Analytics the agile way
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons
[2017]
|
| Edición: | 1st ed |
| Colección: | Wiley and SAS business series.
THEi Wiley ebooks. |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849138906719 |
Tabla de Contenidos:
- 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.
