Information Systems & Big Data Analysis: History, Techniques

This report provides an overview of big data analysis within information systems, starting with its historical roots in 1663 and highlighting John Graunt's statistical analysis of the bubonic plague. It defines big data as large, complex datasets characterized by high velocity and increasing volume, distinguishing between structured and unstructured data. The report outlines key challenges in big data analytics, including a lack of skilled professionals, difficulties in understanding massive data, storage issues, tool selection confusion, and methods to overcome these challenges. It also details the five characteristics of big data: volume, value, variety, velocity, and veracity. Furthermore, the report explores how big data technology supports business by offering competitive advantages and enhancing customer demand understanding, referencing relevant academic sources and techniques such as A/B testing, machine learning, data mining, data fusion and integration, and statistics.

HISTORY ON BIG DATA
Big data was firstly Track down in the year of 1663 when
John Graunt faced the large amount of information at the
time of studied the bubonic plague. The mentioned
individual was the first one who used the statistical data
analysis in order to analyse those data. Later on, the issues
of overwhelming data was highlighted in 1880s.
INFORMATION SYSTEMS AND BIG DATA ANALYSIS
WHAT IS BIG DATA?
Big data determines the large set of information's which are in
structured and unstructured form. These data are indulged with
higher velocity and arrives in the increasing volume. Structured
data an easily formatted as well as stored without any complexions
whereas unstructured data comes in more free form, hard to track
and less quantifiable.
CHARACTERISTICS OF BIG DATA
Five characteristics of Big Data:
• Volume
• Value
• Variety
• Velocity
• Veracity
THE CHALLENGES OF BIG DATA ANALYTICS
The big data already comes in the enormousness amount.
Hence there are number of challenges can be state which
can initiated in regard to the storing, analysing and storing
of these huge data.
• Lack of knowledge professionals
• Lack of proper understanding of massive data
• Data storage issues
• Confusion in selection of tool
REFERENCES
Wang, J., Xu, C., Zhang, J. and Zhong, R., 2021. Big
data analytics for intelligent manufacturing systems: A
review. Journal of Manufacturing Systems.
HOW BIG DATA TECHNOLOGY COULD
SUPPORT BUSINESS & EXAMPLES
• Competitive advantages
• Understanding the customer demand
TECHNIQUES THAT ARE CURRENTLY AVAILABLE TO
ANALYSIS BIG DATA
There are number of techniques available in the market which used
in analysis of big data such as A/B testing, machine learning, data
mining, Data fusion and data integration, statistics etc. hence below
are the brief discussion of some of them from the mentioned list.
• Data fusion and integration
• Machine learning
• Data mining