Manufacturing industry data are distributed, heterogeneous and numerous, resulting in different challenges including fast, exhaustive and relevant querying of data. In order to provide an innovative answer to this challenge, the authors consider an information retrieval system based on a graph database. In this paper, the authors focus on determining the key issues to consider in this context. The authors define a three-step methodology using root causes analysis. This methodology is then applied to a data set and queries representative of an industrial use case. As a result, the authors list four main issues: (i) semantic extension of keyword search, (ii) the treatment of syntactic heterogeneity contained in unstructured data, (iii) the results treatment by relevance order and (iv) the detection of relationships between a priori unrelated data. The authors conclude by discussing potential resolutions of these four issues, suggest adapting the methodology used in the paper to evaluate a future proposal, and finally open the possibility of using the results beyond the manufacturing domain.