Developing a Path to Data Dominance: Strategies for Digital Data-Centric Enterprises

Description

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. 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