Information Systems and Big Data: History, Challenges, and Business

This essay provides an overview of big data analysis, starting with its historical roots in the 17th century and its evolution to the present day. It identifies key challenges faced by analysts, such as a lack of professional data handling skills and insufficient knowledge of massive datasets, hindering worker efficiency. The essay defines big data as the large volume of data requiring management and interpretation for better decision-making. It highlights the main characteristics of big data, including volume, variety, and velocity, and discusses techniques like A/B testing, machine learning, and natural language processing used for analysis. Finally, it emphasizes the role of big data technology in business, particularly in performing risk analysis and monitoring customer desires. Desklib offers solved assignments for students.

History on big Data
The big data was first seen back in the year 1663 when
John Graunt dealt managed the big amounts of
information related to the bubonic plague which was
spreading in the Europe at that time (Salah and et.al.,
2018). This was the first time that such big data was
analysed. With the 20th Century, the data has been evolved
rapidly and now at this day there are many software which
can be used by the analsyts to manage and interpret the
data.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
The big data refers to the large volume of data which is required by
the analsysts to manage and further interpret this data. Big data is
useful to examine insights that works for the attainment of better
decision-making for the next move of companies
Characteristics of Big data
The main Characteristics of Big Data are:
Volume: Volume is one of the main features that
required to be ascertained while challenging big data
solutions. The data acquired is genereally in large
chunks and requires to be managed accordingly.
Variety: This is related to the nature of data. The data
might be heterogeneous in form, structed or semi-
structured etc.
Velocity: The data therein, flows from many sources
of data and hence this is one of the characteristic of
same. The
The challenges of big data
analytics
The challenges that are faced by the analysts while
performing big data analytics are:
The handling of data which is done are not done in a
professional way and the analsyts are not skillful enough.
There is a huge requirement of training sessions which
will help the engineers to operate on the different
softwares related to same (Ngan And et.al., 2019).
The other challenge that is being faced is of the lack of
knowledge of massive data. The employees are required
to tackle the complex data and analyse the same. This
hinders the efficiency of the workers.
References
Salah, M. adnd et.al., 2018. Predicting medical expenses
using artificial neural network. International Journal of
Engineering and Information Systems (IJEAIS). 2(20).
pp.11-17.
The Key factors which shows the role of big data
technology in business are:
It helps in Performing risk analysis: Prediction of risk
factors present within and outside the organisation helps
in constanly monitors the desires of the customer and
work accordingly (Wu, 2021).
Techniques that are currently
available to analysis big data
Following are the techniques that are available in the market to
perform a smooth analysis of big data:
A/B testing: It works on the logics rather than guesswork. It is
useful for optimising website outcomes
Machine Learning: It involves training of computer to learn the
dynamic behaviour of the user
Natural Language Processing: It requires making of computer
natural languages resembles the human language