Most existing companies struggle currently because they lack the tools and strategies to move product departments into independent platforms that can be retrofitted to form dynamic new products based on consumer demands. This book provides managers and professionals with the necessary approaches for designing software and hardware architectures to support data platform organizations. Specifically, it demonstrates how to automate the decomposition of existing platforms into smaller parts that can be reused to form new variations. This task requires significant analysis and design methodologies and procedures to create an infrastructure based on data as opposed to products. These new knowledge bases allow data-centric professionals to pursue actions that can better predict and respond to the unexpected.
Featuring case examples from companies such as Lego, FedEx, General Electric (GE), Pfizer, P&G and more, this book is appropriate for C-level executives engaged in the digital transformation of their firms; entrepreneurs of digital platform companies; and senior software engineers that need to design Internet of Things (IoT) devices and integrate them with block chain and multi-cloud architectures. In addition, this book is also useful for graduate-level coursework in data science.
Author(s): Arthur Langer, Arka Mukherjee
Series: Future of Business and Finance
Publisher: Springer
Year: 2023
Language: English
Pages: 290
City: Cham
Preface and Overview
The Proliferation of Data Platforms
Moving to Data Dominance to Optimize Strategic Resilience
Digital Technology Architecture
References
Acknowledgments
Contents
1: Introduction to Data Dominance
Background
Evolution of Architecture and Data Dominance
Internet of Things (IoT)
Distributed Cloud
Mobility
Advanced Software Applications
The Data Dominance Challenge
Organization Structure
People Skills
Access for Expansion of Data
Investment Costs
Transforming to a Data Platform
The S-Curve and Data Dominance
Data Dominance and New Leadership
Data Dominance and Product Development
Initial Playbook Toward Data Dominance
Reduction in the Need for Elite Skills, Using Bots and AI
Augment Abilities of Less Skilled People
Distribute Skilled Workers Across Broader Geographical Areas
Establishing Cultures Driven More on Thought Ideas Than Experience
Uncover and Create New Needs: “The Data Does Not Lie”
Expose the Flaws in Your Current Business Models
Build a VUCA-Ready Workforce
Overview of the Chapters
Conclusions
References
2: The Digital Data-Centric Enterprise: Case Studies
Introduction
Successful Completion of Digital Transformation
Pfizer
Lego
Home Depot
Schneider Electric
IKEA
Platform Case Studies
General Electric (GE) Digital
Background
The GE Plan
GE’s Approach and Experiment
What Went Wrong?
Challenges in Becoming a Software Company
FedEx: Digital Transformation Through Application Innovation
Background
New Strategies
Workforce Opportunity Services: Non-Profit Supplier of Underserved Talent
Background
Platform Strategy
Proctor & Gamble (P&G): Intrapreneurial Leadership & Multiple S-Curve Journeys
P&G History
Results of the Passerini Era
References
3: The Product Strategy
Introduction
Growth-Oriented Product Strategy (GOPS)
Company Factors
Installed Base of Existing Customers
Locations of Business
Expertise in Platform Business
Outside Firm/Consultant
Recruit Staff
Vision for Differentiation
Product Roadmap
Ability to Finance Transformation
Timeline to Create the Platform to Operations
Business Capability Landscape
Digital Touchpoints
Digital Experience
Commerce Journey
Supply Chain Actions
Systems of Record and Master Data
Hyper-Automation
Digital Twins
References
4: Data Strategy for Exponential Growth
Data Strategy: The Foundation for Exponential Growth
Exponential Growth Is Rare
A Brief History of Databases and Enterprise Data Modeling
The Database and Logic Data Modeling
Logic Data Modeling Procedures
Key Attributes
Normalization
Limitations of Normalization
The Supertype/Subtype Model
Key Business Rules
Integration with Existing Data Models
Determining Domains and Triggering Operations
Summary
Exponential Product Portfolios
Selecting the Right Products
Data Strategy
Increasing Market Share
A Typical Data Strategy
Data Strategy Objectives
Architecture and Governance
Data Architecture: Addressing the Foundational Elements
Data Governance: Leadership and Control of the Function
Prioritization: The Differentiator for Exponential Growth
Why Exponential Data Strategies Are Different
Automation Is the Priority
Machine Processing vs. Human Processing
Aligning Data Strategy with Growth Strategy
How to Construct the Strategy
Emerging Markets for Data-Centric Products
Quantum
MetaVerse
Blockchain
5G
Internet of Things (IoT)
References
5: Organizing the Data Ecosystem
Introduction
Data Assets
Data Architect Challenges
The Physical vs. Logical Business Organization
How Business Data Is Organized
Data Organization: The Current State
Traditional Approaches to Solving the Disparate Data Problem
Best Approaches to Improve Data Aggregation
Data Organization: The Aspirational State of the Future
Business Semantics
Semantic Modeling
Data Linkage and Knowledge Graphs
Systematic Data Organization
Data Asset Inventory
Business Semantics
Master Data and Controlled Vocabularies
Data Products
The Metadata Repository
Conceptual Metadata
Logical Metadata
Physical Metadata
Semantic Metadata
Metadata Linkage
Creating the Data Inventory
Profiling the Data Ecosystem
Pattern Mining
Domain Profiling
Relationship Profiling
ID Profiling
Statistical Profiling
Hierarchy Profiling
Distribution Profiling
Quality Profiling
Dependency Profiling
Subtype Profiling
Numeric Profiling
Time Series Profiling
Classifying and Mapping the Data Ecosystem
Classification
Mapping
Creating the Knowledge Graph
Reference
6: Building Data-Centric Products
Introduction
Designing Digital Twins
Engineering Data
Physical Data
Process Data
Integrating Product Strategy with Enterprise Data Assets
Data Fabric Architecture
Published Data Catalog and Data Engineering
Orchestration and DataOps
Data Preparation and Data Delivery Layer
Insights and Recommendation Engine
Active Metadata
Knowledge Graphs
Persistence Layer
Semantic Layer
Augmented Data Catalog
The Minimal Viable Product
References
7: Culture: Friction in Scaling the Product Portfolio
Introduction
Process Barriers
The Employment Challenge in the Digital Era
Gen Y Population Attributes
Advantages of Employing Millennials to Support Digital Transformation and Data Platforms
Integration of Gen Y with Baby Boomers and Gen X
Designing the Digital Enterprise
Assimilating Gen Y Talent from Underserved and Socially Excluded Populations
Implications for New Pathways for Digital Talent
Demographic Shifts in Talent Resources
Economic Sustainability
Integration and Trust
Global Implications for Sources of Talent
Anticipation of Gen Z
Data-Centricity Mindsets and Organizational Resilience in Data Platform Companies
Understanding Organizational Resilience’s Relationship to Data Platforms
Data Platform Architecture and Resilience
Proliferation of Data Platforms
References
8: Alignment: Data Strategy Management and Leadership
Introduction
Product Planning:
Product Development
Product Introduction
Product Lifecycle Management (PLM)
Product Leadership
Data Platform Executive Leadership
Intrapreneurism Versus Entrepreneurism
Vision Versus Reason
Steering and Build-Measure-Learn
Accelerate
Common Leadership Strategies
Intrapreneurial Leadership Considerations Based on Types of Data Platforms
Data Platforms and GDPR
Data Platform Leadership and AI/ML
Data Platform Marketing Trends and Takeaways
References
9: Effects of Wireless Communication and IoT on Data Aggregation
The Wireless Revolution
5G and Distributed Processing
Data Architecture in a 5G World
User-Generated Data and Performance Measurements
The Smartphone as the Key User Interface
5G Summary
The Internet of Things
Logical Design of IoT and Communication Models
IoT Functional Blocks
IoT Communication Alternatives
IoT as an Inversion of Traditional Data Architecture and Design
Data Sensors, Actuators, and Computation
IoT APIs
Recruitability
IoT Security and Privacy
Immersion
The IoT SDLC
Transitioning to IoT
IoT Summary
References
10: Blockchain Data Architecture and Cyber Security
Understanding Blockchain Architecture
Forecasted Growth of Blockchain
Advantages and Disadvantages of Blockchain
Data Architecture of Blockchain
Cyber Security in Data Architecture
Cyber Security Risk in the S-Curve
Decomposition in Cyber Security Analysis and Data Architecture
Data Risk Responsibility
Developing a System of Procedures
IoT and Security
Cyber Security and Data Architecture Roles and Responsibilities
Summary
References
11: Transforming Legacy Systems to Data Platforms
Introduction
Types of Legacy Systems
Third-Generation Language Legacy System Integration
Replacing Third-Generation Legacy Systems
Approaches to Logic Reconstruction
Enhancing Third-Generation Legacy Systems
Data Element Enhancements
“Leaving as Is”: Third-Generation Legacy Systems
Fourth-Generation Language Legacy System Integration
Replacing Fourth-Generation Legacy Systems
Approaches to Logic Reconstruction
Enhancing Fourth-Generation Legacy Systems
“Leaving as Is”: Fourth-Generation Legacy Systems
Hybrid Methods: The Gateway Approach
Incremental Application Integration
Incremental Data Integration
Converting Legacy Character-Based Screens
The Challenge with Encoded Legacy Screen Values
Legacy Migration Methodology
References
12: Conclusions
Glossary
