Migrating candidate data from a legacy applicant tracking system into SAP SuccessFactors without losing information generally comes down to choosing between three approaches: manual re-entry, a generic file-based export and import, or an automated parsing and enrichment tool built specifically for recruiting data. Only the third approach reliably preserves the structure and completeness of resumes and candidate histories, because it interprets unstructured document content the way a recruiter would, rather than moving raw files or hand-typed fields that inevitably drop information along the way.
For UK-based talent acquisition and HR operations teams weighing a move to SAP SuccessFactors, the choice of migration approach is rarely framed as a strategic decision at the outset. It often looks like a scoping question buried inside a broader systems project. That framing tends to under-value what is actually at stake, because the approach chosen for candidate data migration determines whether the new system launches with usable, standardised records or with a backlog of data quality issues that recruiters will be fighting for years afterwards.
The first approach, manual re-entry, is the default when organisations underestimate the complexity of the task. A project team exports records from the legacy system and has staff or contractors manually key data into SuccessFactors fields. This works passably for small candidate pools but becomes unmanageable at scale, and it introduces exactly the inconsistency you would expect from human judgement applied across tens of thousands of records over several weeks. Skills get categorised differently depending on who is entering them. Employment dates get transposed. Duplicate candidates go unnoticed because no reviewer has time to cross-check every record against every other one.
The second approach, a generic bulk file transfer, treats the migration as an IT data-transfer problem rather than a content interpretation problem. Resumes and candidate notes get exported as files or flat data and imported wholesale, with minimal restructuring. This is faster than manual entry but solves the wrong problem: SuccessFactors expects structured fields, not attached documents, so recruiters searching the system after go-live often find that candidates are technically present but not properly searchable, because the underlying content was never parsed into the fields the search index relies on.
The third approach, purpose-built automated migration, differs by actually reading and interpreting each resume or candidate record using natural language processing trained specifically on recruiting content. This extracts structured data, such as work history, qualifications, and skills, and maps it directly into SuccessFactors’ schema, with validation logic that catches duplicates and incomplete records before they land in production. This is the approach behind Data Migration for SAP SuccessFactors, which is built to deliver a zero-loss transfer with AI-driven enrichment rather than a like-for-like copy of whatever quality the legacy data happened to be in.
When evaluating these approaches, UK organisations should weigh a few concrete criteria. Accuracy of field extraction matters most: does the tool correctly separate a candidate’s most recent role from earlier positions, and does it recognise UK-specific qualification frameworks and terminology rather than defaulting to US-centric assumptions? Data provenance and security matter almost as much, particularly given obligations under UK GDPR and the expectations set by the Information Commissioner’s Office around lawful processing of personal data during a system transition. A migration vendor should be able to demonstrate that candidate data is processed securely, that retention is limited to what is necessary for the migration itself, and that the receiving system, SuccessFactors, becomes the sole system of record once the process is complete.
Speed and scalability are the third criterion, particularly for organisations with candidate pools in the hundreds of thousands. Manual and semi-manual approaches simply cannot process that volume within a reasonable project timeline without a substantial temporary workforce, whereas automated parsing scales linearly without the same quality degradation that manual review experiences as volume increases.
A fourth, less obvious criterion is what happens to data quality after go-live. A one-time clean migration is valuable, but organisations that pair migration with ongoing structured intake, meaning every new resume is parsed and enriched the same way going forward, avoid recreating the same data quality problem a few years later when it is time to migrate again. This is where RChilli for SAP SuccessFactors extends beyond the migration event itself into standing recruiting infrastructure that keeps data consistent over time.
Organisations that have gone through this evaluation, whichever approach they ultimately selected, are worth learning from directly. RChilli’s Customer Case Studies document how employers across sectors have approached data quality and migration challenges within their SuccessFactors environments, offering a useful reference point for teams building their own evaluation criteria rather than starting from a blank page.
The practical takeaway for UK HR operations leaders comparing approaches is that the fastest option on paper, a bulk file transfer, is frequently the most expensive option in practice once the downstream cost of unsearchable, poorly structured candidate data is accounted for. Manual re-entry, meanwhile, is rarely fast or accurate at meaningful scale. An automated, recruiting-specific parsing approach tends to be the only option that satisfies accuracy, compliance, and scalability simultaneously, which is why it has become the default recommendation for organisations moving legacy candidate data into SuccessFactors this year.
Total cost of ownership is a useful lens for UK procurement teams comparing these approaches, because the sticker price of a migration project rarely reflects its full cost. Manual re-entry carries ongoing labour costs for the duration of the project and a long tail of remediation work afterwards. A generic bulk transfer looks inexpensive upfront but generates support tickets and recruiter complaints for months as data quality issues surface gradually. Purpose-built automated migration carries a defined project cost but produces a predictable, auditable outcome, which UK finance and procurement teams increasingly weight more heavily than upfront price alone when comparing vendor proposals.
Procurement teams should also confirm how a prospective migration partner is actually distributed and supported, since this affects contracting complexity and integration risk. Solutions available directly through recognised marketplaces, including the SAP Store, tend to carry a lower integration risk profile because the connection to SuccessFactors has already been validated by the platform vendor, rather than being a bespoke integration built and maintained solely by a third party. That distinction is easy to overlook during an initial comparison but becomes important once the migration moves from a proof of concept into a live production rollout affecting the entire recruiting team.
Ultimately, the right approach for a given organisation depends on candidate pool size, internal data quality standards, and how tightly the migration timeline is fixed to a broader systems programme. But across the comparisons UK HR operations leaders tend to run, the pattern is consistent: automated, recruiting-specific parsing outperforms both manual entry and generic file transfer on accuracy, speed, and long-term maintainability, even when it carries a higher upfront project cost than the alternatives.
Vendor demonstrations are useful, but they rarely reveal how a tool performs against an organisation’s actual legacy data, which is often messier than any demo dataset. A more reliable evaluation step is requesting a trial migration against a representative sample of real records, then reviewing the output field by field alongside the recruiting team who will ultimately rely on it. That single step surfaces more useful information than a week of vendor presentations, because it exposes exactly how well a given approach handles the specific quirks of an organisation’s historic data, from inconsistent date formats to region-specific job titles that a generic parser might misclassify.