Big Data Analysis: Introduction, Concepts, and Business Applications

This report provides a comprehensive introduction to Big Data analysis. It begins by defining Big Data and its characteristics, including velocity, variety, volume, and veracity. The report then explores the concept of data assessment, highlighting the various technologies used for analyzing Big Data, such as data mining, linear statistical analysis, and cluster analysis. It also discusses the challenges of Big Data analysis, such as precision and uncertain patterns. Furthermore, the report examines how businesses leverage Big Data analysis, providing real-world examples. The report concludes by emphasizing the importance of extracting valuable information from vast quantities of data. References are provided for further reading. This assignment is ideal for students looking to understand the fundamentals and applications of big data.

INFORMATION SYSTEMS
AND BIG DATA ANALYSIS

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INTRODUCTION
The aim of this study is to offer users with
knowledge about Big Data Analytics,
covering what it is, how it is used, and why it
is important, and also various examples. It
could give learners a thorough knowledge of
Big Data and how people could use that to
help companies and government agencies.

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What is Big Data
• Velocity
• Variety
• Volume
• Variability
• Veracity

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Big Data analysis and
its Significance
The fundamental causes of defects, difficulties, and flaws are
quickly addressed.
Incentives are calculated based on the user's buying patterns
at the point of purchase.
In moments, the total risk assessment is recompiled.
Identifying unethical practices that has an impact on the
company

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CONCEPT OF DATA ASSESSMENT
Numerical assessment is the practice of
assessing predictive methods (in text,
auditory, or graphical form) using a
wide range of elements, methods.

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Technologies for Analysing Big Data
• Caption Data mining
• Linear statistical analysis assessment
• Correlation study
• Cluster analysis groups

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Difficulties of Big
Data Analysis
• Precision
• Uncertain patterns

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Technology based on
big data analysis
In the age of big data, images and videos represent
disorganised data. Since it lacked a regular structure,
unorganised information is difficult to examine. Hadoop
might be employed to manage this data because it allows
huge amounts of disorganised data to be grouped and
accessed quickly. Semi-structured data (such as XML) on
the other hand, frequently deviates from a pre-determined
shape or duration.

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99
BUSINESSES MAKE
USAGE OF BIG DATA
ANALYSIS
• Uber
• Pendleton and Son Butchers

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CONCLUSION
The extracting of valuable information from
vast quantities of information is among the most
significant components of Big Data analytics.

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REFERENCES
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Al-Qirim, N., Tarhini, A. and Rouibah, K., 2017, August. Determinants of big data
adoption and success. In Proceedings of the International Conference on Algorithms,
Computing and Systems (pp. 88-92).
Antignac, T., Scandariato, R. and Schneider, G., 2016, October. A privacy-aware
conceptual model for handling personal data. In International Symposium on Leveraging
Applications of Formal Methods (pp. 942-957). Springer, Cham.
Chiu, Y.C., Lin, H.H. and Jou, Y.T., 2019, May. A Model Selection Method for Machine
Learning by Differential Evolution. In Proceedings of the 2019 4th International
Conference on Big Data and Computing (pp. 135-139).

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THANK YOU