📌 Data Quality Monitoring Summary
Data quality monitoring is the ongoing process of checking and ensuring that data used within a system is accurate, complete, consistent, and up to date. It involves regularly reviewing data for errors, missing values, duplicates, or inconsistencies. By monitoring data quality, organisations can trust the information they use for decision-making and operations.
🙋🏻♂️ Explain Data Quality Monitoring Simply
Think of data quality monitoring like checking your homework before handing it in. You look for mistakes, missing answers, or anything that does not make sense. This helps make sure that what you turn in is correct and complete, just like making sure data is right before it is used.
📅 How Can it be used?
Set up automated checks to alert the team when sales data in a retail dashboard is missing or contains errors.
🗺️ Real World Examples
A hospital uses data quality monitoring to track patient records, ensuring that important information like allergies, medications, and contact details are always correct. If any values are missing or look suspicious, the system alerts staff to review and fix the records, helping prevent medical errors.
A logistics company monitors the data collected from its fleet of delivery trucks, checking for missing GPS coordinates or incorrect delivery times. When issues are detected, the company can quickly investigate and correct the data, making route planning and customer notifications more reliable.
✅ FAQ
Why is data quality monitoring important for businesses?
Data quality monitoring helps businesses make decisions based on accurate and reliable information. When data is regularly checked for mistakes or missing pieces, companies can avoid costly errors, improve customer satisfaction, and run their operations more smoothly.
What are some common problems that data quality monitoring can catch?
Data quality monitoring can spot things like missing information, duplicate entries, and values that do not make sense. By finding these issues early, organisations can fix them before they affect reports or day-to-day work.
How often should data quality be checked?
It is best to check data quality regularly, as errors can appear at any time. Some organisations monitor their data daily, while others might do it weekly or monthly, depending on how quickly their data changes and how important it is to their work.
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