Word Count: 2000 Words (+/-10%)
Weighting: 30 %
Total Marks: 30
Submission: via Turnitin
Due Date: Monday Week 5, 23:55pm AEST
Your Task
Complete Parts A to C below by the due date. Consider the rubric at the end of the assignment for guidance on structure and content.
Assessment Description
• You are to read case studies provided and answer questions in relation to the content, analytics theory and potential analytics professionals required for solving the business problems at hand.
• Learning outcomes 1 and 2 are addressed.
Assessment Instructions
Part A: Case Study Analysis (700 words, 10 marks)
Instructions: Read the following two case studies. For EACH case study, briefly describe:
a) The industry to which analytics has been applied.
b) A business problem arising from the case study.
c) The type of analytics used, and how it was used to address that business problem
d) The main challenge(s) of using this type of analytics to achieve a business objective
e) Recommendations regarding how to execute the development and deployment of analytics in a way that maximises acceptance and buy-in from stakeholders.
1. Big Data for Consumers: The Internet of Things revolution https://www.bernardmarr.com/default.asp?contentID=704
2. GE Power: Big Data, Machine learning And ‘The Internet of Energy’ https://www.bernardmarr.com/default.asp?contentID=1266
Part B: The Role of Analytics in Solving Business Problems (500 words, 8 marks)
Instructions: Describe two different types of analytics (from Workshop 1) and evaluate how each could be used as part of a solution to a business problem with reference to ONE real-world case study of your own choosing. You will need to conduct independent research and consult resources provided in the subject.
Part C: Developing and Sourcing Analytics Capabilities (800 words, 12 marks)
Instructions: You are the Chief Analytics Officer for Telekonika, the largest telecommunications company in South East Asia and Latin America. The organization is undergoing significant transformations; it is scaling back operations in existing low revenue segments and ramping up investments in next generation products and services – 5G, cloud computing and Software as a Service (SaaS). Telefonica is keen to develop its data and analytics capabilities. This includes using technology for product innovation and for developing a large contingent of knowledge workers.
To prepare management for these changes, you have been asked review Accenture’s report (see link below) and publish a short report that addresses the following key points:
1. How do we best ingrain analytics into our decision-making processes?
2. How do we organize and coordinate analytics capabilities across the organization?
3. How should we source, train and deploy analytics talent?
4. Discuss the key success factors that underpin and define an organisation’s journey toward becoming analytics driven.
To help you draft this report, you should review the following working paper from Accenture: https://www.accenture.com/us-en/~/media/accenture/conversion-
assets/dotcom/documents/global/pdf/industries_2/accenture-building-analytics-drivenorganization.pdf
Important Study Information
Academic Integrity Policy
KBS values academic integrity. All students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct Policy.
What is academic integrity and misconduct?
What are the penalties for academic misconduct?
What are the late penalties?
How can I appeal my grade?
Click here for answers to these questions:
http://www.kbs.edu.au/current-students/student-policies/.
Word Limits for Written Assessments
Submissions that exceed the word limit by more than 10% will cease to be marked from the point at which that limit is exceeded.
Study Assistance
Students may seek study assistance from their local Academic Learning Advisor or refer to the resources on the MyKBS Academic Success Centre page. Click here for this information.
Assessment Marking Guide
Criteria Criteria NN (Fail)
0%-49% P (Pass)
50%-64% CR (Credit) 74%-65% DN
(Distinction)
75%-84% HD (High
Distinction)
85%-100%
Part A:
Case
Study
Analysis Analyse how analytics can enhance business
performance and identify the challenges of integrating analytics into diverse
industries
Incorrect or incomplete interpretation of case study with reference
to the questions
Little or no reference to the course material, methods and analytics applications Basic interpretation of case study with reference to the
questions
Minimum reference to the course material, methods and analytics applications Moderately supported
interpretation of case study with reference
to the questions
Reference to some of the course material, methods and analytics applications Well-supported interpretation of case study with reference to the
questions
Reference to most of the course material,
methods and analytics applications Well-supported and engaging interpretation of case study with reference to the
questions
Reference to all key course material, methods and analytics
applications
A novel approach taken to the representation of the content
Part B:
Role of
Analytics Evaluate the role of analytics processes and procedures in solving business problems and conduct research
into existing business cases where analytics is being used Inadequate description of analytics types and/or inadequate explanation of how analytics could be used as part of a business problem with minimal research conducted Description of the different types of analytics in a basic way with limited consideration of
business applications
Consideration of how analytics can solve business problems with limited examples
Description of the different types of analytics and a brief consideration on how each can be used in business.
Solid exploration of analytics solutions with reference to well-researched
case studies
Description of the different types of analytics and an illustration of how each can be used to address a
business problem
Comprehensive exploration of analytics solutions with reference to wellresearched, relevant case studies Comprehensive
description of the different types of analytics and a critical evaluation of how each can be used to address
a business problem
Convincing and engaging exploration of feasible analytics solutions with reference to well-researched,
detailed case studies
Part C:
Analytics
Jobs Investigate existing analytics jobs and identify the type of analytics involved in Student is not able to identify the types of analytics undertaken by various roles Student identifies minimum amount of
information relating to the types of analytics Student identifies some of the types of analytics undertaken by various roles Student identifies most of the types of analytics Student comprehensively identifies the types of
their associated tasks
Student does not provide feasible recommendations for the type of analytics professionals required by a given scenario
undertaken by various
roles
Student provides minimum
recommendations for the type of analytics professionals required by a given scenario
Student provides adequate recommendations for the type of analytics professionals required by a given scenario undertaken by
various roles
Student provides solid
recommendations
for the type of analytics professionals required by a given scenario analytics undertaken by
various roles
Student provides well supported recommendations for the type of analytics professionals required
by a given scenario
Comments:
Assignment Submission
Students must submit their individual analysis via Turnitin on Monday of Week 5 at 23:55pm AEST.
This file must be submitted as a PDF document to avoid any technical issues that may occur from incorrect file format upload. Uploaded files with a virus will not be considered as a legitimate submission. Turnitin will notify you if there is any issue with the submitted file. In this case, you must contact your lecturer via email and provide a brief description of the issue and a screen shot of the Turnitin error message.
Students are also encouraged to submit their work well in advance of the time deadline to avoid any possible delay with Turnitin similarity report generation or any other technical difficulties.
Late assignment submission penalties
Penalties will be imposed on late assignment submissions in accordance with Kaplan Business School’s Assessment Policy.
Number of days Penalty
1* – 9 days 5% per day for each calendar day late deducted from the student’s total Marks.
10 – 14 days 50% deducted from the student’s total marks.
After 14 days Assignments that are submitted more than 14 calendar days after the due date will not be accepted and the student will receive a mark of zero for the assignment(s).
Note Notwithstanding the above penalty rules, assignments will also be given a mark of zero if they are submitted after assignments have been returned to students.
*Assignments submitted at any stage within the first 24 hours after deadline will be considered to be one day late and therefore subject to the associated penalty.
If you are unable to complete this assessment by the due date/time, please refer to the Special Consideration Application Form, which is available at the end of the KBS Assessment Policy:
https://www.kbs.edu.au/wp-content/uploads/2016/07/KBS_FORM_Assessment-Policy_MAR2018_FA.pdf
Weighting: 30 %
Total Marks: 30
Submission: via Turnitin
Due Date: Monday Week 5, 23:55pm AEST
Your Task
Complete Parts A to C below by the due date. Consider the rubric at the end of the assignment for guidance on structure and content.
Assessment Description
• You are to read case studies provided and answer questions in relation to the content, analytics theory and potential analytics professionals required for solving the business problems at hand.
• Learning outcomes 1 and 2 are addressed.
Assessment Instructions
Part A: Case Study Analysis (700 words, 10 marks)
Instructions: Read the following two case studies. For EACH case study, briefly describe:
a) The industry to which analytics has been applied.
b) A business problem arising from the case study.
c) The type of analytics used, and how it was used to address that business problem
d) The main challenge(s) of using this type of analytics to achieve a business objective
e) Recommendations regarding how to execute the development and deployment of analytics in a way that maximises acceptance and buy-in from stakeholders.
1. Big Data for Consumers: The Internet of Things revolution https://www.bernardmarr.com/default.asp?contentID=704
2. GE Power: Big Data, Machine learning And ‘The Internet of Energy’ https://www.bernardmarr.com/default.asp?contentID=1266
Part B: The Role of Analytics in Solving Business Problems (500 words, 8 marks)
Instructions: Describe two different types of analytics (from Workshop 1) and evaluate how each could be used as part of a solution to a business problem with reference to ONE real-world case study of your own choosing. You will need to conduct independent research and consult resources provided in the subject.
Part C: Developing and Sourcing Analytics Capabilities (800 words, 12 marks)
Instructions: You are the Chief Analytics Officer for Telekonika, the largest telecommunications company in South East Asia and Latin America. The organization is undergoing significant transformations; it is scaling back operations in existing low revenue segments and ramping up investments in next generation products and services – 5G, cloud computing and Software as a Service (SaaS). Telefonica is keen to develop its data and analytics capabilities. This includes using technology for product innovation and for developing a large contingent of knowledge workers.
To prepare management for these changes, you have been asked review Accenture’s report (see link below) and publish a short report that addresses the following key points:
1. How do we best ingrain analytics into our decision-making processes?
2. How do we organize and coordinate analytics capabilities across the organization?
3. How should we source, train and deploy analytics talent?
4. Discuss the key success factors that underpin and define an organisation’s journey toward becoming analytics driven.
To help you draft this report, you should review the following working paper from Accenture: https://www.accenture.com/us-en/~/media/accenture/conversion-
assets/dotcom/documents/global/pdf/industries_2/accenture-building-analytics-drivenorganization.pdf
Important Study Information
Academic Integrity Policy
KBS values academic integrity. All students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct Policy.
What is academic integrity and misconduct?
What are the penalties for academic misconduct?
What are the late penalties?
How can I appeal my grade?
Click here for answers to these questions:
http://www.kbs.edu.au/current-students/student-policies/.
Word Limits for Written Assessments
Submissions that exceed the word limit by more than 10% will cease to be marked from the point at which that limit is exceeded.
Study Assistance
Students may seek study assistance from their local Academic Learning Advisor or refer to the resources on the MyKBS Academic Success Centre page. Click here for this information.
Assessment Marking Guide
Criteria Criteria NN (Fail)
0%-49% P (Pass)
50%-64% CR (Credit) 74%-65% DN
(Distinction)
75%-84% HD (High
Distinction)
85%-100%
Part A:
Case
Study
Analysis Analyse how analytics can enhance business
performance and identify the challenges of integrating analytics into diverse
industries
Incorrect or incomplete interpretation of case study with reference
to the questions
Little or no reference to the course material, methods and analytics applications Basic interpretation of case study with reference to the
questions
Minimum reference to the course material, methods and analytics applications Moderately supported
interpretation of case study with reference
to the questions
Reference to some of the course material, methods and analytics applications Well-supported interpretation of case study with reference to the
questions
Reference to most of the course material,
methods and analytics applications Well-supported and engaging interpretation of case study with reference to the
questions
Reference to all key course material, methods and analytics
applications
A novel approach taken to the representation of the content
Part B:
Role of
Analytics Evaluate the role of analytics processes and procedures in solving business problems and conduct research
into existing business cases where analytics is being used Inadequate description of analytics types and/or inadequate explanation of how analytics could be used as part of a business problem with minimal research conducted Description of the different types of analytics in a basic way with limited consideration of
business applications
Consideration of how analytics can solve business problems with limited examples
Description of the different types of analytics and a brief consideration on how each can be used in business.
Solid exploration of analytics solutions with reference to well-researched
case studies
Description of the different types of analytics and an illustration of how each can be used to address a
business problem
Comprehensive exploration of analytics solutions with reference to wellresearched, relevant case studies Comprehensive
description of the different types of analytics and a critical evaluation of how each can be used to address
a business problem
Convincing and engaging exploration of feasible analytics solutions with reference to well-researched,
detailed case studies
Part C:
Analytics
Jobs Investigate existing analytics jobs and identify the type of analytics involved in Student is not able to identify the types of analytics undertaken by various roles Student identifies minimum amount of
information relating to the types of analytics Student identifies some of the types of analytics undertaken by various roles Student identifies most of the types of analytics Student comprehensively identifies the types of
their associated tasks
Student does not provide feasible recommendations for the type of analytics professionals required by a given scenario
undertaken by various
roles
Student provides minimum
recommendations for the type of analytics professionals required by a given scenario
Student provides adequate recommendations for the type of analytics professionals required by a given scenario undertaken by
various roles
Student provides solid
recommendations
for the type of analytics professionals required by a given scenario analytics undertaken by
various roles
Student provides well supported recommendations for the type of analytics professionals required
by a given scenario
Comments:
Assignment Submission
Students must submit their individual analysis via Turnitin on Monday of Week 5 at 23:55pm AEST.
This file must be submitted as a PDF document to avoid any technical issues that may occur from incorrect file format upload. Uploaded files with a virus will not be considered as a legitimate submission. Turnitin will notify you if there is any issue with the submitted file. In this case, you must contact your lecturer via email and provide a brief description of the issue and a screen shot of the Turnitin error message.
Students are also encouraged to submit their work well in advance of the time deadline to avoid any possible delay with Turnitin similarity report generation or any other technical difficulties.
Late assignment submission penalties
Penalties will be imposed on late assignment submissions in accordance with Kaplan Business School’s Assessment Policy.
Number of days Penalty
1* – 9 days 5% per day for each calendar day late deducted from the student’s total Marks.
10 – 14 days 50% deducted from the student’s total marks.
After 14 days Assignments that are submitted more than 14 calendar days after the due date will not be accepted and the student will receive a mark of zero for the assignment(s).
Note Notwithstanding the above penalty rules, assignments will also be given a mark of zero if they are submitted after assignments have been returned to students.
*Assignments submitted at any stage within the first 24 hours after deadline will be considered to be one day late and therefore subject to the associated penalty.
If you are unable to complete this assessment by the due date/time, please refer to the Special Consideration Application Form, which is available at the end of the KBS Assessment Policy:
https://www.kbs.edu.au/wp-content/uploads/2016/07/KBS_FORM_Assessment-Policy_MAR2018_FA.pdf
