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CMA Intermediate · Financial Management and Business Data Analytics · Data Processing, Organisation, Cleaning and Validation

A firm's database holds Sales (Order ID, Product ID, Product Name, Product Price, Quantity). Product Name and Product Price repeat on every order for the same product, so changing a price needs many edits. Moving product details to a separate Product table linked by Product ID is an example of:

This is data normalisation. Product name and price are moved to a separate table linked by Product ID, so each fact is stored only once. That reduces redundancy and avoids update anomalies when a price changes, unlike aggregation, imputation or masking.

  1. AData normalisation to reduce redundancy and update anomaliesCorrect
  2. BData aggregation to summarise sales by product
  3. CData imputation to fill missing prices
  4. DData masking to hide sensitive prices

Explanation

Splitting repeated product attributes into their own table keyed by Product ID is normalisation. It removes redundancy, so a price is stored once and updated once, avoiding update anomalies. Aggregation summarises values, imputation fills gaps, and masking hides data, none of which is happening here.

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