May 10, 2022
Financially, poor data quality costs organizations some ludicrous amounts of money. Worse, poor data quality is a strong inhibitor to the success of data science: No analytical method can create value from poor quality data. As a consequence, data science projects invest a majority of their resources on cleansing data. However, cleansing resists automation as […]
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