# Big Data Analytic Framework for Organizational Leverage
## 2 Related Works
### Big Data Characteristics  
Big data are categorized by their diverse characteristics, as indicated by early research [19]. The first three main characteristics are volume, velocity, and variety (3 Vs of big data) [2]. Volume refers to adoption and growth in the size of data that is produced by various sources. The ever-growing data volume is now in zettabytes (a unit of information equal to one sexillion). Due to the sheer scale of big data, they surpass traditionally applied storage and analytical tools [3]. The velocity refers to the rate at which data are generated and the speed required to process them. The Her Majesty's Inspectorate of Constabulary (HMIC) report [20] illustrates how quickly social media information can spread. Further, the variety is from big data collected via various sources that are very varied in nature, such as text and images [15]. Some sources, such as e-transactions generated from e-commerce sites, deal with structured data [6], while server log files may comprise semi-structured data, and social media sites provide unstructured data [15]. Variety stresses diverse sources of data in different formats, data types, and frequencies. It emphasizes the data function for processing the type of data. For example, functions for image processing for image analysis and recognition are different from analyzing human interactions for human data sources. A further five characteristics have been put forth as equally important for big data [15; 21; 22; 23]. These include validity, veracity, virtue, value, and variability. Validity is sometimes referred to as data assurance or trustworthiness of data [23]. This characteristic is based on the accuracy or the tidiness of the processed data. Owing to the sheer big data variety, velocity, and volume, achieving error-free and credible data outcomes is still just a goal, not (yet) a reality [21]. Hence, combinations of precise and imprecise data may exist. For the purpose of providing decision-making data, development of a mechanism that deals with imprecise data is essential when developing a big data analytic framework. A recent study [22] has suggested that social media must augment established sources to showcase a longitudinal extension that informs the designs of existing research instead of acting as a surrogate for them. Functional dependency as a property articulates the vital relationship between two attributes in relational database theory, highlighting constraints between two sets of attributes. With the emergence of approximations in data validity, the property of relaxed functional dependency can identify constraints beneficial to different aspects--for example, capture of data discrepancies or semantically related data patterns [24]. Veracity is another significant big data characteristic that explains the worthiness of data sources in defining their eligibility and value for providing data in important focal areas [23]. It highlights how accurate (or uncertain) the data are from the sources. Some researchers have related veracity to validity or a part of it. Validity in relation to application and context must be reviewed to increase the data value in decision-making. In the comparison of veracity with validity, veracity necessarily includes validity, but the converse may not be true. Virtue, as listed in the same report by [PERSON] et al. [22], relates to the ethics behind using big data in social research. The authors argue that very few social media users were concerned about their information being used by third parties. This aspect is often overlooked due to the informal and less structured characteristic of big data [4], and, while this is considered one of its strengths, it can also pose a data security and privacy problem. In the case of sensitive data being processed, whether for regulatory, security, or a privacy condition, their use may present a serious security breach issue. Thus, safety measure is another factor to consider in the development phase of big data analytics.