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Why self-assessment alone is not enough, and how skills validation can improve confidence in workforce decisions.
Organisations can build greater trust in skills data by moving beyond self-declared proficiency and adding appropriate forms of validation, such as structured assessments, endorsements, evidence submissions and workplace observations. The right level of validation depends on how important the skill is and what business decision the data will support.
Many organisations have already invested significant time in defining skills, building job profiles and encouraging employees to populate their skills profiles in SAP SuccessFactors.
But having skills data and trusting skills data are two different things.
This becomes especially important when that data is being used to support decisions around workforce planning, learning investment, internal mobility or assigning people to critical initiatives. If managers and HR teams are not confident that the information accurately reflects what employees can do, they may still rely on manual conversations, personal networks or off-system processes instead.
The challenge, therefore, is not simply how to collect more skills data. It is how to build enough confidence in that data to use it for meaningful workforce decisions.
Self-assessment is useful for building an initial skills baseline because it is quick, scalable and gives employees an opportunity to reflect on their own capabilities.
However, self-assessment is inherently subjective.
Two employees with similar experience and capability may rate themselves very differently. One may be cautious and select a proficiency level of 3, while another may confidently choose 5. The numbers are different, but their actual capability may be very similar.
A consistent rating scale does not always create consistent data.
When that inconsistency is multiplied across an entire workforce, it can become difficult to determine whether a reported skills gap is real, the result of under-rating or simply a difference in how employees interpret the scale.
This does not mean self-assessment has no value. It means organisations may need additional evidence when the decisions being made require a greater level of confidence.
Skills data quality matters because organisations increasingly use skills information to support workforce planning, learning investment, internal mobility and talent decisions.
Workforce planning
If skills data is inconsistent, organisations may struggle to distinguish genuine capability gaps from gaps caused by cautious self-rating or inconsistent interpretation of proficiency levels.
Learning and development
Training investment can be directed towards gaps that may not exist, while real capability gaps remain hidden.
Internal mobility
When managers do not trust the data in the system, they may revert to informal conversations and personal networks to identify suitable people for opportunities. This can reduce the value of a skills-based approach and make internal mobility less transparent.
AI-powered recommendations
Skills data is also becoming increasingly important as organisations introduce AI-powered recommendations, matching and workforce planning.
These capabilities depend on the quality of the underlying data. If skills information is inconsistent or unreliable, the recommendations built on top of it will also be limited.
Skills validation is the process of confirming an employee's stated proficiency using additional evidence.
That evidence can include:
The aim is not necessarily to replace self-assessment. Instead, validation adds further layers of confidence where they are needed.
The webinar outlined two complementary approaches: assessed validation and observed validation.
Organisations do not need to apply the same level of validation to every skill.
A skills validation model can move from lighter-touch approaches to stronger forms of evidence depending on the importance of the capability.
1. Role-based or inferred skills
Skills may initially be associated with an employee based on their role or job profile.
This provides a starting point but does not confirm whether the individual can demonstrate the skill at the expected level.
2. Employee self-assessment
Employees rate their own proficiency.
This is useful for creating an initial skills baseline, but it represents the lightest form of evidence.
3. Structured assessment
Employees complete a formal assessment designed to test their knowledge or understanding of a skill.
4. Endorsement
Another person, such as a manager, colleague or qualified expert, confirms the employee's capability.
5. Evidence submission
Employees provide supporting material, such as certifications or examples of completed work, which can be reviewed by a skills expert.
6. Workplace observation
A qualified observer watches an employee perform a task and records evidence of capability in context.
These approaches are complementary rather than competing. Organisations can use different methods for different skills depending on the level of confidence required.
AssessIQ helps organisations add structured evidence behind employee skills through assessments, endorsements and evidence submissions.
Structured assessments
Employees can complete quizzes designed around a specific skill and proficiency level. The demonstration showed support for different question types, including multiple choice, true or false, fill-in-the-blank and free-text questions.
Endorsements
Employees can request an endorsement from another person, such as a colleague or manager, to provide an additional perspective on their capability.
Evidence submissions
Employees can submit supporting evidence such as certifications or project work.
That evidence can then be reviewed by a skills expert, creating another layer of confidence behind the employee's stated proficiency.
By combining these different forms of validation, organisations can move beyond a single self-declared rating and create a stronger evidence base around important skills.
Administrators can also configure assessment requirements based on the skill and proficiency level, including passing criteria, number of attempts, endorsements and evidence requirements.
ObserveIQ supports skills validation where capability needs to be demonstrated in practice.
A qualified observer can watch an employee perform a task, assess their performance against defined criteria and capture evidence during the observation.
In the webinar, the example involved an employee being observed while demonstrating how to clean up a chemical spill. The employee could see what they would be assessed on before the observation began, while the observer could assess individual tasks against defined success and failure criteria.
ObserveIQ supports:
Workplace observation is particularly useful when a skill is safety-critical, compliance-related or difficult to validate through knowledge testing alone.
The webinar highlighted regulated and safety-critical environments such as:
In these scenarios, knowing that someone understands a task may not be enough. Organisations may need evidence that the person can perform it correctly in practice.
However, observation is not limited to compliance use cases. The session also highlighted growing interest from organisations that want stronger evidence of capability for other business-critical activities.
No. Organisations do not need to formally validate every employee skill. The level of validation should reflect:
For example:
Use case |
Possible validation approach |
|
Broad employee skills profile |
Self-assessment |
|
Development planning |
Self-assessment and endorsement |
|
Specialist capability |
Structured assessment and evidence |
|
Business-critical skill |
Stronger assessed validation |
|
Safety or compliance-critical capability |
Direct workplace observation |
The aim should be to focus validation where it will provide meaningful additional confidence, rather than introducing unnecessary assessment across the entire workforce.
A skills-based organisation needs more than a catalogue of skills.
It needs enough confidence in those skills to use them when:
This is why skills data quality should not be treated as an end goal in itself.
The purpose of better skills data is to support better decisions, reduce reliance on gut feeling or off-system processes, improve compliance where required and give employees greater confidence that their capabilities are recognised.
The most mature skills strategies are therefore unlikely to be the ones with the largest number of skills or the fullest dashboards.
They will be the ones that understand which skills matter most, where additional validation is needed and how that evidence can improve decision-making.
For organisations using SAP SuccessFactors, building a skills foundation is only one stage of becoming a skills-based organisation.
The next question is whether the data can be trusted enough to support the decisions that matter. Self-assessment will continue to play an important role, but where greater confidence is needed, assessments, endorsements, evidence and workplace observations can provide additional validation.
The goal is not to validate everything.
It is to validate the skills that matter most, and create skills data that people can confidently use to make better workforce decisions.
Want to build greater confidence in your skills data?
TalenTeam helps SAP SuccessFactors customers move from defining and collecting skills to validating them through AssessIQ, ObserveIQ and the wider Skills Transformation Suite.
Talk to TalenTeam about your skills validation strategy.
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