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 critical intersection exists between design and development in software engineering.

Drawing from his extensive background in Java development, full-stack design, and system architecture, Manda discusses best practices for aligning technical requirements with user-centric design principles and shares insights from his work on cutting-edge technologies, including his transition from legacy systems to cloud-native architectures and his application of Agile methodologies to streamline workflows.

Learn how companies can scale their data strategies, fuel advanced workloads, and centralize sensitive information without compromising trust.

Data is the lifeblood of modern enterprises, but with every petabyte collected, the stakes grow higher. Whether it’s customer, patient, or financial data, organizations are under mounting pressure to protect sensitive datasets from exposure while navigating an increasingly complex regulatory landscape. Yet many businesses still rely on outdated approaches that not only stifle innovation but also increase vulnerabilities. Kundavaram unpacks lessons learned from Fivetran’s experience working with global enterprises, sharing actionable insights on bridging cloud and on-prem environments, ensuring airtight data governance, and unleashing the full power of your data—without losing control.

AI can streamline your approach to data.

AI is not just transforming data pipelines for applications—it’s also streamlining the process of building these pipelines. AI-assisted tools can automate much of the tedious work traditionally done by data engineers. Join this session to learn about the opportunities to accelerate your data team efficiency and reliability.

Serverless data engineering refers to designing and managing data workflows using mostly cloud computing resources based on certain events.

In a serverless paradigm, developers focus on creating and running data pipelines without managing the underlying server infrastructure. Instead, the cloud provider dynamically allocates resources and handles scaling, availability, and maintenance. Serverless data engineering enables agile, scalable, and cost-effective solutions for modern data workflows. By offloading infrastructure management to cloud providers, organizations can innovate faster and focus more on delivering insights and value from their data.

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.