A 2021 SAPinsider Benchmark Report found that 67% of SAP SuccessFactors implementation teams identified data accuracy, consistency, and completeness as the most significant challenge to project success. Source: SAPinsider Benchmark Report, ‘HR Technology and Solution Adoption,’ 2021.

But the problem is not just about cleansing, accuracy or legacy formatting. It is about ownership. Across multiple client projects, we have been called in, in the lead-up to Mock 1 data loads, when data is either on a critical path or has been raised by the Steering Committee as a key project risk. In nearly every case, the root cause is the same. No one formally owns the data stream, or at least not early enough to prevent issues before they escalate.

Everyone agrees data is important. HR relies on accuracy, payroll depends on completeness, and IT takes care of the extraction. The implementation partner loads the finished files, while SMEs are pulled in as needed to help with mapping. Validation typically falls to whoever has time.

In theory, it sounds collaborative. In practice, it creates a lack of clear ownership—and the results are all too familiar:

• Mapping stalls while teams try to understand data definitions
• Load files are assembled inconsistently and are incomplete
• only a small percentage of data is load ready and there are serious quality issues.
• SMEs push back because they are time-crunched and validation responsibilities were never clearly defined
• Testers scramble to fix structural issues they can’t explain

Eventually, program leadership discovers what should have been obvious. You can outsource configuration. You cannot outsource ownership.

Owning the data stream does not mean doing it all internally. It means creating a structure where decision-making is clear, issues are visible, and accountability is embedded.

That structure must include:

• Named data owners for each object, with business context and authority
• Validators responsible for sign-off at each mock load cycle
• A clear RACI that covers mapping, cleansing, transformation, and load readiness
• Documented entry and exit criteria for every test cycle
• Tooling that tracks version history, error logs, cleansing actions, and approvals

This is not just governance for governance’s sake. It is the difference between:

• Fixing a mapping error in cycle 1 versus discovering it in payroll rehearsal
• A reconciled UAT load versus a last-minute data scramble
• Go-live confidence versus executive escalation

Most implementation partners are not contracted to manage the data stream. Even if data is in scope for the partner, they cannot define your business logic or validate your data.

They are responsible for building and configuring the new system. They are not responsible for transforming your legacy data into that system’s structures. They will generally expect load ready data in the format they have provided and in the timeline they expect it, multiple times thought the project.

They will generally not:

• Own the business meaning of your fields
• Have the capacity to walk your SMEs through the data definitions required by SuccessFactors
• Define which job codes collapse or split
• Map old hierarchies into new org structures
• Validate what counts as an active employee record

These are business decisions that require context, judgment, and formal ownership. Without clear accountability and sign-off, the project quietly absorbs risk. Issues surface too late, responsibilities become unclear, effort is duplicated, and data quality inevitably declines.

At Coriza, we work with clients to build a governance model that de-risks the data stream and embeds accountability.

That model includes:

• Data stream formally integrated and accounted for within the RACI
• Standing checkpoints for mapping, validation, and reconciliation
• Central tooling for Data Object Mapping Documents (DOMDs), load logs, validation reports, and issue tracking
• Entry and exit criteria for every mock load
• A lead who owns the data stream from start to finish

We make ownership visible. We make quality trackable. We make sign-off predictable. Success in SAP SuccessFactors projects depends not only on the data being loaded, but on having the right people making the right decisions about that data throughout the process.

Download the full eBook: here

Our eBook, Why Data Migration is the Silent Risk in SAP SuccessFactors Projects, walks through:

• What most teams miss when they plan their data stream
• How to build a governance model that drives quality
• The tools and roles required for successful mock loads
• Why clean data is not the same as ready data
• What it takes to protect your test cycles and go-live readiness

• Read Our Blog: Your Best Practice Guide to SAP SuccessFactors Data Migration
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