E-commerce Big Data Analytics: Problem Identification and Solutions

This report critically analyzes the challenges faced by e-commerce businesses in leveraging big data analytics. It begins by outlining the nature of big data, highlighting its volume, variety, value, velocity, and veracity. The report identifies key problems such as data redundancy, difficulties in quick analysis, and data security concerns through a SWOT analysis. It recommends building robust data analysis frameworks, investing in internal expertise through employee training, implementing comprehensive data security systems, and utilizing open-source software and AI-driven solutions to streamline data analysis and filtration. These recommendations aim to improve the efficiency, ethical considerations, and sustainability of big data analytics in the e-commerce sector. Desklib provides access to similar reports and study resources for students.

Big Data Analytics

Table of Contents
INTRODUCTION................................................................................................................................3
MAIN BODY.......................................................................................................................................3
Nature of big data analytics..............................................................................................................3
Problem identification......................................................................................................................4
Recommended solutions...................................................................................................................6
CONCLUSION....................................................................................................................................8
REFERENCES.....................................................................................................................................9

INTRODUCTION
The modern business environment has become quite complex and evolved with the passage
of time due to the advent of globalization which has turned the cross-border trading frameworks
appear commonplace all over the world and also due to the major leaps taken by digital
technologies. One of these major technologies which is shaping the way modern businesses conduct
their operations in the target market along with the major strategy formulation by firms is big data
analytics (Aboelmaged and Mouakket, 2020). Big data refers to the streams and sets of data that are
so exponentially large and complex in the way they increase exponentially and continuously that
their analysis by traditional means of management is next to impossible. The use of advanced
analytical techniques and tools to properly analyse such large and complex streams of data is also
called big data analysis. This report will highlight and critically analyse a real life problem
encountered by businesses related to big data analytics and propose needed solutions to address the
same.
MAIN BODY
Nature of big data analytics
Even though big data has gained rapid importance in the area of both domestic and
international business concerns, not every data set can be labelled as big data. Only extremely large
streams of data which often touch the size of petabytes and are ridiculously large to analyse
manually or through normal digital devices that businesses use such as personal computers are
identified as big data. Big data are very important or modern day businesses to not only succeed in
their target market operations but also plays a key role in business forecasting and policy
formulation (Mazanec, 2020). Such importance is derived due to the nature of big data as it contains
a lot of information in regards to a particular industry or target market which if extracted properly
can help businesses to obtain a lot of relevant information which can be used to formulate proper
strategies which can be very beneficial for the business. The nature of big data can be highlighted
by studying its real life characteristics which are listed herein in the form of 5 V's
 Volume – This refers to the volume of big data which is so dense that it is incomprehensible

for traditional systems of data analysis and some real life examples of these characteristics
include social media websites such as Facebook which generates billions of impressions
daily with the help of the like button
 Variety – Big data comes in separate varieties and consists of various types as the traditional
types of data streams are just the tip of the iceberg. Semi-structured and unstructured big
data are also very preventable such as audio, videos and images which are generated in an
incomprehensible number
 Value – This is one the biggest headaches that companies face in real life when it comes to
big data analytics because the nature of big data is such that from the entire data stream,
only some parts are of actual use to the company and that can only be achieved through
proper analysis and filtration of big data
 Velocity – Analysing big data is useless if it is not done in an optimal and speedy manner
which is why apart from building frameworks of analysing such big sets of data, another
potential problem is to build systems for fast delivery of said data
 Veracity - Another major characteristic and nature of big data involves the sheer
unreliability of data as a big part of it is unstructured and irrelevant which companies must
properly analyse in order to obtain any information of organizational use
The nature of big data makes it so that it is very difficult for firms to properly harness the
various utilities and potential perks which the technology has to offer (Bhatnagar, 2018). However
big data cannot be ignored and despite potential real life problems, the firms which have financial
and operational capabilities to exploit and analyse this technology must overcome solutions to such
problems as the upside of mastering big data means nothing short of becoming a major player in an
industry.
Problem identification
It is no doubt an established fact that big data analytics can be very helpful for companies in
order to get their operations supplemented by the most intelligent access of market information
possible but there are many real life problems that companies encounter regularly while working
with big data. A SWOT analysis is given below in context of big data analytics in the field of e-
commerce which is a framework of strategic management which is used to analyse the various
strengths, weaknesses, opportunities and threats to find a particular factor which in this case is
weaknesses.
Strengths Weaknesses

 Allows usage of smart customer
targeting through pre-meditated
advertisements online
 Facilitates customer behaviour analysis
 Big data analytics is very expensive and
an extensive undertaking (Baig, Shuib
and Yadegaridehkordi, 2019)
 It is very difficult to filter and analyse
such large streams of data
Opportunities
 Cloud integration has major implications
in operational streamlining
 The next evolution of big data analytics
can facilitate even more in-depth
analysis of market relevant information
Threats
 E-commerce companies use a lot of
important customer oriented data which
is very sensitive in nature and big data
facilitates frauds quite easily
 Data security concerns are still looming
over big data integrations over retailing
activities such as fake advertisement,
click baits and targeted phishing links
The above model went into detail regarding the potential of big data analytics in the field of
e-commerce which is one of the fastest growing industries in the entire world where big data is
being used extensively by giants such as Amazon and EBay and even by smaller scaled companies
in some capacities using subpar methods where results are still positive yet inconsistent. The
following weaknesses about big data have been identified and listed herein after critically analysing
the role of big data in e-commerce along with using the findings from the above applied framework.
 E-commerce businesses can try and invest as much time and money as possible to devise the
most effective strategy by looking up at big data but it is worthless if the data sets in
question are not relevant. Since e-commerce companies spend a lot of time in analysing the
traffic to their websites and online channels, a big portion of big data being irrelevant can
spell disaster for the companies (Begum and Nausheen, 2018) Even established companies like Amazon along with other entrants, new or old all face
major real life complications when utilizing big data as the amount of data is so
overwhelming that filtering and properly analysing only relevant data sets is turning out to
be very tedious. Another major complication which is increasing this problem is that
tampering with big data analytics is a risky business as privacy of data is a major issue in e-
commerce and companies like Amazon have already been accused and warned for having
subpar data protection systems (Amazon's former head of information security said the
company's customer data protection was a mess: 'It was all put together with tape and

bubblegum', 2022)
Among the above listed problems which have been identified with big data analytics, the
major problems which e-commerce companies are facing in real life that needs urgent solutions and
innovations to overcome is the sheer redundancy and problem of quick analysis when it comes to
big data. Companies like Amazon, Flipkart, Zingdong and even Alibaba which are market
juggernauts along with large scale companies such as retailers of clothes like Marks and Spencer
are all facing a common problem, which is that big data analysis frameworks are just not quick and
sustainable enough. This problem also gets amplified through a majority of other factors such as the
nature of big data sets making it tough for people to specialize in this field and become professional
analysts which has resulted in the entire industry starving for specialists. In order to overcome this
problem which companies seem to be having regarding the major part of big data sets being
redundant and tough to analyse consistently, they must develop measures to facilitate proper data
analysis of big data. These systematic frameworks must not only facilitate timely and consistent
metrics which can be drawn from a given data set but also do so in a manner where the privacy and
data handling of customers is not used for malicious purposes. This is especially important for
companies in e-commerce as online retail channels absorb a lot of personal information from
customers which all are stored in the company’s big data stream (Chen, 2018).
Recommended solutions
According to the above conducted analysis if the big data analytics in e-commerce industry
and the broad weaknesses associated with the real life sue of big data, it is clear that the most
extensive problem encountered in real life is the complexity in proper analysis and filtration of big
data. In order to solve or reduce the intensity of this market problem, e-commerce companies
should try to adopt the following listed recommendations which have been formulated to increase
the efficiency at which big data is analysed, filtered and processed to develop key market insights
along with being as ethical and sustainable as possible.
 E-commerce companies should build a robust data analysis framework which studies their
organizational capability to analyse big data sets and develop comprehensive statistics
regarding the various issues which are preventing integration of speedy big data analysis to
take place. This method is recommended as there are numerous factors that help pose this
problem in real life such as lack of big data analysts to hire, lack of data security and
technical glitches and knowing and regulating such stuff can help boost the productivity of
e-commerce companies. An example of the statistics that should be developed is highlighted

herein to demonstrate its practicality.
 It is also recommended that all companies including e-commerce ones should stop relying
on outside expertise completely for the purpose of performing big data analytics and should
focus on developing inwards capability. This approach majorly involves providing specialist
training to employees to teach them about big data to a point where lack of jobs do not haunt
companies like Amazon which suffer from severe technical staff shortage. This method can
take a lot of time and resource to invest but in the long run, it will help most companies
undertake big data analytics in a much faster and consistent manner
 Amidst the problems of filtration and simplifying the complexity in analysis of big data, one
of the major risks is one that is associated with the concerns over data security system and
proper handling of sensitive data. In order to keep their data analysis frameworks and
systems safe along with fast, e-commerce companies should invest in an extensive data
security system which works on four key parameters which are perimeter security, access
related to authorization, privacy systems along with daily data audits. Some major examples
of such systems which provide a boost to data analysis and make it secure include Apache
ranger and know along with wire encryption technologies (Thind and Simon, 2019)
 In order to solve the main real life problem of big data being complex and time consuming
to filter relevant information out from, companies must use open source and highly
advanced software’s along with intelligent systems built with Artificial intelligence. This can
help e-commerce companies to automate a large portion of their big data analysis needs and
focus on other areas needing their attention such as the regulation checklist recommended
above. Usage of open source software’s such as Apache Hadoop is highly recommended as
it provides specific storage and platform for the analysis of big data which is why half of the

fortune 50 companies use the software including Amazon which rules the e-commerce
marketplace.
CONCLUSION
The above report went into detail regarding the various complexities and problems that are
associated with the practice of big data analytics which has become a crucial part of the modern
business industry due to the need of smart data backed market insights. The report conducted a
detailed research on the nature and characteristics of big data and also conducted a SWOT analysis
of big data in order to deduce the major real life problems that companies face regarding big data
analytics. It was found that problems of data security, filtration and fast analysis of big data sets are
major real life problems and the report was concluded with some recommendations to solve the
same which included usage of smart big data software’s, building data security systems and charting
an industry wide regulation list in context of big data.

REFERENCES
Books and journals
Aboelmaged, M. and Mouakket, S., 2020. Influencing models and determinants in big data
analytics research: A bibliometric analysis. Information Processing & Management, 57(4),
p.102234.
Mazanec, J.A., 2020. Hidden theorizing in big data analytics: With a reference to tourism design
research. Annals of Tourism Research, 83, p.102931.
Bhatnagar, R., 2018, February. Machine learning and big data processing: a technological
perspective and review. In International Conference on Advanced Machine Learning Technologies
and Applications (pp. 468-478). Springer, Cham.
Baig, M.I., Shuib, L. and Yadegaridehkordi, E., 2019. Big data tools: Advantages and
disadvantages. Journal of Soft Computing and Decision Support Systems, 6(6), pp.14-20.
Begum, S.H. and Nausheen, F., 2018, January. A comparative analysis of differential privacy vs
other privacy mechanisms for big data. In 2018 2nd International Conference on Inventive Systems
and Control (ICISC) (pp. 512-516). IEEE.
Chen, H., 2018. Personalized recommendation system of e-commerce based on big data analysis.
Journal of Interdisciplinary Mathematics, 21(5), pp.1243-1247.
Thind, J.S. and Simon, R., 2019, June. Implementation of Big Data in cloud computing with
optimized Apache Hadoop. In 2019 3rd International conference on Electronics, Communication
and Aerospace Technology (ICECA) (pp. 997-1001). IEEE.
Online referencing
Amazon's former head of information security said the company's customer data protection was a
mess: 'It was all put together with tape and bubblegum', 2022 [online] available through <
https://www.businessinsider.in/tech/news/amazons-former-head-of-information-security-said-the-
companys-customer-data-protection-was-a-mess-it-was-all-put-together-with-tape-and-
bubblegum/articleshow/87786065.cms>