Core Benefits
- Eliminate data silos and redundancy
- Enable predictive analytics and ML readiness
- 10x faster query performance
- Future-proof scalable architecture
- Ensure data integrity and compliance
The Foundation of Analytics
Poor data architecture is the #1 reason analytics projects fail. Before you build dashboards or train models, you need a solid, well-structured data foundation. CubeWorks designs data models that capture the true complexity of your operations while remaining query-efficient and maintainable.
Whether you're dealing with time-series sensor data, transactional manufacturing records, or hierarchical asset structures, we architect the schemas that make your data discoverable, reliable, and actionable.
Relational Modeling
Normalized schemas for transactional systems — ERPs, MES, CMMS. Enforced referential integrity with optimized indexing strategies for complex joins and aggregations.
Time-Series Design
Specialized models for IoT and sensor data using TimescaleDB and InfluxDB. Hypertable partitioning, continuous aggregates, and retention policies built in.
Document & Graph Models
MongoDB for flexible, schema-less data; Neo4j for relationship-rich domains like supply chains, asset hierarchies, and network topologies.
Data Warehouse & Lakehouse
Star and snowflake schemas for analytical workloads. Modern lakehouse architectures combining raw storage with structured query layers.
Data Governance & Standards
A great data model is useless without governance. We establish the rules, documentation, and processes that keep your data clean and trustworthy.
Why CubeWorks Data Modeling?
- Industrial Context:
- We model for ISA-95 hierarchies, UNS topologies, and OT/IT convergence patterns — not generic business data.
- Performance by Design:
- Every model is benchmarked against your actual query patterns and data volumes, not theoretical ideals.
- Migration Ready:
- We deliver complete migration scripts, transformation logic, and rollback plans for moving from legacy to modern schemas.