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

    2026
  • 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

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.

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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