BMP4005 - Information Systems and Big Data Analysis Report 2022

This report provides an overview of big data, its characteristics (velocity, value, volume, diversity), and the challenges associated with its analysis. It discusses techniques currently available for analyzing big data, such as A/B testing, data mining, machine learning, and statistics. The report further explores how big data technology can support business objectives, providing examples of improved customer service, enhanced functional efficiency, and risk determination. The document also highlights the ability of organizations to leverage external intelligence services for decision-making. This student-contributed solution is available on Desklib, where students can find a wealth of study resources.

History on big Data
Big data is a technology for storing, analyzing and
managing massive amounts of data. Case studies
describe a combination of structured and unstructured or
semi-structured detail parts collected by the company for
a variety of forecasting purposes, including acquisition of
machine operations, predictive modeling, and more.
They also assist organizations in managing large volumes
of information.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
Corporate big data involves big details, large volumes of data,
largely from innovative or new sources. This information has a
combination of various data; it also has a high volume and
speed. This case study includes the four Vs showing velocity,
volume, value and variety. It empowers companies to make
profit-based decisions.
Characteristics of Big data
There are some illustrative examples of companies leveraging
big information technology, it involves agriculture,
pharmaceuticals, etc. Important characteristics of big
information are explained as follows:
•Velocity: The name Big Data itself has to do with size, which
means huge or gigantic. The size of the data plays a very critical
role in determining the value of the data.
•Value: Diversity refers to the quality of heterogeneous sources
and data, structured or unstructured.
•Volume: This term refers to the rate at which data is generated,
how quickly it can be generated or processed to meet demand
and determine the potential of the data.
•Diversity: This refers to the inconsistencies that data can show
in some cases, thus hindering the process of being able to
process and manage the data effectively.
The challenges of big data
analytics
In today’s era, everything from shopping to school,
education to work is digital, and after the pandemic,
everything is highly digital. Big data analytics is the
process of using available data in different forms,
structured, unstructured, and of various sizes, for analysis
and application for organizational purposes. Big data has
the following characteristics:
• Social
• Internet of Things
• high speed
• high volume
• mobile
• artificial intelligence
Gelernter, J. and Polimanti, R., 2021. Genetics of substance
use disorders in the era of big data. Nature Reviews
Genetics. 22(11), pp.712-729.
Li, Y., Ma, J. and Zhang, Y., 2021. Image retrieval from remote
sensing big data: A survey. Information Fusion. 67, pp.94-115.
Lv, Z and et.al., 2021. Analysis of using blockchain to protect
the privacy of drone big data. IEEE network. 35(1), pp.44-49.
How Big Data technology could
support business & Examples
Big data is a huge collection of data that still has the
potential or capability to grow rapidly over time. It is so
large and tangled that neither traditional data management
techniques nor tools can efficiently store or manipulate it.
•Processing big data has several advantages, such as:
•Clear and improved consumer or customer service
•Improved and healthier functional efficiency
•Preliminary determination of product or service risks
•Organizations can leverage external intelligence services
for decision making.
Techniques that are currently
available to analysis big data
•A/B testing - This technique helps to compare control and
test groups. to determine which changes brought
improvements. Bid data fits the model and helps to run tests
in big data.
•Data Mining - The most commonly used tool is to extract
only the useful data from the massive data and study the
needed data, saving time and resources and providing fast
results.
•Machine Learning - belongs to the field of artificial
intelligence; it applies computer algorithms to generate
solutions to problems based on available data.
•Statistics - Techniques for collection, organization,
interpretation, and experimentation.