Simor Consulting
Category: Data Quality
Organisations often treat data quality as secondary: something to address after building pipelines and training models. This perspective misunderstands modern data systems. In a world where ML models
Most ML projects fail not because of flawed algorithms but because of poor data quality. Data scientists typically spend 80% of their time on data preparation, and even small data quality issues drama
Data quality problems cost organisations between 15% and 25% of revenue. The global cost of bad data runs into trillions annually. Traditional data quality approaches (manual review, rule-based valida
Data quality determines decision quality. Poor data leads to flawed analytics and misguided business decisions. Manual data quality reviews don't scale and catch issues too late. This article covers