Data Profiling Isn't Optional: What We Check Before You Build a Workflow
The report that looked right
The most dangerous data quality problems aren't the ones that crash a query — they're the ones that quietly produce a plausible-looking wrong number. A "unique customer ID" column with duplicates. A "signup date" column that's 30% null. A report can run cleanly and still be wrong.
What we check, and when
DataQloo profiles a dataset automatically at two points. First, in the Import Wizard, before you commit to saving a new dataset — you see each column's fill rate, unique value count, null percentage, and an average or top value, so problems are visible before anything is built on top of them. Second, in every workflow's Profile tab — a richer version of the same idea, including min/max/average/median and a distribution for numeric columns, or top-value bars for categorical ones.
What profiling doesn't do
This is descriptive profiling, computed from the data you're actually looking at — it's not a rules engine. DataQloo doesn't (yet) let you define an assertion like "this column must never be null" and get alerted when it's violated on a schedule; there's no scheduled monitoring at all today, since scheduled runs aren't built yet either. What exists is a fast, automatic sanity check at the two points where you're most likely to make a decision based on the data: importing it, and building on it.
A habit worth keeping
Even with tooling, the fastest data quality check is still asking "does this fill rate look right for this column" before you trust the number downstream.
Related reading
How to Spot Duplicate Records Using Column Uniqueness Stats
"How many customers do we actually have?" is a harder question than it sounds when the same company might be entered three slightly different ways. Here's how to catch that with profiling stats alone.
A Field Guide to Reading a Data Profile Report
Fill rate, unique count, null percentage, inferred type — a data profile puts real numbers in front of you before you build on a dataset. Here's what each one is actually telling you.
PostgreSQL Data Types, and How DataQloo Infers Them
A column's declared PostgreSQL type is only half the story. Here's how DataQloo's Schema tab infers the more useful, semantic type underneath.
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