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Feedback

As explained in Everyday Data Visualization

Feedback is information or evaluation provided about a product or process to guide its improvement.

Feedback is the visible response or useful input that tells people whether an action worked and what should change next. In interactive visualizations, it confirms that a filter, toggle, or other control changed the displayed data. In project work, it keeps requirements, designs, prototypes, and later builds aligned with stakeholder and user needs. section 7.1.3 section 8.4

Make feedback easy to notice and useful to act on:

  • Place a response on screen at or near the control someone used, so the connection between action and result is clear. section 7.1.3
  • Use motion, such as redrawing lines, resizing bars, rearranging marks, or a loading spinner, but avoid slowing the page or creating distraction. section 7.1.3
  • Change labels, highlights, or font styling to show which data is active and what has changed. section 7.1.3
  • Ask for stakeholder and user input before a build stage is finished; frame unfinished work and set expectations before review. section 8.4.2
  • Treat criticism as input about the work rather than a personal attack, especially when reviewing work in progress. section 8.4.2

A filter that answers back

Someone changes a dashboard filter and wonders whether anything happened. A revised title, moved highlight, or animated change in the chart provides an immediate answer. If new data must load, a spinner can make the delay legible rather than mysterious. section 7.1.3

Feedback also powers iteration: plan, build, get feedback, then repeat. Requirements can be revised after stakeholder review; wireframes and mockups can be checked with users; the first development build can be a prototype. Frequent small reviews reduce the risk of discovering late that the result misses what the client wanted. section 8.4 section 8.4.2

Figure 7.3 Some ways to show the user that their interaction has changed the data being displayed are to change labels such as titles, move a highlighter box, and/or change font styling.
07-03

Compare label changes, highlight movement, and font styling as compact ways to show that an interaction changed the data on display. section 7.1.3

How Everyday Data Visualization covers it

How deep it goes
Explains visual response choices and repeated review cycles, while stopping short of tool-specific implementation steps.
Examples
Uses dashboard filters and toggles, animated charts and spinners, labels and highlights, prototypes, wireframes, and mockups.
What you can do after
Apply it via visible interaction cues and repeated stakeholder and user review.
How it connects
Connects feedback with affordances, error messages, mental models, and accessibility.

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This explainer was generated by Manning's knowledge graph from the text of Everyday Data Visualization. The section links point to the parts of the book it draws on.