## 5 Discussions  
The results of the total of weighted counts per DQD are displayed in Table 5, sorted in descending order:  
As seen in Table 5, it is clear that DQDs such as _accuracy_ and _completeness_ were the most cited, with 58 and 52 total counts, respectively. The authors posited that the dimensions with a total count of more than 10 are the ones which are most important in the context of Big Data within the health industry. Therefore, the most important DQDs are _accuracy_, _completeness_, _consistency_, _reliability_, _and timeliness_. This list of dimensions closely matches the 'information' section of the data quality framework proposed in the context of cloud-based health information systems [32]. Four out of five of the proposed dimensions are included in the 'information' section, with only _reliability_ being excluded. In general, taking into account the discussions from the literature review, it is not surprising to see accuracy as the most important DQD. Accuracy is very often cited in most DQD research, but very often, the meaning of accuracy varies. The results were analyzed within the framework of [13], discussed earlier in Section 3.2. Table 6 below shows the summation of individual DQD counts when grouped within the four categories advocated by the hierarchical framework for organizing DQDs developed by [PERSON] and [PERSON] [13]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & \multicolumn{2}{c}{**Individual Dimensions**} & **Count** \\ \hline \multirow{3}{*}{Intrinsic} & Accuracy, Trust, Plausibility, Precision, Compliance, & \multirow{3}{*}{87} \\  & Traceability, Verifiability, Provenance, Confidence, & \\  & Concordance, Correctness & \\ \hline \multirow{3}{*}{Contextual} & Completeness, Timeliness, Currency, Reliability, Availability, & \multirow{3}{*}{108} \\  & Uniqueness, Relevance, Validity, Expandability, Sensitivity, & \\ \cline{1-1}  & Coverage, Data volume, Comprehensiveness, Heterogeneity & \\ \hline \multirow{3}{*}{Representational} & Consistency, Format, Usefulness, Readability, Capture, & \multirow{3}{*}{48} \\  & Coincidence, Understandable, Usability, Presentation quality & \\ \hline \multirow{3}{*}{Accessibility} & Accessibility, Security, Privacy, Compliance & \multirow{3}{*}{14} \\ \cline{1-1}  & & \\ \cline{1-1} \cline{3-3} \cline{5  
Hence, the following might be concluded for each of the research questions:  
_RQ1: Should the 'Intrinsic' category of DQDs still be applicable in the health industry context?_  
The 'Intrinsic' category ranks second, as per Table 5. This suggests that some DQDs, such as _accuracy_ and _trust_ in data, are applicable in all situations in which data could be used, including that of Big Data for the health industry. However, at the same time, the fact that the 'Intrinsic' DQD category is not the most cited category gives some credit to previous researchers who state that data quality might not be impactful in the context of Big Data [26]. Finally, it also points to the fact that data quality work in the health industry context should not be considered intrinsically, but the importance of DQDs vary according to software applications accessing data and users working with data. _RQ2: How does the breadth of health data use cases impact the Contextual category of DQDs?_  
The Contextual category dimension carries the highest importance. The results of the IHC imply that DQDs in the Contextual category have a higher collective importance for Big Data in the health industry. This might be explained by the fact that there is such a huge variety of categories of data (patient-related, genomic, trauma-based, and others) and many different end-consumers of data (doctors, insurance companies, pharmaceutical groups, and others) such that each specific use of data might uphold different perspectives of quality to suit the \"fitness for use\" definition of data quality. Hence, the conclusion is that the context is extremely important for data quality applications in the specific domains of Big Data within the health industry.