Data quality will either make you or break you in the financial services industry. Missing prices, wrong market values, trading violations, client performance restatements, and incorrect regulatory filings can all lead to harsh penalties, lost clients, and financial disaster. This practical guide provides data analysts, data scientists, and data practitioners in financial services firms with the framework to apply manufacturing principles to financial data management, understand data dimensions, and engineer precise data quality tolerances at the datum level and integrate them into your data processing pipelines. You'll get invaluable advice on how to: Evaluate data dimensions and how they apply to different data types and use cases - Determine data quality tolerances for your data quality specification - Choose the points along the data processing pipeline where data quality should be assessed and measured - Apply tailored data governance frameworks within a business or technical function or across an organization - Precisely align data with applications and data processing pipelines - And more "This book is a must for any data professional, regardless of industry. Brian has provided a definitive guide on how to best ensure that data processes - from sourcing and ingestion, to firmwide utilization - are properly monitored, measured and controlled. The insights that he illustrates are born out of a long history of working with content and enabling financial professionals to perform their jobs. The principles presented herein are applicable to any organization that needs to build proper and efficient data governance and data management. Finally, here is a tool that can help everyone from Chief Data Officers to data engineers in the performance of their roles." -- Barry S. Raskin, Head of Data Practice, Relevate Data Monetization Corp. "Brian Buzzelli presents a clear how-to guide for the finance professional to motivate, design, and implement a comprehensive data quality framework. Even in early stages, the data quality program will improve efficiency, reduce risk, and build trust with clients and across functions. Brian demonstrates the connection between data integrity and fiduciary obligation with relevant examples. Borrowing unabashedly from concepts in high precision manufacturing, Brian provides a step-by-step plan to engineer an enterprise level data quality program with solutions designed for specific functions. The code examples are especially applicable, providing the reader with a set of practical tools. I believe these concepts are an important contribution to the field." -- Matthew Lyberg, CFA, Quantitative Researcher, NDVR Inc. "This book is an essential reading not only for the data management specialists but for anyone who works with and relies on data. Brian Buzzelli harnesses his many years of practical, "been there, done that, have scars to prove it" experience to teach the reader how to apply manufacturing quality control principles to "find a needle in a haystack" - that one erroneous attribute that will have an outside impact." -- Julia Bardmesser, SVP, Head of Data, Architecture and Salesforce Development, Voya Financial "This is the perfect playbook that, if implemented, will allow any financial services company to put their data on an offensive footing to drive alpha and insights without sacrificing quality, governance, or compliance." -- Michael McCarthy, Principal Investment Data Architect, Investment Data Management Office, MFS "The approach to data quality expressed in this book is based on an original idea of using quality and standardization principles applied from manufacturing. It provides insights into a pragmatic and tested data quality framework that will be useful to any data practitioner." -- Predrag Dizdarevic, Partner, Element22 Mr. Buzzelli is Senior Vice President, Head of Enterprise Data Management for Acadian, a quantitative institutional asset management firm specializing in active global, emerging and frontier investments utilizing sophisticated analytical models and specialized research expertise. Brian has defined a systematic and rigorous approach to data quality engineering through the application of specific tolerances to data dimensions based on manufacturing principles and his expertise developed over 27years of experience. His leadership in implementing data governance, data usage policies, data standards, data quality measurement, data taxonomies, architecture, and meta-data have supported some of the most complex financial business functions at Acadian, Nomura, Thomson Reuters, and Mellon Financial. Data quality engineering, data management, and the application of manufacturing principles to data dimensions and data quality validation is at the center of his professional focus. He is a graduate of Carnegie Mellon University with a Bachelor of Science degree in Information and Decision Systems and holds two masterâ??s degrees: Management o