Data quality refers to the accuracy, consistency, completeness, and reliability of data, ensuring it is fit for use in decision-making, analysis, and operations. High-quality data is essential for businesses, especially in finance, healthcare, IT, and asset management. 

Key Dimensions of Data Quality

• Accuracy – Data should be correct and free from errors.
• Completeness – No missing values or gaps in the dataset.
• Consistency – Data should be uniform across different systems.
• Timeliness – Data should be up-to-date and available when needed.
• Validity – Data should follow predefined formats and rules.
• Uniqueness – No unnecessary duplicates.

Why Data Quality Matters

• Better Decision-Making: Ensures reliable business insights.
• Regulatory Compliance: Meets industry and legal standards.
• Operational Efficiency: Reduces errors and improves processes.
• Customer Satisfaction: Ensures accurate and personalized services.

Techniques to Improve Data Quality

• Data Cleansing: Removing duplicates and correcting errors.
• Validation Rules: Setting data input standards.
• Automated Data Monitoring: Using AI and machine learning to detect anomalies.
• Master Data Management (MDM): Centralizing and standardizing data across an organization.