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

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

Data Quality Engineering in Financial Services: Applying Manufacturing
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Data Quality Engineering in Financial Services: Applying Manufacturing Techniques to Data Paperback - 2022

by Brian Buzzelli

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Details

  • Title Data Quality Engineering in Financial Services: Applying Manufacturing Techniques to Data
  • Author Brian Buzzelli
  • Binding Paperback
  • Condition New
  • Pages 174
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 2022-11-29
  • Illustrated Yes
  • Features Illustrated, Index
  • Bookseller's Inventory # Conti114111
  • ISBN 9781098136932 / 1098136934
  • Weight 0.65 lbs (0.29 kg)
  • Dimensions 9.1 x 6.8 x 0.6 in (23.11 x 17.27 x 1.52 cm)
  • Category Computers - Data Base Management
  • Library of Congress subjects Financial services industry - Technological, Financial services industry - Data processing
  • Library of Congress Catalogue Number 2023277498
  • Dewey Decimal Code 332.102
  • Quantity available 1

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From the 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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