History and Techniques of Big Data Analysis

History on big Data
Big data was invented in 2005 by Roger
Mougalas. This person created big data just one
year after the creation of web 2.0. At that time no
name was been given to big data. At that time it
was not easy to reference and manage the data. He
was the person that managed and processed the
data with use of traditional tools and business
intelligence.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
As the name suggest big data is collection of data that is large in
volume and is also growing with time. The data is so large in size
that it is not easy to store it or process something effective out of the
data. It is difficult to manage the data that is so large in volume. It is
seen that the data is so complex, fast and large that the traditional
ways can not find the information out of the data. So the act of
accessing this large volume data and gathering meaningful results
out of such data is known as big data. There are three types of big
data and the discussion related to them is dome underneath:
• Structured: The data that can has a fixed format and is in a
specific style is called structured data. As the computer science
has launched various techniques and tools that work towards
formatting of the data and keep them in a structured format. It is
possible to gather information out of structured big data. As it is
easy to analyse and read the data and draw meaningful results out
of it.
• Unstructured: The data that does not has any structure or is in
unknown format is called as unstructured data. The data is huge
in size and volume and it is not possible to understand or gain
any meaningful insight out of that data is called unstructured big
data. The data is of no use for the organisation if it is not
processed because it is important to draw value out of the data.
• Semi-structured: It is combination of both structured as well as
unstructured data. Herein the organisation is able to draw
understanding at a level.
Characteristics of Big data
Big data is used of high volume data, variety and velocity of
information that is important and it demands innovative, cost,
effective processing of the information to gain the enhance
insights and make effective decision. The characteristics of big
data will help to understand the concept further. They are
discussed below:
• It is the data that is massive in nature. Along with that the
data keeps on growing with time.
• The data is so big in volume that no one is able to process it
or analyse something out of the data, so the processing
techniques are formulated.
References
Oussous, A., Benjelloun, F.Z., Lahcen, A.A. and
Belfkih, S., 2018. Big Data technologies: A
survey. Journal of King Saud University-
Computer and Information Sciences, 30(4),
How Big Data technology could support business &
Examples
As the world is getting digitalised. It is important to consider
that the business organisation are using the technology
updates in their business and using them effectively.
• It helps to analyse the information and make effective
decisions.
• It also helps to understater the customers effectively. That
will help the company to serve the customers effectively.
Techniques that are currently
available to analysis big data
There are various techniques that help to analyse the big
data. The discussion related to those techniques is
mentioned below:
A/B testing: Here in the data is grouped and compared
using variety of tests. As that helps to find the variability.
Data fusion and data integration: It is combination of
techniques that hep to analyse and integrate the data and
find out multiple solutions that increases the potential and
analyses the data is a more accurate way as compared to
that of single source.
The above mentioned are the techniques that are
being used to analyse the data.