Open data was step one. Open semantics is step two.

January 28, 2026

Reading time: 3 min

By Maris Svilans, Head of Sales, Co-Founder, The Infotrust

Open data was step one. Open semantics is step two.

In 2025, many organizations have standardized on open, interoperable data formats like Apache Iceberg – giving customers full ownership of their data and freedom to run it across platforms without duplication or lock-in.

But open storage alone is not enough.

Most organizations today use a mix of tools:

• More than one BI platform, I see often combinations of Qlik, Power BI, Sigma, sometimes still SAP BusinessObjects and Tableau.
• SQL and data science notebooks.
• And now, fast-growing AI applications that also need data definitions to provide trustworthy answers.

In this reality, KPIs often drift over time. Business logic gets copied from one tool to another, rewritten during migrations, and slowly becomes inconsistent.

This challenge is not new. Earlier in my career, when working with SAP BusinessObjects, this was addressed through the semantic layer – the Universe.

Within a single platform, it provided one source of truth for business logic and saved significant time: KPIs and dimensions were defined once and reused across many reports and users, instead of being rebuilt again and again.

What is changing now is that this same principle is finally becoming open and vendor-neutral.

The goal of the Open Semantic Interchange (OSI) is simple – stop redefining “Revenue” in every dashboard. Instead, OSI uses a declarative YAML standard (MetricFlow) to define metrics, dimensions, and joins once – and reuse them everywhere.

(Source of graph idea: Modern Data 101)

With Qlik now joining OSI, the open semantic layer complements open data formats by keeping KPIs consistent across BI tools, SQL, and AI applications – independent of the platform.

This is a major step toward:

• Consistent definitions, so Marketing, Finance, and Sales all see the same numbers even if they used different BI tools. And with Apache Iceberg they can even use different query engines – like Snowflake or Amazon Athena and similar.
• AI-ready context will help LLMs to get clear semantic meaning, enabling accurate answers.
• No vendor lock-in as you can now move business logic can move freely between platforms. Iceberg already offers openness to data platforms, now data definitions can be used across BI and AI tools.
• Lower cost and less rework as KPIs are defined once, not rebuilt repeatedly. You invest time once with right methodology and clear data definitions. Save time later, every time.
• Adopt new use cases for Analytics and AI much faster, re-use already defined metrics.

Open data defines where data lives.

Open semantics defines what data means.

And enterprises need both.

Contact us if you have any questions>