Preface (Includes the Reader's Guide)

Typically, once every couple of decades a disruptive new technology emerges that fundamentally changes the business landscape. Innovative, high tech products that often start a trend come to the mainstream market with such rapidity that they transform the existing way of doing business. These trends also create a new market that eventually disrupts the existing market and related network, often displacing the earlier technology.

In most cases, organizations that understand underlying competitive dynamics of innovation and who adapt to these disruptive trends, win. Today such fundamental shifts take place in the world of data and analytics daily, and they are changing the global business landscape significantly.

If one closely observes the global marketplace, it is safe to say that many businesses are trying to harness an unprecedentedly large amount of data to derive new insights that support their competitive analyses. A huge amount of data that is gathered from diverse channels (e.g., social media, clickstream analysis) need to be translated by businesses to enable concrete actions. Organizations that understand the competitive dynamics at play and those that can then predictively analyze that data will win, whereas those that fail to recognize this challenge and respond to it will become extinct.

While data has always been considered an essential part of IT infrastructure across most organizations to support their business operations, today it is recognized as the key commodity upon which an enterprise runs its business and day-to-day operations. A complete paradigm shift has occurred in which data is increasingly recognized as an asset that can 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 covers 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. The book also offers the following:

• Presents a comprehensive approach for introducing Data as a Service (DaaS) 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.

The topics covered in this book are wide ranging, starting with a presentation on the need for providing DaaS and the technical challenges involved in making that transformation. Some of the areas of the book that may particularly appeal to readers include:

• How DaaS can become a strategic enabler for sharing data with customers on company products they are interested in purchasing, browsing online, or viewing on social media.
• How the DaaS framework can help many organizations recognize monetizable intent and dependency of their customers on accessing their data while buying their company products.
• How enhanced on-demand data services can lead to potential clients by organizations that plan on mining customer, social media, and online conversations over a big data platform, using sophisticated predictive algorithms and data analytics tools.
• How to adopt best practices for successfully deploying reusable data services in your organization along with a reference architecture comprising common sets of data standards, guidelines, and processes.

Covering so much ground-from canonical modeling to data governance and XML based services-can be challenging for some readers, so the book offers a roadmap to help guide you through it.

The Reader's Guide

The Reader's Guide is provided to help readers determine who should read the book and why they need to read the book. A summary of each chapter to explain the step-by-step approach required for the successful introduction of DaaS in any organization is also provided.

The successful adoption of DaaS in any organization is based on three fundamental areas-architecture, adopting organizational processes, and ensuring the appropriate technology components are deployed. However, this should be based on real-world experiences and lessons learned from prior IT/DaaS implementations. This is one of the reasons this book includes case studies in several chapters.

The next section will guide readers on how best to use the book by sharing details of every chapter. It will also help guide readers to determine the best approach to use the DaaS framework in their current IT landscape within their organization. Figures 1.1 and 1.2 illustrate key topics in the book along with the suggested roadmap.

Figure 1.1 Key topics covered in the book by chapter

Figure 1.2 Roadmap the book's different chapters

PART 1: Overview of Fundamental Concepts Includes Chapters 1 to 3

The introductory section of the book introduces you to Data as a Service (DaaS). It also provides readers with a clear overview on how an organization can deliver on the promise of providing DaaS to its business stakeholders and end customers.

Chapter 1: "Introduction to DaaS" provides a high-level overview on the core concepts of the DaaS framework. It also explores commercialization aspects of Data as a Service, its immense potential for generating revenues for most organizations, as well as some of its common limitations. It describes the details of service delivery management while suggesting necessary key steps for preparing the blueprint for enterprise data services in your organization.

Chapter 2: "DaaS Strategy and Reference Architecture" provides an overview of DaaS reference architecture along with the key components that make up the DaaS framework. It also explains the long-term significance of formally creating an enterprise data strategy in an organization that formulates a long-term roadmap to deliver Data as a Service (DaaS).

Chapter 3: "Data Asset Management" explores the significance of enterprise data and the foundational role it plays to make enterprise data services successful in any organization. It explains the underlying principles of data asset management and why companies need to treat data as a corporate asset. It also examines the various major types of enterprise data and contrasts their major features.

PART 2: DaaS Architecture Framework and Components Includes Chapters 4 to 8

This section of the book focuses on the architecture framework and components required to deploy DaaS in your organization. It also describes in detail common patterns, standards, and processes that can help shape the DaaS Reference Architecture. This section also provides readers with a high-level overview on best practices from a few related disciplines (e.g., EIM, EA, SOA, data services) to make DaaS a scalable data delivery mechanism for organizations.

Chapter 4: "Enterprise Data Services" describes the core concepts about enterprise data services as a fundamental component of the DaaS framework. It illustrates with examples how several organizations have successfully developed a set of standardized service interfaces (termed EDS) to enable data sharing with their various stakeholders (customers, vendors, regulatory agencies, government, etc.).

Chapter 5: "Enterprise and Canonical Modeling" explains the significance of enterprise and canonical modeling and its foundational role to promote consistent and reliable data exchange across disparate systems spread out over the organization. It also explains the significance of the enterprise data model (EDM) as the foundational component required for building a robust and mature set of data structures that can be reused across the entire organization.

Chapter 6: "Business Glossary for DaaS" environment provides a detailed overview of the underlying reasons why organizations need to develop a standardized business glossary for data services published for user consumption. Storing glossary terms in a shared metadata repository across the organization will improve the overall productivity of both the businesses and the external subscribers to enterprise data services (EDS).

Chapter 7: "SOA and Data Integration" provides a high-level overview on key data acquisition and integration patterns with service-oriented architecture (SOA) as the underlying foundation. It also covers a few technologies, e.g., data virtualization, stream computing for big data, data federation, which can be leveraged by the DaaS framework to publish data services with enhanced efficiency, performance, and a scalable architecture.

Chapter 8: "Data Quality and Standards" provides details on how to ensure that the quality of data published by enterprise data services is suitable and fit for public consumption. It explains the significance of data standards for the...