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

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 will provide provide 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

在金融服務業中，數據質量將決定你的成敗。缺失的價格、錯誤的市場價值、交易違規、客戶績效重申以及不正確的監管申報都可能導致嚴厲的懲罰、失去客戶和財務災難。本實用指南將為金融服務公司的數據分析師、數據科學家和數據從業人員提供框架，將製造業原則應用於金融數據管理，理解數據維度，並在數據水平上工程化精確的數據質量容限，並將其整合到數據處理流程中。

您將獲得寶貴的建議，包括：

- 評估數據維度及其在不同數據類型和使用案例中的應用
- 確定數據質量規範的容限
- 選擇在數據處理流程中應該進行質量評估和測量的點
- 在業務或技術職能或整個組織中應用定制的數據治理框架
- 將數據與應用程序和數據處理流程精確對齊
- 等等

這本書將幫助您建立高質量的金融數據管理流程，以確保您的業務運作順利並避免潛在的風險。