Journal article
A framework for effective data collection, usage and maintenance of DSS
Information & management, v 22(5), pp 257-268
1992
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
Metrics
Details
- Title
- A framework for effective data collection, usage and maintenance of DSS
- Creators
- Bay Arinze - Drexel UniversitySnehamay Banerjee - Drexel University
- Publication Details
- Information & management, v 22(5), pp 257-268
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:A1992JE80100001
- Scopus ID
- 2-s2.0-50749135934
- Other Identifier
- 991019319069704721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Information Systems
- Information Science & Library Science
- Management