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Model-Driven Decision Support Systems (DSS) and Data-Driven Decision Support Systems are two different approaches used in decision support systems. Here's how they differ:

Model-Driven DSS:

Emphasis on Models: In a model-driven DSS, the focus is on mathematical or analytical models. These models represent the decision-making process and help in evaluating different scenarios. Assumptions and Rules: Model-driven DSS often rely on predefined rules and assumptions about the problem domain. These rules guide decision-making. Predictive: They are more predictive in nature, aiming to forecast outcomes based on the models and assumptions. Examples: Financial planning systems, supply chain optimization, and simulation-based decision support systems fall under this category. Data-Driven DSS:

Emphasis on Data: Data-driven DSS, as the name suggests, prioritize data. They collect and analyze large amounts of data to make informed decisions. Data Mining and Analytics: These systems often use data mining, machine learning, and analytics to discover patterns and insights in the data. Descriptive and Diagnostic: Data-driven DSS are more descriptive and diagnostic in nature. They provide insights into past and current data to inform decisions. Examples: Business intelligence systems, data warehouses, and dashboards are common examples of data-driven DSS. In summary, the main difference lies in their approach: model-driven DSS rely on predefined models and assumptions to make predictions about future scenarios, while data-driven DSS use large volumes of data and advanced analytics to provide insights into historical and current data, aiding in understanding the current state of affairs and making data-informed decisions. The choice between these two approaches depends on the specific needs and objectives of the decision support system.

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In a model-driven DSS, decision-making is based on predefined mathematical or statistical models, where users input data to generate output. In a data-driven DSS, decision-making is based on analyzing large volumes of historical data to identify patterns and trends, without necessarily relying on predefined models.

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Model data driven user interacts primarily with a mathematical model and its results while data driven DSS is user interacts primarily with the data

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Q: What is the difference between model driven and data driven DSS?
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A model-driven DSS relies on mathematical or statistical models to analyze data and make predictions, while a data-driven DSS uses historical and real-time data to generate insights and support decision-making without relying heavily on predefined models. Model-driven DSS are more structured and use algorithms to process data, while data-driven DSS focus on exploring patterns and trends in data to inform decisions.


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