Analytics
The Agile Way
Published on 3. July 2017
304 pages
978-1-119-42420-8 (ISBN)
Description
For 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.
This 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.
But what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating?
What if there were a better way to do analytics?
Fortunately, you're in luck...
Analytics: The Agile Way is the eighth book from award-winning author and Arizona State University professor Phil Simon.
Analytics: The Agile Way demonstrates 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.
Through a series of case studies and examples, Analytics: The Agile Way 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.
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Person
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
Content
1 - Praise for Analytics: The Agile Way [Seite 3]
2 - Analytics [Seite 5]
3 - Wiley & SAS Business Series [Seite 6]
4 - Other Books by Phil Simon [Seite 9]
5 - Contents [Seite 15]
6 - Preface: The Power of Dynamic Data [Seite 21]
7 - Figures and Tables [Seite 31]
8 - Introduction: It Didn't Used to Be This Way [Seite 35]
8.1 - A Little History Lesson [Seite 36]
8.2 - Analytics and the Need for Speed [Seite 39]
8.2.1 - How Fast Is Fast Enough? [Seite 40]
8.2.2 - Automation: Still the Exception That Proves the Rule [Seite 42]
8.3 - Book Scope, Approach, and Style [Seite 43]
8.3.1 - Breadth over Depth [Seite 44]
8.3.2 - Methodology: Guidelines > Rules [Seite 44]
8.3.3 - Technical Sophistication [Seite 45]
8.3.4 - Vendor Agnosticism [Seite 45]
8.4 - Intended Audience [Seite 46]
8.5 - Plan of Attack [Seite 47]
8.6 - Next [Seite 48]
8.7 - Notes [Seite 48]
9 - Part ONE Background and Trends [Seite 51]
9.1 - Chapter 1: Signs of the Times: Why Data and Analytics Are Dominating Our World [Seite 53]
9.1.1 - The Moneyball Effect [Seite 54]
9.1.2 - Digitization and the Great Unbundling [Seite 56]
9.1.3 - Amazon Web Services and Cloud Computing [Seite 58]
9.1.4 - Not Your Father's Data Storage [Seite 60]
9.1.4.1 - How? Hadoop and the Growth of NoSQL [Seite 60]
9.1.4.2 - How Much? Kryder's Law [Seite 61]
9.1.5 - Moore's Law [Seite 62]
9.1.6 - The Smartphone Revolution [Seite 62]
9.1.7 - The Democratization of Data [Seite 63]
9.1.8 - The Primacy of Privacy [Seite 63]
9.1.9 - The Internet of Things [Seite 65]
9.1.10 - The Rise of the Data-Savvy Employee [Seite 65]
9.1.11 - The Burgeoning Importance of Data Analytics [Seite 66]
9.1.11.1 - A Watershed Moment [Seite 66]
9.1.11.2 - Common Ground [Seite 67]
9.1.11.3 - The Data Business Is Alive and Well and Flourishing [Seite 68]
9.1.11.4 - Not Just the Big Five [Seite 70]
9.1.12 - Data-Related Challenges [Seite 74]
9.1.13 - Companies Left Behind [Seite 75]
9.1.14 - The Growth of Analytics Programs [Seite 76]
9.1.15 - Next [Seite 77]
9.1.16 - Notes [Seite 77]
9.2 - Chapter 2: The Fundamentals of Contemporary Data: A Primer on What It Is, Why It Matters, and How to Get It [Seite 79]
9.2.1 - Types of Data [Seite 80]
9.2.1.1 - Structured [Seite 80]
9.2.1.2 - Semistructured [Seite 81]
9.2.1.3 - Unstructured [Seite 82]
9.2.1.4 - Metadata [Seite 83]
9.2.2 - Getting the Data [Seite 86]
9.2.2.1 - Generating Data [Seite 87]
9.2.2.2 - Buying Data [Seite 94]
9.2.3 - Data in Motion [Seite 95]
9.2.4 - Next [Seite 97]
9.2.5 - Notes [Seite 97]
9.3 - Chapter 3: The Fundamentals of Analytics: Peeling Back the Onion [Seite 99]
9.3.1 - Defining Analytics [Seite 100]
9.3.1.1 - Reporting ? Analytics [Seite 100]
9.3.2 - Types of Analytics [Seite 103]
9.3.2.1 - Descriptive Analytics [Seite 103]
9.3.2.2 - Predictive Analytics [Seite 104]
9.3.2.3 - Prescriptive Analytics [Seite 106]
9.3.3 - Streaming Data Revisited [Seite 106]
9.3.4 - A Final Word on Analytics [Seite 108]
9.3.5 - Next [Seite 109]
9.3.6 - Notes [Seite 109]
10 - Part TWO Agile Methods and Analytics [Seite 111]
10.1 - Chapter 4: A Better Way to Work: The Benefits and Core Values of Agile Development [Seite 113]
10.1.1 - The Case against Traditional Analytics Projects [Seite 114]
10.1.1.1 - Understandable but Pernicious [Seite 115]
10.1.1.2 - A Different Mind-Set at Netflix [Seite 115]
10.1.2 - Proving the Superiority of Agile Methods [Seite 116]
10.1.3 - The Case for Guidelines over Rules [Seite 118]
10.1.3.1 - Scarcity and Trade-Offs on Agile Projects [Seite 119]
10.1.3.2 - The Specific Tenets of Agile Analytics [Seite 120]
10.1.4 - Next [Seite 122]
10.1.5 - Notes [Seite 122]
10.2 - Chapter 5: Introducing Scrum: Looking at One of Today's Most Popular Agile Methods [Seite 123]
10.2.1 - A Very Brief History [Seite 124]
10.2.2 - Scrum Teams [Seite 125]
10.2.2.1 - Product Owner [Seite 126]
10.2.2.2 - Scrum Master [Seite 126]
10.2.2.3 - Team Member [Seite 127]
10.2.3 - User Stories [Seite 128]
10.2.3.1 - Epics: Too Broad [Seite 129]
10.2.3.2 - Too Narrow/Detailed [Seite 130]
10.2.3.3 - Just Right [Seite 130]
10.2.3.4 - The Spike: A Special User Story [Seite 130]
10.2.4 - Backlogs [Seite 131]
10.2.5 - Sprints and Meetings [Seite 132]
10.2.5.1 - Sprint Planning [Seite 133]
10.2.5.2 - Daily Stand-Up [Seite 134]
10.2.5.3 - Story Time [Seite 134]
10.2.5.4 - Demo [Seite 134]
10.2.5.5 - Sprint Retrospective [Seite 135]
10.2.6 - Releases [Seite 135]
10.2.7 - Estimation Techniques [Seite 136]
10.2.7.1 - On Lawns and Relative Estimates [Seite 136]
10.2.7.2 - Fibonacci Numbers [Seite 138]
10.2.7.3 - T-Shirt Sizes [Seite 139]
10.2.7.4 - When Teams Disagree [Seite 140]
10.2.8 - Other Scrum Artifacts, Tools, and Concepts [Seite 143]
10.2.8.1 - Velocities [Seite 143]
10.2.8.2 - Burn-Down Charts [Seite 143]
10.2.8.3 - Definition of Done and Acceptance Criteria [Seite 144]
10.2.8.4 - Kanban Boards [Seite 145]
10.2.9 - Next [Seite 146]
10.3 - Chapter 6: A Framework for Agile Analytics: A Simple Model for Gathering Insights [Seite 147]
10.3.1 - Perform Business Discovery [Seite 149]
10.3.2 - Perform Data Discovery [Seite 151]
10.3.3 - Prepare the Data [Seite 152]
10.3.4 - Model the Data* [Seite 154]
10.3.4.1 - The Power of a Simple Model [Seite 155]
10.3.4.2 - Forecasting and the Human Factor [Seite 159]
10.3.4.3 - Understanding Superforecasters [Seite 160]
10.3.5 - Score and Deploy [Seite 161]
10.3.6 - Evaluate and Improve [Seite 162]
10.3.7 - Next [Seite 164]
10.3.8 - Notes [Seite 164]
11 - Part THREE: Analytics in Action [Seite 165]
11.1 - Chapter 7: University Tutoring Center: An In-Depth Case Study on Agile Analytics [Seite 167]
11.1.1 - The UTC and Project Background [Seite 168]
11.1.2 - Project Goals and Kickoff [Seite 170]
11.1.2.1 - User Stories [Seite 170]
11.1.2.2 - Business and Data Discovery [Seite 172]
11.1.3 - Iteration One [Seite 173]
11.1.4 - Iteration Two [Seite 174]
11.1.4.1 - Analytics Results in a Fundamental Change [Seite 176]
11.1.4.2 - Moving Beyond Simple Tutor Utilization [Seite 177]
11.1.4.3 - Meeting International Students' Needs [Seite 178]
11.1.5 - Iteration Three [Seite 179]
11.1.6 - Iteration Four [Seite 180]
11.1.7 - Results [Seite 181]
11.1.8 - Lessons [Seite 182]
11.1.9 - Next [Seite 182]
11.2 - Chapter 8: People Analyticsat Google/Alphabet Not Your Father's HR Department [Seite 183]
11.2.1 - The Value of Business Experiments [Seite 184]
11.2.2 - PiLab's Adventures in Analytics [Seite 185]
11.2.2.1 - Communication [Seite 186]
11.2.3 - A Better Approach to Hiring [Seite 187]
11.2.3.1 - Eliminating GPA as a Criterion for Hiring [Seite 188]
11.2.3.2 - Using Analytics to Streamline the Hiring Process [Seite 189]
11.2.4 - Staffing [Seite 190]
11.2.5 - The Value of Perks [Seite 192]
11.2.5.1 - Innovation on the Lunch Line [Seite 193]
11.2.5.2 - Family Leave [Seite 194]
11.2.6 - Results and Lessons [Seite 195]
11.2.7 - Next [Seite 196]
11.2.8 - Notes [Seite 196]
11.3 - Chapter 9: The Anti-Google: Beneke Pharmaceuticals [Seite 199]
11.3.1 - Project Background [Seite 200]
11.3.2 - Business and Data Discovery [Seite 201]
11.3.3 - The Friction Begins [Seite 202]
11.3.4 - Astonishing Results [Seite 203]
11.3.5 - Developing Options [Seite 205]
11.3.6 - The Grand Finale [Seite 206]
11.3.7 - Results and Lessons [Seite 207]
11.3.8 - Next [Seite 208]
11.4 - Chapter 10: Ice Station Zebra Medical: How Agile Methods Solved a Messy Health-Care Data Problem [Seite 209]
11.4.1 - Paying Nurses [Seite 210]
11.4.2 - Enter the Consultant [Seite 212]
11.4.3 - User Stories [Seite 213]
11.4.4 - Agile: The Better Way [Seite 216]
11.4.5 - Results [Seite 217]
11.4.6 - Lessons [Seite 217]
11.4.7 - Next [Seite 218]
11.5 - Chapter 11: Racial Profiling at Nextdoor: Using Data to Build a Better App and Combat a PR Disaster [Seite 219]
11.5.1 - Unintended but Familiar Consequences [Seite 221]
11.5.2 - Evaluating the Problem [Seite 222]
11.5.2.1 - Redesigning the App [Seite 224]
11.5.2.2 - Agile Methods in Action [Seite 225]
11.5.3 - Results and Lessons [Seite 227]
11.5.4 - Next [Seite 229]
11.5.5 - Notes [Seite 229]
12 - Part Four Making the Most Out of Agile Analytics [Seite 231]
12.1 - Chapter 12: The Benefits of Agile Analytics The Upsides of Small Batches [Seite 233]
12.1.1 - Life at IAC [Seite 234]
12.1.1.1 - Data and Data Quality [Seite 234]
12.1.1.2 - Insightful, Robust, and Dynamic Models [Seite 235]
12.1.1.3 - A Smarter, Realistic, and Skeptical Workforce [Seite 235]
12.1.1.4 - Summary [Seite 237]
12.1.2 - Life at RDC [Seite 237]
12.1.2.1 - Project Management [Seite 237]
12.1.2.2 - Frustrated Employees [Seite 238]
12.1.2.3 - Data Quality, Internal Politics, and the Blame Game [Seite 239]
12.1.2.4 - Summary [Seite 239]
12.1.3 - Comparing the Two [Seite 240]
12.1.4 - Next [Seite 240]
12.2 - Chapter 13: No Free Lunch The Impediments to-and Limitations of-Agile Analytics [Seite 243]
12.2.1 - People Issues [Seite 244]
12.2.1.1 - Resistance to Analytics [Seite 244]
12.2.1.2 - Stakeholder Availability [Seite 245]
12.2.1.3 - Irritating Customers, Users, and Employees with Frequent Changes [Seite 246]
12.2.2 - Data Issues [Seite 246]
12.2.2.1 - Data Quality [Seite 247]
12.2.2.2 - Overfitting and Spurious Correlations [Seite 248]
12.2.2.3 - Certain Problems May Call for a More Traditional Approach to Analytics [Seite 248]
12.2.3 - The Limitations of Agile Analytics [Seite 250]
12.2.3.1 - Acting Prematurely [Seite 250]
12.2.3.2 - Even Agile Analytics Can't Do Everything [Seite 251]
12.2.3.3 - Agile Analytics Won't Overcome a Fundamentally Bad Idea [Seite 252]
12.2.4 - Next [Seite 253]
12.3 - Chapter 14: The Importance of Designing for Data: Lessons from the Upstarts [Seite 255]
12.3.1 - The Genes of Music [Seite 256]
12.3.1.1 - From Theory to Practice [Seite 257]
12.3.2 - The Tension between Data and Design [Seite 260]
12.3.2.1 - All Design Is Not Created Equal [Seite 261]
12.3.2.2 - Data and Design Can-Nay, Should-Coexist [Seite 262]
12.3.3 - Next [Seite 263]
12.3.4 - Notes [Seite 263]
13 - Part FIVE Conclusions and Next Steps [Seite 265]
13.1 - Chapter 15: What Now?: A Look Forward [Seite 267]
13.1.1 - A Tale of Two Retailers [Seite 268]
13.1.1.1 - Test for Echo [Seite 270]
13.1.1.2 - Squaring the Circle [Seite 270]
13.1.2 - The Blurry Futures of Data, Analytics, and Related Issues [Seite 273]
13.1.2.1 - Data Governance [Seite 274]
13.1.2.2 - Data Exhaust [Seite 275]
13.1.2.3 - It's Complicated: How Ethics, Privacy, and Trust Collide [Seite 276]
13.1.3 - Final Thoughts and Next Steps [Seite 276]
13.1.4 - Notes [Seite 277]
13.2 - Afterword [Seite 279]
13.3 - Acknowledgments [Seite 281]
13.4 - Selected Bibliography [Seite 283]
13.5 - About the Author [Seite 287]
13.6 - Index [Seite 289]
13.7 - EULA [Seite 303]
2 - Analytics [Seite 5]
3 - Wiley & SAS Business Series [Seite 6]
4 - Other Books by Phil Simon [Seite 9]
5 - Contents [Seite 15]
6 - Preface: The Power of Dynamic Data [Seite 21]
7 - Figures and Tables [Seite 31]
8 - Introduction: It Didn't Used to Be This Way [Seite 35]
8.1 - A Little History Lesson [Seite 36]
8.2 - Analytics and the Need for Speed [Seite 39]
8.2.1 - How Fast Is Fast Enough? [Seite 40]
8.2.2 - Automation: Still the Exception That Proves the Rule [Seite 42]
8.3 - Book Scope, Approach, and Style [Seite 43]
8.3.1 - Breadth over Depth [Seite 44]
8.3.2 - Methodology: Guidelines > Rules [Seite 44]
8.3.3 - Technical Sophistication [Seite 45]
8.3.4 - Vendor Agnosticism [Seite 45]
8.4 - Intended Audience [Seite 46]
8.5 - Plan of Attack [Seite 47]
8.6 - Next [Seite 48]
8.7 - Notes [Seite 48]
9 - Part ONE Background and Trends [Seite 51]
9.1 - Chapter 1: Signs of the Times: Why Data and Analytics Are Dominating Our World [Seite 53]
9.1.1 - The Moneyball Effect [Seite 54]
9.1.2 - Digitization and the Great Unbundling [Seite 56]
9.1.3 - Amazon Web Services and Cloud Computing [Seite 58]
9.1.4 - Not Your Father's Data Storage [Seite 60]
9.1.4.1 - How? Hadoop and the Growth of NoSQL [Seite 60]
9.1.4.2 - How Much? Kryder's Law [Seite 61]
9.1.5 - Moore's Law [Seite 62]
9.1.6 - The Smartphone Revolution [Seite 62]
9.1.7 - The Democratization of Data [Seite 63]
9.1.8 - The Primacy of Privacy [Seite 63]
9.1.9 - The Internet of Things [Seite 65]
9.1.10 - The Rise of the Data-Savvy Employee [Seite 65]
9.1.11 - The Burgeoning Importance of Data Analytics [Seite 66]
9.1.11.1 - A Watershed Moment [Seite 66]
9.1.11.2 - Common Ground [Seite 67]
9.1.11.3 - The Data Business Is Alive and Well and Flourishing [Seite 68]
9.1.11.4 - Not Just the Big Five [Seite 70]
9.1.12 - Data-Related Challenges [Seite 74]
9.1.13 - Companies Left Behind [Seite 75]
9.1.14 - The Growth of Analytics Programs [Seite 76]
9.1.15 - Next [Seite 77]
9.1.16 - Notes [Seite 77]
9.2 - Chapter 2: The Fundamentals of Contemporary Data: A Primer on What It Is, Why It Matters, and How to Get It [Seite 79]
9.2.1 - Types of Data [Seite 80]
9.2.1.1 - Structured [Seite 80]
9.2.1.2 - Semistructured [Seite 81]
9.2.1.3 - Unstructured [Seite 82]
9.2.1.4 - Metadata [Seite 83]
9.2.2 - Getting the Data [Seite 86]
9.2.2.1 - Generating Data [Seite 87]
9.2.2.2 - Buying Data [Seite 94]
9.2.3 - Data in Motion [Seite 95]
9.2.4 - Next [Seite 97]
9.2.5 - Notes [Seite 97]
9.3 - Chapter 3: The Fundamentals of Analytics: Peeling Back the Onion [Seite 99]
9.3.1 - Defining Analytics [Seite 100]
9.3.1.1 - Reporting ? Analytics [Seite 100]
9.3.2 - Types of Analytics [Seite 103]
9.3.2.1 - Descriptive Analytics [Seite 103]
9.3.2.2 - Predictive Analytics [Seite 104]
9.3.2.3 - Prescriptive Analytics [Seite 106]
9.3.3 - Streaming Data Revisited [Seite 106]
9.3.4 - A Final Word on Analytics [Seite 108]
9.3.5 - Next [Seite 109]
9.3.6 - Notes [Seite 109]
10 - Part TWO Agile Methods and Analytics [Seite 111]
10.1 - Chapter 4: A Better Way to Work: The Benefits and Core Values of Agile Development [Seite 113]
10.1.1 - The Case against Traditional Analytics Projects [Seite 114]
10.1.1.1 - Understandable but Pernicious [Seite 115]
10.1.1.2 - A Different Mind-Set at Netflix [Seite 115]
10.1.2 - Proving the Superiority of Agile Methods [Seite 116]
10.1.3 - The Case for Guidelines over Rules [Seite 118]
10.1.3.1 - Scarcity and Trade-Offs on Agile Projects [Seite 119]
10.1.3.2 - The Specific Tenets of Agile Analytics [Seite 120]
10.1.4 - Next [Seite 122]
10.1.5 - Notes [Seite 122]
10.2 - Chapter 5: Introducing Scrum: Looking at One of Today's Most Popular Agile Methods [Seite 123]
10.2.1 - A Very Brief History [Seite 124]
10.2.2 - Scrum Teams [Seite 125]
10.2.2.1 - Product Owner [Seite 126]
10.2.2.2 - Scrum Master [Seite 126]
10.2.2.3 - Team Member [Seite 127]
10.2.3 - User Stories [Seite 128]
10.2.3.1 - Epics: Too Broad [Seite 129]
10.2.3.2 - Too Narrow/Detailed [Seite 130]
10.2.3.3 - Just Right [Seite 130]
10.2.3.4 - The Spike: A Special User Story [Seite 130]
10.2.4 - Backlogs [Seite 131]
10.2.5 - Sprints and Meetings [Seite 132]
10.2.5.1 - Sprint Planning [Seite 133]
10.2.5.2 - Daily Stand-Up [Seite 134]
10.2.5.3 - Story Time [Seite 134]
10.2.5.4 - Demo [Seite 134]
10.2.5.5 - Sprint Retrospective [Seite 135]
10.2.6 - Releases [Seite 135]
10.2.7 - Estimation Techniques [Seite 136]
10.2.7.1 - On Lawns and Relative Estimates [Seite 136]
10.2.7.2 - Fibonacci Numbers [Seite 138]
10.2.7.3 - T-Shirt Sizes [Seite 139]
10.2.7.4 - When Teams Disagree [Seite 140]
10.2.8 - Other Scrum Artifacts, Tools, and Concepts [Seite 143]
10.2.8.1 - Velocities [Seite 143]
10.2.8.2 - Burn-Down Charts [Seite 143]
10.2.8.3 - Definition of Done and Acceptance Criteria [Seite 144]
10.2.8.4 - Kanban Boards [Seite 145]
10.2.9 - Next [Seite 146]
10.3 - Chapter 6: A Framework for Agile Analytics: A Simple Model for Gathering Insights [Seite 147]
10.3.1 - Perform Business Discovery [Seite 149]
10.3.2 - Perform Data Discovery [Seite 151]
10.3.3 - Prepare the Data [Seite 152]
10.3.4 - Model the Data* [Seite 154]
10.3.4.1 - The Power of a Simple Model [Seite 155]
10.3.4.2 - Forecasting and the Human Factor [Seite 159]
10.3.4.3 - Understanding Superforecasters [Seite 160]
10.3.5 - Score and Deploy [Seite 161]
10.3.6 - Evaluate and Improve [Seite 162]
10.3.7 - Next [Seite 164]
10.3.8 - Notes [Seite 164]
11 - Part THREE: Analytics in Action [Seite 165]
11.1 - Chapter 7: University Tutoring Center: An In-Depth Case Study on Agile Analytics [Seite 167]
11.1.1 - The UTC and Project Background [Seite 168]
11.1.2 - Project Goals and Kickoff [Seite 170]
11.1.2.1 - User Stories [Seite 170]
11.1.2.2 - Business and Data Discovery [Seite 172]
11.1.3 - Iteration One [Seite 173]
11.1.4 - Iteration Two [Seite 174]
11.1.4.1 - Analytics Results in a Fundamental Change [Seite 176]
11.1.4.2 - Moving Beyond Simple Tutor Utilization [Seite 177]
11.1.4.3 - Meeting International Students' Needs [Seite 178]
11.1.5 - Iteration Three [Seite 179]
11.1.6 - Iteration Four [Seite 180]
11.1.7 - Results [Seite 181]
11.1.8 - Lessons [Seite 182]
11.1.9 - Next [Seite 182]
11.2 - Chapter 8: People Analyticsat Google/Alphabet Not Your Father's HR Department [Seite 183]
11.2.1 - The Value of Business Experiments [Seite 184]
11.2.2 - PiLab's Adventures in Analytics [Seite 185]
11.2.2.1 - Communication [Seite 186]
11.2.3 - A Better Approach to Hiring [Seite 187]
11.2.3.1 - Eliminating GPA as a Criterion for Hiring [Seite 188]
11.2.3.2 - Using Analytics to Streamline the Hiring Process [Seite 189]
11.2.4 - Staffing [Seite 190]
11.2.5 - The Value of Perks [Seite 192]
11.2.5.1 - Innovation on the Lunch Line [Seite 193]
11.2.5.2 - Family Leave [Seite 194]
11.2.6 - Results and Lessons [Seite 195]
11.2.7 - Next [Seite 196]
11.2.8 - Notes [Seite 196]
11.3 - Chapter 9: The Anti-Google: Beneke Pharmaceuticals [Seite 199]
11.3.1 - Project Background [Seite 200]
11.3.2 - Business and Data Discovery [Seite 201]
11.3.3 - The Friction Begins [Seite 202]
11.3.4 - Astonishing Results [Seite 203]
11.3.5 - Developing Options [Seite 205]
11.3.6 - The Grand Finale [Seite 206]
11.3.7 - Results and Lessons [Seite 207]
11.3.8 - Next [Seite 208]
11.4 - Chapter 10: Ice Station Zebra Medical: How Agile Methods Solved a Messy Health-Care Data Problem [Seite 209]
11.4.1 - Paying Nurses [Seite 210]
11.4.2 - Enter the Consultant [Seite 212]
11.4.3 - User Stories [Seite 213]
11.4.4 - Agile: The Better Way [Seite 216]
11.4.5 - Results [Seite 217]
11.4.6 - Lessons [Seite 217]
11.4.7 - Next [Seite 218]
11.5 - Chapter 11: Racial Profiling at Nextdoor: Using Data to Build a Better App and Combat a PR Disaster [Seite 219]
11.5.1 - Unintended but Familiar Consequences [Seite 221]
11.5.2 - Evaluating the Problem [Seite 222]
11.5.2.1 - Redesigning the App [Seite 224]
11.5.2.2 - Agile Methods in Action [Seite 225]
11.5.3 - Results and Lessons [Seite 227]
11.5.4 - Next [Seite 229]
11.5.5 - Notes [Seite 229]
12 - Part Four Making the Most Out of Agile Analytics [Seite 231]
12.1 - Chapter 12: The Benefits of Agile Analytics The Upsides of Small Batches [Seite 233]
12.1.1 - Life at IAC [Seite 234]
12.1.1.1 - Data and Data Quality [Seite 234]
12.1.1.2 - Insightful, Robust, and Dynamic Models [Seite 235]
12.1.1.3 - A Smarter, Realistic, and Skeptical Workforce [Seite 235]
12.1.1.4 - Summary [Seite 237]
12.1.2 - Life at RDC [Seite 237]
12.1.2.1 - Project Management [Seite 237]
12.1.2.2 - Frustrated Employees [Seite 238]
12.1.2.3 - Data Quality, Internal Politics, and the Blame Game [Seite 239]
12.1.2.4 - Summary [Seite 239]
12.1.3 - Comparing the Two [Seite 240]
12.1.4 - Next [Seite 240]
12.2 - Chapter 13: No Free Lunch The Impediments to-and Limitations of-Agile Analytics [Seite 243]
12.2.1 - People Issues [Seite 244]
12.2.1.1 - Resistance to Analytics [Seite 244]
12.2.1.2 - Stakeholder Availability [Seite 245]
12.2.1.3 - Irritating Customers, Users, and Employees with Frequent Changes [Seite 246]
12.2.2 - Data Issues [Seite 246]
12.2.2.1 - Data Quality [Seite 247]
12.2.2.2 - Overfitting and Spurious Correlations [Seite 248]
12.2.2.3 - Certain Problems May Call for a More Traditional Approach to Analytics [Seite 248]
12.2.3 - The Limitations of Agile Analytics [Seite 250]
12.2.3.1 - Acting Prematurely [Seite 250]
12.2.3.2 - Even Agile Analytics Can't Do Everything [Seite 251]
12.2.3.3 - Agile Analytics Won't Overcome a Fundamentally Bad Idea [Seite 252]
12.2.4 - Next [Seite 253]
12.3 - Chapter 14: The Importance of Designing for Data: Lessons from the Upstarts [Seite 255]
12.3.1 - The Genes of Music [Seite 256]
12.3.1.1 - From Theory to Practice [Seite 257]
12.3.2 - The Tension between Data and Design [Seite 260]
12.3.2.1 - All Design Is Not Created Equal [Seite 261]
12.3.2.2 - Data and Design Can-Nay, Should-Coexist [Seite 262]
12.3.3 - Next [Seite 263]
12.3.4 - Notes [Seite 263]
13 - Part FIVE Conclusions and Next Steps [Seite 265]
13.1 - Chapter 15: What Now?: A Look Forward [Seite 267]
13.1.1 - A Tale of Two Retailers [Seite 268]
13.1.1.1 - Test for Echo [Seite 270]
13.1.1.2 - Squaring the Circle [Seite 270]
13.1.2 - The Blurry Futures of Data, Analytics, and Related Issues [Seite 273]
13.1.2.1 - Data Governance [Seite 274]
13.1.2.2 - Data Exhaust [Seite 275]
13.1.2.3 - It's Complicated: How Ethics, Privacy, and Trust Collide [Seite 276]
13.1.3 - Final Thoughts and Next Steps [Seite 276]
13.1.4 - Notes [Seite 277]
13.2 - Afterword [Seite 279]
13.3 - Acknowledgments [Seite 281]
13.4 - Selected Bibliography [Seite 283]
13.5 - About the Author [Seite 287]
13.6 - Index [Seite 289]
13.7 - EULA [Seite 303]
