Companies are tasked with the need to pinpoint the type of talent they need and find the candidates who most closely match their criteria. The challenge is moving beyond traditional recruiting processes and avoiding the distraction of notional exploration and making inferences from the ever-increasing data available via the Internet. Organizations committed to a superior workforce are embracing mature talent analytics that can lead to improved hiring decisions. This white paper introduces a talent analytics maturity model that addresses the challenges, opportunities, uncertainties, and benefits of making advancements in the use of analytics for staffing process improvement.
Each hiring decision has a consideration of data behind it that suggests a degree of job-fit and supports a level of confidence regarding a candidate’s likelihood of success. Every executive knows that the quality of a decision reflects the quality of the data behind it. Having the right data and deriving the right insight is critical to improving quality of hire.
Consider the level of attention in talent communities regarding the designation of, and potential differences between, passive and active candidates. However, the differences between passive and active data collection has rarely been given the level of consideration that exposes the challenges and opportunities these forms of data create.
To put it more simply, the data you have may not be the data you need. So what can an organization do to increase the effectiveness of using data to identify the best-fit candidates? The answer lies in switching from passively accepting random data to actively collecting relevant and structured data from candidates.
Download the white paper Talent_Analytics_Maturity_Model_SHAKER_WP
The hiring process will always be an act of personal judgment, but mature talent analytics can enable your recruiting team to combine the best of computer-based predictive modeling and human decision making.