Data quality is the degree to which data is accurate, complete, consistent, and up-to-date, allowing it to be reliably used for analysis and business decision-making.
Data quality consists of several key elements:
- Accuracy – data reflects the actual state (e.g., no errors or typos)
- Completeness – no significant gaps in the data
- Consistency – data is aligned across systems and sources
- Timeliness – data is updated regularly
- Uniqueness – no duplicates
High data quality is the foundation of effective analytics, reporting, and BI projects. Low data quality leads to incorrect conclusions, operational problems, and a decrease in trust in data within an organization.
In practice, data quality is monitored and improved through processes such as data validation, data profiling, and data cleansing, often supported by analytical and integration tools.