Data Visualization Best Practices 2013


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Data Visualization Best Practices 2013

  1. 1. Data VisualizationBest Practices
  2. 2. Favorite References
  3. 3. The Value of Data Visualization – Two basic types – find a story the data is telling you – tell a story to an audience – Represents large quantities of data coherently – Help the user to in the data – Does not distort what the data has to say – Takes into account your
  4. 4. The Value of Data Visualization
  5. 5. The Value of Data Visualization
  6. 6. Audience Considerations• What information does the reader need to be successful?• How much detail does the reader need? and what values need action?• What learned or cultural ? – Identity, Motivation, Language and learned Social Context – What do mean? – Which are familiar?• Don’t forget (quite common) – Color palettes friendly to those with color blindness at
  7. 7. Gestalt Principles of Perception : Objects that are close together or connected are perceived as a group : Objects that share similar attributes, color or shape, are perceived as a group : Objects that appear to have a boundary or a continuation around them are perceived as a group : Open structures can be easily perceived as closed, complete
  8. 8. Common Data Visualization Issues• Inappropriate display choices that distort reality i.e. , 3-D charts• Variety for the sake of variety display choices that use noisy fill patterns, line styles, or saturated/bright colors quantitative data and placement• Inconsistent or reversed scales• Proportional axis scaling• Using counts vs. percentages when comparing periods with different totals
  9. 9. Chart with Integrity VS. example of the “charting tricks” politicians use:1. Vertical scaling: Both graphs use a y-axis that is proportionately bigger than the x-axis, exaggerating slope of recent job losses.2. Absolute values: One graph counts actual number of jobs lost, instead of the percentage of jobs lost. The workforce has grown considerably over the years, this exaggerates the downward slope of recent job losses.3. Narrower context: One graph uses fewer past recessions in the comparison, and leaves out the more-severe 1981 recession, and two shorter recessions. This skews extrapolation of what might happen next.
  10. 10. Chart with Integrity VS. example of the “charting tricks”:1. Expressing measures indirectly: One graph more accurately depicts variance, the measure of true concern.2. Absolute values: Variances are better shown when expressed in units of positive and negative percentages.
  11. 11. Bad Data Visualization
  12. 12. Bad Data Visualization
  13. 13. Good Data Visualization
  14. 14. Choosing the Right Chart Type SOURCE: Dr. Andrew Abela
  15. 15. Design Considerations• Part to whole • Exception highlighting• Bar Charts • Avoid meaningless variety• Line Charts • Empty points• Disparate Values • Do not use 3-D charts• Gridlines • Appropriate use of color• Sparklines • Multiple chart areas• Bullet graphs • Use text sparingly• Small multiples • Avoid Too Much Information• Pareto charts • Physical position is easiest to• Status indicators perceive and most powerful visual property
  16. 16. Part to Whole RelationshipsUse Bar Charts versus Pie Charts
  17. 17. Part to Whole Relationships
  18. 18. Bar Chart for comparing across categories, discrete data or continuous data• Orientation: Horizontal for• Proximity – Set white space width separating contiguous bars equal to 50%-150% width of bars (unless bullet graph like)• Fills – Use distinct, but no intense colors for highlighting• Borders – Avoid, – Always start at the axis unless it’s a range bar chart• Tick marks – Don’t overdo the number of tick marks or tick mark labels – Use consistent, sensible tick mark values
  19. 19. Dealing with Disparate Values
  20. 20. GridlinesUse for value look-up, subtle is best
  21. 21. Line Chart Best Practices• Lines should mostly be used for connecting on interval scale except Pareto chart• Intervals should be equal in size• Combination charts should used synchronized axis• Lines should only values in adjacent intervals – If data is missing, indicate it is missing
  22. 22. Sparklines, Data Bars, Indicators• Data-intense, design simple,• Provide of• Highlight Min and Max• Remove or except series
  23. 23. Bullet Graphs• Key measure (value) and Comparative measure (goal) and qualitative ranges (good, bad, or exceed)• Can be oriented horizontally or vertically
  24. 24. Scatter PlotsGreat for correlations between two quantitative dimensions
  25. 25. Small Multiples or Trellis ChartSmall sets of charts arranged in Tables or Matrices to comparethe sense or trend of many values concurrently
  26. 26. TreemapsUse Treemaps to display large numbers of values that exceed thenumber that can effectively be shown in a bar graph
  27. 27. MapsThematic, data overlays, geospatial radius, trade areas, networkanalytics and route performance
  28. 28. Background MappingVisual location context
  29. 29. AnimationView trends over time
  30. 30. Extreme Data SizesNot necessarily a best practice but sometimes needed
  31. 31. Dashboard Design• Designed by consider• Strategic, Analytical and Operational• Combination of individual data visualizations• Must fit• Monitored at a glance so or• Important to and keep context items near each other
  32. 32. Common Dashboard Design PitfallsStephen Few, Perceptual Edge1. Exceeding the boundaries of a single screen2. Supplying for the data3. Displaying or precision4. Expressing measures indirectly5. Choosing of display6. Introducing7. Using poorly designed display media8. Encoding quantitative data inaccurately9. Arranging the data poorly what’s important11. Cluttering the screen with12. Misusing or overusing color13. Designing an unappealing visual display Whitepapers/Common_Pitfalls.pdf
  33. 33. InfoGraphics• Visual information Showcase numbers to provide• Don’t over do it• Avoid text heavy reports• Try to or over by and
  34. 34. Additional Resources• Jen Underwood Blog• Stephen Few• Edward Tufte• Choosing Right Chart Type• Clearly and Simply• Chandoo• R• Word Clouds• Tableau• JavaScript InfoVis Toolkit• D3• Reporting Services• Power View• PowerPivot• Excel 2013• Visio 2013• OpenLayers• PolyMaps• Open Street Map• Charlotte Visualization Center