Tracks and Topics

Together with the BI specialist group of the "Gesellschaft für Informatik", we are presenting the Academic Track at TDWI Munich 2025, with a particular focus this time on research projects for mastering complex data landscapes, taking into account

• federated data storage with heterogeneous data users,
   
• the development of data ecosystems and data spaces across departmental and company boundaries,
   
• the integration of AI services and AI agents,
   
• demanding regulations such as the EU AI Act and
   
• the agile utilization of new types of data sources, for example from the (Industrial) Internet of Things or Industry 4.0.

We invite all researchers to submit their scientific contributions.

Track Chairs: Henning Baars & Carsten Felden

• Practical reports on BI applications in banks and insurance companies
   
• Data-based digitalization
   
• Use of artificial intelligence methods
   
• Consideration of regulatory requirements such as the EU AI Act, DORA or BAIT/VAIT in architecture and organization
   
• Use of data science methods
   
• Use of specific industry solutions for analysis and simulation
   
• Process monitoring and optimization of data-driven business processes
   
• Requirements from the regulatory reporting system
   
• Dealing with individual data processing solutions (IDV)
   
• Designing audit-proof data-driven processes and other governance aspects
   
• Data quality or master data management

Track Chairs: Andreas Totok, Jan Wiltschut & Michael Zimmer

• Serverless / cloud-based DWH architectures
  • Multi-cloud and cross-cloud data strategies
• Hybrid architectures
  • Data collection in hybrid architectures
  • Data synchronization between cloud and on-prem in hybrid architectures
• Data platform concepts - data mesh, data fabric, data lakehouse: practical examples and user reports
   
• Integration of various tools within a modern data stack
   
• DWH and data vault automation
   
• DWH modernization: from re-platforming to redesign
   
• Real-time/streaming and time series data processing and analysis
   
• DataOps and MLOps
   
• Edge computing in the data architecture

Track Chairs: Thomas Benker, Florian Dindorf, Michael Fischer-Dederra & Torsten Priebe

• Data Literacy & Data Culture and the development of organizational and personal skills 
   
• Data-driven culture and data-driven companies
   
• Digitalization of business processes as a driver for data culture & data literacy 
   
• Testimonials, studies and theoretical models related to data culture and data literacy
   
• AI strategy and AI literacy in companies - can we speak of AI strategy or AI literacy without a functioning data literacy and data culture?

Track Chairs: Claudia Koschtial & Lea Zimmer

• Next evolution of the business data fabric 
   
• AI from data management to the front end
   
• Status quo data mesh: still hype or where do we currently stand?
   
• Tension between agility and governance
   
• Trends and challenges in data modeling e.g. data vault/new data modeling concepts
   
• Metadata management, data quality, master data management and data catalog in modern data analytics environments

Track Chairs: Matthias Stemmler & Gordon Witzel

• Business applications for AI
  • Co-Pilots
  • Ubiquitous computing
  • Knowledge management / model base (inference)
  • Customer services
• Deep learning and its applications
  • Large language models 
  • Computer vision
  • Time series
  • Audio recognition
• MLGovernance & change management
   
• Data science platforms, model distribution and operationalization, model management
   
• AIOps / MLOps
   
• Machine learning (mapping?)
   
• Explainability
   
• Regulation

• Success stories: From data strategy to implementation
   
• Data value creation: Making data value measurable
   
• Data governance models from practice: best practices for setting up and implementing data governance structures in organizations. Building a data team. Roles and responsibilities. Interfaces with compliance and specialist departments
   
• Data Mesh: Successful development of data products by specialist departments. Best practices for the successful transformation to data mesh
   
• Operationalizing data governance: Successfully integrating data governance into the organization. Successfully implementing data standards across the board
   
• Data protection and compliance: integrating compliance requirements into data value creation
   
• The data catalog as an instrument of data governance
   
• Change management in data governance projects
   
• Data ethics and social responsibility: ethics in data use, dealing with bias and discrimination, transparent data practices
   
• Advanced data analytics and BI: integrating data analytics into the data strategy
   
• Data literacy: approaches to education and training for employees to promote a data-driven culture, understanding of data and analysis tools
   
• Role of AI in data governance: approaches to automate and support data governance. Data governance for AI products.
   
• Case studies and industry insights: Presentations of case studies and specific challenges in different industries.

Track Chairs: Carsten Dittmar, Christian Fürber & Michael Kolb

A whole track for new ideas and concepts! Do you have an idea for a new format? Submit it here! Do you have a topic that you would like to present at the conference but that doesn't fit into one of the existing tracks? Submit it here! Do you have an innovative start-up or a cool research project that you would like to showcase? Submit it here! Do you know an exciting speaker who would be an asset to the conference? Submit him/her here! Anything that fits into the Data Universe is welcome.

Any idea is welcome. We look forward to your contribution.

Track Chairs: Julian Ereth, Armin Geisler

• HR Governance
   
• Team structures
   
• Skillset
   
• Data Literacy
   
• Further training
   
• New Work

1. maximize effectiveness and efficiency: Which measures really work

• How can data-driven approaches optimize the efficiency and effectiveness of the media mix?
• Which methods improve message effectiveness and resonance with target groups?
• Which data-driven strategies promote long-term customer loyalty and brand loyalty?

2. target your budget and media mix: Achieving the greatest impact with the right investments

• How can marketing and communication expenditure be made more efficient and resources better allocated?
• How can an optimal media mix be identified and which channels deliver the highest ROI?
• What criteria are crucial for prioritizing marketing investments and allocating budgets effectively?

3. use real-time analytics: Faster decisions for agile marketing

• How can marketing analytics incorporate external influencing factors (e.g. competition, seasonal trends) in order to develop flexible and adaptable strategies?
• Which approaches enable dynamic marketing decisions to be made in real time?
• Which tools and methods enable real-time analysis and continuous monitoring for marketing teams?

4. apply customer analytics: Understanding customers and influencing behavior in a targeted manner

• Which data-based methods help to better understand customer behavior along the customer journey?
• How can customer segmentation and persona analyses be used to personalize marketing measures?
• Which customer analytics approaches promote customer loyalty and reduce the churn rate?

5. convincing stakeholders: communicating marketing impact clearly and transparently

• How can the success of marketing ROI be measured and communicated transparently to management teams and stakeholders?
• Which tools and processes facilitate the preparation and presentation of analytics data for stakeholder communication?
• How can analytics teams prepare their results in an understandable way to support the strategic decision-making level?

Track Chair: Philipp Loringhoven

• How is self-service analytics changing decision-making in your company?
   
• What challenges and best practices have you developed when introducing self-service analytics?
   
• How do you ensure data quality and security with increasing data usage in self-service?
   
• How do you strike a balance between data access for users and control by experts?
   
• What specific added value has Citizen AI already brought to your organization?
   
• How does your tool strategy support the scalability and growth of self-service analytics?
   
• What cultural changes has self-service analytics triggered in your company?

Track Chairs: Philipp Loringhoven, Artur König

• Tell your not-so-perfect story
   
• Describe the mistakes you made and what you learned from them
   
• What are the lessons learned?

Submissions of interactive formats and hands-on sessions such as workshops and hackathons are encouraged and will be given preference in the judging process.

Please adapt the format of your session to meet the requirements in terms of efficient use of time, depth of content, interactivity, entertainment, etc.

Present your tools in front of an expert audience!

In the tool track, manufacturers have the opportunity to present a tool in a 30-minute demo session on a predefined, practical task and to receive direct feedback and questions from the expert audience in a subsequent 15-minute Q&A session. This gives you the opportunity to present your solution, explain your benefits in detail and position yourself as a leading provider in your sector.

We are looking for manufacturers and providers of tools and software solutions that specialize in the following areas:

• Data integration and preparation
• Data visualization and business intelligence
• Artificial intelligence and machine learning
• Data quality and security
• Cloud and hybrid solutions for data management