BMP4005 - Information Systems and Big Data Analysis Report 2021

This report provides an overview of big data analysis within the context of information systems, addressing its definition, key characteristics (volume, variety, velocity, variability, and veracity), and importance for businesses. It explores the risks associated with big data analytics, such as failures in providing updated information, incorrect analytics, complexity, and long system response times. Furthermore, the report details how big data technology can support businesses through better customer insights, innovation and creativity, smarter recommendations, audience targeting, and enhanced data safety. Examples are provided to illustrate the practical applications of big data in achieving business objectives, concluding that big data analytics is a crucial method for modern businesses to gain a competitive edge.

Information systems
and Big Data analysis

Table of Contents
INTRODUCTION ..........................................................................................................................3
MAIN BODY...................................................................................................................................3
1 .What big data is and the feature of big data. ..........................................................................3
2 Define the risks faced by utilisation of big data analytics.......................................................5
3 . Explain ways in which Big data technology could support business, use examples
wherever required.......................................................................................................................6
CONCLUSION ...............................................................................................................................6

INTRODUCTION
Big data analytics is a approach for newest and developing analysis of techniques in an
objection to a wide, selective sets of data or categories which combines of arranged, semi
arranged and unarranged data, from multiples different origins, and which is not identical in sizes
in comparison from terabytes to zettabytes (Chae, 2019). In the following report, definition of
big data and its five characteristics volume, velocity, variety, variability and veracity have been
elaborated. It also contains challenges faced by big data analytics and also consists of supports
provided by big data analytics with examples.
MAIN BODY
1.What is big data? Explainthe feature of big data.
It is set or group of data of which is big in terms of capacity, which is expanding
gradually with time. This data is so big in terms of capacity and volume that no tools of
management can store it or with the same frequency as required. Big data is also a data which is
enormous in terms of size.
Features of Big data
 Volume: The size of the data gathered and stored is huge. The volume of the data
represents its reliability and potential. When the data is in large number it is useful for
acquiring new information about the transaction. The size of big data is bigger than
terabytes and petabytes. Big data is gathered from various resources like blogs, websites,
social sites and other ways therefore it is usually unstructured (Kobusińska and et.al.,
2018).
 Variety: It links with diversified origins and class of data, both organised and
unorganised are comprised. In the initial days, sets of data and tables, spreadsheets are
the single origin of information which examined by most of the administration
departments. These days, picture, video, mails and audio clips are also contemplated in
the examination of the systems. It is the main issues which is the industries has to face
worldwide as it can adversely effect the presentation of the entity.
 Velocity: Data is received quickly and moreover faster worked on the velocity at which
data is processed and gathered to meet the requirement of the organisation. Internet and
computer science functions on real time basis needs real time data. (Li and et.al., 2022).

 Variability: This refers to show the unpredictability that is presented by the data in some
scenarios, therefore obstructing the process of being able to handle and management of
data efficiently. It brings up to few distinct things in context with the big data. One of the
main grounds in data is the number of the irregularities. It can be found by various
inconsistent and exceptional methods in order for the fulfilment of the meaningful
analytics.
 Veracity: It refers to show the accuracy and dependability of the data. There are many
methods to get a clear representation of the data. It is a technique of controlling the
management of the data in efficient and effective manner. Big data is also important to
supply the data for the growth of the businesses in the markets all around the world.
Importance of Big Data
Saving of Cost: Organisations uses a number of cost saving tools when they have to collect a
huge amount of data. These kinds of techniques assists the business in identifying more effective
ways of operating a business.
Saving of time: It assists the organisations in gathering the data across the globe from various
sources. It assists the organisations in analysing of data as soon as possible which assists in
taking decisions at a faster speed on the grounds of learnings.
Determining the conditions of market: It assist an organisation to get better understandings of
the circumstances of market. For the development of enterprise it is very essential to have a clear
analysis of the circumstances prevailing in the market (Liang and Liu, 2018).
Assistance from social media: Entities operates more effectively on the basis of feedbacks and
emotional analysing by utilising big data techniques. These helps to manage them to get
suggestions of the entity that what entrepreneur think about the business and their suggestions on
the idea of expansion of the organisation.
Eliminate the issue of the advertisers and offering them the perceptions of the market: It
assists to examine the entity adequately and the functions in entity. It permits the entities to
handle the requirements of consumers and preferences of consumers (Moharm, 2019).
Innovations and development of the product: It assists the business able to start with the
continuing thoughts in the market place and makes them creative for the development of new
product and renewing their current products. It usually defines all the levels which are involved
in bringing up the product by the thought of publishing in the market.

2Define the risks faced by utilisation of big data analytics.
Fails in providing new or updated information: analytics does not have sufficient data for
generation of new reports. This may be caused due to the insufficiency of data collections or
poor data arrangement. In such a scenario. It increases the possibilities to performs data audit
and insure that current data collections can provide the required informations. The collection of
new data can also eradicate the issue of insufficiency of data.
Incorrect analytics: If the data is so huge in amount it will be actually difficult to find relevant
data for analysis. However it becomes hard to manage such huge data and the results. Outcomes
from the examination do not gives correct solution.
Using data analytics is complicated: The system requires more scenarios and provides more
features than required thus blurring the focus. It also intakes more hardware resources and hence
upsurges the costs. As a result, users uses only a portion of the operations. The remaining hangs
like dead weight and it looks that the solution is too difficult. It is essential to recognise excess
operations (Rezaee and Wang, 2018).
Long system response time: The issue can be in the system itself, it means that it has reached
its scalability limit. It also may be that hardware infrastructure of organisation is insufficient.
Upscaling is the only solution here that is addition of more computing resources to the system. It
is good as long as it assists in improvising the system response within an affordable budget, and
as long as the resources are used adequately.
3. Explain ways in which Big data technology could support business, use examples wherever
required.
Better customer insight
Not only online stores but also physical stores can also gather useful informations of their
consumers, by examining and analysing the ways of directing visitors by physical market place
rather than directing by a website.
Innovation and creativity
In order to survive in the dynamic environment modification and alteration in existing product is
necessary to keep the business in popularity. Innovation and creativity is method to keep the
customer in line and connected to the business. Feedback collection and reviews from customer
are really helpful to rectify errors in the business. This helps in redeveloping the product (Wen
and et.al., 2018).

Smarter recommendations and audience targeting
New suggesting applications are better than the old one containing consumer data, with the
outcomes that they can be more sensitive to geographic and consumer behaviour.
Data safety
Big data tools allows the business to map the entire business operations. This allows to the
organisation to find out all the internal threats of the company. It helps in keeping confidential
information safe. This helps in protecting trade secrets and other information related to the
business secure from hackers and unauthorized signing by special looks and privacy features.
CONCLUSION
From above prepared report it can be concluded that big data analytics is method of the
newest and developing examination of techniques in an objection to very wide, selective data
sets or categories that merges or arranges, data, from various different sources, and in non-
identical sizes. The above report concludes meaning of big data along with its characteristics and
challenges faced by organisations and it also concludes ways in which big data analytics support
businesses.
REFERENCES
Books and Journals
Chae, B.K., 2019. A General framework for studying the evolution of the digital innovation
ecosystem: The case of big data. International Journal of Information Management, 45,
pp.83-94.
Kobusińska, A., and et.al., 2018. Emerging trends, issues and challenges in Internet of Things,
Big Data and cloud computing. Future Generation computer systems, 87, pp.416-419.
Li, X., and .et.al., 2022. Big data analysis of the internet of things in the digital twins of smart
city based on deep learning. Future Generation Computer Systems, 128, pp.167-177.
Liang, T.P. and Liu, Y.H., 2018. Research landscape of business intelligence and big data
analytics: A bibliometrics study. Expert Systems with Applications, 111, pp.2-10.

Moharm, K., 2019. State of the art in big data applications in microgrid: A review. Advanced
Engineering Informatics, 42, p.100945.
Rezaee, Z. and Wang, J., 2018. Relevance of big data to forensic accounting practice and
education. Managerial Auditing Journal.
Wen, L., and et.al., 2018. Compression of smart meter big data: A survey. Renewable and
Sustainable Energy Reviews, 91, pp.59-69.