Big Data Analysis: Challenges, Techniques, and Characteristics

This presentation discusses the challenges, techniques, and characteristics of big data analysis. It covers the history of big data, challenges in big data analytics, techniques for analyzing big data, and how big data technology can support businesses. The challenges of big data analytics include inaccurate analytics, failure to deliver new and timely insights, expensive maintenance, complexity, and long system response time. Techniques for analyzing big data include A/B testing, machine learning, and natural language processing. The characteristics of big data include volume, variety, veracity, and velocity. Big data technology can support businesses in various ways, such as dialogue with customers and evaluating risk and ensuring data safety.

History on big Data
Big data is the concept that defines the large and high
volume data that is structured and unstructured. In the
early era of 2000s the concept of big data has gained huge
popularity and then later by the famous big data analyst
Doug Laney the new definition of big data was articulated
that defines the Big data using three V's that is volume,
velocity and Variety.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
Basically, the big data is the large, fast and complex data that is
difficult and complex to be process using the traditional methods .
Characteristics of Big data
Volume: the amount of data is always a concern when
there is word regarding big data.
Variety: by this decision base gain different
perspectives from the wide variety of data obtained
through big data.
Veracity: this characteristic includes quality and
availability of the data or information in the big data.
Velocity: the big data has the great velocity at which the
data is generated and managed.
The challenges of big data analytics
Inaccurate analytics: there are times when the data
analyzed by the bid data analytics system is addresses in
accurate which is a serious problem.
Fails to deliver new and timely insights:
it is analyzed that the big data analytics fails into this and
is providing same level of insights as the before systems
used to do
Expensive maintenance: it is a type of technique or
technology that requires a ongoing investment to repair
and maintain the infrastructure of the software.
Using big data analytics is complicated: the another
challenge is complexity which can make the all the efforts
invested in making this system into vain.
Long system response time: the big data analytics
system consumes large amount of time to analyze the data
even when the input data is available on prior basis.
References
•Ranjan, J. and Foropon, C., 2021. Big data analytics in
building the competitive intelligence of
organizations. International Journal of Information
How Big Data technology could
support business & Examples
Big data comprises of different tools and techniques that
are essential to utilize and manage the large volume data
sets within the business.
•Dialogue with customers
•Evaluate risk and Ensure
Data Safety
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Techniques that are currently available to
analysis big data
A/B testing: A technique to analyse the big data in which a control
team is differentiated with a variety of test teams which aid in
determining the changes or the treatment that can improve a
objective.
Machine learning: this technique is highly popular technique to
analyze the big data.
Natural language processing (NLP): for this technique of big data
analysis there is usage of set of techniques originated from the sub
specialty of the computer science that is from the history of the
artificial intelligence.
The big data is one of
such the technology
that has a data in huge
volume, yet it is
growing explicitly
beyond the time.