BSc Business Management: Information Systems and Big Data Analysis

This report provides an overview of big data, emphasizing its characteristics such as volume, variety, and velocity, and explores the challenges associated with big data analytics, including issues like timely insights, inaccuracy, and high maintenance costs. It discusses various techniques for analyzing big data, such as A/B testing, data fusion, data mining, machine learning, and natural language processing. The report also highlights how big data technology can support business by reducing costs, increasing sales and revenue, improving pricing decisions, providing a competitive advantage, and enhancing efficiency in decision-making. The included poster visually summarizes key aspects of the analysis. It concludes that big data is a crucial component for businesses seeking to make informed decisions and increase profits, while also acknowledging the importance of addressing the challenges and costs associated with its implementation. Desklib provides access to similar solved assignments and resources for students.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
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Contents
Introduction 3
Characteristics of big data 3-4
The challenges of big data analytics 4-5
Techniques currently available to analysis big data 5
How Big Data technology could support business 6
Poster 7
Conclusion 8
References 9
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Introduction
Big data refers to the data which have greater variety,huge volumes and more
velocity. They are referred to as three Vs. It is a bigger,complex set of data from new
data sources. The data is so huge that old processing software can not process it.
They can be used to address business issues. The concept is new but the origins of it
goes back to 1960s. It makes to gain more answers as they have more information and
puts a complete new approach towards tackling problems. It helps in addressing
various business operations like goods improvement,prognostic care,consumer
experience,fraud and abidance,device learning and many more. This report will deal
with the topic of big data,what are the challenges of large data analytic(Ardito and
et,al.,2018). The features of big data and techniques for analyzing the big data will also
be mentioned. Big data technologies support to business will also be studied.
Characteristics of big data
Big data have huge volume of data which can not be processed by old softwares.
It is mainly used by multi national companies for processing data and business of many
companies(Dinh, Karmakar and Kamruzzaman,). Before replication data flow should be
exceed from 150 exabytes. Here are some of the features of Big information-
 Volumes- Big data refers to the larges size of data. It is generated by many
sources on daily basis like process of business,social media
level,system,human like action etc. Facebook and its like many companies
generates billion messages,3.6 million times when the like button is on,only
big data technologies handles these amounts of data( 2020Liang and Liu,
2018).
 Variety- It can be sectioned in to organized,unorganised and semi organized
data which is gathered from various origin. It is from databases and sheets
from past. Now a days data come in different forms like
PDFss,Emails,audios,SM posts,photos etc. Structure is in tabular
forms,semi structured is in tables and unstructured contains log files etc.
 Veracity- It means how much reliable is the data. The data is filtered or
translated by many ways. It is the process of handling and managing the
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data effectively and efficiently. It is essential in business growth also. Like
hashtags in face book posts(Maheshwari, Gautam and Jaggi, 2021).
◦ Value- It is most important characteristics of big information. It is not
processed data or stored data. It is the valuables and dependable
information which business stores ,procedure and analyses.
 Velocity- It show crucial function in comparison to another. It makes the
velocity through which information is created in actual time. It includes the
link of inbound information sets velocity,charge per unit and act busted. Big
data provides the data fast.
The challenges of big data analytics
In present technology global,institution uses big data analytic for determination
devising,gain answerability,rise productiveness,make finer anticipation,display execution
and increase plus(Raut and et, al., 2019) . Many business face problems in anaytics tools.
It can be created by system or infrastructure problems. Here are some of the challenges
of big data analytics-
 Fails to provide timely insights- Businesses invest in analytics solution for
taking decisions and if it provides same information which was earlier
available. Lack of data integration or poor organization will have lack of data
for providing insights. For resolving this a data audit run is done, The use of
new data sources will provide with solution.
 Inaccuracy- Systems relies on data which can have defects,errors or they are
incomplete,which will provide poor results to the business. It happens when
the required systems are not omitted or are not fulfilled while
development ,testing or verification process. Detailed review of the system to
checks if it is fault free(Upadhyay and Kumar, 2020).
 Complicated- If the solution is complicated business may find hard to extract
value from data. It boils down to UX or to technical aspects. Reports have
high complexity which uses more hardware resources and increase costs.
The system hangs and solution is complicated.
 Long effect time- If the group takes more time for analyzing the information
even if input signal data is available and written report is urgent. In real time
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systems it may cost to companies. Data if not organized well creates this
problem,or the system has this problem where it has reached its scalability
limit.
 Very expensive maintenance- It requires high investment for care and
structure and every business organization wants to reduce costs. As new
technology are invented old one is of no use to business ,they have to invest
in new ones which causes high investment to companies.
Techniques currently available to analysis big data
Data analysis refers to the process of analyzing the sets of data and make
conclusions about the information . All these are done by specific systems,software's and
methods. Here are some of the techniques available for analyse big data-
 A/B testing- It involves comparison grouping with various test groups,to know
what attention or alteration that will better the given subjective covariant.
McKinsey provides with the illustration of examine what transcript,textual
matter,images will better taxation in -commerce site. This model fits into big
data as it can test large numbers(Verma, Bhattacharyya and Kumar, 2018).
 Data fusion and data integration- It includes a primed set of method which
combines for analyzing and integrating data from various sources and
solutions. The information is cost-efficient and faithful if formulated individual
source of data.
 Data mining- It is tool used with big data analytics it infusion shape from sets
of data by combine methods by using applied mathematics and device
learning. For ex- consumer data mining to know segments for offering.
 Machine learning- It is used in artificial intelligence for data investigation. It
works with algorithmic program to get postulate based on data. The
predictions can not be done by any human. It consists of softwares that learn
from data. It provides the systems to ability to learn without being
programmed and focuses on predictions.
 Natural language processing(NLP)- It is also known as subsidiary of data
processor science,artificial ability and uses for human language analysis by
algorithms.
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How Big Data technology could support business
Big data is not big for businesses,but needed for patterns,trends,consumers
preferences and various other information. Big data makes it easy for business to make
smart decisions for competing with the competitors and increase revenue and profits.
Some factors by which big data can support business-
 Big data reduces costs- For reducing cost efficiency is needed. Big
data points out the inefficiency of business operations and provide
solution for it. Data may tell consumers have no interest in buying gifts
at check out. It tells the business to remove that offering which result
in reduced cost.
 Increases sales and revenue- It gives businesses to know the
customers preferences to know how they can modify the goods and
services to give consumers what they require and increase revenue.
 Big data improves pricing decisions- The prices of products and
services may have impact on the success of business. Tools of big
data helps business in finances and pricing techniques of competitors.
They can reduce or increase the prices as per the analysis.
 Provides competitive advantage- It provides business to mainly
focus on consumer behavior. Tools provide insight on various buying
behaviors of consumers. Businesses get the idea of liking and disliking
of consumers,so that they can provide them excellent services which
will give them advantage.
 Increases efficiency in decision making- Data mining help business
to take the best decision for their business on the basis of big data
analytics. Like the societal media platforms which gathers information
to know about the consumer behavior,interests etc. These help them
in target marketing for segmenting the markets.
Poster
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Conclusion
This report concludes that big data is important part of business. It compiles the
large data into useful information for business to take effective decisions. It also tells
about its characteristics and challenges faced by big data analytic like
inaccuracy,expensive etc. The project also mentions the various types of big data
analytics measurement tools and how can it support to business . Big data helps
business in many aspects to increase profits and revenue.
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References
Ardito, L., and et,al.,2018. A bibliometric analysis of research on Big Data analytics for
business and management. Management Decision.
Dinh, L.T.N., Karmakar, G. and Kamruzzaman, J., 2020. A survey on context awareness in
big data analytics for business applications. Knowledge and Information
Systems, 62(9), pp.3387-3415.
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.
Maheshwari, S., Gautam, P. and Jaggi, C.K., 2021. Role of big data analytics in supply chain
management: current trends and future perspectives. International Journal of
Production Research, 59(6), pp.1875-1900.
Raut, R.D.and et, al., 2019. Linking big data analytics and operational sustainability
practices for sustainable business management. Journal of cleaner production, 224,
pp.10-24.
Upadhyay, P. and Kumar, A., 2020. The intermediating role of organizational culture and
internal analytical knowledge between the capability of big data analytics and a
firm’s performance. International Journal of Information Management, 52,
p.102100.
Verma, S., Bhattacharyya, S.S. and Kumar, S., 2018. An extension of the technology
acceptance model in the big data analytics system implementation
environment. Information Processing & Management, 54(5), pp.791-806.
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