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Support argument: Although big data can be very powerful, human interaction is still an indispensable part in future decision making. For example, as stated by Janssen, van der Voort and Wahyudi (2017), the outcomes of big data analysis should be interpreted by decision-makers, but should not be manipulated only with fancy graphics. According to Raghunathan (1999), the quality of the decision will increase if the decision maker understands the connection between the issues (cited from: Janssen, van der Voort and Wahyudi, 2017). Conversely, if the decision maker does not understand the relationship between the variables, he or she may have difficulty making effective decisions. Interacting with people who process or collect data can make better decisions than people without data. It can also be applied to big data; interaction and communication with other people involved in big data can lead to higher decision quality (Janssen, van der Voort, & Wahyudi, 2017). Hence, although big data can be very powerful, human interaction is still an indispensable part in future decision making. Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345. Raghunathan, S. (1999). Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis. Decision support systems, 26(4), 275-286. Support argument: The big data problem-solving ability is useful in supporting analytical rather than intuitive decision making. For example, big data analytics tools analyze massive amounts of data and can integrate the data that is difficult to manage; these tools can help to derive analytical results and evaluate alternative decision options (Jarrahi, 2018). However, many human decisions are not the direct result of collecting information, but the subconscious intuition. Human interaction can help obtain insight or business intuition with a new product or investment. When ambiguous - like many organizational decisions - or when the organization is faced with no precedent, communication and interaction-based decisions may be more helpful (Jarrahi, 2018). Hence, although big data can be very powerful, human interaction is still an indispensable part in future decision making. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. Positive area of big data in decision making: In terms of the positive area of big data in decision making, big data technology can fast match different heterogeneous information, which can help identify undiscovered information flows. Predictive analysis can be applied at the highest level, which will result in true evidence-based decision making while improving the quality of scenario plan. For example, big data analysis can help decision makers identify areas of poor performance, support resource redistribution, and improve overall performance. (Höchtl, Parycek & Schöllhammer, 2016) Höchtl, J., Parycek, P., & Schöllhammer, R. (2016). Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26(1-2), 147-169.