Big Data Analysis and Its Application in Business

This presentation discusses the characteristics of big data, challenges faced by organizations while using big data technology, and techniques available to analyze big data to support business. It also includes examples of how big data technology helps various sectors of the business to achieve its targets in an effective manner.

History on big Data
Big data technology is a field of science which takes into account data
in huge volumes. It include the activities such as processing,
organizing and analyzing the data in such a way that helps to take
various operational and financial decisions (Choi, Wallace and Wang,
2018). It has several features such as velocity, variety, volume and
value of the big data. It also include various challenges which are
required to be faced by the organization while using the technology
of big data. These challenges include lack of professional knowledge
and massive data. There are various tools and techniques used by big
data and its application in business to reduce the impact of risk is also
being discussed in the given report.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
The big data is a large data which is having huge volume and it is difficult to
store, process and retrieve large volume of data. It's application can be seen in
the field of product development, predictive maintenance, customer
experience, fraud and compliance and machine learning.
Characteristics of Big data
a.) Volume – The data derived from the various sources are huge in
quantity which require utmost care in its handling.
b.) Variety – There are several forms of data. It can be either
structured, semi structured, Quasi structured and unstructured.
c.)Veracity – It is another feature of big data which reflects the
reliability of the data.
d) Velocity – It determines the speed of the data at which it will
give its output.
e.) Value – It is important feature of big data which says that every
data contains some value and it makes the data accurate and
reliable.
The challenges of big data
analytics
Every technology has to face several challenges while using the
technology which can be elaborated as given below:
Lack of knowledge professionals – The tools and techniques of big
data require qualified personnel to operate the massive data. 2. Data
growth issues – When the data grows exponentially, it becomes
difficult to store large volume data. However, the enterprise is using
various new tools for compressing the size of the data.
3. Integrating data from spread of sources – The most challenging
task of big data is integration or combining the data from several
sources. The distinct sources are pages of social media, ERP
applications, financial reports, e-mail, presentations and reports
created by the personnel
4. Securing data – Another issue with big data is that it carries
various threats to the data.
References
Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics in operations
management. Production and Operations Management, 27(10), pp.1868-
1883.
Dezi, L., and et.al.,2018. The role of big data in shaping ambidextrous
business process management: Case studies from the service
industry. Business Process Management Journal.
Ghani, N.Aand et.al., 2019. Social media big data analytics: A
survey. Computers in Human Behavior, 101, pp.417-428.
How Big Data technology
could support business &
Examples
Big data technology helps various sectors of the business to achieve its
targets in effective manner. The importance of big data technology can be
described as given below-
a.) It helps the company in customer acquisition and
b.)Big data management helps in risk management
Techniques that are currently
available to analysis big data
Techniques are the specific way to carry out a task in unique manner.
There are several techniques to manage the massive data which can be
elaborated as given below -
a.)A/B testing – It is a type of tools which takes into account various
variables and test the performance on a random basis.
b.) Machine learning – It is a method which enables the system to
understand the program mes without being explicitly programmed. It
helps to know the recent trends, preference and demands of the
customer.
C) Statistics – It is the branch of knowledge which includes collection,
organizing, assembling and interpreting the data in such a way that
results in the improved decision making for several institutions.
d.) Natural language processing – It is a new innovation which is included
in fifth generation. It uses simple English words which helps the
programmer to understand the coding easily.