📌 Data Quality Checks Summary
Data quality checks are processes that help ensure the information in a dataset is accurate, complete, and reliable. They involve looking for errors such as missing values, duplicate records, or values that do not make sense. By performing these checks, organisations can trust that their decisions based on the data are sound. These checks can be done automatically using software or manually by reviewing the data.
🙋🏻♂️ Explain Data Quality Checks Simply
Think of data quality checks like proofreading an essay before handing it in. You look for spelling mistakes, missing words, or repeated sentences to make sure everything makes sense. In the same way, data quality checks help make sure the information in a computer system is correct and ready to use.
📅 How Can it be used?
A project team runs data quality checks to catch errors before analysing customer survey responses.
🗺️ Real World Examples
A hospital collects patient information in a database. Before using this data to create health reports, staff run data quality checks to find missing birth dates or duplicate patient records. This helps prevent mistakes in patient care and reporting.
An online retailer gathers sales data from different stores. Data quality checks are used to spot any transactions with negative prices or impossible dates, ensuring the sales reports reflect true business activity.
✅ FAQ
Why are data quality checks important for organisations?
Data quality checks help organisations make decisions with confidence. If the data is accurate and reliable, it means any actions taken based on that information are more likely to be successful. Without these checks, mistakes or gaps in the data could lead to poor choices or missed opportunities.
What are some common problems that data quality checks can find?
Some of the most common issues are missing information, duplicate entries, or numbers that do not make sense. For example, a customer record might be entered twice, or a date of birth could be in the future. Spotting these errors early helps keep the data trustworthy.
Can data quality checks be done automatically?
Yes, many organisations use software to check their data automatically. This can save time and catch problems quickly. However, sometimes a human review is still needed for tricky cases where a computer might not spot something unusual.
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