📌 Data Quality Framework Summary

A Data Quality Framework is a structured approach used to measure, monitor and improve the quality of data within an organisation. It defines clear rules, standards and processes to ensure data is accurate, complete, consistent, timely and relevant for its intended use. By following a data quality framework, organisations can identify data issues early and maintain reliable information for decision-making.

🙋🏻‍♂️ Explain Data Quality Framework Simply

Imagine keeping a recipe book where you want every recipe to be correct, complete and easy to follow. A data quality framework is like a checklist to make sure each recipe has all the ingredients listed, the steps in the right order and no mistakes. This way, anyone using the book gets good results every time.

📅 How Can it be used?

A data quality framework can help a company ensure its customer database is accurate and up to date for marketing campaigns.

🗺️ Real World Examples

A hospital uses a data quality framework to regularly check patient records for missing information, duplicate entries and outdated contact details. This helps doctors access accurate patient histories and reduces errors in treatment or billing.

An online retailer implements a data quality framework to validate product information entered by suppliers. This reduces incorrect pricing or descriptions on their website, improving customer satisfaction and reducing returns.

✅ FAQ

A Data Quality Framework is a set of rules and processes that helps organisations keep their data accurate, complete and useful. It is important because it ensures that decisions are based on trustworthy information, making everyday business smoother and reducing costly mistakes caused by bad data.

A Data Quality Framework helps spot and fix data issues early by setting clear standards for how data should look and behave. Regular checks guided by the framework mean problems can be caught before they cause bigger issues, saving time and headaches later on.

The main parts usually include rules for how data should be collected, stored and shared, as well as tools for checking data quality over time. There are also processes for fixing problems and making sure everyone follows the same standards, so data stays reliable and useful.

📚 Categories

• Data Engineering
• Data Governance
• Data Science

🔗 External Reference Links

Data Quality Framework link

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