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Analytic models in organizational decision management

Analytic models consist of analytic infrastructures that involve applications, services, utilities, services and systems that are employed for preparing data or information for modeling and other related analytic activities.  These infrastructures can include scoring engines, statistical and data mining, data ware houses and data bases. Analytic models entails a strategy that ensures you are asking the right questions and that the output of the model is always actionable. The actions must have a business impact that is in accordance with corporate strategy. In other words the analytic models ensure that there is an appropriate governance procedure in solving various problems or in decision making process (Timothy, 2005).

Decision making is one of the fundamental aspects of management. Companies that can make appropriate decisions are better placed in marketplaces characterized by stiff competition. Therefore analytic models will be required to ensure that an organization makes appropriate decisions. These models break the uncertainty element in decision making thus making sure that an organization makes informed decisions with the best prediction of the future.  Analytic models solve uncertainties in decision making into probabilities and outperform people in finding patterns in complex data. These models play a critical role especially in making decisions that involve trade-offs because they are characterized by a lot of uncertainty.  There are three main analytic models that can be employed in decision making and in analyzing organizational strategies (Timothy, 2005).

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1. Descriptive models: these are models that only identify relation but do not offer predictions. These models are useful especially in classifying customers into different classes which is vital when setting strategies and targeting treatment.

2. Predictive models: these models can be employed in calculating risk or opportunity related to a given decision in real time. For instance an attrition model is used to determine the probability of a consumer to attrite or churn. These models are usually embedded in operational processes. 

3. Decision models: they are employed in designing more effective rules that guide an organization in decision making. The model maps the relation between the data that is available to a decision that is to be made, the outcome of the decision and the business objective.  This model is significant in balancing multiple objectives and constraints.

It is required that when developing these analytic models, emphasis should be put on developing sophisticated models that will  be feasible in real world and that will ensure fast and better decision making to the frontlines of customer interactions. Therefore application of appropriate analytic model forms one of the fundamental aspect for creating a decision centric-enterprise (Bojke, 2009).

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