Big Data Analysis: A Comprehensive Look at Data Analysis

This assignment provides an overview of big data analysis, its characteristics, and techniques. It explores the concept of big data, its various dimensions like volume, velocity, and variety. The assignment discusses the challenges associated with big data, such as data integration and complexity. Furthermore, it highlights the techniques used in big data analysis, including data mining and machine learning. The assignment concludes with real-world examples of big data applications in financial services, manufacturing, and transportation sectors, demonstrating its practical significance. This assignment is available on Desklib, a platform offering AI-based study tools for students.

INTRODUCTION
Information system is an integrated set of components for processing,
storing and collecting data in order to provide digital products,
knowledge and information. It is a data with large, more complex data
sets, specifically from new data sources. This poster will cover the
concept and nature of big data and its characteristics.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
Big data is the data that involves a greater variety, arriving in increasing volumes
and with more velocity. It refers as a term that describe a collection of data that is
large in size and yet it is growing exceptionally with the time. Big data is a
combination of structured, structured and unstructured data collection through
organisations that is mined with information and used in machine learning
projects to predicts modelling and other innovative analytics applications.
Characteristics of Big data
•Volume
•Variety
•Velocity
•Value
•Veracity
The challenges of big data
analytics
• Data integration-
• Data complexity
References
Chinnaswamy, A. and et. al., 2018. Big data visualisation, geographic information systems and decision making in healthcare
management. Management Decision.
Guha, S. and Kumar, S., 2018. Emergence of big data research in operations management, information systems, and healthcare:
Past contributions and future roadmap. Production and Operations Management, 27(9), pp.1724-1735.
Techniques that are currently
available to analysis big data
• Data mining
• Machine learning
• Statistics-
Business Examples
• For example, the financial services firms are using the big
data system for risking the management in real-time analysis
of the market data.
• For example, the manufacturer and transportation companies
are relying on the big data in order to manage the supply
chains and to optimize delivery routes.