| dc.description.abstract | The term of big data represents datasets that is difficult to collect, manage and analyze using traditional information technologies (IT), software and hardware tools within a tolerable time (Chen et al. 2014). It encodes information about the client‘s behavior, interests and needs. Although big data collection is becoming more and more accessible, 60 percent of big data projects fail (Gartner 2015). This is because the barriers to the adoption of big data. It is important to identify and understand them, because it makes it easier to eliminate the potential risks and succeed in big data analysis. Researching the barriers and risks of big data adoption, three groups: the data, process and management challenges are distinguished (Akerkar 2014; Zicari 2014). Data challenges relate to 3V, 5V and 7V models which are based on big data dimensions that characterize the data itself (e.g. volume, variety, velocity) (Krasnow Waterman, Bruening 2014; Akerkar 2014; Zicari 2014; Sivarajah et al. 2017; Ward, Barker 2013). Process challenges are related to big data processing phases: capturing, integrating and transforming data, selecting the right model for analysis and providing the results (Akerkar 2014; Zicari 2014; Sivarajah et al. 2017). And management challenges – cover privacy, security aspects (Akerkar 2014; Zicari 2014; Sivarajah et al. 2017). To overcome these challenges, new skills and IT infrastructure need to be developed, new management practices or new organizational culture across the organization introduced (Manyika et al. 2011). According to that, detailing the barriers and the risks of big data adoption, the technological, human and organizational barriers are identified (Alharthi et al. 2017; Delgado 2016). Technological barriers are defined as IT infrastructure (Alharthi et al. 2017), which do not help manage substantial amounts of data efficiently (Sivarajah et al. 2017). To eliminate this barrier, to insure the usage of commodity hardware is necessary (Alharthi et al. 2017). Human barriers are related to the person‘s activity, which influences the use of big data. In this aspect, much of the focus on big data has been on the protection and personal privacy issues (Ward, Barker 2013; Agrawal et al. 2012). In order to solve this issue, legal regulation is the base. Laws and standards define the conditions for processing data and prevent human rights violations. From the perspective of big data protection, malware is named as an ever-growing threat to data security (Abawajy et al. 2014). For protection of accumulated data, applying of security controls are needed. It helps to ensure malware prevention. Also, it is important to have qualified employees. Demand for data scientists is already high and only on the rise, yet the number of qualified data scientists available is low (Delgado 2016). Because of that, organizations and academic institutions should collaborate on providing practical training to address missing skills in the field of data analytics and big data (Miller 2014). Organizational barriers are an organizational culture that manifests itself through the values, norms and symbols of the organization. The achievement of successful cultural change requires a clear organizational vision and strategy, which is closely related to the big data. Big data presents new opportunities for organizations. Having these barriers in mind, organization will be in an advantageous position to ensure the right technology is available, that their culture will foster big data, that the skilled employees are working (Delgado 2016). Those businesses who are able to identify big data risks will remain competitive in the increasing data-driven economy. | eng |
