Module Code: H9BIBA
Long Title Business Intelligence and Business Analytics
Title Business Intelligence and Business Analytics
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: MICHAEL BRADFORD
Module Author: Jenette Carson
Departments: School of Computing
Specifications of the qualifications and experience required of staff

MSc/PhD in a computing or cognate discipline. May have industry experience also. 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically analyse advanced Business Intelligence and Business Analytics methodologies in order to assess best practice guidance when applied to operational data of a business
LO2 Investigate and evaluate key concepts and advanced Business Intelligence and Business Analytics techniques and assess how to apply which technique on complex datasets and practical problem domains.
LO3 Contextualise, research and utilise current data approaches, applications and technologies in order to develop Business Intelligence and business analytics strategies to address the operational and analytical requirements of an organisation
LO4 Critically review and apply appropriate data mining research and assess research methods
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

A level 8 degree or its equivalent in any discipline
 

Module Content & Assessment

Indicative Content
Intelligent Enterprises
Agile Enterprises, Operating Strategies, Continuous Improvement Programs
Enterprise Systems
Evolution – MRP, CL MRP, MRP II, ERP, ES Packages, Balanced Scorecard
BI and Dashboards
Views v Reports, Types of Dashboards, Advantages of Dashboards, The Funnel
Consumer Behaviour models
Behaviourist v Cognitivist, Lawson’s, EKB, and Howard and Sheth’s models
Operational CRM Systems
Overview and Demo of a commercial system such as Microsoft Dynamics CRM
Implementing Enterprise BI systems
Data Warehousing and Data Marts, Data mining, Online Analytical Process (OLAP)
Implementing CRM systems
Fit-Gap Analysis, Integration with Heterogeneous systems, Data integration, Information Lifecycle Management, Data protection, security and ethical considerations
Customer-Centric Enterprise with CRM
Customer Experience, Customer Loyalty, Customer Relationships, Customer Life Cycle, Customer Value Management
Customer-Responsive Enterprise with SCM
Supply Chain Management, Customer-Responsive Management, B-Webs, Activity Costing techniques
Renewing Enterprise with PLM
Components and Advantages of PLM, Porter’s Framework, Product Life Cycle
Collaborative Enterprise with BPM
BPM, BPR, Business Processes with SOA, Workflows, Analytics
Informed Enterprise with BI
Context-Aware Applications, Decision Patterns and Data mining
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Continuous Assessment % of total: 20
Assessment Date: Week 8 Outcome addressed: 3
Non-Marked: No
Assessment Description:
Multiple-Choice Questions, similar to Industry Certification exams
Assessment Type: Project % of total: 80
Assessment Date: Week 12 Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Analyse Requirements, Design and Implement an end-to-end BI and Analytics system for an organisation.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
The repeat strategy for this module is by repeat assessment/project that covers all learning outcomes.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Every Week 24.00
Tutorial Other hours (Practical/Tutorial) 12 Every Week 12.00
Independent Learning Independent learning (hours) 89 Every Week 89.00
Total Weekly Contact Hours 36.00
 

Module Resources

Recommended Book Resources
  • Vivek Kale, Enhancing Enterprise Intelligence: Leveraging ERP, CRM, SCM, PLM, BPM, and BI (CRC Press).
Supplementary Book Resources
  • Dean Abbott, Applied BI and Consumer Relationship Analytics: Principle and Techniques for the Professional Data Analyst (Wiley, 2014)..
  • John W. Foreman, Data Smart: Using Data Science to Transform Information into Insight (Wiley, 2013)..
  • Gordon S. Linoff and Michael J. A. Berry, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (Wiley, 2011).
  • John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy, Fundamentals of Machine Learning for BI and Consumer Relationship Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press, 2015)..
  • Albrecht, K. The Power of Minds at Work: Organizational Intelligence in Action, Amazon, 2003..
  • Bell, S. Lean Enterprise Systems: Using IT for Continuous Improvement, Wiley, 2006..
  • Davis, F. W. and K. B. Mandrodt, Customer-Responsive Management: The Flexible Advantage, Blackwell, 1996..
  • Dove, R. Response Ability: The Language, Structure, and the Culture of the Agile Enterprise, Wiley, 2001..
  • Koren, Y. The Global Manufacturing Revolution: Product-Process-Business Integration and Reconfigurable Systems, Wiley, 2010..
  • Nightingale, D. J. and D. H. Rhodes, Architecting the Future Enterprise, MIT Press, 2015..
  • Shtub, A. and R. Karni, ERP: The Dynamics of Supply Chain and Process Management, Springer, 2010..
  • Walker, W. T. Supply Chain Architecture: A Blueprint for Networking the Flow of the Material, Information, and Cash, CRC Press, 2005..
  • Waltz, E. Knowledge Management in the Intelligent Enterprise, Artech House, 2003..
  • Weijermars, R. Building Corporate IQ: Moving the Energy Business from Smart to Genius, Executive Guide to Preventing Costly Crises, Springer, 2011..
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: