Customer: IBM
Role: IBM Silver Partner
Industry: Enterprise Data Platforms & Engineering Systems
Challenge
Large engineering-driven enterprises generate massive volumes of operational and product data across vehicles, manufacturing systems, simulations, and validation pipelines.
Despite mature data lake infrastructures, data usability for engineers and product teams remained structurally limited:
- Data models optimized for storage and ingestion, not engineering workflows
- Highly complex schemas requiring SQL and specialist knowledge
- Strong dependency on data engineers for even routine access
- Fragmentation between data lakes and downstream AI or automation systems
Baseline reality:
- Engineering teams spend significant time translating questions into queries
- Access latency blocks daily development and validation workflows
- Large portions of collected data remain underutilized
- Data lakes function primarily as storage, not as operational intelligence
This created a growing gap between available data and usable engineering insight.
Solution
Semantic Data Layer on top of the Data Lake using watson.data
IBM implemented a semantic data layer approach that decouples physical data storage from logical, domain-aligned meaning, using watson.data as the foundation.
Core approach:
- Introduced a semantic abstraction layer above existing data lakes
- Modeled data around engineering and product concepts rather than schemas
- Governed access centrally while simplifying consumption for end users
- Enabled contextual, intent-driven data access instead of query-driven access
In collaboration with Context64AI:
- Engineering-relevant context is explicitly structured
- Context-based retrieval replaces manual query construction
- Agent-driven access patterns handle technical complexity
This ensures the system reasons over engineering context, not raw tables and files.
Result
Strategic Impact
- Engineers and product teams work on engineering scenarios, not schemas
- Data lake complexity is contained at the platform layer
- Operational data becomes directly usable for:
- Engineering development
- Validation and testing
- Decision-making
Most importantly, the semantic data layer establishes a critical prerequisite for the next phase of enterprise AI:
Training and operating physical AI systems on real-world engineering data