Job-based data – the role you do defines the skills you have (but does it?)

HR systems love job titles. They assume that because you have a role, you must have certain skills. A software engineer must know Java, a sales director must be good at negotiation, and a data scientist must know Python—right? 

Wrong. 

Job-based data assumes that experience = expertise. But just because someone has been doing a job for five years doesn’t mean they’ve been doing it well. 

The good stuff (pros): 

1. Easy to implement: If you have job architecture in place, this approach is straightforward.
2. Benchmarking against market trends: Helps HR compare roles internally and externally.
3. Great for role-based learning paths: Learning platforms can tailor content based on job titles. 

The bad stuff (cons): 

1. Ignores individual growth: Not every person in a role develops the same way.
2. Assumes jobs stay the same: In reality, roles evolve faster than HR systems can track.
3. Fails to capture hidden skills: People often have valuable skills that aren’t listed in their job descriptions. 

• Cornerstone OnDemand – Maps learning paths to job roles. 

• SuccessFactors – Aligns job roles with competency models. 

• TechWolf – Infers skills based on job families, creating a structured job-based skills model. 

• Visier – Uses analytics to track job-related skills trends across organisations. 

Job-based data is great for structure but terrible for nuance. If HR relies too heavily on it, we risk forcing people into outdated job descriptions instead of letting skills evolve dynamically.