Presented by John O’Brien

Moving beyond speculation to data. This keynote presents analysis and insights from our comprehensive Q1 2025 market study spanning 200+ organizations. We'll 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'll survey the evolution of data platforms by adding vector databases, knowledge graphs, and semantic layers. The session will cut 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.

Presented by Dave Wilkinson

As enterprises race to implement AI and machine learning, one truth remains: your models are only as good as the data feeding them. Without trusted, consistent, and well-mastered data, even the most sophisticated algorithms will struggle to deliver accurate, explainable, or actionable outcomes. This session explores how mastering your foundational data—through effective data governance, operational master data management, and purpose-driven architecture—can make your AI/ML strategy not only more complete but more impactful. We’ll share insights from real-world transformations, including how poor data hygiene reshaped financial reporting for a Fortune 500, how it took four months to answer a simple executive question, and how digital twins revolutionized predictive maintenance on oil platforms.

You’ll leave having experienced how to well-architect data ecosystems support scalable, trustworthy AI. We’ll dive into why operational efficiency begins with well-mastered data, how behavior change—not just dashboards—is the real measure of value, and why using generic AI models might erase your competitive edge. Whether you're leading a data team, evaluating ML platforms, or advancing enterprise architecture, this session will provide the frameworks, language, and real-world examples to bridge data mastery with artificial intelligence.

Attendees Will Learn:

• Why MDM is foundational to trustworthy AI and ML outcomes
• How to link master data, behavioral change, and operational efficiency
• How to define and apply critical data elements based on real business purpose
• Strategies for aligning MDM and analytics with measurable behavior change
• How to support predictive analytics, digital twins, and advanced visualization through better architecture
• Practical steps for architecting data environments that enable responsible, scalable AI adoption

Presented by Mark Horseman   In this session we'll show how knowledge graphs can be used to bridge the gap between enterprise policies (eg: Information Security, Document Management, Data Privacy, and External Data Sharing) and the actual data elements within a data lake. Linking specific fields to their governing policies, data owners, and usage contexts allows for the creation of a visual representation of data governance. This approach not only clarifies policy applicability but also reveals the centrality of key data assets. Implementation of a solution like this is surprisingly lightweight, requiring only a few steps to deliver a powerful solution.

We will resume at 11:30am

Moderated by Sara Nash with panel members Kevin Fair, Kelle O'Neal, and Dia Adams

As artificial intelligence continues to reshape industries, the foundation it stands on—data architecture—has never been more critical. This keynote panel brings together leading voices to explore how modern data architectures are evolving to meet the demands of AI-driven enterprises. Panelists will discuss the shift from traditional data warehouses to real-time, scalable, and decentralized frameworks; the role of data governance and quality in training reliable AI models; and how organizations can future-proof their infrastructure for emerging AI capabilities. Attendees will gain insights into best practices, common pitfalls, and forward-looking strategies to align their data architecture with the accelerating pace of AI.  

Presented by Swapna Tom

This talk will delve into the intricacies of data product methodology, emphasizing the importance of delivering reusable and certified data products. Attendees will learn about standard dimensions and metrics that enhance quality, predictability, and speed. The methodology ensures transparency of design and seamless transitions driven by milestones across teams, enabling robust governance of technical standards.. The session will also highlight compliance and improved data quality monitoring, featuring key data elements in a centralized location. Furthermore, the talk will cover smooth transitions to the support and maintenance phase by providing essential knowledge artifacts and encouraging re-use over re-invent through accessible and well-defined knowledge libraries.

Presented by Christopher Bergh

Would you like help maintaining high-quality data across every layer of your Medallion Architecture?

Like an Olympic athlete training for the gold, your data needs a continuous, iterative process to maintain peak performance. We will cover how Data Quality Testing, Observability, and Scorecards help you build accuracy, consistency, and trust at each layer—Bronze, Silver, and Gold. Discover how to make quality testing a built-in practice, empower your team to act fast, and ensure reliable insights from raw data to analysis-ready reports.

Whether you’re facing delayed data mismatches or need real-time quality assurance, we’ll give you practical tips and tools to keep your data flowing flawlessly.

What to Expect:

• Winning Strategies for Each Medallion Layer: Discover how to tackle the unique data quality challenges of Bronze, Silver, and Gold layers in Medallion Architecture.
• Get the DataOps Advantage: Learn how to apply DataOps to monitor, iterate, and automate quality checks—keeping data quality high without slowing down.
• Practical Tools to Sprint Ahead: Dive into hands-on tips with open-source tools that supercharge data validation and observability.