The multiple sources, and may have its multiple

The data-driven
decision-making process is widely under recognition now these days, and the
term `Big Data’ is on the top most level for running these kind of data-driven
decision-making process. Big data can be derived from multiple sources, and may
have its multiple formats. It is a high-volume, high-velocity and/or
high-variety information assets that demand cost-effective, innovative forms of
information processing that results insight enhancement, decision-making, and make
the process automatic.

High-performance
analytics lets you do things you never thought about before because the data
volumes were just way too big. For instance, you can get timely insights to
make decisions about fleeting opportunities, get precise answers for
hard-to-solve problems and uncover new growth opportunities – all while using
IT resources more effectively.

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Despite the much lauded
potential, using big data has brought huge challenges in terms of data
acquisition, management, process, storage and analysis. Big data is unstructured,
unlike traditional data in its characteristics of high-volume, high velocity,
high-variety of sources and the requirement to integrate all of it for
analysis. Traditional data management and analytical systems are based on
relational and structured database systems. Those systems are not designed for
the huge volume and heterogeneity of big data.

Technical Institutions are
operating in an increasingly complex and competitive environment. This paper
identifies contemporary challenges being faced in technical institutions worldwide
and explores the potential of Big Data in addressing these challenges. The
paper then outlines a number of opportunities and challenges associated with
the implementation of Big Data in the context of higher education.

The paper concludes by
outlining future directions relating to the development and implementation of
an institutional project on Big Data.

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