A figure means one thing, and it can be traced to the transaction it came from.

Data migration and master data governance

The reporting layer is only as good as the record beneath it, so this work starts at the source: migration designed and rehearsed against the transformation programme, master data cleansed and given an owner, and governance that keeps a material, a customer and a supplier meaning one thing across every system that names them.

• Migration design, mock loads and reconciliation to source
• Master data governance — ownership, rules and workflow
• Data quality rules enforced where data is created
• Reference data agreed once and distributed

BW and BW/4HANA

The warehouse where history, volume and a stable model matter: layered data flows, delta extraction from the operational systems, and a modelled layer that survives the source system’s own changes. Existing BW landscapes are modernised or migrated to BW/4HANA with the models that still earn their place carried forward and the rest retired.

• BW/4HANA modelling and layered data flows
• Delta extraction and load orchestration
• Migration of existing BW landscapes, models triaged
• Performance and housekeeping on HANA

SAP Datasphere

The enterprise data model where SAP is one source among several. Datasphere federates or replicates SAP and non-SAP data into spaces with business semantics, so that a supply chain figure can include the carrier’s data and a finance figure the bank’s, without a copy of everything being made first. Noble designs the spaces, the semantic layer and the boundary with the warehouse.

• Space design, federation and replication decisions
• Semantic modelling with business names, not table names
• Non-SAP sources brought into the same model
• The boundary between Datasphere and BW/4HANA made explicit

SAP Analytics Cloud — reporting and dashboards

Reporting built on the metric layer rather than on ad-hoc queries, so that every story, dashboard and export shows the same figure for the same question. Live connections to S/4HANA where currency matters, imported models where history and performance do, and a design standard so that a hundred dashboards read as one system.

• Metric layer and semantic consistency across reports
• Live and import connections chosen per use
• Dashboard and story design to one standard
• Bilingual reporting, composed in both languages

Planning, budgeting and management reporting

Planning models in Analytics Cloud that share the metric layer with reporting, so that a plan and an actual are compared on the same definition. Budgeting cycles, driver-based planning, version comparison and the management reporting pack, built so that the period-end process is a run rather than a reconstruction.

• Planning models sharing the reporting metric layer
• Budgeting, forecasting cycles and version comparison
• Driver-based and scenario planning
• Management reporting pack produced as a run

Governance and lineage

Every figure in the layer can be followed back to its source, and every transformation between has an owner and a reason. This is what makes a disagreement between two reports a question with an answer, and what an auditor, a regulator or an AI system asks for before it trusts the number.

• Lineage from report to transaction
• Ownership and stewardship of metrics and models
• Classification and residency for reported data
• Governed data served to applications and AI