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Data Governance

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Prospectus 2024
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This subject is available under ICMS undergraduate degrees, please click the button below to find an undergraduate course for you.

BROWSE UNDERGRADUATE DEGREES WITH THIS SUBJECT

Subject Code:

DAT304A

Subject Type:

Specialisation 

Credit Points:

3 credit points

Pre-requisite/Co-requisite: 

ICT301A IT Risk Management 

 Course level of study pre-requisite:  a total of 24 credit points (15 credit points, including ICT101A, ICT102A, ICT103A, DAT101A from level 100 and 9 credit points from level 200 core subjects) prior enrolling into level 300 core and specialisation subjects. 

Subject Level:

300 

Subject Rationale:

The ability to strategically manage data and related policies, procedures, structures, and roles and responsibilities is a major imperative for any organisation to effectively compete in a data-driven world. This subject will cover the relevant pillars of effective data governance frameworks including people, processes, and technology platforms.   

 Students will be introduced to the relevant data governance concepts, including mission and guideline principles, operating models, underlying infrastructure and processes, change management, roles and responsibilities, and standards to maintain data quality, security, integrity, and accuracy. In addition, emerging governance concepts such as big data governance, agile data governance, and cloud governance and related business implications will be addressed. The subject will also cover the key challenges of building and implementing a data governance structure in organisations related to planning, scoping, and executing. 

Learning Outcomes:

a) Explain the structures, concepts, and principles for governing data in organisations.

b) Identify the scope and key responsibilities to effectively govern data in different organisational contexts.

c) Critically evaluate how data governance structures create value for organisations.

d) Critically assess the effectiveness of data governance structures in organisations and identify actions for improvement.

e) Develop data governance concepts that align with organisational IT infrastructures and strategies, taking into account ethical and social considerations

Student Assessment:

WordPress Table

Broad Topics to be Covered:

Topic: 
W1: Introduction to data governance 

  • Definition and relationship between data governance, information governance, and IT governance 
  • Data Governance Institute (DGI) and the DGI data governance framework 
  • Relevance and business value of data governance 
  • Relationship between ethics, trust and data  
  • Data governance tools 
  • Regulatory requirements 
W2: Components of data governance   

  • Scope and elements of data governance programs 
  • Data governance models (centralised/decentralised) 
  • Data lifecycle & management  
  • Data management plans 
  • Data governance policies  
W3: Data governance roles and responsibilities 

  • Different user hats 
  • Data steward council and responsibilities 
  • Data steward manager and responsibilities 
  • Enterprise data steward and responsibilities 
  • Business data steward and responsibilities 
  • Project data steward and responsibilities 
  • Technical and operational data steward and responsibilities  
  • RACI matrix (responsible, accountable, consulted, and informed) to outline roles and responsibilities 
W4: Data governance development planning  

  • Governance approval 
  • Develop a governance team structure 
  • Definition of business units and capabilities 
  • Setting data governance scope 
  • Governance evaluation, ethical and social considerations 
  • Managing change capacity 
  • Defining measures  
W5: Data governance strategy  

  • Identifying business needs  
  • Aligning data governance with business needs  
  • Identifying core data governance principles  
  • Data governance success factors and capabilities 
  • Demonstrate value through use cases 
W6: Data governance architecture 

  • Prioritising data governance capabilities 
  • Data governance tools and technologies 
  • Identification of accountability and ownership 
  • Design of the operating framework 
  • Design of the engagement and workflow structures 
  • Data architecture management (development of enterprise data model, data technology, data integration, and meta data architecture) 
W7: Data governance implementation  

  • Alignment with existing organisational structures and projects 
  • Data governance milestones 
  • Short-term and long-term implementation plans  
  • Managing implementation change/change management plans 
  • Implementation metrics 
W8: Data governance operation  

  • Data governance deployment plan  
  • Implement operational data governance activities 
  • Execute data governance processes 
  • Implement metrics and reporting processes 
  • Data governance monitoring  
W9: Data governance quality management 

  • Definition of data quality and role of data governance  
  • Data quality awareness 
  • Define data quality rules and metrics 
  • Data quality techniques  
  • Data lineage 
  • Data quality audits 
W10: Data governance database and security management 

  • Datawarehouse and characteristics 
  • Datawarehouse processing (ETL) 
  • Data recovery and backup 
  • Database performance 
  • Database technology architecture  
  • Data privacy, confidentiality, and security needs, policies, standards 
  • Design and implementation of data security controls  
  • Managing data access, user authentication, and behaviour 
  • Establishing data security audits 
W11: Data governance for cloud computing and big data & subject review  

  • Cloud computing characteristics and implications for data governance 
  • Agile data governance and technology implications 
  • Big data and implications for data governance  
  • Subject review 

Please note that these topics are often refined and subject to change so for up to date weekly topics and suggested reading resources, please refer to the Moodle subject page.