Module Code: H8BI
Long Title Business Intelligence
Title Business Intelligence
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator: Sophie Flanagan
Module Author: ORLA LAHART
Departments: School of Computing
Specifications of the qualifications and experience required of staff

Msc degree in Computer Science. Experience Lecturing , work experience or projects in the specific domain


Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Develop and build a solid understanding of the value and purpose of an implementation of Business Intelligence technologies and strategies within industry and other domains
LO2 Recognize and evaluate the theoretical and practical requirements for the implementation of strategic decision supporting Business Intelligence technologies
LO3 Comprehend and contrast the technological foundational architectures supporting BI systems and workflows
LO4 Build and evaluate a BI supportive platform through data collection and preparation with supporting complex visualization tools , reports and dashboards. with the ability to become proficient in storytelling of data, addressing audiences in an industry context and build upon key soft skills of communication in a BI role.
LO5 Discover, discuss and critique recent and cutting edge BI and visualization technologies for both current and future implementations
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  

Module Content & Assessment

Indicative Content
Concept of BI
○ Historical Context ○ Business Intelligence through Analytics ○ The Value of Correlation ○ Non-Trivial BI
Informed Business Decision Making with BI
○ Enterprise Strategic Planning ○ Gaining Competitive Edge ○ Securing Longevity through BI ○ Decision Patterns
Benefits of BI
○ Real World Scenarios ○ Positive Case Studies of BI ○ Negative Case Studies of BI ○ Best Practices and Standards
Technologies of BI
○ Data warehousing and Data Marts ○ Business Intelligence ○ Data Mining ○ Online Analytical Process ○ Dashboards / Visualizations
BI Application Framework
○ PowerBI ○ Tableau & Tableau Prep ○ Dynamics ○ ClickSense
BI and the Cloud
○ Altrex ○ Apache Druid ○ Snowflake ○ Crisp DM
Connecting to Data
○ API Implementations ○ Middle-ware Supporting BI ○ Dynamic Sources ○ Text and Sentiment Analysis ○ Real Time BI
Data Preparation
○ Effectively Cleaning Data ○ T in ETL ○ Dealing with Ambiguity ○ Process Automation ○ Tools for Cleaning
Future Implementations and Technologies
○ VR Virtual Reality ○ AR Augmented Reality ○ Machine Learning AI ○ Context Aware Applications ○ Online Analytical Process ○ RPA Robotic Process Automation
Effective BI
○ Dashboards ○ Reports / Stories ○ Design Thinking ○ Data Presentations ○ Soft-skills & BI
Assessment Breakdown%


Full Time

Assessment Type: CA 1 % of total: 20
Assessment Date: n/a Outcome addressed: 1,2,3
Non-Marked: No
Assessment Description:
Theory Based Assessment : Addressing the fundamental topics up to and including topic four, will be a timed assessment encompassing 20-30 questions
Assessment Type: CA 2 % of total: 80
Assessment Date: n/a Outcome addressed: 3,4,5
Non-Marked: No
Assessment Description:
Group Project Based: Encompassing the development lifecycle of an industry based BI solution, including but not limited to ETL, Reporting, visualization composition and CRM implementation. Students will also be assessed on storytelling of Data Visualizations and overall effective presentation skills.
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 learner will be given the opportunity to repeat any of the above assessments which have been failed, this will be a repeat assessment through coursework alone, no written repeat exam will be provided. An opportunity to repeat will be at the end of the semester.

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 Online / Classroom Activities 24 Per Semester 2.00
Tutorial Practical & Tutorial Activities 12 Per Semester 1.00
Independent Learning Independent Learning Activities 89 Per Semester 7.42
Total Weekly Contact Hours 3.00

Module Resources

Recommended Book Resources
  • Vivek Kale. (2017), Enhancing Enterprise Intelligence, 1st. CRC Press, united states of America, p.378, [ISBN: 9781498705974].
  • Rick Sherman. (2014), Business Intelligence Guidebook, Morgan Kaufmann, p.550, [ISBN: 9780124114616].
Supplementary Book Resources
  • Efraim Turban. (2007), Decision Support and Business Intelligence Systems, Prentice Hall, p.772, [ISBN: 0131986600].
This module does not have any article/paper resources
Other Resources
Discussion Note: