1) Data journalism experienced hype but journalists have little time for failures and cannot rush projects as scientists require thorough testing. 2) Readers may not understand charts and data without context, and bias can be hidden behind scientific claims. 3) Data journalism requires open data, code, processes and peer review in order to build community understanding.