A framework for effective data collection, usage and maintenance of DSS

The need for proper, reliable, and accurate data for any DSS is universally accepted. However, in real life, developers and users face ill-structured problems in noisy and difficult environments. While a wide variety of hardware and software exists for data storage, communication, and presentation (e.g., specialized hardware, DBMS’s, and query languages), much less effort has gone into developing methodologies for DSS data capture in less tractable decision environments. Insufficient understanding of potential problems with DSS data and of available methods for dealing with these problems will serve to limit the effectiveness of even sophisticated technologies in DSS development and use. This paper addresses the issue of data collection for DSS in noisy environments, and presents a framework for detecting, preventing, and correcting errors in data collected for DSS use. It employs the metaphor of data communications, and uses analogies from that field in constructing the framework. The approach is illustrated using an actual case study from industrial marketing.