The book’s subtitle best describes what it’s all about. The book does not focus on selling the concepts of big data and the Internet of Things (IoT). Rather, it assumes that big data and IoT are ushering in a new age of information technology (IT). This new age does not replace the existing enterprise information infrastructure, but instead leads to an extended, expanded, and broadened enterprise information architecture that has new capabilities. These new capabilities enable the architecture to meet evolving critical business demands that can only be addressed using big data and IoT. The book does not assume that an enterprise agrees with this thesis, but rather that the enterprise must be willing to explore and see if the world of big data and IoT applies to it. If so, the book describes the various steps, from determining if a case exists for using big data and IoT, to implementing a successful big data and IoT project with the resulting new enterprise information architecture.

The book is also not an in-depth examination of big data and IoT technologies. Necessary technologies, such as Hadoop, are covered as needed. However, the focus is on the information architecture and not the specific technologies, which other books can tackle. The book is a very practical and pragmatic guide that enables an enterprise to do something about big data and IoT rather than just talk about it.

The book is divided into eight chapters. Chapter 1 starts off with a review of analytical data stores, most notably enterprise data warehouses. It then covers evolving data management strategies, including NoSQL databases and Hadoop, before introducing IoT. The rest of the chapter focuses on introducing the methodology for developing and deploying projects that is described in detail in the last seven chapters. The methodology is a traditional IT one that is now being applied and extended to big data and IoT projects. The methodology has seven phases; each of the remaining chapters covers one phase.

Chapter 2 covers developing “the art of the possible” vision, which is the first phase of the methodology. Developing that vision starts with understanding the current state, which includes an information architecture maturity self-assessment. It then moves on to understanding the current state and future state of the data warehouse before determining where NoSQL databases and Hadoop fit in. The chapter concludes with a discussion of validating the vision.

The second phase--understanding the business--is the subject of chapter 3. The focus of this chapter is on determining business drivers and key performance indicators (KPIs), which is part of what the authors call their “method to success.” This chapter covers understanding business initiatives, identifying critical success factors, prioritizing initiatives to an early roadmap, and developing an initial business case.

Chapter 4 covers the next phase: business information mapping (BIM). This chapter discusses mapping data to KPIs. Data flow diagrams are introduced and used to describe both the current and future state. This gives a good graphical representation of both the current and potential future information architectures from a data and KPI perspective.

Chapter 5, “Understanding Organizational Skills,” covers the evaluation skills phase of the methodology. This chapter explores the skills that are needed, as well as how to address the gaps between available needed skills. Those skills include those needed for business architecture, data architecture, application architecture and integration, and technology.

Designing the future state information architecture is the subject of chapter 6. This first involves inventorying the current information architecture state before going on to design the future information architecture state. This chapter includes a number of questions about availability, recoverability, performance, data security and governance, reporting, predictive analytics, and so on, as well as questions relating to the use of hardware components, such as servers, storage, and networking.

Chapter 7, “Defining an Initial Plan and Roadmap,” covers revisiting skills and priorities, validating costs and the business case, delivering the roadmap, and obtaining approval.

The final chapter (8) discusses implementing the plan, which includes the implementation steps, putting the project into operation, and ending the project.

Each chapter is easy to follow because of clear and straightforward diagrams and text. Altogether, the book provides a very useful methodology for the successful implementation of big data and IoT projects.

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