Loading Events


  1. Finding, collecting, modelling and storing data
  2. Building and establishing data governance
  3. Understanding and managing data quality, privacy and security
  4. Building analytical infrastructures (cloud vs. on-premise options)


Data is the fuel that makes big data analytics and artificial intelligence run – this is the central theme of the second module. We cover all organizational, technical and legal requirements for managing data as a strategic asset and enabler for data-driven business transformation. Participants understand that 80% of the effort associated with big data and artificial intelligence projects is data engineering: the process of acquiring, modeling and cleaning data. We introduce central concepts for establishing effective data governance and managing data quality including associated legal requirements such as information privacy and the GDPR. Finally, we provide an introduction to central database and big data technologies such as MapReduce, Hadoop or Pig such that participants are equipped with the knowledge to set-up analytical infrastructures on-premise or in the cloud.

After the module, participants…

  • have gained practical experiences in data engineering in order to understand the nature of big data and artificial intelligence projects

  • have internalized effective practices for managing data governance and data quality

  • are familiar with legal constraints of big data and artificial intelligence

  • can navigate through the big data technology landscape and are equipped with a toolset to identify and evaluate different technology options that meet their purposes