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{{Short description|Potential of a data set}}
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{{For|quality of information in a more general sense|Information quality}}
{{Article for deletion/dated|page=Information Quality (InfoQ)|timestamp=20180815044101|year=2018|month=August|day=15|substed=yes}}
{{technical|date=September 2018}}
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⚫ '''Information quality''' ('''InfoQ''') is the potential of a ] to achieve a specific (scientific or practical) goal using a given ].
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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


== Definition ==
5) Chronology of data and goal
Formally, the definition is <code>InfoQ = U(X,f|g)</code> where X is the data, f the analysis method, g the goal and U the utility function. InfoQ is different from ] and ], but is dependent on these components and on the relationship between them.
⚫
6) ]
InfoQ has been applied in a wide range of domains like healthcare, customer surveys, data science programs, advanced manufacturing and Bayesian network applications.

7) ]
Kenett and ] (2014) proposed eight dimensions to help assess InfoQ and various methods for increasing InfoQ: Data resolution, ], Data integration, Temporal relevance, Chronology of data and goal, ], ], Communication.
<ref>{{cite book
8) Communication.
| first1=Ron S.
| last1=Kenett
Formalizing the concept of InfoQ increases the value of statistical analysis and data mining, both methodologically and practically
| first2=Galit
| last2=Shmueli
| author2-link= Galit Shmueli
⚫ | title=Information Quality: The Potential of Data and Analytics to Generate Knowledge
| date=19 December 2016
| publisher=John Wiley & Sons
| pages=9–
| isbn=978-1-118-87444-8}}</ref>
<ref>{{cite journal
| first1=Ron S.
| last1=Kenett
| first2=Galit
| last2=Shmueli
| title=On information quality
| journal=Journal of the Royal Statistical Society. Series A (Statistics in Society)
| volume=177
| issue=1
| year=2014
| pages=3–38
| issn=0964-1998
| doi=10.1111/rssa.12007| s2cid=62901580
| doi-access=free
}}</ref>
<ref>{{cite journal
| last1=Kenett
| first1=Ron S.
| title=On generating high InfoQ with Bayesian networks
| journal=Quality Technology & Quantitative Management
| volume=13
| issue=3
| year=2016
| pages=309–332
| issn=1684-3703
| doi=10.1080/16843703.2016.1189182| s2cid=63700188
}}</ref>


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== ==References==
{{reflist}} {{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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Latest revision as of 21:22, 8 November 2023

Potential of a data set For quality of information in a more general sense, see Information quality.
This article may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts, without removing the technical details. (September 2018) (Learn how and when to remove this message)

Information quality (InfoQ) is the potential of a data set to achieve a specific (scientific or practical) goal using a given empirical analysis method.

Definition

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. InfoQ is different from data quality and analysis quality, but is dependent on these components and on the relationship between them.

InfoQ has been applied in a wide range of domains like healthcare, customer surveys, data science programs, advanced manufacturing and Bayesian network applications.

Kenett and Shmueli (2014) proposed eight dimensions to help assess InfoQ and various methods for increasing InfoQ: Data resolution, Data structure, Data integration, Temporal relevance, Chronology of data and goal, Generalization, Operationalization, Communication.

References

  1. Kenett, Ron S.; Shmueli, Galit (19 December 2016). Information Quality: The Potential of Data and Analytics to Generate Knowledge. John Wiley & Sons. pp. 9–. ISBN 978-1-118-87444-8.
  2. Kenett, Ron S.; Shmueli, Galit (2014). "On information quality". Journal of the Royal Statistical Society. Series A (Statistics in Society). 177 (1): 3–38. doi:10.1111/rssa.12007. ISSN 0964-1998. S2CID 62901580.
  3. Kenett, Ron S. (2016). "On generating high InfoQ with Bayesian networks". Quality Technology & Quantitative Management. 13 (3): 309–332. doi:10.1080/16843703.2016.1189182. ISSN 1684-3703. S2CID 63700188.
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