From Reactive to Proactive: How Anomaly Detection Revolutionizes Data Quality

For far too long, data quality has been a game of whack-a-mole. We scramble to react after anomalies have already infiltrated our datasets, causing damage and disruption. According to a recent report, only a miserly 7% of data teams resolve data issues before they impact users, why? reactive approach to data quality issues. We don’t hunt for data issues until they haunt our data warehouse, data lakes, or lakehouses. Traditional methods of data quality assurance often leave organizations playing …

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