MIS781 Group Practical Assignment v5July.pdf - MIS781 Business Intelligence Trimester 2 2017 Group Assignment Business Intelligence Solution Development

MIS781 Group Practical Assignment v5July.pdf - MIS781...

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Deakin's Bachelor of Commerce and MBA are internationally EPAS accredited . Deakin Business School is accredited by AACSB . MIS781 Business Intelligence Trimester 2, 2017 Group Assignment: Business Intelligence Solution Development and Report for Social Enterprise DUE DATE AND TIME: 8 September 2017 (Friday; 5pm) PERCENTAGE OF FINAL GRADE: 40% of overall unit assessment mark Word count: Up to 5000 words Assignment: In a team of 3 students (Off-campus/Cloud students can seek permission to do it individually) Learning Outcome Details Unit Learning Outcome (ULO) Graduate Learning Outcome (GLO) ULO3: Collaborate constructively in a team to evaluate business intelligence requirements and implement innovative BI solutions. GLO1: Students are required to demonstrate an understanding of the business intelligence context and explain the BI technologies and associated implementation issues. GLO4: students are required to analyse and critique business intelligence initiatives from technological and organisational perspectives. GLO7: students are expected to collaborate constructively in a team to appraise business intelligence requirements and develop BI solutions. Assessment Feedback: Students who submit their work by the due date will receive their marks and feedback on CloudDeakin on 29 September 2017 (Friday; 5pm) Requirement: BI Solution Development and Report In the BI practicals, we saw how the Watson Analytics tools interact with data sources to produce BI dashboards for managers. This practical assignment requires you to demonstrate your understanding of BI and apply the IBM Watson Analytics skills.
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Page 2 of 5 This group assignment topic will be on social or environmental issues. As the co-founder of a social enterprise, your main task is to apply Watson Analytics and develop innovative analytics solution with regards to environment, e.g. climate change and energy consumption, greenhouse gas emission and economic development, air quality and health, etc. You may apply either real- world or artificial dataset to illustrate your approach (the different datasets can be combined too).
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