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.

As organizations race to implement AI initiatives, many discover that their fragmented data infrastructure is holding them back. Learn how unifying enterprise data in real time with CrateDB not only simplifies your architecture but also creates the foundation for successful AI deployment. Pullepu explores practical approaches to breaking down data silos and building a unified data foundation that's ready for today's AI demands and tomorrow's innovations.

Companies collect data at a great rate, but it takes AI to enhance how users interact with and derive value from it.

Sharing insights from his journey at Crunchbase, Gautam outlines how its new AI-powered tools are designed to anticipate user needs. He is a strong advocate of the jobs-to-be-done framework, believes that AI solutions should solve real customer problems, and that staying agile via a flexible AI architecture is essential. Join Gautam as he describes Crunchbase's approach.

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.

Geek out over unstructured data, vector databases, LLMs, LangChain, and RAG.

Spann explores how Milvus, a high-performance, open source vector database, can be integrated with open source LLMs to implement scalable and efficient RAG pipelines. He shows how any kind of unstructured data from a variety of sources and types can be processed, queried, and used to feed large language models for smart, contextually aware answers. Look for his example utilizing local OLLAMA, LangChain, Milvus, open source tools, libraries, and Jupyter Notebooks.

DevOps, along with good data, is the foundation for MLOps and building responsible, repeatable AI applications. Karam focuses on data management and compliance for fine-tuning, retrieval-augmented generation (RAG), and best practices for extending existing application logic into generative AI (without turning everything into a chatbot!).

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.

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.