The importance of data quality with respect to the strategic planning of any organization cannot be stressed enough. The Data Warehousing Institute (TDWI), in a recent report, estimates that data quality problems currently cost U.S. businesses $600 billion each year. Time and time again, however, people claim that they can't justify the expense of a data quality strategy. Others simply do not acknowledge the benefits.

While a data quality strategy is important, it takes on new significance when implementing a data warehouse. The effectiveness of a data warehouse is based upon the quality of its data. The data warehouse itself is not inherently responsible for cleansing data; if this were true, the same error-prone data would continuously flow to the warehouse, needing to be cleansed repeatedly during iterative operations. The best place to adopt data cleansing efforts is at the source – in production, before loading it to the data warehouse. By cleansing data in production instead of in the data warehouse, organizations save time and money.

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