Why Data Visualization is Important in Delivering Actionable Insight


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Iain Curtain, Ranzal Hyperion Consultant conducted this presentation at OAUG's recent Connection Point conference in Seattle, July 20-21.

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Why Data Visualization is Important in Delivering Actionable Insight

  2. 2. Agenda• Introduction• Design examples• Understanding the customer• What makes good design• How to apply this to reporting• Summary and examples• Questions
  3. 3. About
  4. 4. Customer Sample
  5. 5. Design Examples - Lots of images - Attention not drawn to specific items - Too much color - Visuals used when charts or data could convey messages better - Waste of valuable screen real estate
  6. 6. Design Examples - 3-D rendering makes it harder to interpret the values - Forecasted Units and Dollars lines connect the regions, when they are distinct data points - Dollars and Year Ago Dollars are stacked, when they are also distinct values - Excessive use of the word ‘Region’
  7. 7. Design Examples - Recent example of visuals over substance and meaning - Very difficult to determine correlation between circle sizes
  8. 8. Design Examples - Dark images and background distract the viewer - Cannot determine trends with only 2 data points - Confusing as 2010 values are not circles - Visuals used when charts or data could convey messages better
  9. 9. Customer Maturity Customers are on a reporting journey and determining what their requirements and future plans are Scorecards important in understanding where they are, what their Dashboards needs are and where they think they are going. Mgmt Reports You must satisfy the pre-requisites before climbing up the Operations pyramid. Foundation
  10. 10. Customer Maturity Areas to grow Consider 3 different part of the organization at different stages of maturity: - Finance = Area 1 - Logistics = Area 2 - Manufacturing = Area 3Maintain current operations business Area 1 Area 2 Area 3
  11. 11. User Requirements• Talk to users• Listen to users• Ask why, why, why?
  12. 12. Good design concepts• Memory limits• Encoding data for rapid perception• Gestalt principles of perception
  13. 13. Memory Limits• Iconic memory – visual cues, pre-conscious /pre-attentive processing• Short term memory – conscious processing, 3-9 chunks only• Long term memory
  14. 14. Data Encoding - How many 3’s can you find? - As there is no1723957695026398027384956012 encoding of data, we process9847536970898726547867925019 sequentially – attentive processing2005928976548102985079827158 - very slow!029745647859706987394058869857263271895069729150698712562783789
  15. 15. Data Encoding - 7 is the correct answer - Much easier to see when data is in a1723957695026398027384956012 different color, the same goes for9847536970898726547867925019 bolding, size, shape and orientation2005928976548102985079827158 changes as well029745647859706987394058869857263271895069729150698712562783789
  16. 16. Gestalt Principles Here we see: • Proximity - 2 groups rather than 7 blogs - 2 different sets within the groups - 2 further groups • Similarity within the groups • Enclosure
  17. 17. Gestalt Principles Our minds: • Closure - Close - Continue - Link even though these can • Continuity be seen as discrete items. • Connection
  18. 18. Applying concepts• Focus on the value add you are showing by organising and minimising the data shown.• Arrange information in a way that makes sense, making sure that the important data stands out.
  19. 19. Edward R Tufte• Tufte provided lots of thought around how we view and perceive data
  20. 20. Data Ink Ratio• Key concepts: reduce non data ink from graphics, focus on the values• Reduce graphic paraphernalia (chartjunk)
  21. 21. Chartjunk• Which of these has clutter and unnecessary items?• Which is easier to see the data?
  22. 22. Colin Ware Colin Ware Information Visualization - Perception for Design, 2000 “We can easily see patterns presented in certain ways, but if they are presented in other ways they become invisible.”
  23. 23. Colin Ware We distinguish the items if different in terms of: • Color - Color – either hue or intensity - Form – can also be size, shape, orientation • Form
  24. 24. Colin Ware We distinguish the items if different in terms of: • Position - Position - Motion: flashing/moving should only be used for real time data or issues requiring immediate attention • Motion
  25. 25. Stephen Few• Combined previous theories and melded with current designs and dashboard and communication ideas
  26. 26. Stephen Few This simple example shows how we can’t easily compute size variations in area. The large circle is 16 time larger. Pie and area charts should not be used as we cannot quickly recognize the differences.
  27. 27. How does this help us? • Simplify – reduce the data presented • Simplify – concentrate on important information • Simplify – remove unnecessary color and distractions
  28. 28. Examples Top example is very difficult to read and interpret numbers. Bottom report is cleaner and can easily see the items without distracting border and shading.
  29. 29. Examples Top example is very distracting and difficult to focus on areas that need attention. Bottom report is much cleaner and easier to see items needing attention.
  30. 30. Design Examples Much easier to compare the different market capitalizations when presented as a bar chart. Also much easier to see the best/worse if ordered.
  31. 31. Design Examples
  32. 32. Design Examples 1,600 1,500.0 1,400 1,200 1,000 UK 800 Germany In charting the previous 600 North America graphic, it’s obvious 400 165.8 that there is noUS$ Millions 200 128.573.5 20.4 25.2 4.3 45.0 2.9 relationship between 0 2005 2009 2010 the years or countries. Maybe a table would 1,600 1,500.0 have been better? 1,400 1,200 1,000 2005 800 2009 600 400 2010 128.5 165.8 73.5 US$ Millions 200 4.3 45.0 20.4 25.2 2.9 0 UK Germany North America
  33. 33. Edward R Tufte The Visual Display of Quantitative Information, 1983 “Graphical excellence is that which gives the viewer the greatestnumber of ideas in the shortest time, with the least ink in the smallest space.”
  34. 34. Iain CurtainConsultantiain.curtain@ranzal.comhttp://thinsliced.wordpress.com/202 683 0874www.ranzal.com