Big Data Analysis: Techniques, Challenges, and Business Support

This presentation provides an overview of big data analysis, including its history, characteristics, challenges, and techniques. It also discusses how big data technology can support businesses with insightful decisions. The challenges of big data analytics are also highlighted, including the requirement of knowledgeable and qualified professionals to use such data. The presentation concludes with examples of how big data technology can support businesses.

History on big Data
The concept of big data was introduced in 1663
by Graunt. The main aim of developing this
concept is to collect, analyze and evaluate the
large amount of data efficiently. This concept was
developed by the Egyptians so that insightful
decisions can be taken (Deshpande and Kumar,
2018). The problem of overwhelming of data was
first analyzed in 1880. During 20th century, data
has evolved at high speed. As a result, big data
become the core way of evolution.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
Big data refers to the combination of traditional and
contemporary techniques so that the organization can
analyze the data and information in an insightful manner.
This techniques is related with collection and analysis of
large amount of data that cannot be managed through
traditional methods. This data can be further utilized in
machine learning projects, analytics applications and
more (Sanchez and Rivera, 2017). In organizations, there
are systems and tools to support the analysis of big data.
Characteristics of Big data
Velocity: It is the most important characteristic of big
data which is based on the speed at which companies
get, store and manage the data.
Variety: Data has different varieties including
structured, unstructured, semi-structure and more.
Volume: It basically demonstrates the amount of data
that is managed by an organization. However, high
volume of data helps in taking informative decisions
but, it is quite complex to manage such amount of data
in an appropriate way (Bikakis, Papastefanatos and
Papaemmanouil, 2019).
The challenges of big data analytics
In current time, tools of data analysis and
evaluation are being evolved (Huang and et. al.,
2021). But, there is requirement of
knowledgeable and qualified professionals to use
such data. Majority of the companies do not have
adequate number of professionals to deal with the
information. the employees of the organization
are not familiar with the tools & techniques of big
data. They do not have knowledge what big data
is, how to use it and the way to store, process and
use of data. This insufficient information can
create challenges for the companies in terms of
making significant use of big data.
References
Deshpande, A. and Kumar, M., 2018. Artificial intelligence for big data:
Complete guide to automating big data solutions using artificial
intelligence techniques. Packt Publishing Ltd.
Huang and et. al., 2021. An overview of air quality analysis by big data
techniques: Monitoring, forecasting, and traceability. Information
Fusion, 75, pp.28-40.
Sanchez, A. and Rivera, W., 2017, June. Big data analysis and
visualization for the smart grid. In 2017 IEEE International Congress on
Big Data (BigData Congress) (pp. 414-418). IEEE.
Athmaja, S., Hanumanthappa, M. and Kavitha, V., 2017, March. A survey
of machine learning algorithms for big data analytics. In 2017
International conference on innovations in information, embedded and
How Big Data technology could support business &
Examples
The entire world makes use of data so that appropriate
decisions can be taken. Big data tool and techniques
are used to extract necessary information out of data
which can further used by the business entities to take
decisions (Shukla, Muhuri and Abraham, 2020).
Techniques that are currently available to analysis big
data
Data mining:In big data analysis, data mining is a
common tool that is used to extract patterns from large
set of data through combining different methods
including statistics and machine learning.
Data fusion and integration: It is a set of technique
which is used to analyze integrated data extracted from
multiple sources.
A/B testing: This technique signified comparison of two
groups. One control group is compared with test group
to reach at meaningful conclusion. (Athmaja,
Hanumanthappa and Kavitha, 2017).