As a result of our increased ability to collect data from different sources, many real-world datasets are increasingly becoming multi-featured and these features can also be of different types. Examples of such multi-feature data include different modes of interactions among people (Facebook, Twitter, LinkedIn,..) or traffic accidents associated with diverse factors (speed, light conditions, weather,..). Efficiently modeling and analyzing these complex datasets to obtain actionable knowledge presents several challenges. Traditional approaches, such as using single layer networks (or monoplexes) may not be sufficient or appropriate for modeling and computation scalability. Recently, multiplexes have been proposed for the elegant handling of such data. In this position paper, we elaborate on different types of multiplexes (homogeneous, heterogeneous and hybrid) for modeling different types of data. The benefits of this modeling in terms of ease, understanding, and usage are highlighted. However, this model brings with it a new set of challenges for its analysis. The bulk of the paper discusses these challenges and the advantages of using this approach. With the right tools, both computation and storage can be reduced in addition to accommodating scalability.