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Using predictive analytics to create a map of your workforce

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Data analytics is used by many enterprises to find the right people to hire and then to improve job performance.

But analytics also can be used to predict the career paths of employees as well as which types of jobs would be the best fit for specific types of employees. By using predictive analytics, enterprise HR professionals can create detailed “movement” maps that enable them to better grasp the skills potential within their organizations. This can be a valuable tool both for enterprise asset planning and for staying competitive, particularly in industries where talent acquisition and development are critical to success.

Over at Data Informed, Talent Analytics Corp. co-founder and chief scientist Pasha Roberts talks with editor Scott Etkin about how enterprises can create predictive job maps to create a workforce asset and performance model.

First, Roberts delves into the details of what comprises a predictive job map:
Each node in the map is a role, for example, “Underwriter I” or “Inside Sales Representative.” Then, we map the traffic between each role: hires, promotions, demotions, transfers, terminations, and actual performance in the role. This gives us a set of history and probabilities for how people really move and perform through the organization, which is not always how HR has planned it.
The insights derived can enable macro and micro decision-making, he says:
At the micro level, we can identify attractive paths for candidates or employees through an organization, years before the person has developed the skills or experience to take the later roles.
At the macro level, we can look at workforce planning with much more information. Instead of just knowing that we will need more sales managers in 2018, we can create hiring/promotion models that fill those positions with better people.
Roberts also emphasizes that predictive job maps are tools, not magic or a crystal ball. “Things happen in peoples’ lives. We can’t predict if someone’s parent will get sick or if their spouse will need to move,” he tells Etkin. “I like that people aren’t robots. It makes things more interesting.”
It’s an interesting interview — certainly more so than if Roberts were a robot.

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