Note: this blog post refers to the book Data Visualisation: A Handbook for Data Driven Design by Andy Kirk.
Interactivity in data visualizations opens up new possibilities for how information can be communicated and explored. It can significantly expand the amount of information that can be conveyed within the physical limitations of a single chart. It also enables users to view data from multiple analytical perspectives. Furthermore, interactivity can be used to increase the level of customisation available to the user, thus making the experience more engaging.
Interactivity can be based either on adjusting the data (affecting what data is displayed) or on adjusting the presentation (affecting how the data is displayed). The most appropriate option depends on the specific circumstances.
Data Adjustment
Data adjustment involves changing what data is displayed in the visualization. This is especially valuable because there is a limit to how much data can be shown at once.
With interactive tools like categorical filters or quantitative range sliders, users can narrow down the dataset to focus on specific segments or time frames. This way, all the available data is included, but not all of it at once. For instance, users might want to filter a chart to only show data for a particular period, region, or income level. You can also use filters to increase or decrease the level of detail—for example, by expanding certain categories to reveal more detailed subcategories.
Presentation Adjustment
While data adjustment changes what is shown, presentation adjustment changes how the data is shown. This type of interactivity allows users to manipulate the visual representation of the data without changing the underlying dataset. For example, users might highlight specific values by changing their color or adjust the sorting order of elements to bring particular data points to the forefront.
One common form of presentation adjustment is "linking," whereby hovering over a mark in one chart highlights the corresponding mark in another, thereby drawing attention to their relationship. Another example is the ability to reorder columns in a table, e.g., by clicking on the column header and thereby sorting quantitative data by magnitude.
Summary
Interactivity has the potential to enhance the richness and usefulness of data visualizations by allowing users to control both the content and the form of what they see. When used thoughtfully, it transforms static visuals into dynamic tools for exploration, insight, and personalized discovery.
Source: Kirk, Andy (2016): Data Visualisation: A Handbook for Data Driven Design, Sage.