Automated Payment Program F110: Step-by-Step Guide for SAP Users

Companies building SAP connectors often underestimate the scope of the project, assuming it's just a matter of connecting endpoints. In reality, SAP connectors involve deep data mapping, complex authorization checks, and bidirectional syncing. To avoid this mistake, start with proper scoping, don't skip it, and build a quick proof-of-concept for critical data flows. Additionally, avoid over-customization by sticking to standard procedures and modules, and focusing on reuseable components.

Companies building SAP connectors often underestimate the scope of the project, assuming it's just a matter of connecting endpoints. In reality, SAP connectors involve deep data mapping, complex authorization checks, and bidirectional syncing. To avoid this mistake, start with proper scoping, don't skip it, and build a quick proof-of-concept for critical data flows. Additionally, avoid over-customization by sticking to standard procedures and modules, and focusing on reusable components.

A common pitfall is underestimating the complexity of SAP's business logic and evolving standards. Many teams jump into SAP integrations assuming it’s just a matter of “connecting endpoints.” However, SAP isn't your typical system. It’s layered, business-rule-heavy, and often full of legacy quirks that don’t play nicely with modern tools - unless handled properly. Most issues don’t show up until deep into the dev cycle when fixes are expensive and rollout deadlines are near.

One of the most common mistakes is underestimating the scope of the project. Teams often assume the connector is a “quick build,” thinking it’s just a matter of hitting an API endpoint or exposing a data source. In reality, SAP connectors can involve deep data mapping, complex authorization checks, bidirectional syncing, and often back-end middleware. For example, syncing customer data might sound like a single endpoint call. But what about custom fields, multiple sales data tables, approval flows, or regional tax logic? Now, you're not just calling an API, you’re orchestrating data, managing errors, and tuning performance just to keep things stable.

To avoid this, start with proper scoping. Sit down with your SAP team and map out which modules are in play, what kind of data volumes you're dealing with, and how the integration needs to work. Got custom logic? Flag it early. Even better, build a quick proof-of-concept for your most critical data flows. It’ll help you spot hidden dependencies and test your assumptions before you're knee-deep in development. Trust me, a little clarity upfront saves a ton of cleanup later.

Another mistake is over-customization. Developers often get pulled into adding logic for every Z-table, custom field, and one-off process in a client’s system. The result? A bloated connector that’s impossible to reuse, hard to test, and a nightmare to maintain. For instance, a logistics tech company wants to integrate with customers’ SAP systems to fetch delivery schedules. Instead of mapping to standard delivery BAPIs, developers get pulled into supporting custom date logic, dozens of user exits, and every custom delivery status imaginable. What should have been a 6-week connector project turns into a 9-month maintenance marathon, multiplied by every new client.

To avoid this, resist the temptation to over-engineer. Stick with SAP-standard APIs, BAPIs, or IDocs as much as possible; they’re stable, supported, and documented. Build flexibility into the connector via configuration, not custom code. And ask yourself this: are you solving for a general integration use case or replicating someone’s entire SAP landscape? If it’s the latter, pause and re-scope.

Poor testing strategy is another common mistake. You can write perfect code and still end up with a broken connector if you skip proper testing. One of the most overlooked areas in SAP integration is testing under real-world conditions. Teams often run a few unit tests and assume everything will hold up. But when the connector goes live and hits real data, timeouts, failed transactions, and authorization errors start to surface. SAP environments vary wildly: different data volumes, inconsistent master data, missing authorizations, and custom logic that doesn’t show up in sandbox systems.

To avoid this, plan for multiple test phases: unit, integration, volume, and user acceptance testing. Simulate real data scenarios and edge cases, especially around failed transactions or partial syncs. Invest in a sandbox SAP instance or partner with consultants who have one. Simulate real data, workflows, and user authorizations, and ensure your test environments mirror the production scale as closely as possible.

Good testing doesn’t slow you down; it prevents disaster at go-live.

Lack of SAP expertise on the team is another pitfall. No matter how smart your dev team is, SAP has a steep learning curve. OData services, RFCs, BAPI constraints, and handling user contexts in SAP NetWeaver Gateway require hands-on experience. Without it, teams waste weeks on simple missteps, or worse, build something that breaks at go-live.

To avoid this, involve an experienced SAP technical consultant early. Ideally, someone who has built connectors before. They’ll catch what others miss, guide your team through correct integration paths, and save you from costly detours.

Ignoring security and licensing is another mistake. SAP doesn’t play around with security, and neither should you. Many projects forget to account for SSO configurations, token expiration, and access control within the SAP system. Worse, they overlook SAP licensing implications, which can lead to surprise costs.

To avoid this, consult your SAP security team about user access and role requirements. Understand the difference between named users, technical users, and indirect access. Validate connector licensing with your SAP AE if you’re selling the connector or integrating with third parties.

No versioning or change control can also lead to issues. Many SAP environments differ slightly across dev, test, and production. When connector logic isn’t versioned properly or environments aren’t synchronized, you risk unexpected behavior or even data corruption. Worse yet, some projects hardcode transformations, endpoints, or credentials instead of managing them through configuration. What works in Dev may break in QA or Prod, and you’ll end up chasing vague “it works here but not there” bugs across environments.

To avoid this, use proper source control (Git or similar) for the connector. Separate config from code. Set up CI/CD for your connector, where possible and test every transport. Treat SAP like any enterprise system—with dev ops discipline.

Lastly, disconnected stakeholders can lead to misaligned expectations, late-stage changes, and friction during testing. The development team and the SAP owners often operate in silos. You might have your integration team building against technical specs, only to discover during UAT that key assumptions, like how orders are created or which data triggers updates, were wrong. Why? Because the people writing the code never spoke to the people running the business process.

To avoid this, involve SAP functional consultants early, especially those who understand the source data, business rules, and edge cases. Define clear ownership for data mapping, transformation rules, and validation logic. Set up recurring checkpoints between dev, QA, and business teams. Short weekly syncs can save weeks of debugging. Walk through real data scenarios during development.

Building an SAP connector is a strategic effort, especially if it forms the bridge between your product and your enterprise data. It deserves thoughtful planning, expert input, and rigorous testing. Avoiding these common pitfalls will not only save you from costly rework but also accelerate your delivery and business outcomes.

References
[1] https://community.sap.com/t5/enterprise-resource-planning-blog-posts-by-members/common-mistakes-companies-make-when-building-sap-connectors-and-how-to/ba-p/14145970