Information Systems: Big Data Analysis, Techniques & Business

This essay provides an overview of big data, including its characteristics such as volume, variety, and velocity, and its importance in helping businesses make informed decisions. It discusses the challenges associated with managing and analyzing large volumes of data, as well as the techniques used to overcome these challenges, such as A/B testing and machine learning. The essay highlights how big data technology supports businesses by identifying risks, understanding consumer behavior, improving business decisions, and enhancing overall performance. It also touches upon the need for skilled professionals in business analytics and the costs associated with hiring them. The essay concludes by emphasizing the role of big data in enabling better supervision and business operations, providing insights into customer preferences, and driving sales and demand.

History on big Data
The big data becomes difficult to handle as it is a massive
information which helps organisation in collecting large data of
business. In the form of structured and unstructured the data is
which helps in making several choices to benefit organisation.
The challenges of big data is used for presenting big
technology that can help in storing large volume of data. To the
company it provides deep insights and performance but also it
is very complicated.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
To the business the big data is useful information that is spread in
diverse areas regarding different purposes that is collected, organised and
sorted at one place for preparation important summary. The big data help
company in finding what is important what challenges it may face, in
what way it can overcome by using techniques and solutions. The big
data is also useful in having understanding growth related issues in
organisation and secures important data and information of company
Characteristics of Big data
The big data is all about of collecting large information and
storing them which involves the characteristics of volume,
variety, velocity and many more. This helps business to have
proper guidance which assists in knowing how business can
be expanded. The use of big data also helps business to have
idea of customer preferences, demand and requirements and
how it change the market competition
The challenges of big data analytics
In the big data the company has to face various challenges in
which storing of data is main. As the information obtain by
companies is very large that is related to particulars which
becomes impossible for any immediate person who lacks
knowledge to interpret data. It is a situation of troublesome that
is faced by organisation because of less professionals who have
proper knowledge and qualified in business analysts. Along
with this it also incurs high costs to hire these professionals.
References
Dubey, R and et.al., 2018. Big data analytics capability in
supply chain agility: The moderating effect of organizational
flexibility. Management Decision.
How Big Data technology could support
business & Examples
The use of big data technology in organisation will help in
identifying the risk with analysis and evaluation of large data
sets along with consumer behaviours. The data are observed
by gaining lot of information and targeting email campaigns.
The business decisions are improved by sorting useful
quantitative and qualitative data. It also provides
understanding of areas which can increase sales and demand
of products in market. The improved business decision
because of big data will help in enhancing overall work and
performance of business in environment to facilitate better
supervision and business operations.
Techniques that are currently available to analysis big
data
In the big data the company uses several techniques for gathering large
information that becomes difficult to organise and plan. The system of
a/b testing is used for making comparisons from various data and test is
performed. The use of machine learning provides deeper knowledge
regarding technologies which becomes impossible for human analysts to
predict data. It is helpful in data analysis and assumptions are obtained on
the basis of data collection.