Data Quality Engineering in Financial Services - Helion

ebook

Autor: Brian Buzzelli
ISBN: 9781098136895
stron: 176, Format: ebook
Data wydania: 2022-10-19
Księgarnia: Helion

Cena książki: 220,15 zł (poprzednio: 255,99 zł)
Oszczędzasz: 14% (-35,84 zł)

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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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Spis treści

Data Quality Engineering in Financial Services eBook -- spis treści

Preface
- My Journey and a Brief History of Data in the Financial Services Industry
- Conventions Used in This Book
- Online Figures
- OReilly 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
    - Example
  - Timeliness
    - Example
  - Accuracy
    - Authoritative data source
    - Example
    - Triangulation
    - Example
  - Precision
    - Example: Exchange rate
    - Example: Market values
  - Conformity
    - Example
  - Congruence
    - Prior value comparison
    - Example
    - Comparison to average
    - Example
    - Comparison to standard deviation and z-score
    - Example
  - Collection
    - Example
  - Cohesion
    - Example
- 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
    - Defining data elements and collections
    - Building the data model
    - Defining the data lifecycle
    - Detailing the data flows
    - Defining the data transformations
    - Defining the data distributions
  - Data Integrity Tasks
    - Defining the DQS
    - Performing data quality assessments
    - Performing data realignment and remediation
    - Defining and implementing data quality controls
    - Measuring and scorecarding data quality
    - Data integrity sensors
    - Ensuring proper access controls
  - Data Management Tasks
    - Defining data owners, stewards, and custodian
    - Defining the responsibility assignment matrix (RACI)
    - Certifying data remediation tools
- 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