Decision Support Systems Concepts

The video introduces decision support systems, explaining their broad category and four main types: spreadsheets, relational databases, expert systems, and artificial neural networks. It emphasizes that complexity does not always equate to better suitability, and sometimes simpler systems are more effective. The video then focuses on spreadsheets, highlighting the importance of analysis in making data meaningful for decision-making.

What does the term 'monolithic' imply in the context of decision support systems?

• Diverse and varied

• Uniform and undifferentiated

• Complex and sophisticated

• Simple and straightforward

Which of the following is the simplest type of decision support system?

• Spreadsheets

• Artificial neural networks

• Expert systems

• Relational databases

Why are relational databases specified instead of just databases?

• They are two-dimensional like spreadsheets

• They are more complex than spreadsheets

• They are a type of flat file database

• They are simpler than flat file databases

What is a key characteristic of expert systems?

• They are the most sophisticated type

• They are the simplest type of decision support system

• They are more related to people

• They are unrelated to human decision-making

Which type of decision support system is considered the most sophisticated?

• Artificial neural networks

• Expert systems

• Relational databases

• Spreadsheets

What is the primary goal of analysis in decision-making?

• To collect as much data as possible

• To make data meaningful

• To categorize data

• To ignore patterns and trends

Why might a simpler decision support system be chosen over a more sophisticated one?

• It takes longer to develop

• It might perform the task better

• It is always cheaper

• It requires more complex jargon

What is the relationship between complexity and the time it takes to develop decision support systems?

• More complex systems take less time

• Complexity does not affect development time

• All systems take the same time to develop

• More complex systems take more time

What is the significance of identifying patterns and trends in data analysis?

• To make informed decisions

• To make random decisions

• To ignore them

• To complicate the decision-making process

What is a trivial example of using analysis in decision-making?

• Randomly choosing options

• Identifying weather patterns

• Making decisions without analysis

• Ignoring data