In this paper, we identify the state of data as being an important reason for failure in applied Natural Language Processing (NLP) projects. 

We argue that there is a gap between academic research in NLP and its application to problems outside academia, and that this gap is rooted in poor mutual understanding between academic researchers and their non-academic peers who seek to apply research results to their operations. 

To foster transfer of research results from academia to non-academic settings, and the corresponding influx of requirements back to academia, we propose a method for improving the communication between researchers and external stakeholders regarding the accessibility, validity, and utility of data based on Data Readiness Levels \cite{lawrence2017data}. While still in its infancy, the method has been iterated on and applied in multiple innovation and research projects carried out with stakeholders in both the private and public sectors. 

Finally, we invite researchers and practitioners to share their experiences, and thus contributing to a body of work aimed at raising awareness of the importance of data readiness for NLP.