The business intelligence landscape has a fundamental interoperability problem. Organizations using Power BI, Tableau, Looker, Microstrategy, and Cognos often struggle with semantic silos that block the adoption of AI-driven analytics, natural language query (NLQ), and BI investments.
Today, we’re launching several open-source converters for Power BI, dbt, and Snowflake that solve this problem. These aren’t just migration tools—they’re our contribution to establishing an industry-wide semantic standard that will power the next generation of open BI and Agentic AI analytics.
The Semantic Interoperability Challenge in Modern BI
Enterprise data teams face a costly reality: their semantic investments are trapped in proprietary formats. When organizations want to leverage cutting-edge LLMs, implement NLQ solutions, or deploy AI agents, they encounter the expensive proposition of redefining all their business metrics, calculations, and logic. This semantic redundancy isn’t just frustrating—it’s architecturally unsound and economically wasteful.
Today’s BI ecosystem creates multiple sources of truth, undermining data governance and blocking the adoption of AI-driven analytics.
As our CEO, Christopher Lynch, explains: “Our SML converters break down silos, making it possible for organizations to define their metrics and business logic once, in an open standard, and leverage them everywhere. This is the foundation of Agentic BI, where every question gets the same, trusted answer, no matter the tool or the agent asking it.”
How SML Converters Work
The SML converters preserve the semantic intelligence organizations have built while enabling cross-platform interoperability. By converting proprietary semantic models into Semantic Modeling Language (SML), an open, vendor-neutral standard, enterprises can benefit by having:
- Open Semantic Interoperability: Translate proprietary semantic models into Semantic Modeling Language (SML), ensuring vendor-neutral portability across BI platforms and AI-powered analytics tools.
- Future-Proof Architecture: Protect your organization from vendor lock-in by decoupling business logic from proprietary platforms, enabling semantic layer flexibility as the AI analytics landscape evolves.
- AI-Ready Semantic Foundation: Provide intelligent agents and AI models with standardized semantic definitions for higher accuracy in NLQ and automated insights generation.
- Accelerated Agentic BI Adoption: Enable enterprises to participate in next-generation analytics by aligning existing semantic assets to open frameworks that support conversational BI and AI-driven decision making.
Cross-Platform Semantic Enablement
The semantic interoperability vision extends beyond single-direction conversion. As part of our open source commitment, we’re simultaneously releasing an SML to Snowflake Semantic Views Converter. This enables platforms like Snowflake Cortex Analyst and other AI-driven services to leverage semantic models from external sources.
By combining these open-source converters, organizations can move their semantic investments to where they’re most valuable. For example, you can use the dbt to SML Converter with the SML to Snowflake Semantic Views Converter to bring dbt semantic models into Snowflake for AI-powered analytics.
By open-sourcing these SML converters, SML serves as the universal semantic model converter to break down silos and enable cross-platform semantic integration.
Implementation Availability and Roadmap
The Power BI to SML, dbt to SML, and SML to Snowflake Semantic Views converters are available immediately, with expanded support for additional BI platforms and semantic modeling tools planned for upcoming releases.
This release represents our ongoing commitment to establishing open, trusted, governed, and reusable semantics as the standard across the analytics and AI ecosystem. Semantic investments should accelerate AI adoption, not create barriers to innovation.
Ready to eliminate semantic silos and enable true interoperability? Explore SML Converters and discover how open semantic standards can future-proof your analytics investments while accelerating your AI-driven BI initiatives.
For more insights on semantic layer architecture, AI-powered analytics, and conversational BI, follow AtScale‘s thought leadership on the intersection of data governance and artificial intelligence in enterprise business intelligence.
SHARE
Case Study: Vodafone Portugal Modernizes Data Analytics