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Big Data is an expression used to mean a monstrous volume of both organized and
unstructured information that is so huge; it is hard to process utilizing
conventional database and programming strategies. In most venture situations
the volume of information is too huge or it moves too quickly or it surpasses
current handling limit. Moreover, other challenges of Big Data are capturing,
analyzing, searching, transferring, sharing and updating data. In addition to
this, mathematical, computer science, statistical, artificial intelligence etc.
skills are needed to handle Big Data.

Characteristics of Big Data:

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Often, Big Data is characterized by 4 V’s namely Volume, Variety, Velocity and Veracity.

Volume: Voluminous data can be gathered from number of sources such as business
exchanges, data from sensor or machine to machine, web based etc. Previously,
it was difficult to store such an enormous data, but new innovations like
Hadoop has facilitated the weight.

For e.g. Everyday there are millions
of flights move across the world, data regarding the passengers who board these
flights can be regarded as Big Data, because that data requires large amount of
space if has to be stored on a single machine.

Variety: Data may also exist in a wide variety of file types – from unorganized
information in conventional databases to organized content records, email,
sound, video, monetary transactions etc.

For e.g. Data collected from hospitals
varies according to their departments like data collected from Radiologist will
be in the form of image, data gathered from doctors will be in the form of text
files.

Velocity: Refers to the speed of data processing and how fast
data is coming to us. In other words, Velocity means speed at which big data
must be analyzed. Main objective of big data project analytics is to process,
correlate and analyze the data to get the better result based on inquiry.       

For e.g. Data collected from social
media is an example of velocity, because data can be posted on internet from
different machines simultaneously which leads to increase in volume of data at
fast pace.

Veracity: Refers to abnormality in data, whether the collected
data is meaningful or not, to the problem being analyzed. Collected data’s
quality may vary greatly as a result of which accuracy of analysis may be
compromised.

For e.g. Data collected from social
sites or social media is inherently uncertain.

Big Data is a
combination of all these factors, high-volume, high-variety, high-velocity and
high veracity that acts as the basis for data to be termed as Big Data. 
Big Data platforms and solutions provide the tool and methods used to capture,
store, analyze and search the data to find new correlations and trends that
were previously unavailable.

However, volume,
velocity and variety are the core characteristics of Big Data while veracity
can be regarded as optional or core as per the requirement of problem to be analyzed
using Big Data, because veracity mainly refers to data quality which means more
quality the more value data has. If data collected is relevant to problem to be
analyzed then we can arrive at positive conclusion automatically, at that time
veracity has nothing to do with data, but if data is unorganized and is not
relevant to problem then veracity is of great importance.

Significance of Big Data:

Big Data’s significance does not mean how much data we have, however
what can be done with that data. We can gather information from any source and
investigate it to discover answers that empower cost diminishments, time
decreases and brilliant basic leadership.

By using better strategies and methods related to Big Data, we can
accomplish following tasks related to any organization.

1)          
Determining
main factors behind the failure of a particular product in the market.

2)          
Enhancing
the usability of company’s website.

3)          
Boosting
customer engagement.

4)          
Optimizing
Workforce planning and operations.

5)          
Predicting
market trends and future needs.

6)          
Detecting
fraudulent behaviour before it affects any organization.

7)          
Recalculating
entire risk portfolio in minutes.

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