Cover
Introduction to Business Analytics
Dedications
About the Authors
From the Authors
Key Features
Available in Connect
Also Available with Connect
Acknowledgments
Brief Table of Contents
Table of Contents
Chapter 1: Specify the Question: Using Business Analytics to Address Business Questions
1.1: Businesses Create Value
The Increasing Availability of Data and the Role of Business Analyst
1.2: The Difference Between Data and Information
1.3: Summarizing the Role of the Business Analyst
1.4: Business Analytics Across the Different Business Functions
1.5: An Introduction to the Soar Analytics Model
Illustration of the SOAR Analytics Model: Predicting Inventory Needs
S: Specify the Question
O: Obtain the Data
A: Analyze the Data
R: Report the Results
1.6: Using Exploratory and Explanatory Data Visualizations in Business Analytics
Exploratory Visualizations
Explanatory Visualizations
Chapter Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 1.1: Excel: Assessing Profitability of a Mowing Business
Lab 1.2: Excel: Assessing Customer Profitability
Lab 1.2: Tableau: Assessing Customer Profitability
Lab 1.2: Power BI: Assessing Customer Profitability
Lab 1.3: Excel: Assessing Product (SKU) Profitability
Lab 1.3: Tableau: Assessing Product (SKU) Profitability
Lab 1.3: Power BI: Assessing Product (SKU) Profitability
Chapter 2: Obtain the Data: An Introduction to Business Data Sources
2.1: Internal and External Data Sources
Internal Data Sources
External Data Sources
2.2: Obtaining the Data
Text Data
Tabular Data
2.3: Structured Data Types: Categorical versus Numerical
Categorical Data: Nominal and Ordinal Data
Numerical Data: Interval and Ratio Data
Additional Ways to Classify Data
2.4: Preparing Data for Analysis
Step 1: Ensure Data Quality
Step 2: Validate the Data for Completeness and Integrity
Step 3: Cleanse the Data
Step 4: Perform Preliminary Exploratory Analysis
2.5: Tools Used to Prepare Data for Analysis
2.6: Gathering and Protecting Data Ethically
Chapter Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 2.1: Excel: Identifying and Working with Different Data Types
Lab 2.2: Tableau: Preparing Different Data Types for Analysis
Lab 2.2: Power BI: Preparing Different Data Types for Analysis
Lab 2.3: Tableau: Conducting Preliminary Exploratory Analysis
Lab 2.3: Power BI: Conducting Preliminary Exploratory Analysis
Lab 2.4: Excel: Aggregating and Visualizing Different Data Types
Chapter 3: Analyze the Data: Basic Statistics and Tools Required in Business Analytics
3.1: Defining Populations and Samples
Parameters and Statistics
Descriptive/Summary Statistics, Inferential Statistics, and Hypotheses
3.2: Sampling Methods, Data Reduction, and Bias
Sampling Methods
Data Reduction
The Decision Process of Using a Subset of the Data
Bias in Business Analytics
3.3: Understanding Basic Statistics
Probability Distributions
Measures of Central Tendency
Measures of Dispersion
Continuous Probability Distributions
3.4: Using Software Tools to Create Summary Statistics
Excel Functions
Excel Data Analysis ToolPakรณDescriptive Statistics
Changing the Default Aggregation in Excel Pivot Table, Power BI, and Tableau
3.5: Interpreting and Visualizing Statistics
Interpreting Statistics Through Visualizations
3.6: Hypothesis Testing
Confidence Intervals: Estimating Population Parameters from Sample Statistics
Hypothesis Testing
3.7: t-Tests, Anova Tests, and Chi-Square Tests
t-Tests: Testing for a Difference of Means Between Two Paired (Dependent) Groups
ANOVA: Testing for Difference of Means Among Three or More Groups
Chi-Square Tests: Testing for a Relationship Between Two or More Variables
3.8: Introduction to Correlation and Regression Analysis
Correlation
Regression Analysis and Line of Best Fit
Chapter Supplement
Discrete Data Distributions
Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 3.1: Excel: Using Excel Functions to Calculate Descriptive Statistics to Gain Insights About the Distribution of a Sales Data Set
Lab 3.2: Excel: Using the Excel Analysis ToolPak to Calculate Descriptive Statistics to Gain Insights About the Distribution of a Sales Data Set
Lab 3.2: Tableau: Calculating Descriptive Statistics to Gain Insights About the Distribution of a Sales Data Set
Lab 3.3: Excel: Performing a t-test for Difference in Means to Determine If the Differences Between In-Person and Online Sales Are Statistically Significant
Lab 3.4: Excel: Performing an ANOVA Test for Difference in Means to Determine If There Are Significant Differences Between the Average 4-Year Degree Completion Rate/SAT Average for Public, Private, and For-Profit Colleges
Lab 3.5: Excel: Deriving Cost Drivers for Activity-Based Costing (Regression Analysis)
Chapter 4: Analyze the Data: Exploratory Business Analytics (Descriptive and Diagnostic Analytics)
4.1: The Third Step of the Soar Analytics Model: Analyze the Data
4.2: Matching the Analytics Type to the Business Question Asked
4.3: Defining Exploratory and Confirmatory Business Analytics
4.4: Descriptive Analytics
Systems that Provide Data for Descriptive Analytics
Statistical and Summarization Techniques for Descriptive Analytics
Using Descriptive Statistics as a Descriptive Analytics Technique
Data Visualization in Descriptive Analytics: Graphs and Histograms
Examples of Descriptive Analytics: Horizontal and Vertical Analysis of Performance
4.5: Going from Descriptive Analytics to Diagnostic Analytics
Diagnostic Analytics: Identifying Anomalies and Outliers
Finding Previously Unknown Linkages, Patterns, or Relationships Between Variables
4.6: Introduction to Techniques Used in Data Analysis
The Excel Data Analysis ToolPak
Chapter Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 4.1: Excel: Evaluating Inventory Using Inventory Turnover, Waste, and Profit Margins
Lab 4.2: Excel: Using Conditional Formatting to Perform a Bank Reconciliation
Lab 4.3: Excel: Applying Benfordรญs Law
Chapter 5: Analyze the Data: Confirmatory Business Analytics (Predictive Analytics and Prescriptive Analytics)
5.1: Confirmatory Data Analytics
5.2: Predictive Analytics
Classification
Regression
Time Series Analysis
5.3: Base Rates and Base Rate Fallacy
5.4: Prescriptive Analytics
Prescriptive Analytics Techniques
5.5: Summary of Analytics Performed to Address Business Questions
Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 5.1: Excel: Evaluating the Relationship Between Sales and R&D Expenditures
Lab 5.2: Excel: Evaluating the Relationship Between Sales and R&D Expenditures: Testing for a Nonlinear Relationship
Lab 5.3: Excel: Forecasting Product Demand Using Time Series Analysis
Lab 5.4: Tableau: Forecasting Product Demand Using Time Series Analysis
Lab 5.5: Power BI: Forecasting Product Demand Using Time Series Analysis
Lab 5.6: Excel: Forecasting Product Demand Using Regression
Lab 5.7: Excel: Assessing the Returns to Education: To MBA or Not to MBA?
Lab 5.8: Excel: Applying Scenario Analysis: Possible Trade War
Chapter 6: Report the Results: Using Data Visualization
6.1: The Basics of Data Visualization
Why Use Visualizations?
Sorting Considerations
6.2: Distinguishing Among Chart Types
Charts Appropriate for Categorical Data
Charts Appropriate for Numerical (Quantitative) Data
6.3: Visualizing Exploratory Business Analytics
Bar Charts, Line Charts, and Pie Charts
Bar Charts Versus Histograms
Visualizing Outliers and Anomalies
Pivot Tables
6.4: Visualizing Confirmatory Analytics
Correlation and Regression
Forecasting with Time Series Data
6.5: Presenting Data in a Dashboard
Checklist for Creating Effective Charts That Clearly Answer Business Questions
6.6: Communicating Your Data with Words: Executive Summaries and Reports
Executive Summary
Full Report
Chapter Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 6.1: Excel: Descriptive Analytics: Visualizing Pivot Table Data Using Conditional Formatting and Sparklines
Lab 6.2: Tableau: Descriptive Analytics: Analyzing Sales Revenue by Product with a Tree Map
Lab 6.2: Power BI: Descriptive Analytics: Analyzing Sales Revenue by Product with a Tree Map
Lab 6.3: Tableau: Descriptive Analytics: Analyzing Sales Revenue by Customer with a Bar Chart and Filters
Lab 6.3: Power BI: Descriptive Analytics: Analyzing Sales Revenue by Customer with a Bar Chart and Filters
Lab 6.4: Excel: Descriptive Analytics: Creating a Dashboard Using Pivot Tables and Slicers
Lab 6.5: Tableau: Descriptive Analytics: Creating a Dashboard
Lab 6.5: Power BI: Descriptive Analytics: Creating a Dashboard
Chapter 7: Marketing Analytics
7.1: What Is Marketing?
The Marketing Mix
7.2: Specifying The Marketing Question
7.3: Obtain The Data: What Marketing Data Are Available? A Discussion of Marketing Data Sources
Internal Marketing Data
External Marketing Data
Combining Internal and External Data
7.4: Descriptive Marketing Analytics
Statistical, Summarization, and Data Visualization Techniques for Descriptive Analytics
Examples of Marketing Descriptive Analytics
7.5: Diagnostic Marketing Analytics
Identification of Anomalies and Outliers
Cluster Analysis
Correlation and Summarization
7.6: Predictive Marketing Analytics
Decision Tree
7.7: Prescriptive Marketing Analytics
Goal-Seek Analysis
7.8: Report The Results
Chapter Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 7.1: Excel: Descriptive Analytics: Analyzing Company Historical Performance
Lab 7.2: Excel: Descriptive Analytics: Using a Pivot Table to Analyze Historical Performance by Product Size and Year
Lab 7.3: Tableau: Descriptive Analytics: Using a Histogram to Evaluate Process Time
Lab 7.3: Power BI: Descriptive Analytics: Using a Histogram to Evaluate Process Time
Lab 7.4: Excel Diagnostic Analytics: Analyzing the Steps in the Sales Process with a Sales Funnel Chart
Lab 7.5: Tableau: Diagnostic Analytics: Examining Pricing Strategy with Cluster Analysis
Lab 7.6: Excel: Predictive Analytics: Predicting Sales Revenue from Advertising Expense
Lab 7.6: Tableau: Predictive Analytics: Predicting Sales Revenue from Advertising Expense
Lab 7.7: Excel: Prescriptive Analytics: Calculating Internet CPM Rate Using Goal Seek
Lab 7.8: Excel: Prescriptive Analytics: Calculating Product Price Using Goal Seek
Chapter 8: Accounting Analytics
8.1: The Role of Accounting in Business
The Four Primary Branches of Accounting
8.2: Specifying the Accounting Question
8.3: Obtain the Data: The Sources of Accounting Data
Financial Accounting Data Sources
Managerial Accounting Data Sources
Auditing Data Sources
Tax Data Sources
8.4: Descriptive Accounting Analytics
8.5: Diagnostic Accounting Analytics
Identifying Anomalies and Outliers
Performing Drill-Down Analysis to Determine Relations, Patterns, and Linkages Among Variables
8.6: Predictive Accounting Analytics
Using Time Series Analysis to Predict Sales
8.7: Prescriptive Accounting Analytics
Goal-Seek Analysis
Sensitivity Analysis
8.8: Report The Results
Tables Are Sometimes a More Effective Way to Show Results
Using Visualizations to Highlight Anomalies
Using a Graph to Show Break-Even Sales
Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 8.1: Excel: Descriptive Analytics: Performing Horizontal Analysis
Lab 8.2: Excel: Descriptive Analytics: Performing Vertical Analysis
Lab 8.3: Excel: Diagnostic Analytics: Using Fuzzy Matching to Look for Fraud
Lab 8.4: Excel: Diagnostic Analytics: Estimating Fixed and Variable Costs
Lab 8.5: Excel: Predictive Analytics: Forecasting Future Performance of IBM
Lab 8.6: Tableau: Predictive Analytics: Forecasting Future Performance of IBM
Lab 8.7: Power BI: Predictive Analytics: Forecasting Future Performance of IBM
Lab 8.8: Excel: Prescriptive Analytics: Using Goal-Seek Analysis to Determine the Break-Even Point
Chapter 9: Financial Analytics
9.1: The Role of Finance in Business
The Three Primary Branches of Finance
9.2: Specifying the Finance Question
9.3 Obtain the Data: Financial Data Sources
Stock-Return Data
Summarized Financial Data
Financial Statement Data
9.4: Descriptive Financial Analytics
Statistical and Summarization Techniques for Descriptive Analytics
Descriptive Statistics
9.5: Diagnostic Financial Analytics
Finding Anomalies and Outliers
Drill-Down Analytics Using DuPont Ratios
Abbreviated DuPont Ratio Analysis for Return on Assets
Risk/Return and the Sharpe Ratio
Risk/Return Diagnostic Analysis Using the Sharpe Ratio
Risk/Return Diagnostic Analysis Using Regression Analysis
9.6: Predictive Financial Analytics
Altmanรญs Z and Bankruptcy Classification
Predicting Who Will Be Offered a Loan
9.7: Prescriptive Financial Analytics
Prescriptive Cash Flow Analysis
Sensitivity Analysis
9.8: Report The Results
Line Graphs to Compare Performance
Research Reports and Earnings Forecasts
Line Graphs in Sensitivity Analysis
Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 9.1: Excel: Descriptive and Diagnostic Analytics: Calculating Returns to Investments Using the Sharpe Ratio
Lab 9.2: Excel: Diagnostic Analytics: Applying DuPont Analysis of Financial Performance
Lab 9.3: Excel: Predictive Analytics: Evaluating Loan Acceptance
Lab 9.4: Excel: Predictive Analytics: Predicting Bankruptcy
Lab 9.5: Excel: Prescriptive Analytics: Evaluating Investments Using NPV
Lab 9.6: Excel: Prescriptive Analytics: Evaluating Investments Using IRR
Chapter 10: Operations Analytics
10.1: The Role of Operations in Business
The Three Primary Branches of Operations
10.2: Specifying the Operations Question
10.3: Obtain the Data: Operations Data Sources
Human Resources Data
IT Operations Data
Supply Chain Data
10.4: Descriptive Operations Analytics
Statistical, Summarization, and Data Visualization Techniques
10.5: Diagnostic Operations Analytics
10.6: Predictive Operations Analytics
10.7: Prescriptive Operations Analytics
Optimization
10.8: Report The Results
Chapter Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 10.1: Excel: Descriptive Analytics/Supply Chain: Calculating KPIs for Walmart and Amazon
Lab 10.2: Tableau: Descriptive Analytics/Human Resources: Analyzing Employee Turnover Using a Bar Chart
Lab 10.2: Power BI: Descriptive Analytics/Human Resources: Analyzing Employee Turnover Using a Bar Chart
Lab 10.3: Excel: Diagnostic Analytics/Supply Chain: Analyzing KPI Differences Between Walmart and Amazon
Lab 10.4: Tableau: Diagnostic Analytics/IT Operations: Analyzing Website Response Time Using a Control Chart
Lab 10.5: Tableau: Predictive Analytics/Supply Chain: Forecasting Unit Demand
Lab 10.6: Excel: Prescriptive Analytics/ Human Resources: Optimizing Employee Assignment
Chapter 11: Advanced Business Analytics
11.1: Adaptive/Autonomous Business Analytics
Adaptive/Autonomous Analytics: Mimicking Human Capabilities
11.2: Advanced Analytic Methods
Artificial Intelligence
11.3: Adaptive/Autonomous Analytics in the Business Functions
Marketing Analytics
Accounting Analytics
Financial Analytics
Operations Analytics
11.4: Trending Technologies
Robotic Process Automation (Rpa)
Cockpits
Voice and Image Analytics
Blockchain Analytics
11.5: Data-Driven Organizations and the Future of Business Analytics
Limitations of Business Analytics
The Future of Business Analytics
Summary
Key Terms
Answers to Progress Checks
Multiple-Choice Questions
Discussion Questions
Problems
Lab 11.1: Excel: Advanced Analytics/Marketing: Analyzing Customer Repurchase Rates Using Cohorts
Lab 11.2: Tableau: Advanced Analytics/Marketing: Analyzing Text Using a Word Cloud
Lab 11.3: Excel: Advanced Analytics/ Human Resources: Visualizing Survival Analysis
Lab 11.4: Excel: Advanced Analytics/Supply Chain: Estimating Demand Using Monte Carlo Simulation
Lab 11.5:: Advanced Analytics: Understanding Blockchain
Chapter 12: Using the SOAR Analytics Model to Put It All Together: Three Capstone Projects
12.1: Project 1: Determining the Price of Airbnb Nightly Rentals in New York City
Specify the Question
Obtain the Data
Analyze the Data
Report the Results
12.2: Project 2: Determining the Factors Associated With Loan Repayment
Specify the Question
Obtain the Data
Analyze the Data
Report the Results
12.3: Project 3: Completing your own Project Using the Soar Analytics Model
Using the SOAR Analytics Model
Deliverables
Appendix A: Excel Tutorial (Formatting, Sorting, Filtering, and Pivot Tables)
Appendix B: Tableau Tutorial
Appendix C: Power BI Desktop Tutorial
Appendix D: Basic Statistics Tutorial
Appendix E: Installing Excelรญs Analysis ToolPak Add-In
Appendix F: Installing Excelรญs Solver Add-In
Index