Cleaning up incomplete candidate data in SAP SuccessFactors generally comes down to one of three approaches: manual record-by-record correction, a one-off bulk data cleansing project, or automated reprocessing that re-parses stored resumes against current extraction rules and writes standardised fields back into each profile on an ongoing basis. Of the three, automated reprocessing is the only approach that stays current as new parsing improvements arrive, because it treats data quality as a continuous process rather than a project with a fixed end date. For HR operations teams comparing options, the right choice usually depends on data volume, how frequently the underlying taxonomy changes, and how much recruiter time the organisation is willing to spend on maintenance.

Most organisations running SAP SuccessFactors Recruiting Management arrive at the data reprocessing question from the same starting point: someone notices that candidate search results look thinner or less consistent than the applicant volume would suggest. From there, the paths diverge, and it is worth setting out what each approach actually involves before choosing one.

Manual correction

The most common first response is to have a recruiter, recruitment coordinator, or HR operations analyst manually review flagged profiles and correct missing or mismatched fields. This works reasonably well for small, high-priority candidate pools, such as an executive search shortlist, where the volume is low enough that a careful human review adds genuine value. It breaks down quickly at scale. A team processing hundreds or thousands of applications a month cannot dedicate the hours required, and manual review introduces its own variability, since different reviewers will standardise skills and job titles slightly differently. Manual correction is also inherently retrospective: it fixes what already went wrong but does nothing to prevent the same categories of error from recurring with the next batch of applications.

One-off bulk cleansing projects

A step up from manual correction is a scoped data cleansing project, often run with the help of an external consultant or a temporary internal task force, to correct historical records in one coordinated push. This approach can meaningfully improve data quality at a point in time, and it is often the right move when an organisation is migrating between systems or consolidating multiple SAP SuccessFactors instances. The limitation is durability. Once the project ends, data quality begins degrading again immediately, because new applications keep arriving and parsing logic keeps evolving. Without a mechanism to reprocess data on an ongoing basis, most organisations find themselves commissioning a similar project again within eighteen to twenty-four months.

Automated, ongoing reprocessing

The third approach treats reprocessing as infrastructure rather than a project: existing candidate resumes are automatically re-parsed against the current version of the extraction and taxonomy engine, and standardised results are written back into SAP SuccessFactors without manual intervention. This is the model behind Data Reprocessing for SAP SuccessFactors, which is designed to keep the entire candidate database aligned to current parsing standards continuously, rather than requiring a new project every time the underlying rules improve.

The practical advantage of this approach is that it decouples data quality from staffing decisions. A recruitment operations team does not need to schedule a cleanup sprint or hire temporary help; the reprocessing runs as part of the platform’s ongoing operation. Because the underlying parsing technology handles more than 4.1 billion documents a year across a large global customer base, extraction accuracy tends to improve steadily over time, and an automated reprocessing approach means the existing candidate database benefits from those improvements automatically rather than staying frozen at whatever quality level existed when candidates first applied.

Weighing the trade-offs

None of this means manual review or project-based cleansing are always the wrong choice. A small recruiting team with low application volume and infrequent taxonomy changes may find that occasional manual review is perfectly adequate, and the cost of automating a small workload may not be justified. Larger organisations, or those operating across multiple business units or countries with high application volume, tend to see the calculus shift quickly toward automation, since the recurring labour cost of manual or project-based approaches compounds every year.

There is also a compliance dimension worth weighing when comparing vendors for this kind of work. UK organisations handling candidate data must be able to demonstrate compliance with UK GDPR and respond to ICO expectations around data accuracy and minimisation, both of which are easier to evidence when data handling follows a documented, repeatable automated process rather than an ad hoc manual review with inconsistent documentation.

Looking at how others have approached it

For teams still deciding which approach fits their organisation, it is worth reviewing how comparable recruiting operations have tackled the same problem. RChilli’s Customer Case Studies include examples of how organisations across sectors have approached candidate data quality, screening efficiency, and workflow automation within SAP SuccessFactors, which can offer a useful benchmark before committing to a particular path. More broadly, the wider set of tools available at RChilli for SAP SuccessFactors shows how reprocessing fits alongside parsing, matching, and enrichment as part of a coordinated data strategy rather than an isolated fix.

Ultimately, the comparison is less about which approach is universally correct and more about matching the method to the actual scale and recurrence of the problem. Organisations that have run the same manual cleanup exercise more than once are usually the clearest candidates for moving to an automated model, simply because the recurring cost of the alternative has already made itself apparent.

A practical decision framework

For teams still weighing these three approaches, a simple framework helps: estimate current application volume, estimate how often the organisation’s taxonomy or job title standards have changed in the past two years, and estimate how much recruiter or analyst time is currently spent on manual correction. Organisations with high volume, frequent taxonomy evolution, and measurable recurring labour cost are strong candidates for automation. Organisations with low volume and infrequent change may reasonably continue with manual or project-based approaches for the time being, provided they revisit that decision periodically rather than assuming it will remain the right call indefinitely.

What tends to tip the balance

In practice, the deciding factor for most organisations that move to automated reprocessing is not a single dramatic failure but an accumulation of smaller frustrations: a strong candidate missed in search, a shortlist that looks thinner than the applicant volume suggests, or a second manual cleanup project that makes the recurring nature of the problem impossible to ignore. Once that pattern becomes visible, the case for automation tends to build quickly, since the alternative, continuing to repeat manual or project-based fixes indefinitely, no longer looks like the lower-effort option it once did.

Revisiting the decision periodically

Whichever approach an organisation chooses today, it is worth revisiting that decision on a regular cadence rather than treating it as permanent. A recruiting operation that starts small and relies on manual review may well outgrow that approach within a couple of years as application volume increases or as the organisation expands into new regions with additional taxonomy complexity. Building a periodic review of data quality approach into the broader HR technology roadmap ensures the chosen method keeps pace with the organisation’s actual scale, rather than being locked in by an earlier decision that no longer reflects current reality.