Big Data MBA: Driving Business Strategies with Data Science

372 Pages·2015·8.76 MB·English

by  Bill Schmarzo

Table of Contents
Introduction
Overview of the Book and Technology
How This Book Is Organized
Who Should Read This Book
Tools You Will Need
What's on the Website
What This Means for You
Part I: Business Potential of Big Data
Chapter 1: The Big Data Business Mandate
Big Data MBA Introduction
Focus Big Data on Driving Competitive Differentiation
Critical Importance of “Thinking Differently”
Summary
Homework Assignment
Notes
Chapter 2: Big Data Business Model Maturity Index
Introducing the Big Data Business Model Maturity Index
Big Data Business Model Maturity Index Lessons Learned
Summary
Homework Assignment
Chapter 3: The Big Data Strategy Document
Establishing Common Business Terminology
Introducing the Big Data Strategy Document
Introducing the Prioritization Matrix
Using the Big Data Strategy Document to Win the World Series
Summary
Homework Assignment
Notes
Chapter 4: The Importance of the User Experience
The Unintelligent User Experience
Consumer Case Study: Improve Customer Engagement
Business Case Study: Enable Frontline Employees
B2B Case Study: Make the Channel More Effective
Summary
Homework Assignment
Part II: Data Science
Chapter 5: Differences Between Business Intelligence and Data Science
What Is Data Science?
The Analyst Characteristics Are Different
The Analytic Approaches Are Different
The Data Models Are Different
The View of the Business Is Different
Summary
Homework Assignment
Notes
Chapter 6: Data Science 101
Data Science Case Study Setup
Fundamental Exploratory Analytics
Analytic Algorithms and Models
Summary
Homework Assignment
Notes
Chapter 7: The Data Lake
Introduction to the Data Lake
Characteristics of a Business-Ready Data Lake
Using the Data Lake to Cross the Analytics Chasm
Modernize Your Data and Analytics Environment
Analytics Hub and Spoke Analytics Architecture
Early Learnings
What Does the Future Hold?
Summary
Homework Assignment
Notes
Part III: Data Science for Business Stakeholders
Chapter 8: Thinking Like a Data Scientist
The Process of Thinking Like a Data Scientist
Summary
Homework Assignment
Notes
Chapter 9: “By” Analysis Technique
“By” Analysis Introduction
“By” Analysis Exercise
Foot Locker Use Case “By” Analysis
Summary
Homework Assignment
Notes
Chapter 10: Score Development Technique
Definition of a Score
FICO Score Example
Other Industry Score Examples
LeBron James Exercise Continued
Foot Locker Example Continued
Summary
Homework Assignment
Notes
Chapter 11: Monetization Exercise
Fitness Tracker Monetization Example
Summary
Homework Assignment
Notes
Chapter 12: Metamorphosis Exercise
Business Metamorphosis Review
Business Metamorphosis Exercise
Business Metamorphosis in Health Care
Summary
Homework Assignment
Notes
Part IV: Building Cross-Organizational Support
Chapter 13: Power of Envisioning
Envisioning: Fueling Creative Thinking
The Prioritization Matrix
Summary
Homework Assignment
Notes
Chapter 14: Organizational Ramifications
Chief Data Monetization Officer
Privacy, Trust, and Decision Governance
Unleashing Organizational Creativity
Summary
Homework Assignment
Notes
Chapter 15: Stories
Customer and Employee Analytics
Product and Device Analytics
Network and Operational Analytics
Characteristics of a Good Business Story
Summary
Homework Assignment
Notes
End User License Agreement
End User License Agreement
List of Illustrations
Chapter 1: The Big Data Business Mandate
Figure 1.1 Big Data Business Model Maturity Index
Figure 1.2 Modern data/analytics environment
Chapter 2: Big Data Business Model Maturity Index
Figure 2.1 Big Data Business Model Maturity Index
Figure 2.2 Crossing the analytics chasm
Figure 2.3 Packaging and selling audience insights
Figure 2.4 Optimize internal processes
Figure 2.5 Create new monetization opportunities
Chapter 3: The Big Data Strategy Document
Figure 3.1 Big data strategy decomposition process
Figure 3.2 Big data strategy document
Figure 3.3 Chipotle's 2012 letter to the shareholders
Figure 3.4 Chipotle's “increase same store sales” business initiative
Figure 3.5 Chipotle key business entities and decisions
Figure 3.6 Completed Chipotle big data strategy document
Figure 3.7 Business value of potential Chipotle data sources
Figure 3.8 Implementation feasibility of potential Chipotle data sources
Figure 3.9 Chipotle prioritization of use cases
Figure 3.10 San Francisco Giants big data strategy document
Figure 3.11 Chipotle's same store sales results
Chapter 4: The Importance of the User Experience
Figure 4.1 Original subscriber e-mail
Figure 4.2 Improved subscriber e-mail
Figure 4.3 Actionable subscriber e-mail
Figure 4.4 App recommendations
Figure 4.5 Traditional Business Intelligence dashboard
Figure 4.6 Actionable store manager dashboard
Figure 4.7 Store manager accept/reject recommendations
Figure 4.8 Competitive analysis use case
Figure 4.9 Local events use case
Figure 4.10 Local weather use case
Figure 4.11 Financial advisor dashboard
Figure 4.12 Client personal information
Figure 4.13 Client financial information
Figure 4.14 Client financial goals
Figure 4.15 Financial contributions recommendations
Figure 4.16 Spend analysis and recommendations
Figure 4.17 Asset allocation recommendations
Figure 4.18 Other investment recommendations
Chapter 5: Differences Between Business Intelligence and Data Science
Figure 5.1 Schmarzo TDWI keynote, August 2008
Figure 5.2 Oakland A's versus New York Yankees cost per win
Figure 5.3 Business Intelligence versus data science
Figure 5.4 CRISP: Cross Industry Standard Process for Data Mining
Figure 5.5 Business Intelligence engagement process
Figure 5.6 Typical BI tool graphic options
Figure 5.7 Data scientist engagement process
Figure 5.8 Measuring goodness of fit
Figure 5.9 Dimensional model (star schema)
Figure 5.10 Using flat files to eliminate or reduce joins on Hadoop
Figure 5.11 Sample customer analytic profile
Figure 5.12 Improve customer retention example
Chapter 6: Data Science 101
Figure 6.1 Basic trend analysis
Figure 6.2 Compound trend analysis
Figure 6.3 Trend line analysis
Figure 6.4 Boxplot analysis
Figure 6.5 Geographical (spatial) trend analysis
Figure 6.6 Pairs plot analysis
Figure 6.7 Time series decomposition analysis
Figure 6.8 Cluster analysis
Figure 6.9 Normal curve equivalent analysis
Figure 6.10 Normal curve equivalent seller pricing analysis example
Figure 6.11 Association analysis
Figure 6.12 Converting association rules into segments
Figure 6.13 Graph analysis
Figure 6.14 Text mining analysis
Figure 6.15 Sentiment analysis
Figure 6.16 Traverse pattern analysis
Figure 6.17 Decision tree classifier analysis
Figure 6.18 Cohorts analysis
Chapter 7: The Data Lake
Figure 7.1 Characteristics of a data lake
Figure 7.2 The analytics dilemma
Figure 7.3 The data lake line of demarcation
Figure 7.4 Create a Hadoop-based data lake
Figure 7.5 Create an analytic sandbox
Figure 7.6 Move ETL to the data lake
Figure 7.7 Hub and Spoke analytics architecture
Figure 7.8 Data science engagement process
Figure 7.9 What does the future hold?
Figure 7.10 EMC Federation Business Data Lake
Chapter 8: Thinking Like a Data Scientist
Figure 8.1 Foot Locker's key business initiatives
Figure 8.2 Examples of Foot Locker's in-store merchandising
Figure 8.3 Foot Locker's store manager persona
Figure 8.4 Foot Locker's strategic nouns or key business entities
Figure 8.5 Thinking like a data scientist decomposition process
Figure 8.6 Recommendations worksheet template
Figure 8.7 Foot Locker's recommendations worksheet
Figure 8.8 Foot Locker's store manager actionable dashboard
Figure 8.9 Thinking like a data scientist decomposition process
Chapter 9: “By” Analysis Technique
Figure 9.1 Identifying metrics that may be better predictors of performance
Figure 9.2 NBA shooting effectiveness
Figure 9.3 LeBron James's shooting effectiveness
Chapter 10: Score Development Technique
Figure 10.1 FICO score considerations
Figure 10.2 FICO score decision range
Figure 10.3 Recommendations worksheet
Figure 10.4 Updated recommendations worksheet
Figure 10.5 Completed recommendations worksheet
Figure 10.6 Potential Foot Locker customer scores
Figure 10.7 Foot Locker recommendations worksheet
Figure 10.8 CLTV based on sales
Figure 10.9 More predictive CLTV score
Chapter 11: Monetization Exercise
Figure 11.1 “A day in the life” customer persona
Figure 11.2 Fitness tracker prioritization
Figure 11.3 Monetization road map
Chapter 12: Metamorphosis Exercise
Figure 12.1 Big Data Business Model Maturity Index
Figure 12.2 Patient actionable analytic profile
Chapter 13: Power of Envisioning
Figure 13.1 Big Data Vision Workshop process and timeline
Figure 13.2 Big Data Vision Workshop illustrative analytics
Figure 13.3 Big Data Vision Workshop user experience mock-up
Figure 13.4 Prioritize Healthcare Systems's use cases
Figure 13.5 Prioritization matrix template
Figure 13.6 Prioritization matrix process
Chapter 14: Organizational Ramifications
Figure 14.1 CDMO organizational structure