Information Systems and Big Data Analysis: Key Challenges

This essay provides an overview of big data within the context of information systems. It defines big data, highlighting its key characteristics such as volume, veracity, variety, value, and velocity. The essay further discusses the challenges associated with big data analytics, including uncertainty in data management, the big data talent gap, data integration, synchronization across data sources, and the need for insightful analysis. It also outlines various techniques currently available for analyzing big data, such as association rule learning, classification tree analysis, genetic algorithms, and machine learning. The essay concludes with business examples illustrating the application of big data analytics in industries like telecommunications and education, referencing relevant academic sources.

INTRODUCTION
Many huge companies with large diversified franchises
and broad functionalities in their business needs to
maintain a massive amount of information and analysis of
their work, sales, employees, customers etc. which is
called big data.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
A record of huge amount of such data about the company is known
as big data. Big data is the collection of tiniest to the largest
volumes of data about the sales, customers, search history etc.
related to the company. It is so large that varies in sizes from
terabytes to petabytes. Data is the set of operations performed by
the computer such as symbols, quantities, characters, numeric digits
and information which can be stored
Characteristics of Big
data
• Volume
• Veracity
• Variety
• Value
• Velocity
The challenges of big data
analytics
• Uncertainty to Data Management Landscape
• Big data Talent Gap
• Getting data into big data platform
• Need for synchronization across data sources
• Getting important insights through the use of Big
Data analytic
References
• Rezaee, Z. and Wang, J., 2018. Relevance of big data to forensic accounting practice and education. Managerial Auditing
Journal.
• Pengda, Z., 2019. Characteristics and rational utilization of geological big data. Earth Science Frontiers, 26(4), p.1.
Techniques that are currently
available to analysis big data
• Association rule learning
• Classification tree analysis
• Genetic algorithms
• Machine learning
Business Examples
• By telecommunication industries in distinguishing
between legitimate emails and the one which are
fake or spam
• By schools and universities for assigning each
student different profile in their computer's
database.