  Any large complex data analysis to infer or discover meaningful
information/knowledge involves the following steps (in addition to data
collection, cleaning, preparing the data for analysis such as attribute
elimination): i) Modeling the data -- an approach for modeling and deriving a
data representation for analysis using that approach, ii) translating analysis
objectives into computations on the model generated; this can be as simple as a
single computation (e.g., community detection) or may involve a sequence of
operations (e.g., pair-wise community detection over multiple networks) using
expressions based on the model, iii) computation of the expressions generated
-- efficiency and scalability come into picture here, and iv) drill-down of
results to interpret or understand them clearly. Beyond this, it is also
meaningful to visualize results for easier understanding. Covid-19
visualization dashboard presented in this paper is an example of this.
  This paper covers all of the above steps of data analysis life cycle using a
data representation that is gaining importance for multi-entity, multi-feature
data sets - Multilayer Networks. We use several data sets to establish the
effectiveness of modeling using MLNs and analyze them using the proposed
decoupling approach. For coverage, we use different types of MLNs for modeling,
and community and centrality computations for analysis. The data sets used - US
commercial airlines, IMDb, DBLP, and Covid-19 data set. Our experimental
analyses using the identified steps validate modeling, breadth of objectives
that can be computed, and overall versatility of the life cycle approach.
Correctness of results is verified, where possible, using independently
available ground truth. We demonstrate drill-down that is afforded by this
approach (due to structure and semantics preservation) for a better
understanding and visualization of results.
