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It has been suggested that this article be merged into Information_quality. (Discuss) Proposed since January 2018.

Information quality (InfoQ) is 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 and on the relationship between them. 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.

There are various statistical methods for increasing InfoQ at the study-design and post-data-collection stages—how are these related to InfoQ?

Kenett and Shmueli (2014) proposed eight dimensions to help assess InfoQ and various methods for increasing InfoQ:

1) Data resolution

2) Data structure

3) Data integration

4) Temporal relevance

5) Chronology of data and goal

6) Generalization

7) Operationalization

8) Communication.

Formalizing the concept of InfoQ increases the value of statistical analysis and data mining, both methodologically and practically

The topic of information quality (InfoQ), as presented in this entry, has been applied in a wide range of domains. There are now publications on such applications in the context of healthcare, customer surveys, data science programs, advanced manufacturing and Bayesian network applications, to name a few. The topic is of special importance in the context of data science programs mostly driven by computer science perspectives. It brings in a complementary perspective that emphasizes a wide application perspective.

A detailed introduction to InfoQ with examples from healthcare, education, official statistics, customer surveys and risk management is available in the book by Kenett and Shmueli, Information Quality: The Potential of Data and Analytics to Generate Knowledge, John Wiley and Sons, 2016.

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