Thank you for this insightful article! As a dashboard designer, I often struggle with applying principles of title design to dynamic or interactive visualisations. Unlike static visualisations, dashboards evolve based on user inputs, such as filters, time ranges, or drill-downs. This makes it challenging to craft titles that remain clear and meaningful across different contexts.
I’d love to hear your thoughts on designing titles for interactive contexts and how we can apply these principles to guide users effectively. Thank you again for sharing your framework—it’s already helped me think more systematically about my title design process!
I am not an expert on dashboards. For sure declarative titles are the most appropriate for graphs used in dashboards. But even for that case they should be dynamic and reflect filters, ranges, aggregations. I believe Power BI has something similar. I don’t think the problem can be solved without embedding some logic in the dashboard. The titles necessarily need to adapt to the different states of each chart. Interestingly, visualization researchers have also been researching this idea of extracting “data facts” and annotating charts with them automatically. I don’t have the reference at hand now but I can share it later if you want. Thanks for reading and commenting!
In my opinion, repeating the names of the axes is not enough for the title of the graph. Also, every graph must include the measurement dates. I teach my students that there are 2 types of titles: conclusion and description and their combination.
A conclusion title is like an interpretation title in your taxonomy. "The reason for the largest number of road accidents is..."
A description title must include one of the 5 types of meanings that can be identified in the format (structure) of the graph: hierarchy, seriality, dominance, trend, density. For example:
"Hierarchy of the number of road accidents by causes in 2020."
Ordinality of the number of road accidents by months in 2020."
"Trend in the number of road accidents by years from 2018 to 2020."
"Dominance of the percentage of the cause... in the total number of road accidents in 2020."
"Density of the number of traffic accidents per 1000 residents in the area... in 2020."
Thank you for this insightful article! As a dashboard designer, I often struggle with applying principles of title design to dynamic or interactive visualisations. Unlike static visualisations, dashboards evolve based on user inputs, such as filters, time ranges, or drill-downs. This makes it challenging to craft titles that remain clear and meaningful across different contexts.
I’d love to hear your thoughts on designing titles for interactive contexts and how we can apply these principles to guide users effectively. Thank you again for sharing your framework—it’s already helped me think more systematically about my title design process!
I am not an expert on dashboards. For sure declarative titles are the most appropriate for graphs used in dashboards. But even for that case they should be dynamic and reflect filters, ranges, aggregations. I believe Power BI has something similar. I don’t think the problem can be solved without embedding some logic in the dashboard. The titles necessarily need to adapt to the different states of each chart. Interestingly, visualization researchers have also been researching this idea of extracting “data facts” and annotating charts with them automatically. I don’t have the reference at hand now but I can share it later if you want. Thanks for reading and commenting!
… I forgot to ask. What kind of dashboards do you develop and what tools do you use to create them?
In my opinion, repeating the names of the axes is not enough for the title of the graph. Also, every graph must include the measurement dates. I teach my students that there are 2 types of titles: conclusion and description and their combination.
A conclusion title is like an interpretation title in your taxonomy. "The reason for the largest number of road accidents is..."
A description title must include one of the 5 types of meanings that can be identified in the format (structure) of the graph: hierarchy, seriality, dominance, trend, density. For example:
"Hierarchy of the number of road accidents by causes in 2020."
Ordinality of the number of road accidents by months in 2020."
"Trend in the number of road accidents by years from 2018 to 2020."
"Dominance of the percentage of the cause... in the total number of road accidents in 2020."
"Density of the number of traffic accidents per 1000 residents in the area... in 2020."
I like the 5 types of meaning!
Great article as always. Thank you!
Thanks!