In the light of the fourth industrial revolution, referred to as Industry 4.0, big data is becoming the key resource of modern companies. In order to safely harvest the potential benefits of the Industry 4.0, a high level of data quality is essential. Yet, modern companies experience poor data quality levels, mainly in human generated datasets. To improve human generated data quality, the IT usage behaviour should be studied. This research investigated the influence of team-technology acceptance and satisfaction on human generated data quality. The research was conducted at Royal Vopak and studied the data from the Decision Support System: INFOR. The study followed a multimethod comparative field study design, in which interviews combined with a literature review provided input and practical validation for a survey and a data quality assessment. The Structural Equation Modelling (SEM) regression was conducted to investigate the relation between TAM variables and data quality of the Decision Support System (DSS). Results confirmed that the perceptions of riskiness, ambiguity had effect on data quality levels. Also, evidence for the influence of perceived usefulness and perceived ease-of-use on overall satisfaction was found. Future research should use these results in developing training and development programs to improve team-technology interaction and data quality levels.