What kind of data does a decision support system primarily analyze?

A decision support system (DSS) is designed to assist in making informed decisions by analyzing data that encompasses both quantitative and qualitative aspects. Quantitative data refers to numerical data that can be measured and analyzed statistically, which is essential for understanding trends, patterns, and performance metrics. On the other hand, qualitative data provides context and insight into human experiences, opinions, and behaviors, offering a richer understanding of the data that numbers alone cannot convey.

The integration of both types of data allows a decision support system to provide comprehensive insights and recommendations. By analyzing quantitative data, a DSS can identify measurable outcomes and forecast potential scenarios. Simultaneously, by incorporating qualitative data, it can capture nuances that numbers may overlook, leading to more effective decision-making.

In contrast, solely focusing on qualitative data would limit the system's analytical capabilities by lacking measurable metrics. Similarly, analyzing only unstructured data would present challenges in interpretation without the context provided by structured quantitative information. Lastly, completely raw data that hasn't been processed does not provide actionable insights because it requires various forms of transformation and analysis to yield useful results. Therefore, the ability of a decision support system to analyze both quantitative and qualitative data is what makes it a valuable tool in the decision-making process.