecting your corporate brand and avoiding costly fines by preventing compliance violations that result from conducting business with a person or business – or their respective
    aliases – that appear on one or more government sanction lists

The results of studies like these will give you the hard numbers that you need to demonstrate to executive management that data quality is truly a strategic corporate resource that deserves their
time and investment.

A Battle Worth Fighting

Achieving a corporate commitment to data quality is an objective that’s worth your effort. It can make a real difference to the future success and viability of your organization. There are plenty
of success stories in the marketplace that share how companies worldwide have realized significant benefits – either savings, process improvements, or increased sales – as a direct result of their
commitment to data quality.

Data quality is not a new phenomena – our company has been providing data quality solutions for over 35 years! But application environments of today pose new challenges for ensuring highly accurate
data throughout the enterprise. With increasing numbers of operational and analytical systems, increasing volumes of data, data moving bi-directionally, customers entering data online, and so
forth, data quality issues are no longer local to a system or business group; they are global to the enterprise. Consequently, those organizations that believe data quality is important and treat
customer data as a strategic and enterprise resource will reap the benefits.

What’s Next …

Now that we’ve set the necessary foundation for effective customer data integration by outlining the components of a closed-loop data management environment and discussing the critical importance
of establishing corporate commitment to enterprise data quality, we should next address the often-confusing process of selecting the right data quality solution.

Increasing awareness of data quality’s importance has led to an increasingly complex and costly data quality software selection process. The next article in this series will discuss the need to
focus on process expertise and best-of-breed software implementations, as well as software functionality, when evaluating data quality solutions. It will identify key questions that should be asked
of any prospective software provider.

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Jeffrey Canter

Jeffrey Canter

R. Jeffrey Canter is EVP, Global Marketing and Operations, at Innovative Systems, Inc. He oversees research and development, product management, global marketing and communications, and client service and support.  Since joining Innovative in 1990, Canter has applied his business and technical expertise to the successful development of customer information projects for clients in a variety of industries, including financial services, hospitality and telecommunications.  Prior to his current position, he served as senior consultant and director of R&D for the company.  Canter is a regular speaker and author on topics related to managing and integrating customer data.

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