Clean core is about more than just clean code. A system can have perfectly upgrade-safe custom extensions and still be burdened by years of accumulated data waste — obsolete records, redundant indexes, historical data that no longer serves any business purpose, and bloated table volumes that slow every transaction and inflate infrastructure costs. Clean data is the often-overlooked second pillar of a healthy SAP S/4HANA landscape.
Why Data Volume Efficiency Matters for Clean Core
In many SAP system landscapes, data volume challenges do not come from running the active business alone. They are often caused by unused or obsolete data that is never cleaned up, redundant data stored across systems or table indexes, and historical data that no longer delivers business value. Over time, this leads to slower system performance across all modules, higher storage costs on both on-premise and cloud infrastructure, longer backup and restore windows, and more complex data migrations during system upgrades and S/4HANA transformations.
With the rise of AI-powered analytics and embedded intelligence in SAP S/4HANA, data quality and volume efficiency have taken on even greater importance. AI models require clean, relevant, well-structured data to deliver reliable insights. Poor data quality is not just a technical problem — it directly undermines the business value of your SAP investment.
The Data Principle in RISE with SAP
SAP’s RISE with SAP methodology now includes a formal Data Principle, surfaced in the RISE with SAP dashboard. This principle provides customers with visibility into their data volume posture and guidance on actively managing data across their SAP landscape. The initial focus is on data volume efficiency — helping organizations understand where they have excessive data accumulation and what can be done about it.
The Data Principle introduces structured measurement: customers can track data volume metrics over time, benchmark against industry norms, and set reduction targets. This transforms data management from a reactive task (only addressed when performance degrades) into a proactive, measurable practice.
Practical Data Volume Management Strategies
Data Archiving
Data archiving is the systematic process of moving completed business objects (closed orders, paid invoices, expired contracts) from the active database to long-term archive storage. SAP provides the Data Archiving function (transaction SARA) with archiving objects for all major business processes. Properly implemented archiving can reduce database sizes by 30–60% for mature systems, dramatically improving performance and reducing cloud storage costs.
Data Deletion and Retention Management
Not all data needs to be archived — some can be permanently deleted once it has passed its retention period. SAP’s Information Lifecycle Management (ILM) provides tools to define data retention policies aligned with legal requirements and business rules, enabling automated data deletion while maintaining compliance. This is especially important for GDPR compliance, where personal data must be deleted upon request or after a defined retention period.
Table Optimization and Reorganization
Beyond business data, technical table bloat — caused by change documents, application logs, workflow traces, and RFC audit entries — can accumulate rapidly. Regular housekeeping jobs (scheduled via SM36/SM37) for standard SAP tables like CDPOS, CDHDR, BDCP2, and IDOC tables are essential for maintaining a lean system. In S/4HANA, the ABAP Statistical Records and Application Log tables also warrant regular attention.
Clean Data as Part of Your Clean Core Journey
A clean core strategy that addresses only code — without addressing data — leaves significant value on the table. The most successful S/4HANA transformations treat clean code and clean data as equally important workstreams. They establish data volume KPIs at the outset, implement archiving before migration (to reduce migration scope), and embed ongoing data housekeeping into their operational BAU processes.
At Clean Core ABAP, we help clients assess their data volume posture, design data retention policies aligned with their business and legal requirements, and implement automated housekeeping frameworks that keep systems lean — not just at go-live, but throughout the system lifecycle. Because a truly clean core is both clean in code and lean in data.