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
In today’s data-driven world, the quality and accuracy of your data are paramount. Poor data quality can lead to costly errors, misinformed decisions, and missed opportunities. It is estimated that 88% of all data integration projects fail entirely or significantly overrun their budgets because of poor data quality. Data scientists spend about 60% of their time...Read More