Compare and contrast the potential and limitations of decision support systems in research.
Decision support system in research saves a lot of time as it reduces the decision cycle to a great extent and hence increases productivity. It is most effective as better decision can be taken as decision makers can now collaborate and communicate easily. It results in cost reduction as labor cost and technical equipment cost is reduced. Data collected from this system of decision making serve as the base for the new employees. In this system of decision making we collect and analyze data to produce valuable information that can ease the process of decision making.
There are few limitations of decision making :
1. Cost : Cost is one of major limitations of decision support system as we have gather and analyze data. Also we need skilled people to analyze and setup the system in place.
2. Approx result : Since it needs all the required data related to a problem so it sometimes becomes very difficult to collect all the needed information.
3.Compromised decision : In a group there is always clash of opinions which may result in taking a compromised decison. Such actions may and may not be the effecient solution for the problem.
4. Subjective decision : Decion support sytem we may end up in making a subjective decision but such decision are not good as they are based on experience and knowledge.
Compare and contrast the potential and limitations of decision support systems in research.
Describe the three primary ethical issues Clinical Decision Support Systems. Compare and contrast the two main types of CDSS .
Discussion Topic #1: Descriptive, Prescriptive and Predictive Analytics Distinguish Business Intelligence from Decision Support Systems. Research and distinguish Business Intelligence from Decision Support Systems. Compare the three analytic styles: Descriptive, Prescriptive, and Predictive Analytics. Share the likeness and uniqueness with each style.
3. Historically, what were the differences between decision support and expert systems? What factors led to the narrowing of those differences? What implication does this have for the model management feature in DSS? 4. How do you know when you have included enough "intelligence" in a decision support system? 5. Compare and contrast symbolic processing and numerical processing. Why is the former referred to as "intelligence"? 6. What factors would lead you to recommend selecting a project that is appropriate...
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