Organizational Applications of Business Intelligence Management

Question: Discuss about the Organizational Applications of Business Intelligence Management. Answer: Introduction: MF wants to increase the intelligence density of the market in order to assist them and thereby the investment decisions are undertaken in order to maximize the return on the assets on the investment made for the clients. The business problem that has been identified in this case is presented in two parts which are as follows: The identification of the potential risk is undertaken with the particular investment associated with the particular investment made by the MF and henceforth it can be solved by using the Fuzzy Logic. The second problem depicts the findings regarding the predictions made on the market trends and the optimistic time to buy and sell shares. This problem can be solved by Artificial Neural Network and the Case Base Reasoning. In order to outperform the market, Moira Freeman's investment fund should be looking at tools which can think on more linear cycles. The business intelligence system has been designed for the company should be picking up the complex methodology a market operates. The huge amount of financial data should be picked up from the source from the market. A strategy should be devised to seek help from the past market conditions, then analyzing the present market scenario to successfully predict the future market conditions (Herschel, 2012). This is the first and most complex problem to be solved. Next, the micro and macroeconomic sentiments should be taken into consideration. A market usually reacts on some sentiments. The intelligence system, therefore, should be devised to analyze right from human behavior to short-lived tools to predict the market. The variables which seem non-existent now can disrupt the market tomorrow. So, proper domain knowledge should be implemented for day-to-day analysis and market predictions of stock. The system should be prone to analyzing the pre and post effects of human behavior while and after natural or manmade disasters. It should be updated about wars or other natural disasters because these conditions are important to make or break the market (Herschel, 2013). Last but not the least expert opinion should be taken into consideration. Experts sometimes have not been able to predict the ongoing trend in the market. So a business intelligence system should be working on its analysis of market prediction after analyzing the raw data received from the experts. The business intelligence system should do more accurate and consistent towards the data because the amount of data the system has to calibrate every given day. The system should be able to pick up data from the market source accurately in order to access the market and give a detailed prediction. The scalability of the system is also important because the vast amount of the data the system needs to process every day (Rud, 2009). It has to systematically analyze each and every data to make a market prediction. The business intelligence system devised by MF does not need to provide point predictions, but it should be able to provide decision or examples in front of the expert's calculation every share value. Because the market reacts to different kind of sentiments the market prediction job should be done in tandem by experts and the machine. Also, the system does not have to be real time because then the system will be leaving too many alternatives in front of the experts. MF should let the system evaluate each share against the market because lots of human, economic and natural disasters affect the scenario of the market. Wars or recession affects the market in one way while natural calamities in some other ways (Shan, 2012). So the machine should be designed to formulate according to the day to day socio-political geographic and economic conditions. MF should be using a system which is both accurate and consistent in form and behavior. The market supplies a vast amount of raw data every day. Add to that the huge amount of data in the form of human sentiment and behavior. That is one of the reasons the experts sometimes are not been able to predict the market correctly. The system will give them the research and analysis to choose from. The conceptual framework of the Moira Freemans Business Intelligence System for Financial Market Analysis and Prediction depicts the strategic planning made for the enhancement of the complexity, technological usage and the program management in the business which is used for the appropriate management of the application with programming initiatives (Sonar, 2013). The business creates an appropriate measurement of the business with depicting the inputs and the outputs regarding the people, process, tools, and the technologies. The need of this framework helps in indicating the values that must be depicted with considering the IT architects and the system developers with including the program managers in terms of the inputs and the output processes. It thereby helps Moira Freemans Business Intelligence system to create an interactive framework with depicting the process of appropriate interaction (Wu Zhao, 2014). By the help of this framework, the appropriate market for the Moira Fre emans Business Intelligence System can be gained by MF and thereby the stability can be achieved by MF. The system development team should start with designing a system which can read, analyze and predict the market in a more accurate way. The system should be able to give an alternative to experts on a particular share or security because it becomes difficult for the human mind to evaluate the vast amount of data the world market produces every day. The system should be able to read analyze and evaluate every single data and also human, national or climatic behavior which affects the market every day (Ward, 2010). System designers should be testing the system from day one so that the machine performs its job seamlessly without error. End of the day the system should be designed, the methodology should be applied in a tried and tested principle so that the system performs in predicting the market which can beat the stock exchange. The investment fund is looking for such a system which enables them to get potentially happy and super happy clients every time (Wang Liu, 2006). The design ers should be working in tandem with scientists, mathematicians, and economists in order the create intelligence which can predict or suggest tomorrow's market behavior in a more logical version which produces a more cumulative result. References Herschel, R. (2012).Organizational applications of business intelligence management. Hershey, PA: Business Science Reference. Herschel, R. (2013).Principles and applications of business intelligence research. Hershey, Pa.: IGI Global (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA). Rud, O. (2009).Business intelligence success factors. Hoboken, NJ: Joh Wiley Sons. Shan, C. (2012).Video analytics for business intelligence. Berlin: Springer. Sonar, R. (2013). Towards Automation of Business Intelligence Services Using Hybrid Intelligent System Approach.International Journal Of Business Intelligence Research,4(4), 61-92. Wang, F. Liu, D. (2006).Advances in Computational Intelligence. 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