Scenario: A client has reported inconsistencies in their sales data. How would you use data quality assessment techniques to identify and rectify these inconsistencies?
- Data auditing
- Data cleansing
- Data profiling
- Data validation
Data cleansing involves correcting, enriching, and standardizing data to resolve inconsistencies and errors. By performing data cleansing on the sales data, you can identify and rectify inconsistencies such as misspellings, formatting errors, and duplicate entries, ensuring the accuracy and reliability of the dataset. This process is crucial for improving data quality and supporting informed decision-making based on reliable sales data.
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