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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