Accuracy
Accuracy ensures that the data reflects real-world information and is free of errors such as a customer’s address. Accurate data helps businesses generate dependable insights and enables better decisions.
Introducing Data Quality
Data quality is a measure to check how accurate, complete, consistent, and useful data is for its purpose. Maintaining data quality guarantees high quality data that is reliable and helps businesses make better decisions. It ensures the data is correct, relevant, and ready to be used effectively for analysis, visualization and reporting.
Good data quality helps businesses avoid mistakes, improves customer satisfaction, and facilitates efficient operations. On the other hand, poor data quality will lead to wrong conclusions, wasted resources, and poor outcomes.
High-quality data is essential for organisations to meet a variety of business requirements, like regulatory compliance, maintaining financial accuracy, enhancing operational efficiency, achieving business growth, improving customer satisfaction among others.
Data Quality Dimensions
Consistency
Consistency ensures that data is uniform across different sources. For example, a customer’s name must be the same in both the billing and CRM systems. Inconsistent data can create confusion and reduce trust.
Relevancy
Relevancy ensures that the data is appropriate and useful for the specified purpose. Essentially, it’s about having the right data for the right job. Relevant data can streamline operations, improve processes, and ultimately boost organizational performance. For eg, for a company that wants to improve its marketing strategies, sales data related to customer demographics and purchase history is highly relevant.
Completeness
Completeness ensures all necessary information is present. For instance, a sales record should include details like the product, customer, and transaction amount. Missing information can make the data less useful and harder to analyze.
Validity
Validity checks whether the data follows the required rules and formats. For example, dates should be in “YYYY-MM-DD” format, and phone numbers should have the correct number of digits. Invalid data can cause errors and slow down processes.
Uniqueness
Uniqueness ensures that data is not duplicated. For example, a customer should only have one profile in the system. Duplicate records can be misleading and lead to incorrect analysis.
Timeliness
Timeliness ensures that data is up-to-date and available real-time. For example, stock levels in an inventory system should reflect the current quantities. Outdated data can lead to poor decisions and missed opportunities.
Integrity
Integrity ensures that data relationships are accurate and well maintained across platforms by enforcing strict validation standards. For example, every order should have a valid customer ID that matches an entry in the customer database. Broken links in data can lead to incomplete or incorrect insights.