Data Quality Engineering in Financial Services Applying Manufacturing Techniques to Data
Data Quality Engineering in Financial Services Applying Manufacturing Techniques to Data by Brian Buzzelli, published by O’Reilly in 2022, is a practical guide designed for data analysts, data scientists, and data practitioners within financial services. This 174-page edition addresses the critical importance of data quality in the financial sector, highlighting the potential consequences of issues such as missing prices and incorrect regulatory filings. The book offers a framework for applying manufacturing principles to financial data management, focusing on understanding data dimensions and engineering precise data quality tolerances.
Readers will find valuable insights on evaluating data dimensions relevant to various data types and use cases, determining data quality tolerances, and identifying key points in the data processing pipeline for quality assessment. The guide also discusses the implementation of tailored data governance frameworks, ensuring alignment between data and applications. With its focus on quality control and data analytics, this book serves as a resource for enhancing data management practices in the financial services industry.
Official synopsis Publisher
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
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