Making choices on graph types, data overlays and even color depends on knowing what type of decision-making will come out of the data visualization.
When a dashboard supports short-term operational decision-making — such as determining whether to escalate a potential problem or to approve a request — data visualization can speed decision-making and improve accuracy through the use of colors, graphics such as arrows and limit lines, and graphical and table data on the same visualization.
Although it’s tempting to be transparent and deliver the same information in the same format to agency end users as to the public, doing so can lead to significant misunderstandings. The general public won’t have the same context and subject-matter expertise as internal users, and will require a presentation in a different format with significant additional explanations.
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3. Turn to Data Scientists and Use Accurate Stats
For a simple dashboard with time-based data on a graph, there are no statistics involved. Anytime a visualization tries to show the relationship between different data sets, however, you’re in the world of statistics — and that’s not always a good place for amateurs.
It’s important to ensure that the math is right when you are using correlation, summaries, averaging and other similar statistical manipulations. A full-time data scientist can help ensure the accurate use of statistics. If a full-time data scientist is not available, some short-term help can ensure appropriate use.
A similar concern lies in the graphics themselves. Many business intelligence and visualization tools have a huge variety of graphic options — not just tables and charts but also bubble charts, heat maps, mosaic graphs, tree maps and more.
Selecting the right graph type and the right axis scales is not just a matter of aesthetics; it’s the best way to focus the viewer’s attention and deliver the right information as accurately as possible.
For IT teams that have learned to graph using Excel, some training on choosing and presenting information in graphics is a requirement. A good place to start is with Edward Tufte’s classic book The Visual Display of Quantitative Information.
A natural way for agencies to help end users understand data is to use geographic-based visualizations, since so much of agency work is based on location. When using geographic visualizations, however, it is important to use clear, precise geocoding. For example, if data was coded only to the state level, then users shouldn’t be able to zoom in to a more specific level that implies greater precision.
They say a picture is worth a thousand words, but the right picture can be worth so much more. It can be deceptive and confusing — or it can explain, persuade, clarify and convince.
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