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Data Visualizations That Expand Your Visual Literacy

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Many data displays are compromised representations that may limit our ability to understand the full story or lead us to shortsighted conclusions. Between multiple screen displays, tables of data, and basic charts that only show a limited perspective of the data, we are often left with subpar tools to combine and analyze data. Collectively, we know we need to improve our data experiences, as well as our ability to see the main issues, discover the hidden details, make connections, and compare the top ideas. Increasing amounts of data only heighten the need to do more with the data we have and ensure our decisions are well considered. As a result, we also need better methods to navigate data and extract multiple questions from datasets so that our follow up queries are only a click away.

Julie Rodriguez draws upon examples from her book Visualizing Financial Data to show you how to turn your raw data into meaningful information. Along the way, Julie shares new visual design methods that provide a greater perspective of the data through embedded context, adjustments to commonly used charts, and new chart types that are easier to read and comprehend.

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Data Visualizations That Expand Your Visual Literacy

  1. 1. Julie Rodriguez Expanding Our Visual Literacy @juliargentinaG
  2. 2. External Communications Explain Visually Individual Displays Internal Presentations Collected Information
  3. 3. 4. Show then tell 3. Easy to navigate 1. Relevant 2. Scan & scrutinize Desperately Seeking
  4. 4. Graphical perception 
 Relative ranking Figure 1. Elementary perceptual tasks, William S. Cleveland; Robert McGill Journal of the American Statistical Association, September 1984 Relative accuracy of various graphical forms that convey quantitative information POSITION COMMON SCALE 10 5 0 POSITION NON- ALIGNED SCALES 1 0 0 10 0 LENGTH DIRECTION ANGLE AREA CURVATURE SHADING COLOR SATURATION VOLUME 1 2 3 4 5 6 7 8 9 10
  5. 5. Alignment & sort order Why Is This Method Effective Accuracy & 
 Perception Design Elements Foreground/ background Color pallet w/ associations POSITION COMMON SCALE 1 0 5 0 POSITION NON- ALIGNED SCALES 1 0 0 1 0 0 LENGTH DIRECTION ANGLE AREA CURVATURE SHADING COLOR SATURATIO N VOLUME 1 2 3 4 5 6 7 8 9 10 Figure 1. Elementary perceptual tasks, William S. Cleveland; Robert McGill Journal of the American Statistical Association, September 1984
  6. 6. Why Is This Method Effective Accuracy & 
 Perception Design Elements Foreground/ background Color pallet w/ associations POSITION COMMON SCALE 1 0 5 0 POSITION NON- ALIGNED SCALES 1 0 0 1 0 0 LENGTH DIRECTION ANGLE AREA CURVATURE SHADING COLOR SATURATIO N VOLUME 1 2 3 4 5 6 7 8 9 10 Figure 1. Elementary perceptual tasks, William S. Cleveland; Robert McGill Journal of the American Statistical Association, September 1984
  7. 7. Embed context into your data displays
  8. 8. Why Is This Method Effective Accuracy & 
 Perception Design Elements Color pallet w/ associations POSITION COMMON SCALE 1 0 5 0 POSITION NON- ALIGNED SCALES 1 0 0 1 0 0 LENGTH DIRECTION ANGLE AREA CURVATURE SHADING COLOR SATURATIO N VOLUME 1 2 3 4 5 6 7 8 9 10 Figure 1. Elementary perceptual tasks, William S. Cleveland; Robert McGill Journal of the American Statistical Association, September 1984 Alignment & sort order Proximity/ comparison Details on demand Grouping Foreground/ background
  9. 9. Aggregation enables better data navigation
  10. 10. Statements of Cash Flows
  11. 11. Why Is This Method Effective Design Elements Limited color pallet w/ associations Connectedness Grouping Foreground/ background Accuracy & 
 Perception POSITION COMMON SCALE 1 0 5 0 POSITION NON- ALIGNED SCALES 1 0 0 1 0 0 LENGTH DIRECTION ANGLE AREA CURVATURE SHADING COLOR SATURATIO N VOLUME 1 2 3 4 5 6 7 8 9 10 Figure 1. Elementary perceptual tasks, William S. Cleveland; Robert McGill Journal of the American Statistical Association, September 1984
  12. 12. Lead with
 visuals, then
 layer in the text
  13. 13. Easy to navigate Scan & scrutinize Relevant Show then tell 2. 1. 3. 4. SUMMARY
  14. 14. Structured/Unstructured, Time, Coordinates, String, Numbers, Nominal, Ordinal, Interval DATA Structured/Unstructured, Time, Coordinates, String, Numbers, Nominal, Ordinal, Interval DATA Structured/Unstructured, Time, Coordinates, String, Numbers, Nominal, Ordinal, Interval Industry Clients Firm DATA DRIVERS Structured/Unstructured, Time, Coordinates, String, Numbers, Nominal, Ordinal, Interval Industry Clients Firm DATA DRIVERS Structured/Unstructured, Time, Coordinates, String, Numbers, Nominal, Ordinal, Interval Industry Clients Firm User Centered Approach Information Design Multi-channel Experience DATA DRIVERSDELIVERY
  15. 15. 40+ use cases 100+ new visualization methods 250+ illustrations 450 pages
  16. 16. Explore... Expand...
  17. 17. Questions?
  18. 18. Thank You…
  19. 19. Visualization Tools (spectrum) CUSTOM SOLUTIONSPREBUILT SOLUTIONS RAW Spreadsheet to vector graphics built on D3.JS Plotly JavaScripting graphing library SandDance Collaborative app for visualizing data IBM Watson Analytics Discovery delivered on the cloud Online Web Apps Tableau BI software QlikView BI software Tibco Spotfire Data viz and analytics software SAS Visual Analytics BI, Data exploration & analytics Trifacta Explore and transform data sources Generalized Software Highcharts JavaScript library D3 Opensource JavaScript library Google Charts Ready to use chart types R Software for statistical computing and graphics Processing IDE for visual communication Open Source Programing Languages Charting Packages MATLAB Multi-paradigm numerical computing environment Mathematica A computation platform Technical Computing Gephi Exploration for graphs and networks ArcGIS Mapping software QGIS Mapping software Excel/ Adobe/ HTML5 Separate tools for data, visualizations, and interactivity Mix of Tools Specialized Skillset: Domain Expertise, Data Science, Stats, Programming, Visual Design No Specialized Skillset Required Specialized Software
  20. 20. Graphical perception (relative ranking) Journal of the American Statistical Association, September 1984 William S. Cleveland; Robert McGill Relative accuracy of various graphical forms that convey quantitative information

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