Analytics

Studio builds analytics two ways: classic chart views, and data dashboards — custom views composing the data widgets over saved queries. Prefer data dashboards for new work; a chart view is still the right tool for a single chart embedded as a dashlet.

Creating a chart

  1. Open the revision’s Views tab, click New and pick Chart View.

  2. In the Data Set section, write the query that returns the figures.

  3. Set the Category (the x-axis field) and add one or more Series — each with its field, chart type (bar, line, pie…) and optional aggregate.

  4. Optionally add search fields (user filters), actions and config entries.

  5. Save, and use Preview to see the rendered chart.

Chart designer

Chart views embed anywhere a dashlet fits — dashboards and form panels alike. The full attribute reference is in Charts.

Building a data dashboard

A data dashboard is a custom view whose template composes the data widgets — DataDashboard as the 12-column container, one DataDashlet per figure bound to a saved query, and a visualization inside: DataChart, DataKPI, DataTable, DataPivot, DataProgress, DataList, DataFlow… Each dashlet hands its query result to the widgets inside it as data, and the widgets bind to the query’s columns by name.

Build it in this order:

  1. Create the queries — one per figure, with column metadata filled in so titles and formats render properly. See Queries.

  2. Create a Custom View and compose the template; the designer edits it with syntax highlighting and previews the result live.

  3. Add an action view that opens it, and a menu entry — Actions, Menus.

For filters shared by the whole page (a date range, a status), declare them once on the DataDashboard — they propagate to every dashlet as query parameters.

The complete component catalog with props and examples is in Data widgets.

Making dashboards readable

  • Lead with a KPI row — the 3–5 numbers that answer "how are we doing".

  • One question per dashlet; a chart comparing two measures beats two charts the reader must correlate.

  • Match the component to the shape of the data: trends → line or bar, pipeline → flow, top-N → ranked list, drill-down → table, cross-tab → pivot.

  • Give charts explicit heights (roughly 350–400px); KPIs and cards size themselves.

  • Make column spans total 12 per row — four span-3 KPIs, two span-6 charts, one span-12 table.