Beverly Wright, Executive Director, Business Analytics Center, Georgia Institute of Technology at MLconf ATL 2016

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This document discusses machine learning and its impact on business decision making. It defines machine learning as constructing algorithms that can analyze and learn from data to make predictions. The document contrasts hypothesis-driven analytics, which starts with a business question, versus data-driven analytics, which starts by analyzing patterns in data. It provides examples of how machine learning could be applied to issues like reservation cancellations, home auctions, and encouraging altruistic behavior. The closing remarks discuss the future of machine learning and the need for machines to become more human-centric to work with people.

Technologie

www.scheller.gatech.edu/bac
Solving for Why:
Impact of Machine Learning on
Business Decision-Making
Beverly Wright, PhD, CAP
Executive Director
Business Analytics Center
www.scheller.gatech.edu/bac

Overview 2
Application of Machine Learning for Business Questions
Closing Remarks
Defined: Machine Learning
Reasoning: hypothesis vs data initiation points
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Vision
The Vision of the Business Analytics Center,
Georgia Tech:
To be a nationally recognized Center in
business analytics, sought-after partner
for business analytics opportunities and
challenges, renowned for our emphasis
on experiential learning and innovative
research.
3

4Definition: machine learning
“constructing algorithms that can
analyze and learn from data in
order to categorize such data
and make related predictions.
“…it’s about enabling computers to
learn things they have not
necessarily been programmed to
learn.”
http://www.cc.gatech.edu/research/machine-learning
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5Hypothesis vs Data Driven Analytics Initiation
Business question / Hypothesis / Data / Testing / Confirm / Deny / Conclusion / Action

6Hypothesis vs Data Driven Analytics Initiation
Data / Patterns / Optimal Method / Conclusion / Action

7Efficiencies we can gain…
Data initiated approach
• Supervised
• Unsupervised
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8Illustration: flip flop wearing
Process –
• Identify important
variables
• Grouping variables –
keep and redundancy
• Relationships
• Transformations
• Techniques

@GeorgiaTechBsch 9/23/2016
Examples from Business

10Reservation Cancellations
Meaning of cancellation
Factors to consider
• Traveler
• Purpose
• Weather
• Location
Possible analytics methods
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11Home Auctions
Number of bids, views, page
hits, sale
Location
Home quality
Inventory
Buyer
Macroeconomics
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12Influencing / Encouraging Altruistic Behavior
Behaviors
• Types of altruism
• Frequency
• Intensity
Volunteer & Organization:
Identification
Attributes
Interactions and communications
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@GeorgiaTechBsch 9/23/2016
The Future for Machine Learning

15
“We don’t all have to become data
scientists in order to work with the
machine. The machine needs to
become more human and work with
us”
KRIS HAMMOND
CHIEF SCIENTIST, NARRATIVE
SCIENCE
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Some people don't
like change, but you
need to embrace
change if the
alternative is disaster.
~ Elon Musk

@GeorgiaTechBsch 9/23/2016
Business Analytics Center
Learn More

Upcoming Event:
Business Analytics Think Tank Roundtable –
Use and Impact of Artificial Intelligence in Analytics
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Thanks
Business Analytics Center
Beverly Wright, PhD, CAP
Executive Director

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