Everyone agrees that data transformation is important for creating and maintaining a high-value data warehouse. In simple terms, data transformation is the process of collecting, cleansing, reformatting, validating and consolidating data and is crucial for business users. But data transformation is merely the process--the means to an end. The resulting information provided by a data transformation process must be high quality. High-quality data will be used by the intended users. They will trust the data enough to use it for making high-value business decisions. Trust of the data is critical. Consider the multi-divisional company that developed a data warehouse to provide integrated cross-divisional customer information so that a new customer relationship initiative could be launched. Eighteen months and a couple of million dollars later the warehouse is complete, the applications start using the warehouse and the first customer analysis reports are produced. The company's CEO is particularly interested in a report that summarizes total revenue, across all divisions, by customer, by year. Armed and eager, the CEO visits one of his top 10 customers. The CEO greets his customer and thanks him for the $7 million in business they did last year. The customer appreciates the kudos but points out that it was $12 million. The embarrassed CEO returns to his company and demands that this error, and all errors like it, be found and corrected. The warehouse project team spends another year correcting data errors and regaining trust. Poor quality, "load-and-go" data transformation was the culprit.

Despite its recognized importance, data transformation continues to be partially or wholly neglected in businesses today. Lack of an integrated enterprise-wide approach to collect, structure and maintain data is often the culprit of poor data transformation and, in turn, the barrier to building a credible and successful data warehouse.

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