## 2 Literature Review
### Dimensions of Data Quality for Big Data  
The high _volume_ and _velocity_ properties of Big Data make it more challenging to distinguish between clean and dirty data for further data analysis. Also, due to data coming from multiple sources, there is a need for a more effective method of increasing data quality through machine learning approaches [25]. Other authors argue that the importance of improving data quality for Big Data might not be high, since the amount of incorrect data is deemed to be negligible and hence will not affect the final outcome of data analysis [26]. The amount and impact of the erroneous or 'dirty' data as part of a big dataset is crucial in determining the importance of data quality for Big Data. A better understanding of DQDs that is more relevant for Big Data will support research and industry in building more appropriate data quality tools. [PERSON] et al. [23] posit that the main DQD to be considered for Big Data is _consistency_, which they explain as the capability of information systems to ensure uniformity of datasets when data are being transferred across networks and systems. Their main hypothesis is that the business value of a dataset can only be estimated in its context of use. They further subdivide _consistency_ into three subsequent parts, as discussed below and seen in Figure 1. Additionally, they connected many of the traditional data quality dimensions with the three consistency subdomains, as follows:  
1. _Contextual consistency_ refers to how far big datasets are used within the same domain of interest independently of data format, size, and velocity of the production of data. For the current research, the domain of interest refers to health data. Relevancy, credibility, ease of understanding, accuracy, and confidentiality are key DQDs for this type of consistency. 2. _Temporal consistency_ conveys the idea that data needs to be understood in a consistent time slot such that the same data might not be comparable if it is from another time slot. Time concurrency, availability, and currency are deemed to be essential for temporal consistency. 3. _Operational consistency_ brings in the operational influence of technology on the production and use of data. The sources of data could be more than one in Big Data scenarios; hence, operational consistency is crucial for ensuring the veracity of data. Availability, portability, precision, completeness, and traceability are considered the main connected dimensions here. Table 2 shows a mapping of the 3 Vs of Big Data (volume, variety, velocity) to the 3 Cs (contextual consistency, temporal consistency, operational consistency) of data quality. Other research studies have focused on determining data quality dimensions for Big Data using the IHC research method [8]. However, the context of the research in [8] differs from the current work since the discussions were for Big Data in general, and not specific to the health industry domain. Furthermore, the authors in [8] use a different research hypothesis, focusing on three Big Data 'coordinates', namely: data types, sources, and application domains. They conducted research on specific types of data, such as maps, semi-structured texts, linked open data, sensor and sensor networks, and official statistics. Correlations between DQDs and the Big Data coordinates were reported as: _accuracy_ for maps, _completeness_ of official statistics, _readability_ for semi-structured data, _accessibility_ and _trust_ for linked open data, and _consistency_ for sensor and sensor networks. An IHC was carried out comprising an initial corpus of 1600 papers, related tables, and notes. Keywords as part of the titles and abstracts having a minimum thread of 100 citations for the period of 2005-2014 were used as criteria for sorting. A summary of the literature review results was used to devise their theoretical conceptual framework which detailed the Big Data quality dimensions clustered by the above-cited application areas, ranging from maps to official statistics.