BUSINESS INTELLIGENCE AND BUSINESS DECISIONS
Ans 5: Decision Support System: A decision support system is a way to model data and make quality decisions based upon it. Making the right decision in business is usually based on the quality of data and ability to sift through and analyze the data to find trends in which you can create solutions and strategies for. DSS or decision support systems are usually computer applications along with a human component that can shift through large amounts of data and pick between the many choices. While many people think of decision support systems as a specialized part of a business, most companies have actually integrated this system into their day to day operating activities.
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For instance, many companies constantly download and analyze sales data, budget sheets and forecasts and they update their strategy once they analyze and evaluate the current results. Decision support systems have a definite structure in businesses, but in reality, the data and decisions that are based on it are fluid and constantly changing. The key to decision support systems is to collect data, analyze and shape the data that is collected and then try to make sound decisions or construct strategies from analysis. Whether computers, databases or people are involved usually doesn't matter, however it is this process of taking raw or unstructured data, containing and collecting it and then using it to help aid decision making. Decision support systems that just collect data and organize it effectively are usually called passive models, they do not suggest a specific decision, and they only reveal the data. An active decision support system actually processes data and explicitly shows solutions based upon that data. While there are many systems that are able to be active, many organizations would be hard pressed to put all their faith into a computer model without any human intervention. There are many working theories of DSS; there are many ways to classify DSS. For instance, one of the DSS models available is with the 'relationship of the user in mind. This model takes into consideration active and cooperative DSS models. A cooperative decision support system is when data is collected, analyzed and then is provided to a human component which then can help the system revise or refine it. It means that both a human component and computer component work together to come up with the best solution. The following list of DSS. passive,
(1) Model Driven DSS (2) Communications Driven DSS
(3) Data Driven DSS (4) Document Driven DSS
(5) Knowledge Driven DSS
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Model Driven DSS: A model driven DSS is when decision makers use statistical, simulations or financial models to come up with a solution or strategy. Keep in mind that these decisions are based on models; however they do not have to be overwhelming data intensive. Model-Driven DSS may assist in forecasting product 'demand, aid in employee scheduling, develop proforma financial statements or assist in choosing plant or warehouse locations. All of these systems are Model-Driven DSS. Model-Driven Decision Support Systems (MDSS) provide managers with models and analysis capabilities that can be used during the process of making a decision. The range and scope of this category of DSS is very large. New commercial products are regularly announced, new web-based applications are being developed for established tools, and companies are developing their own proprietary systems. To exploit these opportunities, DSS analysts and managers need to understand analytical tools and modeling. Building some types of models requires considerable expertise. Many specialized books discuss and explain how to implement specific types of models like simulation or linear programming. Companies use both custom and off-the-shelf Model-Driven DSS applications. Mathematical and analytical models are the dominant component in a Model-Driven Decision Support System.
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A typical modeling process begins with identification of a problem and analysis of the requirements of situation. It is advisable to analyze the scope of problem domain and the forces and dynamics of environment. The next step is to identify the variables the model. The identification of variables and the relationships is very important. One should always as using a model is appropriate. If a model is appropriate then one asks what variables and relationships need to specified using an appropriate modeling tool. A solute method or method needs to be chosen. Also, analysts n to specify assumptions and make any needed forecasts Forecasting variables or parameters is sometimes part the construction of an MDSS. Building a MDSS & involves integrating models and other DSS component like data files and data analysis procedures. Model-Driven DSS need to be validated, evaluated and managed. M validation is the process of comparing a model's out with the actual behavior of the phenomenon that has modeled. Validation attempts to answer the ques "Have we built the right model?"
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