Big Data's Role: Information Systems Analysis and Business Support

This report provides a detailed analysis of information systems and big data, emphasizing the critical role of big data analytics in modern business environments. It defines big data in terms of volume, velocity, and variety, highlighting the challenges organizations face in managing and analyzing these massive datasets. Key challenges include the need for skilled professionals, a comprehensive understanding of data, and managing data growth. The report also explores techniques such as data integration and data mining used to analyze big data effectively. Furthermore, it discusses how big data technology supports businesses by providing competitive advantages, enabling product redevelopment based on customer feedback, creating new revenue streams through data sales, and enhancing data security. The report concludes that a proper understanding and management of big data are essential for businesses to improve productivity, efficiency, and overall revenue generation. Desklib provides access to similar solved assignments and past papers for students.

Information Systems
and
Big Data Analysis

Table of Contents
INTRODUCTION ..........................................................................................................................3
MAIN BODY...................................................................................................................................3
Explain the big data and characteristics of big data....................................................................3
Challenges of big data analytics and techniques available to analysis big data.........................3
How big data technology could support business.......................................................................3
CONCLUSION................................................................................................................................3
REFERENCES................................................................................................................................4

INTRODUCTION
Information system has become most important and crucial element for businesses
specially industries such as banking and IT because their work relies on data and information
systems. Companies makes decisions on the basis of data and informations acquired through
computer systems and ensure a strong competitive advantages. Big data analytics can be defined
a process of examination of big data to identify the market trends and customer demands,
expectations hidden patterns to help the firm in making informed decision. The report is about
information systems and big data analysis with a detailed explanation. In the next part of report
challenges of big data analysis are include also comprise the techniques available for analysing
big data. Further more report covers how big data technology support business to improve
business outcomes and provide competitive advantage than its competitors(Mujeeb and et. al.,
2018).
MAIN BODY
Explain the big data and characteristics of big data
Big data can be termed as data which comprise the greater varieties of data with increased
volume and velocity. Big data is large and complex data set that are not easy to manage but these
massive data sets used for resolving business problems. Big data can help the organisation in
developing their product to enhance the customer experiences and will increase the operational
efficiency(Vassakis, Petrakis and Kopanakis, 2018). Big data develop new opportunities for
business by integrating, managing and analysation. Following are the characteristics of big data
which can be understand by 3 V's of data:
 Volume: At present time amount of data and informations becoming important because a
lot of the data are being stored but not all of them is analysed. This is a major problem
that have to be considerate. Data can be related to the environmental, medical, financial
and surveillance. Organisation must have knowledge how to manage these massive data
to utilize it effectively. With the right technology organisation can analyse all the data to
understand their business and bring new innovation in business.
 Velocity: Along with the managing data organisations need to make sure that the data
flows quickly. Velocity typically can be described as understanding of speed of collecting
and storing data. Velocity is more important than volume of data because it will give

more competitive advantage to the business. Hence organisation must ensure that data is
available at right time to make right business decisions. As there are more and more data
is being collecting and storing in a vert short time so companies need to analyse data in
fast speed to assure insights of the data(Berdik and et. al.,2021.
 Variety: Variety is the third V of the big data. Organisations collect data from various
sources such as from smartphones, social media like Facebook twitter Instagram etc. or
from GPS technology. Different kind or nature of business obtain informations from
different sources. For instance a marketing company should give consideration to the
social network compare to the industrial business. Big data become a complex process for
businesses as variety of data available like traditional data, raw, structured or semi
structured data also unstructured data from social media.
Challenges of big data analytics and techniques available to analysis big data
Big data analytics is a complex and very challenging for business as there are large
volume and variety of data is available also need to have great velocity which make it
challenging for businesses. Organisations need to collect right data from accurate source at right
time to fulfil customers demands and wants at right time. Different techniques derived from
fields like statistics, mathematics, computer science and economies can be used to handle this big
data.
Following are the challenges of big data analysis:
 Professionals knowledge: Organisations must have skilled and highly talented
professionals to analyse the data. Data analysts, scientist, engineers are the professionals
who analyse these massive data and make useful implementation of these data. If
companies have lack of data professionals they can not manage the massive set of data
effectively. Company are nowadays investing more in recruiting skilled professionals
also provides training to develop their skills and knowledge to handle the set of big
data(Farboodi, Matray and Veldkamp, 2018).
 Understanding of data: A comprehend knowledge of data is necessary for analysation
of data. Companies fails to analyse the big data because of insufficient understanding of
data storage, sources, process and importance. Even if data professional have knowledge
of the process but other employees might not have idea. This problem can be resolved if

company organised workshops or seminars and training sessions to create a
understanding of big data.
 Data growth: This is the major problem for business face during big data analysation.
Informations and data stored in companies are increasing rapidly with time. As these data
sets are growing fatly over the time it is getting difficult to handle and manage them.
Company can apply the modern techniques and methodology for managing these data
sets such as compression and de duplication.
Techniques available to analysis big data:
 Data integration: Data integration can proved to be a technique that effectively analyse
the data. Data fusion is a process of combining data derived from different sources into a
single unit. Data integration can be done with by involving a common elements, data
source network, clients and master server.
 Data mining: Data mining is a common tool used for big data analytics. Data mining
extracts data from database by using statistics methods and machine learning. For
example customer data is mined to decides which market segment will react to an offer.
 Statistics: Statistics is a method use for collecting, managing, interpreting of big data
with help of survey and experiments. Statistics method is used to obtain insights from
data with mathematics computations.
How big data technology could support business
Big data is a integrating of all tools and process related to managing large and complex
database. The concepts of big data is developed to understand the pattern, trends and preferences.
Through big data analytics organisations can identify the most valuable customers also can
businesses to develop new experiences and products. Big data can help business in many ways
some of them is explained as below:
 Competitive advantages: Big data is crucial for leading businesses to perform better in
competition. Industries use big data to compete in market with strong and established
competitors as well as help in developing and innovating new products. For example in
health care units data analyst analyse the effects of the pharmaceuticals. Companies focus
on finding the risks and benefits of pharmaceuticals during clinical trials.
 Re develop products: Big data is the best way to collect data and use feedbacks from
customers. It helps the business in understanding the customer's perception about the

product and services and at same time they can improve their product quality by different
techniques and innovations. For instance big data help a clothing company that what kind
of clothes are popular in market and can manufacture according to the feedback and
results(Sun, Strang and Li, 2018).
 Create new revenues: Big data helps in creating revenues for businesses. Big data not
only valuable for single business but they can also sell their data and trends to other big
industries of the same sector. Big data can do wonder for business but it is important for
business to train their employees how to manage big data. Proper management of big data
will enhance productivity and efficiency of business thus overall generate more revenues
for business. For instance IT companies can sell their technologies to other IT companies
and earn more income.
 Data safety: Big data techniques allow the business to manage the entire data of the
company and can predict all types of threats. Businesses can can keep their data and
sensitive informations secure or protect in an appropriate manner according to the
requirements. Hence mostly industries are focusing on big data for keeping their
information safe and protected. For example banking companies use big data tools for
securing their data like credit and debit card informations and such other
informations(Corbett, 2018).
CONCLUSION
From the above report it can be concluded that Information system is a collection of
software, hardware and telecommunications network that are mainly set of business
process,people,and technology. Big data is a set of large and complex data required to address
business crisis. Volume velocity and variety are the three most important v's of the big data.
Professional knowledge and data understanding is very essential to meet the challenges of big
data analytics. Data mining and statistics techniques can be helpful to analyse the data. The
results can be derived from the report that big data technology can help the business in
maintaining competitive advantage and generating new revenues.

REFERENCES
Books and Journals
Berdik and et. al.,2021. A survey on blockchain for information systems management and
security. Information Processing & Management. 58(1), p.102397.
Corbett, C. J., 2018. How sustainable is big data?. Production and Operations Management.
27(9), pp.1685-1695.
Farboodi, M., Matray, A. and Veldkamp, L., 2018. Where has all the big data gone?. Available at
SSRN 3164360.
Mujeeb and et. al., 2018, October. Big data analytics for price and load forecasting in smart
grids. In International Conference on Broadband and Wireless Computing,
Communication and Applications (pp. 77-87). Springer, Cham.
Sun, Z., Strang, K. and Li, R., 2018, October. Big data with ten big characteristics.
In Proceedings of the 2nd International Conference on Big Data Research (pp. 56-61).
Vassakis, K., Petrakis, E. and Kopanakis, I., 2018. Big data analytics: applications, prospects and
challenges. In Mobile big data (pp. 3-20). Springer, Cham.