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Revision as of 12:16, 3 March 2018 editCassiopeia (talk | contribs)Autopatrolled, Extended confirmed users, Page movers, New page reviewers, Pending changes reviewers, Rollbackers228,786 edits Nominated page for deletion using Page Curation (speedy deletion-no content)← Previous edit Revision as of 04:41, 15 August 2018 edit undoAnthony Appleyard (talk | contribs)209,150 edits nominated for deletion: see Misplaced Pages:Articles for deletion/Information Quality (InfoQ)Tag: nowiki addedNext edit →
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{{Article for deletion/dated|page=Information Quality (InfoQ)|timestamp=20180815044101|year=2018|month=August|day=15|substed=yes}}
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{{mergeto|Information_quality|discuss=Talk:Information_Quality#Merger proposal|date=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 ] and ], 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) ]
3) Data integration
4) Temporal relevance


5) Chronology of data and goal
the topic of information Quality (InfoQ), as described in the recently deleted Misplaced Pages entry with that name, is gaining further recognition with invited talks and special sessions dedicated to the topic. Can this page by "resurrected"????
6) ]
7) ]
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, , John Wiley and Sons, 2016.
==References==
{{reflist}}
* , Kenett, R.S. and Shmueli, G., John Wiley and Sons, 2016.
* , Shmueli, G. and Kenett, R.S., Proceedings 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.&nbsp;1–13, 2013
* Chapter 1: The Role of Statistical Methods in Modern Industry and Services, in Kenett, R.S. and Zacks, S., , Second Edition, John Wiley and Sons, 2014
* Chapter 1: Risk management: a general view, in Kenett, R.S. and Raanan, Y., , John Wiley and Sons, 2011
* From Data to Information to Knowledge, Kenett, R.S., Six Sigma Forum Magazine, 2008
* , Kenett, R.S. and Salini, S., John Wiley and Sons, 2011
* , Kenett, R.S. and Salini, S., Applied Stochastic Models in Business and Industry, 2011
* , Cugnata, F., Kenett R.S. and Salini S., Procedia Economics and Finance, 2014
* , Kenett, R.S., Quality Engineering, 2015 http://ssrn.com/abstract=2315556
* , Kenett, R.S. and Shmueli, G., Nature Methods, Vol. 12(8), p 699, 2015
* , Dalla Valle L. and Kenett R.S., Quality and Reliability Engineering International, 2015
* , Kenett, R.S. and Shmueli, G., Journal of the Royal Statistical Society, Series A, vol 177 issue 1, pp.&nbsp;3–38, 2014, http://ssrn.com/abstract=2128547
* , Kenett, R.S., Quality Technology and Quantitative Management, 2016
* , Kenett R.S. and Shmueli G., Journal of the International Association for Official Statistics, 2016

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Revision as of 04:41, 15 August 2018

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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.

References

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