The quality of data is often defined as "fitness for use", i.e., the ability of a data collection to meet user requirements. The assessment of data quality dimensions should consider the degree to which data satisfy users’ needs. User expectations are clearly related to the selected services and at the same time a service can have different characteristics depending on the type of user that accesses it. The data quality assessment process has to consider both aspects and, consequently, select a suitable evaluation function to obtain a correct interpretation of results. This paper proposes a model that ties the assessment phase to user requirements. Multichannel information systems are considered as an example to show the applicability of the proposed model.

The authors present arguments of how data quality is dependent upon a user's requirements for the data and the selected data access service. For example, a blog article on the Internet could be viewed as satisfying and informative by a general reader but also viewed as poor and relatively uninformative to a academic researcher. The service component relates to different channels in which data can be accessed (i.e. bank account details through an ATM, telephones, websites) and therefore users would have different expectations of the data quality based on their used channel. This service component is not particularly relevant to my research as there is likely to only be one service channel; websites. The proposed quality assessment model generates a minimum level of acceptance for a user based implicit (specified by the user) and explicit (predefined by a user's groups) requirements. Poor quality data (below a user's acceptance level) when transmitted to a user is accompanied with an alert.

Six main data quality dimensions in existing literature are presented and discussed. These dimensions include accuracy, completeness, consistency, timeliness, interpretability and accessibility. However, objective measurement algorithms have only been developed for accuracy, completeness, consistency and timeliness. The authors however state that it is not possible to define an objective measure for interpretability. These objective measures also have three different functional forms, simple ratio, min or max and weighted average which produce different quality assessment results. These algorithms and forms can be reviewed and implemented, if appropriate, into a (or number of) user contribution measurement parameters.

• Multichannel information systems
• Quality assurance
• Dimensions of data quality
• Forms of objective measures of quality - simple ratio, min or max & weighted average
• Web information systems (WIS) design
• Class quality level & subjective quality level
• Data repository & quality (metadata) repository
• DaQuinCIS Project
• Quality factory architecture
• Multichannel adaptive information systems (MAIS) project