Data Profiling
Data profiling analyzes the structure and content of a dataset — distributions, completeness, anomalies — to understand what you actually have before you act on it.
What is data profiling?
Data profiling is the diagnostic step before any cleanup or migration: scan the dataset and report on field types, value distributions, missing rates, outliers, format inconsistencies, and likely keys. The output guides what cleansing, normalization, and validation rules to apply.
Why it matters
- You can't clean what you haven't measured
- Profiling exposes hidden surprises (a "phone" column with 12 different formats)
- Migration projects without profiling fail in production; profiling catches it in staging
What a profiling pass tells you
- % completeness per field
- Distinct value counts (high cardinality vs. picklist)
- Format consistency (dates, phones, emails)
- Outliers (revenue values that are 100x the median)
- Likely primary keys and natural relationships
How TexAu helps
Drop a list into a TexAu table to get an immediate read on completeness, distinct value counts, and validation failures — the inputs you need to design the cleanup workflow.
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