A data analyst at an e-commerce company is tasked with creating a customer segment for a targeted direct mail campaign. The business rule for this campaign is that every selected customer record must have a complete mailing address (street, city, state, and postal code). The analyst runs a query to check the customer table and discovers that while the customer_id, city, and state columns are fully populated, 30% of the records have null values in the postal_code column. Which data quality issue is the primary concern for the analyst in this scenario?
The correct answer is Incompleteness. Data completeness is a data quality dimension that measures whether all necessary data is present in a dataset. In this scenario, the postal_code is a required field for the direct mail campaign, and its absence in 30% of records makes the dataset incomplete for the intended business purpose.
Duplication is incorrect because it refers to the same record appearing multiple times in a dataset. The problem described is missing data within records, not repeated records.
Redundancy is incorrect as it refers to the same data being stored in multiple places, which can lead to inconsistencies but is not the issue here.
Outliers are incorrect because they are data points that are present but deviate significantly from the other observations. The issue identified is the absence of data (null values), not the presence of abnormal data.
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What does 'data incompleteness' mean in a data quality context?
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Why is a null value in a dataset problematic for data analysis?
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How can missing `postal_code` values be resolved when preparing data?
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CompTIA Data+ DA0-002 (V2)
Data Acquisition and Preparation
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