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Talent Analytics refers to the analysis of "talent-related" data for business decision-making. We also
call this "BigData for HR."
Organizations have millions of data elements about people: demographic data, performance data,
job history, compensation, mobility, assessment, training, and more. This data can be correlated and matched to many different types of business
data to help companies understand profiles and behaviors which create high performance.
For example, a food service company found that its client renewal rate is highly dependent on certain skills, behaviors, and learning activities.
This correlation was so high that the company developed a dashboard for all its account teams which monitor these talent metrics
with red/yellow/green measures for managers to use. There are hundreds of other applications of talent analytics.
For more information on this topic, we recommend reading about the Bersin Talent Analytics Maturity Model (shown below).
"Talent calibration” meetings bring together managers (who are peers) to finalize ratings of all salaried employees within their groups. During these meetings, employees’ individual results are comprehensively calibrated against their peer group (e.g., performance relative to objectives, critical capabilities, potential) and plans for targeted development may be defined.
A process during which organizations consider different variables that affect the future supply and demand for talent, such as strategic plans (e.g., expansion into new geographical regions or new product lines) and limitations on access to qualified talent (e.g., number of engineers graduating annually). Talent forecasting is much more than a simple headcount analysis.