The objective of analytics and data science is to use data, analytical techniques, and explanatory and predictive models to support or automate human decision making. Given the key objective is to support and automate decision making, it is important for analytics and data science professionals to have a good understanding of the human decision-making process and the role data plays in that process. This workshop gives an introduction to the latest research into how people in organisations make decisions. It also shows how human decision-making is often subject to biases that can lead to poor decision outcomes and how to overcome those biases. The workshop concludes with recommendations on how to best match analytics and data science activities with the level and type of decisions so the best decisions can be made, and how to ensure scarce analytics and data science resources are used effectively to support decision-making.

1. Understanding human decision
making and bias: The foundation of
effective analytics and data science
Steve Remington
Principal Consultant

2. Agenda
• Why should we understand decision making?
• Common decision-making approaches
• Cognitive bias
• Business decisions and decision support
• Decision-centric analytics
• Implications for analytics in organisations

3. Why should we understand decision-
making?
DAWS 05/2018 Understanding human decision making and bias: The foundation of effective analytics and data science 3
Adapted from Davenport and Harris, 2009

5. Human Decision Making
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Research
Approaches
Prescriptive
(“should do”)
Rational
Descriptive
(“does do”)
Dual Process
Cognition
Decision making is a mental or behavioural commitment to a course of action.

6. DAWS 05/2018 Understanding human decision making and bias: The foundation of effective analytics and data science 6
How did you make the
decision to attend the
tonight’s MeetUp?

7. Rational Decision Making
■ Often associated with economic modelling
– “Homo Economicus” – Rational Man
■ Assumes people…
– …are consistently rational
– …always have perfect information
– …always have sufficient time
– …always seek to maximise utility
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8. Rational Decision Making
Define
Problem
Identify
Criteria
Weight
Criteria
Generate
Alternatives
Score
Alternatives
Choose Best
Alternative
Humans rarely make purely rational decisions.
Bazerman and Moore, 2008
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10. 17 x 24 = ?

11. Dual Process Cognition
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System1
• “Fast Thinking”
• Unconscious
• High Capacity
• Automatic
• Effortless
• Heuristics-Based
• Skilled
• Error-Prone
Kahneman, 2011
System2
• “Slow Thinking”
• Conscious
• Low Capacity
• Controlled
• Effortful
• Rule-Based
• Process-Following
• Predictable

12. DAWS 05/2018 Understanding human decision making and bias: The foundation of effective analytics and data science 12
A father and his son are in a car accident.
The father dies at the scene and the son,
badly injured, is rushed to hospital. In the
operating room, the surgeon looks at the
boy and says, “I can’t operate on this boy.
He is my son.” How is this possible?
Benaji and Greenwald, 2016

13. DAWS 05/2018 Understanding human decision making and bias: The foundation of effective analytics and data science 13
A father and his son are in a car accident.
The father dies at the scene and the son,
badly injured, is rushed to hospital. In the
operating room, the surgeon looks at the
boy and says, “I can’t operate on this boy.
He is my son.” How is this possible?
Benaji and Greenwald, 2016

14. Cognitive Biases
Ignoring Base Rate Bias
■ Cause
– Representativeness
Heuristic
– Over reliance on
perceptions of stereotypes
■ Example
– Over-optimistic start-up
founders
Retrievability Bias
■ Cause
– Availability Heuristic
– Over reliance on readily
available information
■ Example
– Unintended employment
discrimination
A systematic deviation from rational decision making outcomes
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16. Minimising Cognitive Bias
Use decision support tools
■ Identify decisions prone to
bias
■ Analyse the decision process
■ Build tool to support or make
decision
– Beware of building bias
into the tool
Acquire Domain Expertise
■ Learn when System 1 fails you
■ Move from System 2 > System 1
– Requires long-term,
accurate and immediate
feedback on outcomes
The effect of cognitive biases can never be eliminated, only minimised
Bazerman and Moore, 2008 | Kahneman, 2011
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17. Minimising Cognitive Bias
Debias Judgement
■ Unfreeze
– Show person their biases
■ Change
– Examples with explanations
■ Refreeze
– Practice and review
Review Decision Processes
■ Good for approach strategic
decisions
■ Focus on identifying bias in
advisers
– Use systematic approach to
identify biases in advice
provided
The effect of cognitive biases can never be eliminated, only minimised
Bazerman and Moore, 2008 | Kahneman, 2011
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18. Business Decisions - Types
Arnott, Lizama & Song, 2017
System 1/2
Decisions where
intuition is
checked and
revised with
data
System 1
Decisions that
can only be
made with skill
and intuition
System 2
Decisions that
have a clear
context and
known process
to solve
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19. Business Decisions - Levels
Arnott, Lizama & Song, 2017
Operational
Control
Tasks
completed
efficiently
and
effectively
Strategic
Planning
Set long-term
goals and
determine
resources
required
Management
Control
Obtain
resources and
allocate them
efficiently and
effectively
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20. Types of Analytics
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Descriptive
• Standard
Reports
• Dashboards
• Ad Hoc Reports
• Query / Drill
Down
• Alerts
Explanatory
• Statistical
Analysis
• Numerical
Analysis
Predictive
• Forecasting
• Propensity
• Segmentation
• Network
Analysis
Prescriptive
• Optimisation
• Machine
Learning
• Experiments
Davenport, 2017
← “Traditional”?, “Low-Value”? → ← “Modern”?, “High-Value”? →

21. Decision-Centric Analytics
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Operational Control Management Control Strategic Planning
System 2
System 1/2
System 1
Prescriptive
Predictive
Predictive
Explanatory
Explanatory
Descriptive
Descriptive
Adapted from Arnott, Lizama & Song, 2017

22. The Decision-Centric Approach
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•Who is the
decision
maker?
•How do
they think?
Decision
Maker
•What is the
type of
decision?
•What is the
level of
decision?
Decision
Task
•What
analysis is
required?
•How should
results be
presented?
Analytics
•Is the data
available?
•What
quality is
the data?
Data
We need to reverse the way we think about analytics

23. Implications for Analytics and Data
Science Programs
■ Business Analytics should be decision-driven and business-owned
■ There are no traditional/modern or high-value/low-value types of
analytics
■ Intuitive decision-making is not intrinsically good or bad
■ Just having good analytics systems and technically competent
teams is not enough
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24. Want to Learn More?
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Introduction IntermediateNovice

25. References
■ Arnott, D., Lizama, F., & Song, Y. (2017). Patterns of business intelligence systems use in
organizations. Decision Support Systems, 97, 58–68. doi:10.1016/j.dss.2017.03.005
■ Bazerman, M. H., & Moore, D. A. (2008). Judgment in managerial decision making (7th ed.).
Hoboken, NJ: Wiley.
■ Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning.
Boston, MA, USA: Harvard Business Review Press.
■ Kahneman, D. (2013). Thinking, fast and slow. New York, NY, USA: Farrar, Straus and Giroux.
■ Lehrer, J. (2010). How we decide. New York, NY, USA: Houghton Mifflin Harcourt.
■ Banaji, M. R., & Greenwald, A. G. (2016). Blindspot: Hidden Biases of Good People. New York,
NY, USA: Bantam.
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26. steve.remington@minerra.net
www.minerra.net
www.linkedin.com/in/stevenremington
@MinerraBI
Questions and Comments