Toward quality data: an attribute-based approach
Decision Support Systems - Special issue on information technologies and systems
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Communications of the ACM - Supporting community and building social capital
A Framework for Analysis of Data Quality Research
IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Knowledge and Data Engineering
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
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Decision Support Systems
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Decision Support Systems
Design science in information systems research
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ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Artifact types in information systems design science – a literature review
DESRIST'10 Proceedings of the 5th international conference on Global Perspectives on Design Science Research
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We propose a measure of reliability called information volatility (IV) to complement Business Intelligence tools when considering aggregated data or when observing trends. Two types of information volatility are defined: intra-cell and inter-cell. For each, two types of distributions are considered: normal and lognormal, which is often the case for time series data. The IV measures are based on similar measures found in the finance literature, since there are similarities in the types of data. In order to understand the information volatility metrics, the notion of benchmarking is introduced with three propositions: numerical benchmarking, graphical benchmarking and categorical benchmarking. The IV metric is designed and evaluated using the design science research paradigm: first, the metric is developed and then it is evaluated through the use of focus groups (including several cycles for refinement of the design). The paper concludes with our research contributions and future research directions.