Dimensionality reduction means reducing the number of dimensions or variables that are under consideration. Big Data contains a large number of variables. Most of the time, some of these variables are correlated.   So there is always room to select only the major/distinct variables that contribute in a big way to produce the result. Such variables are also called Principal Components.

In most CASES, some features are redundant. We can always reduce the features where we OBSERVE a high correlation.  Dimensionality Reduction technique is also known as 'Low Dimensional Embedding'.

When the number of variables is huge, it becomes difficult to draw inferences from the given data set.  Visualization also becomes too difficult. So, it is always desirable in such situations to reduce the number of features and utilize only the more significant features. Thus the technique of  Dimensionality Reduction helps a lot in such situations by allowing us to reduce the number of dimensions and speed up our analytics. There are several obvious advantages of Dimensionality Reduction such as:

1. REDUCED storage DUE to data compression.
2. Reduced computation time.
3. Removal of redundant features
4. Visualization becomes easier.

Dimensionality Reduction MAY cause some loss of data but the advantages gain is more.