Data Analysis Research Paper 2022
Added on 2022-09-18
38 Pages11003 Words26 Views
1
Data Brain Box
Student’s Name
Institutional Affiliation
Data Brain Box
Student’s Name
Institutional Affiliation
2
Abstract
Data analysis is an important component for e-commerce businesses. In the
contemporary business environment, businesses have to deal with vast amounts of data. To
help aid the process of analysis, visualizing and drawing important conclusions from business
data, this paper focuses of how to develop a data brain box that can be used for that purpose.
While there are several tools available for data analysis, the data brain box will be unique
since it will be used across different platforms and will have the ability to analyse data from
different sources. Big data analytics are very important in the modern e-commerce businesses
as they help collect and analyse data which can aid a business in making important decisions.
The amount of data that a business has to deal with has been growing exponentially in the last
few years and this trend is expected to continue. The methodology will involve seeking the
services and opinions of different experts including software engineers and business leaders.
The software will be made using some of the latest technologies in artificial intelligence such
as neural networks and natural language processing. All applicable local and international
laws will be looked into to ensure compliance. The potential risk of cybercrime will be
alleviated/ minimised through ensuring that no loopholes are left. In addition, the software
will be constantly monitored by a team of cyber security experts.
Abstract
Data analysis is an important component for e-commerce businesses. In the
contemporary business environment, businesses have to deal with vast amounts of data. To
help aid the process of analysis, visualizing and drawing important conclusions from business
data, this paper focuses of how to develop a data brain box that can be used for that purpose.
While there are several tools available for data analysis, the data brain box will be unique
since it will be used across different platforms and will have the ability to analyse data from
different sources. Big data analytics are very important in the modern e-commerce businesses
as they help collect and analyse data which can aid a business in making important decisions.
The amount of data that a business has to deal with has been growing exponentially in the last
few years and this trend is expected to continue. The methodology will involve seeking the
services and opinions of different experts including software engineers and business leaders.
The software will be made using some of the latest technologies in artificial intelligence such
as neural networks and natural language processing. All applicable local and international
laws will be looked into to ensure compliance. The potential risk of cybercrime will be
alleviated/ minimised through ensuring that no loopholes are left. In addition, the software
will be constantly monitored by a team of cyber security experts.
3
Acknowledgement
Acknowledgement
4
Contents
Abstract ........................................................................................................................................................... 2
Acknowledgement............................................................................................................................................ 3
1.1 Introduction ......................................................................................................................................... 5
1.1.1 Overview of Data Mining .................................................................................................................... 6
1.1.2 Motivation ........................................................................................................................................... 6
1.1.3 Aim and Objectives ............................................................................................................................. 7
1.2 Report Structure................................................................................................................................... 7
2 CHAPTER 2 ............................................................................................................................................ 8
2.1 Literature Review ................................................................................................................................ 8
2.2 Big Data Analytics............................................................................................................................. 10
2.2.1 Volume.............................................................................................................................................. 13
2.2.2 Velocity............................................................................................................................................. 14
2.2.3 Value ................................................................................................................................................. 15
2.2.4 Variety .............................................................................................................................................. 16
2.2.5 Veracity............................................................................................................................................. 17
2.3 Data Mining ...................................................................................................................................... 18
2.4 Data Mining Steps ............................................................................................................................. 19
2.4.1 BUSINESS UNDERSTANDING ...................................................................................................... 20
2.4.2 DATA UNDERSTANDING .............................................................................................................. 20
2.4.3 DATA PREPARATION .................................................................................................................... 20
2.4.4 MODELING.......................................................................................................................................... 21
2.4.5 EVALUATION ................................................................................................................................. 21
2.4.6 DEPLOYMENT ................................................................................................................................ 21
2.5 DATA MINING MODEL AND TECHNIQUES ................................................................................ 22
2.5.1 ASSOCIATION RULES.................................................................................................................... 22
2.5.2 CLASSIFICATION ........................................................................................................................... 22
2.5.3 ARITIFICIAL NEURAL NETWORK (ANN).................................................................................... 23
2.5.4 DECISION TREE.............................................................................................................................. 24
2.5.5 RANDOM FOREST .......................................................................................................................... 24
2.6 RELATED WORK ............................................................................................................................ 25
2.6.1 Market Basket Analysis ..................................................................................................................... 25
2.6.1.1 Sales Forecasting ........................................................................................................................... 25
2.6.1.2 Customer Profiling ........................................................................................................................ 26
2.6.1.3 Click Stream Data.......................................................................................................................... 26
2.6.1.4: Use of Data Brain Boxes in the Past ................................................................................................... 27
3 CHAPTER 3 .......................................................................................................................................... 28
3.1 Evaluation Methodology .................................................................................................................... 28
3.2 Development Methodology ................................................................................................................ 29
4 CHAPTER 4 .......................................................................................................................................... 29
4.1 Requirements Analysis ...................................................................................................................... 29
4.1.1 Functional Requirements.................................................................................................................... 30
4.1.2 Data Collection – ............................................................................................................................... 30
4.1.3 Data Storage – ................................................................................................................................... 31
4.1.4 Data Analytics – ................................................................................................................................ 31
4.1.5 Predictive Analysis – ......................................................................................................................... 31
4.2 Non-Functional Requirements ............................................................................................................ 31
5 CHAPTER 5 .......................................................................................................................................... 32
5.1 PROFESSIONAL, LEGAL AND ETHICAL ISSUES ........................................................................ 32
5.1.1 Professional Issues and Legal Issues................................................................................................... 32
5.1.2 Ethical Issues ..................................................................................................................................... 32
6 CHAPTER 6 .......................................................................................................................................... 33
6.1 PROJECT PLAN ...................................................................................................................................... 33
6.2 METHODOLOGY ................................................................................................................................... 34
6.3 RISK MANAGEMENT............................................................................................................................ 34
Bibliography .................................................................................................................................................. 35
Contents
Abstract ........................................................................................................................................................... 2
Acknowledgement............................................................................................................................................ 3
1.1 Introduction ......................................................................................................................................... 5
1.1.1 Overview of Data Mining .................................................................................................................... 6
1.1.2 Motivation ........................................................................................................................................... 6
1.1.3 Aim and Objectives ............................................................................................................................. 7
1.2 Report Structure................................................................................................................................... 7
2 CHAPTER 2 ............................................................................................................................................ 8
2.1 Literature Review ................................................................................................................................ 8
2.2 Big Data Analytics............................................................................................................................. 10
2.2.1 Volume.............................................................................................................................................. 13
2.2.2 Velocity............................................................................................................................................. 14
2.2.3 Value ................................................................................................................................................. 15
2.2.4 Variety .............................................................................................................................................. 16
2.2.5 Veracity............................................................................................................................................. 17
2.3 Data Mining ...................................................................................................................................... 18
2.4 Data Mining Steps ............................................................................................................................. 19
2.4.1 BUSINESS UNDERSTANDING ...................................................................................................... 20
2.4.2 DATA UNDERSTANDING .............................................................................................................. 20
2.4.3 DATA PREPARATION .................................................................................................................... 20
2.4.4 MODELING.......................................................................................................................................... 21
2.4.5 EVALUATION ................................................................................................................................. 21
2.4.6 DEPLOYMENT ................................................................................................................................ 21
2.5 DATA MINING MODEL AND TECHNIQUES ................................................................................ 22
2.5.1 ASSOCIATION RULES.................................................................................................................... 22
2.5.2 CLASSIFICATION ........................................................................................................................... 22
2.5.3 ARITIFICIAL NEURAL NETWORK (ANN).................................................................................... 23
2.5.4 DECISION TREE.............................................................................................................................. 24
2.5.5 RANDOM FOREST .......................................................................................................................... 24
2.6 RELATED WORK ............................................................................................................................ 25
2.6.1 Market Basket Analysis ..................................................................................................................... 25
2.6.1.1 Sales Forecasting ........................................................................................................................... 25
2.6.1.2 Customer Profiling ........................................................................................................................ 26
2.6.1.3 Click Stream Data.......................................................................................................................... 26
2.6.1.4: Use of Data Brain Boxes in the Past ................................................................................................... 27
3 CHAPTER 3 .......................................................................................................................................... 28
3.1 Evaluation Methodology .................................................................................................................... 28
3.2 Development Methodology ................................................................................................................ 29
4 CHAPTER 4 .......................................................................................................................................... 29
4.1 Requirements Analysis ...................................................................................................................... 29
4.1.1 Functional Requirements.................................................................................................................... 30
4.1.2 Data Collection – ............................................................................................................................... 30
4.1.3 Data Storage – ................................................................................................................................... 31
4.1.4 Data Analytics – ................................................................................................................................ 31
4.1.5 Predictive Analysis – ......................................................................................................................... 31
4.2 Non-Functional Requirements ............................................................................................................ 31
5 CHAPTER 5 .......................................................................................................................................... 32
5.1 PROFESSIONAL, LEGAL AND ETHICAL ISSUES ........................................................................ 32
5.1.1 Professional Issues and Legal Issues................................................................................................... 32
5.1.2 Ethical Issues ..................................................................................................................................... 32
6 CHAPTER 6 .......................................................................................................................................... 33
6.1 PROJECT PLAN ...................................................................................................................................... 33
6.2 METHODOLOGY ................................................................................................................................... 34
6.3 RISK MANAGEMENT............................................................................................................................ 34
Bibliography .................................................................................................................................................. 35
5
1.1 Introduction
Businesses are happy when they have more data about their businesses, the wants of
their customers and mostly importantly results of strategy implementation. However, when
they have this data, they may not know exactly what do with it. The inability of businesses
not knowing how to utilize data can lead to loss of revenue opportunities, lower productivity,
effectiveness and quality issues.
This thesis discusses the under-utilization of data in businesses such as E-commerce
and how this under-utilized data can be processed into something useful.
Ecommerce businesses obtains a lot of information about their customers, data is
obtained whenever purchases are made or whenever products are viewed on a website. Over
the past years, there has been an increase in the need of data in the E-commerce industry.
This is due to the fact that E-commerce companies that are data driven experiences a higher
level of productivity than their competitors (McAfee & Brynjolfsson, 2012).
A recent study carried out by BSA Software Alliance shows that Data analysis
contributes to 15% or more of the growth for 56% of firms. Therefore 91% of fortune 1000
companies are investing in data analysis projects, an 85% increase from the previous years
(Akter & Wamba, 2016). While at the same time, the use of internet-based technologies
provides e-commerce companies with transformative benefits such as real-time customer
service, pricing options or personalized offers. However, Data mining helps solidify these
benefits by providing informed decisions based on critical insights and allows the companies
use data more efficiently to drive a higher conversion rate by customers.
It is very important Ecommerce businesses to have smart way of getting business
insights for what consumers want to see when their site is visited in order to get the best out
of their business. The objective is to develop a data brain box that provides data collection,
data transformation, data storage and visualization.
1.1 Introduction
Businesses are happy when they have more data about their businesses, the wants of
their customers and mostly importantly results of strategy implementation. However, when
they have this data, they may not know exactly what do with it. The inability of businesses
not knowing how to utilize data can lead to loss of revenue opportunities, lower productivity,
effectiveness and quality issues.
This thesis discusses the under-utilization of data in businesses such as E-commerce
and how this under-utilized data can be processed into something useful.
Ecommerce businesses obtains a lot of information about their customers, data is
obtained whenever purchases are made or whenever products are viewed on a website. Over
the past years, there has been an increase in the need of data in the E-commerce industry.
This is due to the fact that E-commerce companies that are data driven experiences a higher
level of productivity than their competitors (McAfee & Brynjolfsson, 2012).
A recent study carried out by BSA Software Alliance shows that Data analysis
contributes to 15% or more of the growth for 56% of firms. Therefore 91% of fortune 1000
companies are investing in data analysis projects, an 85% increase from the previous years
(Akter & Wamba, 2016). While at the same time, the use of internet-based technologies
provides e-commerce companies with transformative benefits such as real-time customer
service, pricing options or personalized offers. However, Data mining helps solidify these
benefits by providing informed decisions based on critical insights and allows the companies
use data more efficiently to drive a higher conversion rate by customers.
It is very important Ecommerce businesses to have smart way of getting business
insights for what consumers want to see when their site is visited in order to get the best out
of their business. The objective is to develop a data brain box that provides data collection,
data transformation, data storage and visualization.
6
1.1.1 Overview of Data Mining
In the 1973 Webster’s New Collegiate Dictionary data is defined as “factual
information used as a basis for reasoning, discussion, or calculation.” The 1996 Version of
the Webster Dictionary defined data as “information, especially information organized for
analysis (Migrant & Seasonal Head Start Technical Assistant Center, n.d.).
From the definitions above, a more practical way of defining data is that data is a
collection of numbers, characters, images or other method of recording, in a form which can
be assessed to make a decision about a specific action. By closely analysing data we can find
patterns to perceive information which can be used to enhance knowledge (Migrant &
Seasonal Head Start Technical Assistant Center, n.d.).
Data mining is therefore a form of business intelligence and data analysis. It is the
process of digging into larger, unstructured data to get useful correlations or predictions from
it (Han & Kamber, 2011).
1.1.2 Motivation
Being a product designer and having worked with several startup businesses in
Ecommerce industry. It has been realized from my experience over the years that most
Ecommerce companies have no idea of what to do after they have their website or
applications developed asides the upload of products and selling to the few consumers, they
have access to. Some don’t even know the true value of the data the get from there sales. So,
the motivation for this thesis is to bridge the gap of the under-utilization.
1.1.1 Overview of Data Mining
In the 1973 Webster’s New Collegiate Dictionary data is defined as “factual
information used as a basis for reasoning, discussion, or calculation.” The 1996 Version of
the Webster Dictionary defined data as “information, especially information organized for
analysis (Migrant & Seasonal Head Start Technical Assistant Center, n.d.).
From the definitions above, a more practical way of defining data is that data is a
collection of numbers, characters, images or other method of recording, in a form which can
be assessed to make a decision about a specific action. By closely analysing data we can find
patterns to perceive information which can be used to enhance knowledge (Migrant &
Seasonal Head Start Technical Assistant Center, n.d.).
Data mining is therefore a form of business intelligence and data analysis. It is the
process of digging into larger, unstructured data to get useful correlations or predictions from
it (Han & Kamber, 2011).
1.1.2 Motivation
Being a product designer and having worked with several startup businesses in
Ecommerce industry. It has been realized from my experience over the years that most
Ecommerce companies have no idea of what to do after they have their website or
applications developed asides the upload of products and selling to the few consumers, they
have access to. Some don’t even know the true value of the data the get from there sales. So,
the motivation for this thesis is to bridge the gap of the under-utilization.
7
1.1.3 Aim and Objectives
The aim of this dissertation is to investigate some effective ways in which businesses
can utilize available data to increase sales and return on investment. Core to this investigation
will be data mining techniques and various algorithms that could help achieve the task
mentioned above. These algorithms include but are not limited to neural networks, decision
trees and machine learning. This paper also aims to develop a prototype for a data drain box
that could help e-commerce businesses collect relevant data and utilize it to the advantage of
the business.
1.2 Report Structure
My report will be outlined as follows.
Chapter 2- This chapter comprises of the literature review, which gives a summary of
various algorithms and technologies on data mining, data warehousing, data visualization and
predictive analysis
Chapter 3- This chapter identifies the requirements analysis of the project
Chapter 4- This chapter project implementation and evaluation
Chapter 5- This chapter describes the professional, legal, ethical and social issues that
can be associated with the project
Chapter 6- This chapter provides the project plan of the project
1.1.3 Aim and Objectives
The aim of this dissertation is to investigate some effective ways in which businesses
can utilize available data to increase sales and return on investment. Core to this investigation
will be data mining techniques and various algorithms that could help achieve the task
mentioned above. These algorithms include but are not limited to neural networks, decision
trees and machine learning. This paper also aims to develop a prototype for a data drain box
that could help e-commerce businesses collect relevant data and utilize it to the advantage of
the business.
1.2 Report Structure
My report will be outlined as follows.
Chapter 2- This chapter comprises of the literature review, which gives a summary of
various algorithms and technologies on data mining, data warehousing, data visualization and
predictive analysis
Chapter 3- This chapter identifies the requirements analysis of the project
Chapter 4- This chapter project implementation and evaluation
Chapter 5- This chapter describes the professional, legal, ethical and social issues that
can be associated with the project
Chapter 6- This chapter provides the project plan of the project
8
2 CHAPTER 2
2.1 Literature Review
This chapter provides a literature on data mining and predictive analysis for business
marketing data. We will introduce some of the core techniques, concepts and solutions for
data mining in order to meet the aims and objectives of this project. In the contemporary
society, technology has been integrated into almost all facets of our lives. Businesses have not
been left behind. Businesses form a significant number of organizations that exist in the
modern day (Lowndes et al, 2017). With technology being advanced now more than have
ever been observed in history, it is very important for businesses to take advantage of these
technologies to increase their sales and consequently maximize on their profits (Gupta &
George, 2016). Let us take a simple example. Consider the number of people who use
smartphones. There are several billion such people in the world. These people are most likely
to search for the product or services that they need online. Businesses can tap into such an
opportunity for their own advantage. It is worth noting that many businesses have not
invested in data mining and data analysis (Tan, Steinbach & Kumar, 2016). If businesses
could tap into the field of data collection, data analysis and use the data to make important
predictions, the chances of business success are increased. Considering the highly
competitive nature of businesses in the modern world, it is only wise for businesses to
consider venturing into data collection and analysis. It is for this reason that this paper aims to
investigate ways in which e-commerce businesses can tap in to the huge amounts of data that
exist, make sense of this data and use it to make important predictions and decisions
concerning their businesses. There exists extensive evidence to show that businesses with
effective social media marketing are more likely to succeed compared to their counterparts
who have not invested in this kind of marketing (Jackson, 2019). It would be important for e-
commerce businesses to consider having a heavy social media presence (Dai, Wong, Wang,
Zheng & Vasilakos, 2019). In fact, it would be appropriate for them to consider hiring a
2 CHAPTER 2
2.1 Literature Review
This chapter provides a literature on data mining and predictive analysis for business
marketing data. We will introduce some of the core techniques, concepts and solutions for
data mining in order to meet the aims and objectives of this project. In the contemporary
society, technology has been integrated into almost all facets of our lives. Businesses have not
been left behind. Businesses form a significant number of organizations that exist in the
modern day (Lowndes et al, 2017). With technology being advanced now more than have
ever been observed in history, it is very important for businesses to take advantage of these
technologies to increase their sales and consequently maximize on their profits (Gupta &
George, 2016). Let us take a simple example. Consider the number of people who use
smartphones. There are several billion such people in the world. These people are most likely
to search for the product or services that they need online. Businesses can tap into such an
opportunity for their own advantage. It is worth noting that many businesses have not
invested in data mining and data analysis (Tan, Steinbach & Kumar, 2016). If businesses
could tap into the field of data collection, data analysis and use the data to make important
predictions, the chances of business success are increased. Considering the highly
competitive nature of businesses in the modern world, it is only wise for businesses to
consider venturing into data collection and analysis. It is for this reason that this paper aims to
investigate ways in which e-commerce businesses can tap in to the huge amounts of data that
exist, make sense of this data and use it to make important predictions and decisions
concerning their businesses. There exists extensive evidence to show that businesses with
effective social media marketing are more likely to succeed compared to their counterparts
who have not invested in this kind of marketing (Jackson, 2019). It would be important for e-
commerce businesses to consider having a heavy social media presence (Dai, Wong, Wang,
Zheng & Vasilakos, 2019). In fact, it would be appropriate for them to consider hiring a
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