Recent headlines predict that artificial intelligence, machine learning, predictive analytics and other aspects of cognitive computing will be the next fundamental drivers of economic growth (Brynjolfsson & McAfee, 2017). We have evidenced several success stories in the recent years, such as those of Google and Facebook, wherein novel business opportunities have evolved based on data-driven business innovations. Our directional poll among companies, however, reveals that at present, only few companies have the keys to successfully harness these possibilities. Even fever companies seem to be successful in running profitable business based on data-driven business innovations. Company’s capability to create data-driven business relates to company’s overall capability to innovate. Therefore, this research builds a conceptual model of barriers to data-driven business innovations and proposes that a deeper understanding of innovation barriers can assist companies in becoming closer to the possibilities that data-driven business innovations can enable. As Hadjimanolis (2003) suggests, the first step in overcoming innovation barriers is to understand such barriers. Consequently, we identify technology-related, organizational, environmental and people-related i.e. attitudinal barriers and examine how these relate to company’s capability to create data-driven business innovations. Specifically, technology-related barriers may originate from the company’s existing practices and predominant technological standards. Organizational barriers reflect the company’s inability to integrate new patterns of behavior into the established routines and practices (Sheth & Ram, 1987). Environmental barriers refer to various types of hampering factors that are external to a company. Environmental barriers are caused by the company’s external environment and thus company has relatively limited possibilities to influence and overcome such factors. Attitudinal barriers are people-related perceptual barriers that can be studied at the individual level, and if necessary, separately for managers and employees (Hadjimanolis, 2003). Future research will pursue to build an empirical model to examine how these different barriers are related to company’s capability to create business based on data-driven innovations.