Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Data Management | |||
| Reference | CMM722 | Version | 3 |
| Created | February 2024 | SCQF Level | SCQF 11 |
| Approved | April 2017 | SCQF Points | 15 |
| Amended | April 2024 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To provide key concepts and principles of development and execution of architectures, policies, practice and procedures to properly manage and to govern a full data lifecycle to meet the needs of a business enterprise. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Appraise the principles and practices of data management for business. |
| 2 | Make informed judgements on the principles and practices of data ethics and security and to apply them on business data applications. |
| 3 | Evaluate data representation methodologies to model complex business data problems. |
| 4 | Design data management solutions to a significant industry-focused problem providing insights and conclusions about challenges, opportunities and risks for data management. |
| Indicative Module Content |
|---|
| Data management overview: planning, oversight, and control over management of data and the use of data and data-related resources. Data security: ensuring privacy, confidentiality, ethical and appropriate access. Data architecture management: the overall structure of data and data-related resources as an integral part of the enterprise architecture. Data integration and interoperability: acquisition, extraction, transformation, movement, delivery, replication, federation, virtualisation and operational support. Meta-data management: collecting, categorising, maintaining, integrating, controlling, managing, and delivering metadata. Data quality management: defining, monitoring, maintaining data integrity, and improving data quality. Document and Content Management: representation of data using XML. Reference and Master Data: managing shared data to reduce redundancy and ensure better data quality through standardised definition and use of data values. |
| Module Delivery |
|---|
| This is a lecture based course enhanced through interactive tutorials and directed reading, supplemented with lab sessions where suitable state-of-the-art data retrieval and representation tools will be used to further their understanding. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | N/A | 48 |
| Non-Contact Hours | N/A | 102 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | N/A | 150 |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Coursework submission worth 100% of total module assessment. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in Component 1. |
| B | The student needs to achieve a B in Component 1. |
| C | The student needs to achieve a C in Component 1. |
| D | The student needs to achieve a D in Component 1. |
| E | The student needs to achieve an E in Component 1. |
| F | The student needs to achieve an F in Component 1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | DAMA INTERNATION TEAM, 2010. The DAMA Guide to the Data, Management Body of Knowledge (DAMA-DMBOK). DAMA International. |
| 2 | BERSON A. and LARRY, D., 2010. Master Data Management and Data Governance. 2nd ed. McGraw-Hill Education. |
| 3 | LADLEY, J., 2012. Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program (The Morgan Kaufmann Series on Business Intelligence). Morgan Kaufmann. |
| 4 | GORDON, K., 2013. Principles of Data Management: Facilitating Information Sharing. 2nd ed. British Computer Society. |