Big Data Analysis in Information Systems: Supporting Business

This essay provides an overview of big data, defining it by its characteristics of volume, variety, and velocity, and highlighting its origins in the 1960s. It discusses the challenges businesses face in utilizing big data analytics, such as failing to provide timely insights, inaccuracy, complexity, long response times, and expensive maintenance. The essay also outlines various techniques available for analyzing big data, including A/B testing, data fusion and integration, data mining, machine learning, and natural language processing. Furthermore, it explores how big data technology supports business by reducing costs, increasing sales and revenue, improving pricing decisions, providing competitive advantage, and increasing efficiency in decision-making. The document concludes with references to support the information presented.

History on big Data
Big data refers to the data which have greater variety,
huge volumes and more velocity. They are referred to as
three Vs. It is a bigger, complex set of data from new data
sources. The data is so huge that old processing software
can not process it. They can be used to address business
issues. The concept is new but the origins of it goes back
to 1960s.
Information Systems and Big Data Analysis
Name of the Student
What is big Data Characteristics of Big data
Before replication data flow should be exceed from 150
exabytes. Here are some of the characteristics of Big
data:
Volumes
Variety
Veracity
Value
Velocity
References
Ardito, L., and et,al.,2018. A bibliometric analysis of
research on Big Data analytics for business and
management. Management Decision.
How Big Data technology
could support business &
Examples
Some factors by which big data can support business:
• Big data reduces costs
• Increases sales and revenue
• Big data improves pricing decisions
• Provides competitive advantage
• Increases efficiency in decision making
Techniques that are currently
available to analysis big data
All these are done by specific systems, software's and methods.
Here are some of the techniques available for analyzing big data
• A/B testing
• Data fusion and data integration
• Data mining
• Machine learning
• Natural language processing(NLP)
Big data refers to the data which have greater variety, huge
volumes and more velocity. They are referred to as three Vs. It is
a bigger, complex set of data from new data sources.
The challenges of big data
analytics
Many business face problems in anaytics tools. It can be
created by system or infrastructure problems. Here are
some of the challenges of big data analytics:
Fails to provide timely insights
Inaccuracy
Complicated
Long response time
Very expensive maintenance