###### Abstract  
In the health industry, the use of data (including Big Data) is of growing importance. The term 'Big Data' characterizes data by its _volume_, and also by its _velocity_, _variety_, and _veracity_. Big Data needs to have effective data governance, which includes measures to manage and control the use of data and to enhance data quality, availability, and integrity. The type and description of data quality can be expressed in terms of the dimensions of data quality. Well-known dimensions are _accuracy_, _completeness_, and _consistency_, amongst others. Since data quality depends on how the data is expected to be used, the most important data quality dimensions depend on the context of use and industry needs. There is a lack of current research focusing on data quality dimensions for Big Data within the health industry; this paper, therefore, investigates the most important data quality dimensions for Big Data within this context. An inner hermeneutic cycle research approach was used to review relevant literature related to data quality for big health datasets in a systematic way and to produce a list of the most important data quality dimensions. Based on a hierarchical framework for organizing data quality dimensions, the highest ranked category of dimensions was determined. Big Data; data quality; health data; data quality dimensions +
Footnote †: journal: Medical  
1. Big Data is being used to improve decision making in the healthcare industry by increasing the potential of evidence-based medicine's (EBM's) \"small data\" [4]. EBM is defined as \"the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients\" [5]. Personalized decision support systems (PDSS) are enhancing personalized medicine or evidence-based medicine through big data analytics [6]. 2. Healthcare frauds are very serious issues in many countries. Big Data, with the help of data mining combined with machine learning, can play a major role in fraud detection. Data mining can identify some fraud as soon as it happens and therefore positively increase prevention. 3. Big Data analytics is being applied with the aim of reducing patients' readmission numbers. Patient readmission is not only very expensive for hospitals, but the ratio of patients' dying after readmission is alarmingly high [7]. 4. Big data is proving to be a very useful tool for medical research. As there are many very large medical datasets, such as the human genomic dataset, pharmaceutical companies are harnessing the power of Big Data analytics to discover new medicines and understand diseases. 5. Through the use of Internet of Things (IoT) in healthcare, data is retrieved in a pervasive manner. The data collected through IoT governs the daily life of the patient. Through integrating Big Data and IoT with healthcare, both patients and health facilities cut down costs by reducing the repetition of tests, so they benefit from more accurate diagnoses. Without proper data quality, most of the above use cases of Big Data in the healthcare industry will not be effective. Many authors involved with data quality argue that understanding the dimensions of data quality is the first step leading toward appropriate data quality activities [8; 9; 10; 11]. Data quality dimensions are the means of expressing the notion of data quality; examples of very frequently cited data quality dimensions (DQDs) involved with Big Data are: consistency, accuracy, completeness, and timeliness [12]. In order to undertake any data quality initiative, it is imperative to ascertain the most important DQDs associated within the domain of data usage. The principal goal of this paper is to discuss, analyze, and recommend DQDs suitable in the context of Big Data application in the health industry. Using the well-established hierarchical framework developed by [13], the paper also aims to confirm the importance and validity of each of the four main data quality dimension categories in the context stated above.