From an SLCP Expert: Not just quality assurance, but quality control
By Daniela Kipke, Manager, Data Quality Monitoring & Investigations
The Social and Labor Convergence Program (SLCP) is raising the bar on data integrity. Recently, we expanded our quality framework beyond what we have always called ‘quality assurance’ to incorporate direct ‘quality control’ measures.
Instead of catching errors after the fact, when bad data has already slipped through the system, we have now implemented new automated data quality checks that stop bad data slipping through. While SLCP has always had automated checks on reports submitted by Verifiers, earlier this year we introduced Blocking Checks - a game-changer for data quality and integrity.
Through these new checks, we catch and correct mistakes before they ever reach the final report. This shift to quality control stops bad data reaching facilities and brands.
How does it work?
After a Verifier submits a report, automated checks run instantly. If no issues are flagged, the report is finalized, and the facility receives a notification their final report is ready to review.
However, when the automated checks run and find mistakes, Verifiers are informed through a ‘Blocking’ or ‘Advisory’ Check.
If a Blocking Check is triggered, the process stops in its tracks. The report automatically bounces back to the Verifier, triggering a notification to both the Verifier and the Verifier Body (VB). The report cannot be resubmitted until all Blocking errors are fixed and resolved.
Advisory Checks are also in place to alert Verifiers to potential issues that warrant a second look before final submission. These checks can still lead to Corrective Action Requests to VBs if they are repeatedly not addressed, but they don’t stop a report from being submitted.
Find out more about the checks on our Helpdesk
What mistakes are Blocking Checks catching?
Blocking Checks are designed to catch critical errors that must be resolved prior to report finalization. For example, if the response to a question indicates that there is a legal non-compliance, but the Verifier hasn’t raised a legal flag, our automated data quality checks will pick this up.
Other key examples include:
Inconsistent answers or contradictions (for example, answering 0 workers are under 18 while also identifying the youngest worker as between the age of 12-17)
Enforcing explanations from Verifiers when a legal non-compliance is raised
Not including explanations for when data is updated during the Verification
A future of better data
By putting these Blocking Checks in place before a final report reaches a facility, we are reducing the likelihood that incomplete, inconsistent, or inaccurate data reaches the customer.
These new checks represent months of dedicated collaboration between the Assessment & Data Quality Team and the Tech Team at SLCP. Following a successful pilot period that allowed Verifier Bodies to adapt, the initial Blocking Checks officially went live in June 2026, with a further Blocking and new Advisory checks rolling out in late 2026.
Quality control means putting up the right barriers so only reliable, high-integrity data gets through. With these new stops in place, we are protecting our customers and elevating data standards.
Daniela is SLCP’s Manager of Data Quality Monitoring & Investigations. She has over 10 years of experience working in a global brand’s social compliance department, with a strong focus on quality assurance of assessment reports and related processes, as well as data governance matters.
