Win with Advanced Business Analytics – Creating Business Value from Your Data

<p>Preface xv</p>
<p>Acknowledgments xvii</p>
<p>Chapter 1 The Challenge of Business Analytics 1</p>
<p>The Challenge from Outside 5</p>
<p>The Challenge from Within 9</p>
<p>Chapter 2 Pillars of Business Analytics Success: The BASP Framework 15</p>
<p>Business Challenges Pillar 18</p>
<p>Data Foundation Pillar 20</p>
<p>Analytics Implementation Pillar 22</p>
<p>Insight Pillar 26</p>
<p>Execution and Measurement Pillar 29</p>
<p>Distributed Knowledge Pillar 31</p>
<p>Innovation Pillar 32</p>
<p>Conclusion 33</p>
<p>Chapter 3 Aligning Key Business Challenges across the Enterprise 35</p>
<p>Mission Statement 36</p>
<p>Business Challenge 38</p>
<p>Identifying Business Challenges as a Consultative Process 39</p>
<p>Identify and Prioritize Business Challenges 41</p>
<p>Analytics Solutions for Business Challenges 45</p>
<p>Chapter 4 Big and Little Data: Different Types of Intelligence 51</p>
<p>Big Data 57</p>
<p>Little Data 61</p>
<p>Laying the Data Foundation: Data Quality 62</p>
<p>Data Sources and Locations 65</p>
<p>Data Definition and Governance 69</p>
<p>Data Dictionary and Data Key Users 72</p>
<p>Sanity Check and Data Visualization 72</p>
<p>Customer Data Integration and Data Management 73</p>
<p>Data Privacy 74</p>
<p>Chapter 5 Who Cares about Data?</p>
<p>How to Uncover Insights 77</p>
<p>The IMPACT Cycle 79</p>
<p>Curiosity Can Kill the Cat 82</p>
<p>Master the Data 86</p>
<p>A Fact in Search of Meaning 87</p>
<p>Actions Speak Louder Than Data 88</p>
<p> Eat Like a Bird, Poop Like an Elephant 89</p>
<p>Track Your Outcomes 91</p>
<p>The IMPACT Cycle in Action: The Monster Employment Index 92</p>
<p>Chapter 6 Data Visualization: Presenting Information</p>
<p>Clearly: The CONVINCE Framework 95</p>
<p>Convey Meaning 97</p>
<p>Objectivity: Be True to Your Data 99</p>
<p>Necessity: Don t Boil the Ocean 101</p>
<p>Visual Honesty: Size Matters 103</p>
<p>Imagine the Audience 104</p>
<p>Nimble: No Death by 1,000 Graphs 107</p>
<p>Context 107</p>
<p>Encourage Interaction 109</p>
<p>Conclusion 109</p>
<p>Chapter 7 Analytics Implementation: What Works and What Does Not 113</p>
<p>Analytics Implementation Model 117</p>
<p>Vision and Mandate 118</p>
<p>Strategy 119</p>
<p>Organizational Collaboration 121</p>
<p>Human Capital 122</p>
<p>Metrics and Measurement 123</p>
<p>Integrated Processes 124</p>
<p>Customer Experience 125</p>
<p>Technology and Tools 125</p>
<p>Change Management 126</p>
<p>Chapter 8 Voice–of–the–Customer Analytics and Insights 131</p>
<p>By Abhilasha Mehta, PhD</p>
<p>Customer Feedback Is Invaluable 132</p>
<p>The Makings of an Effective Voice–of–the–Customer Program 137</p>
<p>Strategy and Elements of the VOC System 152</p>
<p>Common VOC Program Pitfalls 162</p>
<p>Chapter 9 Leveraging Digital Analytics Effectively 165</p>
<p>By Judah Phillips</p>
<p>Strategic and Tactical Use of Digital Analytics 173</p>
<p>Understanding Digital Analytics Concepts 174</p>
<p>Digital Analytics Team: People Are Most Important for Analytical Success 184</p>
<p>Digital Analytics Tools 187</p>
<p>Advanced Digital Analytics 191</p>
<p>Digital Analytics and Voice of the Customer 192</p>
<p>Analytics of Site and Landing Page Optimization 194</p>
<p>Call to Action: Unify Traditional and Digital Analytics 195</p>
<p>Chapter 10 Effective Predictive Analytics: What Works and What Does Not 199</p>
<p>What Is Predictive Analytics? 201</p>
<p>Unlocking Stage 203</p>
<p>Prediction Stage 206</p>
<p>Optimization Stage 210</p>
<p>Diverse Applications for Diverse Business Problems 213</p>
<p>Financial Service Industries as Pioneers 214</p>
<p>Chapter 11 Predictive Analytics Applied to Human Resources 223<br /> By Jac Fitz–enz, PhD</p>
<p>Staff Roles 225</p>
<p>Assessment: Beyond People 226</p>
<p>Planning Shift 229</p>
<p>Competency versus Capability 229</p>
<p>Production 230</p>
<p>HR Process Management 231</p>
<p>HR Analysis and Predictability 232</p>
<p>Elevate HR with Analytics 233</p>
<p>Value Hierarchy 235</p>
<p>HR Reporting 237</p>
<p>HR Success through Analytics 238</p>
<p>Chapter 12 Social Media Analytics 247<br /> By Judah Phillips</p>
<p>Social Media Is Multidimensional 249</p>
<p>Understanding Social Media Analytics: Useful Concepts 251</p>
<p>Is Social Media about Brand or Direct Response? 254</p>
<p>Social Media Brand and Direct Response Analytics 255</p>
<p>Social Media Tools 259</p>
<p>Social Media Analytical Techniques 262</p>
<p>Social Media Analytics and Privacy 265</p>
<p>Chapter 13 The Competitive Intelligence Mandate 271</p>
<p>Competitive Intelligence Defined 273</p>
<p>Principles for CI Success 275</p>
<p>Chapter 14 Mobile Analytics 285<br /> By Judah Phillips</p>
<p>Understanding Mobile Analytics Concepts 290</p>
<p>How Is Mobile Analytics Different from Site Analytics? 291</p>
<p>Importance of Measuring Mobile Analytics 295</p>
<p>Mobile Analytics Tools 296</p>
<p>Business Optimization with Mobile Analytics 298</p>
<p>Chapter 15 Effective Analytics Communication</p>
<p>Strategies 301</p>
<p>Communication: The Gap between Analysts and Executives 303</p>
<p>An Effective Analytics Communication Strategy 305</p>
<p>Analytics Communication Tips 314</p>
<p>Communicating through Mobile Business Intelligence 316</p>
<p>Chapter 16 Business Performance Tracking: Execution and Measurement 321</p>
<p>Analytics Fundamental Questions 324</p>
<p>Analytics Execution 325</p>
<p>Business Performance Tracking 332</p>
<p>Analytics and Marketing 336</p>
<p>Chapter 17 Analytics and Innovation 343</p>
<p>What Is Innovation? 344</p>
<p>What Is the Promise of Advanced Analytics? 347</p>
<p>What Makes Up Innovation in Analytics? 348</p>
<p>Intersection between Analytics and Innovation 352</p>
<p>Chapter 18 Unstructured Data Analytics: The Next Frontier 359</p>
<p>What Is Unstructured Data Analytics? 360</p>
<p>The Unstructured Data Analytics Industry 363</p>
<p>Uses of Unstructured Data Analytics 364</p>
<p>How Unstructured Data Analytics Works 365</p>
<p>Why Unstructured Data Is the Next Analytical Frontier 366</p>
<p>Unstructured Analytics Success Stories 372</p>
<p>Chapter 19 The Future of Analytics 377</p>
<p>Data Become Less Valuable 379</p>
<p>Predictive Becomes the New Standard 380</p>
<p>Social Information Processing and Distributed Computing 381</p>
<p>Advances in Machine Learning 382</p>
<p>Traditional Data Models Evolve 383</p>
<p>Analytics Becomes More Accessible to the Nonanalyst 384</p>
<p>Data Science Becomes a Specialized Department 385</p>
<p>Human–Centered Computing 386</p>
<p>Analytics to Solve Social Problems 387</p>
<p>Location–Based Data Explosion 388</p>
<p>Data Privacy Backlash 388</p>
<p>About the Authors 391</p>
<p>Index 393</p>