Non-SAP Integration Business Scenarios

Pipeline in Salesforce, booked revenue in SAP, forecasts in spreadsheets. Nobody knows real pipeline-to-cash conversion.

Your sales team manages a large pipeline in Salesforce. Finance books revenue monthly in S/4HANA. Between those two numbers lies a black hole. When a deal closes in Salesforce, you don't see the purchase order in SAP for weeks. Customer orders get placed but never invoiced. Deals slip from quarter to quarter with no clear view of why. Reps quote one conversion rate; finance sees a much lower one. The CFO can't answer "how much pipeline will become revenue?" because Salesforce and SAP don't speak.

CPI replicates Salesforce data to Datasphere, joins with S/4HANA actuals. SAC shows unified revenue intelligence.

Cloud Integration in SAP Integration Suite (formerly SAP Cloud Platform Integration, CPI) reads your Salesforce opportunities, account data, and won/lost deals with SAP's Salesforce adapter (or picks up change events with the Salesforce Pub/Sub adapter) and writes them into Datasphere through the JDBC adapter into an Open SQL schema, on a schedule that fits the forecast cycle. Simultaneously, Datasphere federates S/4HANA sales orders, invoices, and cash collections. A semantic model in Datasphere maps Salesforce accounts to SAP customers and Salesforce deals to SAP orders, and builds a pipeline-to-cash view. SAC dashboards now show true conversion: how many deals in each stage actually converted, average conversion time from stage to order, and which products have slipped conversion ratios. Finance and sales finally speak the same language. Account matching is a master-data task, not a feature you switch on: agree the matching rules (account ID first, name rules second) and keep the cross-reference in Datasphere.

CPI + Datasphere + SAC + S/4HANA

• Cloud Integration (CPI)
• SAP Datasphere
• SAP Analytics Cloud
• SAP S/4HANA
• SAP Salesforce adapter (+ Pub/Sub adapter)
• Scheduled data sync & transformation

Pipeline conversion visibility improves. Forecast accuracy typically improves. Reps and finance align on what "closing" means.

Visibility: A typical early finding is pipeline double-counted across Salesforce instances, a discovery that can pay for the project on its own. Accuracy: Consolidating several competing pipeline forecasts into one source of truth usually improves forecast accuracy. Velocity: Sales operations now tracks conversion metrics daily instead of manually exporting weekly. Conversion trends are visible within days instead of weeks.

Salesforce adoption. SAP customer and order data quality. Agreed mapping rules.

• Salesforce is current and used daily by sales teams for deal tracking
• S/4HANA customer master is clean and maps to Salesforce accounts via account ID or name
• Sales orders and invoices are timely and complete in SAP
• Integration Suite connectivity is available and budgeted
• Sales and finance teams have agreed on stage-to-order mapping rules
• Salesforce adapter deployed in Cloud Integration, plus a Datasphere database user (Open SQL schema) for the write path
• CPI configured with a load schedule, delta logic, and error handling

Risk models in Databricks, market data in Snowflake, transaction data in SAP. No unified risk dashboard.

Your financial institution runs market risk models in Databricks—they're sophisticated, fast, and owned by the quants. Market data (prices, volatility, curve data) lives in Snowflake, updated intraday. Transaction data (positions, exposures, counterparty data) sits in SAP. Currently, a risk analyst exports from each system, rebuilds the data in a local model, and produces a daily risk report. This manual process is a bottleneck. Risk dashboards are produced T+3 instead of T+1. Regulators and management want faster, traceable risk reporting, but you can't connect the dots across three platforms.

BDC Connect shares data products with Databricks and Snowflake (zero-copy). Datasphere joins them with SAP positions. SAC risk dashboard on one model.

SAP Business Data Cloud Connect (BDC Connect; generally available for Databricks since October 2025 and for Snowflake since May 2026) shares data products in both directions without copying. SAP data products, such as position and exposure data, reach the quants' Databricks workspace, and the risk model outputs are shared back to SAP Business Data Cloud as data products (from Databricks through SAP's BDC Python SDK; Snowflake publishes data back natively), where they become discoverable in Datasphere. Where you need live queries on tables that aren't data products, Datasphere can also federate Databricks or Snowflake through a Generic JDBC connection (remote tables on the Data Provisioning Agent). The Datasphere semantic layer joins position data from SAP with market data from Snowflake and risk model outputs from Databricks. SAC dashboards now show a unified risk view: exposures with embedded market prices and the latest risk metrics. No manual exports. Analysts review exceptions instead of rebuilding models. How fresh the view is depends on how often the models run and the shared data refreshes, not on the integration alone; zero-copy refers to data movement, so query cost and latency still need testing.

BDC Connect + Datasphere + SAC + Databricks/Snowflake

• Business Data Cloud Connect
• SAP Datasphere
• SAP Analytics Cloud
• Databricks (zero-copy data-product sharing)
• Snowflake (zero-copy data-product sharing)
• Generic JDBC federation (optional)

Risk reporting cycle compresses. Manual data wrangling largely eliminated. Faster regulatory response.

Speed: Removing the export-and-rebuild step between Databricks, Snowflake, and SAP is what lets a bank move a T+3 risk report toward T+1. Risk analysts review exceptions instead of rebuilding models, and zero-copy sharing removes the copy jobs between platforms. Accuracy: Data reconciliation between systems is removed, eliminating a source of daily discrepancies. Compliance: Lineage can be documented from SAP transactions through Snowflake market data to Databricks model outputs. No more "where did this number come from?"

Databricks and Snowflake connectivity. SAP position data quality. BDC Connect readiness.

• Databricks workspace on Unity Catalog, with model outputs in tables you can share back as data products
• Snowflake market data is current and updated on SLA schedule
• SAP contains complete position and exposure data with counterparty mappings
• BDC Connect for Databricks (GA since October 2025) and for Snowflake (GA since May 2026) is licensed and budgeted
• Risk team has defined the required data model and metrics
• Data governance and lineage requirements are documented
• Platform setup meets BDC Connect's prerequisites (for example, Snowflake Standard, Enterprise, or Business Critical edition on AWS or Azure)

Headcount in Workday (legacy acquisition), new hires in SuccessFactors, cost centers in S/4HANA. CFO can't reconcile people costs.

Your organization acquired a company two years ago. The acquired company runs Workday for its workforce. Your core business runs SuccessFactors for everyone else. Both systems feed into S/4HANA for GL accounting, but the account codes don't align. When the CFO asks "how many people do we have per cost center?" the answer depends on which system you query. HR reports headcount one way, Finance reports it another. Reconciliation meetings between HR and Finance consume many hours every month. Workforce planning is separate for each entity because you can't create a unified headcount model.

Integration Suite connects both HR systems to Datasphere. Unified workforce cost model in SAC.

SAP Integration Suite loads employee data from Workday (legal entity, employee ID, salary, cost center) and SuccessFactors (employment status, role, location, manager, department) into Datasphere on a schedule. A semantic model in Datasphere maps both systems to a unified employee dimension and cost hierarchy. SAP GL data is federated to pull actual costs per cost center. SAC dashboards now show unified headcount by cost center, actual salary spend vs. budget, and cost-per-employee by function. HR and Finance reconcile monthly in hours instead of days because the unified model is the single source of truth. Integration Suite's Workday and SuccessFactors adapters handle the two HR APIs, and Open Connectors offers over 160 prebuilt SaaS connectors for future integrations.

CPI + Datasphere + SAC + S/4HANA

• Cloud Integration (CPI)
• SAP Datasphere
• SAP Analytics Cloud
• SAP S/4HANA
• Workday adapter
• SuccessFactors adapter
• Open Connectors for SaaS APIs

Headcount-to-budget alignment improves. Reconciliation time drops sharply. Workforce planning becomes unified.

Cost control: Organizations with dual HR systems often find contractor overspend once headcount and budget finally line up: contractors paid through multiple cost centers, or benefits paid twice. Speed: Monthly reconciliation shrinks from days of meetings to a short review of exceptions. Planning: Workforce planning cycles compress from separate entity plans to a single global plan because the data is unified.

Workday and SuccessFactors adoption. S/4HANA GL data quality. Cost center harmonization.

• Both Workday and SuccessFactors are current and used daily by HR teams
• Both systems contain complete salary, cost center, and employment status data
• S/4HANA GL posting is complete and includes cost center assignments
• Integration Suite adapters for both HR systems are available and budgeted
• HR and Finance have agreed on mapping rules between Workday and SuccessFactors hierarchies
• Cost center structure is documented and consistent across both HR systems
• Daily scheduled loads configured to support reconciliation
• SuccessFactors API access uses OAuth: Basic Authentication for APIs and the SOAP Employee Central SFAPIs (except CompoundEmployee) were deleted on November 20, 2026

Orders in Shopify, fulfillment in SAP, returns split between the two. No end-to-end order visibility.

Your D2C brand processes hundreds of orders a day through Shopify. Fulfillment is coordinated through SAP (inventory allocation, packing, shipping). Returns are managed in Shopify but refunds are processed in SAP. You have no unified order visibility. Marketing doesn't know if their campaigns are driving orders that actually get fulfilled profitably. Finance can't track order-to-cash cycle time because Shopify and SAP operate independently. Returns analysis is fragmented: Shopify shows return rate, SAP shows refund cost, but nobody connects the two to see true net profit per product.

Near-real-time sync via Integration Suite. Datasphere models the complete order lifecycle. SAC shows margin by channel, product, and customer.

SAP Integration Suite pulls Shopify orders, customers, and returns through the Shopify GraphQL Admin API (Shopify has treated the REST Admin API as legacy since October 1, 2024), and order webhooks relayed by an integration flow keep Datasphere close to real time. Simultaneously, Datasphere federates SAP fulfillment data (shipping costs, invoice amounts, cost of goods), creating a complete order-to-cash picture. A semantic model maps Shopify SKUs to SAP materials and calculates true net margin per order: revenue minus COGS minus fulfillment cost minus return/refund cost. SAC dashboards show profitability by product, channel, customer segment, and geographic region. Marketing now sees which campaigns drive profitable orders, not just volume. Event Mesh can distribute the order events to other consumers too; a subscriber writes them into Datasphere, which does not subscribe to topics itself.

CPI + Datasphere + SAC + S/4HANA + Shopify

• Cloud Integration (CPI)
• SAP Datasphere
• SAP Analytics Cloud
• SAP S/4HANA
• Shopify GraphQL Admin API + webhooks
• Event Mesh for order-to-cash events

Product profitability visibility improves. Return rate visibility. Marketing ROI becomes trackable.

Pricing insight: A common discovery: the most popular product turns out to be the lowest-margin one once returns are counted, because a high return rate (from sizing issues, for example) wipes out a thin gross margin. Promotional budget can then shift quickly to products with lower return rates. Efficiency: Order-to-cash cycle time is now visible, enabling faster cash collection. Analytics: Marketing campaigns are now evaluated on profitability, not just order volume or customer acquisition cost.

Shopify adoption. SAP fulfillment data quality. Product mapping.

• Shopify is your primary order channel and contains complete customer and order data
• SAP contains all fulfillment, COGS, and invoice data, updated daily
• Returns process is documented and captured in both systems
• Shopify SKUs can be mapped to SAP materials (1:1 or via a mapping table)
• Integration Suite connectivity to Shopify is available and budgeted
• Finance team has defined required margin calculations and cost allocations
• Event Mesh topics and a subscriber configured for order, shipment, and return events

Incidents in ServiceNow, IT budget in SAP. Can't allocate service costs to business units. IT is seen as a cost center with no accountability.

Your IT department processes tens of thousands of ServiceNow tickets a month. Your SAP GL holds the IT budget split across many cost centers. There's no connection between them. You can't answer "how much did it cost to support Finance?" or "which services consume the most budget?" IT budgets are allocated top-down by headcount, not bottom-up by actual service delivery. Business units view IT as black-box overhead instead of a service provider. IT directors can't prove their efficiency or justify investments because they have no cost-per-service visibility.

ServiceNow data replicated to Datasphere. Cost allocation model in SAC links service tickets to cost centers and business units.

SAP Integration Suite replicates ServiceNow incident data (ticket type, requester, assignment group, resolution time) into Datasphere. Simultaneously, Datasphere federates SAP GL cost data (salaries, infrastructure, tools, licensing). A semantic model creates a cost allocation bridge: incident categories are mapped to IT service groups, service groups are mapped to SAP cost centers, and cost centers are allocated back to consuming business units (Finance, HR, Sales, Manufacturing, etc.). SAC dashboards show true cost per service delivered. IT now can track ticket volume, resolution time, and cost per service by business unit consuming that service. Integration Suite's ServiceNow adapter handles the ServiceNow API, and Open Connectors offers over 160 prebuilt SaaS connectors for future enterprise app integrations.

CPI + Datasphere + SAC + S/4HANA + ServiceNow

• Cloud Integration (CPI)
• SAP Datasphere
• SAP Analytics Cloud
• SAP S/4HANA
• ServiceNow adapter
• Open Connectors for SaaS APIs

Cost visibility per service. IT accountability improves. Business unit chargeback becomes possible.

Visibility: Once business units see their true IT consumption, some start cost-reduction programs as soon as they see their share of the IT budget. Accountability: IT leadership can now prove efficiency improvements. Resolution time improvements translate to visible cost reductions. Strategy: IT can now make data-driven decisions about service consolidation, outsourcing, and automation based on actual cost and volume data.

ServiceNow adoption. SAP GL cost center structure. Service-to-cost center mapping.

• ServiceNow is current and contains complete incident data with types and assignments
• SAP GL contains detailed IT cost data organized by cost center
• IT service groups or incident categories are clearly defined
• Cost centers can be mapped to consuming business units
• Integration Suite's ServiceNow adapter is available and budgeted
• IT leadership has agreed on cost allocation methodology
• ServiceNow API access (integration user and credentials) configured for the adapter

Procurement in Ariba, travel spend in Concur, AP in S/4HANA. No unified view of total supplier exposure or spend compliance.

Your manufacturing company manages supplier contracts in Ariba. Travel and expense are handled through Concur. Accounts Payable processes all invoices in S/4HANA. The three systems don't talk. You have no visibility into total spend per supplier because Supplier A might appear in Ariba as "Supplier A," in Concur as an indirect travel booking, and in S/4HANA with a master record named differently. Compliance is a guessing game: you don't know how much spend is going through contracts versus off-contract. Risk is invisible: a critical supplier could be supporting a large share of your supply chain, but you wouldn't know until they went bankrupt.

Spend data from Ariba, Concur, and S/4HANA flows into one Datasphere model of total supplier spend. SAC shows risk heatmaps and compliance dashboards.

Transactional integration between SAP Ariba and S/4HANA (purchase orders, invoices) typically runs through SAP Integration Suite, managed gateway for spend management and SAP Business Network (formerly Cloud Integration Gateway). For analytics, integration flows load Ariba spend data (Integration Suite includes an Ariba adapter) and Concur expense data (for example, through the JDBC adapter into a Datasphere Open SQL schema, as in SAP's own tutorial) next to S/4HANA invoices and payments. If SAP Business Data Cloud's Spend Intelligence content fits your scope (SAP positions it to unify, classify, and enrich SAP and third-party spend data), start from it instead of modeling everything yourself, after checking which of your sources it covers today. Matching suppliers across systems (recognizing that "Supplier A" in Ariba and "Supplier A Inc." in Concur are the same vendor) is master-data work: SAP Master Data Governance, or SAP Reltio in SAP Business Data Cloud (now generally available), can maintain the cross-reference. SAC dashboards show total supplier spend, split between contract and off-contract. Risk heatmaps highlight concentration: suppliers above a threshold you set (for example, 10% of category spend) are flagged. Compliance views show off-contract spend by category. Procurement moves from managing individual contracts to managing supplier relationships and risk. Where an alert can't wait for the next load, an SAP S/4HANA business event published to SAP Event Mesh can trigger a subscriber that writes the change into Datasphere.

Integration Suite + BDC + Datasphere + SAC

• SAP Integration Suite (managed gateway, Ariba adapter)
• Business Data Cloud (Spend Intelligence content)
• SAP Datasphere
• SAP Analytics Cloud
• SAP S/4HANA
• Master data: SAP MDG or SAP Reltio
• Event Mesh for time-critical alerts

Off-contract spend identified and reduced. Supplier concentration risk visible. Savings opportunity identification automated.

Savings: Unified spend analytics typically surfaces material off-contract spend within the first weeks, and procurement can renegotiate a share of it within months. Risk visibility: Concentration views can reveal that a critical supplier carries a large share of a category's spend, giving procurement time to source alternatives before a supplier crisis instead of after it. Credibility: Procurement's credibility with the CFO rises because they can finally answer "how much total spend do we have with this supplier?"

Spend data consolidation. Supplier master quality. Spend compliance rules.

• Spend data can be extracted from Ariba, Concur, and S/4HANA
• Supplier master data exists and includes supplier names and contact info
• Transaction volume is large enough for meaningful supplier and category analysis
• Contract repository exists and can be linked to supplier data
• Compliance rules are defined, and if you use BDC Spend Intelligence content, its coverage of your sources is confirmed
• Procurement has defined off-contract spend categories and thresholds
• Event Mesh topics and a subscriber configured only for time-critical procurement events