> For the complete documentation index, see [llms.txt](https://docs.rootcause.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rootcause.ai/more-details/data-management/uploading-datasets.md).

# Selecting Data

RootCause adapts to your data in its current format, without requiring lengthy data engineering work.

**Upload a file**, and the platform automatically detects column types, identifies patterns, and prepares your data for analysis.

**Connect a database**, and your data stays in sync without manual exports.

<figure><img src="/files/Hv6OfYz7QpLEm4kmK7V8" alt="Import manager showing file upload area and database connector options"><figcaption><p>The import manager. Choose file upload for one-off imports or a database connector to keep data in sync automatically.</p></figcaption></figure>

If a connector you need is not yet supported, you can export from the source system and upload directly.

***

### Viewing a dataset

Click on any dataset to see its full details: schema (columns and data types), a data preview, row count, and column statistics.

<figure><img src="/files/cSA8sF4qPKTefaM0X0Ot" alt="Dataset detail view showing the data preview with per-column statistics for a joined view of 4,998 rows and 21 columns"><figcaption><p>The dataset detail view. Every column carries its type and value distribution; the panel on the right reports the sources, concepts, and operations behind the view.</p></figcaption></figure>

***

### Refreshing connected data

For connected sources, data can be kept current in two ways:

* **Sync Now** — click to refresh immediately
* **Schedule Sync** — set automatic refresh intervals (hourly, daily, weekly, monthly)

When a sync runs, RootCause pulls fresh data and updates all Data Views and analyses that depend on it.

***

### Schema detection

RootCause automatically analyzes your data to detect column types. This matters because causal discovery algorithms treat numbers, categories, and dates differently.

| Detected type | Description                                               |
| ------------- | --------------------------------------------------------- |
| Number        | Integers and decimals (revenue, counts, measurements)     |
| Text          | Strings and categorical values (names, IDs, labels)       |
| DateTime      | Dates and timestamps (order dates, event times)           |
| Boolean       | True/false values (flags, binary indicators)              |
| Category      | Columns with limited unique values (status, region, tier) |

Automatic detection is usually correct. If a column is detected incorrectly — ZIP codes detected as numbers, for example — open the dataset, click the column type, and select the correct type from the dropdown.

***

### Next steps

Once your data is uploaded:

1. Review the [ontology](/user-guide/ontology-concepts.md) RootCause built automatically — it links related data across sources
2. Create a [Data View](/user-guide/data-views.md) to transform and combine your datasets
3. [Build a Digital Twin](/user-guide/creating-digital-twin.md) using your prepared Data View