  We introduce the idea of Data Readiness Level (DRL) to measure the relative
richness of data to answer specific questions often encountered by data
scientists. We first approach the problem in its full generality explaining its
desired mathematical properties and applications and then we propose and study
two DRL metrics. Specifically, we define DRL as a function of at least four
properties of data: Noisiness, Believability, Relevance, and Coherence. The
information-theoretic based metrics, Cosine Similarity and Document Disparity,
are proposed as indicators of Relevance and Coherence for a piece of data. The
proposed metrics are validated through a text-based experiment using Twitter
data.
