If you were asked to select the most profound
technologies that have impacted human existence and continue to do so, any such
selection would be incomplete without a mention of Big Data.
Big Data is big not only in terms the data output it
throws up, which is so large that traditional computer systems cannot handle
it; but also in terms of the sheer magnitude of the ways by which this data can
be put to use.
Big Data assumes
extreme significance in the context of the tech world we live in today, where
there are mountain loads of data from possibly every source. Every object has
the potential to throw up data, which could consist of three V’s-variety,
velocity and volumes.
These data in themselves may not be of much
value unless they are harnessed. Once this is done, it becomes meaningful
enough to make sense and enable decision-making. This is exactly what Big Data
enables. This in a nutshell is what the whole idea of Big Data is.
When did Big
Data begin?
Big Data has a history that in a
sense stretches to the 1960’s and 1970’s, when it could be said to have begun in
its infantile stages. However, it was much later, only in about the middle of
the 2000’s, that Big Data came to be structured as a subject with the huge
loads of data that social media companies such as Facebook or online services
such as YouTube threw up.
-
Structured, meaning they are already in a state of organized form,
which enables deployment for analysis straightaway;
-
Semi-structured, which means that they are not fully formed into a
database, but can be processed further;
-
Unstructured data, which consists of data that is not traditionally
classifiable as being organized. It is in this area that Big Data has the most
efficient role to play, as it helps organizations to make sense of this kind of
even highly unstructured data, which could contain valuable insights when
tapped into rightly.
Challenges
in Big Data
Big Data is being hailed as one of the technologies
that can transform our way of life for the future generations, because it has
uses in unimaginably wide arenas of activities. Banking, education,
infrastructure, governance, and law and order are only a handful of areas that
can undergo complete change with the adaption of
Big Data.
Yet, this said, there are a few formidable challenges in adapting
Big Data. Let us examine a couple of these:
Data is increasing multifold, which means
that even a technology that is as resilient and robust as Big Data has to
constantly be on its toes in upgrading its capabilities. This is because as of
now, data volumes have a capacity to double every couple of years, and this is
a huge scale of multiplication. Big Data systems have to be constantly
increased and more importantly, made more efficient in collecting data and then
making use of it.
This means that a lot of resources are
required to clean and sift data that gets generated at such a humongous rate. A
good part of any organization’s resources get consumed on this activity, which
although inevitable, is a drain on resources. Organizations have to discover
newer technologies that make this happen more efficiently.
Please feel free to let us know what you
think of this blog! Interested in making a career in Big Data? Then, take a
look at Simpliv’s
courses,
which will help you take your next steps in this profession.
