Data Quality Is Not Optional

by Larissa T. Moss

Everyone wishes that the quality of their data was pristine. And almost everybody agrees that their data is currently far from pristine -- or trustworthy -- and that something must be done about it. Some organizations hope to improve their data quality by moving their data from legacy systems to enterprise resource planning (ERP), customer relationship management (CRM), or data warehouse (DW) packages. Other organizations use data profiling or data cleansing tools to unearth their dirty data, then cleanse it with an extraction, transformation, and loading (ETL) tool for their BI applications. All these technology-oriented data quality improvement efforts are commendable and definitely steps in the right direction. However, technology solutions alone cannot eradicate the root causes of poor quality data. Other enterprise-wide disciplines must be developed, taught, and enforced in the organization to improve data quality in a holistic, cross-organizational way. These cross-organizationally applied disciplines require some changes in the organization, such as a stronger personal involvement by management; high-level leadership for data quality; new incentives; new performance evaluation measures; data quality enforcement policies; data quality audits; additional training of data owners and data stewards on their respective responsibilities; data standardization rules; metadata and data inventory management techniques; and a common data-driven methodology. This Executive Report will guide the reader from data chaos through data cleansing to continuous data quality improvement practices.

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Data Quality Is Not Optional November 2003

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