Datenqualitätsmanagement aus Prozessperspektive (im Handel): Methoden und Modelle

Abstract

Based on personal professional experience involving repeated encounters with faulty, incomplete, or outdated data, the question arises as to how data quality issues can be systematically avoided or minimized. From a process management perspective, it is evident that data quality should not be viewed solely as a technical matter, but also as an organizational and process-oriented challenge. The aim of this thesis is therefore to provide a structured overview of data quality management (DQM) methods and models and to analyze their applicability in the retail sector from a process-oriented perspective. The thesis is divided into ten chapters. Following the introduction, Chapter 2 presents a detailed examination of the concept of data quality, the underlying quality dimensions, as well as the causes and consequences of poor data quality. Chapters 3 and 4 focus on the principles, objectives, and roles of data quality management, as well as relevant reference models for process-oriented analysis. The main section (Chapters 5 and 6) systematically describes and compares key methods and models, assessing their strengths, weaknesses, and areas of application. Chapter 7 contextualizes these approaches within the retail sector, discussing industry-specific requirements, challenges, and success factors. The final chapters address the limitations of the study and outline directions for future research. The findings indicate that data quality management in retail is particularly effective when methods are purposefully applied along the data life cycle and combined with overarching control models. Key success factors include clearly defined roles and responsibilities, as well as the continuous monitoring and improvement of data quality through standardized processes. While the thesis does not directly answer the central research question, it provides practical recommendations for companies and serves as a conceptual and theoretical foundation that may be complemented by empirical case studies in future work.

Date of Award	2025
Original language	German (Austria)
Supervisor	Christina Feilmayr (Supervisor)