InfoQ: Difference between revisions
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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}} |
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'''Information quality''' ('''InfoQ''') is the potential of a [[data set]] to achieve a specific (scientific or practical) goal using a given [[Empirical research|empirical analysis method]]. |
'''Information quality''' ('''InfoQ''') is the potential of a [[data set]] to achieve a specific (scientific or practical) goal using a given [[Empirical research|empirical analysis method]]. |
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== Definition == |
== Definition == |
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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 [[Efficiency (statistics)|analysis quality]], but is dependent on these components and on the relationship between them. |
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 [[data quality]] and [[Efficiency (statistics)|analysis quality]], but is dependent on these components and on the relationship between them. |
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InfoQ has been applied in a wide range of domains like healthcare, customer surveys, data science programs, advanced manufacturing and Bayesian network applications. |
InfoQ has been applied in a wide range of domains like healthcare, customer surveys, data science programs, advanced manufacturing and Bayesian network applications. |
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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. |
Kenett and [[Galit Shmueli|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. |
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<ref>{{cite book |
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<ref>{{cite book|author1=Ron S. Kenett|author2=Galit Shmueli|title=Information Quality: The Potential of Data and Analytics to Generate Knowledge|url=https://books.google.com/books?id=nsUqDQAAQBAJ&pg=PP9|date=19 December 2016|publisher=John Wiley & Sons|isbn=978-1-118-87444-8|pages=9–}}</ref> |
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| first1=Ron S. |
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<ref>{{cite journal|last1=Kenett|first1=Ron S.|last2=Shmueli|first2=Galit|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}}</ref> |
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| last1=Kenett |
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<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}}</ref> |
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| first2=Galit |
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| last2=Shmueli |
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| author2-link= Galit Shmueli |
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| title=Information Quality: The Potential of Data and Analytics to Generate Knowledge |
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| date=19 December 2016 |
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| publisher=John Wiley & Sons |
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| pages=9– |
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| isbn=978-1-118-87444-8}}</ref> |
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<ref>{{cite journal |
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| first1=Ron S. |
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| last1=Kenett |
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| first2=Galit |
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| last2=Shmueli |
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| title=On information quality |
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| journal=Journal of the Royal Statistical Society. Series A (Statistics in Society) |
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| volume=177 |
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| issue=1 |
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| year=2014 |
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| pages=3–38 |
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| issn=0964-1998 |
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| doi=10.1111/rssa.12007| s2cid=62901580 |
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| doi-access=free |
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}}</ref> |
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<ref>{{cite journal |
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| last1=Kenett |
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| first1=Ron S. |
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| title=On generating high InfoQ with Bayesian networks |
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| journal=Quality Technology & Quantitative Management |
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| volume=13 |
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| issue=3 |
|||
| year=2016 |
|||
| pages=309–332 |
|||
| issn=1684-3703 |
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| doi=10.1080/16843703.2016.1189182| s2cid=63700188 |
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}}</ref> |
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==References== |
==References== |
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Latest revision as of 21:22, 8 November 2023
 | This article may be too technical for most readers to understand. (September 2018) |
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
[edit]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. [1] [2] [3]
References
[edit]- ^ 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.
- ^ 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.
- ^ 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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