Module code: MANM342

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

GARN Wolfgang (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

JACs code: N210

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

Overall student workload

Independent Learning Hours: 84

Lecture Hours: 6

Seminar Hours: 12

Tutorial Hours: 6

Laboratory Hours: 6

Guided Learning: 24

Captured Content: 12

Module Availability

Semester 1

Prerequisites / Co-requisites


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

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 two items of coursework that assess learning objectives.

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
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
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; KCT
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

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

Total student learning time 150 hours.

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

  • Workshop activities conducted in teams

  • Self-directed learning

The module consists of regular lectures and discussion sessions (typically, 2.5 hours each). 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
Upon accessing the reading list, please search for the module using the module code: MANM342

Other information


Programmes this module appears in

Programme Semester Classification Qualifying conditions
Master of Business Administration MBA 1 Compulsory A weighted aggregate mark of 50% is required to pass the module

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 2022/3 academic year.