Description
The instructor will have activities in the classroom discussing the topics for the session, including, but not limited to:
Discuss Reducing Cognitive Load with Preattentive Attributes
Consider the matrix of 160 single digits (16 rows x 10 columns) shown in Figure 3.3 (page 79). The textbook describes how we can aid in counting the number of 7s in the matrix using the preattentive attributes of red hues (see Figure 3.4, page 80) and size (see Figure 3.5, page 80.) Regardless of the specific hue and size used in the two mentioned examples, list at least three more ways you could use individual preattentive attributes to aid in counting the number of 7s in the matrix of first digits.
Discuss Reducing Cognitive Load with Gestalt Principles
Reassess the previous question, but this time answer it using the individual Gestalt principles of similarity, proximity, enclosure, and connection, rather than preattentive attributes. Be creative.
Discuss Increasing Data-Ink Ratio in Charts PPT Slide 24)
Consider the Scarf Sales line chart included in the ScarfSalesChart data file. Default charts created in Excel often have a low and suboptimal data-ink ratio. You can build the default line chart from the ScarfSalesChart data file in Excel by selecting the A1:B21 cell range and selecting the ribbon’s Insert Tab. Then, in the CHARTS group, select the Insert Lines or Area Chart icon and click on the top-left option under 2D Line. When building the default line chart, try increasing its data-ink ratio by removing any redundant and unnecessary feature to the data visualization. Do not add any features. List all the steps you implemented to increase the data-ink ratio.
Once you have removed all unnecessary and redundant features, it is now time to make the chart more readable by adding features to look like Figure 3.21 on page 94. List all the steps you implemented.
Review How to Select Appropriate Color Scheme in Charts (PPT Slides 16-17)
Consider the choropleth maps of average annual temperature by state shown in Figure 4.12 (page 138) and 4.13 (-age 139). We learned how to indicate the gradient of a quantitative variable, such as temperature, using different levels of luminance for the same hue, and how we can play on the psychology of color to convey coolness (blue) vs. warmth (brown) in the audience. Suppose you need to create a choropleth map of median family income by state (data not available). What color scheme would you choose to convey the message to the audience and why? Define the color scheme, hue, and luminance selected for this case.
What if you instead need to create a choropleth map of changes in median family income by state (data not available) over the past decade (such as 2010 vs. 2020.) What color scheme would you choose to convey the message to the audience and why? Define color scheme, hues, and lumnance selected for this case.
Information on these aforementioned topics will be presented by your professor during this class.
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Whatever the instructor chooses for you to do, there will be at least one artifact generated from the classroom activity that will be uploaded to this Classroom Activity Assignment representing individuals or assigned groups. This may include a word doc, spreadsheet, presentation or any other artifact as designated by the instructor. At least half of the participation grade (25 points) will be tied to this in-class activity and will be graded by the instructor.
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Unformatted Attachment Preview
Data Visualization
Exploring and Explaining with Data
Jeffrey D. Camm
James J. Cochran
Wake Forest University
University of Alabama
Michael J. Fry
Jeffrey W. Ohlmann
University of Cincinnati
University of Iowa
Australia ● Brazil ● Canada ● Mexico ● Singapore ● United Kingdom ● United States
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text may not be available in the eBook version.
Data Visualization: Exploring and
Explaining with Data,
First Edition
Jeffrey D. Camm, James J. Cochran,
Michael J. Fry, Jeffrey W. Ohlmann
SVP, Higher Education & Skills Product:
Erin Joyner
© 2022 Cengage Learning, Inc.
WCN: 02-300
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Printed in the United States of America
Print Number: 01
Print Year: 2021
Brief Contents
ABOUT THE AUTHORS xi
PREFACE xiii
Chapter 1
Introduction 2
Chapter 2
Selecting a Chart Type 26
Chapter 3
Data Visualization and Design 76
Chapter 4
Purposeful Use of Color 128
Chapter 5
Visualizing Variability 174
Chapter 6
Exploring Data Visually 226
Chapter 7
Explaining Visually to Influence with Data 284
Chapter 8
Data Dashboards 322
Chapter 9
Telling the Truth with Data Visualization 360
References
Index
397
399
Contents
ABOUT THE AUTHORS xi
PREFACE xiii
Chapter 1
Introduction 2
1.1 Analytics 3
1.2 Why Visualize Data? 4
Data Visualization for Exploration 4
Data Visualization for Explanation 7
1.3 Types of Data 8
Quantitative and Categorical Data 8
Cross-Sectional and Time Series Data 9
Big Data 10
1.4 Data Visualization in Practice 11
Accounting 11
Finance 12
Human Resource Management 13
Marketing 14
Operations 14
Engineering 16
Sciences 16
Sports 17
Summary 18
Glossary 19
Problems 20
Selecting a Chart Type 26
2.1 Defining the Goal of Your Data Visualization 28
Selecting an Appropriate Chart 28
2.2 Creating and Editing Charts in Excel 29
Creating a Chart in Excel 30
Editing a Chart in Excel 30
2.3 Scatter Charts and Bubble Charts 32
Scatter Charts 32
Bubble Charts 33
2.4 Line Charts, Column Charts, and Bar Charts 35
Line Charts 35
Column Charts 39
Bar Charts 41
2.5 Maps 42
Geographic Maps 42
Heat Maps 44
Treemaps 45
Chapter 2
vi
Contents
2.6 When to Use Tables 47
Tables versus Charts 47
2.7 Other Specialized Charts 49
Waterfall Charts 49
Stock Charts 51
Funnel Charts 52
2.8 A Summary Guide to Chart Selection 54
Guidelines for Selecting a Chart 54
Some Charts to Avoid 55
Excel’s Recommended Charts Tool 57
Summary 59
Glossary 60
Problems 61
Data Visualization and Design 76
3.1 Preattentive Attributes 78
Color 81
Form 81
Length and Width 84
Spatial Positioning 87
Movement 87
3.2 Gestalt Principles 88
Similarity 88
Proximity 88
Enclosure 89
Connection 89
3.3 Data-Ink Ratio 91
3.4 Other Data Visualization Design Issues 98
Minimizing Eye Travel 98
Choosing a Font for Text 100
3.5 Common Mistakes in Data Visualization Design 102
Wrong Type of Visualization 102
Trying to Display Too Much Information 104
Using Excel Default Settings for Charts 106
Too Many Attributes 108
Unnecessary Use of 3D 109
Summary 111
Glossary 111
Problems 112
Chapter 3
Purposeful Use of Color 128
Color and Perception 130
Attributes of Color: Hue, Saturation, and Luminance 130
Chapter 4
4.1
Contents
Color Psychology and Color Symbolism 132
Perceived Color 132
4.2 Color Schemes and Types of Data 135
Categorical Color Schemes 135
Sequential Color Schemes 137
Diverging Color Schemes 139
4.3 Custom Color Using the HSL Color System 141
4.4 Common Mistakes in the Use of Color in Data
Visualization 146
Unnecessary Color 146
Excessive Color 148
Insufficient Contrast 151
Inconsistency Across Related Charts 153
Neglecting Colorblindness 153
Not Considering the Mode of Delivery 156
Summary 156
Glossary 157
Problems 157
Chapter 5
Visualizing Variability 174
5.1 Creating Distributions from Data 176
Frequency Distributions for Categorical Data 176
Relative Frequency and Percent Frequency 179
Visualizing Distributions of Quantitative Data 181
5.2 Statistical Analysis of Distributions of Quantitative
Variables 193
Measures of Location 193
Measures of Variability 194
Box and Whisker Charts 197
5.3 Uncertainty in Sample Statistics 200
Displaying a Confidence Interval on a Mean 201
Displaying a Confidence Interval on a Proportion 203
5.4 Uncertainty in Predictive Models 205
Illustrating Prediction Intervals for a Simple Linear
Regression Model 205
Illustrating Prediction Intervals for a Time Series Model 208
Summary 211
Glossary 211
Problems 213
Exploring Data Visually 226
Introduction to Exploratory Data Analysis 228
Espléndido Jugo y Batido, Inc. Example 229
Organizing Data to Facilitate Exploration 230
Chapter 6
6.1
vii
viii
Contents
6.2 Analyzing Variables One at a Time 234
Exploring a Categorical Variable 234
Exploring a Quantitative Variable 237
6.3 Relationships between Variables 242
Crosstabulation 242
Association between Two Quantitative Variables 247
6.4 Analysis of Missing Data 256
Types of Missing Data 256
Exploring Patterns Associated with Missing Data 258
6.5 Visualizing Time-Series Data 260
Viewing Data at Different Temporal Frequencies 260
Highlighting Patterns in Time Series Data 262
Rearranging Data for Visualization 266
6.6 Visualizing Geospatial Data 269
Choropleth Maps 269
Cartograms 272
Summary 273
Glossary 274
Problems 275
Explaining Visually to Influence with Data 284
Know Your Audience 287
Audience Member Needs 287
Audience Member Analytical Comfort Levels 289
7.2 Know Your Message 292
What Helps the Decision Maker? 293
Empathizing with Data 294
7.3 Storytelling with Charts 300
Choosing the Correct Chart to Tell Your Story 300
Using Preattentive Attributes to Tell Your Story 304
7.4 Bringing It All Together: Storytelling
and Presentation Design 306
Aristotle’s Rhetorical Triangle 307
Freytag’s Pyramid 308
Storyboarding 311
Summary 313
Glossary 313
Problems 314
Chapter 7
7.1
Data Dashboards 322
8.1 What Is a Data Dashboard? 324
Principles of Effective Data Dashboards 325
Applications of Data Dashboards 325
Chapter 8
Contents
8.2 Data Dashboards Taxonomies 327
Data Updates 327
User Interaction 327
Organizational Function 328
8.3 Data Dashboard Design 328
Understanding the Purpose of the Data Dashboard 329
Considering the Needs of the Data Dashboard’s Users 329
Data Dashboard Engineering 330
8.4 Using Excel Tools to Build a Data Dashboard 331
Espléndido Jugo y Batido, Inc. 331
Using PivotTables, PivotCharts, and Slicers to Build
a Data Dashboard 332
Linking Slicers to Multiple PivotTables 343
Protecting a Data Dashboard 346
Final Review of a Data Dashboard 347
8.5 Common Mistakes in Data Dashboard Design 348
Summary 349
Glossary 349
Problems 350
Telling the Truth with Data Visualization 360
9.1 Missing Data and Data Errors 363
Identifying Missing Data 363
Identifying Data Errors 366
9.2 Biased Data 369
Selection Bias 369
Survivor Bias 372
9.3 Adjusting for Inflation 374
9.4 Deceptive Design 377
Design of Chart Axes 377
Dual-Axis Charts 381
Data Selection and Temporal Frequency 382
Issues Related to Geographic Maps 386
Summary 388
Glossary 389
Problems 389
Chapter 9
References 397
Index 399
ix
About the Authors
Jeffrey D. Camm is Inmar Presidential Chair and Senior Associate Dean of Business
Analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio,
he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior
to joining the faculty at Wake Forest, he was on the faculty of the University of Cincinnati.
He has also been a visiting scholar at Stanford University and a visiting professor of business
administration at the Tuck School of Business at Dartmouth College.
Dr. Camm has published more than 45 papers in the general area of optimization applied
to problems in operations management and marketing. He has published his research in
Science, Management Science, Operations Research, INFORMS Journal on Applied
Analytics, and other professional journals. Dr. Camm was named the Dornoff Fellow of
Teaching Excellence at the University of Cincinnati, and he was the 2006 recipient of the
INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous
companies and government agencies. From 2005 to 2010 he served as editor-in-chief of the
INFORMS Journal on Applied Analytics (formerly Interfaces). In 2016, Professor Camm
received the George E. Kimball Medal for service to the operations research profession, and
in 2017 he was named an INFORMS Fellow.
James J. Cochran is Associate Dean for Research, Professor of Applied Statistics, and
the Rogers-Spivey Faculty Fellow at The University of Alabama. Born in Dayton, Ohio, he
earned his B.S., M.S., and M.B.A. from Wright State University and his Ph.D. from the University of Cincinnati. He has been at The University of Alabama since 2014 and has been a
visiting scholar at Stanford University, Universidad de Talca, the University of South Africa,
and Pole Universitaire Leonard de Vinci.
Dr. Cochran has published more than 50 papers in the development and application of
operations research and statistical methods. He has published in several journals, including
Management Science, The American Statistician, Communications in Statistics—Theory and
Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, INFORMS Journal on Applied Analytics, and Statistics
and Probability Letters. He received the 2008 INFORMS Prize for the Teaching of Operations Research Practice, 2010 Mu Sigma Rho Statistical Education Award, and 2016 Waller
Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran
was elected to the International Statistics Institute in 2005, named a Fellow of the American
Statistical Association in 2011, and named a Fellow of INFORMS in 2017. He also received
the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association, and he received the INFORMS President’s Award in 2019.
A strong advocate for effective operations research and statistics education as a means
of improving the quality of applications to real problems, Dr. Cochran has chaired teaching
effectiveness workshops around the globe. He has served as an operations research consultant to numerous companies and not-for-profit organizations. He served as editor-in-chief of
INFORMS Transactions on Education and is on the editorial board of INFORMS Journal on
Applied Analytics, International Transactions in Operational Research, and Significance.
Michael J. Fry is Professor of Operations, Business Analytics, and Information Systems
(OBAIS) and Academic Director of the Center for Business Analytics in the Carl H. Lindner
College of Business at the University of Cincinnati. Born in Killeen, Texas, he earned a B.S.
from Texas A&M University and M.S.E. and Ph.D. degrees from the University of Michigan.
He has been at the University of Cincinnati since 2002, where he served as Department Head
from 2014 to 2018 and has been named a Lindner Research Fellow. He has also been a visiting professor at Cornell University and at the University of British Columbia.
xii
About the Authors
Professor Fry has published more than 25 research papers in journals such as Operations Research, Manufacturing and Service Operations Management, Transportation Science, Naval Research Logistics, IIE Transactions, Critical Care Medicine, and Interfaces.
He serves on editorial boards for journals such as Production and Operations Management,
INFORMS Journal on Applied Analytics (formerly Interfaces), and Journal of Quantitative
Analysis in Sports. His research interests are in applying analytics to the areas of supply chain
management, sports, and public-policy operations. He has worked with many different organizations for his research, including Dell, Inc., Starbucks Coffee Company, Great American
Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the
Cincinnati Bengals, and the Cincinnati Zoo and Botanical Gardens. In 2008, he was named a
finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, and
he has been recognized for both his research and teaching excellence at the University of
Cincinnati. In 2019, he led the team that was awarded the INFORMS UPS George D. Smith
Prize on behalf of the OBAIS Department at the University of Cincinnati.
Jeffrey W. Ohlmann is Associate Professor of Business Analytics and Huneke Research
Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine,
Nebraska, he earned a B.S. from the University of Nebraska and M.S. and Ph.D. degrees
from the University of Michigan. He has been at the University of Iowa since 2003.
Professor Ohlmann’s research on the modeling and solution of decision-making problems has produced more than two dozen research papers in journals such as Operations
Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science, and European Journal of Operational Research. He has collaborated with
organizations such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections, and three National Football League franchises. Because of the relevance of his work
to industry, he was bestowed the George B. Dantzig Dissertation Award and was recognized
as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice.
Preface
D
ata Visualization: Exploring and Explaining with Data is designed to introduce best
practices in data visualization to undergraduate and graduate students. This is one
of the first books on data visualization designed for college courses. The book contains
material on effective design, choice of chart type, effective use of color, how to explore
data visually, how to build data dashboards, and how to explain concepts and results
visually in a compelling way with data. In an increasingly data-driven economy, these
concepts are becoming more important for analysts, natural scientists, social scientists,
engineers, medical professionals, business professionals, and virtually everyone who
needs to interact with data. Indeed, the skills developed in this book will be helpful to
all who want to influence with data or be accurately informed by data.
The book is designed for a semester-long course at either the undergraduate or graduate
level. The examples used in this book are drawn from a variety of functional areas in the
business world including accounting, finance, operations, and human resources as well as
from sports, politics, science, medicine, and economics. The intention is that this book will
be relevant to students at either the undergraduate or graduate level in a business school as
well as to students studying in other academic areas.
Data Visualization: Exploring and Explaining with Data is written in a style that does
not require advanced knowledge of mathematics or statistics. The first five chapters cover
foundational issues important to constructing good charts. Chapter 1 introduces data visualization and how it fits into the broader area of analytics. A brief history of data visualization
is provided as well as a discussion of the different types of data and examples of a variety of
charts. Chapter 2 provides guidance on selecting an appropriate type of chart based on the
goals of the visualization and the type of data to be visualized. Best practices in chart design,
including discussions of preattentive attributes, Gestalt principles, and the data-ink ratio, are
covered in Chapter 3. Chapter 4 discusses the attributes of color, how to use color effectively,
and some common mistakes in the use of color in data visualization. Chapter 5 covers the important topic of visualizing and describing variability that occurs in observed values. Chapter
5 introduces the visualization of frequency distributions for categorical and quantitative variables, measures of location and variability, and confidence intervals and prediction intervals.
Chapters 6 and 7 cover how to explore and explain with data visualization in detail with
examples. Chapter 6 discusses the use of visualization in exploratory data analysis. The exploration of individual variables as well as the relationship between pairs of variables is considered. The organization of data to facilitate exploration is discussed as well as the effect of
missing data. The special considerations of visualizing time series data and geospatial data
are also presented. Chapter 7 provides important coverage of how to explain and influence
with data visualization, including knowing your message, understanding the needs of your
audience, and using preattentive attributes to better convey your message. Chapter 8 is a
discussion of how to design and construct data dashboards, collections of data visualizations
used for decision making. Finally, Chapter 9 covers the responsible use of data visualization
to avoid confusing or misleading your audience. Chapter 9 addresses the importance of
understanding your data in order to best convey insights accurately and also discusses how
design choices in a data visualization affect the insights conveyed to the audience.
This textbook can be used by students who have previously taken a basic statistics course
as well as by students who have not had a prior course in statistics. The two most technical chapters, Chapters 5 (Visualizing Variability) and 6 (Exploring Data Visually), do not
assume a previous course in statistics. All technical concepts are gently introduced. For
students who have had a previous statistics class, the statistical coverage in these chapters
provides a good review within a treatment where the focus is on visualization. The book offers complete coverage for a full course in data visualization, but it can also support a basic
statistics or analytics course. The following table gives our recommendations for chapters to
use to support a variety of courses.
xiv
Preface
Chapter
1
Intro
Chapter
2
Chapter
3
Chapter
4
Chapter
5
Chapter
6
Chapter
7
Chapter
8
Chapter
9
Chart Type
Design
Color
Variability
Exploring Explaining Dashboards Truth
•
Full Data Visualization Course
•
•
•
•
Data Visualization
Course Focused on
Presentation
•
•
•
•
Part of a Basic Statistics Course
•
•
•
•
•
•
Part of an Analytics
Course
•
•
•
•
•
•
•
•
•
•
•
•
Features and Pedagogy
The style and format of this textbook are similar to our other textbooks. Some of the specific
features that we use in this textbook are listed here.
●●
●●
●●
●●
●●
●●
Data Visualization Makeover: With the exception of Chapter 1, each chapter contains a
Data Visualization Makeover. Each of these vignettes presents a real visualization that
can be improved using the principles discussed in the chapter. We present the original
data visualization and then discuss how it can be improved. The examples are drawn
from many different organizations in a variety of areas including government, retail,
sports, science, politics, and entertainment.
Learning Objectives: Each chapter has a list of learning objectives of that chapter. The
list provides details of what students should be able to do and understand once they
have completed the chapter.
Software: Because of its widespread use and ease of availability, we have chosen
Microsoft Excel as the software to illustrate the best practices and principles contained
herein. Excel has been thoroughly integrated throughout this textbook. Whenever we
introduce a new type of chart or table, we provide detailed step-by-step instructions
for how to create the chart or table in Excel. Step-by-step instructions for creating
many of the charts and tables from the textbook using Tableau and Power BI are also
available in MindTap.
Notes and Comments: At the end of many sections, we provide Notes and Comments
to give the student additional insights about the material presented in that section.
Additionally, margin notes are used throughout the textbook to provide insights and
tips related to the specific material being discussed.
End-of-Chapter Problems: Each chapter contains at least 15 problems to help the student master the material presented in that chapter. The problems are separated into
Conceptual and Applications problems. Conceptual problems test the student’s understanding of concepts presented in the chapter. Applications problems are hands-on and
require the student to construct or edit charts or tables.
DATAfiles and CHARTfiles: All data sets used as examples and in end-of-chapter
problems are Excel files designated as DATAfiles and are available for download by
the student. The names of the DATAfiles are called out in margin notes throughout the
textbook. Similarly, some Excel files with completed charts are available for download
and are designated as CHARTfiles.
Preface
xv
MindTap
MindTap is a customizable digital course solution that includes an interactive eBook,
auto-graded exercises and problems from the textbook with solutions feedback, interactive
visualization applets with quizzes, chapter overview and problem walk-through videos, and
more! MindTap also includes step-by-step instructions for creating charts and tables from
the textbook in Tableau and Power BI. Contact your Cengage account executive for more
information about MindTap.
Instructor and Student Resources
Additional instructor and student resources for this product are available online. Instructor
assets include an Instructor’s Manual, Educator’s Guide, PowerPoint® slides, a Solutions
and Answers Guide, and a test bank powered by Cognero®. Student assets include data sets.
Sign up or sign in at www.cengage.com to search for and access this product and its online
resources.
ACKNOWLEDGMENTS
We would like to acknowledge the work of reviewers who have provided comments and
suggestions for improvement of this first edition of this text. Thanks to:
Xiaohui Chang
Oregon State University
Wei Chen
York College of Pennsylvania
Anjee Gorkhali
Susquehanna University
Rita Kumar
Cal Poly Pomona
Barin Nag
Towson University
Andy Olstad
Oregon State University
Vivek Patil
Gonzaga University
Nolan Taylor
Indiana University
We are also indebted to the entire team at Cengage who worked on this title: Senior Product Manager, Aaron Arnsparger; Senior Content Manager, Conor Allen; Senior Learning
Designer, Brandon Foltz; Digital Delivery Lead, Mark Hopkinson; Associate Subject-Matter
Expert, Nancy Marchant; Content Program Manager, Jessica Galloway; Content Quality
Assurance Engineer, Douglas Marks; and our Senior Project Manager at MPS Limited,
Anubhav Kaushal, for their editorial counsel and support during the preparation of this text.
The following Technical Content Developers worked on the MindTap content for this
text: Anthony Bacon, Philip Bozarth, Sam Gallagher, Anna Geyer, Matthew Holmes, and
Christopher Kurt. Our thanks to them as well.
Jeffrey D. Camm
James J. Cochran
Michael J. Fry
Jeffrey W. Ohlmann
Chapter 1
Introduction
Contents
1-1 ANALYTICS
1-2 WHY VISUALIZE DATA?
Data Visualization for Exploration
Data Visualization for Explanation
1-3 TYPES OF DATA
Quantitative and Categorical Data
Cross-Sectional and Time Series Data
Big Data
1-4 DATA VISUALIZATION IN PRACTICE
Accounting
Finance
Human Resource Management
Marketing
Operations
Engineering
Sciences
Sports
SUMMARY
GLOSSARY
PROBLEMS
LE A R NI N G
O B J E C T I V ES
After completing this chapter, you will be able to
LO 1 D
efine analytics and describe the different types
of analytics
LO 3 D
escribe various examples of data visualization
used in practice
escribe the different types of data and give
LO 2 D
an example of each
LO 4 Identify the various charts defined in this chapter
1-1
Analytics
3
You need a ride to a concert, so you select the Uber app on your phone. You enter the location of the concert. Your phone automatically knows your location and the app presents
several options with prices. You select an option and confirm with your driver. You receive
the driver’s name, license plate number, make and model of vehicle, and a photograph of
the driver and the car. A map showing the location of the driver and the time remaining
until arrival is updated in real time.
Without even thinking about it, we continually use data to make decisions in our lives.
How the data are displayed to us has a direct impact on how much effort we must expend
to utilize the data. In the case of Uber, we enter data (our destination) and we are presented
with data (prices) that allow us to make an informed decision. We see the result of our
decision with an indication of the driver’s name, make and model of vehicle, and license
plate number that makes us feel more secure. Rather than simply displaying the time until
arrival, seeing the progress of the car on a map gives us some indication of the driver’s
route. Watching the driver’s progress on the app removes some uncertainty and to some
extent can divert our attention from how long we have been waiting. What data are presented and how they are presented has an impact on our ability to understand the situation
and make more-informed decisions.
A weather map, an airplane seating chart, the dashboard of your car, a chart of the performance of the Dow Jones Industrial Average, your fitness tracker—all of these involve
the visual display of data. Data visualization is the graphical representation of data and
information using displays such as charts, graphs, and maps. Our ability to process information visually is strong. For example, numerical data that have been displayed in a chart,
graph, or map allow us to more easily see relationships between variables in our data set.
Trends, patterns, and the distributions of data are more easily comprehended when data are
displayed visually.
This book is about how to effectively display data to both discover and describe the
information it contains data. We provide best practices in the design of visual displays of
data, the effective use of color, and chart type selection. The goal of this book is to instruct
you how to create effective data visualizations. Through the use of examples (using real
data when possible), this book presents visualization principles and guidelines for gaining
insight from data and conveying an impactful message to the audience.
With the increased use of analytics in business, industry, science, engineering, and
government, data visualization has increased dramatically in importance. We begin with a
discussion of analytics and data visualization’s role in this rapidly growing field.
1-1 Analytics
Analytics is the scientific process of transforming data into insights for making better
decisions.1 Three developments have spurred the explosive growth in the use of analytics
for improving decision making in all facets of our lives, including business, sports, science,
medicine, and government:
●● Incredible amounts of data are produced by technological advances such as pointof-sale scanner technology; e-commerce and social networks; sensors on all kinds
of mechanical devices such as aircraft engines, automobiles, thermometers, and
farm machinery enabled by the so-called Internet of Things; and personal electronic
devices such as cell phones. Businesses naturally want to use these data to improve
the efficiency and profitability of their operations, better understand their customers,
and price their products more effectively and competitively. Scientists and engineers
use these data to invent new products, improve existing products, and make new
basic discoveries about nature and human behavior.
1
We adopt the definition of analytics developed by the Institute for Operations Research and the Management
Sciences (INFORMS).
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Chapter 1
Introduction
Ongoing research has resulted in numerous methodological developments, including
advances in computational approaches to effectively handle and explore massive
amounts of data as well as faster algorithms for data visualization, machine learning,
optimization, and simulation.
●● The explosion in computing power and storage capability through better computing
hardware, parallel computing, and cloud computing (the remote use of hardware and
software over the internet) enable us to solve larger decision problems more quickly
and more accurately than ever before.
●●
In summary, the availability of massive amounts of data, improvements in analytical methods, and substantial increases in computing power and storage have enabled the explosive
growth in analytics, data science, and artificial intelligence.
Analytics can involve techniques as simple as reports or as complex as large-scale optimizations and simulations. Analytics is generally grouped into three broad categories of
methods: descriptive, predictive, and prescriptive analytics.
Descriptive analytics is the set of analytical tools that describe what has happened.
This includes techniques such as data queries (requests for information with certain characteristics from a database), reports, descriptive or summary statistics, and data visualization.
Descriptive data mining techniques such as cluster analysis (grouping data points with
similar characteristics) also fall into this category. In general, these techniques summarize
existing data or the output from predictive or prescriptive analyses.
Predictive analytics consists of techniques that use mathematical models constructed
from past data to predict future events or better understand the relationships between variables. Techniques in this category include regression analysis, time series forecasting,
computer simulation, and predictive data mining. As an example of a predictive model, past
weather data are used to build mathematical models that forecast future weather. Likewise,
past sales data can be used to predict future sales for seasonal products such as snowblowers, winter coats, and bathing suits.
Prescriptive analytics are mathematical or logical models that suggest a decision
or course of action. This category includes mathematical optimization models, decision
analysis, and heuristic or rule-based systems. For example, solutions to supply network
optimization models provide insights into the quantities of a company’s various products
that should be manufactured at each plant, how much should be shipped to each of the
company’s distribution centers, and which distribution center should serve each customer
to minimize cost and meet service constraints.
Data visualization is mission-critical to the success of all three types of analytics. We
discuss
