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Data quality directly influences decisions on all management levels of enterprises (Redman, 1998). Especially on tactical and strategic levels decision processes are heavily dependent on the quality of underlying data (Price & Shanks, 2005). Thus, data quality plays an important role in a wide variety of information systems applications. From a data consumer point of view it is defined as fitness for use (Wang & Strong, 1996). This definition emphasizes the role of the data consumer who decides whether data is of an appropriate quality for usage in a given context.
Literature provides evidence that supplying decision makers with information regarding the quality of available data positively influences decisions (Chengular-Smith, Ballou, & Pazer, 1999; Even, Shankaranarayanan, & Watts, 2006; Shankaranarayanan, Ziad, & Wang, 2003; Shankaranarayanan & Cai, 2006). According to the definition of Wang and Strong (1996) the context of the decision maker has to be taken into account when measuring data quality and generating information about data quality. Current literature does not consider the context in an adequate manner. Especially the division of a decision processes into four phases (Simon, 1977) and the distinction of three management levels strategic planning, management control and operational control (Anthony, 1965) on which decisions have to be made are not reflected by existing approaches. The combination of the context dimension A: decision process phase and context dimension B: management level determines the overall context in which a decision maker finds himself (Figure 1). As each of the decision process phases and management levels come along with different information needs (Gorry & Scott-Morton, 1971) consequently each overall context requires special information, too. In conjunction with the definition of Wang and Strong (1996) data quality possibly defers in relation to a given combination of both context dimensions. The decision maker as data consumer has to decide on the fitness for use of the underlying data with regard to the overall context he is faced with and the resulting information need.
Figure 1.
Context-dimensions of a decision
This paper proposes a framework for the selection of adequate dimensions of data quality for different overall contexts a decision maker may be faced with. It is intended to be used to support decision makers with context-sensitive information regarding data quality. Thus we serve the claim for differentiated needs for data quality information in specific decision-situations with regard to the context dimensions management level and decision process phase. This will serve the overall goal to improve decision outcomes.
Our research methodology is based on the approaches of Rossi and Sein (2003) and Hevner, March, Park, and Ram (2004). Currently there is no approach for context-sensitive data quality assessment regarding decision making phase and management level (need). Thus we design a framework (artifact) and describe the application with a fictitious example. We claim that decision processes on each management level and in each decision process phase can be enhanced when applying our framework (hypothesis 1) and that different combinations of management level and decision making phase come along with different requirements regarding data quality (hypothesis 2).