Data Quality

Data quality refers to the overall reliability, accuracy, completeness, and consistency of data, determining how well it serves its intended purpose. High-quality data is crucial for making informed decisions, ensuring operational efficiency, and achieving trustworthy analytics in both business and technical environments.

Also known as: Data integrity, data reliability

Comparisons

• Data Quality vs. Data Cleaning: Data cleaning is a step taken to improve data quality by fixing issues like missing or incorrect values.

• Data Quality vs. Data Governance: Data quality focuses on the condition of the data itself, while governance defines policies and responsibilities around data usage and maintenance.

Pros

• Better decision-making: Accurate data supports reliable analytics and business insights.

• Operational efficiency: Reduces errors and rework caused by faulty or incomplete data.

• Regulatory compliance: Helps organizations meet data-related legal and industry standards.

Cons

• Resource-intensive: Assessing and maintaining high data quality can require significant time and tools.

• Subjectivity: Definitions of "quality" can vary depending on context or use case.
  

Example

In an e-commerce database, data quality issues might include missing product prices, duplicate customer accounts, or outdated shipping addresses. Addressing these problems—by validating entries, removing duplicates, and updating information—improves the accuracy of sales reports and the efficiency of order fulfillment.