Analytics : The Agile Way.

By: Simon, PhilSeries: Wiley and SAS Business SerPublisher: Newark : John Wiley & Sons, Incorporated, 2017Copyright date: ©2017Description: 1 online resource (303 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781119424192Subject(s): Business intelligence--Data processingGenre/Form: Electronic books. Additional physical formats: Print version:: Analytics : The Agile WayDDC classification: 658.4033 LOC classification: HD38.7.S535 2017Online resources: Click to View
Contents:
Intro -- Praise for Analytics: The Agile Way -- Analytics -- Wiley & SAS Business Series -- Other Books by Phil Simon -- Contents -- Preface: The Power of Dynamic Data -- Figures and Tables -- Introduction: It Didn't Used to Be This Way -- A Little History Lesson -- Analytics and the Need for Speed -- How Fast Is Fast Enough? -- Automation: Still the Exception That Proves the Rule -- Book Scope, Approach, and Style -- Breadth over Depth -- Methodology: Guidelines > Rules -- Technical Sophistication -- Vendor Agnosticism -- Intended Audience -- Plan of Attack -- Next -- Notes -- Part ONE Background and Trends -- Chapter 1: Signs of the Times: Why Data and Analytics Are Dominating Our World -- The Moneyball Effect -- Digitization and the Great Unbundling -- Amazon Web Services and Cloud Computing -- Not Your Father's Data Storage -- How? Hadoop and the Growth of NoSQL -- How Much? Kryder's Law -- Moore's Law -- The Smartphone Revolution -- The Democratization of Data -- The Primacy of Privacy -- The Internet of Things -- The Rise of the Data-Savvy Employee -- The Burgeoning Importance of Data Analytics -- A Watershed Moment -- Common Ground -- The Data Business Is Alive and Well and Flourishing -- Not Just the Big Five -- Data-Related Challenges -- Companies Left Behind -- The Growth of Analytics Programs -- Next -- Notes -- Chapter 2: The Fundamentals of Contemporary Data: A Primer on What It Is, Why It Matters, and How to Get It -- Types of Data -- Structured -- Semistructured -- Unstructured -- Metadata -- Getting the Data -- Generating Data -- Buying Data -- Data in Motion -- Next -- Notes -- Chapter 3: The Fundamentals of Analytics: Peeling Back the Onion -- Defining Analytics -- Reporting ≠ Analytics -- Types of Analytics -- Descriptive Analytics -- Predictive Analytics -- Prescriptive Analytics -- Streaming Data Revisited.
A Final Word on Analytics -- Next -- Notes -- Part TWO Agile Methods and Analytics -- Chapter 4: A Better Way to Work: The Benefits and Core Values of Agile Development -- The Case against Traditional Analytics Projects -- Understandable but Pernicious -- A Different Mind-Set at Netflix -- Proving the Superiority of Agile Methods -- The Case for Guidelines over Rules -- Scarcity and Trade-Offs on Agile Projects -- The Specific Tenets of Agile Analytics -- Next -- Notes -- Chapter 5: Introducing Scrum: Looking at One of Today's Most Popular Agile Methods -- A Very Brief History -- Scrum Teams -- Product Owner -- Scrum Master -- Team Member -- User Stories -- Epics: Too Broad -- Too Narrow/Detailed -- Just Right -- The Spike: A Special User Story -- Backlogs -- Sprints and Meetings -- Sprint Planning -- Daily Stand-Up -- Story Time -- Demo -- Sprint Retrospective -- Releases -- Estimation Techniques -- On Lawns and Relative Estimates -- Fibonacci Numbers -- T-Shirt Sizes -- When Teams Disagree -- Other Scrum Artifacts, Tools, and Concepts -- Velocities -- Burn-Down Charts -- Definition of Done and Acceptance Criteria -- Kanban Boards -- Next -- Chapter 6: A Framework for Agile Analytics: A Simple Model for Gathering Insights -- Perform Business Discovery -- Perform Data Discovery -- Prepare the Data -- Model the Data* -- The Power of a Simple Model -- Forecasting and the Human Factor -- Understanding Superforecasters -- Score and Deploy -- Evaluate and Improve -- Next -- Notes -- Part THREE: Analytics in Action -- Chapter 7: University Tutoring Center: An In-Depth Case Study on Agile Analytics -- The UTC and Project Background -- Project Goals and Kickoff -- User Stories -- Business and Data Discovery -- Iteration One -- Iteration Two -- Analytics Results in a Fundamental Change -- Moving Beyond Simple Tutor Utilization.
Meeting International Students' Needs -- Iteration Three -- Iteration Four -- Results -- Lessons -- Next -- Chapter 8: People Analyticsat Google/Alphabet Not Your Father's HR Department -- The Value of Business Experiments -- PiLab's Adventures in Analytics -- Communication -- A Better Approach to Hiring -- Eliminating GPA as a Criterion for Hiring -- Using Analytics to Streamline the Hiring Process -- Staffing -- The Value of Perks -- Innovation on the Lunch Line -- Family Leave -- Results and Lessons -- Next -- Notes -- Chapter 9: The Anti-Google: Beneke Pharmaceuticals -- Project Background -- Business and Data Discovery -- The Friction Begins -- Astonishing Results -- Developing Options -- The Grand Finale -- Results and Lessons -- Next -- Chapter 10: Ice Station Zebra Medical: How Agile Methods Solved a Messy Health-Care Data Problem -- Paying Nurses -- Enter the Consultant -- User Stories -- Agile: The Better Way -- Results -- Lessons -- Next -- Chapter 11: Racial Profiling at Nextdoor: Using Data to Build a Better App and Combat a PR Disaster -- Unintended but Familiar Consequences -- Evaluating the Problem -- Redesigning the App -- Agile Methods in Action -- Results and Lessons -- Next -- Notes -- Part Four Making the Most Out of Agile Analytics -- Chapter 12: The Benefits of Agile Analytics The Upsides of Small Batches -- Life at IAC -- Data and Data Quality -- Insightful, Robust, and Dynamic Models -- A Smarter, Realistic, and Skeptical Workforce -- Summary -- Life at RDC -- Project Management -- Frustrated Employees -- Data Quality, Internal Politics, and the Blame Game -- Summary -- Comparing the Two -- Next -- Chapter 13: No Free Lunch The Impediments to-and Limitations of-Agile Analytics -- People Issues -- Resistance to Analytics -- Stakeholder Availability -- Irritating Customers, Users, and Employees with Frequent Changes.
Data Issues -- Data Quality -- Overfitting and Spurious Correlations -- Certain Problems May Call for a More Traditional Approach to Analytics -- The Limitations of Agile Analytics -- Acting Prematurely -- Even Agile Analytics Can't Do Everything -- Agile Analytics Won't Overcome a Fundamentally Bad Idea -- Next -- Chapter 14: The Importance of Designing for Data: Lessons from the Upstarts -- The Genes of Music -- From Theory to Practice -- The Tension between Data and Design -- All Design Is Not Created Equal -- Data and Design Can-Nay, Should-Coexist -- Next -- Notes -- Part FIVE Conclusions and Next Steps -- Chapter 15: What Now?: A Look Forward -- A Tale of Two Retailers -- Test for Echo -- Squaring the Circle -- The Blurry Futures of Data, Analytics, and Related Issues -- Data Governance -- Data Exhaust -- It's Complicated: How Ethics, Privacy, and Trust Collide -- Final Thoughts and Next Steps -- Notes -- Afterword -- Acknowledgments -- Selected Bibliography -- About the Author -- Index -- EULA.
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Intro -- Praise for Analytics: The Agile Way -- Analytics -- Wiley & SAS Business Series -- Other Books by Phil Simon -- Contents -- Preface: The Power of Dynamic Data -- Figures and Tables -- Introduction: It Didn't Used to Be This Way -- A Little History Lesson -- Analytics and the Need for Speed -- How Fast Is Fast Enough? -- Automation: Still the Exception That Proves the Rule -- Book Scope, Approach, and Style -- Breadth over Depth -- Methodology: Guidelines > Rules -- Technical Sophistication -- Vendor Agnosticism -- Intended Audience -- Plan of Attack -- Next -- Notes -- Part ONE Background and Trends -- Chapter 1: Signs of the Times: Why Data and Analytics Are Dominating Our World -- The Moneyball Effect -- Digitization and the Great Unbundling -- Amazon Web Services and Cloud Computing -- Not Your Father's Data Storage -- How? Hadoop and the Growth of NoSQL -- How Much? Kryder's Law -- Moore's Law -- The Smartphone Revolution -- The Democratization of Data -- The Primacy of Privacy -- The Internet of Things -- The Rise of the Data-Savvy Employee -- The Burgeoning Importance of Data Analytics -- A Watershed Moment -- Common Ground -- The Data Business Is Alive and Well and Flourishing -- Not Just the Big Five -- Data-Related Challenges -- Companies Left Behind -- The Growth of Analytics Programs -- Next -- Notes -- Chapter 2: The Fundamentals of Contemporary Data: A Primer on What It Is, Why It Matters, and How to Get It -- Types of Data -- Structured -- Semistructured -- Unstructured -- Metadata -- Getting the Data -- Generating Data -- Buying Data -- Data in Motion -- Next -- Notes -- Chapter 3: The Fundamentals of Analytics: Peeling Back the Onion -- Defining Analytics -- Reporting ≠ Analytics -- Types of Analytics -- Descriptive Analytics -- Predictive Analytics -- Prescriptive Analytics -- Streaming Data Revisited.

A Final Word on Analytics -- Next -- Notes -- Part TWO Agile Methods and Analytics -- Chapter 4: A Better Way to Work: The Benefits and Core Values of Agile Development -- The Case against Traditional Analytics Projects -- Understandable but Pernicious -- A Different Mind-Set at Netflix -- Proving the Superiority of Agile Methods -- The Case for Guidelines over Rules -- Scarcity and Trade-Offs on Agile Projects -- The Specific Tenets of Agile Analytics -- Next -- Notes -- Chapter 5: Introducing Scrum: Looking at One of Today's Most Popular Agile Methods -- A Very Brief History -- Scrum Teams -- Product Owner -- Scrum Master -- Team Member -- User Stories -- Epics: Too Broad -- Too Narrow/Detailed -- Just Right -- The Spike: A Special User Story -- Backlogs -- Sprints and Meetings -- Sprint Planning -- Daily Stand-Up -- Story Time -- Demo -- Sprint Retrospective -- Releases -- Estimation Techniques -- On Lawns and Relative Estimates -- Fibonacci Numbers -- T-Shirt Sizes -- When Teams Disagree -- Other Scrum Artifacts, Tools, and Concepts -- Velocities -- Burn-Down Charts -- Definition of Done and Acceptance Criteria -- Kanban Boards -- Next -- Chapter 6: A Framework for Agile Analytics: A Simple Model for Gathering Insights -- Perform Business Discovery -- Perform Data Discovery -- Prepare the Data -- Model the Data* -- The Power of a Simple Model -- Forecasting and the Human Factor -- Understanding Superforecasters -- Score and Deploy -- Evaluate and Improve -- Next -- Notes -- Part THREE: Analytics in Action -- Chapter 7: University Tutoring Center: An In-Depth Case Study on Agile Analytics -- The UTC and Project Background -- Project Goals and Kickoff -- User Stories -- Business and Data Discovery -- Iteration One -- Iteration Two -- Analytics Results in a Fundamental Change -- Moving Beyond Simple Tutor Utilization.

Meeting International Students' Needs -- Iteration Three -- Iteration Four -- Results -- Lessons -- Next -- Chapter 8: People Analyticsat Google/Alphabet Not Your Father's HR Department -- The Value of Business Experiments -- PiLab's Adventures in Analytics -- Communication -- A Better Approach to Hiring -- Eliminating GPA as a Criterion for Hiring -- Using Analytics to Streamline the Hiring Process -- Staffing -- The Value of Perks -- Innovation on the Lunch Line -- Family Leave -- Results and Lessons -- Next -- Notes -- Chapter 9: The Anti-Google: Beneke Pharmaceuticals -- Project Background -- Business and Data Discovery -- The Friction Begins -- Astonishing Results -- Developing Options -- The Grand Finale -- Results and Lessons -- Next -- Chapter 10: Ice Station Zebra Medical: How Agile Methods Solved a Messy Health-Care Data Problem -- Paying Nurses -- Enter the Consultant -- User Stories -- Agile: The Better Way -- Results -- Lessons -- Next -- Chapter 11: Racial Profiling at Nextdoor: Using Data to Build a Better App and Combat a PR Disaster -- Unintended but Familiar Consequences -- Evaluating the Problem -- Redesigning the App -- Agile Methods in Action -- Results and Lessons -- Next -- Notes -- Part Four Making the Most Out of Agile Analytics -- Chapter 12: The Benefits of Agile Analytics The Upsides of Small Batches -- Life at IAC -- Data and Data Quality -- Insightful, Robust, and Dynamic Models -- A Smarter, Realistic, and Skeptical Workforce -- Summary -- Life at RDC -- Project Management -- Frustrated Employees -- Data Quality, Internal Politics, and the Blame Game -- Summary -- Comparing the Two -- Next -- Chapter 13: No Free Lunch The Impediments to-and Limitations of-Agile Analytics -- People Issues -- Resistance to Analytics -- Stakeholder Availability -- Irritating Customers, Users, and Employees with Frequent Changes.

Data Issues -- Data Quality -- Overfitting and Spurious Correlations -- Certain Problems May Call for a More Traditional Approach to Analytics -- The Limitations of Agile Analytics -- Acting Prematurely -- Even Agile Analytics Can't Do Everything -- Agile Analytics Won't Overcome a Fundamentally Bad Idea -- Next -- Chapter 14: The Importance of Designing for Data: Lessons from the Upstarts -- The Genes of Music -- From Theory to Practice -- The Tension between Data and Design -- All Design Is Not Created Equal -- Data and Design Can-Nay, Should-Coexist -- Next -- Notes -- Part FIVE Conclusions and Next Steps -- Chapter 15: What Now?: A Look Forward -- A Tale of Two Retailers -- Test for Echo -- Squaring the Circle -- The Blurry Futures of Data, Analytics, and Related Issues -- Data Governance -- Data Exhaust -- It's Complicated: How Ethics, Privacy, and Trust Collide -- Final Thoughts and Next Steps -- Notes -- Afterword -- Acknowledgments -- Selected Bibliography -- About the Author -- Index -- EULA.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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