Operator training using a real weapon in a real-world environment is risky, expensive, time-consuming, and restricted to the given environment. The simulator, or a virtual simulation, is usually employed to solve these limitations. As the operator is trained to maximize weapon effectiveness, the effectiveness-focused training can be completed. However, the training was completed in limited scenarios without guidelines to optimize the weapon effectiveness for an individual operator, thus the training will not be effective with a bias. For overcoming this problem, we suggest a methodology on guiding effectiveness-focused training of the weapon operator using big data and Virtual and Constructive (VC) simulations. Big data, which includes structured, unstructured, and semi-structured types, are generated by VC simulations under a variety of scenarios. The big data are stored in a Hadoop Distributed File System (HDFS) and analyzed with processing via MapReduce, followed by an analytics tool. We can discover important human factors influencing weapon effectiveness, along with optimal values for these factors, which can be a guideline for effectiveness-focused training of the individual weapon operator.