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April 22, 2008

Data Quality At Large

Filed under: Data Cleansing, Data Quality — Alena Semeshko @ 10:35 pm

What’s data quality for you? Right customer contact information in your CRM? Think again? Data quality is more than that, much more than that. Product numbers, associated descriptions, part numbers, units of measure, medical procedure codes and patient identification numbers, telephone numbers, email addresses, commodity codes, vendor numbers and vehicle identification numbers, the list goes on.

This article in CXO describes some consequesnes of poor data quality:

For the CEO, whose ultimate responsibility is to increase customer retention and loyalty, the effects of poor data can have long-term, devastating consequences. For example, the inability to eliminate redundant name and address records results in additional mail-order campaign costs. Recipients of duplicate mailings are also likely to become frustrated and question the firm’s overall operating efficiency. If these redundant mailings each consistently misspell the individual’s name or address, the frustration level is likely to approach alienation or even a legal concern – especially if the recipient had previously made a request to the mailer that they be removed from the vendor’s mailing list or asked to be placed on an industry-wide, do-not-mail list.Add to this the cost of the catalogs or merchandise delivered to the wrong address and the real magnitude of the problem only just begins to surface. If a single customer is included in a company’s database multiple times, each time with a different value for the customer identifier, the company will be unable to determine the true volume of this customer’s purchases. It could even be placed in the embarrassing situation of attempting to sell the customer an item that he or she has already purchased. Poor data quality can negatively influence how a company is perceived in the marketplace and damage brand equity.

These data inefficiencies can also result in missed up-sell and cross-sell opportunities. Without a single view of the customer across the enterprise, it’s impossible to aggregate information to make decisions. This makes it impossible to distinguish between single-product and multi-product buyers, or between new and existing customers

For the CFO – who is in charge of regulatory compliance, managing security risk and other methods of limiting exposure – poor data can result in the company facing public embarrassment, loss of credibility, significant fines and even lawsuits. A forward-thinking organization should include data quality as a part of its everyday operations. While this may not happen overnight, recent regulatory and Homeland Security initiatives such as the U.S. Department of Treasury’s Office of Foreign Assets Control (OFAC), Sarbanes-Oxley, the U.S. Patriot Act, and the Health Insurance Portability and Accountability Act (HIPAA) can quickly spur a company to establish a solid data foundation.


For the CIO, who spends his days striving to achieve peak operational efficiency, inferior data quality can lead to missed opportunities to negotiate better rates with suppliers. Large companies can have thousands, or even millions, of suppliers. Unless you have precise data on how much total business you are conducting with a single vendor across all divisions, you are likely to pay too much for their service.

So what do you do to improve? The article suggests the following:

First,  conduct a Data Quality Assessment to help you recognize the severity of data quality issues.

Second,  adopt a well-defined Data Governance Plan across your organization. That is, define who owns the data, who is authorized to access the data, and which specific standards should apply to the data.

Third, choose a technology to serve as the backbone for the intelligent use and preparation of relevant customer data.

Sounds short and sweet, but try following it through. Will take a while, but you won’t regret it.

1 Comment »

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    Comment by Modesto Langbein — March 23, 2011 @ 3:41 am

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