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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) Generalizability

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

Information Quality: The Potential of Data and Analytics to Generate Knowledge, Kenett, R.S. and Shmueli, G., John Wiley and Sons, 2016.
An Information Quality (InfoQ) Framework for Ex-Ante and Ex-Post Evaluation of Empirical Studies, Shmueli, G. and Kenett, R.S., Proceeding of the 3rd International Workshop on Intelligent Data Analysis and Management, Kaohsiung, Taiwan, Springer Proceedings in Complexity, Eds. L Uden, L SL Wang, T-P Hong, H-C Yang and I-H Ting, pp. 1–13, 2013
Chapter 1: The Role of Statistical Methods in Modern Industry and Services, in Kenett, R.S. and Zacks, S., Modern Industrial Statistics: with applications in R, MINITAB and JMP, Second Edition, John Wiley and Sons, 2014
Chapter 1: Risk management: a general view, in Kenett, R.S. and Raanan, Y., Operational Risk Management: A Practical Approach to Intelligent Data Analysis, John Wiley and Sons, 2011
From Data to Information to Knowledge, Kenett, R.S., Six Sigma Forum Magazine, 2008
Modern Analysis of Customer Surveys with Applications using R, Kenett, R.S. and Salini, S., John Wiley and Sons, 2011
Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis, Kenett, R.S. and Salini, S., Applied Stochastic Models in Business and Industry, 2011
Bayesian Network Applications to Customer Surveys and InfoQ, Cugnata, F., Kenett R.S. and Salini S., Procedia Economics and Finance, 2014
Statistics: A Life Cycle View, Kenett, R.S., Quality Engineering, 2015 http://ssrn.com/abstract=2315556
Clarifying the terminology that describes scientific reproducibility, Kenett, R.S. and Shmueli, G., Nature Methods, Vol. 12(8), p 699, 2015
Official Statistics Data Integration for Enhanced Information Quality, Dalla Valle L. and Kenett R.S., Quality and Reliability Engineering International, 2015
On Information Quality, Kenett, R.S. and Shmueli, G., Journal of the Royal Statistical Society, Series A, vol 177 issue 1, pp. 3–38, 2014, http://ssrn.com/abstract=2128547
On Generating High InfoQ with Bayesian Networks, Kenett, R.S., Quality Technology and Quantitative Management, 2016
Helping Reviewers Ask the Right Questions: The InfoQ Framework for Reviewing Applied Research, Kenett R.S. and Shmueli G., Journal of the International Association for Official Statistics, 2016

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