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The problems associated with poor data quality, which include lack of trust in the data, poorly supported decision making and significant costs, have been well known for some time. However, even within those companies that have recognized the problem and which have taken steps to cleanse their data, there are still major data quality issues. This is for two major reasons. Firstly, most data quality programs are retro-active, which means that there is a time lag between the origination of the data and its validation. In an increasingly real-time and 24 x 7 world this is no longer acceptable. Secondly, the actual process of cleansing the data is usually the responsibility of IT, but IT does not understand either the meaning or the value of the data to the business.
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