Vision & Business Value
The Problem
Section titled “The Problem”Roche Global IT manages a growing ecosystem of data products across manufacturing sites, supply chain operations, and research facilities worldwide. Today, each new data product requires:
| Activity | Typical effort | Who |
|---|---|---|
| Define data contract | 1–2 weeks | Data architect |
| Create Snowflake schemas | 3–5 days | Data engineer |
| Build dbt models (Bronze/Silver/Gold) | 1–2 weeks | Analytics engineer |
| Write quality checks | 1 week | Data quality specialist |
| Create API specification | 3–5 days | API developer |
| Set up governance metadata | 1 week | Data steward |
| Total | 6–8 weeks | 4–6 specialists |
This process repeats for every entity in every domain. With hundreds of isolated data products and growing, the cost compounds:
- Inconsistency — Each team interprets standards differently. KPI definitions diverge across systems.
- Fragility — No data contracts means schema changes cascade unpredictably. A column rename in one system breaks three downstream dashboards.
- No AI readiness — Zero semantic definitions exist for natural-language queries. Generative AI tools cannot access governed data.
- Linear scaling — Every new business question requires the same 6–8 week cycle with the same specialist bottleneck.
The Solution
Section titled “The Solution”roche-data is a data infrastructure compiler. It takes a canonical data model from RTiS (Roche’s Terminology and Information System) and automatically generates the complete artifact stack:
One command. One source of truth. Complete automation.
The same entity model that defines “what this data means” in RTiS now drives every layer of the data platform — from physical storage through quality assurance to AI-ready APIs.
Business Benefits
Section titled “Business Benefits”From weeks to minutes
Section titled “From weeks to minutes”What previously required 6–8 weeks of specialist work now happens in a single automated pipeline run. A model change in RTiS triggers regeneration of all downstream artifacts, tested and deployed through CI/CD.
Consistency by construction
Section titled “Consistency by construction”Every data product follows identical patterns because they are generated from the same templates. There is no room for interpretation drift — the compiler enforces the standard.
Quality as a first-class citizen
Section titled “Quality as a first-class citizen”The four-gate data quality architecture (G1–G4) is not bolted on after the fact. Quality predicates are embedded directly into the generated dbt views. Every record earns its trust level through progressive validation.
AI-ready by default
Section titled “AI-ready by default”Every generated artifact includes semantic definitions that Snowflake Cortex Analyst understands natively. The moment data passes quality certification, it becomes queryable through natural language — no additional integration work required.
Governed and auditable
Section titled “Governed and auditable”Every artifact is version-controlled in git. Every policy is compiled from a declarative YAML definition into enforceable OPA rules. Every data access is logged in Snowflake. The compliance story writes itself.
Scales horizontally
Section titled “Scales horizontally”Onboarding a new data domain means defining entities in RTiS and running the compiler. The same tooling, same quality gates, same governance model — applied uniformly across every domain at Roche.
Who Benefits
Section titled “Who Benefits”| Stakeholder | Value delivered |
|---|---|
| Data domain owners | Self-service data product creation. Define the model, the platform handles the rest. |
| Data engineers | No more hand-writing repetitive dbt models and DDL. Focus on complex transformations. |
| Data stewards | Governance metadata generated automatically. Quality gates enforced consistently. |
| Business analysts | Semantic layer available from day one. Natural-language queries through Cortex Analyst. |
| AI/ML teams | Quality-certified, semantically rich data accessible through MCP tools and APIs. |
| Compliance & audit | Full git-based audit trail. Policy enforcement through OPA. Access logging in Snowflake. |
| Leadership | Predictable timelines. Consistent quality. Measurable ROI on data platform investment. |
Strategic Alignment
Section titled “Strategic Alignment”roche-data directly enables three strategic priorities:
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Roche AI Journey — Generative AI requires trusted, semantically defined data. roche-data is the foundation that makes this possible across all domains.
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Operational excellence — Automating the data pipeline lifecycle reduces time-to-value from weeks to minutes while eliminating human error in repetitive engineering tasks.
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Global standardization — A single compiler ensures every data domain at Roche follows identical patterns for quality, governance, and access — regardless of which team or site operates it.