Smarter data science : succeeding with enterprise-grade data and ai projects / Neal Fishman, Cole Stryker.

Format:

Book

Author/Creator:

Fishman, Neal, author.

Stryker, Cole, author.

Language:

English

Subjects (All):

Computer Science/IT.

Physical Description:

1 online resource

Place of Publication:

Indianapolis, Indiana : John Wiley and Sons, 2020.

Summary:

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.

Contents:

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)

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

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

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

Chapter 6 Addressing Operational Disciplines on the AI Ladder

A Passage of Time

Create

Stability

Barriers

Complexity

Execute

Visibility

Compliance

Operate

Quality.

Notes:

Description based on print version record.

ISBN:

1-119-69342-X

1-119-69438-8

1-119-69798-0