.png)
Why Data Quality Matters (Even More Than You Think!): Part 3 – Post-Migration
- Adsotech
- Mar 20
- 6 min read
Espoo, Finland - 20.03.2025 Finalizing a migration to S/4HANA is an incredible achievement, but maintaining success, requires ongoing effort. Without the right post-migration strategies, even the most well-executed migration can run into challenges as data issues emerge over time. Governance, automated quality checks, and data enrichment are vital to keeping your data accurate, consistent, and ready for use.
In this final part of our series, we'll explore key techniques and tools for maintaining data quality after migration, ensuring that your S/4HANA system remains a valuable and evolving business asset.
1. Validation and Reconciliation: Ensuring Data Accuracy After Migration
After a migration—especially one as complex as an S/4HANA transition—validating and reconciling data between the old and new systems is critical. But even in smaller migrations or system upgrades, ensuring data consistency is just as important.
One of the biggest post-migration challenges is confirming that your data remains accurate, complete, and properly structured. While this is essential for an S/4HANA migration, we have seen customers apply the same approach to check for data inconsistencies across their Quality and Production environments. During the pre-migration phase, we also explored how discrepancies can exist across different sources, such as Salesforce and SAP—issues that don’t disappear after go-live.
Key differences between ECC and S/4HANA—such as:
Changes in GL account structures,
Business partners replacing customers and vendors,
Shifts in material numbers, and
Open documents like sales orders or purchase orders
These shifts can lead to discrepancies that affect financial reporting, supply chain operations, and customer service.
If left unchecked, these inconsistencies can result in incorrect financial reports, supply chain disruptions, and customer service failures. Traditional validation methods—often slow, manual, and requiring multiple tools—are unsustainable for large-scale migrations.
Why Do These Differences Occur?
Some of the most common causes of data mismatches include:
Structural differences between ECC and S/4HANA (e.g., customers and vendors being merged into the Business Partner model).
Errors in transformation rules during migration (e.g., missing fields or incorrectly mapped data).
Missing dependencies (e.g., inactive cost centers not carried over to the target system).
Traditionally, addressing these challenges requires heavy IT involvement, making it a time-consuming and complex process. At Adsotech, we advocate for a business-centric approach, empowering business users—who know the data best—to take charge of validation.
The Solution? Automated Validation and Reconciliation
Instead of a slow, labor-intensive process relying on multiple tools, we use Data360 Analyze to automate and centralize validation.
For example, a financial company validated 60 million records in just two hours, uncovering 12,000 errors before they could cause operational disruptions—all without manual effort or complex coding.
If you have two reference datasets you need to compare, we would be happy to explore running a test validation for you. Reach out to us to discuss how we can help!
2. Data Governance: Creating a Framework for Consistent Quality
After migration is completed, a strong governance framework is essential to ensure that data quality remains a priority. Data governance is more than a set of rules—it’s a structured approach to managing data, defining quality standards, and assigning responsibilities. By creating clear data policies, companies can ensure that quality standards are met consistently, and that data remains relevant as the organization evolves.
Effective governance ensures your data is accessible, reliable, and actionable, enabling organizations to make informed decisions and maintain operational efficiency in their S/4HANA systems.
Key Governance Strategies:
Define Data Rules: Establish and enforce rules to maintain data accuracy and relevance. Ensure these rules align with both business objectives and regulatory requirements, creating a clear framework for consistent data handling.
Assign Responsibilities: Appoint data stewards to monitor governance policies and uphold data quality.
Monitor Compliance: Conduct regular audits to ensure adherence to governance standards, proactively identifying and resolving inconsistencies.
Document Governance Policies: Maintaining a centralized repository of data management policies, standards, and processes to ensure accessibility and accountability across the organization.
Enable Collaboration Across Teams: Foster collaboration between IT, data stewards, and business users to bridge the gap between technical and operational data needs.
Measure and Report Data Quality: Establish key performance indicators (KPIs) to track data quality and governance effectiveness. Monitor and report on these metrics to drive accountability and demonstrate improvements over time.
Data360 Govern enhances governance by automating critical tasks such as policy enforcement, compliance tracking, and stewardship assignments. It provides a comprehensive framework for managing data accuracy, relevance, and availability—ensuring that governance efforts are scalable and sustainable. With the ability to integrate data from multiple sources, track quality metrics, and support collaboration, Data360 Govern transforms governance into a streamlined, effortless process.
3. Automated Data Validation: Safeguarding Accuracy Over Time
Manual data accuracy checks are inefficient and prone to human error, especially as data volumes grow. Automated quality checks solve this issue by continuously monitoring for inconsistencies and triggering alerts when issues arise. This proactive approach ensures fast resolution, minimizing disruptions to operations.
Best Practices for Automated Quality Checks:
Establish Routine Reviews: Use automated workflows to schedule regular data quality audits. These audits identify patterns and inconsistencies early, reducing the need for constant manual oversight.
Integrate Alerts: Real-time notifications enable quick responses to errors or discrepancies, ensuring prompt issue resolution without business interruptions.
Continuous data validation: Automated checks maintain consistent data quality across all systems and workflows, ensuring operational accuracy and compliance.
With Automate Evolve, companies can optimize data validation processes without requiring manual intervention. The platform automates workflows to validate data, detect discrepancies, and maintain integrity—ensuring data accuracy from the outset. With robust governance features and deep SAP integration, Automate Evolve enhances efficiency across all systems.
At Adsotech, we’ve championed this approach for years. We firmly believe that data ownership should rest with those who understand it best—not just in terms of quality, but also in terms of context and relevance. Business teams are closer to the data, grasping its nuances and how it fits into the larger business ecosystem.
Empowering Business Users in Process Management
This is why involving business users in the creation and ongoing maintenance of workflows isn’t just beneficial—it’s essential. By giving them ownership over process automation, organizations ensure higher-quality, more reliable data across all business functions.
The Benefits of Business-Driven Automation
When business users take an active role in managing data and workflows, organizations see:
More accurate, contextual, and relevant processes,
Faster issue resolution, since users closest to the problem can make real-time adjustments,
Reduced dependency on IT, enabling more agile and adaptable data management, and
Increased engagement and accountability, as business users take ownership of their data and processes.
Automate Evolve: The Power of Business-Led Automation
With Automate Evolve, companies can put data quality management into the hands of business users, ensuring processes are agile, efficient, and aligned with real business needs. By integrating deep SAP automation with a user-friendly governance framework, Automate Evolve makes it easy to validate and manage data without requiring technical expertise.
By embracing citizen development and business-driven data management, organizations can future-proof their S/4HANA environments, ensuring data remains accurate, accessible, and business-ready.
4. What Else to Keep in Mind in the Post-Migration Phase
Long-term success with SAP S/4HANA goes beyond governance and automation. The post-migration phase is also an opportunity to fine-tune your system and establish ongoing support to ensure continuous efficiency.
System customization and testing
After migration, customize your SAP S/4HANA system to align with business needs and integrate seamlessly with workflows. Thorough testing identifies and resolves potential issues, ensuring smooth operations and reliable system performance under real-world conditions.
Final Deployment and Ongoing Support
The final deployment marks the completion of your migration journey, requiring careful planning and monitoring to quickly resolve any issues. Ongoing support ensures smooth operations, with dedicated teams available to assist employees and optimize system performance.
Conclusion
A successful SAP S/4HANA migration is just the beginning. Maintaining data quality and system performance during the post-migration phase requires an ongoing commitment to strong governance, automated quality checks, and continuous optimization.
By implementing a robust data governance framework, leveraging automation tools like Data360 Analyze, Data360 Govern and Automate Evolve, your organization can ensure that SAP S/4HANA data remains accurate, efficient, and aligned with business goals—not just today, but well into the future.
You can read the first part here: Why Data Quality Matters (Even More Than You Think!): Part 1 – Pre-Migration Essentials for S/4HANA Success
and second part here: Why Data Quality Matters (Even More Than You Think!): Part 2 – During Migration
Watch the recording of the "How to boost the Data Quality for S4/HANA" webinars here: Webcasts | Adsotech.com
Comments