A data specialist is given a large repository of open data from multiple government sites. The dataset has incomplete fields and lacks standardized documentation. Which approach is best for refining the dataset before it is consolidated with local tables?
Mark entries with missing metadata or outliers for manual review to prevent discrepancies
Use data profiling to detect unusual patterns and parse incomplete fields so issues can be addressed
Rely on table shapes in the public repository
Gather each record from the public repository and consolidate it as-is
Data profiling detects unusual patterns, missing fields, and inconsistencies in publicly sourced information, which helps produce a unified and robust dataset when merging with existing tables. Overlooking structure, neglecting validation, or removing incomplete entries may lead to overlooked anomalies or data loss.
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What is data profiling?
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Why is using data profiling better than consolidating as-is?
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What are examples of unusual patterns detected in data profiling?