Introductory description

In the new era of the "data economy", the value of high-quality, granular, and rich data assets is a critical success factor for transformative decision making in many industries and the leveraging of artificial intelligence (AI). The strategic value of data resources, as a means of enabling firms to achieve competitive advantage, is first considered, in order to understand the rationale behind the adoption of data management and business analytics practices. The challenges and opportunities of adopting various business analytics techniques, including using artificial intelligence for many analytical tasks, are investigated in order to obtain critical understanding of the building of organisational capability in these areas.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Strategic nature of data and relevant organizational practices and business models emerging from these.
Management of data and relevant technologies (big data, AI, business intelligence practices, etc.)
Business Analytics techniques including data analytics (use of software) and use in organisations for decision-making (business performance management, dashboards, etc.)
Introduction of artificial intelligence, techniques, and applications (supervised/unsupervised learning, machine learning, data mining).

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate a comprehensive understanding of the strategic nature of enterprise data analytics technologies and artificial intelligence
• Demonstrate a comprehensive understanding of the software and methodology for developing data visualizations and analytical models, and the managerial issues associated with the selection and adoption of such data analytics technologies
• Develop a comprehensive understanding of the managerial and organisational issues associated with the use of data and artificial intelligence practices
• Demonstrate critical analytics skills in the information- and data-driven business environment

Indicative reading list

• Akerkar, R. (2019). Artificial intelligence for business. Springer.

• Vieira, A., & Ribeiro, B. (2018). Introduction to Deep Learning Business Applications for Developers. Apress.

• Barrow, M. (2009). Statistics for economics, accounting and business studies. Pearson Education.

• Bramer, M. (2007). Principles of data mining (Vol. 180). London: Springer.

• Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.

• Stock, J. H., & Watson, M. W. (2020). Introduction to econometrics. Harlow, England : Pearson

• Ertel, W. & Black, N. T. (2011). Introduction to Artificial Intelligence. London: Springer

• Kwartler, T. (2017). Text mining in practice with R. John Wiley & Sons

Subject specific skills

Evaluate management practices and recommend r measures to enable an organisation to exploit data assets and AI-related information technologies effectively
Evaluate and exploit real-world datasets and make business recommendations using data analytics tools (e.g. Tableau and R)

Transferable skills

Written communication.
Oral communication.