TDWI Virtual II

Upcoming Event
Online
Julian Schütt
Ian Shulman
29
.
09
.
2026
/
Virtual conference

Reusable data products for your reporting, analytics, and AI needs

s are considered the cornerstone of reporting, analytics, and AI solutions. But which s actually create long-term value?  

At TDWI Virtual II, we’ll use a real-world example to demonstrate how artificial intelligence can help develop a consistent, reusable, and future-proof data product landscape from a wide range of requirements.

Date: September 29 , 2026
Time:
10:50 a.m. – 11:30 a.m.  

The focus of the session:

  • From Requirement to Data Product: How Companies Develop a Structured Data Product Landscape from Hundreds of Reporting, Analytics, and AI Requirements.
  • AI as an Enabler: Automated analysis, consolidation, and prioritization of business requirements to identify reusable data products.
  • Top-Down Meets Bottom-Up: How Business Requirements, Existing Data Sources, and Governance Rules Are Integrated into a Scalable Data Product Architecture.
  • Governance, Ownership, and Reusability: The Success Factors That Make Data Products Sustainable and Usable Throughout the Organization.
  • Case Study from a Real-World Client Project: Experiences, Challenges, and Lessons Learned in the AI-Driven Development of Data Products.

Take this opportunity to learn how AI can help you identify the data products that create the most value for your business. Sound interesting? Learn more about the free online conference here:

Speaker

Julian Schütt
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Julian Schütt
Business Unit Lead Data & Cloud Services
celver AG

Julian Schütt has been advising our customers for over 15 years, from the conception to the implementation of smart data architectures. As head of the Data & Cloud Services business unit, he is involved in the use of innovative technologies, from agile cloud environments to the efficient use of artificial intelligence.

Ian Shulman
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Ian Shulman
Senior Data Scientist
celver AG

Ian Shulman is a Data Scientist at celver with a particular focus on text generation using Large Language Models. He focuses on simplifying complex and cumbersome processes through machine learning approaches and using data science methods to extract new insights from the data.

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