With the rapid growth of large amounts of data in different types from different sources, it is possible for the industrial automation sector to transform raw data from production processes into meaningful and useful information for business purpose. The leverage of Big Data Analytics helps the Original Equipment Manufacturing companies gain more insights from their internal organizational data and to get faster and better fact-based decision-making support. However, many OEM companies are still reluctant to adopt Big Data Analytics in their daily business activities due to different concerns. The investigation of Big Data Analytics adoption by OEM is rarely seen in the literature. Therefore, how to adopt Big Data Analytics by OEM companies in the industrial automation sector is studied. During the execution of this study, TOE theory and DOI theory are utilized to address how the different factors from technological, organizational and environmental contexts affecting different Big Data Analytics adoption phases regarding OEM companies. This framework was developed to provide an overview and guidelines for OEM companies to utilize Big Data Analytics. The influential factors and the adoption process framework for Big Data Analytics by OEM companies were evaluated by face-to-face interviews in a qualitative approach. It is found that OEM companies will experience several phases for Big Data Analytics adoption, including Awareness phase, Strategy phase, Knowledge phase, Trial phase, Implementation phase, and Internalization phase. The competitive pressure and marketing effort from the Big Data Analytics service providers will positively affect the Awareness phase. The relative advantage, top management support and competitive pressure and marketing effort will positively affect the Strategy phase. The top management support and marketing effort are the main drivers for the Knowledge Phase when the Data security is the barrier for this phase. In the Trial phase, the relative advantage, compatibility and financial readiness will be the main drives. The Implementation phase will be mainly affected by Data security and Top management support. In the last Internalization phase, only external environmental factor such as competitive pressure will affect the maturation of Big Data Analytics at the organizational level of OEM companies. The influential factors for Big Data Analytics adoption and adoption process framework can be further evaluated through quantitative approach.