Big Data Analysis: Characteristics, History, Challenges, Techniques, and Business Support

This presentation covers the characteristics, history, challenges, techniques, and business support of Big Data Analysis. It emphasizes the importance of valuable data and insight for enterprises of all sizes. The challenges of big data analytics are discussed, along with the techniques currently available for analyzing big data. The presentation also explores how Big Data technology could support businesses, with examples such as data fusion, data integration, and A/B testing. Technical learnings of massive details, measurements, and mining data are also included. References are provided for further reading.

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