LEO5304 · Reporting for strategy and change

Overview

High performing and agile organisations need decision makers to embrace the power of data, data analytics, and generative AI. To be most effective, these users need to engage reports and dashboards that deliver up-to-date information (e.g. inventory and supply levels, real-time sales data, varied key performance indicators, etc.) aligned to their unique responsibilities, strategic objectives, and change initiatives. Driving effective and sustained user engagement requires a 'consultative' mindset coupled with sufficient technical skills across the data analytics cycle, particularly when defining data needs, presenting findings, and operationalising the use of analytics and generative AI. This unit highlights the importance of stakeholder engagement and leadership skills necessary to drive adoption and sustained usage of self-service reporting and insight generation platforms by managers. It provides specific instruction on the optimal characteristics (e.g., accessibility, completeness, timeliness, etc.) of reports and/or dashboards to support different levels of management. To do this, the unit introduces modern reporting and data management platforms, focusing on Power BI which has been widely adopted across sectors to advance managerial analytics.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. design and develop high quality reports and dashboards using Power BI and incorporating recommended characteristics for effective data visualisations
2. design and develop sustainable managerial analytics solutions across the data analytics cycle, from definition through to presentation and finally operationalisation
3. apply a ‘consultative’ mindset to identify and categorize user needs and support users to effectively engage with data analytics.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.