Join us for an insightful session where biopharmaceutical leader Pfizer shares valuable lessons learned during the development of their global SMART application suite, which empowers representatives to deliver more patient-centric care. With a mission to create breakthroughs that change patients’ lives, the SMART application suite supports over 20,000 pharmaceutical sales employees across dozens of countries. Deploying at this scale required not just advanced technology, but also transformative ways of working. The result? A threefold increase in the adoption of previous solutions.

Rapid changes demand innovative decision-making tools beyond traditional methods. Businesses are turning to AI, BI, and data science to gain a competitive edge. The perfect blend of these technologies can be a true differentiator.

- Take a quick look at what to expect from this session
- Challenges in data and analytics today
- Unlocking the power of AI, BI, and data science
- The transformative role of AI-powered self-service BI platforms
- Live demos of next-generation analytics in action

Learn how these innovations can drive better decisions to deliver transformative business outcomes.

Whether you are new in your role or a seasoned veteran, you’re likely being asked how to extract more value from your analytics investment. In this session, we will review how to embed AI and conversational analytics into the flow of users’ daily lives to drive adoption and improve overall customer end-user experience. Putting the power of true self-service into the hands of all users, both internal and external, has never been easier and is now a requirement more than ever.

Data and AI are shifting industries and reshaping society, but with great power comes the need for robust governance. In this session, we will focus on governance in data and AI, highlighting why it is a strategic priority for organizations worldwide.

Join this session to discover how trusted AI governance with SAS Viya ensures transparency, ethics, and performance—without slowing innovation. Walk away with real-world strategies to keep your AI trustworthy, efficient, and impactful. Don’t let bad AI decisions cost you.

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.

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.

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.

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.

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.

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.

How can data science accelerate the energy transition? In this session, UK Power Networks’ Data Science team presents real-world tools driving a smarter, more flexible electricity grid on the path to Net Zero. From democratising access to grid insights to automating decision-making for clean energy, this talk highlights how applied AI and analytics are transforming infrastructure at scale, with lessons for any data professional tackling high-impact, real-world problems.

UK Power Networks will share how they extracted data from century-old handwritten and printed archives. Using FME coupled with AI, UKPN was able to automate a task turning a multi-year effort into days. See how All-Data, Any-AI Integration can be used to unlock new insights, drive efficiency, and accelerate innovation.

Join us to learn how the Schwarz Group, the parent company of Lidl and Kaufland and the 4th largest retailer worldwide, leverages containerized Strategy in STACKIT for sustainable growth of their data and AI infrastructure. Over a decade-long partnership, Schwarz has utilized sovereign data and cloud services to maintain a competitive edge. Discover their use of Strategy, the last independent BI tool, with open data formats to avoid vendor lock-in. Get insights into delivering open and sovereign cloud solutions for their business lines and the EU, ensuring data independence and scalability.

Data observability helps to detect and diagnose unknown unknowns in data quality and pipeline performance—but it doesn't fix them. Learn how Bigeye is leveraging AI to enable data teams to resolve data quality and pipeline issues, and even prevent them occurring in the first place.

Disconnected applications slow operations, impacting everything from customer support to case resolution. Eliminating outdated processes is essential for peak performance.

After extensive market research and comparing all of the biggest players, Enate chose SnapLogic to integrate disparate systems, eliminating manual errors and freeing resources for strategic work.

With a unified view, SnapLogic's AI capabilities support Enate’s Business Orchestration Automation Technology (BOAT), helping service providers operate efficiently, reduce complexity, and deliver exceptional customer results.

There is no value from AI without a Data Strategy. AI hallucinations are a significant risk in delivering ROI across the enterprise. Stardog’s knowledge graph-powered agentic architecture delivers an AI-ready data foundation with a semantic layer that provides facts and grounding needed to eliminate hallucinations. Learn why traditional Retrieval-Augmented Generation and straight Text-to-SQL approaches can be insufficient and how you can broaden AI's access to diverse and dense data and ensure timely, secure, and, most importantly, hallucination-free answers from your own data.

As organizations race to harness agentic AI, the challenge is not just prototyping but picking the right use cases and confidently deploying them. Discover how DataRobot is helping organizations like BMW, DHL and VidaCaixa on their journey, and learn the key considerations for developing, deploying, and governing agentic AI:

- Picking the right tools and techniques: Knowing what frameworks and models to use when.
- Streamlining AI Infrastructure: How to efficiently manage and scale your AI infrastructure.
- Examples demonstrating agentic AI in action.

As enterprises race to unlock AI, many face barriers like poor metadata and weak governance. In this session, Rebecca O’Kill (CDAO of Axis Capital), Tim Gasper, and Juan Sequeda share how AI is not just the outcome of governance—it’s the incentive. Framing AI as the “carrot” motivates adoption of governance as a strategic enabler. Learn how AI-powered governance, data marketplaces, and knowledge graphs together provide context, drive smarter metadata, and enable impactful AI use cases like underwriting agents that require structured and unstructured data.

As data becomes the lifeblood of modern enterprises, protecting it must be seamless, scalable and strategic. Discover how leading organisations use automation, tokenisation and AI to embed privacy from ingestion to sharing. Learn how built-in protection boosts breach resilience, automation cuts compliance costs, and controlled sharing enables monetisation. Real-world use cases show how AI and policy-based controls make privacy a driver—not a blocker—of faster, smarter decisions.

KIL Proposal:
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.