|Title||On Information Quality|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Kenett, R. S., and G. Shmueli|
|Keywords||data, data mining, Statistical modeling, study design, study goal|
We define the concept of Information Quality (InfoQ) as the potential of a dataset to achieve a specific (scientific or practical) goal using a given empirical analysis method. InfoQ is different from data quality and analysis quality, but is dependent on these components as well as on the relationship between them. We survey statistical methods for increasing InfoQ at the study design and post-data-collection stages, and consider them relative to the more general concept of InfoQ. We propose eight dimensions that help assess InfoQ: 1) Data resolution, 2) Data structure, 3) Data integration, 4) Temporal relevance, 5) Generalizability, 6) Chronology of data and goal, 7) Construct operationalization and 8) Communication. We demonstrate the notion of InfoQ, its components and assessment and its importance through three studies in online auctions research. We suggest that formalizing the concept of Information Quality can help increase the value of statistical analysis and data mining both methodologically and practically.