# Data Management All analysis in RootCause.ai starts with data. This section covers how to bring data into the platform and what happens to it once it arrives. *** ### Getting data in [**Selecting Data**](/more-details/data-management/uploading-datasets.md) Upload files (CSV, Parquet, Excel, JSON) or connect to external databases and APIs. File uploads are instant; connected sources stay in sync automatically. For step-by-step connector setup, see [Data Connectors](/more-details/data-management/uploading-datasets/data-connectors.md). *** ### Preparing for analysis Once your data is in the platform, two further steps prepare it for causal analysis: * [**Build 360 Data Table**](/user-guide/data-views.md) — join, filter, and transform datasets into the analysis-ready view your digital twin will use * [**Build Ontology**](/user-guide/ontology-concepts.md) — map column names across datasets to shared concepts so RootCause.ai understands your data structure These are Steps 2 and 3 of the [seven-step workflow](/start-here/workflow.md). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.rootcause.ai/more-details/data-management.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.