  Data today fuels both the economy and advances in machine learning and AI.
All aspects of decision making, at the personal and enterprise level and in
governments are increasingly data-driven. In this context, however, there are
still some fundamental questions that remain unanswered with respect to data.
\textit{What is meant by data value? How can it be quantified, in a general
sense?}. The "value" of data is not understood quantitatively until it is used
in an application and output is evaluated, and hence currently it is not
possible to assess the value of large amounts of data that companies hold,
categorically. Further, there is overall consensus that good data is important
for any analysis but there is no independent definition of what constitutes
good data.
  In our paper we try to address these gaps in the valuation of data and
present a framework for users who wish to assess the value of data in a
categorical manner. Our approach is to view the data as composed of various
attributes or characteristics, which we refer to as facets, and which in turn
comprise many sub-facets. We define the notion of values that each sub-facet
may take, and provide a seed scoring mechanism for the different values. The
person assessing the data is required to fill in the values of the various
sub-facets that are relevant for the data set under consideration, through a
questionnaire that attempts to list them exhaustively. Based on the scores
assigned for each set of values, the data set can now be quantified in terms of
its properties. This provides a basis for the comparison of the relative merits
of two or more data sets in a structured manner, independent of context. The
presence of context adds additional information that improves the
quantification of the data value.
