  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.
