Includes bibliographical references (pages 312-314) and index.

Guest Introduction ⁰́₃ Sanjoy Paul xiii -- Guest Introduction ⁰́₃ Christopher Surdak xv -- Preface (Includes the Reader⁰́₉s Guide) xvii -- Acknowledgments xxvii -- Part One Overview of Fundamental Concepts -- 1. Introduction to DaaS 3 -- Topics Covered in this Chapter 3 -- Data-Driven Enterprise 4 -- Defining a Service 6 -- Drivers for Providing Data as a Service 7 -- Data as a Service Framework: A Paradigm Shift 12 -- 2. DaaS Strategy and Reference Architecture 25 -- Topics Covered in this Chapter 25 -- Enterprise Data Strategy, Goals, and Principles 26 -- Critical Success Factors 28 -- Reference Architecture of the DaaS Framework 30 -- How to leverage the DaaS Reference Architecture 41 -- Summary 41 -- 3. Data Asset Management 43 -- Topics Covered in this Chapter 43 -- Introduction to Major Categories of Enterprise Data 46 -- Transaction Data (Includes Big Data) 54 -- Significance of EIM in Supporting the DaaS Program 56 -- Role of Enterprise Data Architect 57 -- Summary 59 -- Part Two DaaS Architecture Framework and Components -- 4. Enterprise Data Services 63 -- Topics Covered in this Chapter 63 -- Emergence of Enterprise Data Services 64 -- Need for an Enterprise Perspective 65 -- Emergence of Enterprise Data Services 66 -- Publication of Enterprise Data 69 -- Interdependencies between DaaS, EIM, and SOA 73 -- Case Study: Amazon⁰́₉s Adoption of Public Data Service Interfaces 76 -- Summary 79 -- 5. Enterprise and Canonical Modeling 80 -- Topics Covered in this Chapter 80 -- A Model-Driven Approach Toward Developing Reusable Data Services 81 -- Defining a Standards-Driven Approach toward Developing New Data Services 82 -- Role of the Enterprise Data Model 83 -- Developing the Canonical Model 84 -- Enterprise Data Model 85 -- Canonical Model 85 -- Implementing the Canonical Model 89 -- Publishing Data Services with the Canonical Model as a Foundation 93 -- Implementing the Canonical Model in Real-life Projects 95 -- Data Services Roll Out and Future Releases 97 -- Case Study: DaaS in Real Life, Electronic-Data Interchange in U.S. Healthcare Exchanges 98.

Summary 102 -- 6. Business Glossary for DaaS 103 -- Topics Covered in this Chapter 103 -- Problem of Meaning and the Case for a Shared Business Glossary 104 -- Using Metadata in Various Disciplines 106 -- Role of an Organization⁰́₉s Business Glossary 108 -- Enterprise Metadata Repository 113 -- Implementing the Enterprise Metadata Repository 115 -- Metadata Standards for Enterprise Data Services 116 -- Metadata Governance 121 -- Summary 121 -- 7. SOA and Data Integration 123 -- Topics Covered in this Chapter 123 -- SOA as an Enabler of Data Integration 124 -- Role of Enterprise Service Bus 127 -- What is a Data Service? 128 -- Foundational Components of a Data Service 131 -- Service Interface 133 -- Major Service Categories 133 -- Overview of Data Virtualization 136 -- Consolidated Data Infrastructure Platform 143 -- Summary 145 -- 8. Data Quality and Standards 146 -- Topics Covered in this Chapter 146 -- Where to Begin Data Standardization Efforts in Your Organization 150 -- Role of Data Discovery/Profiling to Identify DaaS Quality Issues 152 -- Data Quality and the Investment Paradox 156 -- Quality of a Data Service 157 -- Setting Up Standards in a DaaS Environment 158 -- Summary 163 -- Part Three DaaS Solution Blueprints -- 9. Reference Data Services 167 -- Topics Covered in this Chapter 167 -- Delivering Market and Reference Data Using Real-Time Data Services 169 -- Comparing Usage of Reference Data Against Master Data 171 -- Understanding Challenges of Reference Data Management 173 -- Other Reference Data Management Challenges 174 -- Role of Reference Data Standards and Vocabulary Management 177 -- Collaborative Reference Data Management Implementation Using Business Process Management/Workflow 180 -- Summary 185 -- 10. Master Data Services 187 -- Topics Covered in this Chapter 187 -- Introduction to Master Data Services 188 -- Pros and Cons of Master Data Services (Virtual Master Data Management) 192 -- Leveraging the Golden Source to Resolve Deep-Rooted Source Differences 193.

Future Trends in Master Data Management Using DaaS 194 -- Comparing Master Data Services Approach (Virtual) with Master Data Management Approach Involving Physical Consolidation 196 -- Case Study: Master Data Services for a Premier Investment Bank 197 -- Detailed Scope and Benefits 198 -- Proposed Solution Architecture for Master Data Services 199 -- Enterprise and Canonical Model for Master Data Management Implementation 202 -- Summary 208 -- 11. Big Data and Analytical Services 210 -- Topics Covered in this Chapter 210 -- Big Data 212 -- Big Data Analytics 213 -- Relationship Between DaaS and Big Data Analytics 217 -- Future Impact of DaaS on Big Data Analytics 220 -- Extending DaaS Reference Architecture for Big Data and Cloud Services 221 -- Fostering an Enterprise Data Mindset 228 -- Case Study: Big DaaS in the Automotive Industry 231 -- Summary 233 -- Part Four Ensuring Organizational Success -- 12. DaaS Governance Framework 237 -- Topics Covered in this Chapter 237 -- Role of Data Governance 238 -- Data Governance 240 -- People Governance 245 -- Process Governance 248 -- Service Governance 253 -- Technology Governance 258 -- Summary 261 -- 13. Securing the DaaS Environment 262 -- Topics Covered in this Chapter 262 -- Impact of Data Breach on DaaS Operations 263 -- Major Security Considerations for DaaS 264 -- Multilayered Security for the DaaS Environment 266 -- Identity and Access Management 270 -- Data Entitlements to Safeguard Privacy 271 -- Impact of Increased Privacy Regulations on Data Providers 272 -- Information Risk Management 273 -- Important Data Security and Privacy Regulations that Impact DaaS 275 -- Checklist to Protect Data Providers from Data Breaches 277 -- Summary 278 -- 14. Taking DaaS from Concept to Reality 280 -- Topics Covered in this Chapter 280 -- Service Performance Measurement Using the Balanced Scorecard 284 -- Implementing the Performance Scorecard to Improve Data Services 286 -- Embarking on the DaaS Journey with a Vision 287 -- Using AGILE Principles for New Data Services Development 290.

Sustaining DaaS in an Organization: How to Keep the Program Going 292 -- In Conclusion 295 -- Appendix A Data Standards Initiatives and Resources 297 -- Appendix B Data Privacy & Security Regulations 305 -- Appendix C Terms and Acronyms 309 -- Appendix D Bibliography 312 -- Index 315.

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This book provides the nuts-and-bolts information to transform the way your organization designs, manages, and distributes enterprise data to consumers. Data has always been considered as an essential part of the IT infrastructure across most organizations in supporting their business operations. However, a complete paradigm shift has occurred in recent years as data is increasingly recognized as an asset that could be commercially sold as a service, in and of itself. Based on the author⁰́₉s first-hand experience and expertise, this book offers a proven framework for sharing core enterprise data using reusable data services. The book will cover how organizations can generate business revenues by providing data as a service to their clients for fee-based subscriptions. The book goes on to explain, in detail, how to acquire and distribute data across heterogeneous platforms effectively using enterprise SOA principles, industry data standards and leveraging new technologies such as data virtualization, cloud, and Big Data stream computing. . Presents a comprehensive approach for introducing data as a service in any organization for the first time . Recommended best practices and industry standards for sharing master, reference, and big data with data consumers . Commercialization aspects of data as a service and its potential for generating revenues. Covers real world applications of DaaS such as ⁰́₈Big Data as a Service⁰́₉. Real-life case studies on various innovative architecture blueprints and related patterns Topics covered in this book are wide-ranging starting with the presentation of the need for providing data as a service and the technical challenges involved in making that transformation. Pushpak Sarkar is a Corporate Vice President- Enterprise Technology at New York Life Insurance, USA. The author received a bachelor⁰́₉s degree from Indian Institute of Technology(IIT) Kharagpur and his master⁰́₉s in Technology Management from the University of Pennsylvania, and an MBA from FMS, University of Delhi, India. He has been running Data Management & BI/Analytics Service Centers of Excellence (COE) at several globally renowned organizations. His professional interest lies in data management, business intelligence, and big data analytics.

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