In the factory-driven Industrial Revolution, we began to view and measure work as a process. Now, in the AI Revolution, we will need to adopt a different model, where we view and measure work as a story. Building on the neuroscience that makes us wired for story patterns, storytelling uses “story” as a communication strategy, while story thinking uses “story” as an operational strategy. The volume, velocity, and variety of data will be connected to processes but also to the organization’s overall narrative intelligence. Lewis discusses the implications of data visualization through the lens of story visualization, which requires understanding human beliefs and commitments, and provides examples for leadership, change, innovation, healthcare, and organizational design.

The excitement around AI technologies tends to beg the question of how scalable AI products are or could be.

As the tech ecosystem embraces AI, leaders need proven strategies to scale effectively. Drawing from Isler's experience leading AI at Instagram, Spotify, and SiriusXM, he shares battle-tested frameworks that serve billions of users globally. From Instagram Reels recommendations to Spotify's personalization features, these cross-industry insights will help companies navigate their AI journey and compete in the global market.

As AI technologies rapidly advance, it's crucial to prioritize responsible AI development.

Imagine a world where AI systems perpetuate biases, exacerbate inequalities, and undermine trust. Well, you can stop "imagining," because it will happen if we are not careful while developing such systems. AI technology is full of biases, unfairness, and socio-technical problems that can have unexpected results if not properly understood. Join Gupta on a journey to explore the nuances of fairness constraints that can make or break an AI system's integrity. She invites discussion on real-world scenarios of unfair AI and in-depth learning of what fairness constraints are and how to apply them using open source Python libraries.

Learn about foundations for tracking multiple dimensions of the data ecosystem, such as AI model, metrics, and event lineages.

Modern data ecosystems face increasing complexity with the proliferation of AI models, metrics, and event streams. Shetty explores how Spotify tackled this challenge by implementing a comprehensive data lineage framework that serves as the foundation for tracking multiple dimensions of its data ecosystem. He shares the journey from a proprietary solution to an open source approach, demonstrating how data lineage can be extended beyond traditional use cases to encompass AI model lineage, metrics lineage, and event lineage.

The delivery of critical AI/ML initiatives enhances clinical efficiency and user experiences in healthcare and elsewhere.

Challenges and strategies for managing AI/ML projects in the healthcare domain exist. Drawing from more than 19 years of experience in software engineering and project management, Mishra delves into the end-to-end lifecycle of AI/ML projects, from conception to deployment. He includes effective risk mitigation strategies, translating stakeholder needs into technical requirements, and fostering cross-functional collaboration among data scientists, engineers, and product managers.

Assess the maturity of AI governance capabilities and explore the potential impact of future AI regulations.

AI governance remains one of the biggest hurdles to realizing the full potential of AI and ML, even in advanced organizations. While governance frameworks exist, they fail to connect high-level principles with the practical actions needed to manage risk effectively across the AI lifecycle. The result is stalled projects, increased risks, and missed opportunities for impact. Carlsson, host of the Data Science Leaders podcast, discusses how firms can bridge the governance gap, assess their governance maturity, and build scalable capabilities that ensure trust, compliance, and accelerated AI adoption.

Transforming AI hype into business outcomes is the objective of getting your business AI-ready. Based on Welsch’s AI Leadership Handbook: A Practical Guide to Turning Technology Hype Into Business Outcomes, he draws on more than 60 interviews he conducted with AI leaders and experts to offer strategic insights into AI implementations with a nine-step approach. Gain practical knowledge on fostering innovation, driving human-AI collaboration, and leading AI initiatives. This AI leadership keynote talk covers strategy, leadership, culture, and security, equipping you with tools to boost AI literacy and achieve measurable business success.

A semantic layer provides GenAI with a programmatic framework to make organizational context, content, and domain knowledge machine readable.

Enterprise AI’s business potential cannot be overstated: By employing standards-based semantic components such as metadata, business glossaries, taxonomy/ontology, and graph solutions, a semantic layer arms organizations with a framework to aggregate and connect siloed data and unstructured content, explicitly provide business context for data, and serve as the layer for explainable GenAI solutions. Tesfaye and Majumder present case studies explaining semantic layer technical architectures and exploring the components that enable enterprise scale data transformation efforts.

Revolutionize workflows via AI agents.

Think of AI Copilot as a top-secret agent with a special mission to automate tasks, streamline processes, and revolutionize your workflow. Discover how to equip your agent with a variety of gadgets (actions) to infiltrate various business processes and uncover opportunities for automation. Learn how to harness the power of AI to maximize efficiency, reduce costs, and achieve remarkable results.

A quick look at building a data project.

Asnani covers every step of the process of building a customer churn prediction pipeline—from data preprocessing and feature engineering to tracking experiments, building ML pipelines, and training high-performing classification models. The entire workflow is managed within MLFlow, allowing developers to build, track, and deploy pipelines seamlessly. It uses the Streamlit interface to show predictions as a real-time visualization of churn predictions. This session offers a practical and approachable way to implement customer churn prediction for both beginners and experienced data practitioners.

AI, robotic process automation (RPA), and machine learning (ML) can transform government operations.

With efficiency, cost, and service enhancements being demanded of the federal government, the adoption of AI, robotic process automation (RPA), and machine learning (ML) is emerging as a great shift. These technologies can foster innovation and alter the processes and roles of various government agencies. AI-driven systems offer new data analysis possibilities that allow agencies to speed up decision making. These technologies are enhancing processes which include claim processing, records management, and a range of other activities contacted by the citizens, thereby improving the speed and quality of delivery of services to citizens.

Discover the future of conference engagement with an innovative idea that uses AI to record, transcribe, and build an interactive model around presentation content. Experience a live demo of the AI-powered chatbot used at Data Summit, designed to foster dynamic conversations by asking follow-up questions and providing insightful answers. You can interact with the bot to explore topics, dive deeper into sessions, and learn in a whole new way. This groundbreaking approach extends the value of conversations, making knowledge accessible and engaging.

Moving beyond speculation to data, this keynote presents analysis and insights from our comprehensive Q1 2025 market study spanning 200-plus organizations. We examine how companies are actually implementing modern enterprise data architectures to support analytics and AI initiatives, revealing current adoption rates, investment patterns, and expected outcomes. Building on our 2023 study's foundation, which tracked early investments in modern data architectures, we survey the evolution of data platforms by adding vector databases, knowledge graphs, and semantic layers. The session cuts through market hype to present evidence-based results and insights on which architectural patterns—from data fabric to data lakehouse—deliver measurable value and how organizations successfully balance AI innovation with enterprise data management and governance requirements.