1. Communicating and visualizing results effectively
2. Developing and validating data science business cases
3. Developing operating models and scalable organizational structures
4. Establishing and managing data science teams
5. Innovating with data science vendors and startups

While many companies have engaged in starting first big data and artificial intelligence pilots, only very few have managed to scale up their analytics operations to build a data-driven organization. This is what we tackle in this module. We show how results from big data and artificial intelligence projects can be effectively communicated to key stakeholders and how such projects can be used to build a case for change. We provide a toolset of research methods with which big data and artificial intelligence business cases can be developed and validated (e.g., A/B Testing). Finally, we investigate approaches for developing scalable organizational structures for big data and artificial intelligence and data labs including the collaboration with external startups and vendors.