ULMS Electronic Module Catalogue

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Data Strategy and Analytics for Managers
Code MGTK747
Coordinator Mrs EI Forrester
Management School
E.Forrester@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2022-23 Level 7 FHEQ Whole Session 10

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

 

Modules for which this module is a pre-requisite:

 

Programme(s) (including Year of Study) to which this module is available on a required basis:

 

Programme(s) (including Year of Study) to which this module is available on an optional basis:

 

Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours   3

      30

33
Timetable (if known)              
Private Study 67
TOTAL HOURS 100

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
             
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Individual Video Presentation Reassessment Opportunity: 2000-word report with the same brief Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: No  10 minutes    70       
Discussion Board Contribution Reassessment Opportunity: 500-word written coursework based on discussion post prompts Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment:  -500 words    15       
Discussion Board Contribution Reassessment Opportunity: 500-word written coursework based on discussion post prompts Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment:  -500 words    15       

Aims

This module aims to:

Provide students with the knowledge and skills required to analyse contemporary business scenarios and consider how a strategic approach to the use of data and analytics might enable smarter decision-making and generate competitive advantage;

Develop students’ ability to critically discuss the issues and challenges in the use of data and analytics from a global and ethical perspective;

Develop students’ ability to critically evaluate the contribution of data visualisation and analytics tools and techniques to evidence-based decision-making and effective organisational communications.


Learning Outcomes

(LO1) Students will be able to evaluate the value and benefits that accrue from utilising data and analytics applications across a variety of industries and types of organisation.

(LO2) Students will be able to analyse contemporary business scenarios and consider how a strategic approach to the use of data and analytics might enable smarter decision-making and generate competitive advantage.

(LO3) Students will be able to critically discuss the issues and challenges in the use of data and analytics from a global and ethical perspective.

(LO4) Students will be able to critically evaluate how data visualisation contributes to evidence-based decision-making and organisational communications.

(LRE1) Commercially aware.
Students will develop an understanding of the commercial nature of organisations and will use this understanding to underpin both operational and strategic decision-making.

(LRE2) Ethically aware.
The teaching, learning, and assessment strategy ensures that all students are exposed to ideas of sustainable business practice and ethical awareness from an individual, organisational and global perspective.

(LRE3) IT literate.
Students will have opportunities to improve their IT skills. Students will demonstrate skills in the use of software applications including word processing, visual presentations, databases, spreadsheets and using the internet for information searches in the course of researching and presenting coursework.

(LRE4) Numerate.
Students will develop an understanding of how to identify, handle, analyse and interpret appropriate forms of data for complex business decision-making.

(LRE5) Organised and able to work under pressure.
This will be evident in the students’ independent management of their assignments and ability to meet deadlines.


Teaching and Learning Strategies

The module will primarily be delivered through a series of e-lectures, delivered through a variety of methods, covering key topics, theories and case examples. These will be supported by individual online task such as, case studies, blogs, collaborative tasks and discussion boards, which will be used to develop and apply learning. These activities will be moderated by the module instructor. Students will also be directed to key academic and practitioner readings to further develop their learning.

Unscheduled Directed Student Hours: 30 hours
Description: The asynchronous e-lecture and interactive class activity will equate to 5 hours per week over 6 weeks.
Attendance Recorded: Yes – tracked via the learning platform.

One scheduled synchronous seminar of one hour will be delivered in week 1, with the remaining two synchronous seminar hours scheduled during weeks 2 to 6. The dates and times of the seminars will be confirmed at the start of the module (if th ere are issues with time zones another session will be provided). Peer discussion and questions will be encouraged. These sessions will be recorded and moderated by the module instructor.

Scheduled Directed student hours: 3 hours
Description: The synchronous seminars will equate to 3 hours in total over 6 weeks.
Attendance Recorded: Yes – tracked via the learning platform.

Self-directed learning hours: 67 hours
Description: This will involve directed and independent reading, and independent research into data and analytics theories and practices relevant to the module syllabus, aims and learning outcomes.


Syllabus

 

Applications for data and analytics including: understanding different types of data and their relevance; understanding of the impact of different technologies such as quantum computing, artificial intelligence, machine learning, global positioning systems (GPS) and satellite data on organisations and society; applications for data and prescriptive and predictive analytics in real world settings; data driven innovation.

Ethical Issues in data and analytics including: ethical threats, dilemmas and opportunities for business, society and the environment from the use of data and analytics e.g. Zuboff’s surveillance capitalism.

Managing data strategically including: strategic alignment between data strategy and organisational strategy; the importance of processes and practices for data collection, data quality and data management, integration of third party data, data maintenance; data and cyber security; the role of data mining technologies and techniques; the import ance for managers of data visualisation tools such as executive data dashboards.


Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.