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MN50741: Big data and IT governance in higher education

[Page last updated: 15 October 2020]

Follow this link for further information on academic years Academic Year: 2020/1
Further information on owning departmentsOwning Department/School: School of Management
Further information on credits Credits: 12      [equivalent to 24 CATS credits]
Further information on notional study hours Notional Study Hours: 240
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Modular (no specific semester)
Further information on unit assessment Assessment Summary: CW 100%
Further information on unit assessment Assessment Detail:
  • Assessment detail for this unit will be available shortly. (CW 100%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Description: Aims:
This unit aims to equip students with a deep conceptual understanding of the theoretical foundations of data analytics and IT governance in higher education (HE). It aims to develop students' ability to apply critically and creatively relevant bodies of knowledge concerning the potential role of data analytics in management decision-making to specific problems in their own professional areas whilst taking account of the interdependencies between the various areas and functions of universities.

Learning Outcomes:

* Critical awareness of the potential role of big data analytics in key areas of operations management and in the relationship between operations strategy and business strategy both within and between universities in international partnerships;
* Ability to take an international perspective in understanding the impact of IT governance on the safeguarding of data privacy in higher education in different countries.

Skills:
Students who successfully complete this unit should develop:
* The ability to identify, reflect upon and engage critically with current research and advanced scholarship in the field of international higher education data analytics and IT governance.
* The ability to acquire and analyse large datasets, to evaluate their relevance and validity, and to synthesise them in the context of strategic university management.
* The ability to deal systematically and creatively with complex issues of HE data warehousing, access, governance and strategy, make sound judgements in the absence of complete information, and to communicate their conclusions clearly and competently to a range of audiences.
* The ability to reflect on their learning in the unit and current professional practice with a view to integrate new knowledge concerning big data analytics and IT governance with past experience and effectively apply it to their current role.
* The ability to develop a holistic perspective on a university as an organisation and think strategically about how the different functions relate to one another in relation to IT governance.
All skills are initially taught/delivered in seminars, and then facilitated through discussion online and individual work. All skills should also be evident in the final assessed work.

Content:
Typical content covered in the unit includes:
* Global big data, artificial intelligence (AI) and machine learning
* Knowledge discovery (KDD) methodologies - data clustering, feature selection and association
* Sources of 'static' and 'fluid' big data in higher education
* International best practice in big data analytics in HE
* Redesigning core support services to produce strategically useful data
* Consolidating data from multiple sources - data warehousing
* Descriptive, predictive and prescriptive analytics
* Learning, institutional, IT and academic analytics
* The challenge of big data to management decision-making
* Big data, HE and sustainable development in the Global South
* International issues of IT infrastructure and bandwidth
* Safeguarding data privacy and regulating access
* IT governance maturity and frameworks in HE
* Responsibility, executive knowledge and IT alignment
The unit will also address a range of related concepts and issues such as trade-off between sampling error and measurement error; tagging and other non-taxonomic data labels; repurposing data - option value and recombinant, extensible and exhaust data; big data analytics - Hadroop, MapReduce and Spark; bringing data analytics and strategy closer together.
Further information on programme availabilityProgramme availability:

MN50741 is Optional on the following programmes:

School of Management

Notes: