With new pressures coming from AI, CDAOs must be agile in implementing and enabling business innovation. But, with quick adaptation, inevitably comes stress on traditional D&A delivery processes and practices. Join this interactive session to lean how to: clarify strategies to support business innovation, design adaptable and scalable delivery models and collaborate with business units to ensure value of D&A capabilities and services.

This very popular workshop shares the latest toolkit which can be used to diagnostically connect data, analytics, governance and their impact on business processes, decisions and outcomes.

Implement your target AI governance operating model by mapping governance pillars to key AI components and differentiating AI capabilities. This model should connect with other governance bodies and extend existing governance models to AI-specific considerations of trust, transparency and diversity.

Traditional approaches and thinking around data quality are out of date and not sufficient in the era of AI. Data, analytics and AI leaders will need to reconsider their approach to data quality going beyond the traditional six data quality dimensions. This session will help data leaders learn to think about data quality in a holistic way that support making data AI-ready.

D&A value is not possible without data storytelling that offers a better way to engage communication findings than just BI reporting or data science notebooks. Join this session to know about the fundamentals of data storytelling and how to fill the gap between data science speakers and decision makers. It further discusses how to tell the best data storytelling and how to upscale data storytelling for future in landscape of GenAI.

Government organizations are signaling broader adoption of AI capabilities and increasing investments. Various national and regional governments have published directives that provide guardrails to ensure safe, secure and trustworthy implementation of AI. This session addresses the necessary ingredients through example use cases for deploying and scaling AI capabilities in government.

Successful data and analytics strategies today are business-centric, focused on achieving tangible and measurable business outcomes. This session will discuss the anatomy of an effective data and analytics strategy that enables organizations to thrive in today’s highly challenging and dynamic business environments.

The roundtable will explore the benefits and challenges of both the approaches and aim to find a middle ground that combines the strengths of both adaptive and control governance. Participants will discuss how to strike a balance between agility and stability, innovation and risk management, and empowerment and accountability.

When an organization’s thoughts turn to AI, the CDAO’s name should be one of the first they think of. The CDAO must have a leading voice in their organization’s AI conversation. This session discusses techniques for the CDAO to take this position including real-world examples of success.

AI agents are emerging as an important trend since they enable levels of business adaptability, flexibility and agility that traditional AI systems can’t achieve. This flexibility is valuable in unpredictable environments where real-time monitoring and control aren’t practical. Autonomous behaviors, despite significant societal, legal and ethical implications, are the answer to the increasing complexity in our enterprise systems.

Learn about proven techniques to boost the use and productivity of your self-service analytics environment. What works and what doesn't? What should you prioritize if you're just getting started? How can you keep the environment "fresh" as you mature? What should you be thinking about in addition to analytics as you build and grow your approach? Join us to know the answers to such questions.

CDAOs are making investments daily. Perhaps you're looking to grow your team, or maybe making a technology investment to support GenAI, or another investment where you need to build buy-in and gain funding. This workshop will help you develop personal influence skills while also building a strong story for investment.

Discover how Data Mesh is transforming data management by decentralizing delivery and empowering business-driven D&A initiatives. You will find out what data mesh is, its benefits, and the most common challenges. We will provide a successful path based on the experience of early adopters, allowing you to avoid the most common pitfalls and adopt data mesh successfully.

Most metadata in 2024 will remain passive in approaches with stats, reports, schema and business-developed glossary terms. Most organizations must grow their maturity in metadata management. We start with traditional metadata techniques — passive. With AI undergoing confidence issues and the demand to reduce risk, grow AI confidence, and provide data assurance, active metadata becomes key.

With growing focus on AI in organisations, deliveringAI-ready data has become the number one investment priority of data management leaders. This session will define AI-ready data, how it differs from traditional data management and discuss AI-ready data practices and technologies.

D&A value is not possible without data storytelling that offers a better way to engage communication findings than just BI reporting or data science notebooks. Join this session to know about the fundamentals of data storytelling and how to fill the gap between data science speakers and decision makers. It further discusses how to tell the best data storytelling and how to upscale data storytelling for future in landscape of GenAI.

Relying on a single model for AI solutions due to the hype of powerful GenAI models in the market is a common pitfall in AI adoption for enterprises. D&A leaders and the teams should avoid this pitfall by using systems thinking and clarifying the limitations with stakeholders. This session will introduce practices that go beyond a single model for more powerful, reliable and efficient AI solutions.

Welcome to an exciting theater session where we explore the Magic Quadrant for data and analytics governance platforms. In this session, we will delve into the world of data and analytics governance, understand the importance of these platforms, and discover the leading players in the market.

Implementing AI governance can be challenging, navigating this in the public sector is particularly so with risk focus often overshadowing value and benefits. This roundtable will explore practices in overcoming challenges to achieve success in AI Governance with specific focus on public sector aspects including data, regulations and workforce.

GenAI may excelling in some areas but fail in others, complicating the business case. CIOs often underestimate costs, which can scale by 500%-1000%. Productivity is a key benefit but hard to prove to CFOs. Should you invest in chat-style GenAI for 100K employees or near-custom AI for 50 R&D staff? Join our session to master cost scaling, value harvesting beyond productivity, and creating a portfolio of GenAI investment options.

Heads of analytics & AI face many challenges such as keeping afloat, staying the course or racing ahead in the race for AI superiority. This session will focus on the top fears, risks, objections and hurdles leaders of Analytics & AI face and most importantly, how to handle them.

Have you ever struggled to secure buy-in to a no-brainer MDM strategy? MDM programs exist to support business outcomes, yet business cases often focus on the how not the why, leaving stakeholders disengaged. In this session we explore what constitutes a strong MDM value proposition that will engage stakeholders from business case, through execution and stewardship.

We will discuss the latest burning issues that heads of data governance must address, and also provide recommendations to manage and exploit emerging opportunities. With an understanding of the influencing drivers and trends, leaders will be equipped to optimize their data governance strategy.

In this session, we will explore cost transformative vendors such as DeepSeek and MistralAI’s impact on AI development, highlighting the potential to enhance enterprise innovation at a price/performance that is unprecedented. Data, analytics & AI leaders can bring their questions on how they can capitalize on new training and inferencing paradigms in AI, the future of AI model development and the impact of reasoning models on a more autonomous future in AI.

Moving AI projects from pilot to production requires substantial effort for most enterprises. AI Engineering provides the foundation for enterprise delivery of AI and generative AI solutions at scale unifying DataOps, MLOps and DevOps practices. This session will highlight AI engineering best practices across these dimensions covering people, processes and technology.