Data Quality Engineering in Financial Services:

Data Quality Engineering in Financial Services:All Indian Reprints of O'Reilly are Printed in Grayscale. 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

All Indian Reprints of O'Reilly are Printed in Grayscale.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
About the AuthorMr. 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.