BMP4005 Information Systems & Big Data Analysis: A Detailed Report

This report provides a comprehensive overview of big data, starting with its definition and key characteristics such as volume, variety, and velocity. It elaborates on the challenges associated with big data analytics, including difficulties in providing timely solutions, potential for incorrect analytics due to the vast amount of data, and data overload that can hinder effective analysis. The report also discusses techniques currently available for analyzing big data, such as A/B testing, data fusion and integration, data mining, and machine learning. Furthermore, it explores how big data technology can support business objectives, providing examples related to customer satisfaction, innovation, creativity, and risk analysis. The report concludes by referencing relevant academic sources and includes a poster summarizing the main research findings.

History on big Data
Big data is collection of various information from a
number of sources, the data is so huge that it can not
handled by traditional system (Dartmann, Song and
Schmeink, 2019). The underlying report elaborates the
actual meaning of big data and its features. Risk involved
in utilisation of big data analytics and the methods
available to examine big data. And advantages of big data
technology for organisation and the ways in which it
should be applied for benefits of firm.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
It is termed as an enormous data consisting organized and
unorganised data gathered by entity. For extracting information the
data is gathered and utilised for predicting and advanced data
analytics. Big data as the name suggests is a huge collection of data
that is obtained from a number of sources and origins. This data is
maintained by advanced technology, the difficulty and variations
does not permit traditional system to operate on data.
Characteristics of Big data
Volume: The size of the data gathered and stored is
huge. The volume of the data represents its reliability
and potential
Variety: The data is usually in semi organised and
organised format, there are in numerical, non numerical
Velocity: Data is received quickly and moreover faster
worked on the velocity at which data is processed and
gathered to meet the requirement of the organisation
The challenges of big data
analytics
Fails to provide timely solution: Big data have large and
complicated data which makes it very hard to read. Due to
this result get delayed and sometimes results in losses
Incorrect analytics: It the data is so huge in amount it
will be actually difficult to find relevant data for analysis
Overload data: Data quantity unable the analyst to work
on data in a effective manner as they are not utilised to
such complicated data References
Dartmann, G., Song, H. and Schmeink, A. eds.,
2019. Big data analytics for cyber-physical systems:
machine learning for the internet of things. Elsevier.
How Big Data technology
could support business &
Examples
Customer satisfaction
Innovation and creativity
Perform risk Analysis
Techniques that are currently
available to analysis big data
A/B testing
Data fusion and data integration
Data mining
Machine learning