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Plain English guidance for strategic business analytics and big data implementation
In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive…mehr
Plain English guidance for strategic business analytics and big data implementation
In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice.
Provides the essential concept and framework to implement business analytics
Written clearly for a nontechnical audience
Filled with case studies across a variety of industries
Uniquely focuses on integrating multiple types of big data intelligence into your business
Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.
In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice.
Provides the essential concept and framework to implement business analytics
Written clearly for a nontechnical audience
Filled with case studies across a variety of industries
Uniquely focuses on integrating multiple types of big data intelligence into your business
Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.
Produktdetails
- Produktdetails
- SAS Institute Inc .
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 416
- Erscheinungstermin: 9. Oktober 2012
- Englisch
- Abmessung: 235mm x 157mm x 27mm
- Gewicht: 755g
- ISBN-13: 9781118370605
- ISBN-10: 1118370600
- Artikelnr.: 35450532
- SAS Institute Inc .
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 416
- Erscheinungstermin: 9. Oktober 2012
- Englisch
- Abmessung: 235mm x 157mm x 27mm
- Gewicht: 755g
- ISBN-13: 9781118370605
- ISBN-10: 1118370600
- Artikelnr.: 35450532
JEAN PAUL ISSON is an internationally recognized speaker and an expert in advanced business analytics. He is Global Vice President of BI and predictive analytics at Monster Worldwide, Inc., where he has built his team from the ground up and successfully conceived and implemented advanced analytics and web mining solutions. Prior to joining Monster, Isson led the global customer behavior modeling team at Rogers Wireless, implementing churn models and pioneering the Customer Lifetime Value segmentation to optimize services marketing and sales activities. JESSE S. HARRIOTT, PHD, is Chief Analytics Officer for Constant Contact. Previously, Jesse was Chief Knowledge Officer at Monster Worldwide where he helped drive annual revenue from $300 million to over $1.3 billion. Harriott started an international analytics division at Monster and created the Monster Employment Index, now tracked in the United States, Europe, and Asia by millions of people. He also led web analytics, business intelligence, competitive intelligence, data governance, marketing research, and sales analytics departments for Monster. Jesse has taught at the University of Chicago and was named one of Boston's Top 40 Under 40.
Preface xv Acknowledgments xvii Chapter 1 The Challenge of Business
Analytics 1 The Challenge from Outside 5 The Challenge from Within 9
Chapter 2 Pillars of Business Analytics Success: The BASP Framework 15
Business Challenges Pillar 18 Data Foundation Pillar 20 Analytics
Implementation Pillar 22 Insight Pillar 26 Execution and Measurement Pillar
29 Distributed Knowledge Pillar 31 Innovation Pillar 32 Conclusion 33
Chapter 3 Aligning Key Business Challenges across the Enterprise 35 Mission
Statement 36 Business Challenge 38 Identifying Business Challenges as a
Consultative Process 39 Identify and Prioritize Business Challenges 41
Analytics Solutions for Business Challenges 45 Chapter 4 Big and Little
Data: Different Types of Intelligence 51 Big Data 57 Little Data 61 Laying
the Data Foundation: Data Quality 62 Data Sources and Locations 65 Data
Definition and Governance 69 Data Dictionary and Data Key Users 72 Sanity
Check and Data Visualization 72 Customer Data Integration and Data
Management 73 Data Privacy 74 Chapter 5 Who Cares about Data? How to
Uncover Insights 77 The IMPACT Cycle 79 Curiosity Can Kill the Cat 82
Master the Data 86 A Fact in Search of Meaning 87 Actions Speak Louder Than
Data 88 "Eat Like a Bird, Poop Like an Elephant" 89 Track Your Outcomes 91
The IMPACT Cycle in Action: The Monster Employment Index 92 Chapter 6 Data
Visualization: Presenting Information Clearly: The CONVINCE Framework 95
Convey Meaning 97 Objectivity: Be True to Your Data 99 Necessity: Don't
Boil the Ocean 101 Visual Honesty: Size Matters 103 Imagine the Audience
104 Nimble: No Death by 1,000 Graphs 107 Context 107 Encourage Interaction
109 Conclusion 109 Chapter 7 Analytics Implementation: What Works and What
Does Not 113 Analytics Implementation Model 117 Vision and Mandate 118
Strategy 119 Organizational Collaboration 121 Human Capital 122 Metrics and
Measurement 123 Integrated Processes 124 Customer Experience 125 Technology
and Tools 125 Change Management 126 Chapter 8 Voice-of-the-Customer
Analytics and Insights 131 By Abhilasha Mehta, PhD Customer Feedback Is
Invaluable 132 The Makings of an Effective Voice-of-the-Customer Program
137 Strategy and Elements of the VOC System 152 Common VOC Program Pitfalls
162 Chapter 9 Leveraging Digital Analytics Effectively 165 By Judah
Phillips Strategic and Tactical Use of Digital Analytics 173 Understanding
Digital Analytics Concepts 174 Digital Analytics Team: People Are Most
Important for Analytical Success 184 Digital Analytics Tools 187 Advanced
Digital Analytics 191 Digital Analytics and Voice of the Customer 192
Analytics of Site and Landing Page Optimization 194 Call to Action: Unify
Traditional and Digital Analytics 195 Chapter 10 Effective Predictive
Analytics: What Works and What Does Not 199 What Is Predictive Analytics?
201 Unlocking Stage 203 Prediction Stage 206 Optimization Stage 210 Diverse
Applications for Diverse Business Problems 213 Financial Service Industries
as Pioneers 214 Chapter 11 Predictive Analytics Applied to Human Resources
223 By Jac Fitz-enz, PhD Staff Roles 225 Assessment: Beyond People 226
Planning Shift 229 Competency versus Capability 229 Production 230 HR
Process Management 231 HR Analysis and Predictability 232 Elevate HR with
Analytics 233 Value Hierarchy 235 HR Reporting 237 HR Success through
Analytics 238 Chapter 12 Social Media Analytics 247 By Judah Phillips
Social Media Is Multidimensional 249 Understanding Social Media Analytics:
Useful Concepts 251 Is Social Media about Brand or Direct Response? 254
Social Media "Brand" and "Direct Response" Analytics 255 Social Media Tools
259 Social Media Analytical Techniques 262 Social Media Analytics and
Privacy 265 Chapter 13 The Competitive Intelligence Mandate 271 Competitive
Intelligence Defined 273 Principles for CI Success 275 Chapter 14 Mobile
Analytics 285 By Judah Phillips Understanding Mobile Analytics Concepts 290
How Is Mobile Analytics Different from Site Analytics? 291 Importance of
Measuring Mobile Analytics 295 Mobile Analytics Tools 296 Business
Optimization with Mobile Analytics 298 Chapter 15 Effective Analytics
Communication Strategies 301 Communication: The Gap between Analysts and
Executives 303 An Effective Analytics Communication Strategy 305 Analytics
Communication Tips 314 Communicating through Mobile Business Intelligence
316 Chapter 16 Business Performance Tracking: Execution and Measurement 321
Analytics' Fundamental Questions 324 Analytics Execution 325 Business
Performance Tracking 332 Analytics and Marketing 336 Chapter 17 Analytics
and Innovation 343 What Is Innovation? 344 What Is the Promise of Advanced
Analytics? 347 What Makes Up Innovation in Analytics? 348 Intersection
between Analytics and Innovation 352 Chapter 18 Unstructured Data
Analytics: The Next Frontier 359 What Is Unstructured Data Analytics? 360
The Unstructured Data Analytics Industry 363 Uses of Unstructured Data
Analytics 364 How Unstructured Data Analytics Works 365 Why Unstructured
Data Is the Next Analytical Frontier 366 Unstructured Analytics Success
Stories 372 Chapter 19 The Future of Analytics 377 Data Become Less
Valuable 379 Predictive Becomes the New Standard 380 Social Information
Processing and Distributed Computing 381 Advances in Machine Learning 382
Traditional Data Models Evolve 383 Analytics Becomes More Accessible to the
Nonanalyst 384 Data Science Becomes a Specialized Department 385
Human-Centered Computing 386 Analytics to Solve Social Problems 387
Location-Based Data Explosion 388 Data Privacy Backlash 388 About the
Authors 391 Index 393
Analytics 1 The Challenge from Outside 5 The Challenge from Within 9
Chapter 2 Pillars of Business Analytics Success: The BASP Framework 15
Business Challenges Pillar 18 Data Foundation Pillar 20 Analytics
Implementation Pillar 22 Insight Pillar 26 Execution and Measurement Pillar
29 Distributed Knowledge Pillar 31 Innovation Pillar 32 Conclusion 33
Chapter 3 Aligning Key Business Challenges across the Enterprise 35 Mission
Statement 36 Business Challenge 38 Identifying Business Challenges as a
Consultative Process 39 Identify and Prioritize Business Challenges 41
Analytics Solutions for Business Challenges 45 Chapter 4 Big and Little
Data: Different Types of Intelligence 51 Big Data 57 Little Data 61 Laying
the Data Foundation: Data Quality 62 Data Sources and Locations 65 Data
Definition and Governance 69 Data Dictionary and Data Key Users 72 Sanity
Check and Data Visualization 72 Customer Data Integration and Data
Management 73 Data Privacy 74 Chapter 5 Who Cares about Data? How to
Uncover Insights 77 The IMPACT Cycle 79 Curiosity Can Kill the Cat 82
Master the Data 86 A Fact in Search of Meaning 87 Actions Speak Louder Than
Data 88 "Eat Like a Bird, Poop Like an Elephant" 89 Track Your Outcomes 91
The IMPACT Cycle in Action: The Monster Employment Index 92 Chapter 6 Data
Visualization: Presenting Information Clearly: The CONVINCE Framework 95
Convey Meaning 97 Objectivity: Be True to Your Data 99 Necessity: Don't
Boil the Ocean 101 Visual Honesty: Size Matters 103 Imagine the Audience
104 Nimble: No Death by 1,000 Graphs 107 Context 107 Encourage Interaction
109 Conclusion 109 Chapter 7 Analytics Implementation: What Works and What
Does Not 113 Analytics Implementation Model 117 Vision and Mandate 118
Strategy 119 Organizational Collaboration 121 Human Capital 122 Metrics and
Measurement 123 Integrated Processes 124 Customer Experience 125 Technology
and Tools 125 Change Management 126 Chapter 8 Voice-of-the-Customer
Analytics and Insights 131 By Abhilasha Mehta, PhD Customer Feedback Is
Invaluable 132 The Makings of an Effective Voice-of-the-Customer Program
137 Strategy and Elements of the VOC System 152 Common VOC Program Pitfalls
162 Chapter 9 Leveraging Digital Analytics Effectively 165 By Judah
Phillips Strategic and Tactical Use of Digital Analytics 173 Understanding
Digital Analytics Concepts 174 Digital Analytics Team: People Are Most
Important for Analytical Success 184 Digital Analytics Tools 187 Advanced
Digital Analytics 191 Digital Analytics and Voice of the Customer 192
Analytics of Site and Landing Page Optimization 194 Call to Action: Unify
Traditional and Digital Analytics 195 Chapter 10 Effective Predictive
Analytics: What Works and What Does Not 199 What Is Predictive Analytics?
201 Unlocking Stage 203 Prediction Stage 206 Optimization Stage 210 Diverse
Applications for Diverse Business Problems 213 Financial Service Industries
as Pioneers 214 Chapter 11 Predictive Analytics Applied to Human Resources
223 By Jac Fitz-enz, PhD Staff Roles 225 Assessment: Beyond People 226
Planning Shift 229 Competency versus Capability 229 Production 230 HR
Process Management 231 HR Analysis and Predictability 232 Elevate HR with
Analytics 233 Value Hierarchy 235 HR Reporting 237 HR Success through
Analytics 238 Chapter 12 Social Media Analytics 247 By Judah Phillips
Social Media Is Multidimensional 249 Understanding Social Media Analytics:
Useful Concepts 251 Is Social Media about Brand or Direct Response? 254
Social Media "Brand" and "Direct Response" Analytics 255 Social Media Tools
259 Social Media Analytical Techniques 262 Social Media Analytics and
Privacy 265 Chapter 13 The Competitive Intelligence Mandate 271 Competitive
Intelligence Defined 273 Principles for CI Success 275 Chapter 14 Mobile
Analytics 285 By Judah Phillips Understanding Mobile Analytics Concepts 290
How Is Mobile Analytics Different from Site Analytics? 291 Importance of
Measuring Mobile Analytics 295 Mobile Analytics Tools 296 Business
Optimization with Mobile Analytics 298 Chapter 15 Effective Analytics
Communication Strategies 301 Communication: The Gap between Analysts and
Executives 303 An Effective Analytics Communication Strategy 305 Analytics
Communication Tips 314 Communicating through Mobile Business Intelligence
316 Chapter 16 Business Performance Tracking: Execution and Measurement 321
Analytics' Fundamental Questions 324 Analytics Execution 325 Business
Performance Tracking 332 Analytics and Marketing 336 Chapter 17 Analytics
and Innovation 343 What Is Innovation? 344 What Is the Promise of Advanced
Analytics? 347 What Makes Up Innovation in Analytics? 348 Intersection
between Analytics and Innovation 352 Chapter 18 Unstructured Data
Analytics: The Next Frontier 359 What Is Unstructured Data Analytics? 360
The Unstructured Data Analytics Industry 363 Uses of Unstructured Data
Analytics 364 How Unstructured Data Analytics Works 365 Why Unstructured
Data Is the Next Analytical Frontier 366 Unstructured Analytics Success
Stories 372 Chapter 19 The Future of Analytics 377 Data Become Less
Valuable 379 Predictive Becomes the New Standard 380 Social Information
Processing and Distributed Computing 381 Advances in Machine Learning 382
Traditional Data Models Evolve 383 Analytics Becomes More Accessible to the
Nonanalyst 384 Data Science Becomes a Specialized Department 385
Human-Centered Computing 386 Analytics to Solve Social Problems 387
Location-Based Data Explosion 388 Data Privacy Backlash 388 About the
Authors 391 Index 393
Preface xv Acknowledgments xvii Chapter 1 The Challenge of Business
Analytics 1 The Challenge from Outside 5 The Challenge from Within 9
Chapter 2 Pillars of Business Analytics Success: The BASP Framework 15
Business Challenges Pillar 18 Data Foundation Pillar 20 Analytics
Implementation Pillar 22 Insight Pillar 26 Execution and Measurement Pillar
29 Distributed Knowledge Pillar 31 Innovation Pillar 32 Conclusion 33
Chapter 3 Aligning Key Business Challenges across the Enterprise 35 Mission
Statement 36 Business Challenge 38 Identifying Business Challenges as a
Consultative Process 39 Identify and Prioritize Business Challenges 41
Analytics Solutions for Business Challenges 45 Chapter 4 Big and Little
Data: Different Types of Intelligence 51 Big Data 57 Little Data 61 Laying
the Data Foundation: Data Quality 62 Data Sources and Locations 65 Data
Definition and Governance 69 Data Dictionary and Data Key Users 72 Sanity
Check and Data Visualization 72 Customer Data Integration and Data
Management 73 Data Privacy 74 Chapter 5 Who Cares about Data? How to
Uncover Insights 77 The IMPACT Cycle 79 Curiosity Can Kill the Cat 82
Master the Data 86 A Fact in Search of Meaning 87 Actions Speak Louder Than
Data 88 "Eat Like a Bird, Poop Like an Elephant" 89 Track Your Outcomes 91
The IMPACT Cycle in Action: The Monster Employment Index 92 Chapter 6 Data
Visualization: Presenting Information Clearly: The CONVINCE Framework 95
Convey Meaning 97 Objectivity: Be True to Your Data 99 Necessity: Don't
Boil the Ocean 101 Visual Honesty: Size Matters 103 Imagine the Audience
104 Nimble: No Death by 1,000 Graphs 107 Context 107 Encourage Interaction
109 Conclusion 109 Chapter 7 Analytics Implementation: What Works and What
Does Not 113 Analytics Implementation Model 117 Vision and Mandate 118
Strategy 119 Organizational Collaboration 121 Human Capital 122 Metrics and
Measurement 123 Integrated Processes 124 Customer Experience 125 Technology
and Tools 125 Change Management 126 Chapter 8 Voice-of-the-Customer
Analytics and Insights 131 By Abhilasha Mehta, PhD Customer Feedback Is
Invaluable 132 The Makings of an Effective Voice-of-the-Customer Program
137 Strategy and Elements of the VOC System 152 Common VOC Program Pitfalls
162 Chapter 9 Leveraging Digital Analytics Effectively 165 By Judah
Phillips Strategic and Tactical Use of Digital Analytics 173 Understanding
Digital Analytics Concepts 174 Digital Analytics Team: People Are Most
Important for Analytical Success 184 Digital Analytics Tools 187 Advanced
Digital Analytics 191 Digital Analytics and Voice of the Customer 192
Analytics of Site and Landing Page Optimization 194 Call to Action: Unify
Traditional and Digital Analytics 195 Chapter 10 Effective Predictive
Analytics: What Works and What Does Not 199 What Is Predictive Analytics?
201 Unlocking Stage 203 Prediction Stage 206 Optimization Stage 210 Diverse
Applications for Diverse Business Problems 213 Financial Service Industries
as Pioneers 214 Chapter 11 Predictive Analytics Applied to Human Resources
223 By Jac Fitz-enz, PhD Staff Roles 225 Assessment: Beyond People 226
Planning Shift 229 Competency versus Capability 229 Production 230 HR
Process Management 231 HR Analysis and Predictability 232 Elevate HR with
Analytics 233 Value Hierarchy 235 HR Reporting 237 HR Success through
Analytics 238 Chapter 12 Social Media Analytics 247 By Judah Phillips
Social Media Is Multidimensional 249 Understanding Social Media Analytics:
Useful Concepts 251 Is Social Media about Brand or Direct Response? 254
Social Media "Brand" and "Direct Response" Analytics 255 Social Media Tools
259 Social Media Analytical Techniques 262 Social Media Analytics and
Privacy 265 Chapter 13 The Competitive Intelligence Mandate 271 Competitive
Intelligence Defined 273 Principles for CI Success 275 Chapter 14 Mobile
Analytics 285 By Judah Phillips Understanding Mobile Analytics Concepts 290
How Is Mobile Analytics Different from Site Analytics? 291 Importance of
Measuring Mobile Analytics 295 Mobile Analytics Tools 296 Business
Optimization with Mobile Analytics 298 Chapter 15 Effective Analytics
Communication Strategies 301 Communication: The Gap between Analysts and
Executives 303 An Effective Analytics Communication Strategy 305 Analytics
Communication Tips 314 Communicating through Mobile Business Intelligence
316 Chapter 16 Business Performance Tracking: Execution and Measurement 321
Analytics' Fundamental Questions 324 Analytics Execution 325 Business
Performance Tracking 332 Analytics and Marketing 336 Chapter 17 Analytics
and Innovation 343 What Is Innovation? 344 What Is the Promise of Advanced
Analytics? 347 What Makes Up Innovation in Analytics? 348 Intersection
between Analytics and Innovation 352 Chapter 18 Unstructured Data
Analytics: The Next Frontier 359 What Is Unstructured Data Analytics? 360
The Unstructured Data Analytics Industry 363 Uses of Unstructured Data
Analytics 364 How Unstructured Data Analytics Works 365 Why Unstructured
Data Is the Next Analytical Frontier 366 Unstructured Analytics Success
Stories 372 Chapter 19 The Future of Analytics 377 Data Become Less
Valuable 379 Predictive Becomes the New Standard 380 Social Information
Processing and Distributed Computing 381 Advances in Machine Learning 382
Traditional Data Models Evolve 383 Analytics Becomes More Accessible to the
Nonanalyst 384 Data Science Becomes a Specialized Department 385
Human-Centered Computing 386 Analytics to Solve Social Problems 387
Location-Based Data Explosion 388 Data Privacy Backlash 388 About the
Authors 391 Index 393
Analytics 1 The Challenge from Outside 5 The Challenge from Within 9
Chapter 2 Pillars of Business Analytics Success: The BASP Framework 15
Business Challenges Pillar 18 Data Foundation Pillar 20 Analytics
Implementation Pillar 22 Insight Pillar 26 Execution and Measurement Pillar
29 Distributed Knowledge Pillar 31 Innovation Pillar 32 Conclusion 33
Chapter 3 Aligning Key Business Challenges across the Enterprise 35 Mission
Statement 36 Business Challenge 38 Identifying Business Challenges as a
Consultative Process 39 Identify and Prioritize Business Challenges 41
Analytics Solutions for Business Challenges 45 Chapter 4 Big and Little
Data: Different Types of Intelligence 51 Big Data 57 Little Data 61 Laying
the Data Foundation: Data Quality 62 Data Sources and Locations 65 Data
Definition and Governance 69 Data Dictionary and Data Key Users 72 Sanity
Check and Data Visualization 72 Customer Data Integration and Data
Management 73 Data Privacy 74 Chapter 5 Who Cares about Data? How to
Uncover Insights 77 The IMPACT Cycle 79 Curiosity Can Kill the Cat 82
Master the Data 86 A Fact in Search of Meaning 87 Actions Speak Louder Than
Data 88 "Eat Like a Bird, Poop Like an Elephant" 89 Track Your Outcomes 91
The IMPACT Cycle in Action: The Monster Employment Index 92 Chapter 6 Data
Visualization: Presenting Information Clearly: The CONVINCE Framework 95
Convey Meaning 97 Objectivity: Be True to Your Data 99 Necessity: Don't
Boil the Ocean 101 Visual Honesty: Size Matters 103 Imagine the Audience
104 Nimble: No Death by 1,000 Graphs 107 Context 107 Encourage Interaction
109 Conclusion 109 Chapter 7 Analytics Implementation: What Works and What
Does Not 113 Analytics Implementation Model 117 Vision and Mandate 118
Strategy 119 Organizational Collaboration 121 Human Capital 122 Metrics and
Measurement 123 Integrated Processes 124 Customer Experience 125 Technology
and Tools 125 Change Management 126 Chapter 8 Voice-of-the-Customer
Analytics and Insights 131 By Abhilasha Mehta, PhD Customer Feedback Is
Invaluable 132 The Makings of an Effective Voice-of-the-Customer Program
137 Strategy and Elements of the VOC System 152 Common VOC Program Pitfalls
162 Chapter 9 Leveraging Digital Analytics Effectively 165 By Judah
Phillips Strategic and Tactical Use of Digital Analytics 173 Understanding
Digital Analytics Concepts 174 Digital Analytics Team: People Are Most
Important for Analytical Success 184 Digital Analytics Tools 187 Advanced
Digital Analytics 191 Digital Analytics and Voice of the Customer 192
Analytics of Site and Landing Page Optimization 194 Call to Action: Unify
Traditional and Digital Analytics 195 Chapter 10 Effective Predictive
Analytics: What Works and What Does Not 199 What Is Predictive Analytics?
201 Unlocking Stage 203 Prediction Stage 206 Optimization Stage 210 Diverse
Applications for Diverse Business Problems 213 Financial Service Industries
as Pioneers 214 Chapter 11 Predictive Analytics Applied to Human Resources
223 By Jac Fitz-enz, PhD Staff Roles 225 Assessment: Beyond People 226
Planning Shift 229 Competency versus Capability 229 Production 230 HR
Process Management 231 HR Analysis and Predictability 232 Elevate HR with
Analytics 233 Value Hierarchy 235 HR Reporting 237 HR Success through
Analytics 238 Chapter 12 Social Media Analytics 247 By Judah Phillips
Social Media Is Multidimensional 249 Understanding Social Media Analytics:
Useful Concepts 251 Is Social Media about Brand or Direct Response? 254
Social Media "Brand" and "Direct Response" Analytics 255 Social Media Tools
259 Social Media Analytical Techniques 262 Social Media Analytics and
Privacy 265 Chapter 13 The Competitive Intelligence Mandate 271 Competitive
Intelligence Defined 273 Principles for CI Success 275 Chapter 14 Mobile
Analytics 285 By Judah Phillips Understanding Mobile Analytics Concepts 290
How Is Mobile Analytics Different from Site Analytics? 291 Importance of
Measuring Mobile Analytics 295 Mobile Analytics Tools 296 Business
Optimization with Mobile Analytics 298 Chapter 15 Effective Analytics
Communication Strategies 301 Communication: The Gap between Analysts and
Executives 303 An Effective Analytics Communication Strategy 305 Analytics
Communication Tips 314 Communicating through Mobile Business Intelligence
316 Chapter 16 Business Performance Tracking: Execution and Measurement 321
Analytics' Fundamental Questions 324 Analytics Execution 325 Business
Performance Tracking 332 Analytics and Marketing 336 Chapter 17 Analytics
and Innovation 343 What Is Innovation? 344 What Is the Promise of Advanced
Analytics? 347 What Makes Up Innovation in Analytics? 348 Intersection
between Analytics and Innovation 352 Chapter 18 Unstructured Data
Analytics: The Next Frontier 359 What Is Unstructured Data Analytics? 360
The Unstructured Data Analytics Industry 363 Uses of Unstructured Data
Analytics 364 How Unstructured Data Analytics Works 365 Why Unstructured
Data Is the Next Analytical Frontier 366 Unstructured Analytics Success
Stories 372 Chapter 19 The Future of Analytics 377 Data Become Less
Valuable 379 Predictive Becomes the New Standard 380 Social Information
Processing and Distributed Computing 381 Advances in Machine Learning 382
Traditional Data Models Evolve 383 Analytics Becomes More Accessible to the
Nonanalyst 384 Data Science Becomes a Specialized Department 385
Human-Centered Computing 386 Analytics to Solve Social Problems 387
Location-Based Data Explosion 388 Data Privacy Backlash 388 About the
Authors 391 Index 393