Tag Archive: less is more

  1. Define “Better”…

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    In the visual realm, what people “like” is by definition subjective.

    What “works” and what doesn’t in the context of dataviz design, however, is usually something one can determine a little more easily.  Effective data visualization is a combination of what’s visually appealing and what best communicates the creator’s ideas.

    In a 2-part series this week and next, we’re going to take a look at some visualization examples and question what “works” best in terms of being effective and being accurate.  As we’ll show, sometimes it’s a simple matter, and sometimes it’s not.  In most cases, the nature of the data shown will drive the most effective way of displaying it.

    Example 1:
    These charts show the same data points but in very different ways.  Line or trend charts are usually most effective in showing data trends over time; hence the name “trend”.  Showing data across non-date/time categories doesn’t really lend itself to using a line chart.  A simple side-by-side comparison of figures is really suited to using bar (or column) charts.  Hence, Choice B is more effective.

    Example 2:
    Pie charts are designed to show what share of the “pie” each category has.  Using the same data points, the bar chart is again the better choice in this example because it’s a simple numeric comparison.

    Example 3:
    This example is not quite as straightforward and perhaps a little more subjective.  But unless the column colors in Choice B are tied to their respective categories, there’s no reason to use different colors, except just because you can.  For that reason, using a single color for the columns looks less “busy”.

    Example 4:

    If we stick with the “single color” idea, then Choice A would probably be the better of the two, unless you’re an especially big fan of electric lemon yellow, or that’s the client’s corporate color.  In which case, we feel your pain because we’ve been there.

    Example 5:
    Even without the prevailing consensus in the wider design world that “flatter is better”, we think the simpler design of Choice A is nicer to look at.  Just because you CAN contort a chart into an angled, cylindrical format…

    Example 6:

    Should be fairly obvious…  we hope…

    Example 7:

    Enough with the zeroes!

    Example 8:

    This last comparison may not register so quickly.  Go on, take a few seconds, we’ll wait…

    *checks watch impatiently*

    You have to look carefully, which is the inherent danger of using a non-standard Y-axis range on a chart.  (Did you catch it unprompted?)  Unless Choice B appears in the context of other charts which also don’t start their Y-axes at zero, the user’s expectations are that it would start at zero.

    At first glance, the charted column values in Choice B are quite big (c’mon, Shirts, pull your weight!).  But checking the actual numeric values will show the gaps are not so dramatic after all.  Of course, doing a chart like Choice B CAN be used to influence the viewer to a certain point of view, should that be the goal of the chart designer.

    In our next posting, we’ll take a look at some examples that are a bit more definitively “wrong”…


  2. Less Is More!

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    Less Is More.



    See what we did there?

    OK, that’s a little extreme perhaps, but illustrates the point perfectly:  “less is more” really says it all.  (Our boss, however, told us we need to write more, so…)

    Now let’s talk “less is more” in the context of data visualization design.  There are virtually infinite design choices you can make in combining color, axis formatting, tick-marks, grid lines, plot area, and every other dataviz design element.  We’ve all seen presentations where the creator used EVERY option available (because they could!) and how well that worked out.  Ideally your charts shouldn’t look like a Jackson Pollock wannabe does your PowerPoint decks.

    “Non-data pixels” is a dataviz design term which simply refers to any design element that doesn’t directly tell your data’s story; think in terms of everything that ISN’T or doesn’t directly represent “the numbers.”  The concept is to minimize or eliminate outright from your dashboard “canvas” that which is dispensable.  Now, that can range from the fairly obvious – e.g. do you NEED to use a green-to-purple color gradient for your chart’s plot area, even IF those are your client’s corporate colors? – to the subtle – will using horizontal gridlines every 5% help people understand the numbers any better than every 20 or 25%?  Or, will using NO gridlines at all work?

    Examples of minimizing non-data pixels are things such as: making chart axis lines and gridlines a shade of gray rather than black; same (maybe to a lesser extent) with axis label text or numbers; using fewer or lighter colored gridlines in tabular data visualizations; not using special effect “skins” (e.g. to appear like a reflective glass surface) for chart bars or pie wedges.  You want design elements which enhance your data’s story and not detract from it or distract the viewer.

    We created a dashboard for the end-client of one of our market research clients. The users were research data analysts at a consumer goods company based at several locations across the globe. Our MR client’s proposed design featured their client’s logo prominently on every dashboard canvas, but our design team suggested that since the users were all from the same organization and THEY all knew who they worked for – they all had that in common – such obvious branding wasn’t a necessity.  We instead used subtle color themes based on their corporate colors.  As it turned out, the end-client was very pleased with the overall site aesthetic, and especially with the branding!

    A slight twist on minimizing non-data pixels can be illustrated very clearly in a dashboard populated with columns of data in a table where you want to show – via arrow indicators – variance from a benchmark score.  There are essentially two ways to do this:  you can use an arrow icon for EVERY score on the table – green “up” arrows for scores which are above the benchmark and red “down” arrows for below the benchmark – or, ONLY use red arrows for the below benchmark scores.  Using only red arrows makes it easier for the viewer to process the information at an initial glance, because they’re only looking for one symbol and one color in the table.  If a number’s NOT got the red, it’s above the benchmark.  Less (or, in this case, none) really is more.

    When doing dataviz design we try to borrow a few ideas from Gestalt psychology and how the brain subconsciously makes connections.  For example, if you have certain data objects grouped closely together on the dashboard canvas, the viewer’s mind will naturally make an association that those objects are related somehow.  What this means for design is that you could use physical position as a means of grouping related objects, rather than placing them within, say, a rectangular frame with a colored background (a non-data pixels warning sign).  Using colors in the “right” way of course has its advantages, but by avoiding color in some cases you can also avoid the potential for unintended or conflicting associations in the viewer’s mind.

    We believe that every design decision you make should be for the enhancement of your visualizations; if you ask yourself at every step in the design process, “how does this element / color / feature benefit my client / viewer?” and you cannot easily answer that question, you may want to rethink it.  After all, what you decide NOT to do is every bit as important as what you decide to do.


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