Meeting the Challenges of Data Quality Management

Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly.

The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage.

This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses.

《應對數據質量管理挑戰》概述了數據質量管理的基本概念及其挑戰。本書使數據管理專業人員能夠通過應對數據質量管理的五大挑戰來幫助組織從數據中獲得更多價值：意義挑戰（認識數據如何代表現實）、流程/質量挑戰（通過設計創建高質量數據）、人員挑戰（建立數據素養）、技術挑戰（使組織數據能夠被訪問、使用和保護）、責任挑戰（確保組織領導將數據視為資產）。未能應對這些挑戰的組織從數據中獲得的價值比直接應對這些挑戰的組織要少。

本書描述了核心的數據質量管理能力，並向新老資料質量從業人員介紹了從數據分析、資料質量監控和資料質量報告等活動中獲得價值的實用技術。它將這些思想擴展到大數據環境中的數據質量管理。本書將吸引數據質量和數據管理專業人員，特別是那些涉及數據治理的人員，涵蓋各個行業，以及學術和政府組織。讀者還包括組織層次較高的人員（首席數據官、數據戰略家、分析領導者）和組織中不同部門的人員（財務專業人員、運營經理、IT領導者），他們希望利用數據和組織能力（人員、流程、技術）來創造價值並獲得競爭優勢。

對於計算機科學研究生來說，本書將成為一個重要的參考資料，因為這些計劃通常只專注於數據本身，而數據質量管理往往被忽視。