Misplaced Pages

InfoQ: Difference between revisions

Article snapshot taken from Wikipedia with creative commons attribution-sharealike license. Give it a read and then ask your questions in the chat. We can research this topic together.
Browse history interactively← Previous editNext edit →Content deleted Content addedVisualWikitext
Revision as of 07:16, 14 August 2016 editSimakenett (talk | contribs)71 edits added referenced in other entries, expanded the number of categories (which another reviewer has reduced......)← Previous edit Revision as of 19:49, 14 August 2016 edit undoSimakenett (talk | contribs)71 editsm cleaned the entry after making the suggested adjustmentsNext edit →
Line 1: Line 1:

{{multiple issues|
{{dead end|date=August 2016}}
{{no footnotes|date=August 2016}}
{{orphan|date=August 2016}}
}}
'''Information quality (InfoQ)''' is the potential of a dataset to achieve a specific (scientific or practical) goal using a given empirical analysis method. '''Information quality (InfoQ)''' is the potential of a dataset to achieve a specific (scientific or practical) goal using a given empirical analysis method.
Line 52: Line 48:


] ]]]] ] ]]]]

{{improve categories|date=August 2016}}

Revision as of 19:49, 14 August 2016

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

Categories: