Systematic review of data integration methods in the context of data preparation for enterprise information systems

In enterprise information systems, data-driven decision-making is a critical success factor across all organizational levels. With the rise of paradigms such as Industry 4.0, big data, and intelligent production systems, the role of data integration within the knowledge discovery in databases process has gained increasing attention. This paper presents a systematic literature review of data integration methods, with a focus on their role in the data preparation phase of the knowledge discovery process. The study categorizes data integration methods across four key task categories: schema mapping, schema matching, data matching, and data fusion. Using a proven literature research method, 800 publications were reviewed, of which 217 were deemed relevant, to identify 484 distinct data integration methods. The study highlights the fragmented terminology and overlapping categorizations prevalent in the field, proposing a task-based framework for classifying data integration methods. This work serves as a reference for beginning researchers and practitioners who want to select appropriate data integration methods for enterprise information systems.