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Designing Data Visualization

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presented at FITC Toronto 2018
More info at http://fitc.ca/event/to18/

Presented by
Corey Ouellette, Thomson Reuters

Overview
When you think of “data visualization” what is the very first thing that comes to mind? For many, it’s bar graphs, pie charts, and histograms, or maybe some combination thereof. You’re not wrong – but it’s so much more than that. The era of pie and bar charts has come and gone; these traditional visualizations alone are insufficient. Now is the time of data visualized on a rich canvas. A canvas that not only informs, but immerses you in information in much the same way that your favourite book immerses you in its narrative.

Objective
When attendees leave, that they walk away with an understanding of how development, design and data are strongly intertwined with one other. When aligned with customers needs, these aspects create a meaningful and actionable experience.

Target Audience
Designers and developers interested furthering their appetite for visualization



Five Things Audience Members Will Learn
How data visualization can lead to data exploration
Creating an experience with information
New models of data visualization
Telling a story through data
How to blend design and development through data visualization

Published in: Design
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Designing Data Visualization

  1. 1. Designing Data Visualizations
  2. 2. Zurich Region 8 people: formerly Catalyst Lab, data science, Incubator Boston 12 people: data science, data viz, MIT London 7 people: data science, data viz, startups, Imperial College Cape Town 4 people: data science, data viz, startups TR Labs Formula Startup Ecosystem University Partner Customer Access + + Waterloo 7 people: data science, data viz, startups, University of Waterloo Singapore 4 people: Launched Feb 2017, focus on FinTech San Francisco 12 people: Formerly StarMine, data science, quants THOMSON REUTERS LABS
  3. 3. THREE HORIZONS • Data Experience Developer at Thomson Reuters Labs • Avid kayaker/fisher • Can speak mandarin
  4. 4. • Exploring your data • Understanding data types • Visualization choices and considerations • How data visualization can lead to data exploration • Creating an experience with information • New models of data visualization • Telling a story through data TALK OVERVIEW PROCESS EXECUTION
  5. 5. How it Works It all starts with data
  6. 6. HOW IT WORKS – IT ALL STARTS WITH DATA
  7. 7. • Is it statistically accurate? • What is your sample size? • What is the margin of error? • Are you looking at a long enough timeframe? • When you use a sample to represent an audience, you must make sure that the people in your sample are representative of the audience. • Always spot check your data when combining multiple datasets for errors • Examine outliers – nuggets or inaccuracies HOW IT WORKS – IS IT ACCURATE?
  8. 8. •Unstructured text vs. structured text •File format (csv, tsv, txt, json…) •Encoding( vs. ) HOW IT WORKS – IS YOUR DATA CLEAN?
  9. 9. •Gaps in time •Are you exploring all areas of where you can get data? •Can you aggregate other data sources • This can evolve over time HOW IT WORKS – ARE THERE MISSING PARTS?
  10. 10. EXPLORING YOUR DATA Unearthing the narrative of your visualization
  11. 11. • What's your hypothesis? • Are there trends in the data? • Start initial visualizations in excel/simple graph tools • Are there outliers? • Combine datasets • Corresponding data that would be of value for comparison • Deeper analysis • Sentiment analysis • NLP • Consider viewing data with different diagrams/visualization DATA EXPLORATION
  12. 12. •Not all datasets are complete • Data cleaning will never get you to 100% •Some information is confidential •Consider ways to show missing data EXPLORING YOUR DATA – WHY IS THERE MISSING DATA?
  13. 13. Exploring your Data – Are you twisting the narrative? • Hiding the greater picture • Only focusing on a specific time can leave out important comparison information • Excluding data points to give greater merit to a topic • Skewing Visualization in favour of your narrative • Visualizing data can tell false truths when information is not being accurately displayed EXPLORING YOUR DATA – ARE YOU TWISTING THE NARRATIVE?
  14. 14. Exploring your Data – Are you twisting the narrative? EXPLORING YOUR DATA – ARE YOU TWISTING THE NARRATIVE?
  15. 15. UNDERSTANDING DATA TYPES
  16. 16. • Financial • Census/population • Aggregated non-numeric values • Dates/time • Percentage NUMERIC VALUES When to use: • Bar graphs • Scatterplots • Line graphs • Tables/data points • *Pie/Donut
  17. 17. • BAD PIE Not all pies are created equal
  18. 18. NUMERIC VALUES
  19. 19. • Open-ended response • Tweets • Address information • Form data • Text patterns • *Sentiment analysis • *Text extraction STRING /RESPONSE TEXT When to use: • Word cloud • Text snippet • Tooltip • Results output
  20. 20. NUMERIC VALUES
  21. 21. • Longitude and Latitude • IP address • Line interpolation • Directions • Traffic data SPATIAL / GEOGRAPHIC When to use: • Choropleth maps • Time lapse points • Map interfaces • Event detection • Point to point travel
  22. 22. • API access to consolidated data • Twitter • RSS • News feeds • Analytics data • Survey data • Databases • Customer data GROUPED DATA SETS When to use: • Dashboards • Reports • Customer analytics
  23. 23. NUMERIC VALUES
  24. 24. Data Visualization Leads to Exploration Iterative Development
  25. 25. Index all geo-located tweets referencing #UX in North America over a few months and explore the information for insights with the UX space Scenario
  26. 26. VISUALIZING DATA - CONSIDERATIONSPlot the lng/lat of the tweets
  27. 27. VISUALIZING DATA - CONSIDERATIONS • Add radius weighting to follower count • Sentiment analysis of feeling associated with tweet
  28. 28. • Add tooltip to show content of tweets • Group sentiment to show distribution
  29. 29. Index all geo-located tweets referencing #UX in North America over a few months and explore the information for insights with the UX space #UXploration of Data Recap Scenario
  30. 30. CREATING AN EXPERIENCE Illustrating Insight
  31. 31. • Who are your audience? • How much detail do they need? • What is the margin of error in your data? • Is the insight actionable or just informative? • What story does the data tell? • Why do you need a visualisation? VISUALIZING DATA - CONSIDERATIONS
  32. 32. • Test different ways of visualizing your data • Is this for analysisor for story telling? • Multiple ways of seeing the same information can help reinforce • Consider the scales and dimensions on what your visualizing • Leverage the use of *colours VISUALIZING DATA – HOW TO?
  33. 33. • Clear • Specific • Keep it simple • To the point • Inline with audience VISUALIZING DATA – CONSIDERATIONS
  34. 34. • 3D – novelty vs. insight • Visualizing data for the sake of visualizing (number dressing) • Over complicating the information • Avoid graphical distortion – pick the right scale • Too many colours (no more than 6) • Reduce the need for math VISUALIZING DATA – THINGS TO AVOID
  35. 35. CASE STUDY Electric Vehicles – are we making a cleaner planet?
  36. 36. • Who are your audience? • How much detail do they need? • What is the margin of error in your data? • Is the insight actionable or just informative? • What story does the data tell? • Why do you need a visualisation? VISUALIZING DATA - CONSIDERATIONS
  37. 37. VISUALIZING DATA – Iterations .01
  38. 38. VISUALIZING DATA – Iterations .01
  39. 39. VISUALIZING DATA – Iterations .01
  40. 40. VISUALIZING DATA – Iterations .2
  41. 41. VISUALIZING DATA – Iterations .3
  42. 42. VISUALIZING DATA – Iterations .4
  43. 43. VISUALIZING DATA – Iterations .5
  44. 44. VISUALIZING DATA – Iterations .9
  45. 45. VISUALIZING DATA – outcome
  46. 46. VISUALIZING DATA – outcome
  47. 47. • Who are your audience? • How much detail do they need? • What is the margin of error in your data? • Is the insight actionable or just informative? • What story does the data tell? • Why do you need a visualisation? VISUALIZING DATA - CONSIDERATIONS
  48. 48. NEW MODEL OF DATA VIZ Static to Interactive to static and so on.
  49. 49. VISUALIZING DATA – Static
  50. 50. VISUALIZING DATA – Static
  51. 51. VISUALIZING DATA – Sliced Static
  52. 52. VISUALIZING DATA – Article Integration
  53. 53. TELLING A STORY THROUGH DATA
  54. 54. VISUALIZING DATA – Static
  55. 55. VISUALIZING DATA – Static
  56. 56. VISUALIZING DATA – Static
  57. 57. VISUALIZING DATA – Static
  58. 58. DATA DESIGN DEVELOPMENT INTERSECTION DATA DEVELOPMENT DESIGN Explore Define
  59. 59. • Exploring your data • Understanding data types • Visualization choices and considerations • How data visualization can lead to data exploration • Creating an experience with information • New models of data visualization • Telling a story through data IN CLOSING… PROCESS EXECUTION
  60. 60. THANKS

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