Business Analytics

Companies now have access to large amounts of data. This data can be structured or unstructured and derives from various sources, such as enterprise systems (e.g., customer data), social media (e.g., online reviews), mobile devices (e.g., sensor data), or physical ecosystems created by interconnected devices (e.g., smart thermostats). Harnessing this data to create a competitive advantage is imperative. Many have labelled data as the 'new oil' for businesses in this respect.

Unleashing value from data remains a challenge for most businesses, however. Whereas data traditionally was used to support existing business activities, its more extensive volume and diversity offer opportunities for value creation for companies and customers alike. Consider, for example, the strategic value of online product review data generated by customers for the development of new products. Or think about the cost reductions a business can achieve by better predicting equipment failure. Dealing with the data required for such applications poses not only technical challenges (i.e., handling large data volumes, addressing data quality issues, and navigating complex data structures), but it also necessitates a shift in the mindset and skillset of businesses when it comes to working with data. Like oil, data must be unlocked and refined before it can reach its full potential. In this respect, the emerging discipline of business analytics offers valuable tools and techniques.

This course provides a comprehensive overview of business analytics within the context of technology-driven companies. We will undertake diverse business analytics projects, addressing various business-related issues. In each project, the focus is on (1) problem understanding, (2) harnessing, processing, and storing (un)structured data, (3) selecting and using a machine learning technique that fits the problem, and (4) effectively presenting the knowledge and actionable insights that derive from the analytics project. Each project contains elements students can use in their business analytics group project. Simultaneously with describing the projects, aspects related to analytics projects will be discussed, including data types, "big" data, data infrastructures, types of machine learning techniques, typical business analytics roles, responsible analytics, and visualisation principles. We use the R programming language for executing each project and the group assignment.

Business analytics is a broad and rapidly developing discipline that is impossible to cover exhaustively in a single introductory course. Hence, the overall goal of this course is for students to gain an understanding of the broader business analytics landscape and establish a solid foundation by gaining hands-on experience with various techniques. This foundation can then be further developed independently or through more advanced courses.