O'Reilly #StrataHadoop Presentation- George Roumeliotis This talk describes a Design Thinking methodology for tackling Data Science projects. Be warned that the talk is not about machine learning, and it is not about user interfaces. It's about being an effective Data Science practitioner. The talk was originally presented in 2015 at the O'Reilly Strata Conference in San Jose, CA, by George Roumeliotis, a Data Scientist working at Intuit. To view the presentation, visit: http://youtu.be/LQ9HWNtlggU

1. Data
Science
Design
Thinking

2. The truth about Data Science projects

3. The Internet says so

4. I say so, and I’m as old as dirt

5. Data Science is a terrible thing to waste

6. A Data Scientist
can increase the probability
that a project is successful
by 5-10X
if they approach it as a
Design Thinking leader
My conjecture. By me.

7. Yours to keep: A Design Thinking process
for Data Science projects

8. “ We’ve challenged everyone who
works for us — even our lawyers
and accountants — to think deeply
about how design should be part of
their jobs.”
CEO Brad Smith
Harvard Business Review
January 2015

9. “ Design for Delight articulates Intuit’s
approach to design thinking, based
on deep customer empathy, idea
generation, and experimentation.
D4D provides the entire company
with a common framework for building
great products.”
CEO Brad Smith
Harvard Business Review
January 2015
Design for Delight

10. It’s a journey

11. Show me

12. Data Science new products
One-Time Analysis
Decision Support System
Decision Engine

13. Consumers Business
Data Science diverse customers
Engineering

14. The Data Scientist as Design Thinking leader

15. The root cause of Data Science project failure
It’s hard to get an organization
to adopt a new idea

16. Organizations have immune responses, too

17. Finally. The process. Let’s go!

18. Design for Delight ++

20. The Data Scientist as detective

21. Interview

22. Synthesize

23. Problem StatementName
Photo
Background
Role
Rationale
Hopes
Concerns
Personas
Artifact

24. As a __________________
I want to _______________
because ________________
but ___________________
so I feel _______________
Role and 2-3 descriptive facts
Problem Statements
Goal
Rationale
Obstacle
Emotion
Artifact

25. Environment
Processes
Systems
Data
Samples
Sketches
Storyboards
Artifact

26. Design Evaluation
Criteria
How will we evaluate
a proposed design?
Artifact

27. Solution Evaluation Criteria
How will we evaluate
an implemented solution?
Artifact

29. The Data Scientist as convener

30. Brainstorming Rules
• Stay on topic
• One conversation at a time
• Don’t criticize or ridicule
• Build on the ideas of others
Review ettiquete

31. Set context
Personas
Problem
Statements Environment
Evaluation
Criteria

32. Facilitate idea generation and filtering

33. Solution hypothesis
Solution hypothesis
Leap-of-faith assumptions
Experiment
What we’ll learn
How we’ll respond
Succinct statement of the best idea
What must be true for the idea to work?
What rapid experiment can we do?
What will it teach us?
What will we do, either way?
Artifact

35. The Data Scientist as builder

36. Paper prototype
Artifact

37. Algorithm testing rig
Artifact

38. Iterate

39. One step at a time

40. Enjoy!
george_roumeliotis@intuit.com
http://bit.ly/bigdataanddesign