What this solves
- SAP objects spread across many tables → error-prone joins
- KPI logic duplicated across teams and tools
- Data quality issues discovered too late (in reports)
Data Foundations
Data Foundations
Trusted, governed data — the prerequisite for analytics and AI. AI doesn’t fail because of models. It fails because data is inconsistent, undocumented, and not owned. We build foundations that stay reliable as your business and AI use cases evolve.
AI-Ready Data Foundations
AI doesn’t fail because of models — it fails because data is inconsistent, undocumented, and not owned.
CAPABILITIES
Technical Foundations
- Domain modeling & harmonization
- Data quality rules + monitoring
- Governance + metadata (lineage, catalog, access)
- Secure integration patterns
DELIVERABLES
Tangible Outputs
- Bronze → Silver → Gold data layers (medallion)
- Reusable business entities (Sales, Finance, Production)
- Data quality framework (tests, thresholds, remediation)
- Catalog/lineage + access policies
OUTCOMES
Business Value
- Faster KPI delivery (reuse entities, less ad-hoc logic)
- Higher trust (quality gates + traceability)
- Lower cost of change (new use cases don't rebuild)
- AI-ready data products (explainable, governed)
From fragmented sources → consistent entities → KPI-ready marts → AI-ready features.
From Source Systems to Decision-Grade Data
We standardize source-system complexity once into reusable business entities — so every KPI and AI use case uses the same trusted logic.
Source systems hold business data in dozens or hundreds of technical tables, and KPI logic ends up duplicated across teams and tools. We collapse that complexity into a Bronze → Silver → Gold pattern: raw extracts at the bronze layer, harmonized business entities at silver, and KPI-ready data marts at gold.
The same pattern applies whenever you onboard a new source — an ERP, a CRM, a marketing platform, a custom application. Each new source plugs into the same Data Foundations layer with the same domain structure, governance, and quality controls.
Business Outcomes
- Reusable Silver entities (Sales Docs, Deliveries, Billing…)
- Stable logic + performance (less ad-hoc joining)
- Governance + quality controls built into pipelines
- Faster onboarding of new use cases and new sources
Bronze = raw extracts • Silver = harmonized entities • Gold = KPI/Datamarts
Data Domains
We structure the DWH by business domains, not by technical tables.
Example: SAP business entities and their core tables. The same domain approach applies to any source system.
| Business entity | Example: SAP tables |
|---|---|
| Sales Documents | VBAK, VBAP, VBEP, VBKD |
| Deliveries | LIKP, LIPS, VTTK, VTTS |
| Billing | VBRK, VBRP, BSEG |
| Materials | MARA, MARC, MARD, MBEW |
| Customers | KNA1, KNVV, KNVP |
| Vendors | LFA1, LFB1, LFM1 |
| Purchase Orders | EKKO, EKPO, EKET |
| Production Orders | AUFK, AFKO, AFPO |
Domain Approach
- Build one domain at a time (Sales, Finance, Procurement, Production…)
- Each domain delivers: entity model + quality rules + governance
- Domains combine into cross-functional analytics and AI
Why It Matters
- Clear ownership and stewardship per domain
- Reuse across reporting, planning, automation
- Faster delivery without breaking existing logic
DataOps & Platform Reliability
A data platform is a living product — changes must be safe, testable, auditable.
Controlled Releases
- CI/CD pipelines, approvals, versioning
- Rollback strategy and environment parity (dev / test / prod)
Quality Gates
- Automated tests (unit + data tests)
- Reconciliation checks and SLA monitoring
Operational Visibility
- Monitoring, alerting, incident playbooks
- Lineage and change traceability (who changed what, when, why)
Modernizing Legacy ETL & Data Platforms
Reduce cost and complexity while improving governance, scalability, and time-to-change.
Customer Challenges
- Too many ETL tools and fragmented workflows
- High licensing + infrastructure costs
- Limited governance and visibility
- Hard to scale analytics and AI consistently
Our Approach
- Consolidate pipelines onto a governed platform (cloud or hybrid)
- Standardize patterns (naming, security, monitoring, testing)
- Industrialize delivery with DataOps + reusable domain models
Key Outcomes
- Lower run cost and fewer moving parts
- One delivery approach (skills + templates)
- Better governance, observability, and reliability
- Future-proof foundation for analytics and AI
Cloud, Integration & Real-Time
Modern integration patterns and operational visibility — across cloud, hybrid, and on-prem.
Cloud & Data Integration
- Cloud migrations
- API-first pipelines
- Hybrid and multi-cloud delivery
- Integration with ERP, CRM, and operational systems
Real-Time Data & Observability
- Streaming + operational visibility (Grafana-ready)
- Alerts and live KPIs
- Event-driven architectures (Kafka, message brokers)
- Edge-to-cloud data movement for industrial use cases
How we work
Three principles guide every Data Foundations engagement: outcome-first delivery, no vendor lock-in, and business ownership built in.
See our engagement principles