HOLMES INSTITUTE FACULTY OF HIGHER EDUCATION UNDERGRADUATE PROGRAM HI6037 Fundamentals of Business Analytics Academic Integrity Holmes Institute is committed to ensuring and upholding Academic...

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HOLMES INSTITUTE FACULTY OF HIGHER EDUCATION UNDERGRADUATE PROGRAM HI6037 Fundamentals of Business Analytics Academic Integrity Holmes Institute is committed to ensuring and upholding Academic Integrity, as Academic Integrity is integral to maintaining academic quality and the reputation of Holmes’ graduates. Accordingly, all assessment tasks need to comply with academic integrity guidelines. Table 1 identifies the six categories of Academic Integrity breaches. If you have any questions about Academic Integrity issues related to your assessment tasks, please consult your lecturer or tutor for relevant referencing guidelines and support resources. Many of these resources can also be found through the Study Sills link on Blackboard. Academic Integrity breaches are a serious offence punishable by penalties that may range from deduction of marks, failure of the assessment task or unit involved, suspension of course enrolment, or cancellation of course enrolment. Table 1: Six categories of Academic Integrity breaches Plagiarism Reproducing the work of someone else without attribution. When a student submits their own work on multiple occasions this is known as self-plagiarism. Collusion Working with one or more other individuals to complete an assignment, in a way that is not authorised. Copying Reproducing and submitting the work of another student, with or without their knowledge. If a student fails to take reasonable precautions to prevent their own original work from being copied, this may also be considered an offence. Impersonation Falsely presenting oneself, or engaging someone else to present as oneself, in an in-person examination. Contract cheating Contracting a third party to complete an assessment task, generally in exchange for money or other manner of payment. Data fabrication and falsification Manipulating or inventing data with the intent of supporting false conclusions, including manipulating images. Source: INQAAHE, 2020 HOLMES INSTITUTE FACULTY OF HIGHER EDUCATION UNDERGRADUATE PROGRAM HI6037 Fundamentals of Business Analytics Assignment Description: You are expected to complete a critique and conduct a literature review to discuss the topic “Implications of Predictive Analytics & Reporting in Business Analytics” and analyse the literature and find gaps and future works of the area. You need to search in the literature and find at least ten (10) academic research papers (references) related to this topic. (Academic papers can be found in ProQuest. ProQuest instruction can be found in below. Please contact Liberian if you have further question). A draft of the research paper (key points/headings) will be submitted in session (week) 8 whereby every student can receive feedback. Then you should submit the final research paper in the Blackboard by session 13. The final submission must comply with the draft structure and draw heavily from the key references. ProQuest login: Go to: http://www.holmes.edu.au/ > Login > Proquest USERNAME: holmes2004 PASSWORD: holmes HOLMES INSTITUTE FACULTY OF HIGHER EDUCATION UNDERGRADUATE PROGRAM HI6037 Fundamentals of Business Analytics The Draft and Final report submission structure is as follow: What you need to submit for final submission: 1. Assignment File: You need to submit the final version of your assignment in session 13. The final submission must comply with the draft structure and draw heavily from the key references. The structure of the final submission is headings and in discussion critically analyse each reference and discuss how these references reflecting the REPORT STRUCTURE: ➢ Introduction: State the purpose and objectives of the report. ➢ Discussion: Discuss the references, few paragraphs for each reference, and critically analyse them and discuss how they reflect the key points of the topic. ➢ Conclusion: Summarise your findings, consolidating and drawing attention to the main points of the report. ➢ Referencing: reference sources must be cited in the text of the report, and listed appropriately at the end in a reference list using Harvard referencing style (you can find the guidelines in the BB). 2. Academic References: The reference list in the assignment report should contain the HYPERLINK to the original PDF file of the references otherwise you will receive 10% penalty. 3. Assignment Coversheet: Must be uploaded as a separate file. You can find the template in the unit website in BB. PLEASE NOTE: • All assignments must be submitted electronically ONLY, uploaded to Blackboard and Submission of SafeAssign. Submission deadlines are strictly enforced and a late submission incurs penalties. • DO NOT SHARE YOUR ASSIGNMENT WITH OTHER STUDENTS under no circumstances even after the deadline and after you submitted it in the Blackboard or even after you have marked. If there will be any similarity detected by SafeAssign or the marker, it is an academic misconduct case and BOTH of the students will not be marked and reported to the institution for further investigation. • Your document should be a single word or OpenOffice document containing your report. • All submissions will be submitted through the safeAssign facility in Blackboard. HI6037 Fundamentals of Business Analytics Submission boxes linked to SafeAssign will be set up in the Units Blackboard Shell. Assignments not submitted through these submission links will not be considered. • Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized per day late (including weekend days) according to Holmes Institute policies. • The SafeAssign similarity score will be used in determining the level, if any, of plagiarism. SafeAssign will check conference web-sites, Journal articles, the Web and your own class members submissions for plagiarism. You can see your SafeAssign similarity score (or match) when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and resubmit. However, resubmission is only allowed prior to the submission due date and time. After the due date and time have elapsed your assignment will be graded as late. Submitted assignments that indicate a high level of plagiarism will be penalized according to the Holmes Academic Misconduct policy, there will be no exceptions. Thus, plan early and submit early to take advantage of the resubmission feature. You can make multiple submissions, but please remember we grade only the last submission, and the date and time you submitted will be taken from that submission. HI6037 Fundamentals of Business Analytics Marking Criteria Weighting Draft 5 marks Report structure, Layout, Grammar and spelling, Written style and expression 5 marks Quality of evaluation and critically exploring the references 25 marks Recommendations and justification 5 marks Referencing 5 marks TOTAL Weight for this assignment marking 45 marks HI6037 Fundamentals of Business Analytics
Answered 9 days AfterAug 09, 2021

Answer To: HOLMES INSTITUTE FACULTY OF HIGHER EDUCATION UNDERGRADUATE PROGRAM HI6037 Fundamentals of Business...

Abhishek answered on Aug 19 2021
124 Votes
HI6037
FUNDAMENTAL OF BUSINESS ANALYTICS
INDIVIDUAL ASSIGNMENT
ASSESSMENT 3: RESEARCH PAPER
TOPIC: IMPLICATIONS OF PREDICTIVE ANALYTICS & REPORTING IN BUSINESS ANALYTICS
Table of Contents
Introduction    4
Discussion    4
Source 1:    4
Reference    4
Critical Analysis    4
Reflection    4
Source 2:    5
Reference    5
Critical Analysis    5
Reflection    5
Source 3:    6
Reference    6
Critical Analysis    6
Reflection    6
Source 4:    7
Reference    7
Critical Analysis    7
Reflection    7
Source 5:    8
Reference    8
Critical Analysis    8
Reflection    8
Source 6:    9
Reference    9
Critical Analysis    9
Reflection    9
Source 7:    9
Reference    9
Critical Analysis    10
Reflection    10
Source 8:    10
Reference    10
Critical Analysis    10
Reflection    11

Source 9:    11
Reference    11
Critical Analysis    11
Reflection    12
Source 10:    12
Reference    12
Critical Analysis    12
Reflection    12
Conclusion    13
References    15
Introduction
The purpose of the report is to understand how the organisation performance gets affected by information technology and the capability of the HRM through big data predictive analytics.
Discussion
Source 1:
Reference
Mishra, D., Luo, Z., Hazen, B., Hassini, E. & Foropon, C. 2019, "Organizational capabilities that enable big data and predictive analytics diffusion and organizational performance: A resource-based perspective", Management Decision, vol. 57, no. 8, pp. 1734-1755. Available at: https://www.proquest.com/docview/2293573742/fulltextPDF/5B5C9072EE7D45B5PQ/1?accountid=30552
Critical Analysis
The study has been consistent with the prior research in this aspect. The lower order capacities like IT deployment and the HR system can help develop high order capacity that is big data predictive analysis diffusion. Only limited types of organisational capacity have been focused on and other types were not covered in this study. The impact on the supply chain aspect has not been discussed in this article.
Reflection
In business, the impact of predictive analysis has been discussed, which can be able to provide its direct and indirect impact on organisational performance. Two aspects of organisational capacity have been represented that affect the performance of the organisation.
Through the facilitation of BDPA, the impact of IT and HR on the organisation's performance has been clearly defined where IT is proportionately linked with BDPA diffusion, which helps in better decision making by the prediction regarding the future demand in case of uncertainty. In addition, BDPA leads to better organisational performance through better HR capacity development.
Source 2:
Reference
Jeble, S., Kumari, S., Venkatesh, V.G. & Singh, M. 2020, "Influence of big data and predictive analytics and social capital on performance of humanitarian supply chain: Developing framework and future research directions", Benchmarking, vol. 27, no. 2, pp. 606-633. Available at: https://www.proquest.com/docview/2534584175/fulltextPDF/24FF6AB9ABF84DA8PQ/1?accountid=30552
Critical Analysis
The paper has been formed for investigation of how humanitarian supply chain (HSC) performance is dependent on the social capital and predictive analytics while exploring various frameworks of HSC utilised by organisations and providing a future direction for further research.
The study has successfully identified the role of BDPA and social capital in the humanitarian supply chain, which can be useful in the case of natural calamities. However, the study could not shed light on empirical validation, as it has only been able to develop the conceptual framework.
Reflection
The paper is very effective in the formation of a concept, which links the two themes, social capital and BDPA, in case of the performance of the HSC that can be done for the better functioning, which can help in better humanitarian operations and a fast process of life-saving. This will also be able to provide better transparency to the donors and the stakeholders regarding the organisational operations. This study has been beneficial for humanitarian organisations working on disaster management by building social capital networks.
While BDPA is combined worth the social capital, then a better HSC result can be obtained as BDPA improves the efficiency of humanitarian goals achievement. Social capital is a country like people with each other for the development of a network, which can be useful in case of managing disasters.
Source 3:
Reference
Kolomvatsos, K. & Anagnostopoulos, C. 2017, "Reinforcement Learning for Predictive Analytics in Smart Cities", Informatics, vol. 4, no. 3, p. 16. Available at: https://www.proquest.com/docview/1952108115/fulltextPDF/E477783BB54647FBPQ/1?accountid=30552
Critical Analysis
This research aims to develop the concept of query controller receiving the enquiry from the analytics and placing them in a processor in the data partition. The study has aimed to discuss the process of query management and machine learning through clustering and reinforcement learning.
The combination of the two schemes and the comparison will be done to provide a mathematical formula for simulation results. The advantages of the models and the results will be discussed by comparing the framework for focusing on reinforcement learning for predictive analytics in smart cities.
The article successfully provided the models, combined the outcomes and defined an adaptive learning model that can reduce the time of training, which can lead to initiating multiple schemes without hindering the performance outcome. Furthermore, the schemes are able to bring out decisions from a particular perspective where it provides the opportunity for further insight into the query process.
Reflection
The article has focused on the shift of data management and data products in this era of digitalisation. Huge volumes of data are being developed by various models. This article provided two models: clustering and the reinforcement model for data handling and management.
Smart governance of the smart cities that have been developed, in this article, the basis for intelligent application building to enhance that process has been described. Query control and query processor are the modules for handling the queries. Two schemes have been provided and one of them is based on clustering while the other is based on reinforcement learning.
Source 4:
Reference
Calixto, N. & Ferreira, J. 2020, "Salespeople Performance Evaluation with Predictive Analytics in B2B", Applied Sciences, vol. 10, no. 11, p. 4036. Available at: https://www.proquest.com/docview/2413778857/fulltextPDF/C1D251B7C52046DCPQ/1?accountid=30552
Critical Analysis
Measuring the performance level of the salespeople in the business is a tedious job and is time-consuming at the same time. The present article has stated the development of a model, which might be helpful in the performance measurement of salespeople in organisations. For this purpose, the authors have collected data from 594 salespeople to evaluate their performance appraisal. This model is efficient in producing the guidelines that can be practically followed by HR for the automation of the...
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