Large Language Models (LLMs) are transforming analytics, but only when paired with the right foundation. In this episode, AtScale’s CTO and co-founder Dave Mariani, co-founder Dianne Wood, and product director Petar Staykov show how Model Context Protocol (MCP) and the semantic layer unlock trusted, governed, enterprise-ready AI.

You’ll get a full end-to-end walkthrough of Claude using AtScale’s MCP server to:

• Discover available semantic models via list-models tools 
• Understand metrics, attributes, and dimensional context using describe-model 
• Run fully governed analytical queries, without writing SQL, through run-query
• Generate insights, summarize trends, and even build dashboards autonomously 

Learn why LLMs struggle without structured business logic and how semantic layers eliminate hallucinations and governance drift. 

You’ll also learn how AI will soon help build semantic models, not just query them—enabling faster metric definitions, autonomous metadata generation, and an “army of agents” that maintain semantic consistency as businesses evolve. 

Key topics in this episode:
• Why MCP is the JDBC-moment for LLMs
• How semantic layers make AI deterministic, trustworthy, and governed
• Real demos of LLM insights far beyond simple Q&A
• Automating semantic model creation using agents and SML
• The future: AI copilots for BI, analytics, and semantic modeling

Dave Mariani and Jens Kröhnert explore the "AI Frontier:” how semantic layers enable AI agents to operate autonomously without hallucinations or governance drift. Both veterans of scaling Microsoft BI stacks on Hadoop, they discuss why machines need semantic context even more than humans do.

Topics include the AI Frontier operational model from Microsoft Research, automating semantic model development with Oraylis's Datamate tool, and Model Context Protocol (MCP) as universal infrastructure for LLMs. Dave demonstrates pairing Claude with AtScale's semantic layer to generate insights, identify sales patterns, and design campaigns. All grounded in governed metrics. Independent benchmarks show semantic layers improve LLM accuracy by 4-5x.

Essential for data leaders preparing for agentic AI and generative BI at enterprise scale.

Learn more: atscale.com

In this episode of AtScale’s Data-Driven Podcast, host Dave Mariani, Co-Founder & CTO at AtScale, is joined by Petar Staykov, Product Director, and Daniel Gray, VP of Solutions Engineering, for a deep dive into composable analytics—the architectural shift that’s transforming how enterprises model, govern, and scale data in the era of AI agents.

Key Takeaway
Composable analytics isn’t just a design pattern—it’s the foundation for governed, scalable, and AI-ready data ecosystems. Learn how AtScale’s Universal Semantic Layer and open-source Semantic Modeling Language (SML) are redefining interoperability across BI, AI, and agent frameworks.

In this episode of the Data-Driven Podcast, AtScale CTO and Co-founder Dave Mariani is joined by Rich Williams, SVP of Partnerships and Strategy at Hexaware, and Prashant Dahalkar, VP of the Cloud Data Practice. They dive into the future of analytics through the lens of agentic AI, semantic layers, and enterprise AI governance.

Fresh from attending Snowflake and Databricks Summits, the trio unpacks the momentum behind AI agents, the rise of semantic standards, and how organizations can accelerate business outcomes with governed data architectures. Rich offers insight into how modern business users can finally talk to their data. At the same time, Prashant highlights the role of modular agents, MDM, and self-healing data tools in Hexaware’s data strategy.

Whether you’re modernizing legacy stacks or scaling data literacy across your organization, this conversation sheds light on how enterprises can move from experimentation to enterprise-grade AI adoption without sacrificing trust or governance.

In this episode of the Data Driven Podcast, Dave Mariani, CTO and Co-Founder of AtScale, sits down with Dale Williamson, EMEA CTO at Databricks, to explore the transformative role of open source standards and semantic layers in modern analytics. They discuss the importance of interoperability in data infrastructure, the impact of generative AI on democratizing analytics, and how the right semantic layer can bridge complex data to natural language for everyone. Together, AtScale and Databricks envision a future where data insights are accessible to all, empowering organizations to build with flexibility, trust, and freedom from vendor lock-in.

In this episode, AtScale’s CTO and Co-founder, Dave Mariani and VP of Engineering John Langton, discuss the exciting open-source release of the Semantic Modeling Language (SML) and its transformative potential for the data analytics industry. They dive into how SML establishes the first open standard for the semantic layer, enabling businesses to seamlessly define, share, and scale their data models across platforms like Power BI, Tableau, and Looker. John also explores the broader industry impact, highlighting how SML will foster cross-platform collaboration, enhance AI capabilities, and drive innovation by creating a unified data language that empowers businesses to unlock the full potential of their data.

For additional details on SML, check out this blog post.

Welcome to the latest episode of the Data-Driven Podcast! Host Dave Mariani is joined by Jeff Curran, Lead Data Scientist at AtScale, for a deep dive into the transformative power of Generative AI in business intelligence. Jeff shares his pioneering research, demonstrating how AtScale’s semantic layer bridges the gap between natural language queries and accurate business insights.

Explore the challenges and solutions in integrating AI with BI tools and learn how this innovation can make data-driven decision-making more efficient and precise. Don’t miss this opportunity to stay ahead in the evolving landscape of AI and business intelligence.

For more information, download the white paper now!

In this episode, AtScale CTO and co-founder Dave Mariani chats with Greg Deckler, Vice President at a global IT services firm and a seven-time Microsoft MVP. They discuss Microsoft’s strategy with Power BI and its integration with Microsoft Fabric, with Deckler critiquing the rushed release and its early limitations. Both express skepticism about business users adopting more complex tools, citing ongoing challenges like measuring totals.

They also discuss the impact of generative AI on business intelligence tools like Power BI, urging caution about data security and governance. They also highlight the underutilization of natural language queries in Power BI despite frequent demos. Deckler speculates the future of Power BI’s support for various data platforms and Microsoft’s strategy to integrate these with Fabric.

To learn more, read Deckler’s 3 part blog series, “Is Fabric and Direct Lake a Game Changer For Microsoft?”

Dave Mariani interviews Juan Sequeda, the Principal Scientist at data.world. They discuss Juan’s Benchmark report on Knowledge Graphics and semantics. The conversation also covers generative AI and its role in the future of enterprise innovation.

David Mariani interviews Kunal Agarwal, CEO of Unravel Data, delving into his entrepreneurial journey and the innovative world of data observability. Kunal recounts the compelling origin story of Unravel Data, revealing how the company's vision adapted over time. He shares the challenges faced and key learnings, offering priceless advice to future entrepreneurs. The conversation then shifts to the essence of data observability, its distinction from traditional monitoring, and the unique value Unravel Data offers to its customers, illustrated through a notable success story. Kunal concludes by expressing his excitement for future trends, particularly the integration of Generative AI, providing a glimpse into the evolving landscape of data technology.