Instructor Manual: Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, Camm, 6e, ©2027 9798214057361; Chapter 1:
Introduction to Business Analytics
Instructor Manual
Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann,
Camm, 6e, ©2027 9798214057361; Chapter 1: Introduction to Business Analytics
TABLE OF CONTENTS
Purpose and Perspective of the Chapter......................................................................2
Cengage Supplements....................................................................................................2
List of Instructor Downloads..................................................................................................................... 2
List of Student Downloads.........................................................................................................................2
Chapter Objectives........................................................................................................3
Complete List of Chapter Activities and Assessments.................................................3
Key Terms.....................................................................................................................3
Chapter Outline.............................................................................................................5
Appendix.....................................................................................................................15
Generic Rubrics....................................................................................................................................... 15
Standard Writing Rubric.......................................................................................................................... 15
Standard Discussion Rubric.....................................................................................................................16
a
© 2027 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 1
accessible website, in whole or in part.
,PURPOSE AND PERSPECTIVE OF THE CHAPTER
This book is concerned with data-driven decision making and using analytical approaches in the decision-
making process. Three developments spurred explosive growth in analytical methods for business
applications: the availability of massive amounts of data, improvements in analytic methodologies, and
substantial increases in computing power.
This chapter focuses on the dramatic upsurge in using analytical methods in business and a reliance on the
business analysis discipline. This text aims to provide students with a sound conceptual understanding of
the role that business analytics plays in the decision-making process and a better understanding of the
variety of applications in which analytical methods have been used successfully.
CENGAGE SUPPLEMENTS
Additional instructor and student resources for this product are available online at www.cengage.com.
LIST OF INSTRUCTOR DOWNLOADS
The following product-level supplements provide additional information that may help you prepare for
your course. They are available in the Instructor Resource Center.
Solutions and answers guide
Educator’s guide
PowerPoint slides
Test bank powered by Cognero
Excel solution files available for certain book exercises requiring computer-generated solutions
(new)
LIST OF STUDENT DOWNLOADS
Students should download the following items from the Student Companion Center to complete the
activities and assignments:
Excel datasets
Excel workbooks
[return to top]
a
© 2027 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 2
accessible website, in whole or in part.
,CHAPTER OBJECTIVES
This chapter addresses the following objectives:
LO 1-1 Identify strategic, tactical, and operational decisions.
LO 1-2 Describe the steps in the decision-making process.
LO 1-3 Identify examples of descriptive, predictive, and prescriptive analytics.
LO 1-4 Describe applications of analytics for decision making and problem solving.
[return to top]
COMPLETE LIST OF CHAPTER ACTIVITIES AND
ASSESSMENTS
For additional guidance, refer to the Teaching Online Guide.
Chapter Objective PPT slide Activity/Assessment Duration
LO 1-1 33 Decision Making in Your Organization 30 minutes
LO 1-3 34 Future Innovations on the Four Vs of Big Data 30 minutes
[return to top]
KEY TERMS
Artificial intelligence (AI): A broad field focused on using computers, data, and algorithms to
augment or enhance human decision making or to automate tasks that usually require human intelligence.
Advanced analytics: Predictive and prescriptive analytics.
Big data: Any set of data that is too large or too complex to be handled by standard data-processing
techniques and typical desktop software.
Business analytics: The scientific process of transforming data into insight for making better
decisions.
Cloud computing: The use of data and software on servers housed externally to an organization via
the internet. Also known simply as “the cloud.”
Data dashboard: A data-visualization tool that updates in real time and gives multiple outputs.
Data query: A request for information with certain characteristics from a database.
Data scientists: Analysts trained in both computer science and statistics who know how to effectively
process and analyze massive amounts of data.
Data security: Protecting stored data from destructive forces or unauthorized users.
a
© 2027 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 3
accessible website, in whole or in part.
, Decision analysis: A technique used to develop an optimal strategy when a decision maker is faced
with several decision alternatives and an uncertain set of future events.
Descriptive analytics: Analytical tools that describe what has happened.
Generative AI (GenAI): AI that generates text, images, sound, and videos.
Hadoop: An open-source programming environment that supports big data processing through
distributed storage and distributed processing on clusters of computers.
Large language model (LLM): A deep learning model trained on massive data sets to understand
how characters, words, and sentences function together so it can recognize, comprehend, interpret, and
use human language.
Machine learning: The use of analytical techniques to discover patterns and relationships in data
and/or make predictions based on data.
MapReduce: Programming model used within Hadoop that performs the two major steps for which it
is named: the map step and the reduce step.
Metric: A criterion that measures the effectiveness of a course of action.
Neural network: A supervised learning model whose structure is motivated by the biological
functioning of the brain.
Operational decision: A decision concerned with how the organization is run from day to day.
Optimization model: A mathematical model that gives the best decision, subject to the situation’s
constraints.
Predictive analytics: Techniques that use models constructed from past data to predict the future or
ascertain the impact of one variable on another.
Prescriptive analytics: Techniques that analyze input data and yield the best course of action.
Rule-based model: A prescriptive model based on a rule or set of rules.
Simulation: The use of probability and statistics to construct a computer model to study the impact of
uncertainty on the decision at hand.
Simulation optimization: The use of probability and statistics to model uncertainty, combined with
optimization techniques, to find good decisions in highly complex and highly uncertain settings.
Strategic decision: A decision that involves higher-level issues and that is concerned with the overall
direction of the organization, defining the overall goals and aspirations for the organization’s future.
Supervised learning: A category of machine learning techniques in which an algorithm learns how to
predict an outcome variable of interest.
Tactical decision: A decision concerned with how the organization should achieve the
goals and objectives set by its strategy.
Unsupervised learning: A category of machine learning techniques in which an algorithm discovers
patterns and relationships between variables without an outcome variable to guide the process.
a
© 2027 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 4
accessible website, in whole or in part.
Introduction to Business Analytics
Instructor Manual
Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann,
Camm, 6e, ©2027 9798214057361; Chapter 1: Introduction to Business Analytics
TABLE OF CONTENTS
Purpose and Perspective of the Chapter......................................................................2
Cengage Supplements....................................................................................................2
List of Instructor Downloads..................................................................................................................... 2
List of Student Downloads.........................................................................................................................2
Chapter Objectives........................................................................................................3
Complete List of Chapter Activities and Assessments.................................................3
Key Terms.....................................................................................................................3
Chapter Outline.............................................................................................................5
Appendix.....................................................................................................................15
Generic Rubrics....................................................................................................................................... 15
Standard Writing Rubric.......................................................................................................................... 15
Standard Discussion Rubric.....................................................................................................................16
a
© 2027 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 1
accessible website, in whole or in part.
,PURPOSE AND PERSPECTIVE OF THE CHAPTER
This book is concerned with data-driven decision making and using analytical approaches in the decision-
making process. Three developments spurred explosive growth in analytical methods for business
applications: the availability of massive amounts of data, improvements in analytic methodologies, and
substantial increases in computing power.
This chapter focuses on the dramatic upsurge in using analytical methods in business and a reliance on the
business analysis discipline. This text aims to provide students with a sound conceptual understanding of
the role that business analytics plays in the decision-making process and a better understanding of the
variety of applications in which analytical methods have been used successfully.
CENGAGE SUPPLEMENTS
Additional instructor and student resources for this product are available online at www.cengage.com.
LIST OF INSTRUCTOR DOWNLOADS
The following product-level supplements provide additional information that may help you prepare for
your course. They are available in the Instructor Resource Center.
Solutions and answers guide
Educator’s guide
PowerPoint slides
Test bank powered by Cognero
Excel solution files available for certain book exercises requiring computer-generated solutions
(new)
LIST OF STUDENT DOWNLOADS
Students should download the following items from the Student Companion Center to complete the
activities and assignments:
Excel datasets
Excel workbooks
[return to top]
a
© 2027 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 2
accessible website, in whole or in part.
,CHAPTER OBJECTIVES
This chapter addresses the following objectives:
LO 1-1 Identify strategic, tactical, and operational decisions.
LO 1-2 Describe the steps in the decision-making process.
LO 1-3 Identify examples of descriptive, predictive, and prescriptive analytics.
LO 1-4 Describe applications of analytics for decision making and problem solving.
[return to top]
COMPLETE LIST OF CHAPTER ACTIVITIES AND
ASSESSMENTS
For additional guidance, refer to the Teaching Online Guide.
Chapter Objective PPT slide Activity/Assessment Duration
LO 1-1 33 Decision Making in Your Organization 30 minutes
LO 1-3 34 Future Innovations on the Four Vs of Big Data 30 minutes
[return to top]
KEY TERMS
Artificial intelligence (AI): A broad field focused on using computers, data, and algorithms to
augment or enhance human decision making or to automate tasks that usually require human intelligence.
Advanced analytics: Predictive and prescriptive analytics.
Big data: Any set of data that is too large or too complex to be handled by standard data-processing
techniques and typical desktop software.
Business analytics: The scientific process of transforming data into insight for making better
decisions.
Cloud computing: The use of data and software on servers housed externally to an organization via
the internet. Also known simply as “the cloud.”
Data dashboard: A data-visualization tool that updates in real time and gives multiple outputs.
Data query: A request for information with certain characteristics from a database.
Data scientists: Analysts trained in both computer science and statistics who know how to effectively
process and analyze massive amounts of data.
Data security: Protecting stored data from destructive forces or unauthorized users.
a
© 2027 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 3
accessible website, in whole or in part.
, Decision analysis: A technique used to develop an optimal strategy when a decision maker is faced
with several decision alternatives and an uncertain set of future events.
Descriptive analytics: Analytical tools that describe what has happened.
Generative AI (GenAI): AI that generates text, images, sound, and videos.
Hadoop: An open-source programming environment that supports big data processing through
distributed storage and distributed processing on clusters of computers.
Large language model (LLM): A deep learning model trained on massive data sets to understand
how characters, words, and sentences function together so it can recognize, comprehend, interpret, and
use human language.
Machine learning: The use of analytical techniques to discover patterns and relationships in data
and/or make predictions based on data.
MapReduce: Programming model used within Hadoop that performs the two major steps for which it
is named: the map step and the reduce step.
Metric: A criterion that measures the effectiveness of a course of action.
Neural network: A supervised learning model whose structure is motivated by the biological
functioning of the brain.
Operational decision: A decision concerned with how the organization is run from day to day.
Optimization model: A mathematical model that gives the best decision, subject to the situation’s
constraints.
Predictive analytics: Techniques that use models constructed from past data to predict the future or
ascertain the impact of one variable on another.
Prescriptive analytics: Techniques that analyze input data and yield the best course of action.
Rule-based model: A prescriptive model based on a rule or set of rules.
Simulation: The use of probability and statistics to construct a computer model to study the impact of
uncertainty on the decision at hand.
Simulation optimization: The use of probability and statistics to model uncertainty, combined with
optimization techniques, to find good decisions in highly complex and highly uncertain settings.
Strategic decision: A decision that involves higher-level issues and that is concerned with the overall
direction of the organization, defining the overall goals and aspirations for the organization’s future.
Supervised learning: A category of machine learning techniques in which an algorithm learns how to
predict an outcome variable of interest.
Tactical decision: A decision concerned with how the organization should achieve the
goals and objectives set by its strategy.
Unsupervised learning: A category of machine learning techniques in which an algorithm discovers
patterns and relationships between variables without an outcome variable to guide the process.
a
© 2027 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 4
accessible website, in whole or in part.
