Data Quality Engineering in Financial Services
Applying Manufacturing Techniques to Data
Paperback Engels 2022 1e druk 9781098136932Samenvatting
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
Specificaties
Lezersrecensies
Inhoudsopgave
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
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
Anderen kochten ook
Diederick van Thiel€ 29,30
Bas van Gils€ 54,45
Nicola Askham€ 44,17
Markus Enhrenmueller-Jensen€ 80,27
Viola Den Hartog€ 25,–
Michel Dekker€ 86,11
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