Data cleaning usually focuses on fixing ________ of data while data standardization usually focuses on fixing ________ of data.

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BlackTom AI · Accepted Answer

Data cleaning and data standardization address different aspects of data quality, so it's important to parse what each aims to fix.
Option 1: 'rows, columns' — This aligns with common practice: data cleaning often targets issues within rows (e.g., missing values, duplicates, outliers in records) while data standardization focuses on consistency across columns (e.g., uniform formats, canonical values, consistent data types). This pairing makes conceptual sense because cleaning tends to fix record-level problems and standardization tends to fix schema or domain-level consistency.
Option 2: 'columns, rows' — This reverses the typical emphasis. Standardization is not primarily about fixing rows; rather, it is about ensuring that data across a column adheres to a consistent format or standard. Cleaning is more often about problematic rows (records) rather than fixing the entire column structure.
Option 3: 'the dictionary, the validation' — This misstates the focus: while metadata like a data dictionary and validation rules are important, data cleaning and standardization are more about the data values and their formats than about dictionary or validation concepts specifically. This option confuses process objects with the data quality activities themselves.
Option 4: 'the validation, the dictionary' — Similar to option 3, this mixes up the targets. Validation and dictionary concepts are related to governance and metadata, not the primary, practical focus of cleaning versus standardization, which deal with the contents of rows and columns rather than these abstract controls.
Overall, the statement in option 1 captures the conventional distinction: cleaning tends to address issues within rows (records), while standardization concentrates on making data within columns uniform and consistent across the dataset.

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Data cleaning usually focuses on fixing of data while data standardization usually focuses on fixing of data.

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