Information Systems and Big Data: Business Support Analysis

This report provides a comprehensive overview of big data analysis, exploring its core concepts and practical applications within the context of information systems. It begins by defining big data and its characteristics, including variety, velocity, and volume, emphasizing the importance of big data analytics in extracting valuable insights from complex datasets. The report delves into the challenges associated with big data, such as data management complexities, the talent gap, and the need for effective data accessibility and insights generation. It also outlines various techniques used in big data analysis, including A/B testing and data fusion. Furthermore, the report highlights how big data technology supports businesses by enabling better customer understanding, creating new revenue streams, improving operational efficiency, and enhancing risk analysis, ultimately leading to more informed decision-making and competitive advantages. The report concludes by summarizing the importance of big data analytics in modern commercial enterprises.

Information system and big
data analysis

Table of Contents
INTRODUCTION...........................................................................................................................3
What is big data and characteristic..............................................................................................3
Challenges...................................................................................................................................5
Techniques..................................................................................................................................6
How big data technology support business.................................................................................6
CONCLUSION................................................................................................................................8
REFERENCES................................................................................................................................9

INTRODUCTION
Information system is defined as, it is a formal, sociotechnical, organizational system structured
to collect, procedure store and distribute information. It is an integrated set of components and
also gives information knowledge and digital product. Commercial enterprises organizations and
other firm rely on information system to take out and manage their operations. They looking
forward to make interaction with consumer and suppliers and able to compete in marketplace.
The main work of this system is use supply chain and electronic, market. For instance, industry
utilize information system to process financial accounts, to lead and manage theminter
department such as human resources. It also willing to acquire their potential customers with
online promotions. Big data analytics is a field that treats ways to analyse, systematically extract
information from. It deals with data sets that are too tough or huge to be dealt with through data
processing application software. This report determines types of data and their characteristics, it
covers challenges, techniques and support of big data technology in commercial enterprise. This
research will give overall conclusion that assists to give appropriate data which is very helpful
for implementation in any business.
What is big data and characteristic
It is the often complex procedure of examining big data uncover information system like
hidden patterns, correlations, market trends, and consumer preference that can help company
make informed business decisions. On board, data analytics it is a technologies and techniques
provide companies a way to analyse data sets and gather new information (Choi, and et. al.,
2018). Business intelligence have quires answer nominal questions about business operations and
its outcomes. It is a form of advanced analytics that involve hard applications with elements such
as predictive model, statically algorithms and what if analysis powered by analytics systems. It is
very useful in business that use big data systems and software to make data driven decisions that
can change business related results. The advantage may involve more effective marketing, new
revenue opportunities, consumer personalization and changed operational efficiency with an
effective strategy. These benefits can give competitive advantage over rivals. Big data analytics
it is a procedure of examining data sets and containing various types of data such as big data to
uncover hidden patterns, unknown correlation and other useful information. Industries and
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business that implement big data analytics often obtain different. This data system include code
that conduct numerical and statically analysis with massive data sets. Some of the languages
organization have invest time and monetary value in learning such as python, r, java, and C++ or
many others. It is very helpful for companies because it saves time and money and aid in gaining
insights to inform data driven decisions. There are different types of tools that might fall under
umbrella of big data analytics or serve to improve the procedure of analysing data: storage,
management, cleaning, mining, analysis, visualization integration and collection about data.
This is a proactive approach to business its transformative because it gives analysts and
decision makers the power to move ahead with insight available and best knowledge often in real
time. it simply means that organization looking forward to improve their consumer retention and
develop better product, gain competitive advantage by taking rapid action to respond the market
changes and the other metrics that affect enterprise. Business using this data analytics with
loyalty also have the quality to boost sales and marketing results, discover new revenue
opportunities, improve customer service, optimize operational efficiency, reduce risk, and drive
other commercial enterprises outcomes (Singh and El-Kassar, 2019).
Characteristics
Let’s discuss the characteristics of big data
Variety: It covers structure, unstructured and semi structured data that is gathered from various
sources. While in the past data could only be collected from spread sheet and data bases, it
comes in array of forms such as emails, PDF’s photos, jpg videos, audios and many others. It is
essential characteristics of big data.
Velocity: it covers the speed at which data is being created in rea time. in a wider prospect, it
comprises the rate of change, linking of incoming data sets at varying speeds and activity
eruptions.
Volume: Big data is a huge volume that is being generated only a daily basis from different
sources like social media platform, business procedure, machines networks, human’s interactions
etc. such a large amount of data are stored in data are stored data warehouses (Mohamed and et.
al., 2020).There advantages of data analytics, it can predictive result accurately, thereby allowing
business and organizations make better decisions, while simantenously optimizing their
operational efficiencies and reducing risk.
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These analytics could assist organization generate more sales direct which would naturally mean
a boost in revenue. Enterprises are utilizing big data analytics tools to understand goods and
services are doing in the market and how customers are responding to them (Hopkins and
Hawking, 2018).
Challenges
The valuable assetsare data in today’s world. The economics of data is depending on the idea
that data value can be extracted by the utilization of data analytics. Although big data analytics
are still there in basic growth stage, their importance cannot be undervalued. As huge data
expands and grow, the essential of big dataanalytics will continue to grow in everyday lives, both
personal and business. In relation, the size and volume of data is enhancing every single day,
making it essential to address the manner in which data is addressed every day. Let’s discuss
about major challenges such as:
Uncertainty of data management landscape: This data is continuously expanding, there are new
organizations and technologies that are being developed every day. A major challenge for
organization is to find out which technology works bests from them without the introduction of
new risks and problem.
Data talent gap: big data is a growing field, there are few experts available in this field. This is
because it is a complex field and people who understand the complexity and intricate nature of
this field far few and between.
Getting data in big platform: It is enhancing each day, it simply means organization have to
tackle a limitless amount of data on daily basis. The scale and variety of data that is available
today can overwhelming any data runner that is why essential to make data accessibility simple
and convenient for brand managers and owners.
Getting important insights by the use of big data analytics: it is an essential that organization
acquire insights from big data analytics and it is mandatory that the appropriate department has
access to this information. A major challenge is bridging the gap in an effective fashion
(Dey ,and et. al., 2018).
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Techniques
A/B testing: This technique includes comparing a control group with a variety of tests groups. In
order to discern what treatments or changes will improve a given objective variable.
Data Fusion: By mixing a set of techniques that analyse and integrate data from various sources
and solutions, the insights are more efficient and potentially more accurate than if developed by a
single source of data.
How big data technology support business
Big data is a combination of all process and tools related to using and managing large
data sets. Commercial enterprise organizations can use analytics and figure the most valuable
customers. It can also help in business create new experiences, services and product. It re
develop products, big data is one of the best ways to collect and use feedback. It assists to
analyse how consumer perceive goods and services. It allows test numerous variation of high end
computer aided designs with seconds. It also performs risk analysis; it is not about how
organization run. Economic and social factor plays very essential part in determining
accomplishments. Big data leads to predictive analytics. It gives permission to analyse and scan
social media feeds and newspaper report (Belhadi, A and et. al., 2019).
Data safety allows all kinds of internal threats, with this information, company can keep sensitive
information safe. It is protected in an ethical manner and stored accordingly to regulatory
requirements. Most of the enterprises focus on big data to ensure data safety and protection. It is
more mandatory in organizations that deal with financial information. Credit and debit card and
other such practices.
Creates new revenue streams: there is no doubts big data will play import role in every
enterprise. All business is incomplete without data analytical. It can do definitely wonders for a
business organization. It is mandatory to train employees about data management. With proper
management of big data so business is able to run more effectively and efficiently (Manogaran,
Thota and Lopez, 2018).
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CONCLUSION
It is inferred from the above report about findings such as, information system and big
data analytic is very essential is commercial enterprise. It provides basic information to the
business which is very helpful to get appropriate data. It can be concluded different techniques
and characteristics that uses according to requirement and size of business. It plays very crucial
role in every business because as of now online platforms are enhanced. This data is utilizing in
E commerce. That helps in increase the enterprise so they can run significantly and efficiently.
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REFERENCES
Books and Journals
Belhadi, A and et. al., 2019. Understanding big data analytics for manufacturing processes:
insights from literature review and multiple case studies. Computers & Industrial
Engineering, 137, p.106099.
Choi, and et. al., 2018. Big data analytics in operations management. Production and Operations
Management, 27(10), pp.1868-1883.
Dey, N.,and et. al., 2018. Internet of things and big data analytics toward next-generation
intelligence (pp. 3-549). Berlin: Springer.
Ghani, and et. al., 2019. Social media big data analytics: A survey. Computers in Human
Behavior, 101, pp.417-428.
Hopkins, J. and Hawking, P., 2018. Big Data Analytics and IoT in logistics: a case study. The
International Journal of Logistics Management.
Manogaran, G., Thota, C. and Lopez, D., 2018. Human-computer interaction with big data
analytics. In HCI challenges and privacy preservation in big data security (pp. 1-22). IGI
global.
Mohamed, A, and et. al., 2020. The state of the art and taxonomy of big data analytics: view
from new big data framework. Artificial Intelligence Review, 53(2), pp.989-1037.
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.
Tiwari, S., and et.al., 2018. Big data analytics in supply chain management between 2010 and
2016: Insights to industries. Computers & Industrial Engineering, 115, pp.319-330.
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