Praise for Analytics: The Agile Way "As analytics moves from an IT reporting exercise to a mission critical business-led discipline, collaboration, expediency, and flexibility are more important than ever. Information is no longer an asset to be trifled with, but rather one that organizations must harness aggressively. To date, I have seen scant attempts at Agile analytics, but this book will most certainly launch a thousand more. It is a must-read for any analytics leader, and a must-do for any analytics professional." --Douglas Laney, VP and Distinguished Analyst, Gartner "Analytics: The…mehr
Praise for Analytics: The Agile Way "As analytics moves from an IT reporting exercise to a mission critical business-led discipline, collaboration, expediency, and flexibility are more important than ever. Information is no longer an asset to be trifled with, but rather one that organizations must harness aggressively. To date, I have seen scant attempts at Agile analytics, but this book will most certainly launch a thousand more. It is a must-read for any analytics leader, and a must-do for any analytics professional." --Douglas Laney, VP and Distinguished Analyst, Gartner "Analytics: The Agile Way makes accessible two of today's key themes in modern business: data and how we get work done. A great book for analytics, but also a great book for management and leadership. Given that we are all responsible for our own education, perhaps all business books should now be written as this one is: perfect for a formal class but also for lifelong learning." --Terri Griffith, Associate Dean & Professor at Santa Clara University's Leavey School of Business and author of the award-winning book The Plugged-In Manager "Phil Simon adroitly shows the potential of combining Agile thinking and methods with data analytics to provide powerful, distinct competitive advantages to organizations. As Simon demonstrates clearly through multiple real-life examples, Agile analytics can enable organizations to create lasting value from their Big Data efforts without ignoring the privacy and security issues those efforts frequently create." --Robert N. Charette, President, ITABHI Corporation "A thoroughly enjoyable guide to the critical importance of analytics and Big Data. Simon wisely counsels that no one method works for all companies. The key is to be flexible and nimble and follow the guidelines he explains so clearly and convincingly." --Gary N. Smith, Fletcher Jones Professor of Economics at Pomona College and author of Money Machine: The Surprisingly Simple Power of Value InvestingHinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
PHIL SIMON is a frequent keynote speaker and recognized technology authority. He is the award-winning author of eight management books. He consults organizations on analytics, communications, strategy, data, and technology. His contributions have been featured in the Harvard Business Review, the New York Times, and on Fox News, and many other sites. He teaches analytics, system design, and business intelligence at Arizona State University's W. P. Carey School of Business. @philsimon #agileanalytics www.philsimon.com
Inhaltsangabe
Preface: The Power of Dynamic Data xvii List of Figures and Tables xxvii Introduction: It Didn't Used to Be This Way 1 A Little History Lesson 2 Analytics and the Need for Speed 5 Book Scope, Approach, and Style 9 Intended Audience 12 Plan of Attack 13 Next 14 Notes 14 Part One Background and Trends 17 Chapter 1 Signs of the Times: Why Data and Analytics Are Dominating Our World 19 The Moneyball Effect 20 Digitization and the Great Unbundling 22 Amazon Web Services and Cloud Computing 24 Not Your Father's Data Storage 26 Moore's Law 28 The Smartphone Revolution 28 The Democratization of Data 29 The Primacy of Privacy 29 The Internet of Things 31 The Rise of the Data-Savvy Employee 31 The Burgeoning Importance of Data Analytics 32 Data-Related Challenges 40 Companies Left Behind 41 The Growth of Analytics Programs 42 Next 43 Notes 43 Chapter 2 The Fundamentals of Contemporary Data: A Primer on What It Is, Why It Matters, and How to Get It 45 Types of Data 46 Getting the Data 52 Data in Motion 61 Next 63 Notes 63 Chapter 3 The Fundamentals of Analytics: Peeling Back the Onion 65 Defining Analytics 66 Types of Analytics 69 Streaming Data Revisited 72 A Final Word on Analytics 74 Next 75 Notes 75 Part Two Agile Methods and Analytics 77 Chapter 4 A Better Way to Work: The Benefits and Core Values of Agile Development 79 The Case against Traditional Analytics Projects 80 Proving the Superiority of Agile Methods 82 The Case for Guidelines over Rules 84 Next 88 Notes 88 Chapter 5 Introducing Scrum: Looking at One of Today's Most Popular Agile Methods 89 A Very Brief History 90 Scrum Teams 91 User Stories 94 Backlogs 97 Sprints and Meetings 98 Releases 101 Estimation Techniques 102 Other Scrum Artifacts, Tools, and Concepts 109 Next 112 Chapter 6 A Framework for Agile Analytics: A Simple Model for Gathering Insights 113 Perform Business Discovery 115 Perform Data Discovery 117 Prepare the Data 118 Model the Data 120 Score and Deploy 127 Evaluate and Improve 128 Next 130 Notes 130 Part Three Analytics in Action 131 Chapter 7 University Tutoring Center: An In-Depth Case Study on Agile Analytics 133 The UTC and Project Background 134 Project Goals and Kickoff 136 Iteration One 139 Iteration Two 140 Iteration Three 145 Iteration Four 146 Results 147 Lessons 148 Next 148 Chapter 8 People Analytics at Google/Alphabet: Not Your Father's HR Department 149 The Value of Business Experiments 150 PiLab's Adventures in Analytics 151 A Better Approach to Hiring 153 Staffing 156 The Value of Perks 158 Results and Lessons 162 Next 162 Notes 163 Chapter 9 The Anti-Google: Beneke Pharmaceuticals 165 Project Background 166 Business and Data Discovery 167 The Friction Begins 168 Astonishing Results 169 Developing Options 171 The Grand Finale 172 Results and Lessons 173 Next 174 Chapter 10 Ice Station Zebra Medical: How Agile Methods Solved a Messy Health-Care Data Problem 175 Paying Nurses 176 Enter the Consultant 178 User Stories 179 Agile: The Better Way 182 Results 183 Lessons 183 Next 184 Chapter 11 Racial Profiling at Nextdoor: Using Data to Build a Better App and Combat a PR Disaster 185 Unintended but Familiar Consequences 187 Evaluating the Problem 188 Results and Lessons 193 Next 195 Notes 195 Part Four Making the Most Out of Agile Analytics ..........197 Chapter 12 The Benefits of Agile Analytics: The Upsides of Small Batches 199 Life at IAC 200 Life at RDC 203 Comparing the Two 206 Next 206 Chapter 13 No Free Lunch: The Impediments to-and Limitations of-Agile Analytics 209 People Issues 210 Data Issues 212 The Limitations of Agile Analytics 216 Next 219 Chapter 14 The Importance of Designing for Data: Lessons from the Upstarts 221 The Genes of Music 222 The Tension between Data and Design 226 Next 229 Notes 229 Part Five Conclusions and Next Steps 231 Chapter 15 What Now?: A Look Forward 233 A Tale of Two Retailers 234 The Blurry Futures of Data, Analytics, and Related Issues 239 Final Thoughts and Next Steps 242 Notes 243 Afterword 245 Acknowledgments 247 Selected Bibliography 249 Books 249 Articles and Essays 251 About the Author 253 Index 255
Preface: The Power of Dynamic Data xvii List of Figures and Tables xxvii Introduction: It Didn't Used to Be This Way 1 A Little History Lesson 2 Analytics and the Need for Speed 5 Book Scope, Approach, and Style 9 Intended Audience 12 Plan of Attack 13 Next 14 Notes 14 Part One Background and Trends 17 Chapter 1 Signs of the Times: Why Data and Analytics Are Dominating Our World 19 The Moneyball Effect 20 Digitization and the Great Unbundling 22 Amazon Web Services and Cloud Computing 24 Not Your Father's Data Storage 26 Moore's Law 28 The Smartphone Revolution 28 The Democratization of Data 29 The Primacy of Privacy 29 The Internet of Things 31 The Rise of the Data-Savvy Employee 31 The Burgeoning Importance of Data Analytics 32 Data-Related Challenges 40 Companies Left Behind 41 The Growth of Analytics Programs 42 Next 43 Notes 43 Chapter 2 The Fundamentals of Contemporary Data: A Primer on What It Is, Why It Matters, and How to Get It 45 Types of Data 46 Getting the Data 52 Data in Motion 61 Next 63 Notes 63 Chapter 3 The Fundamentals of Analytics: Peeling Back the Onion 65 Defining Analytics 66 Types of Analytics 69 Streaming Data Revisited 72 A Final Word on Analytics 74 Next 75 Notes 75 Part Two Agile Methods and Analytics 77 Chapter 4 A Better Way to Work: The Benefits and Core Values of Agile Development 79 The Case against Traditional Analytics Projects 80 Proving the Superiority of Agile Methods 82 The Case for Guidelines over Rules 84 Next 88 Notes 88 Chapter 5 Introducing Scrum: Looking at One of Today's Most Popular Agile Methods 89 A Very Brief History 90 Scrum Teams 91 User Stories 94 Backlogs 97 Sprints and Meetings 98 Releases 101 Estimation Techniques 102 Other Scrum Artifacts, Tools, and Concepts 109 Next 112 Chapter 6 A Framework for Agile Analytics: A Simple Model for Gathering Insights 113 Perform Business Discovery 115 Perform Data Discovery 117 Prepare the Data 118 Model the Data 120 Score and Deploy 127 Evaluate and Improve 128 Next 130 Notes 130 Part Three Analytics in Action 131 Chapter 7 University Tutoring Center: An In-Depth Case Study on Agile Analytics 133 The UTC and Project Background 134 Project Goals and Kickoff 136 Iteration One 139 Iteration Two 140 Iteration Three 145 Iteration Four 146 Results 147 Lessons 148 Next 148 Chapter 8 People Analytics at Google/Alphabet: Not Your Father's HR Department 149 The Value of Business Experiments 150 PiLab's Adventures in Analytics 151 A Better Approach to Hiring 153 Staffing 156 The Value of Perks 158 Results and Lessons 162 Next 162 Notes 163 Chapter 9 The Anti-Google: Beneke Pharmaceuticals 165 Project Background 166 Business and Data Discovery 167 The Friction Begins 168 Astonishing Results 169 Developing Options 171 The Grand Finale 172 Results and Lessons 173 Next 174 Chapter 10 Ice Station Zebra Medical: How Agile Methods Solved a Messy Health-Care Data Problem 175 Paying Nurses 176 Enter the Consultant 178 User Stories 179 Agile: The Better Way 182 Results 183 Lessons 183 Next 184 Chapter 11 Racial Profiling at Nextdoor: Using Data to Build a Better App and Combat a PR Disaster 185 Unintended but Familiar Consequences 187 Evaluating the Problem 188 Results and Lessons 193 Next 195 Notes 195 Part Four Making the Most Out of Agile Analytics ..........197 Chapter 12 The Benefits of Agile Analytics: The Upsides of Small Batches 199 Life at IAC 200 Life at RDC 203 Comparing the Two 206 Next 206 Chapter 13 No Free Lunch: The Impediments to-and Limitations of-Agile Analytics 209 People Issues 210 Data Issues 212 The Limitations of Agile Analytics 216 Next 219 Chapter 14 The Importance of Designing for Data: Lessons from the Upstarts 221 The Genes of Music 222 The Tension between Data and Design 226 Next 229 Notes 229 Part Five Conclusions and Next Steps 231 Chapter 15 What Now?: A Look Forward 233 A Tale of Two Retailers 234 The Blurry Futures of Data, Analytics, and Related Issues 239 Final Thoughts and Next Steps 242 Notes 243 Afterword 245 Acknowledgments 247 Selected Bibliography 249 Books 249 Articles and Essays 251 About the Author 253 Index 255
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