Discover how SAP Clean Core depends on clean, harmonized data and how DataLark helps reduce customizations for a cloud-ready SAP landscape.

SAP Clean Core in Practice: The Data Factor

In the SAP ecosystem, the concept of Clean Core has become central to conversations about S/4HANA transformation and cloud adoption. At its simplest, Clean Core means keeping the digital core of SAP free from unnecessary modifications so that upgrades are smooth, new innovations can be adopted quickly, and the system remains agile.

Most discussions about Clean Core focus on reducing custom code. That’s an important part of the story — but it’s not the whole picture. Too often, the role of data is overlooked. Yet in many cases, poor data quality or fragmented data landscapes are the real reason why organizations built customizations in the first place.

In this blog, we’ll explore the data dimension of SAP Clean Core: why data problems often drive customization bloat and how clean and governed data can reduce the need for modifications. You’ll come away with a clearer understanding of how Clean Core isn’t just about code hygiene, but also about building the right data foundation for a cloud-ready future.

The Customization Problem

Customizations in SAP aren’t inherently bad. In fact, they often arise from legitimate business needs, for instance:

• A unique industry requirement that isn’t covered by SAP’s standard functionality
• A regulatory or compliance need specific to a country or market
• A process innovation where the business wanted to differentiate itself from competitors
• A quick workaround to bridge a gap until a more permanent solution was available

Over time, though, these adjustments accumulate. Each upgrade cycle introduces new SAP standard features that might make some customizations obsolete; however, organizations often lack the visibility to safely retire them. As a result, the system grows heavier with:

• Z-tables designed to store missing data elements.
• Custom fields created to handle inconsistent master data.
• Bespoke reports built to reconcile mismatched transactions.
• Complex interfaces coded to integrate with siloed legacy systems.

Individually, each customization solves a problem. Collectively, they create technical debt. The more custom code and tables you have:

• the harder it is to upgrade.
• the more regression testing is required.
• the less flexible the system becomes.

What’s often overlooked is that many of these customizations exist not because SAP was lacking, but because the data feeding SAP was inconsistent, fragmented, or incomplete. In SAPinsider’s 2024 migration study, organizations that had already moved to S/4HANA cited cleansing and improving data quality as their #1 challenge — above adapting custom code and integrating third‑party apps. In other words, data issues typically outrank code during real‑world migrations. Moreover, the root cause of customization bloat is frequently a data problem.

The Data Dimension of Clean Core

An SAP Clean Core strategy cannot succeed without addressing data. Code cleanup alone removes symptoms; tackling data addresses root causes.

Here is how poor data can undermine SAP Clean Core:

• Inconsistent master data → drives the creation of custom fields and extra tables. Example: Customer names or addresses are maintained differently in different regions, leading to duplicate entries. Instead of harmonizing the data, IT adds custom fields and reconciliation tables to manage the duplicates.
• Low-quality transactional data → requires duplicate reports and reconciliations. Example: Financial postings are often assigned to the wrong cost centers. To fix this, the finance team requests a custom ABAP report to reconcile errors every month.
• Fragmented system landscapes → lead to point-to-point custom interfaces. Example: Sales orders originate in a legacy CRM, while invoicing happens in SAP. To bridge the gap, developers build a custom interface in ABAP, which has to be maintained through every upgrade.
• Uncontrolled data migration → clutters S/4HANA with legacy or irrelevant data. Example: During migration, thousands of inactive materials are loaded into the new system. The excess data slows performance and forces IT to write custom logic to filter records later.

By contrast, if data is standardized, harmonized, and governed properly, many of these customizations simply aren’t necessary:

• Clean master data means fewer extra tables and fields.
• Reliable transactional data eliminates the need for reconciliation reports.
• Well-managed integration pipelines reduce the number of custom ABAP interfaces.
• Disciplined migration ensures that only relevant data moves forward, keeping the new system lean.

In other words, clean data reduces the need for custom code.

Where DataLark Adds Value

Achieving a Clean Core requires more than reducing custom code — it requires making sure the data that flows into SAP is standardized, high-quality, and well-governed. This is where DataLark brings unique value: helping organizations manage and prepare their SAP data more effectively.

Cleansing and harmonization

Before data enters SAP, DataLark can cleanse, validate, and harmonize it across sources. This ensures master data like customers, suppliers, and materials is consistent and complete. When SAP receives clean data, the need for extra Z-tables or validation logic inside the system disappears.

Example: Instead of creating a Z-table for missing supplier tax attributes, DataLark enriches and harmonizes supplier master data upstream, so standard SAP fields are sufficient.

Migration enablement

During S/4HANA or cloud migrations, organizations often face the risk of carrying forward years of redundant or poor-quality data. DataLark supports controlled data migration by filtering, transforming, and loading only what is truly relevant for modern processes.

Example: Outdated material codes and inactive vendor records can be identified and excluded, keeping the new SAP environment lean and clean.

Integration simplification

Many custom ABAP interfaces are written simply to connect SAP with external systems. DataLark simplifies these scenarios by orchestrating data flows across multiple applications outside the core. This reduces the number of custom interfaces that SAP needs to maintain.

Example: Instead of building a custom integration for sales order data between a legacy CRM and SAP, DataLark pipelines standardize the feed and deliver it into SAP in the right format.

Governance and control

A Clean Core is not just about getting SAP into shape once — it’s about keeping it clean over time. DataLark provides transparency and governance over data pipelines, ensuring that new projects or integrations don’t reintroduce inconsistencies.

Example: When a new business unit is onboarded, DataLark enforces data standards before information is loaded into SAP, preventing the reappearance of duplicates or incompatible formats.

Through these capabilities, DataLark helps organizations remove the data-driven reasons for customization. Clean, harmonized data feeding into SAP reduces the reliance on workarounds, supports fit-to-standard adoption, and sustains the SAP Clean Core principle over the long term.

The DataLark’s SAP Clean Core Approach: Real-World Scenarios

To see how data issues translate into customizations — and how DataLark can help — let’s look at three real-world scenarios.

Case study 1: retiring a Z-table for supplier data

The problem: A global manufacturing company created a Z-table to store tax identification numbers for suppliers, because the master data coming from regional offices was incomplete and inconsistent. Over time, maintaining the Z-table required custom ABAP logic and added complexity to supplier onboarding.

The DataLark approach: By using DataLark to harmonize supplier master data upstream — validating tax IDs, enforcing formats, and enriching missing attributes — the company was able to load complete records directly into SAP standard fields.

The outcome: The Z-table and its associated custom code were retired, reducing technical debt and allowing the company to use SAP’s standard supplier functionality without modifications. Thus, the time to onboard a new supplier was reduced from three days to just a few hours.

Case study 2: eliminating redundant finance reports

The problem: A large retail chain relied on custom ABAP reconciliation reports to fix errors in financial postings. The root cause was poor data quality in cost center assignments, as postings often included outdated or incorrect codes from external systems.

The DataLark approach: DataLark introduced a cleansing pipeline that validated cost centers against a single source of truth before transactions entered SAP. Invalid entries were flagged and corrected upstream, so only clean, accurate postings reached the core system.

The outcome: The finance team no longer needed custom reconciliation reports, and month-end close was shortened by several days, leading to quarterly savings of over 100 person-hours.

Case study 3: simplifying a CRM-to-SAP integration

The problem: A service company had built a custom ABAP interface to connect its legacy CRM with SAP for sales orders. Every SAP upgrade required testing and adjustments to the interface, which consumed IT resources and created risk.

The DataLark approach: Instead of maintaining the ABAP interface, the company used DataLark to manage the integration outside of SAP. Sales order data from the CRM was standardized, validated, and delivered into SAP in the required format through a governed pipeline.

The outcome: The custom ABAP interface was decommissioned, upgrades became smoother, and IT gained a more flexible way to onboard new CRM fields without touching SAP code, thus cutting regression testing costs for upgrades.

Best Practices for a Data-Driven Clean Core

Adopting an SAP Clean Core strategy is not just a technical project; it’s an ongoing discipline that requires both code and data considerations. Here are some practical best practices:

• Combine technical and data perspectives: Use SAP’s native tools like the Custom Code Analyzer and ABAP Test Cockpit to identify redundant or risky custom code. At the same time, use a data management platform like DataLark to ensure the underlying data doesn’t recreate the need for new customizations.
• Start with data cleansing before migration: Don’t wait until you’re in S/4HANA to address poor data quality. Clean and harmonize master and transactional data before migrating, so you don’t carry forward decades of clutter that will weigh down your new system.
• Embrace fit-to-standard processes: Trust SAP’s standard functionality whenever possible. Often, customizations are added to compensate for poor data rather than true business uniqueness. Clean, harmonized data makes it easier to adopt fit-to-standard processes without compromise.
• Simplify integrations outside the core: Keep the SAP digital core free from custom-built ABAP interfaces. Manage data flows with governed pipelines outside of SAP to reduce technical debt and improve upgrade resilience. Always use SAP-published APIs and events (via SAP API Hub) first; inside the S/4HANA core, and only rely on released/whitelisted objects according to ABAP Cloud rules.
• Choose a Clean Core compliant extension path based on your scenario: Choose in-app (key user) for simple adaptations; opt for developer with ABAP Cloud when you need tight integration with core data and transactions; and use side-by-side on SAP BTP for decoupled, scalable, and integration-heavy solutions. Avoid classic extensibility.
• Establish continuous governance: Clean Core is not a one-time achievement. New projects, mergers, or regulatory changes can reintroduce complexity. Implement continuous monitoring and governance of both data and integrations to keep the core clean over time.
• Collaborate across business and IT: Clean Core is successful only if business stakeholders and IT align. Present data and customization insights in business-readable formats, so decisions about what to retire, migrate, or standardize can be made together.

Conclusion

SAP Clean Core is often described as a quest to eliminate unnecessary code — in reality, it’s about eliminating the reasons code was added in the first place. More often than not, those reasons are tied to data: inconsistent master records, poor-quality transactions, fragmented landscapes, or uncontrolled migrations.

By treating data as the foundation of SAP Clean Core strategy, organizations can minimize the need for customizations, simplify integrations, and ensure that their SAP systems remain upgrade-safe and cloud-ready.

This is where DataLark makes a measurable difference. By cleansing and harmonizing data before it enters SAP, orchestrating integrations outside the core, and enabling controlled migration of only relevant information, DataLark helps organizations sustain Clean Core principles long after a project is completed.

The takeaway is clear: a clean core starts with clean data. With DataLark as the data backbone of your SAP landscape, you can unlock the full value of SAP standard functionality, reduce technical debt, and accelerate your journey to a future-ready, cloud-enabled enterprise. Request a demo of DataLark’s capabilities now, and embrace SAP Clean Core to realize its full potential.