Data as a Service: A Framework for Providing Reusable Enterprise Data Services

ISBN-13: 9781119046585 / Angielski / Twarda / 2015 / 368 str.

Pushpak Sarkar

Opis

Data as a Service shows how organizations can leverage -data as a service- by providing real-life case studies on the various and innovative architectures and related patterns

• Comprehensive approach to introducing data as a service in any organization
• A reusable and flexible SOA based architecture framework
• Roadmap to introduce 'big data as a service' for potential clients
• Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions

Spis treści

Guest Introduction – Sanjoy Paul xiii

Guest Introduction – Christopher Surdak xv

Preface (Includes the Reader’s 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: Amazon’s 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 Organization’s 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

O autorze

Pushpak Sarkar is an Executive IT Architect at New York Life Insurance, USA. The author received a bachelor’s degree from Indian Institute of Technology, his master’s from the University of Pennsylvania, and an MBA from FMS, University of Delhi, India. He has been running Data Management & Analysis Service Centers of Excellence (COE) at several globally renowned organizations. His professional interest lies in data management, business intelligence, and big data analytics.

Blurb

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 author’s 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 ‘Big Data as a Service’
• 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 bachelor’s degree from Indian Institute of Technology(IIT) Kharagpur and his master’s 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.