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"Information quality" is a measure of the value which the information provides to the user of that information. [1] "Quality" is often perceived as subjective and the quality of information can then vary among users and among uses of the information. Nevertheless, a high degree of quality increases its objectivity or at least the ...
Data quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning".
The Information Quality Act (IQA) or Data Quality Act (DQA), passed through the United States Congress in Section 515 of the Consolidated Appropriations Act, 2001 (Pub. L. 106–554 (text)). Because the Act was a two-sentence rider in a spending bill , it had no name given in the actual legislation.
Information quality management is an information technology (IT) management discipline encompassing elements of quality management, information management and knowledge management. [1] It further encompasses the COBIT information criteria of efficiency, effectiveness, confidentiality, integrity, availability, compliance and reliability.
Indexing and classification methods to assist with information retrieval have a long history dating back to the earliest libraries and collections however systematic evaluation of their effectiveness began in earnest in the 1950s with the rapid expansion in research production across military, government and education and the introduction of computerised catalogues.
Information quality (shortened as InfoQ) is the potential of a dataset to achieve a specific (scientific or practical) goal using a given empirical analysis method.
Once relevance levels have been assigned to the retrieved results, information retrieval performance measures can be used to assess the quality of a retrieval system's output. In contrast to this focus solely on topical relevance, the information science community has emphasized user studies that consider user relevance. [3]
In computer science, garbage in, garbage out (GIGO) is the concept that flawed, biased or poor quality ("garbage") information or input produces a result or output of similar ("garbage") quality. The adage points to the need to improve data quality in, for example, programming. Rubbish in, rubbish out (RIRO) is an alternate wording. [1] [2] [3]