Data quality will either make you or break you in the financial services industry. Missing prices, wrong market values, trading violations, and incorrect regulatory filings can all lead to harsh penalties, lost clients, and financial disaster. With this practical book, you'll learn how to apply manufacturing principles to financial data management, understand data dimensions, and integrate data quality tolerances into your data processing pipelines. Author Brian Buzzelli teaches you how to define, engineer, and use data validation checks and quality tolerances to deliver high-quality data that meets consumers' data quality specifications. You'll learn how to think like a manufacturer, understand data dimensions, use techniques to define data quality specifications, and assess data quality by applying quantitative tolerances to data dimensions. You'll get invaluable advice to help you: Understand how data dimensions apply to different datatypes and use cases, Apply manufacturing principles to data using DQS, Generate data quality metrics based on standardized data quality measurements, Recognize differences between data quality tolerances for multiple consumers, Determine control points along data pipelines where quality should be measured, Apply data governance frameworks, concepts, policies, and data catalogs