On June 11, industry experts, data management practitioners and visionaries convened on Twitter to discuss trends in data visualization during our bi-weekly #IMChat. Whether you want to view the daily use of the MTA or the daily use of your company’s enterprise social media network, data viz can provide a picture that tells your data’s story. Still, there can be a disconnect between the data and the decision.
Information Management Editor-in-Chief Julie Langenkamp-Muenkel (@JulieLangenkamp) moderated a lively chat which addressed all these questions and more, some of which is encapsulated below:
When do you most appreciate visualizations? What kinds of information do you most prefer visually?
@WhitneyAEden: The simple, but humble table often works best
@jgrecco13: Always. My clients are visual people.
What makes visualizations effective? What features do you appreciate when creating or “consuming” them?
@lewandog: Instantaneous understanding of results, and true insight. Good design, proper techniques, use of color and annotations for meaning
@IMJustinKern: Simplicity. A Pollack painting of data points does less for me than written descriptions, actually.
Along those lines, what are some fundamental design principles – do’s and don’ts – for data viz?
@jgrecco13: Remove extraneous data/visual elements and get to the point quickly.
@JulieLangenkamp: 3D charts are typically a bad idea as are absurd comparisons (giant quantities of numbers)
@KevinTaylorNC: 3D should be removed in almost every case
@KevinTaylorNC: Pie Charts are typically not the best option, Donut chart is worse
@jgrecco13: I've found bubble charts and histograms are received well for communicating activity over time.
@KevinTaylorNC: @tableau has a color blind palette
@JulieLangenkamp: Jonathan Schwabish recommends knowing the differences between exploratory & explanatory data visualizations.
How do you determine what kinds of charts/graphs are the best choice to use for your data?
@jgrecco13: By a lower number of questions about what seems obvious.
@WhitneyAEden: In data viz (and in life), I always refer back to K.I.S.S. (Keep it Simple, Stupid). Often you can do more with less
@KevinTaylorNC: A lot depends on audience & delivery channel (i.e. power point vs interactive)
@KevinTaylorNC: Interactivity is important - ability to drill for more info & explore rather than just deliver predetermined info
How do you balance aesthetic/design choices with the end goal of helping people understand data?
@WhitneyAEden: You should be guided by this question: “Does this design choice support the comprehension of the data?”
@KevinTaylorNC: Above all else, it must inform. Beauty can be an added bonus but should not be the goal
What are important features to be aware and perhaps seek out with data viz tools?
@jgrecco13: Ease of styling. The viz usually lives (should live) in an attractive document.
@WhitneyAEden: If the UX of a data viz platform is horrible, that doesn't leave much hope for the actual output (i.e., data viz)
@JulieLangenkamp: interactivity (graphs, maps, etc.)
@KevinTaylorNC: Lack of Bling, some tools don't even have a 3D or shadow option. Want a tool with chart variety, i.e Treemaps, Network Maps, etc
Join us June 25th at 1PM ET for the next #IMChat.
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