What it is, why you need it, and best practices. This guide provides definitions and practical advice to help you understand and maintain data quality.
Data quality assesses the extent to which a dataset meets established standards for accuracy, consistency, reliability, completeness, and timeliness. High data quality ensures that the information is trustworthy and suitable for analysis, decision-making, reporting, or other data-driven activities.
Data quality management involves ongoing processes to identify and rectify errors, inconsistencies, and inaccuracies. It should be a key element of your data governance framework and your broader data management system.
Data quality is essential because it underpins informed decision-making, reliable reporting, and accurate analysis. Bad data can lead to errors, misinterpretations, and misguided decisions, potentially causing financial losses and reputational damage. Reliable data enables you to have confidence in your business intelligence insights, leading to better strategic choices, improved operational efficiency, and enhanced customer experiences.
Data quality assessment involves multiple dimensions, which can vary based on your data sources. These dimensions serve to categorize data quality metrics, which help you perform an assessment.
The top 6 dimensions most organizations track:
Accuracy ensures correct values based on your single "source of truth." Designating a primary data source and cross-referencing others enhances accuracy.
Consistency compares data records from diverse datasets. This confirms data trends and behaviors across sources for reliable insights.
Completeness reflects the amount of data that is usable. A high absence rate might misrepresent typical data samples, leading to biased analysis.
Timeliness addresses data readiness within expected timeframes. Real-time generation, like immediate order numbers, is essential.
Uniqueness measures duplicate data volume. For instance, customer data should have distinct customer IDs.
Validity gauges data alignment with business rules, including metadata management like valid data types, ranges, and patterns.
Additional dimensions you can track:/ © 1993-2025 QlikTech International AB, All Rights Reserved
