CDAO Germany schedule

During this 10-minute networking session, the aim of the game is to go and meet two people you don't already know.  Have fun! 

• How do organisations determine which datasets require governance when faced with an overwhelming volume of information? 

• What are the key hurdles in maintaining regulatory compliance during governance initiatives, and how can these be effectively managed? 

• In what ways can adaptable governance models promote tailored and secure access to data across diverse business needs? 

• What practical steps can dismantle data silos between departments to drive faster, more informed decision-making? 

• How can aligning data governance efforts with organisational culture and stakeholder priorities strengthen governance frameworks? 

• How do we identify the most relevant metrics that align with our business objectives? 

• What best practices can we use to ensure our metrics are actionable and drive decision-making? 

• How can we effectively communicate the importance and impact of these metrics to stakeholders? 

• What are common pitfalls in metric creation and how can we avoid them to ensure data accuracy and relevance?

• How can we initiate Gen AI projects with board members and business units? What’s the expected ROI? Once we have the mandate, how do we move from Proof of Concept (PoC) to a pilot that significantly impacts business KPIs and ROI? 

• How can we educate business managers and technical developers to use AI tools and solutions effectively and responsibly? 

• How can data leaders address the EU AI Act to ensure it facilitates AI adoption while ensuring secure and compliant solutions? 

• How can businesses comply with GDPR and the EU AI Act without stifling innovation in data and AI? 

• What strategies enable organizations to balance regulatory adherence with creative, competitive data solutions? 

• What are the risks of non-compliance in Germany’s data-driven market, and how can they be mitigated? 

• How can companies' future-proof their data strategies against Germany’s evolving regulatory requirements? 

• How can businesses stand out in the competitive market for data and analytics talent, especially against tech giants? 

• What specific team structures and incentives drive productivity and foster collaboration in high-demand fields? 

• How can leaders align career growth paths with emerging technologies like AI and machine learning to future-proof their teams? 

• What role does hybrid work culture and workplace flexibility play in retaining top performers?

• How can traditional industries overcome the inertia of legacy systems to embrace digital innovation? 

• What strategies enable seamless integration of emerging technologies like AI, IoT, and automation? 

• How can organizations foster a culture that supports agility and innovation in long-established sectors? 

• What are the critical steps to ensure digital transformation delivers measurable value and long-term sustainability?

• How can organisations harness generative AI, predictive analytics, and continuous intelligence to scale efficiency like never before? 

• What lessons can we learn from trailblasers already leveraging AI/ML to transform supply chains, production, and customer engagement? 

• How do we tackle the scalability challenge: from proof of concept to full deployment while ensuring ROI and resilience? 

• What role will cutting-edge advancements like autonomous AI agents and real-time decision-making play in shaping the future of operational efficiency?

• What innovative approaches are transforming legacy systems into agile, future-ready platforms? 

• How can businesses ensure data integrity and operational continuity during modernization efforts? 

• What role do hybrid solutions play in easing the transition to modern platforms while maximizing ROI? 

• How can organizations balance the cost and complexity of modernisation with the urgency of staying competitive? 

• What innovative data strategies are enabling organisations to align with Germany’s ambitious sustainability and ESG targets? 

• How can data analytics unlock actionable insights to reduce carbon footprints and improve resource efficiency? 

• What role do emerging technologies like AI and IoT play in advancing ESG initiatives across key industries? 

• How can businesses ensure transparency and compliance in reporting ESG metrics while delivering measurable impact? 

• How can industries tailor their approach to data architecture for maximum flexibility and scalability? 

• What innovative governance models can ensure both control and agility in managing organisational data? 

• How does fostering a data-centric culture drive business transformation and competitive advantage? 

• What practical strategies can organisations adopt to deliver actionable insights through self-service and collaborative data practices? 

• How can businesses quantify the impact of data initiatives and align them with long-term strategic goals? 

What You’ll Learn: 

• AI Regulatory Landscape: Key regulations and their impact on businesses. 

• Compliance Strategies: Effective ways to ensure your AI initiatives meet regulatory standards. 

• Opportunities in Compliance: Leveraging regulations for innovation and competitive advantage. 

• Implementation Tips: Practical advice for integrating compliant AI solutions. 

Workshop Outline: 

1. Introduction: Overview of AI regulations and their significance. 

1. Regulatory Landscape: Key regulations and their implications. 

1. Compliance Strategies: Practical approaches and shared experiences. 

1. Opportunities in Regulation: How compliance can drive innovation. 

1. Implementation Tips: Best practices for compliant AI solutions. 

How You Can Participate: 

• No preparation needed! 

• Share your experiences and insights. 

• Join discussions and ask questions. 

• Consider regulatory opportunities for your industry. 

What to Expect: 

A solution-driven session where senior leaders collaboratively address challenges in ESG data quality, standardization, and application. Discussions will focus on turning ESG metrics into actionable insights while tackling reporting complexities and greenwashing risks. 

Participation: 

No preparation needed—bring your experience and challenges. Share ideas, exchange strategies, and co-create practical solutions to integrate ESG data into decision-making and compliance. 

Key Focus Areas: 

• Ensuring ESG data quality and transparency. 

• Standardizing metrics to meet global standards. 

• Transforming ESG data into measurable business outcomes. 

Outcome: Practical solutions and fresh perspectives to tackle your ESG data challenges.

• How is edge computing driving real-time operational insights and decision-making in modern industries? 

• What role does blockchain play in enhancing data security and trust across global supply chains? 

• How are businesses leveraging AI, robotics, and 5G to achieve scalable automation and efficiency? 

• What governance frameworks are companies adopting to address ethical concerns in advanced analytics and AI deployment?

• How can organizations ensure their data strategy is effectively aligned with their overall business objectives? 

• What are the common pitfalls organizations face when their data strategy does not align with their business strategy, and how can these be avoided? 

• What role does data governance play in aligning data strategy with business goals, and what are the best practices for implementing it? 

• How can organizations measure the impact of data-driven initiatives on their business performance, and what metrics should be used? 

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! 

This session will explore how organisations can design and structure data teams to maximise efficiency, innovation, and strategic alignment. Key discussions will include: 

• Balancing regulatory compliance, data security, and business agility through robust governance frameworks. 

• Integrating AI and machine learning capabilities within data teams to drive innovation and maintain competitive advantage. 

• Leveraging advanced data architectures and technologies (e.g., data lakes, data warehouses, and cloud platforms) to scale operations and enable data-driven decision-making. 

• Fostering a culture of data literacy and continuous improvement, ensuring employees at all levels are empowered to leverage data effectively.

• How can we accurately quantify the financial return on investment (ROI) from AI initiatives? 

• What are the key risk factors associated with deploying AI technologies, and how can they be mitigated? 

• What methodologies can be used to measure the operational costs of AI projects? 

• How do we balance the cost and benefits to ensure sustainable AI adoption in the organization?

An informal, interactive session where attendees ask questions directly to an AI ethics and data privacy expert. The expert will answer questions on the fly, providing practical insights and advice on navigating regulations and building trustworthy AI systems. 

Focus Areas: AI ethics, data privacy, regulatory challenges. 

• What strategies can organizations adopt to identify and monetize untapped data assets while maintaining compliance with GDPR and other regulations in the German market? 

• How can businesses create scalable data products or services that deliver value to external customers and internal stakeholders without compromising operational efficiency? 

• What role do emerging technologies like AI, IoT, and predictive analytics play in transforming raw data into profitable revenue streams across industries such as manufacturing and finance? 

• How can organizations balance data monetization efforts with ethical considerations and customer trust to ensure long-term commercial success? 

• How will evolving regulations, like the EU Data Act and AI Act, shape the future of data governance frameworks, particularly in Germany? 

• What trends in technology, such as cloud sovereignty, quantum computing, and autonomous AI systems, will redefine how organizations govern and secure their data? 

• How can businesses balance agility and control in their governance models to stay competitive while ensuring compliance and ethical use of data? 

• What strategies can organizations adopt today to prepare for long-term challenges and opportunities in data governance by 2030?

• What are the essential considerations for designing scalable and flexible data architectures to support advanced analytics? 

• How can organisations balance the use of various data sources (internal, external, and third-party) while ensuring quality and consistency? 

• What strategies can simplify the integration of diverse and complex data streams to enable a unified, actionable view? 

• How can organisation's structure and optimise data preparation processes to unlock the full potential of predictive analytics?

• How can leaders establish and promote ethical frameworks that address bias, transparency, and accountability in AI-driven decision-making? 

• What strategies can organizations implement to ensure ethical data practices while navigating complex global regulations and stakeholder expectations? 

• How does ethical leadership influence trust and long-term value creation in an increasingly data-centric economy? 

• What role do leaders play in fostering a culture of ethical innovation that balances technological advancements with societal and environmental impacts? 

• What strategies can organizations adopt to empower non-technical employees with self-service analytics while maintaining data quality and governance? 

• How can leaders ensure data literacy across teams to maximize the value of democratized analytics without overwhelming employees or creating security risks? 

• What tools and technologies are most effective in enabling scalable and user-friendly self-service analytics for diverse organizational needs? 

• How can businesses measure the ROI of self-service analytics initiatives and ensure alignment with broader strategic objectives? 

• What are the core principles of effective data management? 

• How can robust data management practices speed up digital transformation initiatives? 

• In what ways can data management enhance decision-making and drive business innovation? 

• What strategies can overcome common challenges in data management?