Data quality plays an essential role in decision-making, as the latter may incorporate some risks in different application areas. In the context of industry 4.0, the amount, the versatility, and the speed of information flow for decision-making are important issues. The quality and, in particular, the dependability of data is paramount. This paper investigates the leading data quality characteristics in the industry 4.0 environment with the related issues due to various interactions. It proposes a taxonomy of data sources and flows, from acquisition to the information extraction level for decision-making. The authors highlight the specific issues of error and uncertainty propagation management as significant research challenges for designing intelligent data collection and acquisition systems for industrial manufacturing decision support within the framework of the new generation of industry development. They review data quality characteristics definition and assessment requirements, and suggest classifying these characteristics into four categories. Data quality assessment methods and models with relation to decision-making are also examined. They willfully left aside the investigation of data quality improvement processes for a future more detailed paper.