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  2. Data Driven Managerial Decisions [MAN6777]

School: Business and Law

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

  • Unit Title

    Data Driven Managerial Decisions

  • Unit Code

    MAN6777

  • Unit Type

    Learning Unit

  • Year

    2027

  • Enrolment Period

    1

  • Version

    3

  • Credit Points

    20

  • Full Year Unit

     N

  • Mode of Delivery

    On Campus
    Online

  • Unit Coordinator

    Dr Allen AU

Description

In the contemporary business climate of hyper-competition, volatility and increasingly pervasive technologies, the demand for organisational agility and responsiveness accentuate the degree to which success is linked to managerial decision making: more business decisions need to be made, at greater speed, with superior precision in order to achieve effective business outcomes. This unit examines the different types of business decisions that managers make, embedding these within a variety of processes and contexts. Students will examine the role of data analytics and other technologies in the strategic decision making processes of organisations. Students will have an opportunity to familiarise themselves with the business decision-making processes by exploring and applying some analytical and data visualisation techniques.

Equivalent Rule

The online accelerated version of this unit is PRJ6409 Data Driven Managerial Decisions.

Capabilities

In this unit, students will be developing the following capabilities:

  1. Digital Literacy
  2. Communication
  3. Creative Thinking

Unit Content

  1. Creating solutions for structured, semi-structured and unstructured business decisions. Role of information systems in decision-making.
  2. Social media crowdsourcing and customer involvement in decision-making.
  3. Enterprise 2.0 - collaborative systems for decision making.
  4. Types of decision and decision making, distortions, criteria for assessing decision-making approaches.
  5. Business value of managerial decision making. Embedding data analytics into business processes.
  6. Introduction to data analytical tools (such as Power BI).
  7. Application of data analytics and technologies in organisational decision-making activities.

Learning Experience

ONLINE

All learning experiences are delivered online and attendance at scheduled virtual classes is expected.

ON-CAMPUS

On-campus attendance at scheduled classes is expected.

This is a Learning Unit. Learning Units engage students in regular learning activities to develop their knowledge, skills, and capabilities. The learning activities provide each student with feedback to support their development, and create evidence for each student’s progress towards achieving the learning outcomes of the course.

Unit Completion Requirements

To meet the minimum requirements for this Learning Unit, you will actively engage in specified learning activities and produce a curated portfolio of work that demonstrates your knowledge, skills, and developmental progress toward the course learning outcomes. Further details are available in the unit Canvas site.

GS2 GRADING SCHEMA 2 Used for Undifferentiated Pass/Fail units inc. practical units or work-integrated learning

Disability Standards for Education (Commonwealth 2005)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.

Assessment

Students please note: The marks and grades received by students on assessments may be subject to further moderation. Informal vivas may be conducted as part of an assessment task, where staff require further information to confirm the learning outcomes have been met. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.

Academic Integrity

Integrity is a core value at Edith Cowan University, and it is expected that ECU students complete their assessment tasks honestly and with acknowledgement of other people's work as well as any generative artificial intelligence tools that may have been used. This means that assessment tasks must be completed individually (unless it is an authorised group assessment task) and any sources used must be referenced.

Breaches of academic integrity can include:

  • Plagiarism: Copying the words, ideas or creative works of other people or generative artificial intelligence tools, without referencing in accordance with stated University requirements. Students need to seek approval from the Unit Coordinator within the first week of study if they intend to use some of their previous work in an assessment task (self-plagiarism).
  • Unauthorised collaboration (collusion): Working with other students and submitting the same or substantially similar work or portions of work when an individual submission was required. This includes students knowingly providing others with copies of their own work to use in the same or similar assessment task(s).
  • Contract cheating: Organising a friend, a family member, another student or an external person or organisation (e.g. through an online website) to complete or substantially edit or refine part or all of an assessment task(s) on their behalf.
  • Cheating in an exam: Using or having access to unauthorised materials in an exam or test.

Serious outcomes may be imposed if a student is found to have committed one of these breaches, up to and including expulsion from the University for repeated or serious acts.

ECU's policies and more information about academic integrity can be found on the student academic integrity website.

All commencing ECU students are required to complete the Academic Integrity Module.

Academic Integrity in Learning Units

The University is committed to creating an academic environment in which learning with integrity means engaging honestly, responsibly and ethically with the curriculum. Engaging in academic misconduct undermines this commitment, impedes the development of authentic knowledge and skills, and prevents meaningful learning. Academic integrity is therefore essential to the learning process and to the value of the qualifications awarded by the University.

Academic Integrity in a Learning Unit includes:

  • Following the guidance for Artificial Intelligence in your unit, taking responsibility for the validity of any information you get from AI tools, and always acknowledging your use fully and accurately;
  • Completing your own work, without copying from others or asking other people to do your work for you;
  • Referencing your sources of information accurately;
  • Attending classes and engaging with the learning materials and feedback.

Your teaching staff will provide feedback if they have concerns that you are not acting with integrity in your learning. However, it is your responsibility to ensure that you are completing your work ethically.

Extension

In some circumstances, Students may apply for an extension in accordance with ECU policy and procedure - for more information visit https://askus2.ecu.edu.au/s/article/000001386.

Special Consideration

Students may apply for Special Consideration in respect of a final unit grade, where their achievement was affected by Exceptional Circumstances as set out in ECU policy and procedure - for more information visit https://askus2.ecu.edu.au/s/article/000003318.

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