Display highlights

Last modified by Aurelie Bertrand on 2026/06/29 11:12

The key insights dashboard uses advanced algorithms to analyse your data in order to improve your understanding of it. Which factor(s) contribute most significantly to a particular value? Is there a correlation with another metric? ...
Artificial intelligence analyses the dimensions and metrics contained in your data model to provide you with relevant insights relating to the selected data, for example to identify anomalies or trends.

💡Visit the page Artificial Intelligence > Key Findings to configure highlights.

Configure the display of highlights

Selecting the dimensions and measures to be analysed

⚠ For data models created in a version prior to 2025 R1, AI analysis is disabled for all dimensions and measures.

By default, all dimensions and measures in the data model are selected for AI data analysis.

You can modify this selection to include only certain dimensions and measures. The selection will be applied by default to all flows (charts) using this data model.
To do this:

  1. In Studio, edit the data model.
  2. In the Columns tab of the model’s advanced configuration, select the dimensions and/or measures that you do not wish to include in the key insights analysis.
  3. In the Properties section of the right-hand panel, untick theAI Analysis box.
    IA_analysis_checkbox_FR.png

Enabling the display of key insights

Key insights are disabled by default for all flows (charts).

To enable key insights for a chart:

  1. Select the chart in the Dashboard Editor.
  2. Go to theProperties section of the Settings panel on the right.
  3. On the Options tab, tick the ‘Show highlights’ box.
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Displaying highlights on the dashboard

ℹ Highlights are not available in mobile mode.

Simply click (see the section Interaction and Highlights) on an item to display the highlights or key points.
Digdash then searches for relevant information linked to the selected data from among:

  • the metrics present in the Flow that have been selected for AI analysis (whether hidden or not)
  • the dimensions (and all associated hierarchy levels) that are not displayed on the Flow and have been selected for AI analysis.

A window appears showing the key points for the selected chart.
It specifies the selected exploration filters and lists the various highlights or key points identified, sorted by measure and then by dimension.

Highlight_example2_FR.png

If there are a large number of key points, a loading bar appears at the bottom of the window with a button to stop the loading process:

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Interaction and highlights 

By default, charts inherit the click interaction defined when they were configured in Studio (if one exists). See the page Configuring Interactions for more details on chart interactions.

In this case, to view the highlights, right-click on the data and then click Show Highlights.
Highlight_menu_rightclick_FR.png

If you wish to set highlights as the default click interaction in all cases:

  1. Go to the User menu and click on Default Interaction.
  2. Under ‘ Default Interaction’, click on ‘Highlights’.

You can also use the shortcut Ctrl + H. A message will then appear.
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Types of highlights 

💡 Highlights are described here for settings with their default values. You can change these values if you wish to influence how these highlights are identified. See the section Highlights for further details.

Several types of key points can be identified.

Mega contributor Mega_icon_FR.png

A mega contributor is a member of a dimension that accounts for at least 40% of a given metric (total sum).
The dimension must have at least 5 members.

For example, in a retail chain, the United States accounts for 54% of turnover.

Top K contributors TopK_icon.png

ℹ Where a Mega Contributor is displayed, there are no Top K Contributors.

The Top K contributors refer to the K best members of a dimension who contribute at least 40% to a given metric (total sum).
K is between 2 and 5. Its value depends on the number of members. By default, K is equal to 33% of the number of members.
Thus, to obtain a Top 2, there must be at least 6 members. For a Top 3, at least 9 members; for a Top 4, at least 12 members; and for a Top 5, at least 15 members.

For example, in a chain of shops, the ‘Fruit’, ‘Gift Set’ and ‘Basket’ product categories account for 62 per cent of turnover.

Pareto Pareto_icon_FR.png

The Pareto principle refers to the situation where 20% of the members of a dimension account for 80% of a given measure.

For example, in a chain of shops, it is observed that the top 20% of best-selling products account for 80% of total turnover.

Correlation Correlation_example_FR.png

ℹ Correlation analysis is only performed if the selected metric includes a Mega Contributor, Top K Contributors or Pareto.

Correlation refers to the situation where a (non-displayed) metric changes in the same way as the selected metric, according to a given dimension.
The correlation analysis is only performed if the selected metric includes a Mega Contributor, Top K Contributors or Pareto.
Correlation can be positive or negative and have different levels of significance: very strong, strong or moderate.
The calculation of correlation thresholds is dynamic. The minimum threshold starts at 0.7 for 10 members.
As the number of members increases, the minimum threshold becomes more lenient, reaching 0.3 for 50 members or more.

For example, there is a very strong correlation between the CA measure and the Quality Index measure.

Find out more...