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.

1. Julie Rodriguez
Expanding
Our Visual
Literacy
@juliargentinaG

2. External
Communications
Explain Visually
Individual
Displays
Internal
Presentations
Collected
Information

5. 4. Show then tell
3. Easy to navigate
1. Relevant
2. Scan & scrutinize
Desperately Seeking

14. 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

15. 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

20. 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

21. Embed context into
your data displays

35. 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

36. Aggregation enables
better data navigation

37. Statements of Cash Flows

50. 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

51. Lead with
visuals, then
layer in the text

52. Easy to navigate
Scan & scrutinize
Relevant
Show then tell
2.
1.
3.
4.
SUMMARY

54. 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

55. 40+ use cases
100+ new visualization methods
250+ illustrations
450 pages

56. Explore... Expand...

57. Questions?

58. Thank You…

59. 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

60. 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