A comprehensive guide to data quality covering the types of data errors developers commonly introduce (required field errors, format errors, range errors, logical consistency errors, duplicates, relational errors, and structural errors), the six pillars of good data (completeness, uniqueness, validity, timeliness, accuracy, consistency), and the five validation layers every application should implement (frontend, backend, database, service/business logic, and data ingestion). Includes practical code examples in HTML/JavaScript and Laravel PHP, plus testing strategies covering unit, integration, and functional tests to protect data integrity throughout the pipeline.
Table of contents
The Importance of Data QualityTypes of Data ErrorsWhat Makes Good Data?Data Validation LayersTesting Strategies to Protect Data QualityConclusionSort: