Data Quality Engineering in Financial Services

Chapter 9. Data Project Methodology

This chapter presents the data project methodology for analytical tasks and artifacts commonly used throughout the lifecycle of data-intensive development projects—from requirements to implementation. Most development projects that support your business will involve implementing new, or modifying existing, data volumes, data structures, and data processing pipelines in architectures and applications that support one or more business functions.

The data project methodology we introduce in this book is not intended to be an exhaustive list of all business- and technology-related tasks and artifacts that may be required by a project development methodology or by your firm. Instead, this methodology primarily focuses on important tasks and artifacts that involve comprehensive data analysis and that support the implementation of data governance objectives throughout the data definition, data integrity, and data management project phases.

Note

Data definition, data quality analysis, implementation of DQS, and data management activities are agnostic to the methodology used in technology development and implementation. Data project methodology tasks and artifacts can be integrated into traditional waterfall methodologies as well as more contemporary Agile and Kanban development frameworks.

The data project methodology illustrated in Figure 9-1 provides a logical progression of tasks and artifacts, beginning with the business use case and processes, ...