Data-Driven Talent Decisions Flashcards

A talent analytics team wants to measure the ROI of a leadership development program. Which calculation approach is most defensible to finance stakeholders?

Answer: Compare performance improvement and retention rates of program graduates to a matched control group

Using a matched control group isolates the program's impact from other variables, producing the most credible ROI calculation for financial stakeholders.

In the context of talent data governance, why is it essential to establish data definitions before building talent analytics reports?

Answer: Inconsistent definitions cause different business units to calculate the same metric differently, making comparisons meaningless

Without agreed-upon definitions, metrics like 'turnover' or 'high potential' mean different things across the organization, rendering comparisons invalid.

When DDI describes 'talent decision quality,' which of the following outcomes most directly measures it?

Answer: The percentage of people selected who meet or exceed performance standards on the job

Decision quality is ultimately measured by how well selected individuals perform on the job, which directly validates the selection criteria and methods used.

An organization finds that its engagement survey scores predict voluntary turnover with a correlation of r=0.38. How should this finding be interpreted?

Answer: Engagement explains approximately 14% of the variance in turnover, indicating a meaningful but incomplete predictor

R-squared (0.38² ≈ 0.14) shows that engagement accounts for about 14% of turnover variance — significant but leaving 86% explained by other factors.

Which of the following scenarios best illustrates 'Simpson's Paradox' as a risk in talent analytics?

Answer: A selection tool appears fair overall but shows adverse impact when data is segmented by job level

Simpson's Paradox occurs when aggregate data masks opposite trends within subgroups, making segmentation critical for accurate talent analytics interpretation.

In DDI's talent measurement approach, what role does 'incremental validity' play in building a selection system?

Answer: It quantifies how much predictive power a new assessment adds beyond what existing measures already predict

Incremental validity justifies the cost and complexity of adding a new assessment by demonstrating it predicts performance above and beyond existing tools.

A talent team is using machine learning to predict promotion readiness. Which ethical consideration is most critical when deploying this model?

Answer: Auditing the model regularly for disparate impact across protected demographic groups

Algorithmic models can perpetuate or amplify historical biases, so regular disparate impact audits are essential to ensure equitable promotion outcomes.