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

1. Designing Data Visualizations

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. THREE HORIZONS
• Data Experience Developer at Thomson Reuters Labs
• Avid kayaker/fisher
• Can speak mandarin

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. How it Works
It all starts with data

6. HOW IT WORKS – IT ALL STARTS WITH DATA

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. •Unstructured text vs. structured text
•File format (csv, tsv, txt, json…)
•Encoding( vs. )
HOW IT WORKS – IS YOUR DATA CLEAN?

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. EXPLORING YOUR DATA
Unearthing the narrative of your visualization

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. •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. 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. Exploring your Data – Are you twisting the
narrative?
EXPLORING YOUR DATA – ARE YOU TWISTING
THE NARRATIVE?

15. UNDERSTANDING DATA TYPES

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. • BAD PIE
Not all pies are created equal

18. NUMERIC VALUES

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. NUMERIC VALUES

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. • 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. NUMERIC VALUES

24. Data Visualization Leads to Exploration
Iterative Development

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. VISUALIZING DATA - CONSIDERATIONSPlot the lng/lat of the tweets

27. VISUALIZING DATA - CONSIDERATIONS
• Add radius weighting to follower count
• Sentiment analysis of feeling associated with tweet

28. • Add tooltip to show content of tweets
• Group sentiment to show distribution

31. 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

32. CREATING AN EXPERIENCE
Illustrating Insight

33. • 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

34. • 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?

35. • Clear
• Specific
• Keep it simple
• To the point
• Inline with audience
VISUALIZING DATA – CONSIDERATIONS

36. • 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

37. CASE STUDY
Electric Vehicles – are we making a cleaner
planet?

38. • 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

39. VISUALIZING DATA – Iterations .01

40. VISUALIZING DATA – Iterations .01

41. VISUALIZING DATA – Iterations .01

42. VISUALIZING DATA – Iterations .2

43. VISUALIZING DATA – Iterations .3

44. VISUALIZING DATA – Iterations .4

45. VISUALIZING DATA – Iterations .5

46. VISUALIZING DATA – Iterations .9

47. VISUALIZING DATA – outcome

48. VISUALIZING DATA – outcome

49. • 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

50. NEW MODEL OF DATA VIZ
Static to Interactive to static and so on.

51. VISUALIZING DATA – Static

52. VISUALIZING DATA – Static

53. VISUALIZING DATA – Sliced Static

54. VISUALIZING DATA – Article Integration

55. TELLING A STORY THROUGH DATA

56. VISUALIZING DATA – Static

57. VISUALIZING DATA – Static

58. VISUALIZING DATA – Static

59. VISUALIZING DATA – Static

60. DATA DESIGN DEVELOPMENT INTERSECTION
DATA
DEVELOPMENT
DESIGN
Explore Define

61. • 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

62. THANKS