Luciano Vilas Boas presented on data science and decision making. He discussed key topics such as what data science is, the role of data scientists, big data, and challenges in becoming a data-driven organization. He emphasized that data science involves finding patterns in raw data to solve business problems and support data-driven decision making. Becoming truly data-driven requires overcoming roadblocks like a lack of data literacy and changing organizational culture to value data-driven philosophy. Cognitive biases can negatively impact decision making, so data science aims to reduce bias and improve decisions through evidence-based insights.

1. Presenter: Luciano Vilas Boas
October 29th, 2020
Data Science
and
Decision Making
Image by Freepik on Freepik

2. Presenter’s
Bio
Luciano Vilas Boas
Luciano Vilas Boas is currently pursuing his Master's Degree in Data
Science at Texas Tech University (TTU), and is expected to graduate in
December 2020. He also holds an MBA in Business Economics at
Universidade Federal Fluminense (UFF), and a Bachelor's degree in
Business Administration at Universidade Candido Mendes (UCAM).
Luciano is a seasoned professional with international experience and
previous management positions in private and public institutions.

3. Image by Freepik on Freepik
Agenda Data Science01
Decision Making03 • Cracking Decision Making
• Breaking the Wheel
• Data Science and Data Scientist
• Big Data
Data-Driven Culture02 • A Rough Ride
• Organizational Change

4. What is Data Science (DS)?
There is not a consensus on what Data
Science is!
- Data Science as an art/alchemy, not a
Science.
- No magical formula: each dataset is
unique.
“Data Science” term was first introduced in 1794 by Peter Naur.
There is not a consensus on what a Data
Scientist is or do!
- But, we can think about DS as a professional
that holds a collection of practices, wisdoms,
know-hows of data mining, and analysis.
Figure out hidden patterns in the raw data, solve business problems,
and support data-driven decision making.
Data Science Data Scientist

5. Big Data in 60 Seconds“too big to fit on a single server, too unstructured to fit into a row-and-column database”.
Thomas H. Davenport
Photo by Lomen Bros on Library of Congress
Link: https://www.loc.gov/item/99614756/
Is data the New Oil or the New Garbage?
Source: https://www.linkedin.com/company/statista/
Data Mining: transforming garbage into gold.
Difficult to make sound decisions!
Bridging the Gap

6. Image by Freepik on Freepik
Agenda Data Science01
Decision Making03 • Cracking Decision Making
• Breaking the Wheel
• Data Science and Data Scientist
• Big Data
Data-Driven Culture02 • A Rough Ride
• Organizational Change

7. Data-Driven: From Silos to Collaboration
https://www.ibm.com/blogs/business-analytics/data-driven-analytics-vision/
Foster culture that values data-driven philosophy as the
core element of decision making: transforming insights-
to-action supported by data.
“74% of firms say they want to be “data-driven,”
only 29% say they are good at connecting
analytics to action.”
https://go.forrester.com/blogs/16-03-09-think_you_want_to_be_data_driven_insight_is_the_new_data/
Mission:
Top-down and/or Bottom-up
strategies to take “data-driven”
advocates from department to an
enterprise endeavors.
Remember: ”Garbage in, garbage out”
Communicate | Educate | Share the Vision

8. Data-Driven: Roadblocks
 Culture
 Data and Analytics not seen as an engine of
value creation
 Lack of understanding on how to monetize on
(big) data
 Data Literacy
Focus on cultural change as the main driver for
transitioning to a data-driven culture.
Source: https://www.oilandgas-blog.com/en/role-models/
Operational
Tactical
Strategic
“You have to handle adversity well. There are roadblocks you will have to fight through”
- Zach LaVine

9. SWOTA Analysis Case: A Real Life Experience
Retail company with no POS (Point of Sale) System - no barcodes.Situation
Implement the first POS SystemTask
Train managers, team leaders and decision makers on SWOTA AnalysisAction
The SWOTA Analysis gave voice to the team and documented the need
for a system that we all knew. Few months later, we started to implement
the first POS System.
Result
What to fight?
How to fight?
Resistance | Status-quo
Support | Teamwork
When to start? Now | Today
Work on? Resilience
Why do People “Fear” Data? Loss of Power/Control
“Your success in life… It is based on your ability to change faster than your competition, customers and business.”
– Mark Sanborn

10. Cultural Change by Law
Image by B&W Film Copy Neg. on Library of Congress
Link: https://www.loc.gov/resource/cph.3b36072/
Change firm’s “Laws”:
- Code of Conduct
- Operational Policies (OP)
- Standard Operating Procedure (SOP)
“Culture is a product of law. And laws create norms for society. This is why anyone who wants to change the culture of a country must try to change the norms of the country.”
- Myles Munroe

11. Image by Freepik on Freepik
Agenda Data Science01
Decision Making03 • Cracking Decision Making
• Breaking the Wheel
• Data Science and Data Scientist
• Big Data
Data-Driven Culture02 • A Rough Ride
• Organizational Change

12. The first rule in decision making is that one does not
make a decision unless there is disagreement.
Peter Drucker
“

13. Decision Making (DM) Article
https://towardsdatascience.com/the-ultimate-mission-of-a-data-scientist-support-decision-making-824f870aa386

14. Blowing in the Wind DM “Model”
Source: https://pt.wikihow.com/Determinar-a-Dire%C3%A7%C3%A3o-do-Vento
Poor Decisions: Gut Feelings | Emotional | Experience
“When so much is unknown
and unknowable, conventional wisdom says to go
with your gut… worst time to rely on your
intuition is when you’re making high-stakes decisions.
Every scientific test of intuition shows that it’s
profoundly affected by cognitive biases”.
Source: “In high-stakes decisions, sometimes you’ve just got to go with your gut.”, Harvard Business School Publishing Corporation.
"There's no evidence whatsoever that I know of that shows more senior managers make
better decisions," Snijders said. "Experience has really shown to be relatively worthless
when it comes to making more accurate decisions.“
Chris Snijders, professor at Eindhoven University of Technology (Netherlands)
Source: Trust an algorithm with your business? The New York Times

15. Top 5 Cognitive Biases in DM
1. Overconfidence
2. Confirmation Bias
3. Unrealistically Positive Views of Self
4. Illusion of Control
5. Self-serving Attributions
“No problem in judgment and decision making is more prevalent and more potentially catastrophic than overconfidence”
(Plous, 1993, p. 217)
Considered the “mother of all biases” in DM
Anything to support our beliefs and values
Lack of humility
Extreme superstitious mindset
Take a lot of credit when succeed,
and too little blame when fail

16. The Role of Risk in DM
Prospect Theory | Uncertainty | Loss Aversion | Information Asymmetry
Source: https://www.nngroup.com/articles/prospect-theory/
Source: https://www.nngroup.com/articles/prospect-theory/
Risk Averse (for Gain)
Risk Seeking (for Loss)
“the pain of losing is greater
than the satisfaction of an equivalent gain”.
Source: https://www.nngroup.com/articles/prospect-theory/
Decision is usually made based on emotion
(pain, fear) and not rationally (Expected
Value).
Video:
“When good decisions have bad outcomes”,
By Cassie Kozyrkov (Head of Decision Intelligence on
Google).
https://www.youtube.com/watch?v=x84RsnUzNtE
Outcome Bias (regretting a decision based on
a sub-optimal outcome).

17. The Abilene Paradox: The Management of Agreement
“The first rule in decision making is that one does not make a
decision unless there is disagreement.”
Peter Drucker
“The inability to manage agreement, not the inability to
manage conflict, is the essential symptom that defines
organizations caught in the web of the Abilene Paradox.”
Jerry B. Harvey
Source: Google Maps
Source: http://homepages.se.edu/cvonbergen/files/2013/01/The-Abilene-Paradox_The-Management-of-Agreement.htm_.pdf
Source: https://thedailyomnivore.net/2011/03/29/abilene-paradox/
“75% of respondents admit that their projects
are either always or usually “doomed right
from the start.”
https://www.geneca.com/why-up-to-75-of-software-projects-will-fail/

18. Fighting Conformism
Video: “Sociedade dos Poetas Mortos – Autoconhecimento”
Link: https://www.youtube.com/watch?v=vyds5y-d7oQ
Dead Poets Society Movie
Breaking the wheel of:
“This is how we do things here”
In Many Cases You’ll Have To:
Swimming against the stream

19. Additional References
Harvey, J. B. (1974). The Abilene paradox: The management of
agreement. Organizational Dynamics, 3(1), 63–80.
Kahneman, Daniel, and Amos Tversky. “Prospect Theory: An Analysis of Decision
under Risk.” Econometrica, vol. 47, no. 2, 1979, pp. 263–291. JSTOR,
www.jstor.org/stable/1914185. Accessed 29 Sept. 2020.
Shhhh!; Schumpeter 2016, , The Economist Intelligence Unit N.A., Incorporated,
London.
Zweig, J. 2016, The Intelligent Investor: The Worst Advice? 'Just Trust Your Gut',
Eastern edition edn, New York, N.Y.
Walker, J. 2013, Justice by the Numbers: States Turn to Software To Make Parole
Decisions, Eastern edition edn, New York, N.Y.
Rollings, Mike, Duncan, D, Logan, Valerie. “10 Ways CDOs Can Succeed in
Forging a Data-Driven Organization”. Gartner, 2019.
Judgment in Managerial Decision Making, 8th Edition
Max H. Bazerman, Don A. Moore
Articles Book

20. Thank You!