An analogy and framework for effective data collection, usage, and maintenance for decision support systems

The issue of data capture for DSS databases is an important one both for DSS developers and DSS users facing ill-structured problems in noisy and difficult environments. While the available database technologies (e.g. specialized hardware, DBMSs, and query languages) and other DSS-building tools are well advanced, methods for DSS data capture in less tractable environments are lacking. This often serves to limit the effectiveness of the above technologies in DSS development and use. The paper addresses the issue of data collection for DSS and presents a framework and an approach for detecting, preventing, and correcting errors in data collected. The framework employs analogies from the field of data communications to this end, and illustrates the approach using an example from the industrial marketing area