Kristen Sosulski The future of business intelligence: Data Visualization   How can data visualization be used as a platform to reveal intelligent insights and help business analysts make timely decisions? In this talk, Kristen Sosulski will discuss the opportunities for personalized, location aware, context relevant, and platform independent information visualizations as a toolkit for business analysts.

1. The future of business
intelligence
DATA
VISUALIZATIONProf. Kristen Sosulski
@sosulski
ks123@nyu.edu

2. WHY IS DATA
VISUALIZATION
SO POPULAR?

3. DATA is the
reason for this
shift in
emphasis….

4. We are living in the
era of
big data.

5. Think about the data
you share and
generate in a single
day…

6. Searching
Browsing
Communicating
Shopping
Working out
Connecting
Contributing

7. We’ve excelled at
generating and even
collecting data.
---Nathan Yau

8. Now, what do we do
with it?

9. The data must
become easier to
understand and act
upon.

10. How do we get there?

11. There comes a time
when you have to talk
to other people. It’s
communicating to a

12. We can do this
through data
visualization.

13. VISUAL
COMMUNICATI
ON
is an essential skill for all managers -
Scott Berinato, 2016

14. Wisdom
Knowledg
e
Information
Data
Bellinger, G., Castro, D., & Mills, A., 2004;

15. The affordances of
the medium enable
us to:

16. PRESENT
HISTORY

17. INTELLIGENT
INSIGHTS
ABOUT
THE PAST

18. DESCRIBE
WHAT IS
HAPPENING
NOW

19. FOSTER TIMELY
DECISION
MAKING ABOUT
THE PRESENT

20. BEHAVIORS,
PATTERNS, &
TRENDS ABOUT
THE FUTURE

21. AN
INTERFACE…
• Location-aware analysis
• Person-centered analysis
• Context-relevant analysis

22. TO DO IT
EFFICIENTLY
AND
EFFECTIVELY

23. Spreadsheets

24. Many values can be encoded as
one piece of information

25. dashboards would make it appear
that data visualization must be
commonplace, the reality is that
most organizations are in the early
stages of adoption and are still
learning what users need.
--David Stodder, Senior Director of TWDI Research for
Business Intelligence, 2013

26. The Challenge
As visualization designers we
are “melding the skills of
computer science, statistics,
artistic design, and
storytelling”
K. Cukier (2010). Show me: New ways of vi
http://www.economist.com/node/15557455

27. A picture is worth a thousand
words…
Creative Commons

28. Not all pictures are….
• Readable
• Interpretable
• Meaningful
• Relevant
• Timely

29. We need to better understand the
use cases. How do we go
beyond the models and
technology?

30. Models
Technol
ogy
Audien
ce
Design
Deliver
y

31. 1. Consider humans
are your audience

32. Underlying assumptions
Dual
channels
Limited
capacity
Active
Processing

35. Techniques for using
presenter-driven
visuals• Annotation  highlight what’s important
• Animation  guide the pathway

36. Annotations
Reserve the use of
pre-attentive attributes as
cues for your audience.
COLOR

37. Annotations
Reserve the use of
pre-attentive attributes as
cues for your audience.
FORM

38. Annotations
Reserve the use of
pre-attentive attributes as
cues for your audience.
MOVEMENT

39. Annotations
Reserve the use of
pre-attentive attributes as
cues for your audience.
SPATIAL POSITION

40. Annotations
Highlight a data point
using a pre-attentive
attribute.

41. Annotations
Avoid highlighting every
data point.

42. Animation
Progressively reveal content
PANNING AND ZOOMING

43. Animation
Show time series data.
TEXT AND SIZE

44. Animation
Mark as specific data point
and see how it changes
over time
HIGHLIGHT AND POSITION

45. 2. Design for
comprehension and
retention.

46. Remember, it is a translation
process.
Real world Data
Shapes
and colors
Nathan Yau

47. Representing data through
shape, size, and color

48. The top 10 data visualization
design principles1. Chart type
2. Color
3. Text and labels
4. Readability
5. Scales and proportions
6. Data integrity & the lie factor
7. Chart Junk
8. Data density
9. Data-ink ratio
10. Data Richness

49. 3. Delivery

50. Delivery of
visualizations
Projected Paper Screen

51. Delivery of
visualizations
Animated Interactive Static

52. Delivery of
visualizations
Narrated Written Presented

53. Models
Technol
ogy
Audien
ce
Design
Deliver
y

54. Q & A

56. Thank You!
Prof. Kristen Sosulski
@sosulski
ks123@nyu.edu