Data Management

General Information

This subject introduces students to the principles and practices of managing data effectively in modern computing environments. It covers foundational topics such as database design, relational models, and the use of SQL for querying and manipulating data. Students will learn to construct Entity-Relationship diagrams, apply normalisation techniques to optimise database structure, and explore strategies for ensuring data reliability through backup and recovery. The subject also examines the evolution of data systems by introducing NoSQL and non-relational databases, highlighting their applications in handling large-scale, unstructured data. Through practical exercises and conceptual analysis, students will gain the skills to design, implement, and maintain robust data management solutions, using AI to support the learning of the syntax for specific database management software.

  • Academic unit: Bond Business School
    Subject code: ENAI71-202
    Subject title: Data Management
    Subject level: Postgraduate
    Semester/Year: May 2027
    Credit points: 10.000
  • Timetable: https://bond.edu.au/timetable
    Delivery mode: Standard
    Workload items:
    • Computer Lab: x12 (Total hours: 24) - Computer Lab 1
    • Computer Lab: x12 (Total hours: 24) - Computer Lab 2
    • Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
    Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible.
  • Prescribed resources:

    No Prescribed resources.

    After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.
    iLearn@Bond & Email:

    iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications.

    Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.

    To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
    Class recordings:

    The primary workload items for this subject will be recorded for the purpose of revision.

    These recordings are not a substitute for attending classes. Students are encouraged to attend all sessions as there may be instances where a session is not recorded due to the presence of a guest speaker, the inclusion of sensitive or protected content, or technical issues. Students are advised not to rely solely on these recordings for revision.

    See the Recording policy for further details.
Academic unit: Bond Business School
Subject code: ENAI71-202
Subject title: Data Management
Subject level: Postgraduate
Semester/Year: May 2027
Credit points: 10.000

Enrolment requirements

Requisites:

Nil
Restrictions:

Nil

Assurance of learning

Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.

At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.

Program Learning Outcomes (PLOs)

Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.

Find your program

Subject Learning Outcomes (SLOs)

On successful completion of this subject the learner will be able to:

  1. Critically evaluate data management paradigms, including relational and non-relational models, to determine their suitability for diverse applications.
  2. Design and optimise complex SQL queries to retrieve, manipulate, and manage data in relational databases.
  3. Develop and refine data models using Entity-Relationship (ER) diagrams to model real-world data scenarios and create the corresponding tables using SQL.
  4. Assess and improve database designs for redundancy, efficiency, and reliability using normalisation techniques.
  5. Formulate and implement complex backup and recovery strategies in database environments.

Generative Artificial Intelligence in Assessment

The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.

  • Type Task % Timing* Outcomes assessed AI category
    Computer-Aided Examination (Open) Conceptual questions about the topics taught until week 6 30.00% Week 7 (Mid-Semester Examination Period) 1, 2, 3, 4
    Assignment Given a business scenario and data, use ER and normalisation to propose a new database design that is efficient and avoids redundancy. 20.00% Week 8 1, 2, 3, 4, 5
    Assignment Given an ER for a database, write the SQL code to create the database and the tables for this ER. Populate the tables and test the database for efficiency by testing queries and measuring the runtime. 20.00% Week 11 1, 2, 3, 4, 5
    Assignment Develop and test a backup and recovery plan for a simulated database environment using a high-level language (e.g., Python or R). 30.00% Week 13 1, 2, 3, 4, 5
    • * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
    • C = Students must reach a level of competency to successfully complete this assessment.

    AI Categories

    • Ai Prohibited: Learning to develop AI-free knowledge and skills.

    • Ai Supported: Learning with the help of AI as directed.

    • Ai Focussed: Learning AI expertise and mastery as directed.

    Refer to the assessment task sheet for specific AI instructions and review the Bond University Gen-AI Guide.

  • Assessment criteria

    High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking.
    Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas.
    Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above.
    Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment.
    Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines.

    Quality assurance

    For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Type Task % Timing* Outcomes assessed AI category
Computer-Aided Examination (Open) Conceptual questions about the topics taught until week 6 30.00% Week 7 (Mid-Semester Examination Period) 1, 2, 3, 4
Assignment Given a business scenario and data, use ER and normalisation to propose a new database design that is efficient and avoids redundancy. 20.00% Week 8 1, 2, 3, 4, 5
Assignment Given an ER for a database, write the SQL code to create the database and the tables for this ER. Populate the tables and test the database for efficiency by testing queries and measuring the runtime. 20.00% Week 11 1, 2, 3, 4, 5
Assignment Develop and test a backup and recovery plan for a simulated database environment using a high-level language (e.g., Python or R). 30.00% Week 13 1, 2, 3, 4, 5
  • * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
  • C = Students must reach a level of competency to successfully complete this assessment.

AI Categories

  • Ai Prohibited: Learning to develop AI-free knowledge and skills.

  • Ai Supported: Learning with the help of AI as directed.

  • Ai Focussed: Learning AI expertise and mastery as directed.

Refer to the assessment task sheet for specific AI instructions and review the Bond University Gen-AI Guide.

Study Information

Submission procedures

Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.

Policy on late submission and extensions

A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the subject coordinator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.

Academic Integrity

Bond University‘s Student Code of Conduct Policy , Student Charter, Academic Integrity Policy and our Graduate Attributes guide expectations regarding student behaviour, their rights and responsibilities. Information on these topics can be found on our Academic Integrity webpage recognising that academic integrity involves demonstrating the principles of integrity (honesty, fairness, trust, professionalism, courage, responsibility, and respect) in words and actions across all aspects of academic endeavour.

Staff are required to report suspected misconduct. This includes all types of plagiarism, cheating, collusion, fabrication or falsification of data/content or other misconduct relating to assessment such as the falsification of medical certificates for assessment extensions. The longer term personal, social and financial consequences of misconduct can be severe, so please ask for help if you are unsure.

If your work is subject to an inquiry, you will be given an opportunity to respond and appropriate support will be provided. Academic work under inquiry will not be marked until the process has concluded. Penalties for misconduct include a warning, reduced grade, a requirement to repeat the assessment, suspension or expulsion from the University.

Feedback on assessment

Feedback on assessment will be provided to students according to the requirements of the Assessment Procedure Schedule A - Assessment Communication Procedure.

Whilst in most cases feedback should be provided within two weeks of the assessment submission due date, the Procedure should be checked if the assessment is linked to others or if the subject is a non-standard (e.g., intensive) subject.

Accessibility and Inclusion Support

Support is available to students where a physical, mental or neurological condition exists that would impact the student’s capacity to complete studies, exams or assessment tasks. For effective support, special requirement needs should be arranged with the University in advance of or at the start of each semester, or, for acute conditions, as soon as practicable after the condition arises. Reasonable adjustments are not guaranteed where applications are submitted late in the semester (for example, when lodged just prior to critical assessment and examination dates).

As outlined in the Accessibility and Inclusion Policy, to qualify for support, students must meet certain criteria. Students are also required to meet with the Accessibility and Inclusion Advisor who will ensure that reasonable adjustments are afforded to qualifying students.

For more information and to apply online, visit BondAbility.

Additional subject information

As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Subject curriculum

Approved on: Oct 24, 2025. Edition: 1
Last updated: Feb 4, 2026