D&A and AI leaders including CDAOs continue to struggle to prove the value of D&A and AI. This session will provide a roadmap for gaining stakeholder buy-in on D&A initiatives, quantifying the value and building the business case, learning from the success of other organizations, and continuously measuring results and adjusting.

Models have become a commodity and the true differentiator lies in your data. This session will showcase seven real case examples from Uber, Rechat, J.P. Morgan, ChatDOC, Arize AI, Qodo, and Unstructure that span the entire AI-delivery life cycle, demonstrating how to transform your data into AI-ready assets to unlock its full value.

Get ready for a high-energy, action-packed session that brings AI agents to life. Build and deploy a fully functional AI agent in minutes using IBM watsonx Orchestrate. Learn how to:
• Create a domain-specific agent in minutes.
• Explore our pre-built agents for Sales, Procurement, and HR, which integrate seamlessly with 80+ enterprise tools your teams already use.
• Manage multiple agents through a centralised chat interface, automating even the most complex workflows.
• Discover proven strategies to boost productivity and ROI without long development cycles or heavy IT demands.
Whether you’re starting out or scaling your automation, this session will inspire and equip you to build your own AI agents confidently.

Explore how a global CPG leader revolutionized forecasting by integrating AI, internal data, and 5M+ external signals. By embedding macroeconomic indicators and modeling consumer behavior, they enhanced forecast precision—where even a 1% improvement equates to millions in value.

Across industries, AI-driven forecasting has boosted accuracy to over 90%, reduced out-of-stocks by 32%, and cut inventory costs by 25%.

See how advanced analytics drives agility and measurable impact.

Introducing Raden, the world’s first AI Data Engineer brought to you by Revefi and powered by MIP.
Today’s cloud data platforms are complex, costly, and often chaotic. Broken pipelines, hidden inefficiencies, and manual firefighting dominate most data teams’ time. In this session, we’ll explore how Revefi’s metadata-first observability platform, powered by Raden, transforms how enterprises detect, resolve, and prevent data issues with zero disruption and instant value. From automatically creating over 665,000+ data monitors, to cutting warehouse costs by 50% in just four weeks, Raden helps teams regain trust, control, and ROI fast.

Discover how leading enterprises are transforming financial planning with AI-driven automation, predictive insights, and dynamic decision-making. In this session, we’ll share best practices and real-world use cases of clients leveraging
IBM Planning Analytics to streamline forecasting, enhance agility, and gain a competitive edge. We’ll explore the latest advancements in Agentic AI; intelligent assistants that proactively analyse data, detect outliers, and recommend next steps, empowering finance teams to operate with greater speed and precision.

Deploying AI agents into business operations offers significant potential – but also demands careful execution to deliver tangible results. In this session, discover practical strategies for embedding agentic AI into frontline workflows while maintaining responsible AI governance.

We’ll explore how agentic AI can drive real performance gains – such as in call centre operations, where organisations have achieved a 20% increase in complaint resolution volume, a 30–40% reduction in average response times, and a 15% drop in handling costs. Learn how to turn AI ambition into operational success.

Urgent Investments in data, analytics and AI use cases has put the spotlight once more on strong data management foundations. Is our Data even Ready for upcoming AI, analytics and data sharing initiatives is now top of mindshare for heads of data, CDAOs and their counterparts. Data Fabrics have emerged as a long term, foundational data management architecture that you should now pursue for sustained D&A success. This session will:
1. Help understand what data Fabrics are and what they mean for your data strategy and architecture
2. Help decide how to build and where to buy
3. Navigate the vendor landscape to assist in tech procurement decisions to aid your fabric journey

Join your peers to explore effective strategies and address challenges in preparing your data for AI applications. Exchange insights and valuable lessons with other Data & Analytics leaders. Discuss the essential elements of AI readiness and their significance for your data strategy. Discover methods to evaluate your existing data infrastructure and pinpoint areas for enhancement. Peer Meetups are networking sessions that allow you to connect and share with a small group of your peers without Gartner facilitation. Please make every effort to attend your peer meetup, as other attendees look forward to meeting with you.

With rapid technological advancements, AI and advanced analytics are reshaping business landscapes. Explore the latest trends and strategies for integrating these tools into your business. Learn from peers and strategically position your organization in the AI era. Peer Meetups are networking sessions that allow you to connect and share with a small group of your peers without Gartner facilitation. Please make every effort to attend your peer meetup, as other attendees look forward to meeting with you.

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.

Data architects are increasingly tasked with provisioning quality unstructured data to support AI models. However, little has been done to manage unstructured data beyond data security and privacy requirements. This session will look at what it takes to improve the quality of unstructured data and the emerging best practices in this space.

Summary In this episode of the Data Engineering Podcast we welcome back Nick Schrock, CTO and founder of Dagster Labs, to discuss the evolving landscape of data engineering in the age of AI. As AI begins to impact data platforms and the role of data engineers, Nick shares his insights on how it will ultimately enhance productivity and expand software engineering's scope. He delves into the current state of AI adoption, the importance of maintaining core data engineering principles, and the need for human oversight when leveraging AI tools effectively. Nick also introduces Dagster's new components feature, designed to modularize and standardize data transformation processes, making it easier for teams to collaborate and integrate AI into their workflows. Join in to explore the future of data engineering, the potential for AI to abstract away complexity, and the importance of open standards in preventing walled gardens in the tech industry.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementThis episode is brought to you by Coresignal, your go-to source for high-quality public web data to power best-in-class AI products. Instead of spending time collecting, cleaning, and enriching data in-house, use ready-made multi-source B2B data that can be smoothly integrated into your systems via APIs or as datasets. With over 3 billion data records from 15+ online sources, Coresignal delivers high-quality data on companies, employees, and jobs. It is powering decision-making for more than 700 companies across AI, investment, HR tech, sales tech, and market intelligence industries. A founding member of the Ethical Web Data Collection Initiative, Coresignal stands out not only for its data quality but also for its commitment to responsible data collection practices. Recognized as the top data provider by Datarade for two consecutive years, Coresignal is the go-to partner for those who need fresh, accurate, and ethically sourced B2B data at scale. Discover how Coresignal's data can enhance your AI platforms. Visit dataengineeringpodcast.com/coresignal to start your free 14-day trial. Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. This is a pharmaceutical Ad for Soda Data Quality. Do you suffer from chronic dashboard distrust? Are broken pipelines and silent schema changes wreaking havoc on your analytics? You may be experiencing symptoms of Undiagnosed Data Quality Syndrome — also known as UDQS. Ask your data team about Soda. With Soda Metrics Observability, you can track the health of your KPIs and metrics across the business — automatically detecting anomalies before your CEO does. It’s 70% more accurate than industry benchmarks, and the fastest in the category, analyzing 1.1 billion rows in just 64 seconds. And with Collaborative Data Contracts, engineers and business can finally agree on what “done” looks like — so you can stop fighting over column names, and start trusting your data again.Whether you’re a data engineer, analytics lead, or just someone who cries when a dashboard flatlines, Soda may be right for you. Side effects of implementing Soda may include: Increased trust in your metrics, reduced late-night Slack emergencies, spontaneous high-fives across departments, fewer meetings and less back-and-forth with business stakeholders, and in rare cases, a newfound love of data. Sign up today to get a chance to win a $1000+ custom mechanical keyboard. Visit dataengineeringpodcast.com/soda to sign up and follow Soda’s launch week. It starts June 9th.Your host is Tobias Macey and today I'm interviewing Nick Schrock about lowering the barrier to entry for data platform consumersInterview IntroductionHow did you get involved in the area of data management?Can you start by giving your summary of the impact that the tidal wave of AI has had on data platforms and data teams?For anyone who hasn't heard of Dagster, can you give a quick summary of the project?What are the notable changes in the Dagster project in the past year?What are the ecosystem pressures that have shaped the ways that you think about the features and trajectory of Dagster as a project/product/community?In your recent release you introduced "components", which is a substantial change in how you enable teams to collaborate on data problems. What was the motivating factor in that work and how does it change the ways that organizations engage with their data?tension between being flexible and extensible vs. opinionated and constrainedincreased dependency on orchestration with LLM use casesreducing the barrier to contribution for data platform/pipelinesbringing application engineers into the mixchallenges of meeting users/teams where they are (languages, platform investments, etc.)What are the most interesting, innovative, or unexpected ways that you have seen teams applying the Components pattern?What are the most interesting, unexpected, or challenging lessons that you have learned while working on the latest iterations of Dagster?When is Dagster the wrong choice?What do you have planned for the future of Dagster?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Links Dagster+ EpisodeDagster Components Slide DeckThe Rise Of Medium CodeLakehouse ArchitectureIcebergDagster ComponentsPydantic ModelsKubernetesDagster PipesRuby on RailsdbtSlingFivetranTemporalMCP == Model Context ProtocolThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Three out of four companies are betting big on AI – but most are digging on shifting ground. In this $100 billion gold rush, none of these investments will pay off without data quality and strong governance – and that remains a challenge for many organizations. Not every enterprise has a solid data governance practice and maturity models vary widely. As a result, investments in innovation initiatives are at risk of failure. What are the most important data management issues to prioritize? See how your organization measures up and get ahead of the curve with Actian.

Join Transurban as they share their journey, highlighting how enhanced data visibility and effective remediation have transformed their data culture and driven improved business value in infrastructure and road operations. Gain insights into Transurban’s plans for leveraging AI governance in the future to unlock further benefits and optimize industry practices.

Join us for an exclusive roundtable discussion featuring industry leaders and experts as we delve into the transformative power of the SAP and Databricks partnership. This session is designed to provide actionable insights and foster a collaborative dialogue on the ways this collaboration is reshaping the landscape of data management, AI, and business strategy.

This will be a dynamic, interactive roundtable where participants can share their viewpoints, explore real-world use cases, and address challenges and opportunities. The session is designed to encourage open discussion and provide valuable insights for navigating the evolving data and AI landscape.

Enter the agentic era of data and analytics with Tableau and Agentforce. Discover how AI agents are accelerating data modeling and unlocking conversational analytics. Hear how leading organizations are harnessing agents to reimagine decision-making, supercharge insight delivery, and unleash the full potential of their data-driven workforce.

This session will look at the quickly emerging domain of agentic AI. What are AI agents? What are the solutions and applications that will most benefit from an agent-based approach? What are the pitfalls to watch for when considering this fast-growing software engineering discipline? Join this session to know the answers to such questions and more.

Chief data and analytics officers play a critical role in driving and overseeing major business changes to deliver enterprise value. Clear and actionable change-management plans are essential for both proactive and reactive data-driven change.

Data and AI is playing a pivotal role in the energy transition, enhancing customer centricity and unlocking operational efficiency. Come and hear how AGL Energy is empowering it's people with the knowledge and skills to thrive in a data & AI driven future.