Information Systems and Big Data Analysis - Desklib

This presentation covers the history, definition, challenges, techniques, and characteristics of Big Data. It also explores how Big Data technology can support businesses with examples. The subject is Information Systems and the course code is not mentioned. The presentation is relevant for students studying Big Data Analysis in any college or university.

History on big Data
The big data is a collection of data that is to be stored on
the internet with the help of technology. This data is very
wide and huge in terms of numbers and storage. The
storage capacity of this data is very high. This data is very
wide as compare to the historical data. This has become
possible only because of the new technological
inventions in the field of science. It also aids in keeping
the data in a systematic and arranged manner.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
The big data is a collection of the unorganized and organized data
with reference to the valuable data on the internet. Moreover, the
data is collected from the server, client, online transactions such as
the purchase, sale, online bank transfers, online bookings, past
history of online trading, etc. All these data is to be collected and
selected in order to maintain the big data. The big data is to be
used to take the effectual and efficient decision for the company in
the long run.
Characteristics of Big data
• Volume
• Variety
• Velocity
• Variability
The challenges of big data
analytics
• Lack of proper knowing of big data
• Growth of data related issues
• Difficulty in selecting the tool References
Agrawal, R. and Prabakaran, S., 2020. Big data in digital
healthcare: lessons learnt and recommendations for
general practice. Heredity, 124(4), pp.525-534.
Baig, M.I., Shuib, L. and Yadegaridehkordi, E., 2019. Big
data adoption: State of the art and research
challenges. Information Processing &
Management, 56(6), p.102095.
How Big Data technology
could support business &
Examples
• Develop better structural decision
• Apprehension the consumer
• Enhance the business operations
• Create income
Techniques that are currently
available to analysis big data
• A/ B testing
• Data defense