• Demonstrate how the course models and mechanisms for decision making can be used to analyse strategic decisions in your own organization or other chosen empirical context.
• Have the competence to recognize unconscious biases when making decisions and solving problems and reflect on common decision making traps that lead to fallacious reasoning and unfavorable outcomes.
• Have the ability to identify criteria for when to trust intuition and when to push for analysis and evidence-based decisions.
• Reflect on how to make strategic decisions involving multiple (and changing) goals and stakeholders.

In the current competitive business environment, decision makers need to make decisions quickly and effectively based on abundant data. Data-driven decision making refers to organizations systematically collecting and analyzing various types of data, including input, process, outcome and satisfaction data, to guide, inform and/or automate a range of decisions from operational to strategic decisions. Typical examples are recommender systems that drive product recommendation decisions, credit scoring that drive lending decisions, employee analytics that drive hiring or promotion decisions etc. Making decisions includes many considerations such as weighing risk, understanding the specific situation encountered, identifying available options as well as considering long-range implications for the organization.
This course is about understanding and applying data-driven decision making while taking into consideration the decision maker’s experience and expertise. By knowing how data-driven decisions are actually made the students can learn how various decision techniques and strategies improve the quality of decisions. Some of these techniques and strategies are founded on mathematical models or computer software like algorithmic decision making; others build on theories about awareness and mindfulness. The course presents a wide range of such techniques covering the different theoretical approaches to decision making.