Data Quality Engineering in Financial Services: Applying Manufacturing Techniques to Data

In the financial services industry, data quality is critical, as missing prices, wrong market values, trading violations, client performance restatements, and incorrect regulatory filings can lead to harsh penalties, lost clients, and financial disaster. This practical guide provides data analysts, data scientists, and data practitioners with the framework to apply manufacturing principles to financial data management, understand data dimensions, and engineer precise data quality tolerances at the datum level.

Format: Paperback / softback
Length: 125 pages
Publication date: 04 November 2022
Publisher: O'Reilly Media

In the fiercely competitive financial services industry, data quality holds the key to success or failure. A multitude of factors, including missing prices, incorrect market values, trading violations, client performance restatements, and incorrect regulatory filings, can result in severe penalties, lost clients, and financial disaster. To navigate this complex landscape, data analysts, data scientists, and data practitioners need a comprehensive framework to manage financial data effectively.

This practical guide is designed to empower these professionals with the knowledge and tools to apply manufacturing principles to financial data management. It delves into understanding data dimensions, engineering precise data quality tolerances at the datum level, and seamlessly integrating them into data processing pipelines. By following the guidance provided, you will gain invaluable insights on how to:

• Evaluate data dimensions and their relevance to different data types and use cases.
• Determine data quality tolerances tailored to your specific data quality specification.
• Identify critical points along the data processing pipeline where data quality should be assessed and measured.
• Apply tailored data governance frameworks within business or technical functions, or across organizations.
• Align data with applications and data processing pipelines to ensure seamless integration.
• Implement best practices for data cleansing, validation, and reconciliation to maintain high data quality.
• Utilize advanced analytics techniques, such as machine learning and natural language processing, to enhance data accuracy and reliability.
• Adopt a culture of continuous improvement and data quality monitoring to stay ahead of evolving regulatory requirements and industry trends.

By embracing the principles of manufacturing and applying them to financial data management, you can position your organization for success in the ever-changing financial services landscape. This guide provides the roadmap to achieve data excellence, enabling you to make informed decisions, drive innovation, and maintain a competitive edge.

Weight: 314g
Dimension: 178 x 233 x 16 (mm)
ISBN-13: 9781098136932