  Data exploration and quality analysis is an important yet tedious process in
the AI pipeline. Current practices of data cleaning and data readiness
assessment for machine learning tasks are mostly conducted in an arbitrary
manner which limits their reuse and results in loss of productivity. We
introduce the concept of a Data Readiness Report as an accompanying
documentation to a dataset that allows data consumers to get detailed insights
into the quality of input data. Data characteristics and challenges on various
quality dimensions are identified and documented keeping in mind the principles
of transparency and explainability. The Data Readiness Report also serves as a
record of all data assessment operations including applied transformations.
This provides a detailed lineage for the purpose of data governance and
management. In effect, the report captures and documents the actions taken by
various personas in a data readiness and assessment workflow. Overtime this
becomes a repository of best practices and can potentially drive a
recommendation system for building automated data readiness workflows on the
lines of AutoML [8]. We anticipate that together with the Datasheets [9],
Dataset Nutrition Label [11], FactSheets [1] and Model Cards [15], the Data
Readiness Report makes significant progress towards Data and AI lifecycle
documentation.
