Can you briefly explain your course module and some of the teaching modalities used?
Our course focuses on business analytics, where we teach students methods and tools to for analysing organisational challenges and resolving them with digital means. The module is structured around four key steps: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics:
- Descriptive Analytics: Identifying and describing the problem (‘pain point’), understanding the variables involved, such as people, processes, products, and ethical, legal, economic, political and sustainability aspects and requirements influencing the problem. In this process, we remind ourselves that there will always be an “obvious” problem description that is simple, clear, and entirely wrong.
- Diagnostic Analytics: Understanding why certain outcomes occurred by analysing relationships between variables.
- Predictive Analytics: Forecasting future outcomes if current trends continue (‘what if’?).
- Prescriptive Analytics: Recommending actions to achieve desired future outcomes.
Throughout the course, we emphasize the real importance of understanding the human factors within a business context; these include the human values, assumptions, beliefs, biases, other contexts that may shape the system and behaviours and contribute to the problem.
How will students leverage this module and knowledge later in their career?
This module equips students with a comprehensive understanding of business analytics, crucial for digital transformation initiatives. They learn to identify and analyse pain points, consider human and organizational factors, and apply analytical tools to recommend data-driven solutions. These skills are vital for managerial roles, enabling students to drive efficiency and innovation within their organizations.
To what extent does the course integrate real-world scenarios and simulations to help students develop practical skills?
The course heavily integrates real-world scenarios and examples from various industries. For instance, we discuss a case where a new, completely new payment system was implemented from one day to the next in a large organization without considering human factors, leading to significant operational issues. By examining such cases, students learn to anticipate potential challenges and understand the importance of aligning technological solutions with human and organizational needs.
What are the main challenges that students seem to struggle with on this course?
One of the primary challenges students face is the misconception that having access to vast amounts of data and advanced tools like AI will automatically identify the problem areas and provide effective solutions. They often overlook the importance of human factors and the complexity of integrating new systems into existing workflows. Students can struggle with qualitative research, such as interviewing stakeholders to gather unstructured data, which is essential for understanding the full scope of business problems.
What kind of mindset is best for students to succeed in this course?
The most successful students are those who are curious, open-minded, and willing to put themselves in the shoes of different stakeholders. They need to be empathetic and non-judgmental, ready to gather and analyse qualitative data, and willing to engage in thorough problem decomposition. Trusting their ability to use the tools provided and being open to surprises and new insights are also crucial for success.
