> For the complete documentation index, see [llms.txt](https://docs.rootcause.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rootcause.ai/user-guide/data-views.md). # Build 360 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 Table**: one row per entity of interest, with all the variables you want to analyze 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. *** ## The recommended path After the ontology finishes processing, open the **Datasets** section in the top navigation bar.
Datasets list showing two auto-generated single-source views and the joined Customer Subscription info × Customer Details dataset

Two single-source Data Views were created automatically during ontology processing; the joined dataset was created from the ontology's recommendation.

You will see: * **Auto-generated views** — one per dataset, created automatically, with zero operations (a direct pass-through to the raw data). * **Recommended Datasets** — 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. *** ## The Operations editor Clicking a recommendation creates the view and opens it in the Operations editor.
The joined dataset with its data preview and the Operations panel showing two source datasets and an auto-generated left join on Customer Id

The joined view with its Operations panel: two source datasets joined on Customer Id, 4,998 rows out. 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 Subscription info - customerID = Customer Details - 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. *** ## 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, Time Series Join, Concatenate | | Filtering and cleaning | Filter, Remove Duplicates, Drop Nulls, Impute Nulls, Clip Outliers | | Reshaping | Aggregate, Sort, Transpose Panel | | Column manipulation | Split Column, Drop Columns, Replace Values, Cast Type, Normalize | | Categories | Map Categories | | Time series | Interpolate Time, Time Series Aggregate, Frequency Analysis, Cyclic Aggregate, Vertical Diff, Date Span, Cumulative Sum | | Arrays | Decompose Array, Consolidate into Array, Explode Array (long), Aggregate Array | | Custom logic | SQL Query | The specialized time-series and array operations, briefly: * **Time Series Join** — join two sources on time, with exact, nearest (as-of), or granularity-based matching. * **Transpose Panel** — collapse a long-format panel dataset into a single wide-format time series, one column per environment and value. * **Frequency Analysis** — run FFT or Welch's method over a time series to surface its frequency content. * **Cyclic Aggregate** — group rows by a cycle component (hour of day, day of week, …) and aggregate, revealing the shape of a cycle. * **Vertical Diff** — the change from each row to the next along the time axis (first difference). * **Date Span** — the interval between two date columns in the same row, in the unit you choose. * **Cumulative Sum** — a running total along the time axis. * **Decompose Array** — spread an array column into one scalar column per position, or one boolean column per distinct value. * **Consolidate into Array** — merge several scalar columns into one array column. * **Explode Array (long)** — unnest an array column into one row per element. * **Aggregate Array** — reduce each row's array to a single value (mean, sum, min, max, median, or count). 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. *** ## What's next When the view looks right, you're ready for causal discovery. It runs when you build a Digital Twin from this view — either click **Create & build twin** on the Ontology page's recommended dataset, or start from **Digital Twins → New Digital Twin** and pick the view in the builder. Next step: [Step 4: Build Causal Graph](/user-guide/causal-graph.md)