Big Data Analysis: Techniques, Challenges, and Business Benefits

This report delves into the realm of big data analysis, examining its fundamental concepts and practical applications. It begins by defining big data and its characteristics, including volume, variety, value, veracity, and velocity. The report explores the challenges associated with big data, such as insufficient data, the inability to understand data, and the use of poor quality data. It then outlines various techniques used in big data analysis, including classification tree learning, machine learning, association rule learning, genetic algorithms, regression analysis, sentiment analysis, and social network analysis. The report also highlights how big data technology supports businesses, leading to reduced costs, increased revenue, improved pricing decisions, a competitive advantage, and enhanced decision-making processes. The report includes references to relevant research and publications on big data and its applications.

INFORMATION SYSTEMS AND BIG DATA
ANALYSIS

INTRODUCTION
The Big data is basically a combination of large
data which could be collected from various
methods such as purchasing product, social media
platforms and apps. The big data could be divided
into two parts such as unstructured data and
structured data. In reference to structured data, it is
managed and used by the organization in the data
base and spreadsheet.

Big data and its characteristics
In reference to big data, it basically defined as the quantity of data which cannot be
collected through a data system which traditional. Moreover, big data is used by
multinational companies as it could support companies in helping preferences of the
consumers.
 Volume
 Variety:
 Value:
 Veracity:
 Velocity:

Challenges of big data analytics
 Insufficient data
 Inability to understand data:
 Usage of bad quality in sourcing
 Expensive Maintenance:
 Technical issue
 Excess Pressure

Techniques available to analyze the big data
 Classification tree leaning
 Machine Learning: In machine learning
 Association rule learning
 Genetic algorithms
 Regressing analysis
 Sentiment analysis
 Social network analysis

Big Data Technology supporting Business
 Reduces overall costs
 Increases revenue and sales
 Helps in improving pricing decisions
 Provides a competitive advantage
 Boosts efficiency in decision-making

REFERENCES
 Cohen, S. and Macek, J., 2021. Cyber-Physical Process Monitoring Systems, Real-
Time BigData Analytics, and Industrial Artificial Intelligence in Sustainable Smart
 Wiech, M. and et.al., 2022. Implementation of big data analytics and Manufacturing