Exploring Information Systems in Big Data Analysis and Business

This report provides a comprehensive overview of big data analysis within information systems, starting with its historical context and evolution since the 1990s. It defines big data analytics as the application of advanced techniques to diverse datasets, including structured, semi-structured, and un...Read More

Information Systems and Big Data
Analysis

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
CONCLUSION................................................................................................................................6
REFERNCES...................................................................................................................................7

INTRODUCTION
MAIN BODY
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. Big data means volume of information, velocity or speed at which it is created or
information is collected (Choi, Wallace and Wang, 2018). This term was used for large
information and used by different people. This term is used in all the sectors like, education,
retail industry, government sector, health care industry and other organisations. This term was
used for punching cards designed for controlling mechanical looms. This machine reduced ten
years of labour into three months of labour. In early time big data term was used for large or
huge information and data is collected from different sources. Nowadays, big data analytics
refers to use of new and latest technology so that organisations can perform better than others
and it helps in achieving goals and objectives.
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). It is essential to use new and latest technology so that relevant information can be
collected and targets of firm can be accomplished. It is important to use techniques for gathering
information as social media is one of the best way which can be used by businesses to gather
details and it helps in making better plans so that targets can be accomplished. The purpose of
using big data analytics is it describes process of uncovering trends and patterns in market, large
amount of material is provided which is beneficial in making data informed decisions. Big data
analytics is used by organisations, government and individuals to make plans for growth of firm

and relevant information can be collected. It is essential to identify market situation and make
plans accordingly so that goals and objectives can be accomplished. There are three types of big
data analytics i.e., descriptive analysis, diagnostic analysis and predictive analytics. This is used
by different organisation so that they can collect relevant details and it helps in achieving targets
of firm. When information is collected from market by using latest techniques then it becomes
easy to make plans and satisfy need of people. Big data analytics help organisations to identify
new opportunities in market and plans can be made accordingly. This helps in performing better
than others and more profit can be generated. Big data analytics is used by private organisations
to determine choice of people and new opportunities in market which helps in growth of firm.
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). Volume is the speed of data processing. This includes, rate of change, linking of incoming
data sets, activity burst. Value refers to benefits which an organisation derives from data
collected. Variety refers to different types of data and this is an issue which is faced by
companies because choice of people is changing and affects performance of firm. There are
different types of data that is collected from different sources. It is important to collect relevant
information so that better plans can be made and it helps in achieving goals and objectives.
Volatility means big data is changing constantly and data which is gathered from different
sources can be used to collect information. These are some characteristic of big data analytics
and companies are using this to collect relevant details.
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).

Securing data - using big analytics, managers has to face challenge i.e., securing data. It is
important to secure data because relevant details are collected from different sources and if
information is leaked then company has to suffer loss. It is essential to secure data and keep it in
safe place so that goals and objectives of company can be accomplished.
Lack of proper understanding - another challenge which is faced by managers is lack of proper
understanding. Employees need time to understand things and technology which is applied.
Competition is increasing nowadays and to gain competitive advantage it is important to apply
new technology. Due to lack of proper understanding work is not completed on time and targets
are not accomplished (Mikalef and et.al., 2019).
Integrating data from different sources - collecting data from different sources is difficult for
managers because it is essential to gather relevant information so that better plans can be made.
This helps in achieving goals and objectives of company. Managers are making plans after
analyzing market situation and new technology should be applied so that targets can be
accomplished. Integrating data from different sources is important for growth and development
of firm. It is essential to determine choice of people and make plans accordingly so that better
products can be provided to people.
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.
A/B testing - this technique is used to analyze data and relevant information can be gathered to
run business smoothly. A/B testing is helpful for managers so that correct details can be collected
and helps in achieving targets. Benefits of A/B testing are, it improves user engagement, reduced
bounce rates, ease of analysis, quick results can be find out, everything is testable, increased
conversion rates, high conversion values.

Data integration - this technique is used by organizations because it helps in gathering data
from different systems and it is beneficial for organization. There are some advantages of data
integration technique such as, better collaboration and deployment, availability of real time
integrated data, data from multiple distributed sources. It saves time, boost efficiency of
employees and reduces chances of mistakes or errors. This technique is beneficial for firm
because it helps in achieving targets and more profit can be generated (Singh and El-Kassar,
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. It is important to use big data analytics as it
lead to smarter business moves, more efficient operations, more revenue is generated and
customers are happy. Big data analytics plays a crucial role in growth of business because it
reduces cost, increases efficiency, improves pricing, increases sales and loyalty, ensures right
employees.
CONCLUSION

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.
Mehta, N. and Pandit, A., 2018. Concurrence of big data analytics and healthcare: A systematic
review. International journal of medical informatics. 114. pp.57-65.
Mikalef, P., and et.al., 2019. Big data analytics and firm performance: Findings from a mixed-
method approach. Journal of Business Research, 98, pp.261-276.
Saggi, M. K. and Jain, S., 2018. A survey towards an integration of big data analytics to big
insights for value-creation. Information Processing & Management. 54(5). pp.758-790.
Singh, S. K. and El-Kassar, A. N., 2019. Role of big data analytics in developing sustainable
capabilities. Journal of cleaner production, 213, pp.1264-1273.
Zhu, L., and et.al., 2018. Big data analytics in intelligent transportation systems: A survey. IEEE
Transactions on Intelligent Transportation Systems. 20(1). pp.383-398.