Every business intelligence project involves significant data cleansing efforts. It is like building a data quality firewall to stop bad data before it enters data warehouse.
Why is data cleansing essential?
By number of reasons, which I’ll explain in the next posts, the quality of corporate data is 10 times less than most of respondents believed. Poor data quality leads to inaccurate reporting, wasted time, and lost money.
Unfortunately, most companies in North America and Europe are unrealistically optimistic about the quality of their data. Less than a third of companies are actually measuring that quality. (Q&A: Survey Shows Organizations Overly Optimistic about Data Quality).
Yet, as Jonathan Hulford-Funnell, Chief Operating Officer of QAS, comments, “The research by The Data Warehousing Institute shows that inaccurate customer data costs U.S. businesses $611 billion per year… More time needs to be dedicated to data accuracy.” (Full details of the QAS research report can be found at: www.qas.com/report.)
The lack of support from senior management was named one of the biggest barriers to improving data quality, and so the first step in improving data quality ought to be initial acknowledgement that there is an issue.
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