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Designing with Data: Creating Visualizations to Tell Your Story

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A presentation explaining the importance of visualizations. I begin by reviewing some general theories about translating data into visuals, and then dive deeper into some specifics for using qualitative and quantitative information to tell your story. Finally I close by discussing some more technical details that everyone making visualizations should be aware of.

It was geared towards an internal audience that has varying levels of technical understanding regarding the artistic, psychological, and narrative principles that inform well made visualizations and infographics. ‎

Published in: Design, Technology
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Designing with Data: Creating Visualizations to Tell Your Story

  1. Welcome to the presentation on Designing with Data. I hope you’re excited to learn. Dominic Prestifilippo | andCulture | Design Methods Training | December 4, 2013
  2. AGENDA • Intro • General Theories • Quantitative • Qualitative • Details • Critique
  3. introduction People are visual learners Visualizations help everyone
  4. introduction “… 80% of the information we take in is provided by our eyesight.” People are visual learners. http://www.vision1to1.com/EN/HomePage.asp?BGColor=1&Category=6&Article=122
  5. Introduction Visualizations help everyone. 1. Making them provides further insight into the information 2. Visualizations invite comments and inspired discussion 3. Enable presentations that aren’t reliant on scripts or memorization Dan Roam, Back of the Napkin, pg 11
  6. General Theories Storytelling Levels of Information Layer Information Proportions Sanity Check
  7. General Theories | Storytelling Tell a story. Provide context. Don’t let data lie. have intent.
  8. General Theories | Storytelling Tell a story. Have a point to make when creating an infographic and let that guide your decisions. http://visual.ly/most-popular-baby-names-girls
  9. General Theories | Storytelling Tell a story. Have a point to make when creating an infographic and let that guide your decisions. My interpretation is, anyone with these names should hope they have interesting middle names. Is that the intent? http://visual.ly/most-popular-baby-names-girls
  10. General Theories | Storytelling provide context. 380,000 Number Of Locations Worldwide Information without context is un-relatable. People don’t know what it means or what to do with it. Western Union http://issuu.com/dpresto/docs/remas_book
  11. General Theories | Storytelling provide context. Sure it seemed like a lot before, but you may have also thought there was a lot of these other locations. This helps highlight the differences in perception of “a lot.” 380,000 Number Of Locations Worldwide Information without context is un-relatable. People don’t know what it means or what to do with it. 31,000 16,700 8,500 Wal-Mart Starbucks McDonalds Western Union http://issuu.com/dpresto/docs/remas_book
  12. General Theories | Storytelling Don’t let Data Lie. Percentages hide absolute values, skewing real scale. http://visual.ly/most-popular-content-management-systems-2013
  13. General Theories | Storytelling Don’t let Data Lie. Percentages hide absolute values, skewing real scale. Earlier in the graphic, we’re told Wordpress has 50.07% of the CMS market while Joomla only has 6.44% http://visual.ly/most-popular-content-management-systems-2013
  14. General Theories | Storytelling have intent. Treat each decision as if it is crucial to the entire piece, because it is. http://visual.ly/knife-skills
  15. General Theories | Storytelling have intent. Treat each decision as if it is crucial to the entire piece, because it is. I assume the decision to illustrate this as a sketch is to make something potentially scary and dangerous seem more approachable. http://visual.ly/knife-skills
  16. General Theories | levels of info Broad Points. Visible from 4’ or more Very Specific Details. visible from less than 1’
  17. General Theories | levels of info 4 feet 12 inches http://visual.ly/how-startup-funding-works
  18. General Theories | Layer Information Average wait times Juxtaposing relevant data can produce even more interesting results, highlighting potential relationships and making both data sets more valuable. http://visual.ly/waiting-time-week
  19. General Theories | Layer Information Average wait times per day is much more interesting http://visual.ly/waiting-time-week
  20. General Theories | Proportions The Golden Ratio. The Fibonacci Sequence.
  21. General Theories | Proportions The Golden Ratio. a/b = (a+b)/a ≈ 1.618033988 a b Sample Pattern.
  22. General Theories | Proportions The fibonacci sequence. 1 0+1=1 1+1=2 1+2=3 2+3=5 3+5=8 5+8=13 8+13=21 13+21=34 • • • Sample Pattern. • • •
  23. General Theories | Sanity Check • Is this important? • Does this provide value? • Does this make sense? • Can this be done better? • Does this help convey my message? • • •
  24. Quantitative Graph Types Statistics
  25. Graph Types | Basic Bar Charts whiskers bar chart bar chart Bar Chart. “The biggest benefit of bar charts is that different tems of data can easily be compared visually.” whiskers whiskers histogram histogram Stacked Bar Chart. histogram histogram. “Stacked bar charts describe totals while allowing a degree of internal breakdown of the data.” “…in a histogram it is important to retain and display the empty space. It contributes to the picture of the data as a whole.” stacked bar chart stacked bar chart candlestick candlestick Brian Suda, A Practical Guide to Designing with Data, pg 114, 119, 120
  26. Graph Types | Advanced Bar Charts bar chart whiskers Whiskers. bar chart whiskers “…whisker is a small vertical line representing plus or minus two per cent from the value, with some horizontal histogram histogram lines to make the ends easier to see and measure.” stacked bar chart candlestick stacked bar chart candlestick Candlestick chart. “The whiskers, or wicks, that extend up and down do not measure margin of error, but the maximum and minimum…” where the bar represents the starting and finishing points. Brian Suda, A Practical Guide to Designing with Data, pg 121, 122
  27. Graph Types | Pie Chart “…a pie chart can only represent relative amounts.” “The most effective pie charts comprise only two items, such as the percentage of male or female customers.” “The total value of the information must add up to one hundred per cent…” Unknown Female Male Brian Suda, A Practical Guide to Designing with Data, pg 132
  28. Graph Types | Others line graph. scatter plot. “Line graphs work best when the data is continuous.” “Scatter plots are a useful tool to reveal relationships between any amount of independent values. …The data points are placed in a grid in an attempt to build a larger picture.” “One of most common variables used in line graphs is time…” Brian Suda, A Practical Guide to Designing with Data, pg 111, 161
  29. Statistics | Average Σ( Σ( )= M #of elementsof elements in the series # in the series )= M =M =M =M =M MEan. MEdian. Mode. “We add together all of our test results and then divide it by the sum of the total number of marks there are.” “The Median is the ‘middle value’ in your list.” “The mode in a list of numbers refers to the list of numbers that occur most frequently.” http://math.about.com/od/statistics/a/MeanMedian.htm
  30. qualitative Statements Relationships
  31. Statements | Bold Statements Make Bold Statements
  32. Statements | Highlighting “Use this to highlight a piece of a quote you would like cited.” http://www.plantbasedpeople.com/misc.php?do=bbcode
  33. Statements | Iconography Include relevant iconography to help with wayfinding and make the written content more memorable http://pictos.cc/
  34. Relationships | Mind Map Sub-idea 1 a2 b-ide Su a de I Idea 3 It is an unstructured visual outline that allows people to move through the related content in any order they choose. Connected information logically as its produced so that train-of-thoughts and conversations can be easily documented by topic. a1 ide ub S Sub -id ea 2 1 Mind Map Id ea 2 a1 de -i ub S Sub-idea 2 Su bid ea 3
  35. Relationships | Affinity Map Using proximity and position to indicate relationships between statements. These clusters develop organically depending on the content under review.
  36. Relationships | Flow Charts Flow charts are a very detailed, standardized way of mapping processes. Start action Decision Decision action Stop
  37. Details Data to Pixel Ratio Chart Junk Resolution Color Legends
  38. Details | Data to Pixel Ratio “the amount of ink representing the data divided by the total ink on the graph” Don’t be confused; the data–ink ratio is not advocating the use of as little ink as possible, but only as much ink as needed to convey the data 10 8 6 4 2 2 4 6 8 10 Brian Suda, A Practical Guide to Designing with Data, pg 25, 27
  39. Details | Chart Junk “…if you remove something from the chart and it doesn’t change the meaning, it’s chart junk “ Brian Suda, A Practical Guide to Designing with Data, pg 25, 27
  40. Details | Resolution DPI Dots per Inch For Print Media. It is preferable that documents are at least 300dpi. For Digital Media. It is preferable that documents are at least 72dpi.
  41. Details | Color Color can do a lot to help clarify information on a chart. However, mis-use and it will only add to the confusion. Be mindful of how you use color. It can easily be overdone. Try starting with black and white, then adding color later.
  42. Details | Legends As nice as it can be to have a very “clean” visualization or chart, if it doesn’t convey the necessary information it is useless. Make sure, if you do use distinctions such as color, shape, size, etc. to differentiate data, make sure it is labeled and clear. 10 8 6 4 2 2 4 6 8 10
  43. Further References A Practical Guide to Designing with Data by Brian Suda The Back of the Napkin by Dan Roam The Visual Display of Quantitative Information by Edward Tufte Envisioning Information by Edward Tufte Visual Explanations by Edward Tufte Visual and Statistical Thinking: Displays of Evidence for Making Decisions by Eward Tufte
  44. AGENDA • Intro • General Theories • Quantitative • Qualitative • Details • Critique
  45. Thank you for learning more about Designing with Data. Do you have any questions? Dominic Prestifilippo | andCulture | Design Methods Training | December 4, 2013
  46. Critique http://visual.ly/

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