| MODULE TITLE & CODE EFFECTIVE FROM
MODULE LEVEL 7 |
Data Analytics
January 2023
|
BMG880 | ||||||
| CREDIT POINTS 20 | ||||||||
| MODULE INSTANCE(S) | Location | Semester | Module Coordinator
Teaching Staff |
|||||
| QAHE
(London) |
1 | Muhammad Ateeb
Hakim Mezali Mohammad Khurram Celia Nikolaidi Mohammad Alzbaidi
|
||||||
| QAHE
(Birmingham) |
1 | |||||||
|
HOURS |
Lectures |
24 |
hrs |
|||||
| Workshops | 12 | hrs | ||||||
| Independent study | 164 | hrs | ||||||
| TOTAL EFFORT HOURS | 200 | hrs | ||||||
| ACADEMIC SUBJECT | Business Management | |||||||
RATIONALE
Organisations are being compelled to collect, organise, store and disseminate data in large volumes to support decision making in order to improve business operations and achieve competitive advantage. Data management and analytics technologies have become widely accessible and affordable to businesses which are increasingly adopting these technologies to create useful insights from big data. Businesses now require managers, accountants and auditors to become expert users of business analytics tools to create valuable insights from both financial and non-financial data.
This module introduces students to the strategic role that business intelligence and analytics play in creating an enterprise-wide data set that can then be transformed into valuable insights to enhance strategic decision making. The module also provides students with an opportunity to use statistical analysis tools and data visualisation features of analytics software to deliver reports with real-time information to managers to facilitate faster decision making. The module also incorporates fundamental concepts of business performance management.
AIMS
The aim of this module is to provide students with the knowledge and practical skills for applying business intelligence and data analytics principles to support management decision making in a business context. The module also equips students’ with quantitative analysis and data visualisation skills to derive valuable insights from the data in a business context.
The module adopts ‘learn by doing’ approach to implement relevant features of a performance dashboard as part of the design and implementation of performance management system for a case organisation.
LEARNING OUTCOMES
Successful students will be able to:
- Demonstrate in-depth knowledge and understanding of business intelligence and data analytics methods, tools and technologies within a business context
- Analyse business data using appropriate statistical techniques
- Show competence in the use of business intelligence and analytics software
- Design, implement and evaluate an appropriate business analytics solution within the context of business performance management.
- Interpret outputs from data analysis and communicate findings in a
professional manner
CONTENT
Enhancing decision making by using business intelligence and analytics tools Systems Analysis and Design Methods
Entity Relationship Modelling
Performance Management Systems
Business Reporting using performance dashboards
Data Visualisation and data presentation methods
Descriptive, Predictive and Prescriptive Analytics
Big Data Analytics
LEARNING AND TEACHING METHODS
Lectures will be used to provide students with a review of the main concepts and theories of business intelligence and analytics. Topics will be introduced through lectures or learning materials and explored through a variety of directed learning activities. Each lecture will have its own specific learning objectives such as to enable students to develop their own understanding of a particular aspect of business intelligence, data analytics, systems analysis and design, entity relationship modelling, performance dashboards, and big data analytics. An interactive approach will be adopted in lectures which draw upon case examples to facilitate application of theory to ‘real-life’ situations, critically analysing and making recommendations for appropriate ways forward to enhance business decision making.
Student learning is further consolidated to in workshops where practical activities are designed to support ‘learn by doing’ and case scenarios are presented for further application and analysis of the knowledge gained through the lectures and independent study.
Students will be encouraged to use both directed and independent learning to read academic and professional articles to expand their knowledge of current issues in business intelligence and business analytics. The module will use blended learning approach to further enhance students’ learning.
ASSESSMENT AND FEEDBACK
Report and Dashboard [100%]
SUBMISSION DATE: 21/04/2023
Students will select an organisation of their choice. (This could be the organisation they work for.
Taking the role of an external Consultant, they will be required to conduct a performance review of the organisation and will submit a 2500-word report that will critically review and evaluate a chosen area of interest, department, or sector.
The report will include the design and a deployment plan for a business intelligence/data analytics system (using appropriate data analytics software).
Secondary data sources will be used to direct the analysis as well as frame the problem/opportunity statement regarding the case company.
There is no expectation that students should collect primary data.
As part of the written assignment, students will be required to develop a decision support dashboard for the organisation.
The dasboard will be designed and developed by using appropriate data analytics software and should be submitted separately using Blackboard.
A 2500 word report justifying the use of business intelligence and data analytics solution methods should also be submitted using Blackboard.
Students will be given written feedback so they will be able to feed into the workplace and/or further study.
Formative feedback will be provided to students during the semester by the module tutor during interactive workshop sessions. In addition, assignment briefing session will be run which will provide further opportunities of formative feedback to students.
Summative feedback will be provided to students on completion of their assignment to assess their performance.
The following marking criteria will be used to mark the report and the decision support dashboard;
- Introduction to the problematic situation/opportunity regarding your selected organisation – 400 words (10%)
- Theoretical frameworks to link problem/opportunity of the case organisation- 800 words (20%)
- Evidence of knowledge and understanding of business intelligence/data analytics systems. Marks will be awarded on the quality of analysis, data visualisations, interactive use of data analytics software and dashboard features (30%)- submitted separately as a dashboard
- Critical analysis and justification of dashboard solution to address the problematic/opportunity situation in the selected case company- 800 words (20%)
- Conclusion and recommendations for the implementation of business intelligence/analytics system in the case organisation for the focused area of investigation- 400 words (10%)
- Presentation including appropriate language, references used, clarity of expression and style; appropriate structure and format; length and the relevance of appendices if used (10%)
100% Coursework
0% Examination
READING LIST
Required:
Cadle, J., Paul, D. & Turner, P. (2014), Business analysis techniques: 99 essential tools for success, British Computer Society (BCS), London.
Eckerson, Wayne W. (2006), Performance dashboards: measuring, monitoring, and managing your business, John Wiley & Sons, New Jersey.
Sharda, R., Turban, E., Delan, D. (2014) Business intelligence and analytics; systems for decision Support, Boston: Pearson.
Wisniewski, M. (2010). Quantitative methods for decision makers with mathxl. Pearson Education.
Recommended:
Brynjolfsson, E., Hitt, L.M. and Kim, H.H. (2011). Strength in numbers: how does data-driven decision making affect firm performance?. Available at SSRN 1819486.
David Santiago Rivera & Graeme Shanks (2015) A dashboard to support management of business analytics capabilities, Journal of Decision Systems, 24:1, 73-86, DOI: 10.1080/12460125.2015.994335.
Graeme S. and Nargiza B. (2012). Achieving benefits with business analytics systems: an evolutionary process perspective, Journal of Decision Systems, 21:3, 231-244, DOI: 10.1080/12460125.2012.729182
Gross, D., Akaiwa, F. & Nordquist, K. (2014), Succeeding in business with microsoft excel 2013: A problem solving approach, Cengage Learning, Stamford, USA.
Hashmi, A. (2015), Do you need a balanced scorecard for performance measurement? Print Replica Kindle Edition.
Kaplan, R. & Norton, D. (1992), The balanced scorecard as a strategic management system, Harvard Business Review pp61-66.
Kaplan, R. & Norton, D. (1996), The balanced scorecard-translating strategy into action, Harvard Business School press, Boston.
Kopanakis, I., Vassakis, K. and Mastorakis, G., (2016), Big data in data-driven innovation: the impact in enterprises’ performance. In Proceedings of 11th Annual MIBES International Conference, 22nd of June-24th of June (pp. 257-263).
Nørreklit, H, Nørreklit, L, Mitchell, F, Bjørnenak, T, (2012) ‘The rise of the balanced scorecard! Relevance regained?’, Journal of Accounting & Organizational Change, Vol. 8 Iss: 4, pp.490 – 510.
Russom, P., 2011. Big data analytics. TDWI best practices report, fourth quarter, 19(4), pp.1-34.
Srinivasa, S. and Bhatnagar, V., 2012. Big data analytics. In Proceedings of the First International Conference on Big Data Analytics BDA (pp. 24-26).
Winston, W. L. (2011), Excel 2010: Data analysis and business modelling, Microsoft Press, Washington, USA.
SUMMARY DESCRIPTION
This module provides students with the knowledge and practical skills for applying business intelligence and data analytics principles to support management decision making in a business context. The module also help develop students’ quantitative analysis and data visualisation skills to derive valuable insights from the data in a business context. The module will help students to design, develop and deploy a performance dashboard as part of their performance management system case organisation.
LEARNING RESOURCES
QA Library website.
QA’s library website is a great place to start your research. This site brings together the resources provided by Ulster University library and the resources provided by QAHE library. Everything you need is in one place!
- The Ulster University pageincludes links to databases, ebook collections, open access sources, referencing, and much more.
- The Homepagecontains information on research skills, extracting information from textbooks and accessible learning recommendations.
- The contact uspage contains a live chat box so you can ask questions and receive immediate answers from a librarian. You can also book one-to-one sessions or join the regular ‘online hangout with a librarian’. There are lots of ways to ask for help with your research!
SCONUL
Ulster University is part of the SCONUL network. This allows students to visit other university libraries and use their books. If you’d like to do this, contact QA Library and we will arrange SCONUL membership for you.
Studiosity
Ulster University subscribes to Studiosity, which enables students to receive feedback on draft assignments. Simply upload your draft, select the type of feedback you want (eg grammar, referencing etc), and submit. Feedback will be provided within 24 hours.