  Big data, i.e. collecting, storing and processing of data at scale, has
recently been possible due to the arrival of clusters of commodity computers
powered by application-level distributed parallel operating systems like
HDFS/Hadoop/Spark, and such infrastructures have revolutionized data mining at
scale. For data mining project to succeed more consistently, some methodologies
were developed (e.g. CRISP-DM, SEMMA, KDD), but these do not account for (1)
very large scales of processing, (2) dealing with textual (unstructured) data
(i.e. Natural Language Processing (NLP, "text analytics"), and (3)
non-technical considerations (e.g. legal, ethical, project managerial aspects).
  To address these shortcomings, a new methodology, called "Data to Value"
(D2V), is introduced, which is guided by a detailed catalog of questions in
order to avoid a disconnect of big data text analytics project team with the
topic when facing rather abstract box-and-arrow diagrams commonly associated
with methodologies.
