Data and analytics leaders can use the concept of a “franchise” to communicate the optimal organizational model, data architecture and governance framework that large enterprises require to scale self-service analytics programs.

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.

Organizations struggle to make sense of numerous programs and projects that overlap or operate in silos. This research will weave together data and analytics governance, MDM and data quality into one organized initiative that every CDAO should be interested in.

Data and analytics leaders and their data engineering teams are tasked with evaluating and selecting data integration tools. However, there are many options, which can be confusing. This session will explain the various types of data integration tools and technologies available in the market, and help you select the right data integration tool for your needs.

As organisations scale AI and move towards Data Products, success depends on trusted, high-quality data underpinned by strong governance. In this fireside chat, Chemist Warehouse shares how domain-aligned metadata, data quality, and governance, powered by Alation, enable a unified delivery framework using Critical Data Elements (CDEs) to reduce risk, drive self-service, and build a foundation for AI-ready analytics and future data product initiatives.

Organizations can face many challenges in operationalizing D&A and AI strategies. In this session, we discuss how to capitalize on value-based opportunities, engage with stakeholders and get to what matters.

CDAOs implement self-service analytics (SSA) to enable data-driven decision making across their organizations. However, SSA initiatives often fail to deliver on their full potential due to governance, trust, scalability and adoption challenges. This presentation provides a framework for CDAOs to balance control and agility within SSA to drive meaningful business value from data.

Asking your colleagues how analytics can help them often results in blank stares, defensiveness, or wildly incoherent suggestions involving AI. This session will show you how to work with your colleagues to pinpoint how you can help, identify the most helpful capabilities to build, and explain how to measure your impact.

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.

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.

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

To position their teams as a must-have discipline that can fulfill cross-functional use cases, D&A leaders must optimize their organizational model. The optimal organizational model is one that balances enterprisewide capabilities focused on enablement with decentralized needs focused on outcomes. In this session you will learn the design principles behind the right D&A organizational model and how to balance a centralized team (focused on enablement) and decentralized needs (focused on outcomes).

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.

Discuss with your peers the approaches, tips and pitfalls in communicating D&A strategy, approaches and value with executive leadership within the government. Understand how to communicate that the fast-changing world of D&A can be difficult, especially within areas of long-range planning and budgeting. Learn from your peers and build relationships in this session.

D&A is full of politics. What people say they want is often not what they really want with D&A. Resistance to becoming more data-driven is often left unspoken. This session will help you to recognize the most common political issues and the best practices to overcome them.

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

Since 2022, Gold Coast Health have been on a digital transformation journey. Their objective, to “liberate” clinicians and health staff from burdensome digital and administrative processes by combining data analytics, automation and workflow solutions. Hear about their journey from an on premise enterprise data warehouse to their Advanced Data Platform and their recent success using this platform to automate communications between tertiary and primary care.

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.

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.