For me, BIG DATA means money. It may sound so direct, but that is
true. One may ask, how? It means an increase in the organizational
Return on Investment (ROI) if the organization can afford the platform.
Also, when the organization can afford a big data system, it requires
money to maintain the storage and experts required to manage the
platforms. As the trend goes, that data is the new oil, every
organization wants to play in the big data league. Different kinds of
data sources, such as social media (Facebook, Instagram, and so on) are
available for organizations to draw from while making a business
decision. To some big data means exactly what the name suggests,
“Big Data”, while to others it means something different. For example,
“Pope et al. (2014), these are large and complex digital datasets that
typically require nonstandard computational facilities for storage,
management, and analysis”, (as cited by Aytas, 2021). Going by Pope's
definition, big data requires special computation, storage,
management, and analysis. What does this mean, and why will any
organization be interested or invest in gathering such large and
complex digital datasets? The only reason that comes to mind is
money, I mean there is gain collecting, storing, managing, analyzing,
and applying these digital datasets.
Having mentioned that big data require nonstandard computational
facilities for storage, management, and analysis. “Nonstandard” could
mean a special approach such as artificial intelligence (AI). AI requires
large data to learn and perfect the outcome of any analysis. Big data
and AI are mutually related. As AI leverages the avalanche of data
within big data, big data in return work with AI to achieve more. They
both complete each other.
The roles of big data in the industry and the society at large, cannot be
over-emphasized. Industries (organizations) and the entire society
want to improve their business and minimize risk and losses.
Therefore, the analytics of big data helps industries to identify new
opportunities and risks. As they harness these opportunities such as
higher profits and better customer relations, then, on the other hand,
identify and avoid (or minimize) risks such as potential fraud or bad
credits. For the society at large, good analytics of big data will provide
insights, help in policymaking, discover trends, create jobs, minimize
waste, and improve productivity. Using CDC as an example, different
kinds of guidance have been provided for the masses to follow, during
this COVID-19 pandemic.
Presently, the IT industries have been moving on a very fast trend, and
the direction is towards the maximization of data (big data). As a young
person, who planned to still be useful and make an impact in society, I
became interested in the learning of big data techniques. The future
business revolves all-around data and the analysis of data. This has
been my motivation for learning big data techniques.
Hadoop is the platform for big data. Remember how Pope and friends
described big data, as large and complex digital datasets that typically
require nonstandard computational facilities for storage,
management, and analysis. As there was no traditional commercially
available software to process large sets of data, Hadoop became the
tool for processing Big Data, (Aytas, 2021). Hadoop has a special
feature of handling any failure automatically, in hardware that is prone
to fail.
The term big data is more popular than the actual meaning. To many
ordinary people, big data is just too much data and nothing more. I
would love to see clarifications, teachings, and awareness of this great
field of study because the future is highly dependent on data and its
analysis.
References:
Aytas, Y. (July 2021),
Designing Dig Data Platforms: How to Use,
Deploy, and Maintain Big Data Systems
. Retrieved from
https://learning.oreilly.com/library/view/designing-big-
data/9781119690924/
