Information Systems and Big Data Analysis: Techniques and Business

This report provides a comprehensive overview of big data analysis within the context of information systems. It begins by defining big data and tracing its historical roots, highlighting key milestones such as Fremont Rider's consideration of digitalization and John Mashey's coining of the term. The report identifies challenges associated with big data analytics, including understanding and handling large volumes of structured and unstructured data, selecting appropriate tools, and ensuring data security. Furthermore, it explores various techniques currently available for analyzing big data, such as machine learning, statistical classification analysis, regression analysis, social analysis, and association rule learning. The report also discusses the characteristics of big data, emphasizing volume, variety, velocity, value, and veracity. Finally, it examines how big data technology supports businesses by observing marketing trends, competitor strategies, and product pricing, ultimately leading to competitive pricing, improved efficiency, cost advantages, and increased sales through customer loyalty.

History on big
Data
At first in the year 1944, Fremont Rider, considering the
requirement of storage capacity, presented Big data as
“Information Explosion” but did not consider the
digitalization of libraries. Then, in the year 1998, John
Mashey gave the title for big data as “Big Data and the Next
Wave of Infrastress”.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
Big data is a collective information of massive and complex data
collected from big data sources. Earlier, the data was collected from
traditional softwares and databases but now the quantity per second
of data has been increased to the extent that it cannot be supported
by traditional softwares. Companies use modern applications such
as Hadoop, Cassandra and Spark etc. to process and store
organizations' big data.
Characteristics of
Big data
Big data is applied to the huge set of information that
cannot be supported by traditional systems or processes.
Volume
Variety
Velocity
Value
Veracity
The challenges of
big data analytics
Big data challenges include understanding and handling the
large amount of data collected in multiple structured or
unstructured forms from various online as well as offline
sources.
• Understanding the Big data
• Issues in handling data
• Choosing appropriate Big data Tool
• Security of Data
• Accumulation of Data
References
• Egger, R., Kroner, M. and Stöckl, A., 2022. Web scraping. In Applied Data Science in Tourism (pp. 67-82). Springer, Cham.
• Goundar, S., and et. al., 2022. Using online student interactions to predict performance in a first-year computing science
course. Technology, Pedagogy and Education, pp.1-19.
How Big Data technology could
support business & Examples
Big data technology helps the businesses in observing the marketing trends and
competitors' strategies in calculating the change in product price. It also assists in
reducing cost of product return by the estimations. Below are the advantages of
big data technology to business:
• Develop competitive pricing
• Improve Efficiency
• Cost Advantage
• Increased sales through customer loyalty
Techniques
that are
currently
available to
analysis big
data
• Machine Learning
• Statistical Classification Analysis
• Regression Analysis
• Social network Analysis
• Association Rule Learning Analysis