What is data transformation?
Data transformation is the process of converting data from one format, structure, or value to another during import or processing.
Understanding data transformation
Data transformation modifies data as it moves from source to destination. This is necessary because source data rarely matches exactly what your system expects.
Common transformations include: - Format changes: Converting dates from "MM/DD/YYYY" to "YYYY-MM-DD" - Case normalization: Converting "JOHN SMITH" to "John Smith" - Value mapping: Converting "M/F" to "Male/Female" - Calculations: Computing totals, percentages, or derived fields - Cleaning: Trimming whitespace, removing special characters - Splitting/merging: Breaking "John Smith" into first/last name fields
Transformations can be applied automatically based on rules, or offered as suggestions for users to apply selectively.
Key points
Converts data from source format to target format
Includes format changes, normalization, and calculations
Can be automatic or user-directed
Essential for handling real-world data variety
Should be reversible/auditable when possible
Frequently asked questions
What is the difference between data transformation and data validation?
Validation checks if data meets requirements (is this a valid email?). Transformation changes data to meet requirements (convert this text to a valid email format). They often work together: validate first, then transform or suggest transformations.
Should transformations be automatic or manual?
It depends on the transformation. Safe, reversible transformations (trimming whitespace) can be automatic. Transformations that change meaning (mapping values) should be shown to users for confirmation.
How do I handle transformation errors?
When a transformation fails (e.g., can't parse a date), flag the cell as an error, show the original value, and let the user fix it manually or skip the row.