Garbage in, garbage out—this computing principle applies especially to data and AI. Machine learning models trained on low-quality data learn to make poor decisions. Business decisions based on unreliable data lead to poor outcomes. Yet data quality is often neglected until problems become obvious.

Data quality dimensions include accuracy (are values correct?), completeness (are values missing?), consistency (does the same entity have conflicting information?), and timeliness (is the data current?). Different applications weight these dimensions differently. A customer database needs high accuracy and consistency. A recommendation system accepts lower accuracy if patterns are strong.

Data validation catches problems early. Schema validation ensures data matches expected structure. Range validation catches outliers. Uniqueness validation detects duplicates. Cross-field validation ensures internal consistency. These automated checks prevent garbage data from entering systems.

Data profiling reveals actual data characteristics. What percentage of values are null? What's the value distribution? Are there unexpected patterns? Profiling creates baselines, enabling detection of anomalies. A field that was always present might suddenly have 30% null values—indicating upstream problems.

Master data management solves consistency problems. A customer named 'John Smith' might appear as 'john.smith@email.com', 'J. Smith', 'Smith, John'. Systems need canonical representations to avoid duplication and inconsistency. MDM platforms manage this complexity.

Data lineage tracks data flow through systems. Where did this data come from? What transformations were applied? Which systems depend on it? Understanding lineage enables debugging when data problems occur. When accuracy drops, lineage helps identify whether the problem is source data or transformation logic.

Privacy and governance constraints increasingly affect data quality. GDPR requires deletion of personal data ('right to be forgotten'). Regulations restrict how long you can retain data. Governance questions—who owns this data? Who can access it? How is it used?—become technical concerns affecting data systems.

Organizations that excel at data quality gain competitive advantage. They make better decisions faster. Their AI models are more accurate. Their customers receive better experiences. Data quality requires investment—tools, processes, governance, people. But the return on investment is substantial. In the data-driven economy, data quality is increasingly the difference between winners and losers.