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Standardizes the definition and framework of analytics

ABOK stands for Analytics Body of Knowledge. Based on the authors’ definition of analytics—which is “a process by which a team of people helps an organization make better decisions (the objective) through the analysis of data (the activity)”— this book from Institute for Operations Research and the Management Sciences (INFORMS) represents the perspectives of some of the most respected experts on analytics. The INFORMS ABOK documents the core concepts and skills with which an analytics professional should be familiar; establishes a dynamic resource that will be used by practitioners to increase their understanding of analytics; and, presents instructors with a framework for developing academic courses and programs in analytics.

The INFORMS ABOK offers in-depth insight from peer-reviewed chapters that provide readers with a better understanding of the dynamic field of analytics. Chapters cover: Introduction to Analytics; Getting Started with Analytics; The Analytics Team; The Data; Solution Methodology; Model Building; Machine Learning; Deployment and Life Cycle Management; and The Blossoming Analytics Talent Pool: An Overview of the Analytics Ecosystem. 

Across industries and academia, readers with various backgrounds in analytics – from novices who are interested in learning more about the basics of analytics to experienced professionals who want a different perspective on some aspect of analytics – will benefit from reading about and implementing the concepts and methods covered by the INFORMS ABOK. 

Preface xv
List of Contributors xix
1 Introduction to Analytics 1(30)
Philip T. Keenan
Jonathan H. Owen
Kathryn Schumacher
1.1 Introduction
1(2)
1.2 Conceptual Framework
3(3)
1.2.1 Data-Centric Analytics
3(1)
1.2.2 Decision-Centric Analytics
4(1)
1.2.3 Combining Data-and Decision-Centric Approaches
5(1)
1.3 Categories of Analytics
6(10)
1.3.1 Descriptive Analytics
7(3)
Data Modeling
7(3)
Reporting
10(1)
Visualization
10(1)
Software
10(1)
1.3.2 Predictive Analytics
10(4)
Data Mining and Pattern Recognition
11(1)
Predictive Modeling, Simulation, and Forecasting
11(1)
Leveraging Expertise
12(2)
1.3.3 Prescriptive Analytics
14(2)
1.4 Analytics Within Organizations
16(7)
1.4.1 Projects
17(4)
1.4.2 Communicating Analytics
21(1)
1.4.3 Organizational Capability
21(2)
1.5 Ethical Implications
23(2)
1.6 The Changing World of Analytics
25(3)
1.7 Conclusion
28(1)
References
28(3)
2 Getting Started with Analytics 31(18)
Karl G. Kempf
2.1 Introduction
31(1)
2.2 Five Manageable Tasks
32(11)
2.2.1 Task 1: Selecting the Target Problem
33(1)
2.2.2 Task 2: Assemble the Team
34(2)
Executive Sponsor
35(1)
Project Manager
35(1)
Domain Expert
35(1)
IT Expert
35(1)
Data Scientist
36(1)
Stakeholders
36(1)
2.2.3 Task 3: Prepare the Data
36(3)
2.2.4 Task 4: Selecting Analytics Tools
39(3)
Analytical Specificity or Breadth
39(1)
Access to Data
40(1)
Execution Performance
40(1)
Visualization Capability
40(1)
Data Scientist Skillset
40(1)
Vendor Pricing
41(1)
Team Budget
41(1)
Sharing and Collaboration
41(1)
2.2.5 Task 5: Execute
42(1)
2.3 Real Examples
43(3)
Case 1: Sensor Data and High-Velocity Analytics to Save Operating Costs
43(1)
Case 2: Social Media and High-Velocity Analytics for Quick Response to Customers
44(1)
Case 3: Sensor Data and High-Velocity Analytics to Save Maintenance Costs
44(1)
Case 4: Using Old Data and Analytics to Detect New Fraudulent Claims
45(1)
Case 5: Using Old and New Data Plus Analytics to Decrease Crime
45(1)
Case 6: Collecting the Data and Applying the Analytics Is the Business
45(1)
References
46(1)
Further Reading: Papers
47(1)
Further Reading: Books
48(1)
3 The Analytics Team 49(28)
Thomas H. Davenport
3.1 Introduction
49(1)
3.2 Skills Necessary for Analytics
50(7)
3.2.1 More Advanced or Recent Analytical and Data Science Skills
51(2)
3.2.2 The Larger Team
53(4)
3.3 Managing Analytical Talent
57(4)
3.3.1 Developing Talent
58(1)
3.3.2 Working with the HR Organization
59(2)
3.4 Organizing Analytics
61(11)
3.4.1 Goals of a Particular Analytics Organization
62(1)
3.4.2 Basic Models for Organizing Analytics
63(2)
3.4.3 Coordination Approaches
65(5)
Program Management Office
66(1)
Federation
67(1)
Community
67(1)
Matrix
67(1)
Rotation
67(1)
Assigned Customers
67(1)
What Model Fits Your Business?
68(2)
3.4.4 Organizational Structures for Specific Analytics Strategies and Scenarios
70(1)
3.4.5 Analytical Leadership and the Chief Analytics Officer
70(2)
3.5 To Where Should Analytical Functions Report?
72(3)
Information Technology
72(1)
Strategy
72(1)
Shared Services
72(1)
Finance
73(1)
Marketing or Other Specific Function
73(1)
Product Development
73(1)
3.5.1 Building an Analytical Ecosystem
73(1)
3.5.2 Developing the Analytical Organization over Time
74(1)
References
75(2)
4 The Data 77(22)
Brian T. Downs
4.1 Introduction
77(1)
4.2 Data Collection
77(9)
4.2.1 Data Types
77(3)
4.2.2 Data Discovery
80(6)
4.3 Data Preparation
86(7)
4.4 Data Modeling
93(4)
4.4.1 Relational Databases
93(2)
4.4.2 Nonrelational Databases
95(2)
4.5 Data Management
97(2)
5 Solution Methodologies 99(56)
Maly E. Helander
5.1 Introduction
99(7)
5.1.1 What Exactly Do We Mean by "Solution," "Problem," and "Methodology?"
99(2)
5.1.2 It's All About the Problem
101(1)
5.1.3 Solutions versus Products
101(2)
5.1.4 How This
Chapter Is Organized
103(2)
5.1.5 The "Descriptive-Predictive-Prescriptive" Analytics Paradigm
105(1)
5.1.6 The Goals of This
Chapter
105(1)
5.2 Macro-Solution Methodologies for the Analytics Practitioner
106(10)
5.2.1 The Scientific Research Methodology
106(3)
5.2.2 The Operations Research Project Methodology
109(3)
5.2.3 The Cross-Industry Standard Process for Data Mining (CRISP-DM) Methodology
112(2)
5.2.4 Software Engineering-Related Solution Methodologies
114(1)
5.2.5 Summary of Macro-Methodologies
114(2)
5.3 Micro-Solution Methodologies for the Analytics Practitioner
116(26)
5.3.1 Micro-Solution Methodology Preliminaries
116(1)
5.3.2 Micro-Solution Methodology Description Framework
117(2)
5.3.3 Group I: Micro-Solution Methodologies for Exploration and Discovery
119(8)
Group I: Problems of Interest
119(1)
Group I: Relevant Models
119(1)
Group I: Data Considerations
120(1)
Group I: Solution Techniques
120(6)
Group I: Relationship to Macro-Methodologies
126(1)
Group I: Takeaways
126(1)
5.3.4 Group II: Micro-Solution Methodologies Using Models Where Techniques to Find Solutions Are Independent of Data
127(10)
Group II: Problems of Interest
127(1)
Group II: Relevant Models
127(1)
Group II: Data Considerations
128(1)
Group II: Solution Techniques
128(7)
Group II: Relationship to Macro-Methodologies
135(2)
Group II: Takeaways
137(1)
5.3.5 Group III: Micro-Solution Methodologies Using Models Where Techniques to Find Solutions Are Dependent on Data
137(4)
Group III: Problems of Interest
137(1)
Group III: Relevant Models
138(1)
Group III: Data Considerations
138(1)
Group III: Solution Techniques
139(1)
Group III: Relationship to Macro-Methodologies
140(1)
Group III: Takeaways
141(1)
5.3.6 Micro-Methodology Summary
141(1)
5.4 General Methodology-Related Considerations
142(2)
5.4.1 Planning an Analytics Project
142(1)
5.4.2 Software and Tool Selection
142(1)
5.4.3 Visualization
143(1)
5.4.4 Fields with Related Methodologies
144(1)
5.5 Summary and Conclusions
144(5)
5.5.1 "Ding Dong, the Scientific Method Is Dead!"
145(1)
5.5.2 "Methodology Cramps My Analytics Style"
145(1)
5.5.3 "There Is Only One Way to Solve This"
146(2)
5.5.4 Perceived Success Is More Important Than the Right Answer
148(1)
5.6 Acknowledgments
149(1)
References
149(6)
6 Modeling 155(76)
Gerald G. Brown
6.1 Introduction/SS
6.2 When Are Models Appropriate
155(6)
6.2.1 What Is the Problem with This System?
159(1)
6.2.2 Is This Problem Important?
159(1)
6.2.3 How Will This Problem Be Solved Without a New Model?
159(1)
6.2.4 What Modeling Technique Will Be Used?
159(1)
6.2.5 How Will We Know When We Have Succeeded?
160(1)
Who Are the System Operator Stakeholders?
160(1)
6.3 Types of Models
161(1)
6.3.1 Descriptive Models
161(1)
6.3.2 Predictive Models
161(1)
6.3.3 Prescriptive Models
161(1)
6.4 Models Can Also Be Characterized by Whether They Are Deterministic or Stochastic (Random)
161(1)
6.5 Counting
162(1)
6.6 Probability
163(2)
6.7 Probability Perspectives and Subject Matter Experts
165(1)
6.8 Subject Matter Experts
165(1)
6.9 Statistics
166(3)
6.9.1 A Random Sample
166(1)
6.9.2 Descriptive Statistics
166(1)
6.9.3 Parameter Estimation with a Confidence Interval
166(1)
6.9.4 Regression
167(2)
6.10 Inferential Statistics
169(1)
6.11 A Stochastic Process
170(3)
6.12 Digital Simulation
173(1)
6.12.1 Static versus Dynamic Simulations
174(1)
6.13 Mathematical Optimization
174(1)
6.14 Measurement Units
175(1)
6.15 Critical Path Method
176(2)
6.16 Portfolio Optimization Case Study Solved By a Variety of Methods
178(3)
6.16.1 Linear Program
178(1)
6.16.2 Heuristic
179(1)
6.16.3 Assessing Our Progress
179(1)
6.16.4 Relaxations and Bounds
179(1)
6.16.5 Are We Finished Yet?
180(1)
6.17 Game Theory
181(3)
6.18 Decision Theory
184(3)
6.19 Susceptible, Exposed, Infected, Recovered (SEIR) Epidemiology
187(2)
6.20 Search Theory
189(1)
6.21 Lanchester Models of Warfare
189(3)
6.22 Hughes' Salvo Model of Combat
192(1)
6.23 Single-Use Models
193(2)
6.24 The Principle of Optimality and Dynamic Programming
195(2)
6.25 Stack-Based Enumeration
197(3)
6.25.1 Data Structures
197(2)
6.25.2 Discussion
199(1)
6.25.3 Generating Permutations and Combinations
199(1)
6.26 Traveling Salesman Problem: Another Case Study in Alternate Solution Methods
200(6)
6.27 Model Documentation, Management, and Performance
206(9)
6.27.1 Model Formulation
206(1)
6.27.2 Choice of Implementation Language
207(1)
6.27.3 Supervised versus Automated Models
207(1)
6.27.4 Model Fidelity
208(2)
6.27.5 Sensitivity Analysis
210(1)
6.27.6 With Different Methods
211(1)
6.27.7 With Different Variables
212(1)
6.27.8 Stability
213(1)
6.27.9 Reliability
213(1)
6.27.10 Scalability
213(1)
6.27.11 Extensibility
214(1)
6.28 Rules for Data Use
215(2)
6.28.1 Proprietary Data
215(1)
6.28.2 Licensed Data
215(1)
6.28.3 Personally Identifiable Information
216(1)
6.28.4 Protected Critical Infrastructure Information System (PCIIMS)
216(1)
6.28.5 Institutional Review Board (IRB)
216(1)
6.28.6 Department of Defense and Department of Energy Classification
216(1)
6.28.7 Law Enforcement Data
216(1)
6.28.8 Copyright and Trademark
216(1)
6.28.9 Paraphrased and Plagiarized
217(1)
6.28.10 Displays of Model Outputs
217(1)
6.28.11 Data Integrity
217(1)
6.28.12 Multiple Data Evolutions
217(1)
6.29 Data Interpolation and Extrapolation
217(1)
6.30 Model Verification and Validation
218(2)
6.30.1 Verifying
219(1)
6.30.2 Validating
219(1)
6.30.3 Comparing Models
219(1)
6.30.4 Sample Data
220(1)
6.30.5 Data Diagnostics
220(1)
6.30.6 Data Vintage and Provenance
220(1)
6.31 Communicate with Stakeholders
220(7)
6.31.1 Training
221(1)
6.31.2 Report Writers
221(1)
6.31.3 Standard Form Model Statement
222(1)
6.31.4 Persistence and Monotonicity: Examples of Realistic Model Restrictions
223(1)
6.31.5 Model Solutions Require a Lot of Polish and Refinement Before They Can Directly Influence Policy
224(2)
6.31.6 Model Obsolescence and Model-Advised Thumb Rules
226(1)
6.32 Software
227(1)
6.33 Where to Go from Here
228(1)
6.34 Acknowledgments
228(1)
References
229(2)
7 Machine Learning 231(44)
Samuel H. Huddleston
Gerald G. Brown
7.1 Introduction
231(1)
7.2 Supervised, Unsupervised, and Reinforcement Learning
232(3)
7.3 Model Development, Selection, and Deployment for Supervised Learning
235(8)
7.3.1 Goals and Guiding Principles in Machine Learning
235(1)
7.3.2 Algorithmic Modeling Overview
236(1)
7.3.3 Data Acquisition and Cleaning
236(1)
7.3.4 Feature Engineering
237(1)
7.3.5 Modeling Overview
238(2)
7.3.6 Model Fitting (Training) and Feature Selection
240(1)
7.3.7 Model (Algorithm) Selection
241(1)
7.3.8 Model Performance Assessment
242(1)
7.3.9 Model Implementation
242(1)
7.4 Model Fitting, Model Error, and the Bias-Variance Trade-Off
243(4)
7.4.1 Components of (Regression) Model Error
243(2)
7.4.2 Model Fitting: Balancing Bias and Variance
245(2)
7.5 Predictive Performance Evaluation
247(7)
7.5.1 Regression Performance Evaluation
248(1)
7.5.2 Classification Performance Evaluation
249(4)
7.5.3 Performance Evaluation for Time-Dependent Data
253(1)
7.6 An Overview of Supervised Learning Algorithms
254(13)
7.6.1 k-Nearest Neighbors (KNN)
255(1)
7.6.2 Extensions to Regression
256(1)
7.6.3 Classification and Regression Trees
257(2)
7.6.4 Time Series Forecasting
259(2)
7.6.5 Support Vector Machines
261(1)
7.6.6 Artificial Neural Networks
262(3)
7.6.7 Ensemble Methods
265(2)
7.7 Unsupervised Learning Algorithms
267(5)
7.7.1 Kernel Density Estimation
267(1)
7.7.2 Association Rule Mining
268(1)
7.7.3 Clustering Methods
269(1)
7.7.4 Principal Components Analysis (PCA)
270(1)
7.7.5 Bag-of-Words and Vector Space Models
271(1)
7.8 Conclusion
272(1)
7.9 Acknowledgments
272(1)
References
273(2)
8 Deployment and Life Cycle Management 275(36)
Arnie Greenland
8.1 Introduction
275(1)
8.2 The Analytics Methodology: Understanding the Critical Steps in Deployment and Life Cycle Management
276(27)
8.2.1 CRISP-DM Phase 1: Business Understanding
278(1)
8.2.2 JTA Domain I, Task 1: Obtain or Receive Problem Statement and Usability
278(1)
8.2.3 JTA Domain I, Task 2: Identify Stakeholders
279(2)
8.2.4 JTA Domain I, Task 3: Determine if the Problem Is Amenable to an Analytics Solution
281(1)
8.2.5 JTA Domain I, Task 4: Refine the Problem Statement and Delineate Constraints
281(1)
8.2.6 JTA Domain I, Task 5: Define an Initial Set of Business Benefits
281(1)
8.2.7 JTA Domain I, Task 6: Obtain Stakeholder Agreement on the Business Statement
282(1)
8.2.8 JTA Domain II, Task 1: Reformulate the Problem Statement as an Analytics Problem
283(2)
8.2.9 JTA Domain II, Task 2: Develop a Proposed Set of Drivers and Relationships to Outputs
285(1)
8.2.10 JTA Domain II, Task 3: State the Set of Assumptions Related to the Problem
286(1)
8.2.11 JTA Domain II, Task 4: Define the Key Metrics of Success
287(1)
8.2.12 JTA Domain II, Task 5: Obtain Stakeholder Agreement
287(1)
8.2.13 CRISP-DM Phases 2 and 3: Data Understanding and Data Preparation
288(2)
8.2.14 JTA Domain III, Task 1: Identify and Prioritize Data Needs and Sources
290(1)
8.2.15 JTA Domain III, Task 2: Acquire Data
290(1)
8.2.16 JTA Domain III, Task 3: Harmonize, Rescale, Clean, and Share Data
291(1)
8.2.17 JTA Domain III, Task 4: Identify Relationships in the Data
292(1)
8.2.18 JTA Domain III, Task 5: Document and Report Finding
293(1)
8.2.19 JTA Domain III, Task 6: Refine the Business and Analytics Problem Statements
293(1)
8.2.20 CRISP-DM Phase 4: Modeling
293(1)
8.2.21 CRISP-DM Phase 5: Evaluation
294(3)
8.2.22 CRISP-DM Phase 6: Deployment
297(1)
8.2.23 Deployment of the Analytics Model (Up to Delivery)
298(3)
8.2.24 Post-deployment Activities (Domain VI: Model Life Cycle Management)
301(2)
8.3 Overarching Issues of Life Cycle Management
303(8)
8.3.1 Documentation
303(2)
8.3.2 Communication
305(2)
8.3.3 Testing
307(1)
8.3.4 Metrics
308(3)
9 The Blossoming Analytics Talent Pool: An Overview of the Analytics Ecosystem 311(16)
Ramesh Sharda
Pankush Kalgotra
9.1 Introduction
311(1)
9.2 Analytics Industry Ecosystem
312(13)
9.2.1 Data Generation Infrastructure Providers
314(1)
9.2.2 Data Management Infrastructure Providers
315(1)
9.2.3 Data Warehouse Providers
316(1)
9.2.4 Middleware Providers
316(1)
9.2.5 Data Service Providers
316(1)
9.2.6 Analytics-Focused Software Developers
317(2)
Reporting/Descriptive Analytics
317(1)
Predictive Analytics
318(1)
Prescriptive Analytics
318(1)
9.2.7 Application Developers: Industry-Specific or General
319(2)
9.2.8 Analytics Industry Analysts and Influencers
321(1)
9.2.9 Academic Institutions and Certification Agencies
322(1)
9.2.10 Regulators and Policy Makers
323(1)
9.2.11 Analytics User Organizations
323(2)
9.3 Conclusions
325(1)
References
326(1)
Appendix: Writing and Teaching Analytics with Cases 327(28)
James J. Cochran
Index 355
JAMES J. COCHRAN, PHD, is Associate Dean for Research, Professor of Applied Statistics, and the Rogers-Spivey Faculty Fellow with The University of Alabama's Culverhouse College of Business.
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