With is omnipresent today, ‘Big Data’ as

With data pervading into every
aspect of our life, the reverence for data in decision-making has become so
foreseeable. While it is omnipresent today, ‘Big Data’ as a concept in itself
is yet nascent. The trend of “Big Data” has become so popular that every other
person or firm talks about it. The cycle of innovations has not only
become more frequent, but along with it the hype associated with certain emerging
terms also keeps alternating now and then. For example, the so-called Hype of
the term “Big Data”.  If not for the
volume, variety, velocity and veracity of the increasingly expanding data, this
has been something that we have been working on for years in the forms of
statistics and information management. Data, essentially the information cannot
be merely labelled in terms of “Big” or “Small”. In fact, Google trends
suggests that this hype of the word “Big Data” is eventually being replaced/
overtaken by terms like “Machine Learning” / “IoT” and the same will keep
happening. Hence, this hype, driven by words is not so important because data
small or big, alone cannot resolve all challenges—even when compiled into astoundingly
large data sets on our most powerful computers. Fundamentally what matters will
be the questions that it can answer and the insights that come along with this
new approach.

 

 

Now the next question is, with the
mammoth of data that we are capable to exploit today, how valuable is it in
giving us business insights? Does bombarding an organization with the plethora
of data indeed bring about growth? As per PwC survey 2016, “62% of respondents
believed that Big Data will help deliver commercial advantage but 58% felt that
moving from data to insight is a major challenge.”  In fact, a
large proportion of emerging start-ups and small firms who have successfully stored
massive amount of data do not have a clear understanding of how to analyse this
resource. We do have so much of data being generated every fraction of
seconds from variety of sources, yet the major portion of this is not utilized
and interpreted correctly. One of the epic example (“article”)
would be the data that suggests perfect anti-correlation between honey bee population
and juvenile
arrests for possession of marijuana in the US between 1990 and 2009, where the relationship
between the data fails to sufficiently hypothesize the direction or rather existence
of any underlying causality.

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In addition to understanding the
significance of all the data that we collect, Big Data evolution requires a
universal shift from traditional databases and analysis paradigm. We still lack
the appropriate investment and talent adequacy in this area. The reality
indicates that unlike the leading global firms, mediocre companies are not
leveraging this data to its best. We know that Big Data has become significantly
important to all the sectors of industry (health care, electronics, social
networking, e-commerce, banking etc.) for deriving benefits and valuable
insights, however, there is a dearth of domain expertise in the Big Data
Analytics. Hence, it is crucial that companies that want to embark on the Big
Data Journey, should device out a clear course of action and collaboration plan
for developing the expertise needed in data science. Building a stronger
foundation can give a good grasp and control over information management, as
well as the risks and compliance policies involved with it, especially when it
comes to sensitive issues data privacy and security.

 

Of course, it is true that Big Data
empowers us with the ability to unravel many new aspects of science and
industrial research spanning over consumer behaviour, demand forecast, diagnostics
patterns etc., but it demands a holistic understanding of core organisational
goals to be drawn at first by standard analyses. Data certainly can be the fuel
to catalyse and supplement our fundamental understanding, however, the
assumption that simply collecting and storing terabytes of data could bypass
the core fundamentals possibly cuts down this very explanatory power of Big
Data. In order to reinforce our knowledge and gain competitive advantage in
data driven business decisions, we still need to address the right questions
and drill deeper into the data. Bringing in interdisciplinary knowledge and intuition
is vital to make sense of data to the fullest and ensure scalability of domain
expertise. Once we take this leap, we can look forward to the conglomeration of
Big Data/Machine Learning/ Internet of Things in a wider framework.

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