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Decision making is a very crucial activity engaged at any level, by managers in all types of organizations. One of the major examples of data that can be used in managerial decision making processes is called data mining. It is the process of discovering important new patterns, correlation and trends through sifting of large amounts of data, by employing the use of pattern recognition technologies also statistical techniques. Data mining tools are helpful in the formulation of both tactical and strategic decision which can be necessary for organizations to survive in the business. Better understanding of statistic leads to better data interpretation hence better decision can be made.
Regression analysis involves three steps, which are; modeling, predicting, and characterization. The logical order of tackling these three steps such that each task leads to or justifies the other tasks clearly depends on the key objectives. Modeling can be done to get prediction, which will enable better control during business decision making.
Qualitative fore casting incorporates judgmental and subjective factors into forecasting, while quantitative forecasting model attempts to predict the future by employing the use of historical data. Quantitative forecasting tends to capture only seasonal factors at the expense of ignoring trend also assumes data from some periods to be more important than data from other periods. All these will not be very appropriate for managerial needs. In qualitative forecasting judgment is combined at higher levels to reach an overall forecasting which can result to better managerial needs.
Risk analysis involves qualitative and quantitative methods. Quantitative method will develop the level of risk for each hazard, while qualitative methods assume that a loss cannot be expressed as discrete event or monetary value. These can help in anticipating any possible business outcome before any actual performance.
Well organized statistical report will enables users to make effective utilization of the available information, in acquiring a better understanding of the past to predict future through better decision making by managers.