Practical DataOps : Delivering Agile Data Science at Scale

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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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500 |a Data Quality Assessment 
505 0 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
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650 0 |a Big data. 
650 0 |a Agile software development. 
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contents 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
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spelling Atwal, Harvinder., Practical DataOps Delivering Agile Data Science at Scale Harvinder Atwal, Berkeley, CA Apress L.P ©2020, 1 online resource (289 pages)., text rdacontent, computer rdamedia, online resource rdacarrier, Data Quality Assessment, 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, Print version record., Big data., Agile software development., Agile software development. fast (OCoLC)fst01743753, Big data. fast (OCoLC)fst01892965, Electronic books., ELD ebook, LDL ebooks ONIX to marcxml transformation using Record_Load-eBooks_Legal_Deposit_onix2marc_v2-1.xsl 20191211 com.springer.onix.9781484251041 Uk, (OCoLC)1132426602
spellingShingle Atwal, Harvinder., Practical DataOps: Delivering Agile Data Science at Scale, 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, Big data., Agile software development., Electronic books.
title Practical DataOps: Delivering Agile Data Science at Scale
title_auth Practical DataOps Delivering Agile Data Science at Scale
title_full Practical DataOps Delivering Agile Data Science at Scale Harvinder Atwal
title_fullStr Practical DataOps Delivering Agile Data Science at Scale Harvinder Atwal
title_full_unstemmed Practical DataOps Delivering Agile Data Science at Scale Harvinder Atwal
title_short Practical DataOps
title_sort practical dataops delivering agile data science at scale
title_sub Delivering Agile Data Science at Scale
topic Big data., Agile software development., Electronic books.
topic_facet Big data., Agile software development., Electronic books.