The clear audience for this book is the data management professional. Data architects, data modelers, data stewards and data administrators will find this book to be very useful. In addition, students and others who want to enter the ranks of data management professionals will benefit. The book should appeal to a diverse audience beyond the data management professional including: data stewards, managers, business intelligence people, business analysts, educators, researchers and consultants.
The book is clearly written and well illustrated. Editors Mark Mosley, Michael Brackett and Susan Earley have done an exceptional job in producing a book with a consistent style. This is amazing considering the hundreds of people who have contributed their ideas and expertise to this work. A data management hub-and-spoke diagram gives a unifying view to what could be complex and diverse subject. The organization and approach is patterned on other books of knowledge such as the Project Management Book of Knowledge (PMBOK).
Looking Inside
Chapter 1 introduces data and the data management functions. Data is an enterprise asset and provides the basis for information, knowledge, and in the end, fact-based action. The book emphasizes that data has value as an asset only when it is used to produce business value. This part of the data lifecycle includes planning, specifying, enabling, creating and acquiring, maintaining and using, archiving and retrieving, and finally purging. The DAMA-DMBOK Functional Framework consists of data management functions (data governance, data development, etc.) mapped to environmental factors (goals and principles, primary deliverables, technology, etc.).
Chapter 2 explains the functions of data management. The stage is set by describing the definition, missions and goals of data management as well as guiding principles in the conduct of data management. The chapter is organized around the Data Management Context Diagram, which centers on data management functions and includes inputs, suppliers, participants, tools, primary deliverables, consumers, and metrics. Helpful explanations are provided about IT roles and business roles, particularly roles of different types of data stewards.
Chapter 3 describes the concepts, activities and roles needed for successful data governance. In fact, data governance is placed in the center of the DMBOK data functional diagram. Data governance is defined as “the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.” Shared decision-making by business and IT management is critical to data governance success. The accountability for data specifications and data quality of assigned business domains is assigned to the data stewards who are in turn organized into groups such as the data stewardship committees and data governance council. Each role and activity is described.
In chapter 4, data architecture is the data management function that supplies the blueprints for managing data assets that satisfy enterprise requirements. I can relate to this topic because I am an enterprise architect with data architecture responsibilities. Deliverables produced by data architects include: the enterprise data model, the information value chain analysis, database architecture, data integration architecture, business intelligence architecture, document control architecture and metadata architecture. The data architect establishes a common vocabulary that defines critical business entities and their data attributes. These deliverables are mapped to other architectural deliverables such as process definitions, application portfolios and technology platforms through information value chain analysis. This chapter provides a very good explanation of the Zachman Enterprise Framework.
Chapter 5 focuses on data development and explains data design and build approaches. This is where data management meets the IT systems development lifecycle (SDLC). Data specification activities support the SDLC activities of plan, analyze, design solution, and detail design while data enabling activities support the SDLC activities of build, test, prepare and deploy. This chapter provides a good high-level overview of data modeling, which includes notations and approaches.
Chapter 6 is about data operations management and describes concepts and activities related to database support and data technology management. In this chapter, support provided by production database administrators comes to the forefront. It is the role of data operations management to protect the integrity of data, manage data availability and optimize database performance. Protecting the integrity of data includes backup and recovery mechanisms as well as data archiving. Optimizing database performance includes performance turning, monitoring, and error reporting. This is a rich chapter which gave me insights into the physical side of data.
Chapter 7 explains the critical tasks and policies needed to secure data in the modern environment. Performing this function well requires a balance between protecting and securing data on the one hand while enabling user access to needed data on the other hand. This must be based on enterprise goals, requirements, and strategies. Data security management produces data security policies, data privacy and confidentiality standards and well-documented classifications. Execution is needed beyond these policies, standards and classifications. This means managing user profiles, passwords, memberships security permissions and access views, and performing data security audits.
Chapter 8, on reference and master data management, addresses the need to provide a 360-degree view of information about critical business entities such as customers. It describes and illustrates the concepts and activities required for the ongoing reconciliation and maintenance of reference and master data. Reference data consists of domain values such as codes with associated descriptions. Master data is data that describes essential business entities such as customers, products, locations, and financial accounts. MDM seeks to build and make available a “golden record” of the most critical shared data. Activities include identifying requirements, developing data models and documentation and improving data quality. The chapter presents numerous reference data examples as well as descriptions of major MDM data subjects. In addition, data architecture for an MDM data hub is presented.
