With the growth in the use of knowledge graphs (KGs) in a variety of applications, it is more important than ever to understand how such KGs are constructed. A core component of many construction pipelines is entity alignment, the identification of corresponding entities across KGs. A number of embedding-based approaches have been developed in the literature to address this problem. However, these algorithms are generally based on a few benchmark datasets. These benchmark datasets are not reflective of reality in regard to data quality, given that real-world KG data will often include false triples and incorrect alignments. This paper presents an investigation into how embedding-based approaches can be affected by low data quality, and what this might mean for real-world application. We find that the effects of noise can be both dataset and algorithm specific, but that relative performance remains unchanged. We also show that data cleaning can in some cases be far more effective in improving performance than collecting more data.