Information Systems: Big Data Analysis, Challenges, and Techniques

This report provides a comprehensive overview of big data analysis, emphasizing its importance in contemporary business environments for making strategic decisions. It details the characteristics of big data, including volume, velocity, variety, value, and veracity, and discusses various challenges associated with its utilization, such as the lack of skilled professionals, the complexity of massive datasets, and the difficulty in selecting appropriate tools. The report also outlines several big data analysis techniques, including A/B testing, data fusion and integration, data mining, and machine learning. Furthermore, it illustrates how businesses can benefit from leveraging big data, such as gaining better customer insights, improving operations, and enabling data-driven innovation. The report concludes that big data is instrumental in achieving organizational objectives and maintaining competitiveness in the current business landscape.

History on big Data
Big data is concerned with large volume which is found in both
structured and unstructured that helps business to take strategic
decision. In addition to this, there are number of insights that
can be derived from big data which helps in achieving
confidence and ability to improve strategic business moves. In
the current era, it is essential for the organization to pay
attention on having effective strategic decision in turn greater
capability to cope up with prevailing circumstances in effectual
pattern to gain competitiveness. The current report will
comprise characteristics, challenges, techniques, etc. Present
report will provide crucial information regarding how big data
can support business along with example.
Information Systems and Big Data Analysis
Name of the Student
Techniques that are currently available to analysis big data
• A/B testing
• Data Fusion and integration
• Data mining
• Machine learning
• Natural language processing
• Associate rule learning
• classification tree analysis,
• genetic algorithm,
• Regression analysis, sentiment evaluation, social network
process, etc
characteristics of big data
Volume
Variety
Velocity
Value
Veracity
The challenges of big data analytics
• Lack of knowledge possessing professional
• Complexity in understanding massive data
• Rapid data growth issue
• Confusion in selecting big data tool
• Sourcing data is highly daunting problem
CONCLUSION
From the above report it can be concluded that big data is highly
useful in order to make strategic decision. The current report has
comprised characteristics of BD such as volume, velocity, variety,
value, etc. There are differnt kinds of challenges that occur in
company due to utilization of big data such as lack of knowledge,
complexity of data, confusing in choosing tool, etc. In addition to
this, present report has comprised BD analysis techniques such as
A/B testing, Data Fusion and integration, mining, machine learning,
etc. There are several ways in which business get benefited from
utilization of big data which involves getting customer insights, etc.
How Big data can support business
Making strategic decision
Better customer insights
Improved operation
Agile supply chain management
Data driven innovation and improved operation
What is big Data
Big Data (BD) is essential in the present environment of
organization in turn higher ability to accomplish predetermined
objectives can be derived. It is widely taken into consideration for
having effectual ability to coordinate with changing situation of
business environment. In order to be prompt and effectual business
need to highlight larger data sources in order to get sustainability
(Khalajzadeh and et.al., 2020). BD’s characteristics helps in
gaining accurate & fair source of information in turn order to get
proper insights about it.
REFERENCES
Books and Journals
Khalajzadeh, H. and et.al., 2020, October. User-centred tooling for modelling of big
data applications. In Proceedings of the 23rd ACM/IEEE International Conference on
Model Driven Engineering Languages and Systems: Companion Proceedings (pp. 1-
5).
Lu, K. and et.al., 2020. A review of big data applications in urban transit
systems. IEEE Transactions on Intelligent Transportation Systems. 22(5). pp.2535-
2552.
Nachiappan, R. and et.al., 2017. Cloud storage reliability for big data applications: A
state of the art survey. Journal of Network and Computer Applications. 97. pp.35-47.
Rahul, K., Banyal, R. K. and Goswami, P., 2020. Analysis and processing aspects of
data in big data applications. Journal of Discrete Mathematical Sciences and
Cryptography. 23(2). pp.385-393.
Online
Top 6 Big Data Challenges. 2020. [Online]. Available through:
<https://www.xenonstack.com/insights/big-data-challenges>