The quality of information generated in data-driven empirical studies is of central importance in Industry 4.0. However, despite the undeniable and widely accepted importance, not sufficient attention has been devoted to its rigorous assessment and analysis. Consequently, if information quality cannot be measured, it also cannot be improved, and therefore current efforts for extracting value from big data empirical studies and data collectors are exposed to the risk of generating limited findings and insights, leading to suboptimal solutions. In this article, we describe and apply a framework for evaluating, analyzing and improving the quality of information generated in empirical studies called InfoQ, in the context of the Chemical Processing Industry (CPI). This systematic framework can be used by anyone involved in data-driven activities, irrespectively of the context and specific goals. The application of InfoQ framework to several case studies is described in detail, to illustrate its practical relevance. (C) 2018 American Institute of Chemical Engineers