Siren Platform User Guide

Adding visualizations to a dashboard

Once you have created a new dashboard, you can add visualizations to it.

Click Edit in the toolbar panel — the options change to Editing mode.

Click Add, then select a previously created visualization from the list:

Adding a visualization to the dashboard

You can filter the list of visualizations by typing a filter string into the Visualization Filter field.

The visualization you select appears in a container on your dashboard.


If you see a message about the container’s height or width being too small, resize the container.

If you select another visualization, it appears in another container beside the first one that was added.

Visualizations and searches in a dashboard

A visualization is usually linked to a search. For example, the Article Authors visualization is linked to the Articles search, as is the Articles Wordcloud visualization. Similarly, the Companies Table and Companies Timeline visualizations are linked to the Companies search.

A dashboard can contain visualizations that are all linked to the same search (this provides a logical focus for the dashboard). A dashboard can also contain visualizations that are linked to multiple searches (Dashboard 360). This is a new feature, introduced in Siren 10.3, which can provide great benefits for data analysis.

Before you create a dashboard, you should decide whether it will be based around a single search or multiple searches, as this determines the kind of data model to be used.

  • Single search: Click Data Model, then select the Dashboard is about a search option. You can then select a search from the dropdown list.

  • Multiple searches: Click Data Model, then select the Dashboard 360 with filter strategy option.

When you start with Siren Investigate, it is easier to use dashboards based around a single search. It is very straightforward, and easy to get consistent results.

Before creating a dashboard based around multiple searches (Dashboard 360), you should read The Dashboard Data Model topic, which provides a comprehensive explanation of the feature.