  Modern smart distribution system requires storage, transmission and
processing of big data generated by sensors installed in electric meters. On
one hand, this data is essentially required for intelligent decision making by
smart grid but on the other hand storage, transmission and processing of that
huge amount of data is also a challenge. Present approaches to compress this
information have only relied on the traditional matrix decomposition techniques
benefitting from low number of principal components to represent the entire
data. This paper proposes a cascaded data compression technique that blends
three different methods in order to achieve high compression rate for efficient
storage and transmission. In the first and second stages, two lossy data
compression techniques are used, namely Singular Value Decomposition (SVD) and
Normalization; Third stage achieves further compression by using the technique
of Sparsity Encoding (SE) which is a lossless compression technique but only
having appreciable benefits for sparse data sets. Our simulation results show
that the combined use of the 3 techniques achieves data compression ratio to be
15% higher than state of the art SVD for small, sparse datasets and up to 28%
higher in large, non-sparse datasets with acceptable Mean Absolute Error (MAE).
