Build 360 Data Table

Causal discovery requires a single, coherent table that represents the domain you want to model. If your data lives across multiple datasets — customers in one file, transactions in another — you need to join them before the engine can work.

RootCause calls this joined table a Data View. The friendly name for it in this workflow is the 360 Data Table: one row per entity of interest, with all the variables you want to analyse in a single flat structure.

The good news: RootCause builds it for you. After the ontology runs, it has already identified which datasets share common identifiers and can recommend a join recipe ready to use in one click.

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The Recommended Data Views path

After the ontology finishes processing, navigate to Data → Data Views.

Two single-source Data Views were created automatically during ontology processing. The recommended joined view appears below.

You will see:

• Auto-generated views — one per dataset, created automatically, with zero operations (a direct pass-through to the raw data).

• Recommended Data Views — on the Ontology page's right panel, RootCause identifies datasets that share an identifier and proposes a join. Click the recommendation to create the joined view instantly.

The recommendation is derived from the ontology: when two datasets share the same identifier concept (for example, Customer Id), RootCause proposes joining them on that field, with the larger dataset as the main table.

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The Operations editor

Clicking a recommendation creates the view and opens it in the Operations editor.

The Operations editor with an auto-generated recipe: two source datasets joined on Customer Id. The join type, key columns, and table order are all editable.

The editor shows your recipe as a visual graph:

• Source nodes (blue cards) — one per input dataset.

• Operation nodes — transformations applied in sequence. The recommended view starts with a single JOIN node: "Left join on Customer Details — customerId, Customer Subscription info — customerId."

• Bezier edges connecting each source into the join.

The recipe is fully editable. Each node has pencil (edit) and trash (delete) controls. You can change the join type from left to inner, switch which table is primary, or add further operations — a filter to exclude inactive records, an aggregate to roll up transaction-level data to customer level, or a derived column calculation.

The accept-as-is path takes one click. The edit path uses the same interface. You do not need to rebuild from scratch to change what the platform inferred.

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Adding operations manually

To build a view from scratch, or to add steps to an existing view, use Add Source and Add Operation in the top-right of the Operations panel. Available operations include:

Category	Operations
Data combination	Join, Concatenate
Filtering and cleaning	Filter, Remove Duplicates, Drop Nulls, Impute Nulls, Clip Outliers
Reshaping	Aggregate, Pivot, Unpivot, Sort
Column manipulation	Split Column, Drop Columns, Replace Values, Replace Type, Normalize Numeric
Categories	Merge Categories, Map Categories, Fill Missing Categories
Time series	Interpolate Time, Time Series Aggregate
Custom logic	SQL Query

Operations execute in sequence — each takes the output of the previous step as input. Preview results after each addition to verify the output before continuing.

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What's next

When the view looks right, click Discover in the top-right of the editor. This passes the 360 Data Table to the causal discovery engine and starts building your causal graph.

Next step: Step 4: Build Causal Graph