Data Viz Done Right

April 22, 2013

Notes from the Visual Business Intelligence Workshop: Day 2 – Information Dashboard Design

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Day two of Stephen Few’s three-day Visual Business Intelligence Workshop centered around his book Information Dashboard Design.  This class included quite a bit of critiquing of dashboards from “BI” vendors, a look at some of the better work, and a bit of hands on creating our own designs.

Like day one, these are the key points I wrote down (nowhere near the entire content of the course) that we should all reinforce in our work.

  • Well designed dashboards and well designed software allow for rapid visual monitoring, which has a three-phase analytical approach:
    1. Scan the big picture
    2. Zoom in on important details
    3. Links to supporting detail
  • The visual display of a dashboard needs to match the reader’s mental model.  If the reader does not have a mental model, then you should sit with them to develop one.  Avoid asking them “What do you want your dashboard to look like?”, rather get a sense for what questions the reader expects to be able to answer.
  • Be aware of the 13 common mistakes in dashboard design
  • A great way to convince people of how simple data visualize can be is through Stephen’s “Graph Design IQ Test”.  Answering the questions wrong is pretty funny.
  • There are four characteristics of a good dashboard design:
    1. Exceptional organization
    2. Data is condensed in summaries
    3. Data is specific to and customized for the task at hand
    4. Concise, clear and often contain small display mechanisms
  • Never ask people what they want their dashboard to look like.
  • Common dashboard data consists of:
    1. Measures of what’s currently going on
    2. Each compared to something to provide context
    3. Each evaluated to declare its qualitative state
  • Don’t design a dashboard only to highlight problems and exceptions.  The dashboard should be meaningful even when all is well.
  • Objectives of visual design:
    1. Eliminate clutter and distraction
    2. Group data into logical sections
    3. Highlight what’s most important (Place what’s always important on the upper-left)
    4. Support meaningful comparisons / give your data context (this was a them that came up over and over again)
    5. Design for aesthetic appeal (but don’t add fluff to add fluff)
      • Use soft, natural colors
      • Soften the background of the dashboard (Stephen likes to use a soft yellow)
      • Charts and text should be crisp and clear
      • Use good fonts (stick to Sans Serif on dashboards)
      • Only include one font style per screen
    6. Navigating to additional important needs to be easy and should support our train of thought.
      1. Scan the big picture
      2. Zoom in on important specifics
      3. Link to supporting details

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