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Tuesday, January 17, 2006

METRIC MATTERS: Ensuring data quality

Data quality management is critical to a successful business, but it doesn't have to be difficult. In general there are three main steps organizations can follow in order to ensure data quality:
1. The first step is to gain a clear understanding of the data, including how it is captured, its intended uses, its structure and content quality. By carrying out an in-depth audit, the organization is able to identify common data defects such as missing, incomplete, inconsistent or inaccurate data.
2. The second step is to improve the data by filtering it to eliminate errors and resolve inconsistencies. Once this has taken place, there is a need to protect the data quality. It's vital that new data defects are prevented from infiltrating the system. Real-time defect prevention is the most pro-active way of protecting the standard of the data, and eliminating defects when new data is entered.
3. The final step is control. Data quality management cannot be just a one-off quick-fix solution. In order to achieve optimum results, data quality management needs to become a controlled and integral part of day-to-day business processes. Clear performance measurements need to be devised to ensure visibility, as well as compliance with regulatory standards.

Excerpted from "Ensuring the quality of information" by Laurie Mascott, in the current issue of KM Review.

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