MQM2DAS-Data Analytics Strategy in Business

Module Provider: Business Informatics, Systems and Accounting
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Prof Keiichi Nakata
Email: k.nakata@henley.ac.uk

Type of module:

Summary module description:

This module focusses on the strategic use of data analytics in business in the era of Big Data. Given the availability of large amounts of data in business and organisation, there is an increasing need for organisations to assess how effectively data analytics and relevant emerging technologies can be utilised for business. In this module, students consider how organisations can benefit from Big Data and analytics, and analyse business and technological requirements to create value though these technologies. Students will also explore recent developments in technologies surrounding Big Data and data analytics such as text analytics, visual analytics and artificial intelligence, and assess types of tools that can be utilised, including the evaluation and use of state-of-the-art tools. Students will also assess the legal and ethical implications of data analytics in business.

Aims:

The aim of this module is to evaluate the business value in utilising data analytics and develop information and data management solutions with an appreciation of data analytics methods and technologies. It also develop understanding of the legal, social and ethical concerns involved in data management and analysis.

Assessable learning outcomes:

Upon successful completion of this module, students should be able to:

• Assess the business opportunity and value creation through the utilisation of business data analytics by analysing the business environment and requirements;


• Critically assess suitable data analytics technologies and business analytics approaches;


• Formulate a solution for achieving value through data analytics;


• Scope and deliver data analysisprojects, in response to business priorities, create compelling business opportunities reports on outcomes suitable for a variety of stakeholders including senior clients and management;


• Demonstrate the awareness and understanding of the information governance requirements that exist in the UK, and the relevant organisational and legislative data protection and data security standards that exist.


• Assess and address the organisational and technical impact of implementing the solution, including the legal, social and ethical concerns involved in data management and analysis

Additional outcomes:

Upon successful completion of this module, students should be able to:

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

• Become familiar with state-of-the-art developments and data analytics tools

Outline content:

• Introduction; Business opportunity of data analytics in the era of big data


• Big data and data analytics strategy


• Business analysis for data analytics and business intelligence


• Methods, techniques and tools for big data and analytics


• Developing a data analytics strategy


• Big Data visualisation


• Strategic use of artificial intelligence and machine learning technologies


• Professional, leadership, social, legal and ethical issues in implementing data analytics solutions


• Emerging issues and impacts of big data analytics

Brief description of teaching and learning methods:

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

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures	14
Work-based learning	24
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (independent)	30
Ìý Ìý Wider reading (directed)	30
Ìý Ìý Advance preparation for classes	8
Ìý Ìý Preparation for presentations	8
Ìý Ìý Preparation of practical report	40
Ìý Ìý Group study tasks	24
Ìý Ìý Reflection	22
Ìý	Ìý	Ìý	Ìý
Total hours by term	200	0	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Written assignment including essay	100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Assessment will consist of a written coursework assignment (up to 20 pages of A4) (100%) due three weeks after the completion of the learning content. In completing the coursework assignment, students will be expected to produce a data analytics strategy for a particular business context, based on which an individual report is produced. The assignment will provide students an opportunity to communicate critically and concisely their findings which demonstrate their extended understanding of the subject.

Formative assessment methods:

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy £Penalties for late submission for Postgraduate Flexible programmes£, which can be found here: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmissionpgflexible.pdf
The Support Centres will apply the following penalties for work submitted late:

• 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 five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/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.

Assessment requirements for a pass:

50% in the coursework assignment

Reassessment arrangements:

Resubmission of the coursework

Additional Costs (specified where applicable):

Last updated: 22 September 2022