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This is an old revision of this page, as edited by Simakenett (talk | contribs) at 06:21, 20 August 2016. The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

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) [structure]

3) Data integration

4) Temporal relevance

5) [[1]]

6) Chronology of data and goal

7) Operationalization

8) Communication.

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

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.

References

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