Automated Validation Rules

Automated validation catches data quality issues at the point of entry, not after they have propagated into reports and decisions. Three levels of validation. Schema validation: does the data conform to the expected format? A date field that contains text fails. A numeric field with negative values where only positive are valid fails. Business rule validation: does the data make logical sense? A deal value of $0 in an active opportunity fails. A close date in the past for an open deal fails. Statistical validation: does the data fall within the expected distribution? A deal value ten times the historical average is an outlier that warrants investigation, not automatic rejection. Each level catches different types of errors. Together, they form a quality firewall that prevents bad data from entering the system.