Adapting on-line analytical processing for decision modelling: the interaction of information and decision technologies
The introduction of new technologies and concepts has redefined the relative positioning of information systems (IS) and decision technologies in a corporate context. Corporate IS have been extended to include not only transaction processing databases but also analytical databases, often known as Data Warehouses. On-line analytical processing (OLAP), as introduced by Codd et al. [E.F. Codd, S.B Codd, C.T. Salley, Providing On-Line Analytical Processing to UserAnalysts: An IT Mandate, E.F. Codd and Associates, 1993], is capable of capturing the structure of the real world data in the form of multidimensional tables which are known as `datacubes' by management information systems (MIS) and statistical systems specialists. Manipulation and presentation of such information through multidimensional views and graphical displays provide invaluable support for the decision-maker. We illustrate the natural coupling, which exists between data modelling, symbolic modelling and `What if' analysis phases of a decision support system (DSS). In particular, we explore the power of roll-up and drill-down features of OLAP and show how these translate into aggregation and disagreggation of the underlying decision models. Our approach sets out a paradigm for analysing the data, applying DSS tools and progressing through the information value chain to create organisational knowledge.
decision models analytic database olap datacube dss information value chain decision-maker multidimensional databases (dis)aggregation information technology or/ms models forecasting eis interfaces model granularity