Information quality (InfoQ) is the potential of a data set to achieve a specific (scientific or practical) goal using a given empirical analysis method.
Formally, the definition is InfoQ = U(X,f|g) where X is the data, f the analysis method, g the goal and U the utility function. InfoQ is different from data quality and analysis quality, but is dependent on these components and on the relationship between them.
InfoQ has been applied in a wide range of domains like healthcare, customer surveys, data science programs, advanced manufacturing and Bayesian network applications.
Kenett and Shmueli (2014) proposed eight dimensions to help assess InfoQ and various methods for increasing InfoQ: Data resolution, Data structure, Data integration, Temporal relevance, Chronology of data and goal, Generalization, Operationalization, Communication.   
- ^Ron S. Kenett; Galit Shmueli (19 December 2016). Information Quality: The Potential of Data and Analytics to Generate Knowledge. John Wiley & Sons. pp. 9–. ISBN 978-1-118-87444-8.
- ^Kenett, Ron S.; Shmueli, Galit (2014). “On information quality”. Journal of the Royal Statistical Society. Series A (Statistics in Society). 177 (1): 3–38. doi:10.1111/rssa.12007. ISSN 0964-1998.
- ^Kenett, Ron S. (2016). “On generating high InfoQ with Bayesian networks”. Quality Technology & Quantitative Management. 13 (3): 309–332. doi:10.1080/16843703.2016.1189182. ISSN 1684-3703.