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Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics(FT Press Analytics)

Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics(FT Press Analytics)

          
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About the Book

Bridge the gap between analytics and execution, and actually translate analytics into better business decision-making! Now that you've collected data and crunched numbers, Applied Business Analytics reveals how to fully apply the information and knowledge you've gleaned from quants and tech teams. Nathaniel Lin explains why "analytics value chains" often break due to organizational and cultural issues, and offers "in the trenches" guidance for overcoming these obstacles. You'll discover why a special breed of "analytics deciders" is indispensable for any organization that seeks to compete on analytics… how to become one of those deciders… and how to identify, foster, support, empower, and reward others to join you.   Lin draws on actual cases and examples from his own experience, augmenting them with hands-on examples and exercises to integrate analytics at all levels: from top-level business questions to low-level technical details. Along the way, you'll learn how to bring together analytics team members with widely diverse goals, knowledge, and backgrounds. Coverage includes:   How analytical and conventional decision making differ — and the challenging implications How to determine who your analytics deciders are, and ought to be Proven best practices for actually applying analytics to decision-making How to optimize your use of analytics as an analyst, manager, executive, or C-level officer Applied Business Analytics will be invaluable to wide audiences of professionals, decision-makers, and consultants involved in analytics, including Chief Analytics Officers, Chief Data Officers, Chief Scientists, Chief Marketing Officers, Chief Risk Officers, Chief Strategy Officers, VPs of Analytics and/or Big Data, data scientists, business strategists, and line of business executives. It will also be exceptionally useful to students of analytics in any graduate, undergraduate, or certificate program, including candidates for INFORMS certification.

Table of Contents:
Foreword    xv Acknowledgments    xviii About the Author    xix Preface    xxi Why Another Book on Analytics?    xxi How This Book Is Organized    xxii After Reading and Working Through This Book    xxvi Chapter 1: Introduction    1 Raw Data, the New Oil    1 Data Big and Small Is Not New    2 Definition of Analytics    3 Top 10 Business Questions for Analytics    5 Financial Management    6 Customer Management    8 HR Management    11 Internal Operations    11 Vital Lessons Learned    12 Use Analytics    13 Reasons Why Analytics Are Not Used    13 Linking Analytics to Business    14 Business Analytics Value Chain    14 Integrated Approach    17 Hands-on Exercises    17 Reasons for Using KNIME Workflows    17 Conclusion    18 Chapter 2: Know Your Ingredients—Data Big and Small    21 Garbage in, Garbage out    21 Data or Big Data    22 Definition of Big Data    22 Data Types    23 Company Data    24 Individual Customer Data    31 Sensor Data    34 Syndicated Data    35 Data Formats    38 Structured, Poorly Structured, and Unstructured Data    39 Conclusion    42 Chapter 3: Data Management—Integration, Data Quality, and Governance    43 Data Integration    44 Data Quality    45 Data Security and Data Privacy    46 Data Security    47 Data Privacy    48 Data Governance    53 Data Preparation    56 Data Manipulation    58 Types of Data    58 Categorize Numerical Variables    59 Dummy Variables    60 Missing Values    60 Data Normalization    61 Data Partitions    62 Exploratory Data Analysis    64 Multidimensional Cube    65 Slicing    65 Dicing    65 Drilling Down/Up    66 Pivoting    66 Visualization of Data Patterns and Trends    66 Popularity of BI Visualization    66 Selecting a BI Visualization Tool    67 Beyond BI Visualizations    70 Conclusion    70 Chapter 4: Handle the Tools: Analytics Methodology and Tools    73 Getting Familiar with the Tools    73 Master Chefs Who Can’t Cook    74 Types of Analytics    75 Descriptive and Diagnostic Tools: BI Visualization and Reporting    75 Advanced Analytics Tools: Prediction, Optimization, and Knowledge Discovery    77 A Unified View of BI Analysis, Advanced Analytics, and Visualization    77 Two Ways of Knowledge Discovery    79 Types of Advanced Analytics and Applications    81 Analytics Modeling Tools by Functions    81 Modeling Likelihood    82 Modeling Groupings    86 Supervised Learning    87 Value Prediction    97 Other Models    102 Conclusion    111 Chapter 5: Analytics Decision-Making Process and the Analytics Deciders    115 Time to Take Off the Mittens    115 Overview of the Business Analytics Process (BAP)    116 Analytics Rapid Prototyping    120 Analytics Sandbox for Instant Business Insights    122 Analytics IT Sandbox Database    125 People and the Decision Blinders    125 Risks of Crossing the Chasms    126 The Medici Effect    127 Analytics Deciders    129 How to Find Analytics Deciders    130 Becoming an Analytics Decider    132 Conclusion    139 Chapter 6: Business Processes and Analytics (by Alejandro Simkievich)    141 Overview of Process Families    142 Enterprise Resource Planning    143 Customer Relationship Management    145 Product Lifecycle Management    147 Shortcomings of Operational Systems    147 Embedding Advanced Analytics into Operational Systems    150 Example 1: Forecast    152 Example 2: Improving Salesforce Decisions    154 Example 3: Engineers Get Instant Feedback on Their Design Choices    155 Conclusion    155 Chapter 7: Identifying Business Opportunities by Recognizing Patterns    157 Patterns of Group Behavior    157 Importance of Pattern Recognition in Business    158 Group Patterns by Clustering and Decision Trees    161 Three Ways of Grouping    162 Recognize Purchase Patterns: Association Analysis    167 Association Rules    167 Business Case    169 Patterns over Time: Time Series Predictions    173 Time Series Models    174 Conclusion    179 Chapter 8: Knowing the Unknowable    181 Unknowable Events    181 Unknowable in Business    182 Poor or Inadequate Data    185 Data with Limited Views    185 Business Case    186 Predicting Individual Customer Behaviors in Real-Time    192 Lever Settings and Causality in Business    197 Start with a High Baseline    199 Causality with Control Groups    199 Conclusion    201 Chapter 9: Demonstration of Business Analytics Workflows: Analytics Enterprise    203 A Case for Illustration    204 Top Questions for Analytics Applications    209 Financial Management    210 Human Resources    212 Internal Operations    213 Conclusion    218 Chapter 10: Demonstration of Business Analytics Workflows—Analytics CRM    219 Questions About Customers    220 Know the Customers    220 Actionable Customer Insights    222 Social and Mobile CRM Issues    226 CRM Knowledge Management    227 Conclusion    228 Chapter 11: Analytics Competencies and Ecosystem    231 Analytics Maturity Levels    233 Analytics Organizational Structure    234 The Centralized Model    236 The Consulting Model    237 The Decentralized Model    238 The Center of Excellence Model    239 Reporting Structures    241 Roles and Responsibilities    242 Analytics Roles    242 Business Strategy and Leadership Roles    243 Data and Quantitative Roles    247 Analytics Ecosystem    250 The In-House IT Function    250 External Analytics Advisory and Consulting Resources    251 Analytics Talent Management    256 Conclusion    260 Chapter 12: Conclusions and Now What?    263 Analytics Is Not a Fad    263 Acquire Rich and Effective Data    264 Start with EDA and BI Analysis    265 Gain Firsthand Analytics Experience    265 Become an Analytics Decider and Recruit Others    266 Empower Enterprise Business Processes with Analytics    266 Recognize Patterns with Analytics    267 Know the Unknowable    268 Imbue Business Processes with Analytics    269 Acquire Competencies and Establish Ecosystem    270 Epilogue    271 Appendix A: KNIME Basics    273 Data Preparation    274 Types of Variable Values    274 Dummy Variables    275 Missing Values    275 Data Partitions    277 Exploratory Data Analysis (EDA)    279 Multi-Dimensional Cube    279 Slicing    281 Dicing    281 Drilling Down or Up    281 Pivoting    281 Index    285    


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Product Details
  • ISBN-13: 9780133481518
  • Publisher: Pearson Education (US)
  • Publisher Imprint: Pearson FT Press
  • Language: English
  • Series Title: FT Press Analytics
  • Weight: 1 gr
  • ISBN-10: 0133481514
  • Publisher Date: 03 Dec 2014
  • Binding: Digital download
  • No of Pages: 320
  • Sub Title: Integrating Business Process, Big Data, and Advanced Analytics


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