BMP4005 - Big Data Analysis: Information Systems & Business Use

This report provides a comprehensive overview of big data analysis within the context of business management and information systems. It defines big data, outlines its key characteristics (volume, variety, velocity, value, and veracity), and explores the challenges associated with its analysis, such as synchronization, lack of trained personnel, overwhelming data volume, and data security. The report details various techniques used for big data analysis, including classification tree analysis, genetic algorithms, machine learning, and regression analysis. Furthermore, it illustrates how big data technology supports business by providing better insights into buyer behavior, improving consumer targeting, and enhancing supply chain management, with examples from companies like Coca-Cola, Netflix, and PepsiCo. The report concludes by emphasizing the immense scope of big data analysis in facilitating better quality products and services.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
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Table of Contents
Introduction................................................................................................................................3
Main body ..................................................................................................................................3
What big data is and the characteristics of big data........................................................3
The challenges of big data analytics.................................................................................4
The techniques that are currently available to analyze big data.....................................5
How Big Data technology could support business, an explanation with examples.........6
Conclusion .................................................................................................................................6
Poster.........................................................................................................................................7
References .................................................................................................................................8
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Introduction
Data sets are used heavily in the current business landscape and are used to
supplement and design a wide spectrum of company operations and policy and plan
formulation. The aforementioned streams and collections of data come in different types and
from along them big data is one of the most important avenues for modern businesses as it
refers to the pool of data which is immense in nature and contains large volume of
information pertaining to multiple dimensions of the business environment (Awan, and et.al.,
2021).
This report will highlight the importance and prevalence of big data in current market
proceedings by business enterprises by describing the nature and characteristics along with
the various difficulties that companies face in utilizing streams of data of such large volume.
The process of big data analytics will also be explained in detail which refers to the
exhaustive and technological advanced process of extracting and filtering large amounts of
data.
Main body
What big data is and the characteristics of big data
The type of data which contains huge amounts of information variables and keeps on
increasing at a pace which renders it unable to be processed and analyzed using traditional
tools and techniques of business management is known popularly as big data. The examples
of big data include stock exchanges and social media where large volumes of unprocessed
data flows inwards daily (Dahiya, and et.al., 2021). There are certain characteristics
associated with big data which are all listed herein.
 Volume – Whether or not a given collection of various sources of information can be
labeled as big data depends upon the size and volume of the data as business
technically gain a lot of statistics from business logs, transactions and market
research.
 Variety – Big data can also be identified and labeled based on the different sources
where it is collected from it is also formatted and stored in multiple forms and devices
such as audio and video files along with PDF's.
 Velocity – This characteristic is related to the speed and overall flow of big data as
the data of such large volume is always continuous and keeps on increasing over time.
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Analysis can only be performed on big data by accounting for each and every change
in it's flow daily which is also the reason why routine analytical tools are not
compatible with big data.
 Value – Big data is very useful which is the reason for the popularity of it's analytics
despite the process being overly time consuming and costly. Businesses only invest
their time on a data set if it correlates directly with their market success which big
data often does.
 Veracity – The overall security, reliability and authenticity of data is defined through
this characteristic as big data contains huge amounts of statistics which might contain
a lot of information which is fake, harmful and not of any use to a company at the
given time which must be filtered out.
The challenges of big data analytics
The process of analyzing, segregating, filtering and statistically evaluating the large
amount of big data in order to extract valuable information for use in strategic planning and
business operations is called big data analytics. This procedure is employed by various
companies across a wide spectrum of industries due to it's appeal and the amount of resources
one can gain from studying it (Giacalone, Cusatelli and Santarcangelo, 2018). However, there
are many challenges that corporate face while undertaking such complex analysis which are
detailed below.
 Constant need of synchronization – Since the amount of big data being analyzed by
businesses have been increasing in complexity and size due to the different channels
from which they are generated, keeping them incorporated and with sync with each
other on a single platform is very hard.
 Lack of trained personnel – There is a large disparity between the amount of big
data being generated with each passing second and the amount of actual researchers
and analysts of big data which has made it very difficult for firms to recruit talented
candidates which are capable to work on big data.
 Overwhelming volume of modern data – The sheer amount of big data that is
getting compiled nowadays by big businesses is mindbogglingly large and exists in
multiple zettabytes which is enough to overwhelm most big data experts and analysts
operating around the globe. The process of due analysis of big data is a very
exhaustive and laborious undertaking.
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 Data security – Even though big data analytics open up multiple opportunities for
companies to gain advantages in their inner workings and their target market
operations, the tools and techniques used for this process can leave the extracted data
vulnerable across multiple platforms risking exposure of sensitive data which can be
detrimental for businesses and their clientele (Huadong, 2018).
The techniques that are currently available to analyze big data
Despite big data being way too complex and large for most regular businesses to give
up their pursuit of it's collection and analysis, large businesses are increasingly harnessing
complex data sets using various technical approaches which facilitates the solutions to
difficult market problems that they encounter on a routine basis. Some techniques which are
currently available to analyze big data are described below.
 Classification tree analysis – This analysis is used to filter and group big data
statistics into different homogeneous categories based on the identification of
common tendencies and displayed behavioral properties (Sedkaoui, 2018). The use of
this approach has been widely reported in organism classification and arranging
business data in a proper order such as number of students attending school.
 Generic Algorithms – Big data is not only analyzed by this technique but it is also
streamlined and optimized as the information is extracted and sorted with the intent of
developing evolutionary solutions for complex business problems. Starting and
developing social media campaigns and planning TV broadcast timings are some
examples.
 Machine Learning – When it comes to conducting complex and large amount of
analyses under a short duration, machines are far more efficient than humans which is
why special software's and commands are put in place which study and restructure big
data according to the customized needs of the firm. Many businesses run their own
machines and applications for analysis of big data.
 Regression Analysis – This is another commonly used technique to dissect and form
solutions from big data as it consists of recording changes made to a dependent
statistic when there is manipulation made in the independent variable deliberately. For
example this technique is used by retailers to analyze the change in the amount of
time people spend inside their store premises when the music being played inside the
store changes.
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How Big Data technology could support business, an explanation with examples
The usage scope of big data is immense for companies who can afford to run costly
and time consuming analysis techniques on it and possess competent staff which can design
the policies in accordance with the information found. Big data technology can help support
businesses in a variety of ways, some of which are described below.
 Gaining better insight into buyer behavior and habits – Big data supports business
enterprises by helping supply accurate information about the current trends which are
dominating a particular industry and the way consumers have been reacting to the
product lines of the firm along with it's competitors. This helps companies like Coca-
Cola stay relevant in global beverage industry despite operating for more than 50
years as they collect and analyze buyer behavior statistics using big data from their
social media and hardware store channels.
 Better and more accurate targeting of consumers – Big data also helps in refining
the current existing STP and marketing protocols of businesses by facilitating much
more focused targeting of people for their particular product and service lines which
makes the process of market penetration much more effective (Zhang, and et.al.,
2021). An example is the firm Netflix, which is a streaming content giant and uses big
data analytic to recommend consumers certain TV series based on their content
watching history.
 Improvement in supply chain management – Through the use of big data
technologies and analytical tools, the supply chain of firms can be bolstered and
constraints and disruptions which occur in traditional approach can be altered and
avoided through big data. PepsiCo is a large company in the packaged goods industry
and uses a large volume of big data analysis facilitated through cutting edge
technology to make sure the products in demand are fully stocked for delivery.
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Poster
Conclusion
The above concluded report highlighted the current role that big data and it's analysis
plays in business and it's immense scope in facilitating much better quality of products and
services to the final consumer. The report included the definition and characteristics of big
data along with the difficulties which companies face during their analysis of such a large and
unstable volume of data. The various currently available techniques and technologies which
are helping in analyzing big data by companies was described along with the various ways I
which big data helps support businesses with examples.
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References
Books and Journals
Awan, and et.al., 2021. Big data analytics capability and decision-making: The role of data-
driven insight on circular economy performance. Technological Forecasting and
Social Change, 168, p.120766.
Dahiya, and et.al., 2021. Big data analytics and competitive advantage: the strategic role of
firm-specific knowledge. Journal of Strategy and Management.
Giacalone, M., Cusatelli, C. and Santarcangelo, V., 2018. Big data compliance for innovative
clinical models. Big data research, 12, pp.35-40.
Huadong, G., 2018. Scientific big data—A footstone of national strategy for big data.
Bulletin of Chinese Academy of Sciences (Chinese Version), 33(8), pp.768-773.
Sedkaoui, S., 2018. Data analytics and big data. John Wiley & Sons.
Zhang, and et.al., 2021. Edge learning: The enabling technology for distributed big data
analytics in the edge. ACM Computing Surveys (CSUR), 54(7), pp.1-36.
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