The quality of the information is a key factor, because the success of the decisions made by organizations depends heavily on the quality data on which those decisions are based. However, the organizations often lack the means to be able to assess the quality of their data. Therefore, AQCLab has developed a framework for evaluating and improving the data quality. The assessment and improvement of data quality aims to analyze the quality characteristics of the data stored by an organization, detecting the weaknesses and proposing the improvements necessary to ensure that the data stored have the desired quality. Request information.
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The Data Quality model represents the grounds where the system for assessing the quality of data products is built on. In a Data Quality model, the main Data Quality characteristics that must be taken into account when assessing the properties of the intended data product are established. The Quality of a Data Product may be understood as the degree to which data satisfy the requirements defined by the product-owner organization.
Specifically, those requirements are the ones that are reflected in the Data Quality model through its characteristics Accuracy, Completeness, Consistency, Credibility, Currentness, Accessibility System-Dependent Data Quality : System dependent data quality refers to the degree to which data quality is reached and preserved within a computer system when data is used under specified conditions.
From this point of view data quality depends on the technological domain in which data are used; it is achieved by the capabilities of computer systems' components such as: hardware devices e. The degree to which data has attributes that correctly represent the true value of the intended attribute of a concept or event in a specific context of use. It has two main aspects:. The degree to which subject data associated with an entity has values for all expected attributes and related entity instances in a specific context of use.
Semantic Accuracy : Semantic accuracy is defined as the closeness of the data values to a set of values defined in a domain considered semantically correct.
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