"Garbage in, garbage out", the age-old adage holds true now more than ever. Whether you're generating a marketing report, training a machine learning model, or forming strategic decisions, poor data quality leads to flawed insights, missed opportunities, and costly mistakes. But what exactly makes data "high-quality"? The answer lies in the six key dimensions of data quality: completeness, accuracy, consistency, timeliness, validity, and uniqueness. Mastering these dimensions ensures your data works for you, not against you.
The Six Dimensions of Data Quality
"Garbage in, garbage out", the age-old adage holds true now more than ever. Whether you're generating a marketing report, training a machine learning model, or forming strategic decisions, poor data quality leads to flawed insights, missed opportunities, and costly mistakes. But what exactly makes data "high-quality"? The answer lies in the six key dimensions of data quality: completeness, accuracy, consistency, timeliness, validity, and uniqueness. Mastering these dimensions ensures your data works for you, not against you.