Big Data Analysis: Characteristics, Techniques, Challenges, and Business Examples

History on big Data
Big data is a word which describes huge volumes of
data,which come in business on daily basis. It is used for
providing information to take effective decisions and plan
business strategy. It is large complex and impossible to
process it using old softwares.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
Big data refers to the collection of the data from various sources
like traditional and digital internal and external sources of
organization which represents continuous discovery and
analysis(Bag and et. al.,2021). People constrain big data by inputs
like web behavior and social network interactions. Big data consists
of large volumes of data which is growing at fast speed.
Characteristics of Big data
It helps companies to put data on work for finding new
opportunities and build models of business.
Characteristics of big data areas follows-
• Volume-
• Velocity-
• Veracity-
• Variability
• Value-
References
Galetsi, P., Katsaliaki, K. and Kumar, S., 2019. Values, challenges
and future directions of big data analytics in healthcare: A
systematic review. Social science & medicine, 241, p.112533.
How Big Data technology could support
business & Examples
Each business requires valuable data and insights. For
understanding target consumer preferences big data
plays important role.
• Create new revenue streams
• Re develop products
• Dialogue with customers-
• Perform risk analysis-
• Data safety-
Techniques that are currently available to analysis big
data
Data analytics refers to the process of analyzing data into insights.
They are used to take better decision.
• Regression analysis
• Monte Carlo simulation
• Factor analysis-
• Cohort analysis
The challenges of big data analytics
The challenges of big data includes processing large
amount of data which involves storage, analysis of
information.
• Lack of knowledge professionals
• Lack of proper understanding of massive data-
• Data growth issues
• Confusion while big data tool selection-
• Integrating data from a spread of sources-
• Securing of data-