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

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Description

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 casesDetermine data quality tolerances for your data quality specificationChoose the points along the data processing pipeline where data quality should be assessed and measuredApply tailored data governance frameworks within a business or technical function or across an organizationPrecisely align data with applications and data processing pipelinesAnd more Read more

Publisher ‏ : ‎ O'Reilly Media; 1st edition (October 19, 2022)


Publication date ‏ : ‎ October 19, 2022


Language ‏ : ‎ English


File size ‏ : ‎ 5450 KB


Simultaneous device usage ‏ : ‎ Unlimited


Text-to-Speech ‏ : ‎ Enabled


Enhanced typesetting ‏ : ‎ Enabled


X-Ray ‏ : ‎ Not Enabled


Word Wise ‏ : ‎ Enabled


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If you place your order now, the estimated arrival date for this product is: Friday, Jun 6

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Top Amazon Reviews

    • Required reading for investment practitioners
This book remarkably captures the key concepts and methods needed to engineer a robust data ecosystem for an investment management firm. I’ve read it a few times now.
Reviewed in the United States on March 27, 2024 by Matthew Lyberg
    • This book is concise with detailed model examples applying data quality concepts to financial data
The book is easy to understand by applying qualitative metrics to financial data. The examples use real world financial data to demonstrate the engineering concepts used to provide the assessment of data quality. It provides a comprehensive overview of the process as well as providing a framework for Data Governance and Management for the Enterprise. The author does an exemplary job conveying the best practices in modeling methodology and quality management with respect to data analysis. As with other O'Reilly books , the content is succinct and to the point. ... show more
Reviewed in the United States on December 17, 2022 by anne galovich
    • Practice advise for data professionals
Not fluff! This book covers practical ideas for professionals looking to make a difference in their firms.
Reviewed in the United States on December 8, 2022 by Oresa33
    • The book is concise with detailed model examples applying data quality concepts to financial data.
The book is easy to understand, applying qualitative metrics to financial data. The examples use real world financial data to demonstrate the engineering concepts used to provide the assessments of data quality. It provides a comprehensive overview of the process as well as providing a framework for Data Governance and Management for the Enterprise. The book does an exemplary job conveying best practices in modeling, methodology, and quality management with respect to data analysis. As with other Oreilly books, the content is succinct and to the point. ... show more
Reviewed in the United States on December 1, 2022 by DGALO

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