The amount of open information available on-line from heterogeneous sources anddomains is growing at an extremely fast pace, and constitutes an important knowledge base for the consideration of industries and companies. In this context, two relevant data providers can behighlighted: the "Linked Open Data" (LOD) and "Social Media" (SM) paradigms. The fusion of these data sources - structured the former, and raw data the latter -, along with the informationcontained in structured corporate databases within the organizations themselves, may unveil significant business opportunities and competitive advantage to those who are able to understand andleverage their value. In this paper, we present two complementary use cases, illustrating the potential of using the open data in the business domain. The first represents the creation of an existingand potential customer knowledge base, exploiting social and linked open data based on which any given organization might infer valuable information as a support for decision making. Thesecond focuses on the classification of organizations and enterprises aiming at detecting potential competitors and/or allies via the analysis of the conceptual similarity between their participatedprojects. To this end, a solution based on the synergy of Big Data and semantic technologies will be designed and developed. The first will be used to implement the tasks of collection, data fusionand classification supported by natural language processing (NLP) techniques, whereas the latter will deal with semantic aggregation, persistence, reasoning and information retrieval, as well aswith the triggering of alerts based on the semantized information.