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.
Autorentext
NEAL FISHMAN is a Distinguished Engineer and CTO of Data-Based Pathology at IBM. He is an IBM-certified Senior IT Architect and Open Group Distinguished Chief Architect.
COLE STRYKER is a journalist based in Los Angeles. He is the author of Epic Win for Anonymous and Hacking the Future.
Klappentext
PRAISE FOR SMARTER DATA SCIENCE
"This work provides benefit to a variety of roles, including architects, developers, product owners, and business executives. For organizations exploring AI, this book is the cornerstone to becoming successful."
Harry Xuegang Huang Ph.D., External Consultant, A.P. Moller Maersk
"Presents a holistic model that emphasizes how critical data and data management are in implementing successful value-driven data analytics and AI solutions. The book presents an elegant and novel approach to data management."
Ali Farahani, Ph.D., Former Chief Data Officer, County of Los Angeles; Adjunct Associate Professor, USC
"The authors seek and speak the truth, and penetrate into the core of the challenge most organizations face in finding value in their data. Our industry needs to move away from trying to connect the winning dots by 'magical' technologies and overly simplified approaches. This book provides the necessary guidance."
Jan Gravesen, M.Sc., IBM Distinguished Engineer, Director and Chief Technology Officer, IBM
BUILD A ROBUST INFORMATION ARCHITECTURE THAT SCALES AND DELIVERS LONG-TERM VALUE
Large organizations are racing to implement advanced data science. All too often, our AI endeavors turn out to be dead-end science projects that never deliver sustainable business value. What are we missing? In Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects, you'll discover the pillars of information architecture that you must understand and implement.
Data analytics and AI only add value when they can predictably and consistently deliver business insights and scale across the organization. Smarter Data Science outlines an effective and practical way for organizing, managing, and evaluating data, so you can establish an information architecture to better drive AI and data science.
You'll learn how to:
- Simplify data management, making data available when and where it is needed
- Improve time to value for operationalizing AI use cases
- Make AI and data insights accessible across the enterprise
- Scale complex AI scenarios dynamically and in real time
- Develop an information architecture that brings predictable, repeatable value
Inhalt
Foreword for Smarter Data Science xix
Epigraph xxi
Preamble xxiii
Chapter 1 Climbing the AI Ladder 1
Readying Data for AI 2
Technology Focus Areas 3
Taking the Ladder Rung by Rung 4
Constantly Adapt to Retain Organizational Relevance 8
Data-Based Reasoning is Part and Parcel in the Modern Business 10
Toward the AI-Centric Organization 14
Summary 16
Chapter 2 Framing Part I: Considerations for Organizations Using AI 17
Data-Driven Decision-Making 18
Using Interrogatives to Gain Insight 19
The Trust Matrix 20
The Importance of Metrics and Human Insight 22
Democratizing Data and Data Science 23
Aye, a Prerequisite: Organizing Data Must Be a Forethought 26
Preventing Design Pitfalls 27
Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time 29
Quae Quaestio (Question Everything) 30
Summary 32
Chapter 3 Framing Part II: Considerations for Working with Data and AI 35
Personalizing the Data Experience for Every User 36
Context Counts: Choosing the Right Way to Display Data 38
Ethnography: Improving Understanding Through Specialized Data 42
Data Governance and Data Quality 43
The Value of Decomposing Data 43
Providing Structure Through Data Governance 43
Curating Data for Training 45
Additional Considerations for Creating Value 45
Ontologies: A Means for Encapsulating Knowledge 46
Fairness, Trust, and Transparency in AI Outcomes 49
Accessible, Accurate, Curated, and Organized 52
Summary 54
Chapter 4 A Look Back on Analytics: More Than One Hammer 57
Been Here Before: Reviewing the Enterprise Data Warehouse 57
Drawbacks of the Traditional Data Warehouse 64
Paradigm Shift 68
Modern Analytical Environments: The Data Lake 69
By Contrast 71
Indigenous Data 72
Attributes of Difference 73
Elements of the Data Lake 75
The New Normal: Big Data is Now Normal Data 77
Liberation from the Rigidity of a Single Data Model 78
Streaming Data 78
Suitable Tools for the Task 78
Easier Accessibility 79
Reducing Costs 79
Scalability 79
Data Management and Data Governance for AI 80
Schema-on-Read vs. Schema-on-Write 81
Summary 84
Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail 87
A Need for Organization 87
The Staging Zone 90
The Raw Zone 91
The Discovery and Exploration Zone 92
The Aligned Zone 93
The Harmonized Zone 98
The Curated Zone 100
Data Topologies 100
Zone Map 103
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