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4 people answered: Dhari, Nurdes, Seena and 1 more
Prioritize GDPR Cleanup with Automated Controls
The most important thing I learned when integrating a Human Resources Information System (HRIS) for a technology firm in Berlin was to not underestimate the amount of data that needed to be cleaned up due to the General Data Protection Regulation (GDPR) and that it would result in out-of-date consent fields in 15% of employee records, which puts employers at risk of being fined up to €20 million because of the German Federal Data Protection Act (BDSG).
The project migrations I was involved with had to be halted twice for this reason and took an additional three weeks to complete. Using an anonymized sample and performing a pre-clean audit, we were able to determine that the estimated accuracy of the data was 95%, which allowed us to move forward with the system implementation.
We tested the data in the system with a pilot of 500 records, which resulted in no compliance flags, whereas before, it was complete chaos. If I were to do this again, I would implement automated tools such as OneTrust from the beginning of the project to minimize issues after the initial audit. To new entrepreneurs, I encourage you to conduct a privacy audit first because the German authorities will not tolerate sloppiness!
Own Your Data End to End
This one is close to my heart because I've led three HRIS-level implementations, and they are absolutely character builders.
The most valuable lesson I learned? No one cares about your data as much as you do.
Not your vendor. Not the implementation team. You do. And that means you must stay focused, detail-oriented, and deeply involved from start to finish.
What Data Migration Really Teaches You
1. Implementations are never as fast as they say they will be.
Timelines look clean in project plans. Reality includes nuances, system errors, and configuration gaps you couldn't have anticipated. There are always edge cases 0 payroll timing, tax setup, accrual rules, permissions - that require deeper review.
2. "Like-to-like" rarely means identical.
Systems don't use the same language. Field names may match, but functionality often doesn't. You have to carefully map true equivalents, understand calculation logic, and validate how workflows operate. Assumptions are expensive in HRIS transitions.
3. You don't know what you don't know.
Implementation teams can be siloed. Payroll looks at payroll. Benefits looks at benefits. But HR sees how everything connects. That's why it's critical to ask tough, cross-functional questions and source feedback from your HR community. Someone else has likely experienced the issue you're about to encounter.
4. Audit and test more than you think you need to.
Large configuration fixes late in the process can create even larger problems once you transition to service. Take the time to truly test — parallel runs, reporting audits, workflow validation. Slow down to speed up.
How I'd Approach It Again
1. Build in more testing time than recommended
2. Document every assumption and create SOPs
3. Challenge "quick fixes"
4. Clarify data governance before go-live
Implementations are intense. They expose every operational gap. But with the right vendor partnership and strong internal ownership, once the system is live and clean, you'll be happy you did it.
Treat Conversion as Strategic Transformation
The most valuable lesson I learned during an HRIS integration—particularly in a Workday implementation—was this: data migration isn't a technical task, it's a transformation strategy.
In one large-scale integration I led, we treated data cleanup as a parallel workstream rather than the foundation of the entire project. On paper, our legacy data looked "good enough." In reality, it carried years of inconsistencies—duplicate employee records, outdated org structures, mismatched job codes—that only surfaced once we began mapping into Workday's structured framework. We spent weeks in reactive cleanup, eroding stakeholder confidence and compressing testing timelines. We went live successfully—but harder than it needed to be.
That experience reframed my approach in three important ways, all aligned with best practices highlighted by Kandor Solutions in their Workday implementation guidance.
1. Start data discovery early—before configuration.
Data readiness should begin 3-6 months before build. Kandor emphasizes rigorous audits and validation cycles at the outset of implementation. When you cleanse and standardize data before design decisions are finalized, you avoid retrofitting configuration to flawed inputs.
2. Run multiple mock migrations with structured reconciliation.
Migration should be iterative, not event-based. I now advocate for at least three full-volume mock loads with reconciliation reports baked into the plan. Kandor's approach reinforces the importance of validation checkpoints—comparing legacy totals to Workday outputs so discrepancies are resolved before cutover, not after go-live.
3. Establish shared data governance across functions.
Workday touches HR, payroll, finance, and recruiting. Data ownership can't sit in a silo. Kandor's implementation philosophy underscores cross-functional accountability, especially around foundational elements like positions, supervisory organizations, and cost centers. When governance is shared, data integrity improves—and so does long-term reporting confidence.
If I were approaching that integration again, I'd shift the timeline forward, formalize governance from day one, and treat mock conversions as rehearsals—not milestones.
Because in an HRIS transformation, clean data isn't just operational hygiene. It's organizational trust.
Define Rules and Single Source of Truth
The most valuable lesson was that data migration is a business rules problem, not a spreadsheet problem. If you do not define a single source of truth, precedence rules between systems, and an audit trail for changes, you end up arguing about whose record is "correct" the moment payroll, leave, and headcount reports do not match. Next time I would start with a tight data dictionary, owners for every field, and two dry runs with reconciliation and exception reports, plus a short parallel run for anything that touches pay or entitlements.
