Data, Analytics & AI

SAP Data & Analytics

SAP-native data, modernized for the cross-functional enterprise.

Datasphere, BTP, and SAP Analytics Cloud — engineered into a federated data fabric that connects SAP and non-SAP estates without a rip-and-replace.

Reference architecture

The case

SAP estates rarely
live alone anymore.

Modern enterprises run SAP for the system-of-record and a sprawl of non-SAP systems for everything else — CRMs, planning tools, custom applications, third-party data, partner feeds. The analytics estate has to honour that reality without forcing a five-year consolidation programme.

We architect SAP-native data fabrics that federate where it pays back and replicate where it must — with semantic models, governance, and reporting that work across S/4, BW, Datasphere, and the rest of the modern stack.

What we build

From SAP estate
to federated data fabric.

01

SAP Datasphere implementation

Federated SAP data product fabric — semantic models, replication, analytical views, and integration with non-SAP estates.

02

BTP integration & extensions

Extension applications and integrations on SAP BTP — CAP, Fiori, HANA Cloud — built for clean-core S/4HANA strategies.

03

SAP Analytics Cloud

Planning, BI, and predictive scenarios deployed on SAC with governed live connections and SAP-native security propagation.

04

S/4HANA migration analytics

Pre- and post-migration data quality, reconciliation, and reporting continuity through phased S/4HANA cutovers.

05

SAP–non-SAP federation

Federated query patterns and replication strategies between SAP and modern lakehouses (Snowflake, Databricks, BigQuery).

06

Master data & governance

SAP MDG patterns, data ownership models, and stewardship workflows that travel cleanly across S/4, Datasphere, and downstream non-SAP estates.

Reference architecture

Federate where it pays.
Replicate where it must.

Most SAP analytics estates fail because the architecture defaults to either pure replication or pure federation. The right answer is layered.

01

Source plane — SAP

Layer 01

Operational SAP estate with no impact on transactional performance.

S/4HANA
ECC
BW/4HANA
C/4HANA
Ariba / SuccessFactors
02

Source plane — non-SAP

Layer 02

First-class citizens in the data fabric, not afterthoughts.

Salesforce
ServiceNow
Workday
Custom apps
Partner feeds
03

Datasphere fabric

Layer 03

Federation, replication, and semantic modeling — chosen per data product.

SAP Datasphere
HANA Cloud
Smart Data Integration
Replication flows
Federation views
04

Consumption plane

Layer 04

Planning, BI, and predictive on governed semantic models.

SAP Analytics Cloud
Power BI
Tableau
AI / ML connectors
Joule integrations

Stacks we work with

Inside SAP and out —
both fluencies, one engagement.

We carry the SAP partner certifications and the cross-platform engineering practice. Almost every SAP engagement we run involves material non-SAP integration work, and the architecture has to honour that reality.

01

SAP core

The systems-of-record. We work in S/4, BW/4, Datasphere, and BTP without forcing a clean-core compromise on operations teams that still rely on legacy extensions.

S/4HANABW/4HANADatasphereBTPHANA CloudJoule
02

SAP analytics

Where SAP-native reporting and planning live. SAC for live connections to S/4 and Datasphere; Analytics Designer where custom planning UX matters.

SAP Analytics CloudAnalytics DesignerPredictive scenarios
03

Federation & ETL

The fabric between SAP and the rest. We federate when data gravity wins; we replicate when latency, governance, or downstream consumers demand it.

SDISDACDS viewsReplication flowsBTP Integration Suite
04

Non-SAP analytics

First-class citizens in the analytics estate, not afterthoughts. Snowflake, Databricks, BigQuery, and modern BI surfaces sit alongside SAC — federated, not subordinated.

SnowflakeDatabricksBigQueryPower BITableau

Where this applies

Anywhere SAP is
the system-of-record.

If you run SAP at scale and your reporting estate spills past it, this work fits. The vertical varies; the federation problem doesn't.

  • Manufacturing
  • Consumer Goods
  • Pharma & Life Sciences
  • Energy & Utilities
  • Oil & Gas
  • Chemicals
  • Industrial Equipment
  • Automotive
  • Aerospace & Defense
  • Logistics & Mobility
  • Retail
  • Hospitality
  • Public Sector
  • Telecom & Media

Related in Data, Analytics & AI

Adjacent capabilities
in this practice.

All of Data, Analytics & AI

Data Strategy & Consulting

Capability audits, target-state architectures, and a sequenced investment thesis tied to operating KPIs.

Data Lakehouse & Warehouse

Lakehouse on Databricks, Snowflake, or BigQuery — engineered for governed scale and downstream AI.

Advanced AI & Analytics

Forecasting, segmentation, and anomaly detection wired into operational workflows.

Start the conversation

SAP-native, modern,
and federation-aware.

Whether you're inside an S/4 cutover or scaling Datasphere as your enterprise data fabric, we'll meet you in the architecture you already run.