CDAO GERMANY, FRANKFURT

• Overcome data inertia by implementing initiatives with clearly measurable monetary value
• Bring together the story of data with the language of business to maximise investment
• Is a top-down or bottom-up approach better for creating a data-driven culture?
• Remove the perception of data as a technical business component to building agility

Alexander Borek, Head of Data Analytics, Zalando

• What impact will the 2024 EU regulations have on AI Ethics?
• How do you make sure human values are kept within AI initiatives?
• What do you think will be the core components of AI related challenges up to 2030?
• How can AI help meet the demands of sustainability challenges businesses face?

• Enable seamless individual data usage and pipeline management for streamlined efficiency.
• Delivering comprehensive access to trusted data for advanced productivity across your broader workforce.
• Encourage innovation by providing the right tools that make stakeholders aware of the data available to them.
  
  

Markus Lohman, Chief Data & Technology Officer, Allianz Global Investors

• Allow data usage to evolve correctly by discovering the type of data culture that is right for your business
• What meanings can be given to data so that it is effectively understood across multiple business units?
• Building your own jargon-free data phrasebook to overcome the semantic challenges of data adoption
• How data champions can break down silos between teams and foster collaboration and creativity

• How to overcome the security implications of sensitive data in the cloud
• Carefully manage the questions of dependency and complexity in your transition
• Cut costs by understanding the different cloud options that fit your needs

Susan Wegner, VP, Artificial Intelligence & Data Analytics, Lufthansa Industry Solutions

• How to extract maximum value from unstructured data sets by investing in NLP
• Be resource efficient by utilising a reusable approach to developing NLP models
• Ensure sophisticated data privacy requirements are met when deploying high accuracy NLP models
• Maintain clear AI governance policies to generate explainable NLP models

• How, and why, should you build automated data governance into your architecture?
• Strategies for securing buy-in for automating data governance tools
• Making data governance built into DevOps and along pipelines achievable
• How can you move from projects to products with data as part of a bigger picture?

Improve productivity and transparency with enhanced collaboration between data scientists, engineers, IT and DevOps

Keep disparate models already in production agile with standardised model validation, monitoring and re-training processes

Turbocharge AI enterprise and innovation with a holistic view of models and pipelines

Demonstrate the ROI of AI initiatives by optimising ModelOps to gathering insights to business KPI’s

• Building a framework to identify and locate your most valuable data
• Embed data analytics into your products to ensure customer loyalty
• Optimise your data to derive business intelligence that enhances performance and reduces risks
• Boost your data maturity to get ahead of the curve when data asset trading

Gareth Farr, Head of Data and Analytics, HypoVereinsbank – UniCredit
Jo Coutuer, Chief Data Officer - Member of the Executive Committee, BNP Paribas Fortis
Hiek Van der Scheer, Chief Data & Analytics Officer, Aegon

• Advance the data integration process and gain access to actionable insights with ease
• Increase agility with MSD that can be used with legacy solutions and the cloud
• Leverage the benefits of data accessibility with better visualisation capabilities for data-users

Bogdan Pirvu, Head of Data Science, Novomatic

• Incorporating data storytelling into your marketing strategy to effectively attract, gain and retain new customers
• How to train a workforce in data storytelling that moves beyond visualisation towards the power of narrative
• Generate better and faster decision making capabilities by providing personalised data stories

• Increase resilience and precisions by motivating your data scientists to apply A/B testing
• Building processes that are capable of supporting software deployment and model lifecycle management
• Is it useful to stay cloud-agnostic, or are you better off concentrating on one cloud provider and its services?
• Enhance flexibility for data scientists by choosing the right ML-Ops tools for them
• How to gain investment for distinct ML-Ops personnel and maximise the potential of AI

Sudhir Mahakali, Global Head, Data & AI (Sandoz Chief Digital Office), Novartis

Dubravko Dolic, Head of Applied Analytics & AI, Continental

• How will quantum computing affect the optimisation of business?
• Understanding how high levels of technological maturity can bring value to your business
• What do you need to prepare your company for Quantum computing?
• How do new ideas around quantum computing move into production?

  Gabriele Compostella, CTO Volkswagen Data:Lab, Volkswagen

• Deploying a system to monitor the accuracy of machine learning models
• Understanding why drifts in quality occur and how to avoid them
• How to make data science models accurate and robust to avoid accuracy loss in AI decision making

Sven Freisendorf, Head of Data and Intelligence, HypoVereinsbank – UniCredit *NEW*

• What is the importance of federated data governance as part of your data mesh architecture?
• Increase efficiency by giving data product teams end-to-end responsibility in a data mesh architecture
• Deliver democratised data to build an advanced data stack to boost innovation

• Moving from isolated projects to wide scale accessibility of D&A solutions
• Deliver ROI from data by developing products and use-cases appropriate to your organisations maturity
• Offering D&A solutions in an incumbent enterprise IT landscape

Lukas Jan Stroemsdoerfer, Head of Data Science & Engineering, Mercedes-Benz

• Boost data reliance by creating a data catalogue as the lynchpin for strong data usage
• Decrease dependency on subject matter experts and legacy systems to enable democratization capabilities
• Creating a unified data ecosystem, for different users with diverse needs the streamlines agility

• How do you properly embed the tools required to implement Self-Service BI?
• Leverage predictive analytics to inform decisions, test them for success and fortify strategic aims
• Gain an competitive edge by optimising a workforce to speak the language of data

Alexander Agronovsky, Head of BI - Customer Fulfilment, Zalando