Vertical integration, also known as make-or-buy, defines whether activities are conducted by company or provided by external parties. There are different models to support decision making for vertical integration in the literature. However, they ignore the uncertainty aspect of vertical integration. As a strategic decision, vertical integration is multidimensional and less frequent. This study contributes a new data-driven model that includes all these characteristics of vertical integration decisions. In this study, a methodology is suggested that benefits from the models in the literature and assesses the results with data obtained from real IT cases. Different methodologies were followed to reach a model that accurately predicts make-or-buy decisions in IT projects at a retail company. Firstly, three different knowledge-based generic models derived from the literature were applied to predict decisions for twenty-one different make-or-buy cases in IT. The highest accuracy rate reached among these knowledge-based models was 76%. Secondly, the same cases were also analyzed with Naive Bayes using factors originally introduced by these generic models. The Naive Bayes algorithm can represent the uncertainty inherent in the decision model. The highest accuracy rate obtained was 67%. Thirdly, a new data-driven model based on Naive Bayes using IT-related factors was proposed for the decision problem of vertical integration. The data-driven model correctly classified 86% of the decisions.