> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dataerai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Understand your data

> When a tabular dataset version is ready, Dataerai has inferred its schema and computed summary statistics for every column.

When a tabular [dataset version](/datasets/overview) 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**.

## 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.

<Frame caption="The inferred table — column names and a row preview (here, 3 columns × 43 rows).">
  <img src="https://mintcdn.com/dataerai/JYQfiA1164D5XDuO/images/dataset-table.png?fit=max&auto=format&n=JYQfiA1164D5XDuO&q=85&s=5b989721456f8be062a60908379e440f" alt="Dataerai inferred table preview showing columns and rows" width="320" height="235" data-path="images/dataset-table.png" />
</Frame>

## 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.

<Frame caption="Statistics surface as charts — here, category counts for a categorical column.">
  <img src="https://mintcdn.com/dataerai/JYQfiA1164D5XDuO/images/visualize-category.png?fit=max&auto=format&n=JYQfiA1164D5XDuO&q=85&s=0d05b6b6412172b99fb7c2e5d410b54a" alt="Dataerai category counts chart derived from column statistics" width="320" height="225" data-path="images/visualize-category.png" />
</Frame>

## Why it matters

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

## Next steps

<Card title="Visualize your data" icon="chart-column" href="/datasets/visualize">
  Turn these columns into histograms and category charts.
</Card>