Multicollinearity is a problem that is the main issue in data modeling- overfitting. The reason for this is the presence of correlated features. In most cases, the volume of data available is overwhelming due to numerous features, which makes exploring the data very difficult. The huge quantity of data comes with many disadvantages; for instance, it can increase the time to compute, reduce the storage space, and, perhaps most importantly, cause multicollinearity, which can cause models to be overfitted. 

Dimensionality Reduction aids in reducing processing time and enabling us to execute more complicated algorithms. Another benefit of keeping the data in a smaller size is that it allows storage space. These advantages are, however, all hardware-related. One of the most significant benefits that come from Feature Reduction is that it addresses the issue of multicollinearity. 

This is why Dimensionality Reduction can help make an analysis of information more efficient and more precise. 

The two most commonly used methods with an Unsupervised Setup that can be employed to reduce the information size include Principal Component Analysis and Factor Analysis. 

Principal Component Analysis is where the information from the high-dimensional area is reduced less dimensions. In simple terms, we transform our data into fewer artificial ones without losing a lot of the data. 

Factor Analysis Features are grouped according to their similarities, which their common variance will determine, and then the user can select the most relevant features from these groups. This makes the feature set distinct and less susceptible to multicollinearity.