Final answer:
Raw data, whether it is quantitative or qualitative, needs to be organized and analyzed to be useful for decision-making. Quantitative data is easier to analyze mathematically, while qualitative data provides descriptive insights. Data visualization is key to interpreting and gaining insights from both types of data.
Step-by-step explanation:
In decision-making, raw data may not be very helpful without proper analysis. For decision-makers, quantitative data, which includes counts and measurements, and qualitative data, which describes attributes that can't be measured, require organization and interpretation to be useful. Raw data needs to be processed to uncover patterns, trends, or correlations which can inform effective decisions. This might involve statistical analysis for quantitative figures or thematic analysis for qualitative insights. Quantitative data, such as the number of correct answers on a quiz or weights of sumo wrestlers, is helpful because it can be easily analyzed mathematically. However, qualitative data, like opinions on educational opportunities or hair color descriptions, can be more challenging to quantitatively analyze but can provide rich, descriptive information that quantitative data cannot. To make sense of any type of data and gain actionable insights, data visualization through graphs, charts, or other plotting methods is often necessary.