RootCause.ai 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.
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.
The dataset detail view. Schema is on the left; the data preview and statistics are on the right.
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)
When a sync runs, RootCause.ai pulls fresh data and updates all Data Views and analyses that depend on it.
Schema detection
RootCause.ai automatically analyses 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:
Create a Data View to transform and combine your datasets
Tag columns with Ontology Concepts to link related data across sources
