Practical DataOps : Delivering Agile Data Science at Scale
Bibliographische Detailangaben
- Titel
- Practical DataOps Delivering Agile Data Science at Scale
- verantwortlich
- veröffentlicht
- Erscheinungsjahr
- 2020
- Medientyp
- E-Book
- Datenquelle
- British National Bibliography
- Tags
- Tag hinzufügen
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Inhaltsangabe:
- Intro
- Table of Contents
- About the Author
- About the Technical Reviewer
- Acknowledgments
- Introduction
- Part I: Getting Started
- Chapter 1: The Problem with Data Science
- Is There a Problem?
- The Reality
- Data Value
- Technology, Software, and Algorithms
- Data Scientists
- Data Science Processes
- Organizational Culture
- The Knowledge Gap
- The Data Scientist Knowledge Gap
- IT Knowledge Gap
- Technology Knowledge Gap
- Leadership Knowledge Gap
- Data-Literacy Gap
- Lack of Support
- Education and Culture
- Unclear Objectives
- Leaving It to Data Scientists to Figure Out
- Summary
- Endnotes
- Chapter 2: Data Strategy
- Why We Need a New Data Strategy
- Data Is No Longer IT
- The Scope of the Data Strategy
- Timeframe
- Sponsorship
- Start with Situational Awareness
- The Organization
- People
- Technology
- Processes
- The Data Asset
- Identify Analytics Use Cases
- Missions, Visions, and KPIs
- Ideate
- What Could We Do?
- Benchmark Capabilities of the Data Lifecycle
- Gap Analysis
- What Needs to Change?
- Define Data Strategy Objectives
- Where Do We Need to Go?
- Deliver the Data Strategy
- Define Data Strategy Initiatives
- How Do We Get There?
- Execution and Measurement Plan
- How Do We Know if We're There?
- Summary
- Endnotes
- Part II: Toward DataOps
- Chapter 3: Lean Thinking
- Introduction to Lean Thinking
- Origins at Toyota
- Lean Software Development
- Lean Product Development
- Lean Thinking and Data Analytics
- Seeing Waste
- Value Stream Mapping
- Deliver Fast
- Pull Systems
- See the Whole
- Root Cause Analysis
- Summary
- Endnotes
- Chapter 4: Agile Collaboration
- Why Agile?
- Waterfall Project Management
- Agile Values
- Agile Frameworks
- Scrum
- XP and Scrum/XP Hybrid
- Kanban Method
- Scrumban
- Scaling Agile
- Scrum of Scrums
- Disciplined Agile Delivery
- Scaled Agile Framework (SAFe)
- Agile For DataOps
- DataOps Manifesto
- DataOps Principles
- Data Science Lifecycle
- Agile DataOps Practices
- Ideation
- Inception
- Research and Development
- Transition/Production
- Summary
- Endnotes
- Chapter 5: Build Feedback and Measurement
- Systems Thinking
- Continuous Improvement
- Feedback Loops
- Team Health
- Retrospectives
- Health Check
- Starfish Retrospective
- Sailboat Retrospective
- Premortem
- Service Delivery
- Service Delivery Review Meeting
- Improving Service Delivery
- Product Health
- KPIs for Data Product Monitoring
- Monitoring
- Concept Drift
- Product Benefit
- Benefit Measurement
- Benefit Measurement Challenges
- Alternatives to A/B Testing and Measurement
- Metric Challenges
- Summary
- Endnotes
- Part III: Further Steps
- Chapter 6: Building Trust
- Trust People with Data and Systems
- Accessing and Provisioning Data
- Data Security and Privacy
- Resource Utilization Monitoring
- People Can Trust Data
- Metadata
- Tagging
- Trust During Ingestion