Definition: Data Completeness

What is data completeness?

Data completeness is the degree to which data is complete and contains all of the necessary information or attributes. It measures how much data is present in a product record or a set of product records and how much of that data is accurate and up-to-date. Data completeness is a common feature of Product Information Management (PIM) systems.

For example, a product record for a laptop may have fields for the brand, model, screen size, processor speed, RAM, hard drive capacity, and weight. If any of these fields are missing or incomplete, such as the weight field being blank, then the data is considered incomplete and will be highlighted as such in the system.

How is data completeness calculated?

The completeness score is calculated by dividing the number of complete product records by the total number of product records and multiplying by 100 to get a percentage. The formula is portrayed as such:

(Number of complete product records / Total number of product records) x 100% = Completeness Score

Let's say a retailer sells clothing items online and maintains a product database in a Product Information Management system that includes various attributes such as brand, size, color, material, and price. The retailer has 1,000 products in its database, but only 800 have complete and accurate information for all attributes, while the remaining 200 products are missing some information. In this case, the completeness score in the PIM system would be:

(800 / 1000) x 100% = 80%

This means that the retailer's product database is 80% complete. By calculating the completeness score, the retailer can identify which products have missing information and take steps to update the database, ensuring that the product information is complete and that they maximize every sales opportunity.

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