Situation
- Large global manufacturing organization
- SAP ECC landscape
- 200+ integrations
- Thousands of reports
- No enterprise architecture capability
- Growing AI ambitions
- Significant operational complexity
- Executive concern around future ERP decisions
Challenge
Despite years of successful delivery:
- Architecture had evolved organically
- Reporting was tightly coupled to operational systems
- Enterprise data strategy was absent
- AI initiatives were moving faster than platform maturity
- Technical debt was becoming a business risk
Approach
Within weeks, dataMaven:
- Interviewed executive leadership
- Reverse-engineered the enterprise architecture
- Mapped data flows
- Assessed governance
- Assessed master data management
- Assessed analytics
- Built an enterprise capability model
- Produced an executive-ready transformation roadmap
Outcome
The executive team gained:
- A shared understanding of current state
- Enterprise architecture principles
- A data strategy
- An AI-readiness roadmap
- A modernization roadmap
- An executive decision framework
- A target operating model
- Transformation sequencing
Key insight
The greatest obstacle to AI was not AI technology — it was the absence of an enterprise data foundation.
Engagement snapshot
Global manufacturing
Industry
20,000+
Employees
40+
Factories
50+
Countries
200+
Integrations assessed
Thousands
Reports reviewed
Executive leadership
Stakeholders
Deliverables
- Enterprise Architecture Assessment
- Enterprise Data Assessment
- AI Readiness Strategy
- Executive Roadmap
- Transformation Plan
What changed — week by week
Week 1Executive interviews
Week 2Architecture discovery
Week 3Current-state blueprint
Week 4Enterprise data assessment
Week 5Executive roadmap
Week 6Transformation strategy
Facing a complex landscape — or an ambitious build — and a decision you need to make with confidence? That’s the work.
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