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. These expensive quagmires can be avoided, and this book explains precisely how.
Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments.
When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise.
By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements:
• Improving time-to-value with infused AI models for common use cases
• Optimizing knowledge work and business processes
• Utilizing AI-based business intelligence and data visualization
• Establishing a data topology to support general or highly specialized needs
• Successfully completing AI projects in a predictable manner
• Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing
When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.
Author(s): Neal Fishman, Cole Stryker
Edition: 1
Publisher: Wiley
Year: 2020
Language: English
Commentary: Vector PDF
Pages: 304
City: Indianapolis, IN
Tags: Artificial Intelligence; Data Science; Management; Analytics; Statistics; Best Practices; Ontologies; Data Lake; Data Warehouse; Data Pipelines; Data Management; Data Governance; Data Quality; Accessibility
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
Reliance
Reusability
The xOps Trifecta: DevOps/MLOps, DataOps, and AIOps
DevOps/MLOps
DataOps
AIOps
Summary
Chapter 7 Maximizing the Use of Your Data: Being Value Driven
Toward a Value Chain
Chaining Through Correlation
Enabling Action
Expanding the Means to Act
Curation
Data Governance
Integrated Data Management
Onboarding
Organizing
Cataloging
Metadata
Preparing
Provisioning
Multi-Tenancy
Summary
Chapter 8 Valuing Data with Statistical Analysis and Enabling Meaningful Access
Deriving Value: Managing Data as an Asset
An Inexact Science
Accessibility to Data: Not All Users Are Equal
Providing Self-Service to Data
Access: The Importance of Adding Controls
Ranking Datasets Using a Bottom-Up Approach for Data Governance
How Various Industries Use Data and AI
Benefiting from Statistics
Summary
Chapter 9 Constructing for the Long-Term
The Need to Change Habits: Avoiding Hard-Coding
Overloading
Locked In
Ownership and Decomposition
Design to Avoid Change
Extending the Value of Data Through AI
Polyglot Persistence
Benefiting from Data Literacy
Understanding a Topic
Skillsets
It’s All Metadata
The Right Data, in the Right Context, with the Right Interface
Summary
Chapter 10 A Journey’s End: An IA for AI
Development Efforts for AI
Essential Elements: Cloud-Based Computing, Data, and Analytics
Intersections: Compute Capacity and Storage Capacity
Analytic Intensity
Interoperability Across the Elements
Data Pipeline Flight Paths: Preflight, Inflight, Postflight
Data Management for the Data Puddle, Data Pond, and Data Lake
Driving Action: Context, Content, and Decision-Makers
Keep It Simple
The Silo Is Dead; Long Live the Silo
Taxonomy: Organizing Data Zones
Capabilities for an Open Platform
Summary
Glossary of Terms
Index
EULA
