Not long time ago specified professionals had to take responsibility for the success of decision making processes, weight all pros and cons of the concepts chosen, and complete a considerable amount of work. It is time to trust such work to machines and be ready to get interesting and, what is more effective, reliable help. Nowadays, huge organizations prefer to use different intelligence systems to achieve success and apply thoughtful ideas within a short period of time (Thierauf, 2001).

An expert system and a conventional business intelligence system are the two systems, which are frequently used by many organizations to find solutions to different problems and organize their work in the most appropriate way. Each of these systems has its own peculiarities, as well as weak and strong points. In brief, the main difference between the conventional business intelligence system and the expert system is that the former focuses on the explanation of “how-type” questions and the latter considers “why-type” questions in addition to “how-type” ones. On one hand, the privilege of the expert system seems to be evident. On the other hand, there are many experts, who still prefer to use a conventional business intelligence system. Therefore, it is necessary to understand the main issues of both models and define their worth.

A conventional business intelligence system is usually used when some extracts or information summaries are given. It monitors business as something usual that cannot be underwent some changes. Unpredictable exceptions in data are not acceptable within the frames of such kind of a system. Though people can get the answers to how-questions and make use of a considerable list of helpful tips, they cannot give clear and appropriate answers to why-questions as the system is limited.

However, it is necessary to underline that a conventional business intelligence system is regarded to be a model system. It may be applied on an everyday basis. People should not spend much time to analyze information but use small units of information and monitor them. There is a number of organizations that try to create a certain order in their work by means of the effectiveness improvement of small units. For example, the U.S. Army makes use of the Ground Soldier System (Vane & Toguchi, 2010). The chosen approach shows how effective the idea of rapid reactions to changing situations can be. Army is the place where strong leaders are developed. There is no place for mistakes or misunderstanding, this is why their experience and preference is aimed to the system where small units are considered should be taken into account. A conventional business intelligence system is a good option for the organizations that have deal with constantly changing environment, still, the changes should be regular in order the system can evaluate them.

The peculiarities of an expert system differ considerably from those of a conventional business intelligence system. First, it is necessary to admit that an expert system depends on the amounts of information that has to be analyzed. Second, there are two possible outcomes of the system’s work: the system may support the decisions made by the experts, or the system denies all the ideas and tries to replace them within a short period of time (Rainer & Watson, 2012). There are also many other benefits of the system under analysis: its reliability, quality and productivity. Still, the costs spend on the accomplishment of the chosen system are considerable indeed.

In comparison to conventional business intelligence systems, expert systems touch upon many aspects of the decision-making process. The experts know that they can reduce the number of errors within the preferred system. Provision of training is one of the most successful benefits of expert systems (Rainer & Watson, 2012). The point is that expert systems may become credible training devices for beginners. In comparison to conventional business intelligence systems, expert systems give explanations of why a decision is made and what consequences of the decision-making process may be. Therefore, the use of expert systems seems to be more effective and promising; though, the costs which are necessary for this intelligence system are also meaningful. Only properly prepared organizations are able to make proper use of this kind of a system.

It is not a new conception to trust the decision-making process to a machine. There are so many steps which have to be taken to evaluate information, weight all aspects, and explain the reasons of why this or that decision is made. The presence of different intelligence systems facilitates the work of organizations and offers a number of interesting ideas. The evaluation of conventional business intelligence systems and expert systems has been made to clarify pros and cons of both systems.

As a result, the following implementations are defined: conventional business intelligence systems are easily used by people with different level of knowledge and practice, and expert systems offer a lot of credible information about the decision-making process that is usually more interesting for novices. Conventional business intelligence systems aim at giving suggestions considering information that is clearly defined. There is no place for some irregular changes or improvements. Expert systems are able to give clear ideas and explain why a particular concept is chosen. Sometimes, more time is necessary to understand how expert systems work and why they work this way. Many organizations prefer to save their time on making decisions; this is why they like to use conventional business intelligence systems as time saving ones. Still, the organizations which take care of quality and effectiveness of their work should think over the priorities set.

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