Information Systems and Big Data Analysis: Techniques & Examples

This report provides an overview of big data and its characteristics, highlighting its volume, variety, velocity, veracity, and value. It discusses the challenges associated with big data analytics, including the lack of skilled professionals, integrating data from various sources, data growth issues, and the need for proper data understanding. The report also explores various techniques used in big data analysis, such as A/B testing, data fusion and integration, statistics, natural language processing, and data mining. Furthermore, it examines how big data technology can support businesses by enhancing data safety, facilitating dialogue with customers, and optimizing resource utilization. The document concludes by referencing academic works on big data and sentiment analysis, as well as influencing models in big data analytics research.

History on big Data
The big data is a form of massive data which is having
huge volume and difficult to manage the same. It is a
modern branch of technology which helps in
understanding the behaviour of the consumers to know
the preferences of them.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
It can be defined as a set of structured, semi structured and
unstructured information that is collected, stored, sorted by
different enterprises that helps in efficient and effective
decision making process that would facilitate growth and
expansion of organisation. There are various sets that provide
assistance to company for implementing analysis of market and
development of strategies.
Characteristics of Big data
There are various features of big data which can be
explained as given below:
a.) Volume- The big data is large in volume and carries
variety of data such as homogeneous or heterogeneous
data.
b.) Variety – The data is a raw facts and figures. It can be
either structured, semi structured and unstructured.
c.)Velocity – This term defines the rate at which data is
processed and retrieved from the huge collection of data.
d.)Veracity – It indicates the validity and accuracy of the
big data which helps to take several decisions.
e.) Value – The value of big data must carry some value
and have authentic source as well.
The challenges of big data
analytics
There are various challenges of big data which can be
explained as given below:
• Lack of knowledge professionals – The new
technology require experienced personnel which
creates problem for the organisation to hire them.
• Integrating data from various sources – There are
various sources of collecting data such as PDF, video
and audio.
• Data growth issues – The size of the data is very
huge and it is difficult to handle the same.
• Lack of proper understanding of the data – There are
some data which can not be classified in any
category. Thus, it requires proper sorting of the data. References
Hajiali, M., 2020. Big data and sentiment analysis: A
comprehensive and systematic literature review.
Concurrency and Computation: Practice and Experience,
32(14), p.e5671.
Aboelmaged, M. and Mouakket, S., 2020. Influencing
models and determinants in big data analytics research: A
How Big Data technology could
support business & Examples
• Data safety – There are numerous data of the
consumers which is kept safely by using various
software and modern technology.
• Dialogue with customers -Every organisation works
with the motive of satisfying consumers which can be
possible by identifying the preferences of the
consumer.
• Optimum utilisation of resources – The resources of
the organisation are scare. Thus, big data technology
helps in effective allocation of resources.
Techniques that are currently
available to analysis big data
1.)a/b testing- It is a technique which helps to compare the
two different web pages and takes decisions regarding the
best performing web page.
2.)data fusion and integration- The analysis of data is done
when data is inspected closely and integration includes the
synthesis of the several components of the data.
3.)Statistics- It is branch of knowledge which collect,
organize and interpret the data to take various decisions.
4.)Natural language processing – It is a language which uses
simple English words and easily understandable by the
humans.
5.)data mining – it is the process of extracting data from
several sources to know the patterns of the consumer.