With evolving requirements and technology, databases hold large amounts of heterogeneous data, collected by different types of sensors, systems, and agents. Even though data quality evaluation research dates back many years, the big data concept has raised many issues in this field. This work presents a new methodology for assessing information quality in a system while also making use of existing frameworks.

The methodology that is proposed in the paper has three phases. The first phase is a divide-and-conquer approach that decomposes a system into smaller modules to locally define the information quality. In the second phase, the authors identify a module’s influence over the information quality of the overall system with a quality transfer function. The third phase estimates the entire information quality of the system by bringing together the locally defined quality of modules and their influence over the system.

The strongest point of this paper is that the solid features the authors identify in the study can help other methodologies arise, and the methodology proposed in the paper is also very comprehensive and mature. Another advantage of the proposed methodology is that it computes both local and overall quality, which should be useful in cases that give users the opportunity to evaluate smaller parts of their data sources if they wish to increase the quality.

The weakest point of this paper is that the authors don’t provide a comparison between their method and other existing methods while claiming that the other methods treat a system as a black box without citing which methods they considered. Also, they don’t consider that some systems may have only one module dealing with data, which cannot be evaluated like the bigger systems. However, these are very minor points that can be disproved or refuted.

Overall, the authors have great insights on the subject matter and this paper should attract significant attention from the research community. The methodology proposed in the paper is very well defined and should be beneficial for all who are involved in this track of research.