Abstract :Big data refers to datasets that are difficult to sense, collect, manage, store, and analyze using conventional information systems. The prevalence of the internet, recent developments in information and communication technologies, and the ubiquity of electronic devices whose numbers are constantly increasing lead to the accumulation of large amounts of data. Big data is seen as the most important strategic resource of the 21st century, like gold and petroleum. Considering workplace settings that are highly dynamic, this situation is observed as a result of the decision-making processes of managers that are carried out based on data rather than intuition. Firms that have understood the importance of data have started to improve their organizational and technological capabilities to obtain value from data for competitive advantage. However, although many firms are aware that big data analytics is a significant resource of competitive advantage, there are some barriers to the complete utilization of opportunities that could be provided by big data. The implementation of big data analytics may be delayed, especially due to behavioral and organizational problems or indecisiveness in understanding potential utility. Research has also emphasized that to improve firm performance through big data analytics, first, a set of obstacles (or challenges) should be overcome. To overcome these obstacles, it is highly important to identify these obstacles and uncover reciprocal interactions between them. This is because the examination of relationships among obstacles will make it easier to determine which obstacle, or obstacles, should have priority. In light of this information, the purpose of this study is to identify obstacles to the implementation of big data analytics, reveal the relationships among these obstacles, and group them based on their driving and dependence power. In light of the literature review and feedback received from experts, fourteen obstacles that make the implementation of big data analytics difficult are identified. A survey that is created accordingly is administered to employees of the sector. In the study, these obstacles are analyzed by using Interpretive Structural Modeling in the first step and the MICMAC method in the second step. In Interpretive Structural Modeling, it is aimed to identify relationships among obstacles. This way, it becomes possible to determine which obstacle, or obstacles, should be in a more strategic position. The MICMAC method is used to categorize obstacles as driver factors, dependent factors, linkage factors, and autonomous factors. Hence, the driving and dependence power of obstacles to the implementation of big data analytics can be determined. The information that is obtained as a result of the analyses will be helpful in the formation of suitable strategies by managers for the effective utilization of big data analytics. Furthermore, the significance of this study is even higher considering that the number of studies investigating the reciprocal interactions of obstacles to the implementation of big data analytics in Turkey is low. Keywords : Big data, big data analytics, interpretive structural modeling, MICMAC method
