## 2 Literature Review
### Data Quality Dimensions  
Throughout the literature, there have been different sets of DQDs considered by several authors. A brief discussion of some of the varied DQDs follows below.  
Some authors have investigated how to measure or assess the level of the quality of data. They have argued that some assessments of data quality could be task-independent and, therefore, not restrained by the context of application, while others are task-dependent [20]. Table 1 depicts the main DQDs they thought were worthy of discussion, together with some accepted definitions.  
There have been several other research studies undertaken which cite the above as potential DQDs [2; 21]. _Timeliness_ was investigated in the context of information manufacturing systems [21]. The aim was to assess the trade-off between maximizing _accuracy_ and _completeness_ versus improving  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Dimensions** & **Definitions** \\ \hline Accessibility & Extent to which data is available, or easily and quickly retrievable \\ Appropriate amount of data & Extent to which volume of data is appropriate for the task at hand \\ Believability & Extent to which data is regarded as true and credible \\ Completeness & Extent to which data is not missing and is of sufficient breadth and depth for \\  & the task at hand \\ Consistent representation & Extent to which data is presented in the same format \\  & Ease of manipulation & Extent to which data is easy to manipulate and apply to different tasks \\  & Free-of-error & Extent to which data is correct and reliable \\  & & Extent to which data is in the appropriate languages, symbols, and units, and \\  & the definitions are clear \\  & \\ Objectivity & Extent to which data is unbiased, unprejudiced, and impartial \\  & \\  & \\  & \\  & \\  & \\  & \\  & \\  & \\  & \\  & \\  & \\  & \\ \end{tabular}
\end{table}
Table 1: List of well-cited traditional data quality (DQ) dimensions [20].  
timeliness._ The results show a direct positive relationship between _timeliness_ and _accuracy_ in the sense that data recently created has been found to be more accurate, and that completeness depends upon the input time of data. This type of research demonstrates that the context of data use, in this case, information manufacturing systems, is very important in determining relevant DQ dimensions.  
[PERSON] et al. [2] also discussed the trade-off between _accuracy_ and _timeliness_ while aiming to investigate the dependencies between four different DQ dimensions, namely: _accuracy_, _completeness_, _consistency_, and _timeliness_. The authors created a questionnaire called the ACCTI (accuracy, completeness, consistency and timeliness, improvement process) framework to assess correlations between the four DQDs cited above. After the application of multivariate statistical tests, the use of [PERSON]'s test appeared to imply that the four dimensions denote a high level of dependency based on answers about different information systems. Hence, there seems to be some indication that the above four DQ dimensions are normally very relevant for different types of information systems, even if the research did not involve Big Data.  
However, there seems to be a lack of uniformity and standardization in the terms used to describe DQDs [9]. Some ideas have been expressed by interchangeable terms: for example, some authors have described the idea of _accuracy_ by use of the term _completeness_[22]. In other cases, other terms have been used to describe the same dimension of accuracy. _Accuracy_ has been referred to as _precision_ and also _semantic accuracy_[23]. [PERSON] et al. [24] explained the notion of accuracy as 'true value' in the context of data fusion, which refers to a process of integrating data from different sources together while maintaining a standard of data quality.