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Everyday Analytics for Everyone: Communicating Effectively with Data Visualization

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Presented to the Canadian Association of Research Libraries for the Canadian Library Assessment Workshop

October 17, 2013 at Ryerson University, Toronto ON

http://www.carl-abrc.ca/en/research-libraries/claw.html

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Everyday Analytics for Everyone: Communicating Effectively with Data Visualization

  1. 1. Everyday Analytics for Everyone Communicating Effectively with Data Visualization Myles Harrison Canadian Library Assessment Workshop October 17th, 2013
  2. 2. Why Are We Here? – A Story
  3. 3. The Process • • • • Question or Story Data Please Make with the visuals Communicate Question Data Visuals Winning
  4. 4. i.
  5. 5. Thinking Like an Analyst
  6. 6. Thinking about Data – The Exercise – – – – – – – Books Employees Students Budget Size / Capacity Age Location – – – – – – Date Published Subject Author Length Type Publisher – – – – – – Gender Age Program Level Income Nationality
  7. 7. Let’s talk about relationships Doing analysis is as simple as asking the question: “What is the relationship between x and y?”
  8. 8. Thinking about Data – Relationships Libraries Students Size / Capacity Books Nationality Location Employees Budget Income Age Level Students Gender Program Age ………….
  9. 9. Types of Variables • Categorical vs. Quantitative • Categorical: • Nominal • Ordinal • Quantitative: • Discrete • Continuous
  10. 10. Nominal • From nominalis – Latin for name • Named categories without quantity or order • e.g: - color of vehicle - gender of participant - flavour of ice cream
  11. 11. Ordinal • Think “ordered” • Not a quantitative measure but has specific order • e.g: - size (small, medium, large) - quality (poor, fair, good, excellent) - education (bachelor’s, master’s, PhD)
  12. 12. Quantitative Data • Discrete: set values at interval - e.g. number of people • Continuous: any value along the number line - e.g. temperature in degrees celsius
  13. 13. ii.
  14. 14. Visual Encoding Adapted from Show Me The Numbers, 2nd ed. by Stephen Few. Analytics Press, 2012
  15. 15. My God it’s full of graphs
  16. 16. Pie Chart • Probably most common type of chart • Used to compare relative quantities of categorical data using angular area • Consensus amongst data visualization experts: avoid if possible
  17. 17. Many versus few As the number of values of the categorical variable being compared increases, legibility and usefulness decrease rapidly.
  18. 18. Bar Chart • Used to compare absolute values of nominal or ordinal data • Horizontal or vertical (bar and column) • Colour should encode meaning • Bars should not overlap
  19. 19. Bar vs. Column • Either is acceptable • Horizontal (bar) charts may be more suitable if long labels in categorical variable • Vertical (column) charts should used for time intervals
  20. 20. Start axes from zero (please) Because the height of the bar from the x-axis is interpreted as being the total quantity, it is important to start using it as the zero point.
  21. 21. (in thirty seconds)
  22. 22. Scatterplot • For (and only for) showing relationship between quantitative variables • Sizing, colour and symbol shape are important • For large amounts of data, a trend line or other moving average helps to see the overall shape of the data
  23. 23. More data…?
  24. 24. Line Chart • Shows connection and continuity by connecting points with lines • Intervals should be equal and gaps depicted • As number of quantities being visualized increases, readability decreases
  25. 25. Visualizing with integrity Because we subconsciously “fill-in” data gaps it is important to depict data in such a way that this tendency does not result in misinterpretation.
  26. 26. Smooth move An often overlooked nuance is the difference between smooth and straight lines between data points. This becomes more salient as the number of data points decreases.
  27. 27. More lines…?
  28. 28. iii.
  29. 29. Simplicity Elegance Clarity Consistency
  30. 30. The Process Question Data Visualize Insight
  31. 31. Recommended Resources
  32. 32. Conclusion • Today have covered a small portion of realm of data visualization • Always give thought to both your data and design decisions around its visualization • Simplicity and clarity above all • The goal is communication
  33. 33. “Above all else, show the data.” – Edward R. Tufte

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