Data Quality Engineering in Financial Services: Applying Manufacturing Techniques to Data
- Length: 174 pages
- Edition: 1
- Language: English
- Publisher: O'Reilly Media
- Publication Date: 2022-11-29
- ISBN-10: 1098136934
- ISBN-13: 9781098136932
- Sales Rank: #1572358 (See Top 100 Books)
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
Preface My Journey and a Brief History of Data in the Financial Services Industry Conventions Used in This Book Online Figures O’Reilly Online Learning How to Contact Us Acknowledgments 1. Thinking Like a Manufacturer Operational Efficiency Lessons from Lean Manufacturing Coca-Cola: Excellence in Manufacturing Quality DASANI®: Purifying Water Manufacturing Control Specifications Water Quality Specifications Quality Control and Anomaly Detection Summary 2. The Shape of Data Data as Physical Asset Data Shape Concept Model Data Element Datum Data Universe Time Series Data Cross-Section Data Panel Data Data Volumes Data Dimensions and Attributes Data Attributes Data Dimensions Summary 3. Data Quality Specifications Manufacturing Controls DQS Overview Data Quality Tolerances Completeness Timeliness Accuracy Precision Conformity Congruence Collection Cohesion Summary 4. DQS Model Example Completeness DQS Timeliness DQS Accuracy DQS Precision DQS Conformity DQS Congruence DQS Collection DQS Example Cohesion DQS Example Fit for Purpose Summary 5. Data Quality Metrics and Visualization Data Quality Metrics Data Quality Visualization Summary 6. Operational Efficiency Cost Model Model Details Model Cost Assumptions Pre-Use Data Validations Versus Reconciliation Summary 7. Data Governance Establishing a Data Governance Function Principles of Data Governance Data Governance Function Data Governance Models Creating a Data Governance Program Organizing the Program Establishing the Data Governance Council Engaging the Data Management Function Engaging Business Functions Enhanced Data Governance Operating Model Data Governance Program Activities and Deliverables Data Governance Business Value Data Management Maturity Summary 8. Master Data Management Mastering Data Data Governance Synergies Data Management Synergies Summary 9. Data Project Methodology Business Requirements Defining the Business Use Case Mapping Business Processes and Data Flows Impact Analysis Defining Data Quality Scorecards Data Usage Policies Technology Requirements Defining the Application Data Processing Use Case Mapping Application Functions and Data Flows Data Governance Requirements Data Definition Tasks Data Integrity Tasks Data Management Tasks Summary 10. Enterprise Data Management Where to Begin? Understanding Data Volumes Engineering Data Quality Improving Efficiency Scaling Data Architectures and Pipelines Achieving a Data-Quality-First Culture Making It Happen Index About the Author
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