Data Quality: Dimensions, Measurement, Strategy, Management and Governance (Book Review)

Abstract

If you only have one slot on your shelf for a book about data quality, this should definitely be your choice. Mahanti, who is one of the associate editors of the Software Quality Professional, has done a masterful job compiling, organizing, and explaining all aspects of data quality. She takes a cross-industry perspective, producing a handbook that is applicable for solving quality challenges associated with any kind of data. There are eight chapters, each covering a critical aspect of data quality. In Chapter 1, the foundation is set by discussing general categories of data and what causes bad data quality. Cost of poor data quality (CPDQ) is introduced as a mechanism for assessing and improving data quality in a quantitative way. Chapter 2 explains different ways to store data, including data models, schemas, relational databases, and data warehouses. Next, Chapter 3 explores 25 different data quality attributes and why they are important to organizations. Chapter 4 extends the previous chapter and explains how to measure each of the attributes. Chapter 5 steps back to start examining the management and leadership aspects of data quality. Data quality strategy is explained in terms of a five-stage maturity model that reflects the Capability Maturity Model for Integration (CMMI). This chapter also explains how a chief data officer might apply these approaches. Chapter 6 examines enterprise-scale data management, including master data management, cleaning, migration, and integration. In Chapter 7, 21 critical success factors are explored by debunking myths (for example, No. 9, “Our Data Are Very Different from Others”), which makes it particularly useful for building the business case for data quality. Finally, Chapter 8 explores how effective governance processes can solidify the gains from data quality management. Throughout the book, examples and stories are emphasized. Explanations supplement most concepts and topics in a way that it is easy to relate your own challenges to the lessons within the book. In short, this is the best data quality book on the market, and will provide immediately actionable guidance for software engineers, development managers, senior leaders, and executives who want to improve their capabilities through data quality.