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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.

Import manager showing file upload area and database connector options
The import manager. Choose file upload for one-off imports or a database connector to keep data in sync automatically.

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.

Dataset detail view showing the data preview with per-column statistics for a joined view of 4,998 rows and 21 columns
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.

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 RootCause built automatically — it links related data across sources

  2. Create a Data View to transform and combine your datasets

  3. Build a Digital Twin using your prepared Data View

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