Information Systems: History, Challenges, and Techniques in Big Data

This essay provides a comprehensive overview of big data analytics, starting with its historical roots in the 1990s and the evolution of the term. It identifies key challenges in big data analytics, such as the lack of employee knowledge and the difficulties managers face when implementing new technologies. The essay defines big data analytics as the application of advanced techniques to diverse datasets, including structured, semi-structured, and unstructured data, sourced from social media, cloud applications, and machine sensors. It highlights machine learning as a crucial technique for data collection and independent adaptation, emphasizing its advantages like automation and efficient data handling. Furthermore, the characteristics of big data analysis, including velocity, variety, volume, veracity, and value, are discussed. The essay concludes by emphasizing how businesses leverage big data analytics to understand customer preferences, create new products and services, and identify market opportunities, ultimately driving growth and development.

Information Systems and Big Data Analysis
History on big data
Big data has been used in the year 1990s by John Mashey.
Earlier big data was based on the punch cards which are
designed for controlling the patterns which is used by
mechanical looms. Big data was generated from three
sources i.e., social data, machine and transactional data. The
term big data was used in 1987 by John Mashey to quantify
huge volume of information. At that time this word was new
for all and people are not knowing for what purpose this
term is being used.
What is big data
Big data analytics means to use advanced techniques
against diverse data sets. This includes different
types of data such as, structured, semi structured and
unstructured data. Information is collected from
different sources and it is beneficial for businesses
because they can make plans after gathering
information. Big data analytics help businesses to
collect data from different sources such as, social
media, cloud applications and machine sensor data.
This helps in making better products which is
beneficial for growth and development of company
(Ghani and et.al., 2019).
Characteristics of big data analysis
There are some characteristics of big data analytics such as,
velocity, variety, volume, veracity, validity, variability,
volatility, value and visualization. These are some
characteristics of big data and it is beneficial for satisfying
need of people and identifying new opportunities in market.
Velocity means amount of data which an organisation has.
Velocity of data is measured in gigabytes, zettabytes.
Velocity will rise as per industry trends (Hariri, Fredericks
and Bowers, 2019).
How big data technology could support
business
Big data is the used by businesses to identify
choice of customers and plans can be made
accordingly. This helps businesses to create
new experiences, services and products. It is
essential to use big data analytics so that
better plans can be made and company can
more profit. With the help of new techniques
choice of people can be identified and it helps
in achieving targets. Businesses use big data
analytics for growth and development of firm
and better products can be provided to
people. This is useful as need of customer is
satisfied and good quality products are
provided. Big data analytics is beneficial for
business in identifying new opportunities and
plans can be made accordingly.
The Challenges of big data analytics
There are some challenges of big data analytics are:
Lack of knowledge - it is difficult to apply big data
analytics because employees are not having proper
information. This is a challenge which is faced by
managers because when new technology is applied
then training should be provided to employees so that
they can work efficiently. Managers face difficulty
when applying new technology because employees are
not knowing how to work and they face problem
(Mehta and Pandit, 2018).
Techniques that are currently available to analysis big data
There are different techniques which are available currently to
analyses big data are:
Machine learning - this technique is used currently so that
data can be collected. Machine learning is important because it
helps in collecting data and able to independent adapt. There
are some advantages of machine learning such as, automation
of everything, wide range of applications, scope of
improvement, efficient handling of data, data acquisition,
possibility of high error (Saggi and Jain, 2018). This technique
is used by organization to identify choice of people and make
plans accordingly. It is essential to use new techniques so that
better plans can be made and it helps in achieving goals and
objectives of company.
REFERNCES
Books and Journals
Choi, T. M., Wallace, S. W. and Wang, Y., 2018.
Big data analytics in operations
management. Production and Operations
Management. 27(10). pp.1868-1883.
Ghani, N. A., and et.al., 2019. Social media big
data analytics: A survey. Computers in Human
Behavior, 101, pp.417-428.
Hariri, R. H., Fredericks, E. M. and Bowers, K.
M., 2019. Uncertainty in big data analytics:
survey, opportunities, and challenges. Journal of
Big Data. 6(1). pp.1-16.