In today’s world SAP remains the enterprise backbone for thousands of organizations. With SAP S/4HANA adoption accelerating, especially ahead of the 2027 support deadline for ECC, companies are investing millions in modernizing their ERP landscapes. SAP migrations—whether to the cloud, S/4HANA, or through mergers and consolidations—are strategic initiatives designed to deliver agility, real-time insights, and long-term competitiveness.
But there’s a silent killer that continues to derail these ambitions: data. Data is often called the lifeblood of an enterprise, but in SAP migration projects, it can also be a hidden threat. While organizations pour immense time, money, and energy into technical configurations, change management, and training, data quietly slips under the radar—until go-live exposes the cracks. While working on ERP projects in the manufacturing, real estate, retail, and service sectors, if your data is messy, the entire ERP project can fall apart. .
Why Poor Data Quality Destroys ERP Projects
In every ERP implementation I’ve led, data quality has been the most underestimated risk. Bad data—like duplicates, wrong entries, or missing values—can seriously damage operations once you go live.
The cases where poor data led to:
- Delays in production
- Wrong stock levels
- Invoice mismatches
- Decision-making based on incorrect reports
By Oracle more than 80% of data migration projects run over time and/or over budget. Cost overruns average 30%. Time overruns average 41%. 83% of data migration projects either fail or exceed their budgets and schedules.” Read more
The Silent Risk in SAP Migrations
SAP migrations—whether transitioning from ECC to S/4HANA, moving to SAP Public Cloud, or consolidating systems post-M&A—are complex beasts. Everyone focuses on functionality, integrations, testing, and change management. But the data risk sits quietly in the background, waiting to explode when the system goes live.
System Integrators (SIs) are often caught in the crossfire. The blame game begins when reports are inaccurate or transactions fail, and nobody remembers that the source data was never validated, deduplicated, or cleaned up in the first place.
The Real Cost of Ignoring Data Quality
Poor data doesn’t just break systems; it breaks trust. When users see incorrect master data, failed transactions, or inconsistencies, confidence in the new system plummets. That’s when adoption slows, change fatigue increases, and projects start hemorrhaging money.
Let’s look at the real-world consequences:
- Manufacturing: Inaccurate BOM data leading to wrong product builds.
- Retail: Incorrect SKU hierarchies causing inventory mismatches.
- Real Estate: Errors in contract data leading to legal exposure.
- Service Industry: Duplicate customer records affecting SLA commitments.
Why This Problem Persists
Despite decades of ERP transformations, the problem persists because:
- Data ownership is unclear: Who owns the data? IT? Business?
- Data is everywhere: Spreadsheets, legacy systems, SaaS tools—data silos abound.
- Migration tools are misused: ETL tools can move data, but they don’t cleanse or validate it unless properly configured.
- Timelines are aggressive: Cleaning data takes time, and project deadlines are rarely flexible.
“According to Gartner, over 90% of data migration projects run over time or budget, mainly due to poor data quality.”
What System Integrators (SIs) Can Do Differently
Here’s the opportunity for forward-thinking SIs: make data a core pillar of your SAP migration strategy.
- Integrate Data Discovery in the Planning Phase: Before you even think of blueprinting, do a full data landscape assessment. Understand where the data lives, who owns it, and what state it’s in. Use profiling tools to get an empirical view of data quality.
- Treat Data Like a Product: Just like you have project managers, developers, and testers, and have a data product owner who drives quality and user alignment.
- Define a Data Cleansing Strategy: Don’t leave it to business users to clean spreadsheets manually. Set up structured data cleansing using proven frameworks:
- Deduplication algorithms
- Master data governance tools
- Standardization templates
- Business rule-based validations
- Validate with the Business: Business users must sign off on data. This is not optional. Use sample data walkthroughs, pilot migrations, and parallel runs to ensure alignment.
- Adopt a Phased Migration Approach: Big-bang migrations are a high-risk gamble. Consider a phased migration where core entities go live first (e.g., customer, materials) followed by more complex datasets.
Best Practices for Clean, Validated, and Trusted Data
Here’s a simple 4-step process that works:
- Data Assessment
- Identify all data sources (Excel sheets, legacy systems, cloud apps).
- Profile the data for completeness, consistency, and accuracy.
- Data Cleaning
- Eliminate duplicates.
- Correct outdated or incorrect records.
- Standardize formats across systems.
- Data Validation
- Involve business users in validating sample records.
- Perform dry runs before go-live.
- Data Migration
- Break migration into stages.
- Run pilots and get user validation.
- Post-migration, monitor data actively.
Where Datavapte Comes In
This is where platforms like Datavapte offer a distinct advantage.
Datavapte empowers SIs and customers to:
- Profile and cleanse large volumes of SAP and non-SAP data.
- Automate rule-based validation and transformation.
- Maintain traceability and audit logs for compliance.
- Align IT and business stakeholders through intuitive workflows.
Unlike generic ETL tools, Datavapte is built for SAP-centric environments, and dashboards tailored to complex ERP migration landscapes.
For SIs, using Datavapte doesn’t just improve quality—it reduces time to value, lowers project risk, and helps win client trust.
Key Use Cases Where Data Matters Most
- ECC to S/4HANA Transition: Clean vendor and material master data to avoid purchase order rejections.
- M&A Integration: Harmonize customer data across merging organizations.
- Cloud Migration: Standardize pricing conditions for consistent invoicing.
- SAP Central Finance: Consolidate financial data distributed from SAP and non-SAP systems.
Recommendations for SAP Projects
- Make data a first-class citizen in every ERP project.
- Embed data quality KPIs in your project health metrics.
- Invest in data readiness early rather than firefighting post go-live.
- Use purpose-built tools like Datavapte to bring structure and automation.
Final Thoughts
ERP is a powerful tool—but only if the data inside it is clean and reliable. I’ve seen how skipping proper data prep can turn a promising project into a nightmare.
If you’re planning an ERP implementation, don’t treat data migration as a technical task. Make it a business priority. The cost of ignoring data is not just financial—it’s organizational.
For System Integrators, the path forward is clear: own the data challenge and partner with solutions that understand the stakes.
With platforms like Datavapte, there’s finally a way to bring rigor, repeatability, and transparency to SAP data migrations.
Don’t let bad data be the silent killer of your next project. Clean it. Validate it. Trust it. :
Ready to Stop Letting Bad Data Derail Your SAP Projects?
If you’re leading or supporting an SAP migration—whether it’s ECC to S/4HANA, a move to the cloud, or a complex integration—you can’t afford to leave data quality to chance.
Make data your first priority—not your final regret.
Discover how Datavapte can help you take control of your data migration journey:
- Cleanse and validate data before go-live
- Align business and IT teams around a single source of truth
- Reduce project risk and timeline overruns
- Build client trust through transparent, audit-ready workflows
💡 Get a personalized walkthrough of how Datavapte empowers successful SAP migrations.
Schedule a demo or talk to a data specialist today.
