{"product_id":"analytics-isbn-9781119423478","title":"Analytics","description":"\u003cp\u003eFor years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line...if they get there at all.\u003c\/p\u003e \u003cp\u003eThis has always been a problem. Today, though, it's downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends—and act upon them until it's too late.\u003c\/p\u003e \u003cp\u003eBut what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating?\u003c\/p\u003e \u003cp\u003eWhat if there were a better way to do analytics?\u003c\/p\u003e \u003cp\u003eFortunately, you're in luck...\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAnalytics: The Agile Way\u003c\/i\u003e is the eighth book from award-winning author and Arizona State University professor Phil Simon.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAnalytics: The Agile Way \u003c\/i\u003edemonstrates how progressive organizations such as Google, Nextdoor, and others approach analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years. They have replaced large batches in favor of smaller ones...and their results will astonish you.\u003c\/p\u003e \u003cp\u003eThrough a series of case studies and examples, \u003ci\u003eAnalytics: The Agile Way\u003c\/i\u003e demonstrates the benefits of this new analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors.\u003c\/p\u003e \u003cp\u003ePreface: The Power of Dynamic Data xvii\u003c\/p\u003e \u003cp\u003eList of Figures and Tables xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction: It Didn’t Used to Be This Way 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Little History Lesson 2\u003c\/p\u003e \u003cp\u003eAnalytics and the Need for Speed 5\u003c\/p\u003e \u003cp\u003eBook Scope, Approach, and Style 9\u003c\/p\u003e \u003cp\u003eIntended Audience 12\u003c\/p\u003e \u003cp\u003ePlan of Attack 13\u003c\/p\u003e \u003cp\u003eNext 14\u003c\/p\u003e \u003cp\u003eNotes 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Background and Trends 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Signs of the Times: Why Data and Analytics Are Dominating Our World 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eMoneyball \u003c\/i\u003eEffect 20\u003c\/p\u003e \u003cp\u003eDigitization and the Great Unbundling 22\u003c\/p\u003e \u003cp\u003eAmazon Web Services and Cloud Computing 24\u003c\/p\u003e \u003cp\u003eNot Your Father’s Data Storage 26\u003c\/p\u003e \u003cp\u003eMoore’s Law 28\u003c\/p\u003e \u003cp\u003eThe Smartphone Revolution 28\u003c\/p\u003e \u003cp\u003eThe Democratization of Data 29\u003c\/p\u003e \u003cp\u003eThe Primacy of Privacy 29\u003c\/p\u003e \u003cp\u003eThe Internet of Things 31\u003c\/p\u003e \u003cp\u003eThe Rise of the Data-Savvy Employee 31\u003c\/p\u003e \u003cp\u003eThe Burgeoning Importance of Data Analytics 32\u003c\/p\u003e \u003cp\u003eData-Related Challenges 40\u003c\/p\u003e \u003cp\u003eCompanies Left Behind 41\u003c\/p\u003e \u003cp\u003eThe Growth of Analytics Programs 42\u003c\/p\u003e \u003cp\u003eNext 43\u003c\/p\u003e \u003cp\u003eNotes 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 The Fundamentals of Contemporary Data: A Primer on What It Is, Why It Matters, and How to Get It 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTypes of Data 46\u003c\/p\u003e \u003cp\u003eGetting the Data 52\u003c\/p\u003e \u003cp\u003eData in Motion 61\u003c\/p\u003e \u003cp\u003eNext 63\u003c\/p\u003e \u003cp\u003eNotes 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Fundamentals of Analytics: Peeling Back the Onion 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Analytics 66\u003c\/p\u003e \u003cp\u003eTypes of Analytics 69\u003c\/p\u003e \u003cp\u003eStreaming Data Revisited 72\u003c\/p\u003e \u003cp\u003eA Final Word on Analytics 74\u003c\/p\u003e \u003cp\u003eNext 75\u003c\/p\u003e \u003cp\u003eNotes 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Agile Methods and Analytics 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 A Better Way to Work: The Benefits and Core Values of Agile Development 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Case against Traditional Analytics Projects 80\u003c\/p\u003e \u003cp\u003eProving the Superiority of Agile Methods 82\u003c\/p\u003e \u003cp\u003eThe Case for Guidelines over Rules 84\u003c\/p\u003e \u003cp\u003eNext 88\u003c\/p\u003e \u003cp\u003eNotes 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Introducing Scrum: Looking at One of Today’s Most Popular Agile Methods 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Very Brief History 90\u003c\/p\u003e \u003cp\u003eScrum Teams 91\u003c\/p\u003e \u003cp\u003eUser Stories 94\u003c\/p\u003e \u003cp\u003eBacklogs 97\u003c\/p\u003e \u003cp\u003eSprints and Meetings 98\u003c\/p\u003e \u003cp\u003eReleases 101\u003c\/p\u003e \u003cp\u003eEstimation Techniques 102\u003c\/p\u003e \u003cp\u003eOther Scrum Artifacts, Tools, and Concepts 109\u003c\/p\u003e \u003cp\u003eNext 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 A Framework for Agile Analytics: A Simple Model for Gathering Insights 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePerform Business Discovery 115\u003c\/p\u003e \u003cp\u003ePerform Data Discovery 117\u003c\/p\u003e \u003cp\u003ePrepare the Data 118\u003c\/p\u003e \u003cp\u003eModel the Data 120\u003c\/p\u003e \u003cp\u003eScore and Deploy 127\u003c\/p\u003e \u003cp\u003eEvaluate and Improve 128\u003c\/p\u003e \u003cp\u003eNext 130\u003c\/p\u003e \u003cp\u003eNotes 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Analytics in Action 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 University Tutoring Center: An In-Depth Case Study on Agile Analytics 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe UTC and Project Background 134\u003c\/p\u003e \u003cp\u003eProject Goals and Kickoff 136\u003c\/p\u003e \u003cp\u003eIteration One 139\u003c\/p\u003e \u003cp\u003eIteration Two 140\u003c\/p\u003e \u003cp\u003eIteration Three 145\u003c\/p\u003e \u003cp\u003eIteration Four 146\u003c\/p\u003e \u003cp\u003eResults 147\u003c\/p\u003e \u003cp\u003eLessons 148\u003c\/p\u003e \u003cp\u003eNext 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 People Analytics at Google\/Alphabet: Not Your Father’s HR Department 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Value of Business Experiments 150\u003c\/p\u003e \u003cp\u003ePiLab’s Adventures in Analytics 151\u003c\/p\u003e \u003cp\u003eA Better Approach to Hiring 153\u003c\/p\u003e \u003cp\u003eStaffing 156\u003c\/p\u003e \u003cp\u003eThe Value of Perks 158\u003c\/p\u003e \u003cp\u003eResults and Lessons 162\u003c\/p\u003e \u003cp\u003eNext 162\u003c\/p\u003e \u003cp\u003eNotes 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 The Anti-Google: Beneke Pharmaceuticals 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProject Background 166\u003c\/p\u003e \u003cp\u003eBusiness and Data Discovery 167\u003c\/p\u003e \u003cp\u003eThe Friction Begins 168\u003c\/p\u003e \u003cp\u003eAstonishing Results 169\u003c\/p\u003e \u003cp\u003eDeveloping Options 171\u003c\/p\u003e \u003cp\u003eThe Grand Finale 172\u003c\/p\u003e \u003cp\u003eResults and Lessons 173\u003c\/p\u003e \u003cp\u003eNext 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Ice Station Zebra Medical: How Agile Methods Solved a Messy Health-Care Data Problem 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePaying Nurses 176\u003c\/p\u003e \u003cp\u003eEnter the Consultant 178\u003c\/p\u003e \u003cp\u003eUser Stories 179\u003c\/p\u003e \u003cp\u003eAgile: The Better Way 182\u003c\/p\u003e \u003cp\u003eResults 183\u003c\/p\u003e \u003cp\u003eLessons 183\u003c\/p\u003e \u003cp\u003eNext 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Racial Profiling at Nextdoor: Using Data to Build a Better App and Combat a PR Disaster 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnintended but Familiar Consequences 187\u003c\/p\u003e \u003cp\u003eEvaluating the Problem 188\u003c\/p\u003e \u003cp\u003eResults and Lessons 193\u003c\/p\u003e \u003cp\u003eNext 195\u003c\/p\u003e \u003cp\u003eNotes 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Four Making the Most Out of Agile Analytics ..........197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 The Benefits of Agile Analytics: The Upsides of Small Batches 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLife at IAC 200\u003c\/p\u003e \u003cp\u003eLife at RDC 203\u003c\/p\u003e \u003cp\u003eComparing the Two 206\u003c\/p\u003e \u003cp\u003eNext 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 No Free Lunch: The Impediments to—and Limitations of—Agile Analytics 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePeople Issues 210\u003c\/p\u003e \u003cp\u003eData Issues 212\u003c\/p\u003e \u003cp\u003eThe Limitations of Agile Analytics 216\u003c\/p\u003e \u003cp\u003eNext 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 The Importance of Designing for Data: Lessons from the Upstarts 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Genes of Music 222\u003c\/p\u003e \u003cp\u003eThe Tension between Data and Design 226\u003c\/p\u003e \u003cp\u003eNext 229\u003c\/p\u003e \u003cp\u003eNotes 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Five Conclusions and Next Steps 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 What Now?: A Look Forward 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Tale of Two Retailers 234\u003c\/p\u003e \u003cp\u003eThe Blurry Futures of Data, Analytics, and Related Issues 239\u003c\/p\u003e \u003cp\u003eFinal Thoughts and Next Steps 242\u003c\/p\u003e \u003cp\u003eNotes 243\u003c\/p\u003e \u003cp\u003eAfterword 245\u003c\/p\u003e \u003cp\u003eAcknowledgments 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSelected Bibliography 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBooks 249\u003c\/p\u003e \u003cp\u003eArticles and Essays 251\u003c\/p\u003e \u003cp\u003eAbout the Author 253\u003c\/p\u003e \u003cp\u003eIndex 255\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePHIL SIMON\u003c\/b\u003e 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 \u003ci\u003eHarvard Business Review\u003c\/i\u003e, the \u003ci\u003eNew York Times\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003e@philsimon\u003c\/b\u003e\u003cbr\u003e #agileanalytics\u003cbr\u003e \u003cb\u003ewww.philsimon.com\u003c\/b\u003e   \u003c\/p\u003e\u003cp\u003eFor years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line … if they get there at all. \u003c\/p\u003e\u003cp\u003eThis has always been a problem. Today, though, it’s downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends—and act upon them—until it’s too late. \u003c\/p\u003e\u003cp\u003eBut what if the process of turning raw data into meaningful insights didn’t have to be so painful, time-consuming, and frustrating? \u003c\/p\u003e\u003cp\u003eWhat if there were a better way to do analytics? \u003c\/p\u003e\u003cp\u003eFortunately, you’re in luck. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eAnalytics: The Agile Way\u003c\/i\u003e is the eighth book from award-winning author and Arizona State University lecturer Phil Simon. \u003c\/p\u003e\u003cp\u003eIn this book, you’ll learn how progressive organizations such as Google, Nextdoor, and others approach analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years, replacing large batches in favor of smaller ones … and their results will astonish you. \u003c\/p\u003e\u003cp\u003eThrough a series of case studies and examples, \u003ci\u003eAnalytics: The Agile Way\u003c\/i\u003e demonstrates the benefits of this new analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988736262373,"sku":"NP9781119423478","price":49.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119423478.jpg?v=1761781387","url":"https:\/\/k12savings.com\/products\/analytics-isbn-9781119423478","provider":"K12savings","version":"1.0","type":"link"}