BSBMKG528 Mine Data to Identify Industry Directions Assignment

This document presents a comprehensive solution to the BSBMKG528 assignment, focusing on data mining to identify industry directions for Momentum College. The assignment is divided into two events: knowledge questions and a simulation. The knowledge questions explore the uses of data mining in marketing communications, definitions of data validity, reliability, and completion, comparisons of datasets, and the identification of current industry tools and techniques. The simulation task involves determining the purpose of data mining, identifying data sources, and negotiating access rights. The solution includes detailed responses to the questions, identifying potential uses of data mining outcomes, recognizing legislative and organizational requirements, and providing example emails for data access. The assignment emphasizes practical application and understanding of data mining principles within an educational context.

Assessment Workbook
BSBMKG528
Mine Data to Identify Industry Directions

1. Assessment Information
Welcome to your Student Assessment Workbook for BSBMKG528 Mine Data to Identify Industry
Directions.
This Workbook is where you will write all your responses for the knowledge questions and simulation tasks.
Please refer to the Student Assessment Guide for more information.
This assessment has the following two events:
To complete the Simulation, you will need to refer to the following resources:
Please note that your responses for both assessment events can (where appropriate) use dot point format. See
below for an example of a dot point response and a full sentence response:
To Achieve Competence
To be deemed competent for this unit, you will need to meet the following requirements:
 complete all of the questions and tasks listed in this Student Assessment Workbook
 meet all the requirements listed in the Student Assessment Guide
 your responses to the questions and tasks must be relevant, accurate and specific
Assessment Event 1 – Knowledge Questions
There are five questions that will provide us with the evidence of your general knowledge of the current
data mining concepts, applications, tools, practices, and the regulatory context within the marketing
industry field.
Assessment Event 2 – Simulation: Momentum College
You will complete a number of tasks in developing and presenting a well-structured data analysis report.
These tasks will be based on your role of a Market Analyst in a simulation for a digital marketing agency,
DigiGeek, where you will be assisting a new client, Momentum College, an Australian registered training
organisation (RTO) that offers nationally accredited courses seeking to expand its business.
Data Mining Policy and
Procedures
DigiGeek’s policy and procedures for data mining. You will abide these
organisational requirements when performing data mining activities and
producing the required report in Assessment Event 2.
Market Analysis
Project Brief
Document that provides background information about the agency DigiGeek,
the client Momentum College, and other relevant information for the client’s
data mining requirements. You will conduct their data mining project on the
basis of the requirements specified in the Brief.
Dot point format Presentation Plan includes the following:
 outcomes
 needs of the audience
 context.
Full sentence format When you are preparing for a Presentation, there are a number of tasks that must
be carried out. These are; listing the outcomes that you want to achieve, followed
by the identification of the needs of your audience. When you have completed
these two tasks you then check on the room that you will be conducting the
simulation in etc.

 submit your completed Student Assessment Workbook to your Assessor within the set timeframes
 your work must be in your own words
 where you use an external source of information, you must provide citation.
2. Pre-assessment Checklist
Your assessor will go through the assessment for this unit, BSBMKG528 Mine Data to Identify Industry
Directions. It is important that you understand this assessment before taking on the questions and tasks. To
confirm that you have been given this overview, we ask you to complete the following Pre-Assessment
Checklist.
You are required to carefully read each checklist item provided below and tick either ‘Y’ to confirm your
understanding or ‘N’ if you disagree. In case you disagree with an item, please provide your reason under
the ‘Comments’ column.
When you have done this, we ask you to sign this Pre-Assessment Checklist. This acknowledges that your
Trainer/Assessor has discussed all of the information with you prior to undertaking this assessment.

Pre – assessment Checklist
C
o
m
m
e
n
t
s
Y   N I, the student, understand the purpose of the assessment. Y
Y   N
I understand when and where the assessment will occur, who
will assess and in what format the assessment will be
submitted. Y
Y   N I understand the methods of assessment. Y
Y   N I understand what resources are required to complete this
assessment. Y
Y   N I understand the performance level required for each
assessment event. Y
Y   N
I understand that it must be my own work. I have been
explained and understand the serious consequences in case this
work is found plagiarised. Y
Y   N I understand the process if I am deemed not yet competent. Y

3. Assessment Event 1 – Knowledge Questions
The information contained in this assessment event lists the questions that you will need to develop a written
response. These questions are theoretical and provide evidence of your understanding of data mining
concepts and principles.
Note you must answer these questions in your own words. Remember, you must get a satisfactory result
with each question to be deemed satisfactory for the whole of Assessment Event 1.
1.Question 1
Using the table below, outline the various uses of data mining in the context of marketing communications.
Write your response into the table below:
2.
Q
uestion 2
Explain the terms 'data validity', 'reliability' and 'completion'.
Write your response here:
Data life cycle is termed as various stages of data unit which is considered from initial stage to
deletion of data at end. The data life cycle management is important as with the increase in exploration of
big data and development of internet on things. It is important for every organisation to adopt various data
protection for increasing trust and satisfaction of service users. In case of given college, it is important for
this organisation to provide valid, reliable and accurate data for users so that they can get all authentic
information regarding college. The concept of data validity, data reliability and data completion are
explained below -
 Data validity – This is termed as correctness and accuracy of data. The data collected from mining
must provide valid and correct data. The validation of data simply means how accurate data is. In
Uses of Data Mining Explanation
Cluster analysis This gives an opportunity for identifying a single user target group in
accordance to similar features within database like education level, age,
class, course, etc.
Regression analysis This helps in enabling to study habits, changes, customer satisfaction level
for making marketing forecasts. This will help in analysing the market
effectively.
Classification analysis This helps in allowing to classify various information that is received from
potential clients and customers while identifying patterns within a database.
Intrusion detection System security is the most important concept of an organisation so data
mining is helpful in protecting data from various intruders.
Fraud Detection This will be used in detecting any fraud within the organisation.
There is requirement of perfect fraud detection system so that

context of college, it is important to provide useful information of data to people so that there will
not be any issue.
 Data Reliability – Data reliability means that data is reasonably complete and accurate and it fulfils
intended purpose. Reliability helps in assessing way which data mining model has to perform on
various data sets. Model of data mining is said to be reliable when it creates similar kind of
predictions.
 Data completion – This is defined as data is finally completed and it includes all important and
valuable information about the topic. This includes different metrics which will explain completion
of model that provides useful information. Data regarding college courses and students is available to
different websites, social media sites and e-portals.
3.Question 3
Using the table below, compare the characteristics of public, client and organisational datasets.
Write your response into the table below:
4.
Q
uestion 4
Identify and list the uses of current industry tools used in data mining.
Write your response here:
Data mining is effective for valid, hidden and all the possible patterns in datasets. Data mining is
defined as a technique that serves as a primary purpose of introducing patterns among large data and
transformation of this data from into a more refined information. The major objective of data mining is
Characterist
ics
Public Client Organisational
The public datasets are those
which are available to whole
population around the world.
These are available for only
specific clients and
customers.
Such datasets are for organisation
only.
Security The public datasets are those by
which college will market it's
courses and facilities. It is less
secure than other two datasets.
This is more secure dataset
and information in this data
is more secured.
Organisational dataset is available to
individuals who are authentic to know
organisation's data.
Validity and
legitimacy
This data is less valid and
legitimate.
Data of clients is more valid
and legitimate.
Organisational dataset has more valid
and legitimate data.
Accessibility This data can be used by all
public.
This data is limited to
only particular clients
and customers.
This data is accessible to
individuals to organisation.

extracting useful information from data sets and converting to understandable structure. The different
industry tools used in data mining are listed below -
RapidMiner – This is known as best predictive analysis system that is developed by a company
naming Rapid Miner. This is written in Java programming.
Strength Weakness
 This gives an integrated environment for text
mining, predictive analysis, deep learning
and machine learning.
 This tool is useful for wide range of business
application and education.
 This offers server on premise as well as
public and private cloud infrastructure.
 Coding is not used in this application.
 For using this tool, expensive and
commercial tool is required.
 Use case of this application is limited to set
of modules and processors.
Orange – This is a software suite for data mining and this helps in data visualisation and this is a
component based software. This is written in python language.
Strength Weakness
 Orange application helps in bringing more
interactive vibe in analytical tool.
 Data that is coming to Orange is quickly
transformed to requisite pattern.
 This application helps users to make smart
decisions in less time.
 The installation of this application is big as
there is need of installing QT.
 It contains limited list of learning algorithms.
 This application is limited to exporting
representing data visually.
Weka – Waiketo Environment is used for analysis of knowledge. This is a collection of algorithm of
machine learning used for data mining activities. This is supported by Java and this consists of collection of
different tools for data analytics.
Strength Weakness
 This is suitable for making schemes for
machine learning.
 It is open source , extensible and free and it
can be integrated to other Java packages.
 This is a secure way of using data.
 This application lacks adequate and proper
documentation.
 It has bad connectivity with Excel
spreadsheet and database with non Java
programs.
 This application is weaker in classical
statistics.

KNIME – Konkstanz Information Miner is an open source data analytics, integration and reporting
platform which is used in research. This is generally used in different fields like business intelligence, CRM
customer data analytics and financial data analysis.
Strength Weakness
 It integrates different analysis modules.
 This is an easy data mining tool which offers
access to different statistical routines.
 This has ability for interface with programs
which allows for visualisation and analysis of
data.
 Limited error measurement method.
 There is no wrapper method for descriptor
selection.
 There is no facility for parameter
optimisation.
KEEL – This is Knowledge extraction based on Evolutionary which is an application of machine
learning software tools. This is used for data manipulation in research.
Strength Weakness
 This consists of classification, regression,
pattern mining and clustering.
 This includes huge collection of
preprocessing techniques and knowledge
extraction algorithms.
 The main limitation of this is that efficiency
is restricted by a number of algorithms.
 This is less used in organisations.

5.Question 5
Using the table below, identify and list the uses of current industry techniques used in data mining.
Write your response into the table below:
Techniques Explanation Application in Real-life
Tracking Patterns This is a technique in data mining used for recognising
patterns in datasets.
Organisations can use this for analysing sales of particular product
on website.
Classification This is a complex type of data mining which forces for
collecting different attributes.
Financial background or purchase histories are classified as low,
medium or high credit risks.
Association This is associated with tracking patterns. When customers buy a particular item, they sometimes buy another
product with it and this leads to increase the section of people also
brought. to
Clustering This is similar to classification but it also
consists of grouping data chunks.
Various demographics can be clustered and how much
income individuals have.

4. Assessment Event 2: Momentum College
Simulation
In this assessment, you will undertake a number of tasks associated with mining data for an educational
institution, Momentum College. The focus is on undertaking data mining practices to help
Momentum College to identify new directions in accordance with its proposed
initiative.
6.Task 1: Determine Purpose of Data Mining
In this task, you will review client requirements for the data mining activity and suggest which areas it can
bring solution to the client. Also, you will summarise the regulatory and organisational requirements that are
closely linked to the data mining activity. These will involve you to review and analyse the documents
provided (see separate attachments: the ‘Market Analysis Project Brief’ and ‘Data Mining Policy and
Procedures’), and conduct external research on relevant legislative requirements.
7. 1.1 Identify and review relevant client and organisational
requirements for data mining
Insert your response here: There is a need of protecting data provided by the organisation. In the present
scenario, personal information and client data is present which can be public or private. DigiGeek
organisation wants to manage, protect, collect and allow access to information of employees to them. Thus
data mining is used.
8. 1.2 Confirm potential uses of data mining outcomes and
recommendations
Insert your response here:
The potential uses of data mining are mentioned below - Market segmentation – This includes identifying the similar characteristics of individuals working
in the organisation. Customer churn - This predicts which customers are going to leave the organisation. Fraud Detection - Determines transactions that are fraudulent. Direct Marketing – This determines those prospects which are included in a mailing list. Interactive marketing - This predicts what an individual access at website.
9. 1.3 Recognise legislative and organisational requirements
Insert your response here:

The three type of legislations that are currently in force are listed below-
 This policy is related to communicate with staff of DigiGeek. This helps in managing, collecting,
dealing with and allowing access to personal data according to Privacy Act 1988 and Australian
Privacy Principles.
 Data mining activities are conducted without violating copyright in alignment with Copyright Act
1968.
 Data and information are accessed by Australian Government agencies according to Freedom Act
1982.