DATA4000_T2_2022_Assessment_01 Page 1 XXXXXXXXXXKaplan Business School Assessment Outline Document Classification: Public Assessment 1 Information Subject Code: DATA4000 Subject Name: Introduction to...

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DATA4000_T2_2022_Assessment_01 Page 1 Kaplan Business School Assessment Outline Document Classification: Public Assessment 1 Information Subject Code: DATA4000 Subject Name: Introduction to Business Analytics Assessment Title: Individual Case Study Assessment Type: Written assessment Word Count: 2000 Words (+/-10%) Weighting: 30 % Total Marks: 30 Submission: via Turnitin Due Date: Tuesday 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. Page 2 Kaplan Business School Assessment Outline Document Classification: Public 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 potential and meaningful business problem to be solved c) The type of analytics used, and how it was used to address that potential and meaningful business problem d) The main challenge(s) of using this type of analytics to achieve your business objective (from part b) e) Recommendations regarding how to be assist stakeholders with adapting these applications for their business. 1. Transforming customer experience with AI and Machine Learning – Telecoms with Ericsson https://www.ericsson.com/en/blog/2021/3/transforming-customer-experience-with-ai-and- machine-learning 2. Artificial Intelligence in the Healthcare Industry https://www.acc.com/resource-library/artificial-intelligence-healthcare-industry# 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. https://www.ericsson.com/en/blog/2021/3/transforming-customer-experience-with-ai-and-machine-learning https://www.ericsson.com/en/blog/2021/3/transforming-customer-experience-with-ai-and-machine-learning https://www.acc.com/resource-library/artificial-intelligence-healthcare-industry Page 3 Kaplan Business School Assessment Outline Document Classification: Public Part C: Developing and Sourcing Analytics Capabilities (800 words, 12 marks) Instructions: You are the Chief Analytics Officer for a large multinational corporation in the communications sector with operations that span 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). The business 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 of your own that addresses the following key points: 1. How do we best ingrain analytics into the organisation’s 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? To help you draft this report, you should review the following working paper from Accenture: https://pdfcoffee.com/accenture-building-analytics-driven-organization-pdf-free.html The report is prepared for senior management and the board of directors. It must reflect the needs of your organization and the sector you operate in (communications). https://pdfcoffee.com/accenture-building-analytics-driven-organization-pdf-free.html Page 4 Kaplan Business School Assessment Outline Document Classification: Public 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. http://www.kbs.edu.au/current-students/student-policies/ https://elearning.kbs.edu.au/course/view.php?id=1481 Page 5 Kaplan Business School Assessment Outline Document Classification: Public Assessment Marking Guide Page 6 Kaplan Business School Assessment Outline Document Classification: Public DATA4000 Assessment 1 Rubric /30 Has demonstrated limited achievement: Has achieved all or most of: Part A: Case Study Analysis - Analyse how analytics can enhance business performance and identify the challenges of integrating analytics into diverse industries 0-4 5-10 /10 • Incorrect or incomplete interpretation of case study with reference to the questions • Has not analysed how analytics can enhance business performance • Little or no reference to the course material, methods and analytics applications • No originality, minimal effort • Well-supported and engaging interpretation of case study with reference to the questions • Has clearly analysed how analytics can enhance business performance • Reference to all key course material, methods and analytics applications • A novel approach taken to the representation of the content 3 3 2 2 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 0-3 4-8 /8 • 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 • 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 4 4 Page 7 Kaplan Business School Assessment Outline Document Classification: Public Part C: Analytics Jobs - Investigate existing analytics jobs and identify the type of analytics involved in their associated tasks 0-5 6-12 /12 • Student is not able to identify the types of analytics undertaken by various roles • Student does not provide feasible recommendations for the type of analytics professionals required by a given scenario • Student comprehensively identifies the types of analytics undertaken by various roles • Student provides well supported recommendations for the type of analytics professionals required by a given scenario 6 6 Page 8 Kaplan Business School Assessment Outline Document Classification: Public Assignment Submission Students must submit their individual analysis via Turnitin on Tuesday 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
Answered Same DayAug 15, 2022

Answer To: DATA4000_T2_2022_Assessment_01 Page 1 XXXXXXXXXXKaplan Business School Assessment Outline Document...

Rochak answered on Aug 15 2022
76 Votes
Part A
Introduction
Analytics is one of the core concepts which is used in all the industry to generate various insights among which the most popular and used one is to study consumer behaviour which is very vital in selling any product (Wang 2017)
Industry
The industry in which analytics is used in the case studies are:
· In the first case study whi
ch is ‘Transforming customer experience with AI and Machine Learning – Telecoms with Ericsson’, analytics is used in the telecom industry which has changed quickly in the last couple of decades since the dot com bubble
· The second case study which is ‘Artificial Intelligence in the Healthcare Industry is where analytics is used in the Healthcare industry which is another industry where the use of AI (Artificial Intelligence) is booming to predict and find out the cure for many diseases which is next to impossible when done by humans
Business Problem
The business problem which is solved in the case studies with the use of analytics are:
· In the telecom case study the analytics, i.e., Artificial Intelligence is used to study customer experience and generate insights to predict customer behaviour which is used to study customer data and other data that the telecom companies collect from their customers and through this data which is fed into the algorithms is used to generate various insights on what the customer want. This is done because the companies are now seeking what the customers want and through AI (Artificial Intelligence) and Machine Learning the algorithms are created to study the customer behaviour and using that the products are designed which then more suited to the customer's needs
· In the healthcare industry, the potential business problem solved is the distance problem in the industry. The distance here means that the patients who need a cure are to be treated by a specific doctor and that doctor is not present at the place where the patient is residing using AI the doctor can digitally do the diagnosis to cure the patient. The other business problem which is solved using analytics in the healthcare industry is to keep iterating the research to find new cures for the diseases which require multiple (millions) of iterations to predict the final cure of the disease and for this, the algorithm of artificial intelligence and machine learning is best suited
Type of Analytics
The type of analytics used in both the case studies are:
· Artificial Intelligence: Artificial Intelligence is the next big thing which is used in everything and especially the industries where the customer's preferences change pretty rapidly therefore here in the telecom industry it is used to see what kind of products customers need, this can only be done using the study of the customer data which the companies collect
· Machine Learning: Machine learning is used to predict the things which cannot be predicted by humans, as it is assumed that humans trigger emotions in clusters and therefore this is used to predict customer behaviour in both telecom and healthcare
Main Challenge
The main challenge of using this type of analytics is that this requires a lot of customer data to predict anything and also the analytics such as artificial intelligence and machine learning are used to predict customer behaviour or find a cure to diseases in the healthcare industry and therefore the main challenge is the data collection, but with the collection of data increasing these analytics are getting more and more effective and efficient
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