## 6 Conclusions and Implications  
This research was set in a multidisciplinary context involving three main fields: data quality (as an integral part of data governance), Big Data, and health informatics. Although data quality has been a well-researched field for the last two decades, there is still a lack of precise nomenclature when it comes to data quality dimensions. Big Data and health informatics are two emerging fields with very little past research focusing on the data quality perspective. The use of DQDs as part of data governance initiatives is arguably very important, as it ensures that data users can extract the maximum value from data use. The aim of the research discussed in this paper was to investigate methodically which DQDs could be more important within the context of Big Data in the health industry. Initial reviews of literature suggested that previous research for this specific field is extremely limited. The inner hermenetic cycle (IHC) was adopted as a research method due to the emerging degree of novelty of Big Data and health informatics, but also due to the impracticality of implementing other research methods because of organizational, geographical, legal, and ethical constraints. The results confirm the popularity of some well-discussed DQDs, such as _Accuracy_, _Completeness_, _and Consistency_. Based on this research, the DQDs of _reliability and timeliness_ were added to the three aforementioned DQDs to make up a list of the five most adequate DQDs for the context of Big Data within the health industry. When mapping the results of the IHC to the hierarchical DQ category framework of [PERSON] and [PERSON] [PERSON] and [PERSON] (2013), the Contextual category of DQDs was considered to be the most important. This could be easily explained by the broadness of the three domains (i.e., Big Data, data quality, and health informatics) involved, where there could be thousands of unique applications of Big Data for the health industry. Thus, for each application, the probability of selecting different DQ dimensions increases. This current research study provides a hierarchy of the most important DQDs in the specific context of Big Data within the healthcare industry. It is one of the first studies within this area to use a systematic method to achieve this. This research study can be further validated by the use of the same research methods but applied in different contexts. Taking into account the very contextual nature of DQ, it would be expected that different contexts would produce a different list of the most important DQDs. The comparatively small volume of literature, and, more specifically, papers denoting a high weight, is one of the main limitations of this research study. Other research methods which could be applicable using the same corpus of literature are being planned, with the main aim of reducing the amount of author bias introduced when considering the weights allocated to papers. This research study will serve as a foundation for further research in the context of Big Data in the health industry to be conducted by the authors. Using the DQDs as features for machine learning algorithms, future work will distinguish quality data from non-quality data from very large streams of health datasets. Also, the application of more enhanced statistical methods, such as least semantic analysis (LSA), for determining the most important DQD dimensions will be used in further research. Further, empirical research methods will be applied with a wider choice of data variety in order to probe deeper into the Representational and Accessibility data quality categories (derived from [PERSON] and [PERSON]'s framework described above) to confirm the findings of this current research. Finally, the five main DQ dimensions determined would serve as a basis to help determine which machine learning algorithms could be more efficient in classifying incorrect data. Following this, an optimal data repair algorithm which will not be computationally expensive will be developed.