This document discusses reducing noise in data presentations. It defines noise as irrelevant or misleading information, while signals are potentially meaningful data that can prompt good outcomes. Reducing presentation noise involves more than just visual design and includes the entire data life cycle from collection to user perception. Some strategies discussed are providing context, emphasis and clear design, considering how data is perceived and distorted, allowing for feedback and tuning of the data, and handling errors and data entry issues. The goal is to filter out noise and emphasize meaningful signals to help with decision making.

1. Reducing Presentation Noise!
SF Data Mining Meetup
November 2015
Hunter Whitney

2. Consultant, Author, and Instructor
User Experience (UX) Design

3. Listening to Data
Noise!
Irrelevant, meaningless, and
can distract or mislead from the
best course of action
Signals
Potentially meaningful and
can prompt actions that
lead to good outcomes

4. Reducing Presentation Noise Doesn’t
Begin or End with Visual Design
Data Life Cycle
User Perception

5. •
Context, Emphasis, and Design
•
Perception and Distortion
• Feedback, Tuning, Muting
•
GIGO, Data Entry, and Error Handling
4 UX Design Considerations

6. Context, Emphasis, and Design

7. An Alarming Problem

8. Design Choices

9. Signal

10. Noise

11. Murmurs
8
7
6
Sensitivity, Specificity, and Severity

12. Crosstalk

13. Detector Proliferation

14. If it ain’t broke, don’t show it

15. Prioritization and Disclosure
Reactor 1 Core Temp.
Other 9
✔
Water Level in Tank 2
Workplace Accidents

16. You Can’t Always Think Your Way Out

17. Perceptual Distortions

18. Cognitive Bias and
Communication Distortions

19. The Data

20. The Same Data

21. Feedback, Tuning, Muting

22. Muting

23. GIGO, Data Entry, and
Error Handling

24. "Organizations haven't realized until recently,
the majority of their data sets have something
wrong with them. Using visualization allows
us to sometimes see potentially dangerous
errors and omissions.”
“Data visualization is worthwhile if for the data
cleaning capabilities alone."
999-Year-Old Patients
Ben Shneiderman Human Computer Interac4on Laboratory, University of Maryland

25. A Case in Point

26. Personal Resonance
http://www.cdc.gov/genomics/update/file/images/spotlight/2014-03_sequencing.jpg

27. Finding Meaningful Signals

29. Decision-Making

30. Another Example

31. ExpressionPlot

35. Thank You!
Questions?
@hunterwhitney

36. Cell. 2011 Mar 18;144(6):860-3. doi: 10.1016/j.cell.2011.03.007.
Boos4ng signal-to-noise in complex biology: prior knowledge is power.
Ideker T1, Dutkowski J, Hood L.