The data analytics landscape has long suffered from a fundamental challenge: every platform speaks its own dialect. Metrics defined in one tool rarely translate directly to another. Business logic encoded in proprietary formats becomes trapped within specific vendor ecosystems. Data teams duplicate effort maintaining parallel semantic definitions across multiple systems.
The Open Semantic Interchange (OSI) initiative aims to solve this problem through industry-wide collaboration. By establishing common standards for semantic data interchange, OSI promises to break down the walls between data platforms and enable genuine interoperability.
What Is the Open Semantic Interchange Initiative?
OSI is a collaborative standardization effort focused on creating open specifications for exchanging semantic metadata—the business meaning and context layered on top of raw data.
When we talk about semantic metadata, we mean:
Metric Definitions: How business KPIs are calculated (formulas, aggregation logic, filters)
Dimensional Relationships: How data entities connect (customers to orders, products to categories)
Business Glossary Terms: Standardized definitions and terminology
Data Governance Policies: Rules governing access, usage, and quality
Currently, each analytics platform maintains these definitions in proprietary formats. If you define “Monthly Recurring Revenue” in Tableau, replicating that exact calculation in Power BI requires manual translation. OSI seeks to create interchange standards that allow semantic definitions to move between platforms without loss of fidelity.
Why Semantic Standardization Matters
The Current State: Islands of Meaning
Organizations typically employ multiple analytics tools simultaneously:
- BI Platforms: Tableau, Power BI, Looker for visualization
- Data Cataloging: Alation, Collibra, DataHub for metadata management
- Transformation Tools: dbt, Dataform for data modeling
- Data Warehouses: Snowflake, BigQuery, Redshift with native semantic layers
Each tool requires separate definition of the same business concepts. A data analyst working across platforms becomes a semantic translator, manually ensuring “Customer Lifetime Value” means the same thing everywhere.
This fragmentation creates several critical problems:
Inconsistent Metrics: The same KPI calculated differently in different tools leads to conflicting reports and lost trust in data.
Duplicate Effort: Data teams waste time reimplementing identical logic in multiple systems.
Vendor Lock-In: Proprietary semantic definitions make platform migration painful and risky.
Collaboration Barriers: Sharing semantic models between organizations requires custom integration work.
The OSI Vision: Portable Semantics
OSI proposes a future where:
A metric defined once exports to a standard format usable across any compliant platform. Business logic travels with your data instead of being recreated from scratch. Organizations share semantic models as easily as they share datasets. Platform choice becomes about features and performance, not which tool holds your semantic definitions hostage.
The Growing OSI Ecosystem
The initiative has gained significant momentum with major technology companies joining as partners:
Cloud Infrastructure Leaders
Amazon Web Services: Brings perspective from Amazon Athena, AWS Glue, and QuickSight’s semantic layer capabilities.
Google Cloud Platform: Contributes experience from BigQuery’s semantic modeling and Looker’s LookML framework.
These partnerships signal that semantic standardization isn’t just a data tool concern—it’s fundamental to cloud data infrastructure.
Data Platform Innovators
Snowflake: Contributes insights from Snowflake Semantic Views and Cortex AI integration.
DataHub: Brings open-source metadata management expertise and existing interchange formats.
The inclusion of both commercial and open-source platforms ensures OSI standards serve diverse implementation needs.
How OSI Standards Will Work in Practice
While detailed specifications are still under development by working groups, the likely architecture follows proven patterns from other successful standardization efforts:
Core Components of an OSI Specification
Semantic Definition Format: A structured schema (likely JSON or YAML-based) describing metrics, dimensions, and relationships.
# Conceptual example - not final OSI spec
metric:
name: monthly_recurring_revenue
display_name: Monthly Recurring Revenue
description: Sum of all active subscription values on a monthly basis
calculation:
aggregation: SUM
measure: subscription_value
filters:
- field: subscription_status
operator: equals
value: active
- field: billing_period
operator: equals
value: monthly
dimensions:
- customer_segment
- product_category
- region
data_type: currency
format: USD
Interchange Protocol: APIs or file formats for exporting and importing semantic definitions between systems.
Validation Framework: Tools for verifying that imported semantics maintain correctness and consistency.
Version Management: Mechanisms for tracking semantic definition changes over time and managing compatibility.
Example Workflow: Cross-Platform Metric Sharing
Imagine a data analyst working with both Snowflake and Tableau:
Step 1: Define “Customer Acquisition Cost” as a Snowflake Semantic View:
CREATE SEMANTIC VIEW customer_acquisition_metrics AS
SELECT
marketing_campaign,
acquisition_month,
SUM(marketing_spend) AS total_marketing_spend,
COUNT(DISTINCT new_customer_id) AS new_customers,
SUM(marketing_spend) / COUNT(DISTINCT new_customer_id) AS customer_acquisition_cost
FROM marketing_campaigns
WHERE campaign_type = 'acquisition'
GROUP BY marketing_campaign, acquisition_month;
Step 2: Export semantic definition to OSI format:
snowflake semantic export customer_acquisition_metrics --format osi --output cac_definition.osi.yaml
Step 3: Import into Tableau as a calculated field:
tableau semantic import cac_definition.osi.yaml --workspace sales_analytics
The metric definition, including calculation logic, dimensional relationships, and formatting, transfers seamlessly. Changes made in Snowflake can be re-exported and synced to Tableau, maintaining consistency.
Benefits for Different Stakeholder Groups
For Data Engineers
Reduced Maintenance Burden: Define metrics once, use everywhere. Updates propagate through export/import rather than manual changes.
Improved Testing: Standard formats enable automated validation that semantic definitions remain consistent across platforms.
Platform Flexibility: Evaluate new tools without fear of losing accumulated semantic work.
For Data Analysts and Business Users
Consistent Metrics: The same metric produces identical results regardless of which tool you’re using.
Faster Onboarding: Learning metric definitions in one tool transfers knowledge to all others.
Greater Tool Choice: Pick the best visualization or analysis tool for each task without worrying about metric availability.
For Data Platform Vendors
Customer Flexibility: Reduce lock-in concerns by supporting semantic portability.
Ecosystem Integration: Connect more easily with complementary tools in customers’ analytics stacks.
Innovation Focus: Compete on features and performance rather than proprietary semantic formats.
Technical Challenges OSI Must Address
Standardization initiatives face inherent technical hurdles. OSI will need to navigate several complex areas:
Challenge 1: Dialect Differences in Calculation Engines
Different platforms support different SQL dialects and function libraries. A metric using Snowflake-specific functions like QUALIFY or window functions must translate to platforms without equivalent capabilities.
Potential Solution: Define a common semantic calculation language that compiles to platform-specific SQL, similar to how dbt handles cross-database macros.
Challenge 2: Performance Optimization Variations
Platforms optimize queries differently. A metric definition that performs well on Snowflake’s architecture might be inefficient on BigQuery’s columnar storage.
Potential Solution: Allow platform-specific hints and optimizations in the interchange format while preserving semantic equivalence.
Challenge 3: Governance Policy Mapping
Access control models vary significantly between platforms. Row-level security in Snowflake differs from BigQuery’s authorized views and Databricks’ table ACLs.
Potential Solution: Abstract governance policies to intent-based rules that platforms interpret according to their security models.
Challenge 4: Versioning and Change Management
Semantic definitions evolve. How do you handle breaking changes while maintaining backwards compatibility across platforms?
Potential Solution: Semantic versioning with explicit compatibility guarantees and migration paths for definition updates.
The First Working Group: From Vision to Reality
The announcement of “the first working group meeting” marks OSI’s transition from planning to active development. Working groups typically focus on:
Requirements Gathering: Cataloging real-world use cases and pain points to ensure standards address practical needs.
Draft Specification Development: Creating initial format proposals and soliciting feedback from implementers.
Reference Implementation: Building proof-of-concept tools demonstrating interoperability between partner platforms.
Compliance Testing: Establishing test suites for verifying implementations conform to specifications.
This collaborative process ensures the final standards reflect diverse perspectives and technical constraints.
How OSI Fits Into the Broader Data Ecosystem
OSI complements other standardization efforts in the data space:
Apache Arrow: Standardizes in-memory data representation for efficient cross-platform exchange.
Apache Iceberg / Delta Lake: Standardize table formats for data lake interoperability.
SQL Standards (ANSI SQL): Provide common query language across databases.
OpenLineage: Standardizes data lineage metadata interchange.
OSI fills the semantic layer gap—ensuring business meaning and context transfer as easily as data itself.
Together, these initiatives create a more interoperable data ecosystem where organizations choose tools based on merit rather than vendor lock-in concerns.
Practical Steps for Data Teams Today
While OSI standards are still emerging, data teams can prepare:
1. Centralize Semantic Definitions
Move toward a single source of truth for metric definitions:
-- Create a dedicated schema for semantic definitions
CREATE SCHEMA semantic_layer;
-- Define metrics as views with comprehensive documentation
CREATE OR REPLACE VIEW semantic_layer.customer_lifetime_value AS
-- Customer Lifetime Value (CLV)
-- Definition: Total revenue from a customer over their entire relationship
-- Calculation: SUM(order_value) - SUM(acquisition_cost) - SUM(support_cost)
-- Owner: Analytics Team
-- Last Updated: 2025-12-01
SELECT
customer_id,
SUM(order_value) - SUM(acquisition_cost) - SUM(support_cost) AS clv
FROM customer_financials
GROUP BY customer_id;
Document definitions thoroughly so they’re ready to export when OSI tools become available.
2. Abstract Business Logic from Tool-Specific Implementations
Separate “what” (metric definition) from “how” (platform-specific calculation):
# Store metric definitions in version-controlled config files
metrics:
- name: customer_lifetime_value
description: Total profit from customer relationship
calculation: "SUM(revenue) - SUM(costs)"
dimensions: [customer_segment, acquisition_channel]
filters:
- field: customer_status
value: active
Use templating or code generation to implement these definitions across platforms, making future migration to OSI formats straightforward.
3. Monitor OSI Development and Participate in Feedback
Follow OSI’s progress through working group updates and draft specification releases. If your organization has significant semantic modeling needs, consider providing use case feedback to shape standards.
4. Evaluate Platform OSI Readiness
As OSI specifications mature, prioritize platforms with committed implementation roadmaps. Ask vendors:
- When will you support OSI semantic interchange?
- Which OSI specification versions will you implement?
- What’s your migration path for existing semantic definitions?
The Road Ahead: Realistic Expectations
Standardization takes time. OSI likely faces a multi-year journey from initial working group to widespread adoption:
Year 1: Draft specifications, reference implementations, proof-of-concept integrations
Year 2: Platform vendors begin implementing OSI export/import capabilities for basic metrics
Year 3: Broader ecosystem adoption, advanced features (complex calculations, governance policies)
Year 4+: OSI becomes standard practice, with most major platforms offering full interoperability
Early adopters will benefit from gaining experience with portable semantics, while mainstream adoption requires proven stability and broad tool support.
Potential Risks and Limitations
Not every aspect of semantic modeling may be fully portable:
Platform-Specific Features: Advanced capabilities unique to specific platforms might not translate to OSI’s common denominator.
Performance Tuning: Generic semantic definitions may require platform-specific optimization for production workloads.
Governance Complexity: Nuanced access control policies might lose fidelity in translation.
OSI’s success depends on finding the right balance—comprehensive enough to be useful, simple enough to be implementable across diverse platforms.
Conclusion
The Open Semantic Interchange initiative represents a significant step toward breaking down barriers in the analytics ecosystem. By creating standards for semantic metadata exchange, OSI promises to free organizations from the vendor lock-in that has plagued enterprise analytics for decades.
The participation of major cloud providers (AWS, Google), established data platforms (Snowflake), and open-source projects (DataHub) signals genuine industry commitment to solving this problem collaboratively.
For data teams, OSI’s eventual success means focusing energy on deriving insights rather than duplicating semantic definitions across tools. It means choosing analytics platforms based on capabilities rather than fear of losing accumulated semantic work. And it means creating a more interoperable data ecosystem where collaboration and sharing become natural rather than burdensome.
While detailed specifications and production-ready implementations remain on the horizon, OSI’s first working group meeting marks the beginning of a journey toward portable, platform-independent semantic definitions. Organizations that prepare by centralizing and documenting their semantic layer today will be well-positioned to benefit when OSI standards mature.
The future of data analytics is interoperable. OSI is building the foundation to make that future real.
Stay informed about OSI progress by following Snowflake’s blog and open-source metadata management communities. Consider contributing use cases and feedback as specifications evolve to ensure standards serve real-world analytics needs.