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Smarter data science : succeeding with enterprise-grade data and AI projects /
Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. Th...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Indianapolis :
Wiley,
2020.
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- Cover
- Praise For This Book
- Title Page
- Copyright
- About the Authors
- Acknowledgments
- Contents at a Glance
- Contents
- Foreword for Smarter Data Science
- Epigraph
- Preamble
- Chapter 1 Climbing the AI Ladder
- Readying Data for AI
- Technology Focus Areas
- Taking the Ladder Rung by Rung
- Constantly Adapt to Retain Organizational Relevance
- Data-Based Reasoning Is Part and Parcel in the Modern Business
- Toward the AI-Centric Organization
- Summary
- Chapter 2 Framing Part I: Considerations for Organizations Using AI
- Data-Driven Decision-Making
- Using Interrogatives to Gain Insight
- The Trust Matrix
- The Importance of Metrics and Human Insight
- Democratizing Data and Data Science
- Aye, a Prerequisite: Organizing Data Must Be a Forethought
- Preventing Design Pitfalls
- Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time
- Quae Quaestio (Question Everything)
- Summary
- Chapter 3 Framing Part II: Considerations for Working with Data and AI
- Personalizing the Data Experience for Every User
- Context Counts: Choosing the Right Way to Display Data
- Ethnography: Improving Understanding Through Specialized Data
- Data Governance and Data Quality
- The Value of Decomposing Data
- Providing Structure Through Data Governance
- Curating Data for Training
- Additional Considerations for Creating Value
- Ontologies: A Means for Encapsulating Knowledge
- Fairness, Trust, and Transparency in AI Outcomes
- Accessible, Accurate, Curated, and Organized
- Summary
- Chapter 4 A Look Back on Analytics: More Than One Hammer
- Been Here Before: Reviewing the Enterprise Data Warehouse
- Drawbacks of the Traditional Data Warehouse
- Paradigm Shift
- Modern Analytical Environments: The Data Lake
- By Contrast
- Indigenous Data
- Attributes of Difference
- Elements of the Data Lake
- The New Normal: Big Data Is Now Normal Data
- Liberation from the Rigidity of a Single Data Model
- Streaming Data
- Suitable Tools for the Task
- Easier Accessibility
- Reducing Costs
- Scalability
- Data Management and Data Governance for AI
- Schema-on-Read vs. Schema-on-Write
- Summary
- Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail
- A Need for Organization
- The Staging Zone
- The Raw Zone
- The Discovery and Exploration Zone
- The Aligned Zone
- The Harmonized Zone
- The Curated Zone
- Data Topologies
- Zone Map
- Data Pipelines
- Data Topography
- Expanding, Adding, Moving, and Removing Zones
- Enabling the Zones
- Ingestion
- Data Governance
- Data Storage and Retention
- Data Processing
- Data Access
- Management and Monitoring
- Metadata
- Summary
- Chapter 6 Addressing Operational Disciplines on the AI Ladder
- A Passage of Time
- Create
- Stability
- Barriers
- Complexity
- Execute
- Ingestion
- Visibility
- Compliance
- Operate
- Quality