The objective of this article is to explore the potential of text mining for foresight by considering different data sources, text mining approaches, and foresight methods.
Here we review recent data on foresight in animals and conclude that the evidence to suggest episodic memory so far is equivocal.
Contrary to predictions that are based on statistical methods10 the base data of Foresight 2.0 processes are generated by the users for the users.
Thirdly, stronger integration of qualitative and quantitative data in foresight is also called for as different methods have their strengths and weaknesses in different areas [16, 17, 18].
Three factors contribute to the quality of base data in Foresight 2.0 processes: The composition of the participant pool The motivation of the participants The expertise of the participants.
Such research requires enormous foresight, to allow for appropriate data collection, as well as integration with other data systems.
First of all, many foresight methods rely on statistical data to elicit relevant information about the future.
Corporate foresight practitioners should provide quantifiable data in order to document the return-on-investment for foresight activities.
The advantage of such approach was to benefit from two existing sets of data, one about foresight studies in the domain of the sea (prepared by Ifremer) and one about main trends in economics and societies (proposed by Futuribles).
The combination of quantitative and qualitative methods and data in expert foresights supports the assumption that the nature of imagining the future also needs evidence gathered about the past and present.
