Information Systems and Big Data Analysis: Trends and Applications

This essay provides an overview of big data analysis within the context of information systems. It begins by tracing the history of big data, highlighting John Graunt's early use of statistical data analytics. The essay then discusses the challenges associated with big data, including incorrect analytics, security issues, and a lack of understanding among analysts. It defines big data as large and complex data sets that require specialized tools for analysis, revealing patterns in human behavior and marketplace trends. The characteristics of big data, such as volume and variety, are explored, and the essay details how big data technology can support business through techniques like A/B testing, machine learning, and data mining. An example of big data in the healthcare industry is provided, illustrating how companies use trial data to assess risks and benefits. The essay concludes with a list of references.

History on Big
Data
Concept of big data has
been founded by data
management pundits.
John Graunt had dealt
with huge amount of data
for which he started usage
of statistics in 1663.
Hence, he was honoured
as a first person to use
statistical data analytics.
Information Systems and Big Data Analysis
Name of the Student
What is Big Data
Big data may be referred as larger data sets which may only be
analysed with the help of Big data tools in order to reveal human
behaviour, marketplace trends and its patterns and much more. It
is a concept implies larger and complexing data sets which are
processed up with data processing applications & software. Data is
a term refers to raw material, i.e, unprocessed data.
Characteristics of Big data
Volume- The Quantity of data provides an insight that
the data must be considered Big data or Not. If Such
data is large enough and involves much complexities
require a number of data analytics techniques to
process it, then it is classified as big data.
Variety- Traditionally, RDBMS was enough to handle
the stored larger data easily. But as changes occurred
in data types and their structures, traditional data
analytics techniques were challenged as then Big Data
Came into Play.
The challenges of big data
analytics
Incorrect analytics: If the firm has wrong and defective
data, the analysis done on such data would result in
defective results which eventually impact the validity
and authenticity of the data. Sometimes the system has
some errors due to the omission at the time of
development and testing of it which increases the
problems related to processing of the data.
Security issues: The tools that are being used for the
analysis uses various sources to extract the data that
could make the data vulnerable. Hence, increase in the
data would increase the security issues related to the
data.
Lack of understanding: The demand for the analysts are
ever increasing due to continuous rise in the data that is
being created. It has now became a crucial responsibility
of the business to appoint a data analysts who acquires
the skills of properly analysing the data of the firm.
References
Ahamad, M.V. and et.al., 2018. Insight Into Big Data
Analytics: Challenges, Recent Trends, and Future
Prospects. In Handbook of Research on Pattern
Engineering System Development for Big Data
Analytics. (pp. 67-79). IGI Global.
Benoit, D.F. and et.al., 2020. On realising the utopian
potential of big data analytics for maximising return on
marketing investments. Journal of Marketing
Management. 36(3-4). pp.233-247.
Choi, T.M. and et.al., 2018. Big data analytics in
operations management. Production and Operations
Management. 27(10). pp.1868-1883.
How Big Data technology could
support business & Examples
In business usage terms, Big data may be referred to
processes and tools for utilization and management of
a large data set. It can be very helpful to create new
services and products for a grand consumer
experience. example:
Big Data in healthcare industry: Under this Companies
then on basis of trial data frame risks and beneficial
outcome to its users.
Techniques that are currently available
to analysis big data
A/B Testing: It is a technique that compares the control group
with the different test groups to identify what changes can be
adopted to improve the main variable for which the analysis is
done.
Machine Learning: It is a software that helps the computer to
learn new programs and it makes predictions of the data or make
assumptions based on the entered data.
Data Mining: It is a technique used on large amount of data to
draw out a pattern with the help of combination of two
techniques, i.e., machine learning & statistics within the
framework of management of database.