  This paper focuses on numeric data, with emphasis on distinct characteristics
like varying significance, unstructured format, mass volume and real-time
processing. We propose a novel, context-dependent valuation framework
specifically devised to assess quality in numeric datasets. Our framework uses
eight relevant data quality dimensions, and provide a simple metric to evaluate
dataset quality along each dimension. We argue that the proposed set of
dimensions and corresponding metrics adequately captures the unique quality
antipatterns that are typically associated with numerical data. The
introduction of our framework is part of a wider research effort that aims at
developing an articulated numerical data quality improvement approach for Oil
and Gas exploration and production workflows that is based on artificial
intelligence techniques.
