Discover key SAP S/4HANA migration challenges and learn how to overcome data, process, and integration pitfalls for a smooth move from SAP ECC to S/4HANA.

Top 7 SAP S/4HANA Migration Challenges and How to Overcome Them

Migrating from SAP ECC to SAP S/4HANA is one of the most significant technology transitions most enterprises will undertake this decade. SAP S/4HANA promises a modern digital core with real-time analytics, streamlined data models, and intelligent automation. But getting there isn’t simple.

The journey is filled with complexities that span data quality, system dependencies, process redesign, and organizational alignment. Many companies underestimate these factors, leading to overruns in cost, time, and effort. The stats are daunting: 59% of companies that have migrated to S/4HANA claim that they ran over the planned schedule, while 65% report that they exceeded the budget and failed to achieve a satisfactory result quality.

In this post, we’ll unpack the most common SAP S/4HANA migration challenges, explore their root causes, and share practical strategies to address them. Throughout, we’ll also highlight how tools like DataLark can help migration teams gain better control over data extraction, transformation, and loading into S/4HANA — improving visibility into the migration pipeline.

Challenge #1: The Data Problem

Across industries, one truth consistently emerges: data quality defines migration success. Among all SAP S/4HANA migration challenges, poor data readiness remains the most underestimated. Even organizations with well-planned timelines and sufficient budgets often find that their real bottleneck isn’t technology — it’s messy, inconsistent, or incomplete data inherited from years of decentralized maintenance.

When SAP S/4HANA introduces a unified and simplified data model, legacy data complexity becomes painfully visible. In many cases, projects lose months of valuable time correcting master-data inconsistencies or resolving structural mismatches that could have been addressed earlier.

Typical pain points

• Inconsistent or incomplete master data: Customer, vendor, and material records often contain duplicates, missing attributes, or conflicting key fields. These discrepancies cause load errors during conversion and create post-go-live problems, such as failed billing or mismatched partner accounts.
• Fragmented data ownership and governance: In ECC, responsibility for master data is typically spread across departments: sales owns customers, procurement owns vendors, finance owns cost centers. Without a unified data governance framework, cleanup activities stall because no one “owns” cross-domain issues.
• Overloaded historical data: Many organizations attempt to migrate every transaction from the past decade, leading to unnecessarily large datasets and slower migration runs. Old, low-value data consumes time, infrastructure, and budget, while providing little business benefit.
• Mapping and model conversion challenges: ECC’s legacy data structures often don’t align neatly with S/4HANA’s simplified model. Converting from Customer/Vendor to Business Partner, classic GL to Universal Journal, or legacy material classifications to the new Material Ledger model requires careful mapping and iterative testing.
• Hidden dependencies between master and transactional data: For instance, open purchase orders tied to obsolete vendor codes or closed cost centers can block migration loads. These dependencies surface late, unless discovered through detailed pre-migration analysis.
• Inaccurate or incomplete data in non-SAP sources: Many enterprises maintain satellite systems or spreadsheets for pricing, assets, or supplier information. If these are not reconciled before migration, data mismatches occur when integrated into S/4HANA.
• Inadequate validation and reconciliation processes: Some teams focus heavily on the ETL mechanics but neglect systematic validation. As a result, discrepancies are found only after go-live, when fixing them is far more disruptive.

Practical ways to overcome the challenge

• Conduct early data profiling and quality assessment: Use SAP Readiness Check and complementary tools to profile master and transactional data in the SAP system; use DataLark or similar tools to analyze data in non-SAP sources. Quantify duplicates, missing mandatory fields, and outdated records to define the real scope of cleanup.
• Define and enforce data governance: Establish clear ownership for each data domain (Customer, Vendor, Material, Finance, etc.) and create accountability for cleansing decisions.
• Set data retention and archiving rules: Organize data into active, reference, and historical categories. Migrate only what’s required for business continuity and compliance. Archive older or low-value data using SAP Information Lifecycle Management or external storage.
• Align transformation logic with S/4HANA’s target model: Map legacy tables to the new structure, validate conversions for key objects, and test transformation logic using mock runs.
• Automate data cleansing and enrichment: Apply scripts or tools to standardize fields (addresses, tax numbers, units of measure) and remove duplicates automatically rather than manually.
• Run multiple mock conversions and reconcile results: Each test migration should measure error counts, missing records, and load performance to refine transformation rules.
• Involve business stakeholders early: IT alone cannot fix data issues. Business users must validate data accuracy and confirm that cleansed records reflect operational reality.
• Monitor data quality continuously: Establish KPIs such as duplicate rate, validation error ratio, and completeness score; track improvement over time, leading up to go-live.

Expert insight: S/4HANA migration best practices require that at least 25-30% of total migration effort be allocated to data preparation and validation. Treating this phase as an afterthought nearly always results in rework and cut-over delays.

How DataLark supports this

Using its built-in connectors, DataLark can extract data from SAP ECC, R/3, or even non-SAP systems and prepare it for migration through:

• Structured extraction pipelines that ensure data is pulled consistently across systems.
• Transformation and cleansing capabilities that allow teams to align source data with S/4HANA’s target model by merging duplicate master records, standardizing formats, and validating field integrity.
• Data enrichment and rule application to fill missing attributes or harmonize coding structures before loading.
• Validation and reconciliation reporting that compares pre- and post-migration results, highlighting discrepancies for correction.
• Audit and traceability features that document each migration step, providing transparency for compliance and internal review.

In practice, DataLark’s approach complements SAP’s Migration Cockpit and API-based loading processes. For example, during a multinational retailer’s migration project, the IT team used DataLark to apply standardization rules to the data from multiple ECC instances and validate it against S/4HANA’s Business Partner structure. This prevented duplicate loads and reduced reconciliation time by over 50%.

By bringing visibility and consistency to the extraction-to-load pipeline, DataLark helps organizations mitigate one of the most common challenges of migrating from SAP ECC to S/4HANA: the risk of moving inaccurate or incomplete data into the new environment. The result is a cleaner, faster, and more predictable migration that is grounded in reliable data rather than last-minute fixes.

Challenge #2: Custom Code and Integration Issues

Beyond data readiness, one of the most underestimated SAP S/4HANA migration challenges lies in managing custom developments and integrations accumulated over years of system evolution.

While SAP provides a clean, simplified core in S/4HANA, most ECC systems are anything but clean. They’re heavily customized, with hundreds, or even thousands of Z-programs, enhancements, and third-party interfaces that touch every process.

If not addressed early, these hidden dependencies can derail the migration timeline and disrupt critical business processes after go-live.

Typical pain points

• Incompatible Z-code and deprecated objects: Many custom programs built in ECC rely on database tables or structures that no longer exist in S/4HANA. For example, the removal of aggregate and index tables like BSIS or BSAS breaks direct database reads, causing syntax or runtime errors. Custom ABAP logic that worked in ECC often fails under HANA’s in-memory paradigm.
• Lack of visibility into custom object usage: Over time, organizations lose track of which custom reports, transactions, or interfaces are actively used. As a result, teams waste effort remediating unused or obsolete code.
• Unsupported or redundant third-party add-ons: Some ECC add-ons are not certified for S/4HANA or they overlap with new built-in functionality (e.g., output management or credit management). Continuing to maintain them increases technical debt.
• Integration failures and protocol mismatches: Many ECC interfaces use outdated methods like IDocs, BAPIs, or flat-file transfers. S/4HANA encourages API-based integration via OData, CDS views, or SAP Cloud Integration. Without early alignment, legacy integrations can fail or perform poorly.
• Complex middleware landscapes: Enterprises running hybrid landscapes (SAP PI/PO, SAP Integration Suite, plus third-party middleware) face overlapping responsibilities and inconsistent message mappings. This often leads to duplicate or failed transactions post-migration.
• Limited documentation of system dependencies: Legacy systems often rely on point-to-point connections and undocumented field mappings. During migration, teams spend significant time rediscovering how data flows across applications.

Practical ways to overcome the challenge

• Conduct a comprehensive custom code impact analysis: Use SAP’s Custom Code Migration App and ABAP Test Cockpit (ATC) to identify objects that reference obsolete tables or functions. Flag each object as Retain, Rework, or Retire.
• Analyze actual usage patterns: Use ST03N workload statistics or SAP’s Usage and Procedure Logging (UPL) to identify custom objects that haven’t been executed in over a year. Decommission them to reduce remediation scope.
• Standardize where possible: Replace redundant customizations with standard S/4HANA functionality. For example, adopt built-in Fiori apps instead of maintaining custom reports for basic analytics.
• Document and prioritize integration points: Create an integration inventory detailing all inbound and outbound interfaces, data volumes, and connection methods. Prioritize business-critical integrations and test them in early mock conversions.
• Validate middleware readiness: Ensure message mappings, adapters, and certificates in SAP PI/PO or Integration Suite are updated for S/4HANA compatibility. For hybrid or multi-cloud environments, check latency and throughput.
• Adopt API-first integration design: S/4HANA’s architecture supports OData and CDS-based services — use this opportunity to modernize integrations instead of carrying forward technical debt.
• Run early end-to-end integration tests: Don’t wait until final migration rehearsal. Test integrations across finance, logistics, and supply chain domains at least one cycle before the production cutover.
• Plan for code transport and sequencing: When converting sandbox, development, and QA systems sequentially, align transport paths carefully to avoid overwriting fixes.

Expert insight: On average, 30-60% of ECC custom code is unused or redundant, according to SAP’s internal benchmarks. Removing or refactoring that code before migration can shorten conversion timelines by months and reduce testing overhead.

How DataLark supports this

While SAP provides the core tooling for code and interface remediation, DataLark complements this effort by giving teams a unified, data-centric view of how systems and integrations interact across the landscape.

DataLark can connect to both SAP and non-SAP systems, helping migration teams to:

• Map data dependencies between source systems and target S/4HANA objects, ensuring that custom code and interfaces relying on legacy data structures are identified early.
• Trace data flows across integrations, providing visibility into which systems exchange which data objects; this is essential for planning API replacements or middleware updates.
• Validate transformed data before it reaches S/4HANA, verifying that integration payloads meet the target structure.
• Generate reconciliation reports comparing pre- and post-migration data volumes across connected systems, ensuring that integrations transmit complete and accurate data.
• Support ETL workflows for data migration from legacy non-SAP systems, maintaining consistent transformation logic across all interfaces.

For example, during a large-scale manufacturing migration, DataLark provided a centralized view of data transformation and load results, and the project team detected interface mismatches weeks before go-live, thus avoiding costly production downtime.

By improving transparency into cross-system dependencies and data transformations, DataLark helps SAP and IT leaders ensure that integrations and custom logic align with the new architecture, without introducing unnecessary risk or redundancy.

Challenge #3: Business Process Misalignment

Migrating to SAP S/4HANA is not simply a technical upgrade — it’s an opportunity to rethink and modernize business processes. Yet, many companies treat migration as a “lift-and-shift,” aiming to replicate legacy SAP ECC processes exactly as they are. This approach often leads to rework, inefficiencies, and lost potential.

S/4HANA introduces best practices, embedded analytics, and streamlined data structures designed to simplify operations. Organizations that fail to realign their processes often discover post-go-live that their new system behaves differently — not because of technical issues, but because legacy business logic no longer fits the new model.

Typical pain points

• Over-customization of legacy processes: ECC landscapes often contain years of tailored workflows and Z-transactions. Migrating them unchanged leads to conflicts with S/4HANA’s streamlined model.
• Limited process visibility: Many organizations lack a clear picture of how current processes actually run, making it difficult to identify inefficiencies or justify redesign.
• Unclear ownership between business and IT: Without shared accountability, teams struggle to agree on what to standardize or retire.
• Resistance to harmonization: Regional units may resist adopting global templates, leading to inconsistent requirements.
• Underestimating S/4HANA’s structural changes: Simplifications, such as the Universal Journal or new ATP logic, can impact reporting and transaction flow if not aligned early.

Practical ways to overcome the challenge

• Run Fit-to-Standard workshops early: Use SAP best-practice templates to identify which processes can be standardized.
• Use process mining or benchmarking: Tools like SAP Signavio or Celonis reveal how current ECC processes deviate from expected flows.
• Assign joint ownership: Create a cross-functional design board where business and IT jointly decide what to redesign or retain.
• Document and validate every decision: Keep a traceable record of transformation choices for testing and audit purposes.
• Perform end-to-end testing with real data: Validate that redesigned processes hold up under realistic business scenarios.

Expert insight: Top migration programs dedicate 30-40% of project time to process analysis and redesign — a key factor in smoother adoption and lower hypercare costs.

How DataLark supports this

Although process transformation is a business-led task, DataLark enhances it by improving visibility into how data supports those processes. Furthermore, DataLark helps to validate transformation consistency and fosters collaboration by giving IT and business users a shared, data-driven perspective.

Challenge #4: Organizational and Change Management Issues

Even the best-planned SAP S/4HANA migrations can struggle if organizational readiness is overlooked. Many enterprises focus on system conversion, only to discover that resistance to change, unclear communication, and skill gaps create bottlenecks post-go-live.

Successful migrations hinge as much on people and process alignment as on technical execution. S/4HANA introduces new interfaces, roles, and reporting structures that can improve productivity. However, if these aren’t understood or embraced, adoption suffers and the expected business value remains unrealized.

Typical pain points

• End-user resistance to change: Employees accustomed to SAP GUI often resist changing to Fiori apps. Without proper training, adoption lags and productivity drops.
• Insufficient communication and stakeholder alignment: Business units frequently operate in silos. Project teams may underestimate how deeply process and reporting changes affect daily operations.
• Lack of change ownership: In many migrations, IT leads the project, but business units feel that changes are “imposed.” This disconnect fuels pushback and slows decision-making.
• Limited in-house expertise: S/4HANA requires new competencies in HANA database management, Fiori UX, CDS-based reporting, and modern integration patterns. Many internal teams lack these skills.
• Overlooked role and authorization redesign: S/4HANA introduces a new security model. If not redefined early, it can cause access conflicts and business disruption at go-live.
• Underestimating hypercare and stabilization effort: Organizations often allocate too few resources for post-go-live support, leaving users frustrated when issues emerge in production.

Practical ways to overcome the challenge

• Create a structured change management strategy: Define how information will flow between leadership, IT, and end-users. Regular communication updates reduce uncertainty and foster buy-in.
• Involve business users early: Include functional leads and key users in testing and validation phases to ensure the new system supports real-world scenarios.
• Deliver hands-on training with real data: Generic demos aren’t enough. Users should experience their own processes, such as order entry or reconciliation, in S/4HANA’s Fiori environment before go-live.
• Build internal champions: Empower a network of “super users” across departments who can support colleagues, share feedback, and identify early issues.
• Redesign authorizations and roles proactively: Align security roles with redesigned business processes to prevent access bottlenecks at launch.
• Establish a hypercare plan: Allocate adequate time and resources for post-go-live monitoring, feedback collection, and rapid issue resolution.

Expert insight: SAP benchmarks show that organizations investing early in structured change management experience up to 50% faster user adoption and significantly fewer stabilization issues in the first six months post-migration.

How DataLark supports this

While change management is primarily about people and communication, DataLark helps project teams coordinate and communicate more effectively through shared, data-driven visibility.

Here’s how DataLark contributes within its real scope:

• Shared visibility into migration progress: DataLark allows IT and business stakeholders to track the readiness of data domains and migration tasks in one unified view, helping managers communicate progress transparently.
• Evidence-based discussions: During training or testing, teams can use DataLark’s analytical insights to demonstrate how data accuracy or structure changes affect specific business processes, thus turning abstract discussions into concrete evidence.
• Improved confidence in go-live readiness: When users see migration metrics and data validation results clearly, they gain trust in the project outcomes, which eases adoption anxiety.

By providing transparency and a common data language across business and IT, DataLark reinforces change management activities. It helps teams communicate with facts, not assumptions, which is essential for building confidence and ensuring a smooth transition to S/4HANA.

Challenge #5: Technical and Infrastructure Setbacks

Recent data from the German-Speaking SAP User Group (DSAG) reveals that 48% of SAP users have enrolled, or plan to enroll, in RISE with SAP in 2025. This is a dramatic increase from just 16% in 2024, reflecting accelerated cloud adoption driven by the approaching 2027 support deadline.

Even the most carefully planned S/4HANA projects encounter unexpected technical challenges. Unlike ECC, which evolved incrementally over decades, S/4HANA demands a modern infrastructure, optimized architecture, and new runtime patterns. Missteps in sizing, system configuration, or performance tuning can turn an otherwise smooth migration into an operational bottleneck.

From hybrid cloud decisions to downtime planning, technical readiness determines how effectively business operations can resume after go-live. When infrastructure is underestimated, recovery is costly — both in time and trust.

Typical pain points

• Incorrect system sizing and landscape design: Many organizations underestimate HANA’s memory requirements or unnecessarily overprovision cloud resources. Poor sizing leads to performance degradation, unstable reporting, and wasted budget.
• Downtime and cutover issues: Without realistic mock conversions and performance testing, migration cutovers often exceed planned downtime windows, impacting operations.
• Insufficient hardware and network readiness: HANA’s in-memory architecture relies heavily on CPU and I/O throughput. Network latency, storage bottlenecks, or unoptimized load balancing can cause system slowdowns.
• Complex hybrid or multi-cloud environments: Running part of the landscape on-premise and part in the cloud (for example, using RISE with SAP or private hosting) introduces integration complexity, identity synchronization issues, and new monitoring requirements.
• Security and authorizations mismatches: The S/4HANA security model differs from ECC’s, particularly for Fiori and OData services. Incomplete role redesign leads to access errors and compliance gaps.
• Inadequate testing and performance tuning: Many projects only test functional accuracy, but not performance under production data volumes. This causes unexpected delays during posting, reporting, or integration.
• Missing backup and failover strategies: With HANA’s in-memory design, backup and recovery strategies must be re-evaluated. Some organizations carry forward legacy approaches that don’t meet current SLAs.

Practical ways to overcome the challenge

• Start with comprehensive sizing and infrastructure planning: Use SAP’s Quick Sizer or Engage Cloud Architecture services to determine CPU, memory, and disk requirements based on real transaction volumes, not estimates.
• Conduct multiple mock migrations: Run at least two full technical dry-runs to validate load times, cutover duration, and post-load verification. This helps define realistic downtime windows.
• Adopt a hybrid infrastructure governance model: Clearly define ownership and monitoring for each layer (database, application, integration, network). Hybrid environments demand proactive coordination across IT teams.
• Integrate security redesign into early planning: Redefine roles, authorizations, and access patterns before go-live, testing them with real users to ensure compliance and usability.
• Use continuous monitoring and performance baselining: Monitor system health metrics — CPU utilization, cache ratios, query times — before and after migration to catch regressions early.
• Review disaster recovery and high availability plans: Verify backup frequency, restore procedures, and failover capabilities for both HANA and application layers.
• Engage both SAP Basis and network teams jointly: S/4HANA performance often depends as much on network and storage latency as on database tuning. Collaboration prevents finger-pointing post-launch.

Expert insight: In large-scale S/4HANA conversions, mock migrations and technical rehearsals can reduce go-live downtime by up to 40% — but only if results are analyzed and used to refine procedures, not treated as a formality.

How DataLark supports this

While DataLark is not an infrastructure or performance monitoring platform, it plays a valuable supporting role in improving visibility and validation throughout the technical migration lifecycle.

In one example, a global chemicals company used DataLark alongside SAP’s Migration Cockpit to verify data integrity across multiple HANA tenants. When discrepancies emerged due to inconsistent load sequences, DataLark’s validation reporting helped isolate affected business objects within hours, preventing cascading downstream errors.

Challenge #6: Cost Overruns and Post-Migration Stabilization

Too often SAP S/4HANA migrations face budget pressure and extended stabilization cycles. Unexpected remediation work, delayed testing, and rework caused by data or integration issues repeatedly inflate costs beyond the original business case.

At the same time, once go-live is complete, many organizations underestimate how much effort is needed to stabilize the environment. Post-migration, teams must monitor data consistency, system performance, and business continuity — or risk user frustration, reporting errors, and process interruptions.

Typical pain points

• Scope creep during migration: As the project progresses, new requirements that increase workload and cost appear, such as additional reports, extra data objects, or unplanned process redesign.
• Underestimated data-cleansing and validation effort: Teams often allocate minimal time to data preparation. When migration loads fail, remediation costs surge due to repeated testing and extended cutover windows.
• Hidden integration and interface rework: Legacy interfaces frequently need more adaptation than expected to align with S/4HANA’s API structure. Each modification adds both time and expense.
• Insufficient testing coverage: When testing focuses on technical success instead of business validation, defects surface after go-live, which forces emergency fixes and overtime.
• Short or under-resourced hypercare period: Organizations that minimize post-go-live support find themselves overwhelmed by tickets in the first few months of operation.

Practical ways to overcome the challenge

• Establish a data-driven governance model: Track key migration KPIs in near real time (such as data load success rates, reconciliation accuracy, open defect count, and integration test progress). Use this data for steering decisions rather than relying on subjective status reports.
• Implement strict change-control and scope management: Document every requested change and tie it to business value. A clear approval workflow prevents uncontrolled scope expansion.
• Budget explicitly for testing and remediation: Include multiple mock conversions and extended UAT cycles in the financial plan; these are not optional costs but essential insurance.
• Integrate cost and timeline tracking with quality metrics: Monitor spending and milestones, as well as data-quality improvement trends, to ensure project health.
• Define a structured hypercare phase: Provide at least 6-8 weeks of enhanced support after go-live, with dedicated resources for issue resolution, data reconciliation, and performance tuning.

Expert insight: SAP’s project benchmarks show that organizations allocating at least 20% of their total migration budget to data quality, testing, and post-go-live stabilization experience up to 35% fewer unplanned cost overruns.

How DataLark supports this

Although DataLark is not a financial or project-management system, it plays a crucial role in maintaining data visibility, validation discipline, and progress transparency — three factors that directly influence project cost and post-migration stability.

For instance, during a financial-services migration, DataLark was used to reconcile ledger balances between ECC and S/4HANA automatically after each mock load. Early detection of inconsistent postings helped the team avoid an expensive emergency patching effort two weeks before go-live.

By embedding data validation and visibility into project governance, DataLark supports organizations in controlling costs, reducing rework, and achieving a faster return on their S/4HANA investment — ensuring the transition doesn’t just go live, but stays stable.

Challenge #7: Post-Migration Operations

Once the S/4HANA system stabilizes and users settle into daily operations, the focus shifts from migration success to operational excellence. This is the stage where organizations begin realizing — or failing to realize — the long-term business value promised by S/4HANA.

Post-migration operations require sustained attention to performance optimization, proactive monitoring, and continuous data-driven improvement. Without these, systems can quietly degrade, analytics can lose accuracy, and the initial investment may yield only partial returns.

Typical pain points

• Performance degradation over time: As transactional data grows, query performance and job execution times can decline if indexes, CDS views, and caching parameters are not tuned regularly.
• Unmonitored system usage and load: After go-live, usage patterns evolve. Reports or apps that weren’t stress-tested during migration may become performance bottlenecks.
• Inefficient job scheduling and background processing: Batch workloads inherited from ECC may not align with S/4HANA’s in-memory performance model, leading to unnecessary strain on system resources.
• Data drift and integrity issues: If reconciliation and validation aren’t maintained, data may slowly become inconsistent across modules or integrated systems, even after successful migration.
• Neglected process optimization: Many organizations postpone leveraging Fiori analytics, embedded AI, or process automation features, thus missing opportunities to streamline operations and extract full value.

Practical ways to overcome the challenge

• Establish continuous monitoring and performance governance: Use SAP Solution Manager, SAP Cloud ALM, or equivalent APM tools to track system health, response times, and background job performance on an ongoing basis.
• Tune applications and queries regularly: Optimize long-running CDS queries, ABAP logic, and reporting layers to effectively leverage HANA’s columnar capabilities.
• Reassess infrastructure allocation quarterly: Cloud-based S/4HANA environments allow dynamic resource scaling. Review CPU, memory, and I/O metrics to ensure optimal cost-to-performance balance.
• Automate reconciliation and exception handling: Schedule periodic checks between integrated systems (e.g., S/4HANA and data warehouses) to catch anomalies early.
• Activate incremental innovation: After stabilization, incrementally enable new S/4HANA features such as predictive MRP, embedded analytics, or automation scenarios — guided by business priorities.

Expert insight: Continuous monitoring and performance tuning can improve query response times by 20-40% and reduce system incidents by half within the first year of steady-state operation.

How DataLark supports this

DataLark enhances post-migration operations by ensuring ongoing data consistency and transparency across systems. It helps teams detect and analyze recurring data anomalies that may affect reporting or process efficiency, enabling faster resolution and informed optimization. By providing a shared view of validation metrics, DataLark supports IT and business teams in maintaining long-term operational confidence and continuous improvement.

Conclusion: Turning S/4HANA Migration Challenges into Opportunities

Migrating from SAP ECC to S/4HANA is far more than a technical milestone — it’s a fundamental transformation of how an organization manages, integrates, and uses its data to make decisions.

The SAP S/4HANA migration challenges outlined throughout this article — from data quality and custom code to organizational readiness and post-go-live optimization — are complex, but not insurmountable. In every case, success depends on one recurring theme: visibility and preparation.

Organizations that approach migration as a strategic data and process modernization journey, rather than a one-off IT project, consistently achieve smoother transitions, faster user adoption, and better long-term ROI. Early investment in data quality, governance, and cross-team collaboration pays off during migration, but also throughout the system’s lifecycle — enabling agility, better analytics, and operational resilience.

Within this context, DataLark serves as a practical enabler of clarity. By providing transparent insights into data dependencies, validation results, and migration progress, it helps SAP and IT leaders make informed decisions and maintain confidence at every stage — from preparation prior to migration to continuous improvement afterwards.

S/4HANA migration is ultimately a test of organizational discipline: how well teams can align technology, data, and people toward a unified goal. With the right preparation, tools, and governance, it becomes a transition to a new platform, as well as a launchpad for smarter, more connected, and future-ready enterprise operations.

Not sure where to start? Request a DataLark demo and explore how it can streamline your S/4HANA migration.