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In this paper, we identify the state of data as being an important reason forfailure in applied Natural Language Processing (NLP) projects. We argue thatthere is a gap between academic research in NLP and its application to problemsoutside academia, and that this gap is rooted in poor mutual understandingbetween academic researchers and their non-academic peers who seek to applyresearch results to their operations. To foster transfer of research resultsfrom academia to non-academic settings, and the corresponding influx ofrequirements back to academia, we propose a method for improving thecommunication between researchers and external stakeholders regarding theaccessibility, validity, and utility of data based on Data Readiness Levels\cite{lawrence2017data}. While still in its infancy, the method has beeniterated on and applied in multiple innovation and research projects carriedout with stakeholders in both the private and public sectors. Finally, weinvite researchers and practitioners to share their experiences, and thuscontributing to a body of work aimed at raising awareness of the importance ofdata readiness for NLP.

We Need to Talk About Data: The Importance of Data Readiness in Natural Language Processing