Practical DataOps : Delivering Agile Data Science at Scale

Bibliographische Detailangaben

Titel
Practical DataOps Delivering Agile Data Science at Scale
verantwortlich
Atwal, Harvinder.
veröffentlicht
Berkeley, CA: Apress L.P, ©2020
Erscheinungsjahr
2020
Medientyp
E-Book
Datenquelle
British National Bibliography
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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