Value Creation using Big Data

This report discusses the benefits of big data for the retailing industry, including better customer insight, new revenue opportunities, and introducing new products. It also covers the four drivers of big data and how they can create value for a business, as well as Porter's value chain analysis and Porter five forces analysis for Morrison in the retailing industry.

Value Creation using
Big Data

Executive summary
Big data is a type of management of digital data where companies use software and other
computer tools to manage and arrange their huge dataset in a proper manner so that they will
utilize the data for decision making process of the company. There are four drivers of big data
such as Structured, where the data is structured in a proper manner, Unstructured, where the
data is collected first and then it is sorted or evaluated by the software. Latency is another big
data driver which will provide the appropriate time and Predictive analytics which will help to
analyse future trends and demand for the company. The following report is based on usage of big
data in retailing industry and how the industry grow with various kinds of opportunities and
value creation strategies by big data.

Table of Contents
Executive summary.........................................................................................................................2
Introduction......................................................................................................................................4
Main Body.......................................................................................................................................4
PART A...........................................................................................................................................4
Big data opportunities for retailing industry................................................................................4
Value creation using big data......................................................................................................5
Porter’s value chain analysis.......................................................................................................5
Porter five forces analysis............................................................................................................7
Conclusion.......................................................................................................................................8
References........................................................................................................................................8
Online-.............................................................................................................................................9
PART B.........................................................................................................................................11
Dataset identification.................................................................................................................11
Metadata of the chosen database...............................................................................................12
Business opportunities to the chosen dataset.............................................................................13

Introduction
Big data refers to those data of a company which is huge in size and complex to manage
by traditional data-processing application software (Hancock and Khoshgoftaar, 2020). Retailing
industry is one of the fastest growing industries in UK. Various retailing companies’ huge
updated and modern software for managing their digital data effectively and keep them safe and
secure from external threats. Value of the company can be created through effective management
of organisational data. Morrison is the British retailing company which is founded on 1899 by
William Morrison and it is headquartered in Bradford, UK. The following report covers big data
opportunities for the company, value creation for big data, porter chain analysis and porter five
forces analysis for Morrison in retailing industry.
Main Body
PART A
Big data opportunities for retailing industry
For retailing company, big data is one of the best opportunities for them to analyse
purchasing habit of their customers and the ways to attract their customers toward their product
and services (Cockcroft and Russell, 2018). There are few of the benefits which can be utilize by
a company like Morrison to gain high benefit within the market and few of the benefits are
mentioned below-
Better customer insight- Big data management will help the company to analyse their
customer’s habit and their influencing factors within the market. E-commerce activities are best
example for big data where companies can get to know that which item their customers are
searching in search box and which they are adding to their cart and then remove from the cart
(Zhang and et. al., 2018). Each activity of customer can be tracked through big data. This could
help the company to analyse customer’s needs and wants till the payment is made.
Create new revenue opportunities for the company- When the data is effectively
managed by the company and there is no hacking activity by hackers related to the business then
this will help the company to gain high reputation in market which further helps them to gain
more customers (Weersink and et. al., 2018). Therefore, the revenue will be enhanced by the
industry. On the other hand, by using effective and updated software for managing big data will

help the company to sort out their productive activities and unproductive activities so that they
will avoid investing their unproductive activities and saving their earnings.
Using big data to introduce new service and product within the market- Companies
like Morrison collect their customer’s personal data like their phone number, e-mail address and
many others. They send e-mails or text messages to their customers collectively through digital
technology about their new product launch within the market.
Value creation using big data
There are four big data business drivers which can create value for the retailing industry and
these four drivers are mentioned below-
Structured data- It is that data which has perfect format and structure before being placed in
data storage (Côrte-Real and et. al., 2019). The advantages of this kinds of data is that it can be
easily used by machine learning algorithms, easily used by business users, increase access to
more tools and many other advantages.
Unstructured data- It is the data which is stored in a native format and it is not processed until
it is used. The advantages of unstructured data for retailing industry is that it faster accumulation
rates which means there is no need to predefined the data and it can be collected quickly and
easily.
Latency data- It is the time which is taken by computers programs to process a high volume of
data messages with minimum delay (Wenzel and Van Quaquebeke, 2018). This will help to
collect data quickly and arrange them in a structured manner which is useful for the company so
that they will respond to their customers in a quick manner to gain high market reputation.
Predictive analytics- It is used to predict about future outcomes and trends within the industry
by using historical data which is combined with data mining techniques, statistical modelling and
machine learning (El-Kassar and Singh, 2019). This will help the Morrison Company to analyse
their future trends and predict their future demand so that they will manage their inventory
accordingly. The retailing companies who predict their future trends and demand can gain high
value of their company in the market.
Porter’s value chain analysis
Porter’s value chain analysis will help to analyse all activities within the company to produce the
product and gain high value within the market. There are two types of activities according to this
model which are explained below-

Primary activities- It includes all the actions that go into the creation of business offerings. Few
of the primary activities of Morrison is mentioned below-
 Inbound logistics- Here, the raw material is gained by suppliers (Fonseca and et. al.,
2019). Big data can be used to identify how the quantity which Morrison required to
order from their suppliers. Hence, low latency data can be used here by analysing
requirement as quick as possible and order rapidly.
 Operations- It includes converting raw material into finished goods. Identifying the cost
of running machinery, warehouse and assembly is essential.
 Outbound logistics- It will describe how products is distributed from one place to other.
Structured driver of big data can be used here by the chosen business in chosen industry.
When manufactured product is getting distributed from one place to another than it is
essential for the company to analyse which warehouse has more capability to store their
products hence, this is identified by structured data.
 Marketing and sales- Here, company is focused to promote and sell the product within
the market. Predictive analytics can be used here because it will help to analyse trends
and new techniques to promote and sell their products in market which will gain more
attention of customers.
 Services- This is the support which is provided to customer after they purchase the
product to gain high market value and loyal customers. Structure data can be used here by
the company where they can collect data of their customers and analyse their issue after
tracking their purchase activities.
Support activities- These are the activities which support primary activities to gain competitive
advantage within the market. Few of the supporting activities are mentioned below-
 Human resource management- It include all activities and system which is used by
company to manage their employees and their performance (Munasinghe and et. al.,
2019).
 Technology development- Modern and advance technology will help the company to
produce good quality of products. Hence, it will help the company to gain competitive
advantage.
 Procurement- It is the way to found suppliers and required resources appropriately. The
main aim is to find quality suppliers which can fit into the business.

Porter five forces analysis
This is the framework which is used to analyse the level of competition within the industry and
what all strategies can help a company to gain competitive advantage. Porter five forces for the
retailing industry is mentioned below-
 Threat of new entrants- This element will describe how a company will get impacted
when new companies will enter the industry (Chesula and Kiriinya, 2018). In context of
Morrison, they keep the records of their competitors within their digital records and use
software to analyse the investment made by the new company in market. Hence,
unstructured data of used to collect information about new company in a quick manner
and then make arrangements of information by using appropriate software.
 Threat of substitute products- There are various kinds of companies in retailing
industry of UK. Hence, it might possible that the companies will copy each other’s
strategy and products as well as brand name to gain high profit. Big data is also used in
eliminating the threat of substitute products by using predictive analytics. Here, company
can predict future requirement of their customers and adopt the technique and ways as
soon as possible to meet future trend.
 Bargaining power of suppliers- Here, Morrison can use unstructured analytics of big
data where they first collect information of various suppliers available within their
industry and then they can sort the data by comparing the quality and budget.
Unstructured analytics will help the Morrison to analyse suitable supplier for them within
the industry.
 Bargaining power of customers- It is essential for every company to satisfy their
customers and Morrison can satisfy their customers by providing them information such
as discounts, offers, closing and opening time of store, introduction of new product and
services and many others. Hence, they can use structured analytics of big data where they
can collectively send e-mail or text messages to their customers collectively by using
computers and software.
 Industry Rivalry- It helps to analyse the available competitors and their power to impact
upon chosen company (Juliana and Nyoman, 2019). In context of retailing industry, they
are considered as one of the most competitive rivalry industry in UK. Hence, Morrison
can gain competitive advantage through adopting trends and innovative method to satisfy

customers quicker than their competitors. Hence, Predictive analytics driver of big data
can be used by the company to analyse their future trends and new techniques to satisfy
their customers so that they will change their procedure quicker than other companies in
the industry.
Conclusion
From the above information it is concluded that big data are those data which consist of
collecting, storing and utilizing huge number of information by a company. Various digital tools
and technology is used to sort out the information for a company and help them to use the data
for their business growth. There are various big data opportunities for the companies operating in
retailing industry and this opportunities are Better customer insight, it create new revenue
opportunities for the company and it is also used to introduce new service and product within the
market. There are four drivers to big data for which help the business to create value for their
business and these four drivers are structured, unstructured, latency and predictive analytics.
Porter value chain analysis will help to identify primary activities of a business which help them
to produce their goods and services whereas supportive activities support primary activities for
business success. The four drivers of big data can be used in value chain analysis of a company
to mitigate their issues and perform their business activities appropriately. These four drivers can
also be used for porter five forces to gain competitive advantage by a company.
References
Books and Journals

Chesula, O.W. and Kiriinya, S.N., 2018. Competitiveness In The Telecommunication Sector In
Kenya Using Porters Five Forces Model. International Journal of Research in Finance
and Marketing, 8(7), pp.2231-5985.
Cockcroft, S. and Russell, M., 2018. Big data opportunities for accounting and finance practice
and research. Australian Accounting Review, 28(3), pp.323-333.
Côrte-Real, N. and et. al., 2019. Unlocking the drivers of big data analytics value in
firms. Journal of Business Research, 97, pp.160-173.
El-Kassar, A.N. and Singh, S.K., 2019. Green innovation and organizational performance: the
influence of big data and the moderating role of management commitment and HR
practices. Technological Forecasting and Social Change, 144, pp.483-498.
Fonseca, C.M.B. and et. al., 2019. Value chain analysis: Overview and context for
development. Journal of Agriculture and Food Science, 7(12), pp.356-361.
Hancock, J.T. and Khoshgoftaar, T.M., 2020. CatBoost for big data: an interdisciplinary
review. Journal of big data, 7(1), pp.1-45.
Juliana, J.P.E. and Nyoman, Y.N., 2019. Factors influencing competitiveness of small and
medium industry of Bali: Porter’s five forces analysis. Russian Journal of Agricultural
and Socio-Economic Sciences, 89(5).
Munasinghe, M. and et. al., 2019. Value–Supply Chain Analysis (VSCA) of crude palm oil
production in Brazil, focusing on economic, environmental and social
sustainability. Sustainable Production and Consumption, 17, pp.161-175.
Wang, Z., Lachmann, A. and Ma’ayan, A., 2019. Mining data and metadata from the gene
expression omnibus. Biophysical reviews, 11(1), pp.103-110.
Weersink, A. and et. al., 2018. Opportunities and challenges for big data in agricultural and
environmental analysis. Annual Review of Resource Economics, 10, pp.19-37.
Wenzel, R. and Van Quaquebeke, N., 2018. The double-edged sword of big data in
organizational and management research: A review of opportunities and
risks. Organizational Research Methods, 21(3), pp.548-591.
Zhang, N. and et. al., 2018. Synergy of big data and 5G wireless networks: opportunities,
approaches, and challenges. IEEE Wireless Communications, 25(1), pp.12-18.
Online-
E-Commerce Data, 2017 [Online] available through:

https://www.kaggle.com/datasets/carrie1/ecommerce-data/

PART B
Dataset identification
A dataset is collection of data at a single place. Today, most of the companies use various kinds
of software and other technological tools to collect their business related data such as their
transaction history, customer ID, description of product which is sold and many others. Here, a
example of dataset of retailing industry is mentioned below-

This data is chosen among other data set because it is well structured and easy to understand. It is
managed in a proper manner by using appropriate raw and columns. Other datasets are not well
structured and the information about the customers and other requirements are mentioned in a
paragraph wise which does not seems interesting to read and understand. Hence, that’s why only
this dataset is selected over others from retailing industry.
Metadata of the chosen database
Metadata is a set of data which describes and provide easy information about other data so that it
can be understood easily by others (Wang, Lachmann and Ma’ayan, 2019). Hence, the
explanation of above chosen dataset is mentioned below-
 The first column of the chosen dataset is invoice number of bills of different customers.
At the bottom it is mentioned that 25,900 invoices has been used by the company till the
date.
 Second column is stock code of the product. Every product is given a special kind of
code which is different from other code of products which can be scanned digitally. 4090
are the unique values of the stock code.
 Third column is related to the description of product that which product is purchased by
customers. It mentioned the name of product and 4224 is the unique value which is
purchased by the retail customers of this store.

 Forth column is related to quantity which describe the total number of quantity of
mentioned product is purchased by the customer. For example, 6 white hanging heart T-
shirt holders is purchased by a customer, 32 assorted colour bird ornaments are purchased
by another customer. Hence, this column will describe the total number of quantity of
specific product purchased by customer.
 Fifth column of the chosen dataset will describe the invoice date and time, it means that
at which date and time the customer has purchased their selected items. For example, 6
white hanger hearts T-shirt holder is purchased on 1 December 2010 at 8:26 Am.
 Sixth column is unit price which will describe the price of product per unit. For example,
white metal lantern is having price of $3.39 per unit and cream cupid hearts coat hanger
is having the price of $2.75 per unit. This price is get multiplied by the total number of
quantity purchased by the customer to analyse the total price for the specific product
purchased by customer.
 Seventh column is related to customer ID and the eight columns which is the last column
of the given dataset are related to country. It means that the product is purchased from
which specific country by the customer.
Business opportunities to the chosen dataset
From the chosen dataset it is evaluated that the companies can identify the information very
easily about the product which they want to search. The chosen dataset is well structured and
helpful for the retailers to identify the information easily. One of the best business opportunities
for the chosen dataset to the retailing industry is that they can identify those products which are
having more sales as well as those products which are having least sales by analysing total
number of quantity of the product purchased by their customers. Retailing companies can also
analyse their per day sales by using chosen dataset structure.