📌 Data Cleansing Strategy Summary

A data cleansing strategy is a planned approach for identifying and correcting errors, inconsistencies, or inaccuracies in data. It involves setting clear rules and processes for removing duplicate records, filling missing values, and standardising information. The goal is to ensure that data is accurate, complete, and reliable for analysis or decision-making.

🙋🏻‍♂️ Explain Data Cleansing Strategy Simply

Think of a data cleansing strategy like cleaning out your backpack at the end of a school term. You throw away rubbish, organise your notes, and make sure everything is in its right place. This way, when you need something important, you know you can trust what you find.

📅 How Can it be used?

A data cleansing strategy can help a marketing team ensure their customer contact list is accurate before launching a campaign.

🗺️ Real World Examples

A hospital uses a data cleansing strategy to standardise patient records, ensuring that all addresses follow the same format and duplicate entries are removed. This helps staff quickly access accurate information and reduces the risk of medical errors.

An online retailer applies a data cleansing strategy to its product database, correcting misspelt product names and removing outdated listings. This improves search results for customers and helps maintain inventory accuracy.

✅ FAQ

A data cleansing strategy helps make sure that information is accurate and ready to use. Without it, mistakes or missing details could lead to wrong conclusions or poor decisions. By keeping data tidy and consistent, teams can trust their results and save time fixing problems later.

Common steps include checking for duplicate records, filling in missing details, and making sure information follows the same format. These actions help spot and fix issues that might otherwise be missed, so the data is reliable for reports and analysis.

It is a good idea to review and clean data regularly, not just once. The frequency depends on how often new data is added or changed. Regular checks help catch errors early and keep information up to date.

📚 Categories

• Data Engineering
• Data Governance
• Data Science

🔗 External Reference Links

Data Cleansing Strategy link

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