Analysis of Information Systems and Big Data: Trends and Applications

This report provides a comprehensive overview of big data analysis within the context of information systems. It begins with a historical perspective on the evolution of big data, tracing its origins to early data storage efforts and its formal naming in 2005. The report defines big data as a combination of structured and unstructured data used for machine learning, predictive modeling, and advanced analytics, emphasizing its role in discovering relevant information for effective decision-making. Key characteristics of big data, including volume, velocity, variety, and veracity, are discussed in detail, highlighting the challenges and opportunities they present. The report also addresses challenges faced during big data analytics, such as real-time data problems, expensive maintenance, and inaccurate analytics, along with the importance of finding skilled experts. Furthermore, it explores various techniques for analyzing big data, including data fusion, data integration, data mining, machine learning, and statistical methods. The report concludes by illustrating how big data technology can support business operations, using Debenhams Plc as an example of personalized and multichannel marketing communication, ultimately leading to increased revenue and profits. Desklib provides access to similar solved assignments and resources for students.

Information Systems
And
Big Data Analysis

Contents
Contents...........................................................................................................................................2
INTRODUCTION...........................................................................................................................1
MAIN BODY..................................................................................................................................1
History on Big Data.....................................................................................................................1
What is Big Data?........................................................................................................................1
Characteristics of Big Data..........................................................................................................1
Challenges faced while doing Big Data analytics.......................................................................2
Techniques that are currently available to analyse Big Data.......................................................3
How Big Data Technology could support Business and Explain with Example........................4
CONCLUSION................................................................................................................................4
REFERENCES................................................................................................................................5

INTRODUCTION
Big Data is huge number of information or data which is stored and contains greater variety,
increased in volume and have more velocity. Big Data is having larger, more complex set of data
and especially from their new data source. The report covers history related to big data,
characteristics of big data, techniques to analyse big data and uses of big data in a company.
POSTER
SUMMARY REPORT
History on Big Data
The Big Data was established in 1963 by John Graunt for storing big amount of data
when they studied about bubonic plague. Further in 2005, Big Data was name and label by Roger
Mougalas and it was done for representing tools which was used for set large data at that time
which was impossible to manage and process of business (Huang, Wang and Huang, 2020).
People and establishments cannot store huge amount of data in systematic way before because
there was no tools and application are available for storing huge amount data in one application.
Now after 1963, people have application for storing large variety of information at one place and
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it is easy to manage. The historical data is broader concept which is used to collection of data
about their past events and at their particular subject. It is historical data which includes almost
generated data which either manually and automatically in their enterprise.
What is Big Data?
Big Data is combination of both structured and unstructured data of collection by an
enterprise which can be classified information and it will have used in learning machine projects,
predictive modelling and other advanced analytic applications. The main objectives of Big Data
is to discovering relevant information which can help organization to make an effective decision
(Jiang, Huo and Song, 2018). This will improve efficiency level of their business operation, it
also helps in cost optimization of their production and it also help in identifying risk factors
which impact on business operation.
Characteristics of Big Data
There are mainly 4 characteristics of Big data which is explained below:
 Volume: In Big Data, it is estimated that it must have large amount of volume is required
to store data. This grow and developed very fastly and rapidly then It must need new data
base management system and IT employees. This create many new IT job opportunities
for managing Big Data flow in organization.
 Velocity: It refers to speed where data is operated, generated and processed and earlier it
takes time to process for right data and get right information. Now due to up-gradation in
technology it takes very less time to operate and get effective and right information. This
is not possible by internet speed but also have presence of Big Data itself. If more data
create then more methods are required to monitor this all data and it creates various
circles.
 Variety: In high speed and considerable volume both are related to wide variety of forms
of data. This is possible due to smart IT solution in all sectors in every types of
organization (Khan and Javaid, 2021). If in every part of world is having internet in
future, then their volume and variety is increased so for that IT teams required to manage
the big data in this way that it become easy for user to store information in quick way and
get effective information from this storage.
 Veracity: In organization, it is not possible to make effective decision which is based on
Big data because information which is provided by big data is get quickly outdated and
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sometime information which is provide it is not necessarily that it is correct for company.
Data Scientist and IT professional is organizing and assessing right data. This is based on
organization to how they used Big Data software in effective way and right way so that it
can be prevented from any crime which will affect their business.
Challenges faced while doing Big Data analytics
The challenges faced by an Enterprise while doing Big data analytics is explained below:
 Real-Time Big Data Problem: The organization cannot have real time information in
big data due to advancement in technologies then enterprise goals also changed
accordingly (Kolisetty and Rajput, 2020). The data which is present in Big Data becomes
outdated so, it creates problem for company when they need to make any effective
decision for company.
 Expensive Maintenance: If organization is using Big Data then it have to bear more
expensive for their maintenance. The information which is stored is outdated with their
technologies then management of company need to maintain their technologies and need
to update their Big data accordingly which help to provide significant information.
 Inaccurate Analytics: In Big Data, sometime which information is provided is
inaccurate analytics then it will create create very big problem because information which
is provided is not correct and decision which is taken in organization on basis on basis of
Big Data information. Then organization have to apply ETL process which can help to
enhance quality of their coming information at their different levels like syntactic,
semantic, grammatical, business, etc. This help enterprise to make accurate analytics
from their big data and how it will impact the business function which are totally based
on Big Data information.
 Finding Experts who are able to analyse Big Data: This is also a very big issues for
organization to find expert who operate Big Data in effective way. It is not possible for
everyone to manage Big Data, it required expert skills and knowledge then only they
have potential to manage Big Data in Enterprise (Lin and Yang, 2019). This is why it is
important to find suitable person in company who have all required skills and knowledge
so they can easily manage Big Data and provide effective information to organization
which will improve their performance level of business and provide accurate guidance
and advise how to improve their operation of their business.
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Techniques that are currently available to analyse Big Data
In advancement of technology in country there many techniques which are currently available to
operates to analyse Big Data are explained below:
 Data fusion and Data integration: It is combination of techniques which is set to
analyse and integrate data which have multiple source of solutions. It has more efficient
and potentially accurate then it developed through single source of data (Palacio and
López, 2018). In this enterprise have many options from which management get useful
data. The
 Data Mining: The information which is present in Big Data have in very large quantity
then it sets to identifies relevant pattern and relationship which can used by management
of organization to find out appropriate solution of their problem through their data
analysis. This techniques and tools which enables company to forecast their future trends
and their business decision.
 Machine Learning: In machine learning, enterprise is well known about field of
artificial intelligence which help in to do data analysis. It works with computer
algorithms which produce assumption based data. It provides forecast which is not
possible for human analyst. This enables companies that they able forecast their future
production which is based on Big Data Information.
 Statistics: The techniques which is used to collect information for organization by doing
surveys and experiments. The data analysis techniques include many more methods like
spatial analysis, predictive modelling, association rules of learning, network analysis, etc.
by which the Big Data information is become more useful for management that they will
used to make effective decision in their business process.
How Big Data Technology could support Business and Explain with Example
The Big Data technologies will impact organization process by many ways to improve their
business operations, it enhances the performance of customer service, It also create personalized
marketing campaigns, and take over other actions which ultimately increase revenue and profits
(Ren and Ding). If company collect more data, then management have more option and ways can
be identifying to analyse their big data. It is also described as large, hard to manage volume of
data which is both structured and unstructured of overflow of business information on their day
to day basis. For Example, in Debenhams Plc, a Big Data plays very important roles and steps in
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date and forms for eventuating strategy which is more personalised and multichannel marketing
communication. In future it could also help in different areas of digital activity which includes
videos and augmented reality and it is potential for location based in mobile activity according to
their Exon. This will help enterprise in dealing with making effective and quick appropriate
solution for their problem which is faced in organization. This will help to developed their
business and grow in market of UK.
CONCLUSION
From the report, it has been analysed that big data allows business to examine various
customer related patterns and trends. While examine customer behaviour it is essential for
organization to make their customer loyalty.
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REFERENCES
Books and Journals:
Huang, C. K., Wang, T. and Huang, T. Y., 2020. Initial evidence on the impact of big data
implementation on firm performance. Information Systems Frontiers, 22(2), pp.475-
487.
Jiang, D., Huo, L. and Song, H., 2018. Rethinking behaviors and activities of base stations in
mobile cellular networks based on big data analysis. IEEE Transactions on Network
Science and Engineering, 7(1), pp.80-90.
Khan, I. H. and Javaid, M., 2021. Big data applications in medical field: A literature
review. Journal of Industrial Integration and Management, 6(01), pp.53-69.
Kolisetty, V. V. and Rajput, D. S., 2020. A review on the significance of machine learning for
data analysis in big data. Jordanian Journal of Computers and Information Technology
(JJCIT), 6(01), pp.155-171.
Lin, H.Y. and Yang, S.Y., 2019. A cloud-based energy data mining information agent system
based on big data analysis technology. Microelectronics Reliability, 97, pp.66-78.
Palacio, A.L. and López, Ó.P., 2018, May. From big data to smart data: A genomic information
systems perspective. In 2018 12th International Conference on Research Challenges in
Information Science (RCIS) (pp. 1-11). IEEE.
Ren, P. and Ding, R., 2019, March. The application and development of big data in transport
logistics industry in China. In 2019 IEEE 3rd Information Technology, Networking,
Electronic and Automation Control Conference (ITNEC) (pp. 149-154). IEEE.
Yang, X., 2021. Business big data analysis based on microprocessor system and mathematical
modeling. Microprocessors and Microsystems, 82, p.103846.
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