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What Is Data Profiling?
The practice of examining a dataset's structure and content — fill rates, distinct values, distributions — before you rely on it.
What it looks like in practice
Profiling a column means asking basic questions before you trust it: How many values are missing? How many distinct values are there? What type of data is actually in it — text, a number, a date? For numeric columns, what's the range, and how are values distributed?
Why it's easy to skip — and shouldn't be
Profiling takes extra time, so it's often skipped under deadline pressure. The cost shows up later: a report built on a column that's 40% null, or a "unique ID" column that turns out to have duplicates, producing numbers that look plausible but are wrong.
Where this shows up in DataQloo
DataQloo profiles data automatically at two points: during the Import Wizard, before you save a new dataset, and in every workflow's Profile tab — fill rate, min/max/average/median, and a distribution for numeric columns, or top-value bars for categorical ones. There's no separate profiling step to remember to run.
See also: What Is ETL?
Frequently asked questions
To catch problems early — missing values, unexpected duplicates, a column that isn't the type you assumed — before they quietly skew a report.
See DataQloo’s Visual Designer for yourself.