Big Data Analysis for Business: Techniques and Examples

This project, submitted for the BMP4005 Information Systems and Big Data Analysis module, delves into the realm of big data. The assignment begins by defining big data and outlining its core characteristics, such as volume, variety, and velocity. It then explores the challenges inherent in big data analytics, including data quality, organization, and the application of older approaches to new systems. The project also examines current techniques for analyzing big data, such as machine learning and regression analysis, providing examples like Google Cloud and Excel. Furthermore, it details how big data technology supports business objectives, with examples like Amazon Inc.'s use of real-time analytics and dashboards to manage customer information. The project includes a poster presentation summarizing the research findings, and a 1500-word paper providing a comprehensive overview of the topic, referencing relevant academic sources.

History on big Data
The term has come into domain in early 1990s. It is still
not clear that who used the term very first time but it is
supposed that it was first time used by John R. Mashey.
Latter, the term got wider applause, since the evolution of
internet made it popular and worthy to use by business
organizations.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
Big data can be defined as a mammoth constellation of information
which is collected to meet certain purpose. It can also be interpreted
as greater variety, volumes and higher velocity information which
are intended for business or some other objectives.
Characteristics of Big data
The challenges of big data
analytics
Lack of meaningful data
Poor data organization
Application of old approach to the new system
Vulnerable quality of data source
Visualization of data is messy
Over engineered system
References
Ristevski, B. and Chen, M., 2018. Big data analytics in medicine
and healthcare. Journal of integrative bioinformatics, 15(3).
Mehta, N. and Pandit, A., 2018. Concurrence of big data analytics
and healthcare: A systematic review. International journal of
medical informatics. 114. pp.57-65.
How Big Data technology could support business & Examples
In the present market circumstances usefulness of
big data analytics and not be denied, there are number of big data
technologies which are being used. These technologies are having
these advantages to support businesses-
By using big data technologies business can cut
their overall cost of operating business. It makes their task easier
to manage customer information to govern and operate different
business functions. For example- Amazon Inc. is using big data
analytics tools in order to manage it vastly disseminated customers.
The organization is using Real time analytics, operational analytics,
dashboards and some other visualization etc.
……………………………………………………………………...
Techniques that are currently available to analysis big data
Machine learning- Now machine learning technologies are
available, the big data is directly studied and concluded by
machines itself. For example- Google cloud, IBM ML, Amazon ML
are some of the best example.
Regression Analysis- By using normal tools such as Excel,
Regression analysis can be conducted on big data. It gives insights
for future and makes the work of big data analytics simpler.
Volume- The big data is known
for the volume. There would be
wide amount of data which is the
biggest characteristics of big data.
Variety- There would be number
of types of data will be available.
Different format, quality,
usefulness, size, importance these
all dimensions can be seen
variation.
Big data is always comes in
mammoth size, so intensive volume is
one of the main characteristic of big
data. It may be structured or
unstructured and would be having
need of sort of smart technology to
make it useful.