# 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**.

<figure><img src="/files/oby2E0DWQPkYGTsZwCu2" alt="Data Views tab showing two auto-generated single-source views and a Recommended section"><figcaption><p>Two single-source Data Views were created automatically during ontology processing. The recommended joined view appears below.</p></figcaption></figure>

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.

<figure><img src="/files/aIbmFZgv68e9rzKC31Zq" alt="Data View editor showing two source nodes and an auto-generated left join operation"><figcaption><p>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.</p></figcaption></figure>

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](/user-guide/causal-graph.md)


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