Breaking Data Silos with Cross-Cloud Federation and Semantic Layers: Key Takeaways from the TDWI Expert Panel

Recently, I had the opportunity to join the TDWI expert panel onBreaking Data Silos with Cross-Cloud Federation and Semantic Layers, alongside industry leaders John Korsak, Enterprise Solutions Engineer from Cube, and Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics. It was a great discussion about organizations’ challenges in managing data across multiple cloud environments and how semantic layers are crucial in solving them.

The Growing Need for Cross-Cloud Data Unification

AI and data-driven decision-making are at the forefront of enterprise strategies. As organizations modernize, they increasingly rely on cloud platforms to store and process data. However, with data spread across multiple environments—data warehouses, data lakes, and SaaS applications—ensuring seamless access while maintaining governance and cost efficiency has become a major challenge.

One key insight from the discussion was the definitive move to the cloud for AI-driven initiatives. TDWI’s 2025 survey showed that over 40% of organizations now use cloud data warehouses, and nearly 30% leverage cloud data lakes or lakehouses. However, despite these advancements, data integration across hybrid environments remains one of the top challenges—alongside rising cloud costs.

How a Semantic Layer Bridges the Gap

Our panel discussion explored why semantic layers are becoming essential for modern data strategies. A semantic layer serves as an abstraction layer between raw data and business users, enabling consistent and trusted access to data across multiple clouds. Here’s why it matters:

1. A Unified Business View of Data
   

   Organizations struggle with inconsistent business metrics across different teams and BI tools. A semantic layer standardizes key business definitions, ensuring that all users, regardless of tool preference (Tableau, Power BI, Excel, etc.), rely on the same governed definitions for KPIs and metrics. This eliminates conflicting reports and builds trust in data.

2. Cost and Performance Optimization

   One of the biggest surprises organizations encounter when moving to the cloud is cost—querying large-scale cloud data can quickly become expensive. The semantic layer optimizes query execution by reducing redundant computations, leveraging intelligent query acceleration, and eliminating costly direct access to raw data.

3. Enabling AI and Generative BI

   A hot topic in our discussion was the role of semantic layers in AI-driven analytics. Large Language Models (LLMs) are transforming how users interact with data, but without structured business definitions, they can generate incorrect or inconsistent results. Our research shows that AI-driven queries are wrong 80% of the time without a semantic layer. Semantic layers ensure that AI-generated insights are reliable and meaningful by bridging AI with governed data.

Fern Halper emphasized the importance of this, stating, “Organizations need a unified, scalable view of their data for AI. Without proper data governance and a semantic layer, AI applications struggle to deliver accurate and trustworthy insights.”

The Future of Data Strategy: Governance Meets Agility

Our discussion reinforced that governance and agility are not opposing forces—they must coexist. A semantic layer enables organizations to balance control with flexibility, empowering business users with self-service analytics while maintaining data integrity. As enterprises continue their AI and multi-cloud journeys, investing in a semantic layer will be key to breaking down data silos, improving performance, and ensuring that AI-driven insights are trusted.

Why AtScale is the Best Choice for an Enterprise Semantic Layer

AtScale provides a robust semantic layer solution that helps organizations streamline data access, enhance governance, and improve AI-driven decision-making. Here’s what sets AtScale apart:

• Universal Semantic Layer: AtScale provides a unified semantic layer that works across all major cloud platforms, ensuring compatibility with Snowflake, Databricks, Google BigQuery, AWS Redshift, and more.
• Optimized Query Performance: With intelligent query acceleration and aggregate awareness, AtScale ensures that users receive fast, efficient query responses without overloading cloud resources.
• AI & Natural Language Query (NLQ) Enablement: By bridging AI models with governed business definitions, AtScale enables accurate and context-aware AI-driven insights, reducing hallucinations and inconsistencies in LLM outputs.
• Live Data Access Without Movement: AtScale eliminates the need for data duplication or ETL-heavy processes by virtualizing access to source data while enforcing governance rules in real time.
• Multi-Tool Compatibility: Whether using Tableau, Power BI, Excel, or custom AI applications, AtScale seamlessly integrates with all BI and AI consumption tools, ensuring a consistent data experience across teams.
• Granular Security and Governance: AtScale enforces strict row- and column-level security while centralizing governance policies, making compliance and data protection seamless across an enterprise.

At AtScale, we’re committed to helping organizations navigate these challenges. If you’re looking to unify data access, enhance AI-driven analytics, and optimize costs across your cloud environments, see the AtScale semantic layer in action.