In times of data transformation and the change to data-driven corporate models, such as the Data Mesh, one of the greatest challenges is cultivating a new mindset among employees. The transformation towards decentralization brings with it new questions. In this workshop we will address this topic and discuss the following aspects:

• Dealing with the new data-related roles and responsibilities in the specialist departments in a decentralized organization (data literacy and mindset)
• Processes to increase know-how (“what does that mean” or “what does that NOT mean”)
• Designing change management and cultural change correctly
• Practical insights through case studies and tools to actively implement the change in your complex data organization

Tobias Riedner

Head of Data Analytics
Danone

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In this masterclass, we delve deep into the practical aspects of implementing a Data Mesh architecture from an Analytics Platform perspective, drawing from Lufthansa’s own experiences and lessons learned. This session will provide actionable insights and realworld examples of how a global enterprise like Lufthansa has tackled challenges and reaped benefits from a Data Mesh approach.

Platform Strategy for Advanced Analytics

Building Scalable and Cost-Efficient Analytics Platforms

Self-Service Analytics Capabilities

Integration and Harmonization of Data for Analytics

Governance in Decentralized Environments

• Establishing clear ownership roles for data assets within a decentralized architecture to enhance accountability and responsibility
• Implementing robust governance frameworks to regulate data usage, access, and quality across decentralized data domains
• Ensuring compliance with regulatory requirements and industry standards while promoting transparency and trust in data handling practices within the data mesh paradigm

Pascal Moritz

Head of AI Architecture and Data Engineering
Miele

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• Definition of the agile way of working in data governance
• Measurable improvements in efficiency compared to “classic” methods
• Balance between agility and governance, scalability and security

• Automated Data Quality: AI enhances data accuracy by automating cleansing, deduplication, and enrichment processes
• Predictive Data Management: AI anticipates data issues and optimizes future data needs through predictive analytics
• Efficient Data Governance: AI ensures compliance by automating governance rules and monitoring data integrity
• Applying generative AI in MDM

Dr. Yaniv Noar

Director - Head of Global Master Data Management
Sandoz

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• What is Data Lifecycle & Ownership Management?
• Why is it important?
• How will it help your organization?
• Insights from Volvo Cars’ Data Lifecycle journey

• Operational and analytical data
• How is the data architecture at E.ON ?
• Challenges and opportunities with legacy systems
• How to implement AI scenarios to data

• Why data management benefits from enterprise architecture (and vice versa)
• How enterprise architecture and data mesh enable the scaling of data management solutions
• Best practices: successful examples of the integration of data management and enterprise architecture
• How a flexible architecture enables adaptation to changing business requirements

Carsten Forstner

Enterprise Architect
Nordex Group

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• Data product: data, metadata, semantic, template
• Data Marketplace
• Practical implementation

Giorgia Daniele

Bank Julius Bär
Responsible of Enterprise Data Capabilities - Chief Data Office

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• Short intro to van Oord and her business
• Already decentralized IT and extensive analytics capabilities, but
• Still somewhat siloed and maturity varies from department to department, requires tailored approach
• Propagating hub&spoke and pub/sub for more active data ownership and data sharing to come to all-enveloping data landscape
• Use case: show what works, build traction

• From Traditional Data Warehousing to Lakehouse Architecture
• Cultivating a Data Culture for Scalability
• Setting the Stage for Future Decentralization

Manuel Valero Méndez

Head of Big Data
Santalucia Seguros

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• How the important principles of the Data Mesh approach were integrated into the bank’s data governance and data architecture
• Central functions, tasks and teams to meet certain organizational and operational requirements
• Cultural issues and organizational frameworks that must be taken into account in the partial transformation to the Data Mesh philosophy

Sven Wilbert

Stellvertretender Gruppenleiter Data Steering
NordLB

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