Big Data Analysis: Techniques, Challenges, and Characteristics

This presentation discusses the evolution of big data, challenges in big data analytics, techniques for analyzing big data, and characteristics of big data. It also explores how big data technology can support businesses with examples.

History on big Data
The evolution of big data based practices in recent times
have directed different traditional business organisations
to shift to digital business. In the corporate world also big
data techniques are emerging in an effective manner. All
major companies in the international and national
marketplace focus on developing IT practices in a
professional manner.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
In the recent times it is important for a business to
focus on analysing big data with different techniques.
The big data is a practice of management of data
through software and cloud based services in order to
lead the marketplace.
Characteristics of Big data
In the management of big data practices, there are
different characteristics which put a major impact on
development of crucial aspects.
The different characteristics of big data management
are mentioned below:
Volume of data
Variety of data
Velocity of data
The challenges of big data
analytics
In the functioning of a business, big data management
play a crucial role in development of data based
practices.
The major challenges related to big data management
are mentioned below:
Data integration
Complexity of data
Security of data References
Hopkins, J. and Hawking, P., 2018. Big Data Analytics
and IoT in logistics: a case study. The International
Journal of Logistics Management.
How Big Data technology
could support business &
Examples
The big data technology is an important approach
which is beneficial for a company to measure different
types of data techniques in order to develop and
expand.
Techniques that are currently
available to analysis big data
The techniques available to analyse big data are mentioned
below:
• Mining of data
• Machine learning
• Statistics based approach