Data Quality Management
With the increasing availability of Big Data and their attendant analytics, the importance of data quality management has increased. Poor data quality represents one of the greatest hurdles to effective data analytics, computational linguistics, machine learning, and artificial intelligence. If the data are inaccurate, incomprehensible, or unusable, it does not matter how sophisticated our algorithms and paradigms are, or how intelligent our “machines.”
J. M. Juran provides a definition of data quality that is applicable to current Big Data environments: “Data are of high quality if they are fit for their intended use in operations, decision making, and planning” (Juran and Godfrey 1999, p. 34.9). In this context, quality means that Big Data are relevant to their intended uses and are of sufficient detail and quantity, with a high degree of accuracy and completeness, of known provenance, consistent with their metadata, and presented in appropriate ways.
Big Data provide...