In the era of big data, integrating heterogeneous data is a critical process where diverse data sources from various structures and forms need to be combined into a centralized data source for further analysis. However, this process often poses data quality problems such as duplication, inconsistencies, missing values, and incomplete and unpleasant data. All these problems are considered waste in heterogeneous data integration. Waste is defined as any activity that consumes resources but creates no value for customers. When dealing with heterogeneous data sources, the presence of waste can be particularly problematic as it can significantly impact the effectiveness and reliability of the integration process and also produce a huge mistake in data analysis. Hence, it is crucial to understand the concept of waste in heterogeneous data integration to determine strategies to enhance data quality through the reduction of waste. Therefore, this paper systematically reviews the literature on waste in the data integration processes. The main steps in conducting this paper include planning, conducting, and reporting the results. The review underscores the importance of strategies and practices for handling waste in data integration. By synthesizing findings from multiple studies, this paper contributes to the broader discourse on data management, notably to support educational administrators, data scientists, and IT professionals in optimizing data management, ultimately promoting more accurate and actionable insights within heterogeneous data integration environments.