BUSINESS ANALYTICS
AND IMPLICATIONS FOR
APPLIED STATISTICS
EDUCATION
Sam Woolford, Bentley University
2016 Joint Statistical Meetings
Chicago, IL
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August 4, 2016
2016 JSM Chicago, IL
OUTLINE
 Defining Business Analytics
 Historical Context
 Business Analytics Requirements
 Implications for Applied Statistics
Education
 Discussion
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August 4, 2016
2016 JSM Chicago, IL
WHAT IS BUSINESS
ANALYTICS?
 Is it
Applied statistics?
 Data science?
 Statistical engineering?
 Machine learning?
 Operations research?

 Who had heard of Business Analytics
ten years ago?
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August 4, 2016
2016 JSM Chicago, IL
WHAT IS BUSINESS
ANALYTICS
 Wikipedia
“Business analytics makes extensive use of
data, statistical and quantitative analysis,
explanatory and predictive modeling …to drive
decision making.”
 Translation (from a business perspective)
What happened?
 Why is this happening?
 What if the trend continues?
 What will happen next?
 What is the best that can happen?

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August 4, 2016
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HISTORICAL CONTEXT
 Analytics has a long history in
business
Frederick Taylor in the 18th century
 Quality Management

• Focus on process and customer

Reengineering
• Added a systems component to attain
higher performance

Enterprise data management
• ERP and CRM systems
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August 4, 2016
2016 JSM Chicago, IL
HISTORICAL CONTEXT
Data, Data, Data
Traditional structured data
 New unstructured data
 Big data

Business issues
Complexity of global business
 Compressed decision time frames
 The HIPPO is dead

Business Analytics
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August 4, 2016
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REQUIREMENTS FOR
BUSINESS ANALYTICS
 Problem definition
Requires business and context knowledge
 Requires quantitative frameworks

 Problem complexity
Interdisciplinary
 Multiple stakeholders
 Requires multiple interconnected analyses
 Messy data doesn’t conform to assumptions
 Unknown sources of variation

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August 4, 2016
2016 JSM Chicago, IL
REQUIREMENTS FOR
BUSINESS ANALYTICS
 Analytics frameworks
Also multidisciplinary
 Belts and suspenders

 Ancillary issues
Team and project management
 Communications
 Innovation and creativity

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IMPLICATIONS FOR
MASTERS PROGRAMS
 Typical MS in Applied Stat

Courses (30-33 hours/10-11 courses)
•
•
•
•
Background courses (Probability and Math Stat)
Statistical Methods (Linear models, Multivariate)
Statistical computing/data management
Topic areas (time series, DOE, Bayesian analysis,
stochastic processes)
• Machine learning
• Big Data

Duration (1.5 to 2 yrs)
• Shorter time frames being driven from students and
competitive pressures
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August 4, 2016
2016 JSM Chicago, IL
IMPLICATIONS FOR
MASTERS PROGRAMS
 Ancillary skill requirements
Business understanding is important
 Capabilities beyond statistical methodology
 Multiple analyses required for complex problems

 New career paths for applied
statisticians
Data Scientist, Chief Data Officer, VP Big Data,
Chief Analytics Officer, Director Decision Science
 Analytical career paths may more closely parallel
existing corporate career paths

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August 4, 2016
2016 JSM Chicago, IL
IMPLICATIONS FOR
MASTERS PROGRAMS
 No one ‘owns’ the discipline



ASA
INFORMS
Others (big data)
 Issues for statistics departments



Lack of business orientation
Methodology oriented as opposed to case oriented
Requires coordination with other departments
 Opportunity to enhance stature and
relevance of statistics
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2016 JSM Chicago, IL
Discussion
Contact: [email protected]
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August 4, 2016
2016 JSM Chicago, IL