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When a tabular dataset version reaches Ready, Dataerai has analyzed it so you don’t have to open the file to know what’s inside. Two things are produced automatically: a schema and summary statistics.

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Inferred schema

Dataerai infers each column’s name and type — for example:
  • Quantitative — numeric measurements.
  • Nominal — categories and labels.
  • Temporal — dates and times.
  • Boolean and string values.
The schema is what lets Dataerai pick the right chart for a column and helps you reason about a dataset before you dig in.
Dataerai inferred table preview showing columns and rows

The inferred table — column names and a row preview (here, 3 columns × 43 rows).

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Summary statistics

For each column, Dataerai computes a profile so you can spot issues and understand distributions at a glance, including:
  • Counts and how many values are missing.
  • The number of distinct values.
  • Ranges and quantiles for numeric columns.
  • The most common values (category counts) for categorical columns.
Dataerai category counts chart derived from column statistics

Statistics surface as charts — here, category counts for a categorical column.

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Why it matters

The schema and statistics power the rest of the analysis experience: they drive the built-in charts and help you catch problems — an unexpected type, a column full of nulls, a surprising range — early.

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Next steps

Visualize your data

Turn these columns into histograms and category charts.