Are You Practicing “Bad Data Science” with your Pre-Hire Talent Assessments?

Talent Analytics, Corp. uses data gathered from our own proprietary talent assessments as one input variable to predict hiring success – pre-hire. We treat this dataset just like any other dataset in our predictive work. We are careful to analyze it for a strong (or weak) correlation to actual job performance. Our theory? If there is no correlation between data gathered via this method - our clients should stop using it. 

Continuing any practice in an organization without proof of success would be a little like a doctor “knowing” a certain medication doesn’t work, but they continue prescribing the medication. Malpractice at the very least.

Like all successful predictive solutions, we use the most current predictive analytics methods any top data scientist would use - with any dataset - to find if there are strong patterns in human attributes that predict either “lasting in a role” or achieving some kind of KPI performance like sales performance, calls per hour, balanced cash drawers, customer satisfaction scores, errors and the like. 

We use methodologies that include training datasets, validation datasets and lots of cross validation which all lead to the highest level of rigor called Criterion Validation of our talent assessment. 

Criterion validation proves the correlation between certain assessment characteristics – and specific performance in a specific role.  Many global gov't looks for this level of criterion validation before they "approve" talent assessments for the hiring process.

If your business (or your talent assessment vendor) can repeatedly show that your talent assessments accurately increase your hiring success, then it clearly makes sense to continue using them. If you can’t – what’s the point?

I am stunned by how few businesses (or assessment vendors) take the time to analyze their talent assessment dataset to see if it provides any positive or negative value.

We recently evaluated another vendor’s solution to see if it accurately predicted customer service scores (pre-hire) in Bank Tellers. It was predictive – but negatively so meaning, (click here to read more)