In the last years, the amounts of data have increased considerably and therefore, it is becoming more complex to handle these volumes of information. Measuring the data quality is a pivotal aspect to assess the classifier's discriminatory power as the classifiers accuracy heavily depends on the data used to build the model. Multi-label classification is one specific type of classification problem, which has generated an increasing interest in recent years. However, there are no quality measures for multi-label datasets implemented in cluster computing frameworks to evaluate large datasets. This work aims to implement a measure of data quality for multi-label datasets based on Granular Computing under the Apache Spark framework. As a result, it was possible to calculate the values of the quality measure for the datasets, and even in relatively short times.