SAP implementations are transformative but fraught with risk—especially when it comes to data. As Tjaart Malan, Head of Services for SAP Middle East and Africa, succinctly puts failed SAP implementations: “It’s either data that fails, or it’s change management.”

1. Why Data Causes SAP Implementation Failures
   1. Inaccurate or Poor-Quality Data
   2. Integration Challenges
   3. Insufficient Testing and Validation
2. How to Prevent Data-Related SAP Failures
   1. Rigorous Data Cleansing & Migration
   2. Strengthen Integration Strategies
   3. Enforce Robust Testing Protocols
   4. Establish Strong Governance & Training
3. Final Thoughts

High-profile disasters like the SPAR Group’s $107M disaster, Lidl’s €500M SAP failure and Target Canada’s $5.4B collapse underscore how poor data quality, integration gaps, and inadequate testing can derail even the most well-planned projects.

The good news? These risks are preventable with the right strategies.

Why Data Causes SAP Implementation Failures

Inaccurate or Poor-Quality Data

Faulty data migration is a silent killer. When Lidl launched its SAP system, only 33% of the data was accurate, leading to supply chain breakdowns and inventory chaos. Similarly, Target Canada’s SAP failure—fueled by over 30% data inaccuracies—left shelves empty and forced the company into a $5.4B write-down.

Lesson: Garbage in, garbage out. If your source data is flawed, your SAP system will fail.

Integration Challenges

A staggering 51% of organizations struggle with SAP data integration, leading to silos and delayed decisions. Hybrid cloud/on-premise environments and a lack of skilled resources make this worse, preventing real-time data access.

Lesson: Without seamless integration, SAP becomes an expensive bottleneck rather than a business enabler.

Insufficient Testing and Validation

Revlon’s SAP disaster—which triggered shipping delays and lawsuits—could have been avoided with proper testing. Too often, companies rush migrations without validating data accuracy and processes, only discovering errors post-launch.

Lesson: Skipping thorough testing is like launching a rocket without a pre-flight check.

How to Prevent Data-Related SAP Failures

Rigorous Data Cleansing & Migration

• Audit before migrating: Identify duplicates, missing fields, and inconsistencies.
• Adopt a phased approach: Migrate in stages to catch issues early (as SAP experts recommend).
• Automate where possible: Use data cleansing tools to standardize formats.

Strengthen Integration Strategies

• Use middleware & APIs: Tools like the Precisely Automate SAP Data API simplify SAP-to-external-system transfers.
• Prioritize pre-built connectors: Reduce manual errors and speed up cloud/on-premise unification.

Enforce Robust Testing Protocols

• Simulate real-world scenarios: Test high-volume transactions and edge cases.
• Involve end-users in UAT: Let business teams validate data before go-live.

Establish Strong Governance & Training

• Assign a dedicated data migration team: Ensure SAP expertise oversees the process.
• Train employees on data standards: Prevent post-launch degradation with proper workflows.

Final Thoughts

Data is the backbone of SAP success. Companies that invest in cleansing, integration, and testing avoid the catastrophic failures that have plagued others. As Malan’s quote reminds us—when SAP fails, it’s usually data or change management at fault. By tackling data risks head-on, you set your implementation up for long-term success.

Need help with your SAP migration? 

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