INMR89: Big Data in Business

Module code: INMR89

Module provider: Business Informatics, Systems and Accounting; Henley Business School

Credits: 20

Level: 7

When you'll be taught: Semester 2

Module convenor: Professor Keiichi Nakata, email: k.nakata@henley.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: Yes

Last updated: 19 November 2024

Overview

Module aims and purpose

The aim of this module is for students to be able to evaluate the business value in utilising Big Data and develop business technology solutions with an appreciation of Big Data and business analytics methods and technologies. 

This module focusses on the methods and techniques of using Big Data in business. Given the availability of large amounts of data in business and organisation, there is an increasing need for organisations to assess how effectively Big Data can be utilised for business. In this module, students consider how organisations can benefit from Big Data, and analyse business and technological requirements to create value though Big Data and business analytics. Students will also explore recent developments in technologies surrounding Big Data such as text analytics, cognitive analytics and visualisation, and assess types of tools that can be utilised, including the use of state-of-the-art analytics tools. 

Module learning outcomes

By the end of the module, it is expected that students will be able to: 

1. Assess the business opportunity and value creation through the utilisation of Big Data and business analytics by analysing the business environment and requirements; 

2. Critically assess suitable Big Data technologies and business analytics approaches; 

3. Formulatea solution for achieving value through Big Data; Demonstrate the solution using an existing Big Data and business analytics tool; 

4. Assess the organisational and technical impact of implementing the solution. 

5. Critically assess the suitability of a range of business analytics tools against a set of requirements; 

6. Demonstrate the awareness of state-of-the-art developments and commercial tools such as AI tools 

Module content

1. Introduction; Business opportunity in the era of Big Data

2. Business analysis for big data and business intelligence 

3. Methods, techniques and tools for Big Data 

4. Developing a Big Data strategy 

5. Big Data visualisation 

6. Artificial intelligence and machine learning 

7. Professional, leadership and ethical issues in Big Data solutions 

8. Emerging issues and impacts of Big Data 

Structure

Teaching and learning methods

This module takes a form of project-based learning as a team project, and combines lectures, seminars and practical workshops to develop Big Data strategies. It also uses state-of-the-art analytics tools as part of developing a Big Data solution in business.

Study hours

At least 30 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Obtain a mark of 50% or above based in the coursework.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Reassessment

Additional costs