Community Question Answering (CQA) systems, such as Yahoo! Answers and Stack Overflow, represent a well-known example of collective intelligence. The existing CQA systems, despite their overall successfulness and popularity, fail to answer a significant amount of questions in required time. One option for scaffolding collaboration in CQA systems is a recommendation of new questions to users who are suitable candidates for providing correct answers (so called question routing). Various methods have been proposed so far to find appropriate answerers, but almost all approaches heavily depend on previous users' activities in a particular CQA system (i.e. QA-data). In our work, we attempt to involve a whole community including users with no or minimal previous activity (e.g. newcomers or lurkers). We proposed a question routing method which analyses users' non-QA data from a CQA system itself as well as from external services and platforms, such as blogs, microblogs or social networking sites, in order to estimate users' interests and expertise early and more precisely. Consequently, we can recommend new questions to a wider part of a community as well as more accurately. Evaluation on a dataset from Stack Exchange platform showed that considering non-QA data leads not only to better recognition of users with low activity as suitable answerers, but also to higher overall precision of the recommendations. It implies that non-QA data can supplement QA data during expertise estimation in question routing and thus also improve a success rate of a questions answering process.