Data and analytics leaders can use the concept of a “franchise” as a metaphor to communicate the optimal organizational model, data architecture and governance framework that large enterprises require to scale self-service analytics programs.

Data products are all the rage within D&A functions as the next wave of potential solutions to reduce the business-IT friction and provide sustained means to data delivery. However, there are many challenges. This session will help D&A leaders understand and build data products, manage and govern them through contracts, and sustain them through marketplaces, governance, and ops best practices.

Data and analytics leaders expect their data and analytics investments to deliver business results. But unless they address data, analytics and AI risk, their initiatives will fail, leading to higher business risk exposure. This session will help D&A leaders target three key areas where better data and analytics risk practices will yield better business results.

Join us for this roundtable, tailored for Black and Brown technology leaders and open to all attendees, to explore the impact of GenAI on the workforce and underrepresented talent. As AI continues to shape the future of work, the high quality of diverse thought and talent holds both the promise of inclusivity and the hope of overcoming perpetuating biases. Together we’ll discuss how AI can be a double-edged sword — potentially amplifying existing inequalities and amplifying how diverse talent is highly qualified talent overall

The enormous potential business value of AI is not going to materialize spontaneously. AI leaders should guide their organization toward an era in which AI is not only creating tangible business value but goes beyond to become a critical competitive differentiator and industry disruptor.

More than 50% of generative AI projects fail. This session will help data & analytics leaders learn the common causes of failures and understand best practices to mitigate those failures and scale AI across the enterprise.

AI is having a huge impact, but is not the only thing with societal, technological, and organizational implications driving change in data and analytics. We examine trends in areas such as complexity, trust, and empowerment facing leaders and teams as they make decisions in all aspects of their bet-the-business D&A strategy.

Data and analytics leaders can struggle to translate and assess the necessary investment and return value of D&A initiatives in the public sector. In this session, participants will learn frameworks for public sector value determination and communication and then build out value stories for their initiatives.

AI architects are challenged to define a technology roadmap in an environment that is currently defined by a rapid pace of AI technology, specifically with generative AI initiatives, evolving product capabilities, needs for up-skilling, all of this with the goal of improving business outcomes. This session will provide the framework, architectures and tools to define such a roadmap.

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.

Metadata, data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI initiatives. Metadata tools help discover, and inventory your data assets. Data quality tools help business users manage their data at sources by setting rules and policies. Data observability tools give organizations integrated visibility over the health of data, data pipeline and data landscape. Together the tools help organizations lay good foundation in data management for BI and AI initiatives.

To position their teams as a must-have discipline that can fulfill cross-functional use cases, D&A leaders must optimize their organizational model. The optimal organizational model is one that balances enterprisewide capabilities focused on enablement with decentralized needs focused on outcomes. In this session you will learn the design principles behind the right D&A organizational model and how to balance a centralized team (focused on enablement) and decentralized needs (focused on outcomes).

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.

In this session, we will explore DeepSeek’s transformative impact on AI development, highlighting its 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.

The most difficult part of establishing a data governance program is making it stick. This session will discuss challenges with identifying, motivating, and supporting data governance, ownership, and stewardship roles and tactics to build momentum for the program.

Modernize your analytics capabilities by identifying the products that best meet your needs. See side-by-side, scripted demonstrations of three leading vendors: Strategy, Oracle and Tableau. What are the key features to consider and how do they compare in action? What are the main strengths and weaknesses of these vendors? What innovations are coming?

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.

D&A leaders face challenges in identifying and engaging with data stewards to advance D&A governance efforts. This session will provide actions that leaders can take back to their organizations and begin to implement.

Data and analytics leaders need to support the opportunities and challenges of today’s digital business with the right competencies. This is the time to evaluate data and analytics roles and skills that are fit for now and the future. This session will provide key considerations for D&A and AI roles and skills.

Find out about the emerging, most hyped and ready for prime-time concepts and technologies in data, and analytics, and AI programs and practices.

Join this roundtable of trailblazers and thought leaders for peer discussions and lively exchanges on advancing women in IT leadership today. Women attendees are invited to explore visible opportunities and challenges, and to uncover and share best practices for successfully navigating the path to IT leadership.

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.

Data ecosystems, built on data fabric design and infused with AI, promise an integrated, cost effective, and operationally simple approach to varied data management challenges. However, they don't yet always deliver on that promise. This research explores the maturity of various ecosystem components and provides a guide for D&A leaders and others looking to invest in data foundations for competitive differentiation.

This workshop will cover the end-to-end process of managing a data product, from ideation to launch. It will include product roadmap development, stakeholder management and go-to-market stra tegies.

Join this session to get oriented about where you are on the AI maturity curve and develop the next steps to achieve your goals. Explore AI maturity by addressing its building blocks such as use case selection, technology operationalization and AI adoption.