Big data technologies are rapidly gaining popularity and become widely used, thus, making the choice of developing methodologies including the approaches for requirements analysis more acute. There is a position that in the context of the Data Warehousing (DW), similar to other Decision Support Systems (DSS) technologies, defining information requirements (IR) can increase the chances of the project to be successful with its goals achieved. This way, it is important to examine this subject in the context of Big data due to the lack of research in the field of Big data requirements analysis. This paper gives an overview and evaluation of the existing methods for requirements analysis in Big data projects. In addition, we explore solutions on how to (semi-) automate requirements engineering phases, and reason about applying Natural Language Processing (NLP) for generating potentially useful and previously unstated information requirements.