# Data Management

This section covers how to get your data into RootCause.ai and prepare it for analysis.

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### Getting Data In

[**Uploading Datasets**](https://docs.rootcause.ai/user-guide/data-management/uploading-datasets)

Import data from files **(CSV, Parquet, Excel, JSON)** or connect to external sources like **databases and APIs**. Datasets are the raw material for all analysis in RootCause.ai.

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### Preparing Data

[**Data Views**](https://docs.rootcause.ai/user-guide/data-management/data-views)

Transform and combine datasets into analysis-ready views. Apply filters, joins, aggregations, and other operations without modifying your source data.

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### Organizing Data

[**Ontology Concepts**](https://docs.rootcause.ai/user-guide/data-management/ontology-concepts)

Unify columns across datasets by mapping them to common concepts. When "customer\_id" in one dataset means the same thing as "cust\_id" in another, ontology concepts link them together.

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### Workflow

A typical data workflow in RootCause.ai:

1. **Upload or connect** – Bring data into the platform via files or connectors
2. **Create a Data View** – Join, filter, and transform as needed
3. **Tag ontology concepts** – Help RootCause.ai understand your data structure
4. **Build a Digital Twin** – Use your prepared Data View for causal discovery

For data connector setup, see the [Data Connectors](https://docs.rootcause.ai/user-guide/connecting-data) section.