Big Data Analysis in Information Systems

Information
Systems and Big
Data Analysis

Table of Contents
Big Data...........................................................................................................................................3
Big data analytics.............................................................................................................................4
Various techniques for analysing big data are as follows................................................................5
Challenges of big data analytics......................................................................................................6
Big Data in business.........................................................................................................................7
References........................................................................................................................................9

Big Data
Big data refers to a field which provide systematic extraction of information from a number
of data sets that are too large and complex to be dealt with traditional ways of data processing. It
is often seen that the data that have a number of cases a rose with great statistical power and high
complexity a leading towards false discovery rate (Kache and Seuring, 2017). Big data helps in
including the challenge of capturing data, storing data, analysing data, sharing data, transferring
data, updating data, visualizing data and soon. Big data can be generally associated with three
different concepts which are volume, variety and velocity. When a big size of data is handled it
is not simple to track all the information regarding the data. In current context the term big data
refers to using of predictive analytics and different behavioral analytics in order to extract value
from certain data and also particularize the size of data set.
The characteristics of big data can be characterized into four components as mentioned below:
Volume: Volume is the first and major characteristic of big data which makes the dataset
big in its size which is to be evaluated. The date upsets are usually stretch to Peter bites and
exabytes. There are huge volumes of powerful data present and powerful processing techniques
are required in order to assess this data. Example can be taken to Facebook which is most
popular platform for social media and there are more than two .2 billion active users who were
spending huge amount of their day posting, commenting and liking on Facebook. Due to this
volume becomes a major characteristic of big data.
Variety: The variety which is present in big data is very high. Examples can be taken of
different email, CRM system, mobile data as well as Google advertisements that are included in
different data sets. These are all the sources which produce different data which are to be
collected, stored as well as analyse successfully. Along with this the data scientist as well as
analyst or not only limiting their job to collecting data from one source but there are a number of
different sources which are providing data. This can be structured, semi structured as well as
unstructured sources. The structured sources of data comprises of the well organised data
whereas the semi structured sources data are the data that are not very well analysed or
structured.On the other hand the unstructured data consist of different images, media post, instant

messages which do not have any internal structure and a disorder. Due to this the variety in big
data is increasing.
Velocity: When considering the amount of data its volume and the variety there is
consistent flow of data. This gives the birth to 3rd characteristic that is velocity (Dey and
Satapathy, 2018). Velocity of data means that more data is available on certain days and due to
this the velocity of data analysis is also required to be high. There are data professionals who
gather data over time and the date at the end of week or a month or quarter or rather hide and that
at other time of days. Due to this velocity is a major characteristic of big data which is to be
analyse successfully.
Veracity: Voracity refers to the accuracy, quality as well as trustworthiness of the data that
is collected. The reliability of data needs to be distinctive in order to make sure that the data
which is collected is accurate and conclusions can be drawn from it. It is often required to
understand the valuable sources of information from which the big data can be analysed
successfully. When the veracity of data is low it is often estimated that bad decisions can be
evaluated and drawn due to the data.
Along with all these mentioned characteristic of fifth characteristic can also be taken that is
value. Value refers to the importance of data that is collected. It is the top in the pyramid of all
the organisations ability that will help the business to successfully use the data and bring out
conclusions from it. The values can be found in real time as well as in industries. There are a
number of government agencies who are using value of data in the current time in order to deal
with different challenges.
Big data analytics
Big data analytics refers to the specialized analytic software in high-powered computing
system which is enabling scientist and analytics to use the volume of structured as well as
unstructured data in order to draw conclusions from it for business benefits. These benefits can
be the revenue opportunities, improving operational efficiency as well as effectiveness in
marketing campaigns in a business.