The accelerated proliferation of the Internet of Things (IoT) has laid the foundations for the new paradigm of Industry 4.0 and of digital transformations that now arise in organizations. However, these changes have also created challenges related to the management of the large amounts of data; how to process them, store them and convert them into valuable information enabling for effective and efficient decision making.
Currently, the research is in its initial stage; we have reviewed literature on multisensor data fusion, which will provide a complete overview of the methodologies, techniques and recent developments in this field. Then, we examine the data fusion model proposed by Bedworth and O’Brien (2000) called the Omnibus Model, since we will be able to use it in the recognition and extraction of unstructured data patterns, such as those coming from IoT sensors. After applying this technique of extracting patterns with less uncertainty and imprecision, we could establish a predictive model oriented at Industry 4.0 for a multi-sensor industrial environment.