An organization stores data with inconsistent field names and varying date formats across multiple sources. It wants to standardize both the naming conventions and dates in a unified way. Which practice best meets these goals?
Make periodic manual edits in separate files for each dataset
Build a procedure that references standardized field definitions and date variables
Export all data as text and reimport
Divide tasks among multiple spreadsheets without a central reference
A transformation procedure referencing standard conventions applies consistent rules for field names and date formats across all records. Manual edits can create inconsistent outcomes over time. Spreading tasks across various spreadsheets does not guarantee uniform updates. Exporting data as text files and reimporting does not systematically apply a standard naming scheme or date formatting rules.
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Why are standardized naming conventions important in datasets?
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How do date variables improve data standardization?