DATA FOUNDATION ERP & AI READINESS
OUTCOME
TYPICAL DELIVERABLES
When clients come to us
KPIs vary by department
Numbers do not match
Master data quality slows down processes
Duplicates, missing fields, corrections
Forecast and margin pressure
Lack of transparency over costs and working capital
Growth/M&A
Multiple systems, inconsistent KPI logic
Self-Service BI
Fails due to standards and governance
Starting AI initiatives
Data is not accessible/reliable
Audit/Compliance pressure
Traceability, Data Lineage, Permissions
Typically included
End-to-end
KPI definitions, data model and data flow design
Single Source of Truth
Development of a consistent reporting/semantic layer
Data quality rules, responsibilities (RACI) and governance setup
Integration and transformation logic
In the client tenant / client systems
Documentation and enablement
For operation and further development
Prioritization
Via roadmap/backlog (use cases & sources)
Explicitly not included*
*(unless ordered separately)
Data Science / Model Building as the main service
Hosting/operation of own data platforms outside of the client tenant
Full volume data cleansing "by hand" without DQ process
Unlimited source system connections without prioritized backlog
TYPICAL TIMEBOXES
ASSESSMENT
1-2 weeks
IMPLEMENTATION SPRINT
4-6 weeks
HYPERCARE
2 weeks
TYPICAL DELIVERABLES
Data Assessment + Target Illustration
Architecture, data flows, priorities
Power BI Semantic Model
Single-Source-of-Truth layer (stack-dependent)
KPI/Metric Catalog
Incl. definitions, owners and calculation logic
Governance Package
Roles, processes, standards + technical documentation
Canonical Data Model / Domain Model + Data Quality Rules
Incl. definitions, owners and calculation logic
Roadmap/Backlog for Expansion
Use case and source prioritization