Information Systems and Big Data Analysis: Challenges, Techniques

This report provides an overview of big data analysis within information systems, highlighting the characteristics of big data such as volume, velocity, variety, value, and veracity. It discusses the challenges associated with big data analytics, including the deficiency of professional education, understanding massive information, issues in data growth, and confusion during the selection of big data techniques. The report also explores how big data technology supports business through data fusion, data integration, and A/B testing, leading to improved decision-making, increased rivalry benefits, improvements in goods and services, information security, and operational risk analysis. Techniques currently available for analyzing big data, along with relevant references, are also included, emphasizing the importance of valuable data and insights for enterprises to understand their users and effectively target specific markets.

History on big Data
Big data is the utilisation of advance analytic tools
against very huge collected information that considers
system, unorganized data and many others. With this the
management can make development with in the
organisation, outlining the system structure and assuming
the upcoming life.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
This is refers to huge collection of precious information which is
organized or unorganized. The information are gather by the
individual, details from internet and transactions just as
buying, order and accounting transmissions with records of
workers. The understanding of details gives aid in making of
systematic records in the details which gives help in whole
action of business activity operations
Characteristics of Big data
Here are some characteristics of massive details
which is listed in below:-
Volume
Variety
Velocity
Value
Veracity
The challenges of big data
analytics
This analysis is mandatory for an undertaken uncertainty
as it assist to develop the procedure of making decision
making.
• Deficiency of professional education
• Deficiency of appropriate understanding of
massive information
• Issues in data growth
• Saving information
• Confusion during selecting big data techniques
References
• Guedea-Noriega, H.H. and García-Sánchez, F., 2019. Semantic (big) data analysis: an extensive literature review. IEEE Latin
America Transactions, 17(05), pp.796-806.
• Nateghi, R. and Aven, T., 2021. Risk analysis in the age of big data: the promises and pitfalls. Risk Analysis, 41(10), pp.1751-
1758.
How Big Data technology could
support business & Examples
It is important for all kind of enterprise of its size to
have valuable data and insight in order to
understand their users and posses to mark a
particular market and users with analysing their taste.
Increase rivalry benefits
Improvements in goods and services
Information security
Operate risk analysis
Techniques that are currently
available to analysis big data
Data fusion and data integration
A/B testing
Technical learnings
Mining data
Measurements