Data Management
This section covers how to get your data into RootCause.ai and prepare it for analysis.
Getting Data In
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.
Preparing Data
Transform and combine datasets into analysis-ready views. Apply filters, joins, aggregations, and other operations without modifying your source data.
Organizing Data
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.
Workflow
A typical data workflow in RootCause.ai:
Upload or connect – Bring data into the platform via files or connectors
Create a Data View – Join, filter, and transform as needed
Tag ontology concepts – Help RootCause.ai understand your data structure
Build a Digital Twin – Use your prepared Data View for causal discovery
For data connector setup, see the Data Connectors section.
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