Information Systems and Big Data Analysis: Business Growth Strategies

This report provides a comprehensive overview of big data, including its definition, characteristics (volume, variety, velocity, variability, and value), and the challenges associated with its analysis, such as incorrect analytics and the lack of skilled professionals. It also outlines methods for analyzing big data, including machine learning, regression analysis, and social network analysis. Furthermore, the report discusses how big data can contribute to business growth by improving operations, generating income, and delivering smarter products and services, with examples from companies like People Doc, American Express, and Royal Bank of Scotland (RBS). The report concludes that while big data presents risks, it also offers significant opportunities for organizational growth and competitive advantage.

Information Systems
and Big Data Analysis

Table of Contents
INTRODUCTION ..........................................................................................................................3
MAIN BODY...................................................................................................................................3
1. What is meant by Big Data and what are the characteristics?................................................3
2. What are the challenges of big data analytics and what are the methods used to analyse Big
data?............................................................................................................................................4
3. How does Big data help the businesses to grow, provide examples to support the answer?..5
CONCLUSION ...............................................................................................................................6
REFERENCES................................................................................................................................7

INTRODUCTION
An assemblage of organised, semi organised and unorganised data gathered by an
enterprise that can be excerpt for information and can be utilised in anticipatory modelling,
technological projects and other advanced analytical methods is referred as big data (Hou and
et.al., 2020). This report includes Big data definition and its characteristics. It also consists of
challenges faced due to analytics of Big Data and the methods which are currently available for
Big data analysis. Support to business due to the usage of Big Data technique have also been
determined in the following report.
MAIN BODY
1. What is meant by Big Data and what are the characteristics?
Meaning of Big Data
It is an accumulation of data that is enormous in quantity but still has a potential or power
to grow rapidly with time. The capacity of data is so large and big in terms of size that none of
the conventional data management methods and techniques can store it or operate it with
efficiency. Applications or techniques that functions or stores big data have become a general
element of data management framework in enterprises that assists usage of big data analytics.
Enterprises utilises big data techniques to improve their operations or working, and also to make
available better and improve services to its customers and promotional or marketing activities
which in turn results in increase in revenue and profits of the entity (Ianni and et.al., 2020).
Big data technology consists of various benefits with it like:
 Provides better consumer or customer services
 Improves operational efficiency of the organisation
 Understands the risk to the product or services offered by the enterprise Entities can utilise external intelligence services in decision making procedure.
Categories of Big Data
 Structured Data
 Semi-Structured Data Unstructured Data
Features of Big Data

It is a collection of data from various and different sources and commonly describe its
five characteristics:
1. Volume : It refers to the unthinkable quantity of information that is obtained from social
media, cell phones, photographs, videos and credit cards. The name big data is itself
related to a size which means huge or enormous. Size of data plays a important role in
determination of value of data (Mangla and et.al., 2020). Also, the size of data
completely relies on the volume of the data. While dealing with Big data solutions,
volume should be kept into mind.
2. Variety : This refers to the heterogenous sources and the quality of data, both organised
or unorganised are included. In the initial days, the only source of data considered by
most of the applications were databases and spreadsheets. IT is generated in different
varieties. In present scenario, photos and videos are also considered in the examination of
application.
3. Velocity : This term refers to the generation of speed of the data that how speedily a data
can be generated or processed to meet the demands and understands the capability of the
data. It plays an important role as compared to the other features of Big data. It make
accessible data on demand and at a faster speed.
4. Variability : This refers to the non regularity which can be depicts by the data in few
cases, therefore constraining the process of being capable to handle the data efficiently.
5. Value : It is the major issue of the enterprise utilising Big data technology. It is the
amount of important, dependable and honest data that requires to be preserved,
progressed and analysed to find and understand the insights.
2. What are the challenges of big data analytics and what are the methods used to analyse Big
data?
In this technical era, entities utilises big data techniques for better decision making, growth in
accessibility, increased productivity, make better and healthier enunciations, monitoring of
performance and to obtain a competitive advantage over its competitors. Although various
business issues find it complicated to utilise business intelligence analytics on the strategic level
of management (Nguyen, 2018). Entities having business intelligence and maturity in analytics,

lacks support and guidance of data. Immeasurable systems or framework issues along with
analytics issue together constitutes business data analytics problems.
Major problems faced by utilising Big data technology are discussed below :
Incorrect analytics : This is the main and important risk faced by the entity and it requires
special and speedily attention of the management of the entity to resolve it. Incorrect analytics
can be happen due to poor or inappropriate quality of data which is not complete or consists of
defects or errors and also due to faults in the system of flow of data (Sharma, Borah and
Namasudra, 2021).
Absence of informed and experts: For managing these big data's in an enterprise, it needs the
professionals and knowledgeable individuals to deal with them. These professionals can be data
scientists, data analytics. But the insufficiency of these experts makes it very complex for the
entity to handle the huge data amount and results in failure in dealing with them. .
There are a some big data analysis methods that can be utilised by entities to manage and
analyse the huge amount of data that pertains in the entities. These methods are of varied origins
and can be applicable by the enterprises according to their databases Machine learning: It includes software program that can learn from data. It gives
systems the ability to acquire without being defining programmed, and emphasis on
building projections based on known properties taken from sets of instructing data. It
helps in distinguishing between junk and non-junk mail messages and make
recommendations based on the given substance. Regression Analysis: It relates with planning some independent variable to understand
how it impacts a dependent variable. It states how the value of a dependent variable
changes when the independent variable is deviated. It works best with continual
quantifiable information like mass, velocity or age group (Vo and et.al., 2019).
 Social network analysis: A technique that was utilised firstly in the telecom sector and
then rapidly obtained by sociologists to learn interpersonal relation and applied to analyse
the relationships between groups in many sectors and commercial undertakings .
3. How does Big data help the businesses to grow, provide examples to support the answer?
With the usage of Big Data, entities can use analytics, and illustrate or build out the
most valuable or cherished consumers. It can also help industries in generation of new

experiences or content, services and products. Big data technology can assist businesses in
following ways:
Improving business operations
The outgrowth in automation is substantiated by Big Data. Robotics and high technology
may be outdated in production industry lines. But, increasingly, a number of business sectors
and operations are becoming more efficient, effective and automated. People Doc, a HR software
company, that has launched a Robotic Process Automation platform, that functions besides
present system of the company and perceive for procedure or events that could be automate.
Generating an Income
Big Data is not just about rising processes and conclusions, or knowing more about its
customer's data can be procedures and decisions, or determining more about customers data can
be monetised to encourage or create an auxiliary income stream. American Express is generating
income by the help of Big Data technology (Zhao, Xu and Wang, 2019).
Delivering Smarter services or products
When an entity come to know about its customers, it starts delivery of smarter or suitable
products or services for its consumers which meet their requirements entirely and satisfies them
to the fullest. Royal Bank of Scotland (RBS) is a great example of entity utilising Big Data to
deliver a better service to its clients. RBS is beginning to support the calibre of this knowledge to
modify and improvise its efficiency in order to meet its customer requirements.
CONCLUSION
From the above prepared report it can be concluded that, the Big data technology helps
the businesses along with facing various risks and providing techniques or methods to face those
challenges and help in the growth of the organisation.

REFERENCES
Books and Journals
Hou, R., and et.al., 2020. Unstructured big data analysis algorithm and simulation of Internet of
Things based on machine learning. Neural Computing and Applications. 32(10).
pp.5399-5407.
Ianni, M., and et.al., 2020. Fast and effective Big Data exploration by clustering. Future
generation computer systems. 102. pp.84-94.
Mangla, and et.al., 2020. Mediating effect of big data analytics on project performance of small
and medium enterprises. Journal of Enterprise Information Management.
Nguyen, T.L., 2018, December. A framework for five big v’s of big data and organizational
culture in firms. In 2018 IEEE International Conference on Big Data (Big Data) (pp.
5411-5413). IEEE.
Sharma, P., Borah, M.D. and Namasudra, S., 2021. Improving security of medical big data by
using Blockchain technology. Computers & Electrical Engineering, 96. p.107529.
Vo, A.H., and et.al., 2019. An overview of machine learning and big data for drug toxicity
evaluation. Chemical research in toxicology, 33(1). pp.20-37.
Zhao, Y., Xu, X. and Wang, M., 2019. Predicting overall customer satisfaction: Big data
evidence from hotel online textual reviews. International Journal of Hospitality
Management. 76. pp.111-121.