Data quality refers to how accurate, complete, consistent, timely, and reliable data is for its intended use. High-quality data is critical for trustworthy analytics and confident decision-making.

Common dimensions of data quality include:

• Accuracy: data reflects real-world values

• Completeness: required fields are not missing

• Consistency: values match across systems

• Timeliness: data is up to date

• Validity: data follows expected formats and rules

In BI, poor data quality leads to incorrect dashboards, misleading insights, and loss of confidence in analytics. Even advanced tools cannot compensate for bad data.

Data quality issues often arise from:

• Broken pipelines

• Schema changes

• Inconsistent business logic

• Manual data entry

• Poor source system design

Modern analytics stacks address data quality through:

• Automated validation tests

• Freshness checks

• Anomaly detection

• Row count comparisons

• Schema enforcement

Tools like dbt, Great Expectations, and Monte Carlo help teams monitor and enforce data quality at scale.

From a business standpoint, data quality is not just a technical issue. It impacts revenue forecasting, customer trust, compliance, and operational efficiency.

High-quality data builds trust. When stakeholders trust the data, analytics adoption increases across the organization.