CDAO Germany schedule

Elizabeth Press is Deputy Chief Digital Officer at the Center for Hybrid Electric Systems Cottbus (chesco) and Founder of D3M Labs, a media platform focused on profitable and secure digital business. She also advises startups on fundraising, product, and go-to-market strategy, drawing on 20 years of experience at the intersection of finance, strategy, data, compliance, and cybersecurity, including leadership roles in Silicon Valley fundraising and M&A advisory. She has worked with organizations including Dell, the German Foreign Office, and Rolls-Royce, and holds degrees from the Stockholm School of Economics and Tufts University, with published research in venture capital, fintech, and AI. 

You can follow D3M Labs (The Data-Driven Decision Making Media Platform) on LinkedIn and subscribe to the D3M Labs YouTube to join the conversation about creating a secure and profitable digital economy. 

During this 5-minute networking session, the aim of the game is to go and meet two people you don't already know.  Have fun! 

• Why data readiness, not model quality, is the real bottleneck to scaling AI 
• What AI-ready data actually means in federated European organisations 
• How geopolitical fragmentation increases the need for sovereign, execution-grade AI systems  
• Why most governance fails—and how to move from static control to execution enablement 
• What CDAOs must prioritise in 2026 to build resilient, reusable AI foundations 

• How AI maturity has evolved from experimentation to industrialisation
• Where GenAI is delivering measurable value today and where expectations are inflated
• What agentic AI changes for governance, accountability, and enterprise risk
• How organisations drive adoption by redesigning roles and workflows and upskilling the business
• What the next phase of enterprise AI will demand from CDAOs in org design, team integration, and platform/vendor readiness

A Playbook for Data & AI Impact Management

Despite substantial investments in data & AI, many organizations struggle to demonstrate any visible returns. The pressure is mounting to prove its impact.

Recent studies show that the problem is rarely technological – it's operational. What’s missing is a structured approach to value management and data product reuse that turns isolated experiments into compounding business impact.

This session introduces Data & AI Impact Management: a practical shift from one-off projects (which often fail) to demonstrating actual business impact from AI investments.

You’ll learn how to:

• Transform unstructured and ad hoc business demands into value-driven use cases with clear hypotheses defined upfront.
• Prioritize initiatives based on business outcomes, not just technical feasibility.
• Scale impact beyond single initiatives, reduce redundancy, and accelerate time-to-value by linking business use cases to reusable data products.

This session is for data & AI leaders who want to stop managing projects and start managing impact.

• Where AI strategies most commonly break down between design and execution 

• Organisational, data, and governance challenges that are underestimated early on 

• Aligning ambition with real capabilities, timelines, and constraints 

• What companies wish they had known before launching their AI strategy 

Moderator: Aleksejs Plotnikovs, Executive Coach – DATA MASTERCLASS  

Panellists: 

Mark Zakhvatkin, Director AI & Data - IU GROUP  

Florian Leser, Executive Advisor/ Former Head of Data Analytics and AI – KKH 

 Dr. Ahmed Ebada, Senior Product Manager, (Professor & CEO of HOPn) - BMW  

AI agents don't just consume data—they reason with it. Most enterprises have data built for dashboards. Agents demand more: the why behind metrics, the business rules that govern exceptions, and context that travels with the data. Without it, agents don't just underperform - they produce wrong answers at GPU speed.

The result is an intelligence gap - showing up as three failure modes: context blindness, quality exposure, and trust erosion.
Fixing it starts with a structural shift: data is the fourth pillar of enterprise architecture, and it needs its own operating model. That means governed Data & AI Products, Context Graphs that make business semantics machine-readable, and AI in data management itself—creating a flywheel that continuously raises quality at scale humans can't maintain alone.
Join Salesforce's Field CTO and Informatica's Chief Architect EMEA as they unpack what it takes to make agentic analytics trustworthy, explainable, and auditable at enterprise scale.

You'll Learn:

• Why semantic layers aren't enough - and what Context Graphs add that closes the reasoning gap
• Data as the fourth pillar—how to build a Data & AI operating model with real ownership and governance
• MDM, CDP, and CRM - who does what for AI - which layer owns the golden record, and why confusing them breaks agentic architectures
• AI to scale data management - the quality, lineage, and governance flywheel that scales beyond what humans can maintain
• How to start with imperfect data - scoped use cases, incremental value, and governance that works for regulated industries

Siddharth Rajagopal, Chief Architect EMEA-LATAM – SALESFORCE

Timo Tautenhahn, Field CTO – SALESFORCE

• Why organisations still struggle to translate customer data into measurable revenue: Customer single source of truth (omni-channel), where is it, CDP, data warehouse?
• Aligning marketing, product, and data teams around shared commercial metrics, What are the ’shared commercial metrics’ - vs. Marketing ROI, product ROI
• Building data products that business teams actively use
• 4. Governance, consent, and trust as enablers of value creation, GDPR, Digital Omnibus, AI Act, GPC, where are we? How do we do the right thing to retain consumer trust and at the same time help achieve our commercials goals using data?

Do you trust the data, is it reliable?, How and where do you ‘activate’ the data to yield commercial results? How do you measure the success?

Back to the ’single source of truth’ - do you use the same tool to measure the success of common goals?

Moderator: Marie Fenner, Global Senior Vice President, Analytics - PIANO

Facilitators:

Alexandra Rahe, Team Lead Customer Analytics - LUFTHANSA

Solaiyappan Shanmugam, Head of Product, Analytics & Platform – CONRAD.DE

Unlike visible failures such as plane crashes, AI risks are often invisible. With the EU AI Act and other emerging legislation, AI is increasingly required to meet minimum standards for quality and safety — making proactive risk management essential. Organizations operating in regulated environments must systematically identify, assess, and mitigate AI risks, leveraging standards, transparency, and education to build trustworthy, high-quality AI products.

This talk will include:

• How organizations can understand the emerging legal landscape and proactively integrate regulatory requirements into their AI strategy
• Best practices for governance, risk management, and quality management
• Building a healthy compliance culture
• Seeing compliance not as a constraint, but as a competitive advantage

Investing in AI governance and cybersecurity builds market trust, opens new opportunities, and demands a cultural shift — away from the "move fast" era toward interdisciplinary collaboration, resilience, and a governance mindset that coexists with innovation.

In this interactive session, attendees explore the real challenges of building data-driven organizations. Through guided discussion and a live poll, participants will tackle barriers to scaling data initiatives, benchmark their strategies, and walk away with actionable insights. 

Discussion Topics: 

• 
  Why is a reliable data foundation still so hard to achieve at scale?
  • What does “good enough” data really mean for decision-making and AI? 
  • How can traditional enterprises overcome legacy system inertia? 
  • What role do leadership commitment and organisational culture play in long-term success? 
  • Where do data initiatives most often fail — technology, people, or governance? 

Live Poll (5 mins) 

• 
  Which barrier is most critical — data, culture, or governance?
  • Where should focus and investment be prioritised in 2026? 
  • How confident are you that your data strategy will deliver measurable impact this year? 

Moderator: Nadine Heine, Strategic Business Development Manager – LEXIS NEXIS

Florian Leser, Executive Advisor/ Former Head of Data Analytics and AI – KKH

Dr. Katharina Behme, Director Analytics & Insights – LUXEXPERIENCE

Dr. Ahmed Ebada, Senior Product Manager, (Professor & CEO of HOPn) - BMW

• Why strong platforms still fail to drive adoption and impact 

• The critical success factors behind high-performing data teams 

• Building the right competencies across technical and business roles 

• Working with shared principles instead of rigid rules 

• How to shift mindsets, not just tools 

In this interactive discussion group, participants explore the data foundations required to scale AI and analytics in complex enterprises. Through guided peer discussion and live polling, attendees will unpack where data initiatives break down, compare approaches to data management and governance, and identify what truly enables trusted, reusable, AI-ready data at scale. 

Discussion Topics: 

• Why do data foundations still struggle to scale in large, federated organisations? 

• What does “fit-for-purpose” data mean in practice for analytics and AI use cases? 

• How can organisations balance decentralisation with consistency and control? 

• Where do data quality, integration, and governance most often fail to keep pace with AI ambition? 

• What foundations must be in place before scaling AI beyond pilots? 

Rebeca Meyer, AI Strategist – KNAUF

Naveen Kanneganti, Global Lead Data & AI Enterprise Architect Technology & Engineering – EON

Solaiyappan Shanmugam, Head of Product, Analytics & Platform – CONRAD.DE

• Managing risk and quality while accelerating AI and analytics delivery
• Why traditional QA models struggle in data and AI environments
• Continuous assurance as an enabler — not a blocker — of innovation
• Aligning data, analytics, IT, and business teams around shared accountability
• What “confidence at scale” really looks like in complex enterprises

• When an AI decision causes financial or reputational damage — who is responsible?
• Why “human in the loop” often fails in practice
• The hidden gap between legal accountability and operational ownership
• What boards and regulators now expect from CDAOs
• Real cases where accountability broke down — and what changed after

Moderator: Elizabeth Press – Deputy Chief Digital Officer, Center for Hybrid Electric Systems Cottbus (CHESCO)

Veera Babu Manyam, Global Enterprise Architect – EON

Dr. Ahmed Ebada, Senior Product Manager, (Professor & CEO of HOPn) - BMW

Total Cycle Time (TCT), measures the time from order initiation to product delivery a vital metric that reflects overall supply chain agility and responsiveness.

By integrating TCT analysis within supply chain analytics frameworks, companies can:

• Identify bottlenecks across procurement, production, and distribution
• Quantify delays and forecast their downstream impact
• Continuously improve customer satisfaction through faster, more reliable delivery.

During this 5-minute networking session, the aim of the game is to go and meet two people you don't already know.  Have fun! 

Capital Market is navigating a structural inflection point. Margin compression, regulatory fragmentation, geopolitical volatility and AI-driven technological disruption are no longer isolated pressures — they are converging forces reshaping capital markets.

• In this environment, data strategy is no longer a support function. It has become a central capability enabling firms to translate strategic intent into operational performance and sustainable advantage.
• Yet many organisations remain trapped in incremental fixes: siloed data domains, manual reconciliations, duplicated controls and fragmented ownership. The consequences are familiar — operational friction, slower product launches, data quality issues, AI initiatives stalled by data lineage gaps, etc.

To drive consistent business impact, data teams need more than just a modern stack — they need a clear, structured operating system. In this session, Pier Martin will explore how embedding human-centered principles and soft skills into your data culture can radically improve ROI. From real-world day-to-day team challenges to practical leadership strategies, he’ll offer insights and immediate actions for building stronger, more effective data functions. 

• Data Teams Need an Operating System—Not Just a Stack: Why focusing only on tools and technology misses the mark, and how process, mindset, and communication routines create consistency across data functions. 

• Soft Skills as a Multiplier for ROI: How skills like storytelling, empathy, and influence-building amplify technical output and help data teams embed more deeply into business strategy. 

• Prioritization as a Leadership Capability: Why leaving prioritization to tooling or backlog grooming is a mistake—and how data leaders can guide their teams by aligning decisions to business impact. 

• From Insight to Action: Common roadblocks data teams face and practical changes leaders can implement immediately to improve alignment, output, and morale. 

• Data should not lose its business context and semantics when it leaves the ERP context
• Fragmented, re-modelled data creates inconsistencies and breaks integrity
• Impact on AI: loss of business context limits AI effectiveness and scalability - AI models can't deliver value if they don’t have access to unified, context-rich data
• Integrated data platforms share data without loosing the business context and ideally support zero copy data sharing
• Business data delivery via (managed) data products guarantees consistency, reusability, and trust in data across the organization
• The role of SAP’s Business Data Cloud and other enabling technologies in that context
• Empowering business units to access and analyze data
• SAP's own "data for all" approach as an example
• Use cases for data architectures that allow scalable AI (predictive analytics, process automation, agentic AI)
• Leveraging SAP’s ecosystem (integration with Microsoft, Databricks, Snowflake, etc.)
• Real-world examples of increased productivity and efficienc

Join an interactive session with a seasoned leader at the intersection of strategy, finance, data, and cybersecurity.  With extensive experience driving growth and digital transformation across sectors—including digital business, Industry 4.0, the public sector, blue chips, and scale-ups—this expert will share practical guidance on leveraging data strategically, aligning with compliance requirements, and strengthening cyber-resilience. 

Key Focus Areas: Data-driven ROI, data strategy in regulated industries, cyber-resilience, digital transformation, AI-driven innovation, and the socio-economic impact of digital business models. 

• Identifyingwhich metrics and insights truly drive decisions 
  • Overcoming silos to enable cross-functional collaboration 
  • Aligning governance frameworks with business priorities 
  • Ensuring adoption and accountability for data-driven decisions 

Moderator: Pier Martin, Vice President, Data and Analytics - ZEAL Network SE

Panellists:

Wayne Cavanough, CTO & CIO – JAJA FINANCE

Dmytro Pavlichenko, Manager Data Analytics – AUTODOC

Abdul Maten Khan, Group Head of Data Analytics – DISH DIGITAL

Agathe Chomel de Jarnieu, Director of Analytics Europe – AUTO1 GROUP

Florian Hottner, Senior Director, Marketing Data, Analytics & Insights – PENGUIN RANDOM HOUSE

• Monetising data while maintaining trust, quality, and compliance
• Turning data investments into measurable business outcomes
• Nothing comes for free – keeping an eye on the costs
• Aligning commercial ambition with operational and data reality

Moderator: Kolja Manuel Rodel, Head of Data and Analytics – RAMEDER

Mariam Yakub, Master Data Manager – SWISSLOG

Maximilian Ehrlich, Director Data & Insights – TONIES

Discussion Points:

• Why data teams fail without the right culture
• The human side of data transformation
• Internal branding: positioning the data team as a business partner
• From data experts to business influencers

• What really breaks once AI models are live — data drift, model decay, and operational blind spots 
• How organizations detect failure early before business impact escalates 
• Who owns remediation when models behave unexpectedly? 
• Balancing transparency, explainability, and speed when things go wrong 
• Lessons learned from AI incidents that forced course correction