Tracks and Topics

Together with the BI section of the Gesellschaft für Informatik, we present the Academic Track at TDWI Munich.
In the environment of BI & Analytics, the focus is on the consideration of polystructured data sources, the integration of more sophisticated analytical procedures and an orientation towards agility aspects. Among other things, this raises questions about data quality, data security and data protection, which will be discussed from a scientific perspective.
We invite all researchers to submit their scientific contributions.

• BIA und Big Data / NoSQL / In-Memory BIA, Analytical Databases, Business Process Intelligence  
   
• Business applications of artificial intelligence und deep learning: Convolutional Neural Networks, Deep Recurrent Neural Networks, Deep Reinforcement Learning, Deep Autoencoder, Generative Adversarial Networks, NLP 
   
• DevOps / Data Ops / AIOps 
   
• Data Science Platforms, Model Distribution and Operationalization, Model Management

• Practical reports on BI applications in banks and insurance companies
   
• Data-based digitalisation
   
• Use of artificial intelligence methods
   
• Integration of data from social media and mobile devices
   
• Establishment of data science approaches
   
• Use of specific industry solutions for analysis and simulation
   
• Process monitoring and optimisation of data-driven business processes
   
• Requirements from regulatory reporting
   
•  Dealing with individual data processing solutions (IDV)
   
• Design of audit-proof data-driven processes and other governance aspects
   
• Data quality or master data management

• Serverless/cloud-based DWH architectures
   
• Hybrid architectures
  • Data ingestion in hybrid architectures
     
  • Data syncing between cloud und on-prem in hybrid architectures
     
• Data Platform concepts – Data Mesh, Data Fabric, Data Lakehouse: practical examples und user reports
   
• Data lineage in complex data preparation settings
   
• Tools of the Modern Data Stack: hands-on experience reports
   
• DWH und Data Vault automation
   
• DWH modernization: from re-platforming to redesign
   
• Low-Code/No-Code Development Technologies 

• Data Literacy & Data Culture and the Development of Organisational and Personal Skills
   
• Data-driven culture and data-driven companies
   
• Digitisation of business processes as a driver for data culture & data literacy
   
• Field reports, studies and theoretical models related to data culture and data literacy

• Data Modeling e.g. Data Vault / New Data Modeling Concepts  
   
• Metadata management, data quality, master data management und data cataloguesin the BIA environment
   
• Data Management 4 AI

• Objects and contents of a data strategy
   
• Fields of action and core tasks of data governance
   
• Structural organisation and committees of data governance
   
• Principles, guidelines and standards of data governance
   
• Roles and responsibilities: Data owner, data steward and co
   
• Organisational embedding and tasks of a data office

• Organisation of a self-service BIA
   
• Frameworks & best practice for data modelling for BIA
   
• Definition of KPIs in organisations
   
• Best practices in data visualisation
   
• Data Culture & Data Literacy in the context of Self Service Analytics

• Data Culture
   
• HR Governance
   
• Team structures
   
• Skillset
   
• Data Literacy
   
• Training
   
• New Work

Submissions of interactive formats and practical sessions such as workshops and hackathons are encouraged and will be given preference in the evaluation.
Please adapt the format of your session to meet the requirements in terms of efficient use of time, depth of content, interactivity, entertainment, etc.

• Tell your less perfect story
   
• Describe mistakes you made and from which you could learn
   
• What are the lessons learned?