Datenqualität als Basis für eine erfolgreiche Digitalisierung im Controlling

Abstract

The thesis explores the crucial role of data quality in ensuring a successful digital transformation in controlling. While digital technologies such as Business Intelligence, Robotic Process Automation and Artificial Intelligence offer significant opportunities for optimization, their effectiveness depends on the quality of underlying data. Historically grown information technology structures, a lack of standardization and decentralized data maintenance often result in inconsistent and flawed data, undermining reliable analyses and management decisions. Based on literature research, key concepts, technological developments and dimensions of data quality are defined. The thesis highlights that ensuring high data quality requires a combination of strategic, technical and organizational measures. Frameworks like Data Governance, Data Quality Management and Master Data Quality Management provide the foundation for structured data management, while technical approaches such as data cleansing, system integration and automated validation secure data accuracy. Additionally, clear responsibilities and employee training play a crucial role. The study is enriched by two best-practice examples and a case study. DRACOON GmbH demonstrates how centralized data management and well-defined responsibilities can significantly enhance data quality. Nestlé illustrates how simple yet effective measures, such as automated validation rules at the point of entry, can lead to substantial improvements in data quality. The case study of MARK Metallwarenfabrik GmbH provides insights into the risks posed by poor system integration and a lack of data awareness, resulting in significant time requirements and increased costs. The findings emphasize that high-quality data is indispensable for digital transformation in controlling. Companies must strategically integrate data management and implement both technical and organizational measures to fully leverage digital technologies.

Date of Award	2025
Original language	German (Austria)
Supervisor	Melanie Lubinger (Supervisor)