BUSINESS INTELLIGENCE AND ANALYTICS - 2023/4

Module code: MANM520

Module Overview

This module looks at manager decision-making and the creation of organisational value using concepts from the area of Business Intelligence and Analytics. Data science and big data technologies using a decision-making perspective give MBA's an overview of how disruptive technologies are changing industries.

We focus on how digital technologies are changing business, government and society. A main objective of the module is to help MBA's to understand how to use these technologies to help their own firms and their own careers.

Machine Learning and Artificial Intelligence promises to become a fundamental source for competitive advantage and a driver for the creation of value in the business organisation, through the support to manager decision-making, automation of multiple business functions and the promise for emergent products, services and markets.This module looks at how managers can use new digital data technologies in their firms - such as Business Intelligence tools and data science. The emphasis of the module is to present the benefits and pitfalls of Business Intelligence and Business Analytics related  technologies to managers as they strive to create competitive advantage and value for their organisations. 

Module provider

Surrey Business School

Module Leader

FU Colin (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 90

Lecture Hours: 12

Seminar Hours: 30

Guided Learning: 6

Captured Content: 12

Module Availability

Semester 2

Prerequisites / Co-requisites

N/A

Module content

Indicative content includes:


  • Knowledge Discovery Process

  • Digital Data: How data produces insights (Journey based-thinking)

  • Decisions using Big Data Analytics

  • Databases & Data visualisations

  • Story Telling from Data Analytics perspective

  • Disruptive Business Models & value creation

  • Statistical Learning

  • Machine Learning and AI

  • Modelling and Optimisations

  • Simulation


Assessment pattern

Assessment type Unit of assessment Weighting
Oral exam or presentation GROUP PRESENTATION 40
Coursework INDIVIDUAL DATA ANALYTICS PROJECT REPORT 60

Alternative Assessment

Business Intelligence and Analytics coursework

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they  
•    value Business Intelligence and Analytics for decision-making;
•    can gain business insights from data;
•    demonstrate abilities of finding optimal solutions for business opportunities.

The summative assessment for this module consists of:


  • a group presentation and

  • a piece of individual coursework, both of which assess learning objectives.



Formative assessment for this module occurs as the module progresses, with students receiving regular feedback during the workshop and guided learning components, as well as in the lead up to the group presentation.

Module aims

  • The module's main aims is to introduce topics in the area of Business Intelligence and Analytics.
  • To gain insights about the relevance of data analytics for decision-making.
  • To critically evaluate and select relevant prescriptive analytical methods to improve business productivity

Learning outcomes

Attributes Developed
001 Synthesise the fundamental concepts and topics around manager decision-making in a data-rich environment; and devise the challenge of turning data into business value; CKT
002 Combine the concepts in the module to analyse specific functions and activities related to challenges in managing data and in transforming data & information into knowledge, new business models using Data Analytics; KP
003 Formulate and solve optimisation challenges that improve competitiveness in a business environment; KCT
004 Devise decision-making rules from Artificial Intelligence and Machine Learning reports. KP

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to focus on learning by doing and reflection. 

The learning and teaching methods include: 



  • Theories of Business Intelligence and Analytics applied to company case examples and examples based on MBAs interests and experience 


  • Teaching concepts and then learning through group work to analyse real business situations 


  • Presentations and Q&A sessions 


  • Seminar activities conducted in teams 


  • Self-directed learning 



The module consists of regular lectures, seminars and discussion sessions. The lecture component will introduce the topics, concepts, and relevant issues and problems. The discussion will look at critical issues and examples of the topics from the lectures in order to facilitate student understanding and the overall mastering of the material. Students will be expected to have read the assigned readings prior to each session in order to generate a discussion of the concepts. Students will be expected to read materials outside the regular class sessions (environmental scanning) in order to improve their environmental awareness and ability to work with concepts in the module. 

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM520

Other information

Digital Skills: The module provides students with an opportunity to be proficient in applying software such as Power Business Intelligence (PBI), SimVenture or others to demonstrate their digital skills. This includes using a business simulation environment; reading and creating business intelligence reports for strategic and operational decision making; familiarity with Artificial Intelligence; understanding Machine Learning models from a business perspective. 

Resourcefulness and resilience: Students will develop their collaboration skills through group work to simulate a business environment, enhance reflection on experience of contribution and learn from others. These skills will be developed and refined through the student’ active participation in the group discussions and project team meetings either f2f or remotely via MS Teams, WhatsApp and Zoom when/if geographical dispersed whilst understanding the critical roles of information systems and technologies in project success. 

Global and cultural capabilities: The Surrey MBA cohorts consists out on an international community which integrates various cultural backgrounds. During the in-class discussions students develop their global and cultural capabilities by being exposed to and discussion different perspectives. Multicultural teams are formed when preparing for the group presentation and doing the business simulation. This gives students the possibility to appreciate different cultural aspects.

Employability: The group work provides an opportunity to build a support network, interpret theory to practice and it requires decision-making skills when doing the business simulation and dealing with a variety of business opportunities. There is an application of critical thinking skills and the use of qualitative/quantitative information for analysing stakeholders, business case and risks that will affect the chosen business case. 

 

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.