Machine learning – defined as systems that can learn from data – traditionally hasn’t been a concept associated with talent management since often it brings to mind artificial intelligence and robotics, abstract concepts for this market. But, as the talent conversation becomes more sophisticated and learning professionals are better able to understand the promise of big data, the idea of machine learning, in this atmosphere, no longer elicits fear and confusion. In fact, when applied to crowdsourced data, it can be a key ingredient in creating a successful talent management program.
The ability to effectively identify high (and low) performers and make intelligent recommendations regarding career development can help address both the succession planning needs of HR and the career development needs of employees looking to grow within their organizations. Combining formal and informal inputs – gathered from employees’ impressions, peer endorsements and influence within an organization – is the key to getting this right. On its own, machine learning allows a computer to learn a better model for solving a problem – performing complex analysis, fast and in real-time while new data is being generated. But, when the data is rich and made up of the correct, timely and personal inputs, the system gets even smarter and benefits increase.
For example, companies like Netflix or Amazon often know little about their consumers when they create an account. But then consumers begin to input formal data about themselves, like movies they enjoy, shipping address or the date of a baby shower. As consumers use these services more, the intelligent recommendation engines of Netflix or Amazon also gather informal information about them from product reviews, movie ratings, searches and more. Taking these formal and informal inputs, Netflix and Amazon are able to learn more about each user and tailor their experience for better results.
Applying this process to talent management is much the same. Formal inputs from employees and managers coupled with informal, crowdsourced information can inform the ever-changing process for hiring, onboarding, retaining, rewarding and developing any company’s greatest asset, its people. Understanding the interests and aspirations of employees – beyond the typical, identified career path of someone in their position – allows for the identification of talent that might otherwise be overlooked. A standout in the engineering department may have all of the qualifications for a product management position, but may never be considered for the role because their formal qualities (years of experience, past positions) don’t exactly match the job description. However, by gathering informal information from other employees on their peers regarding skills and mentor or role model status, managers may be able to identify a diamond in the rough.
Oftentimes, organizations assume that the human resources department or department manager has the greatest insight into employee performance based on formal reviews, sales results, etc. In many cases, though, managers don’t directly observe the daily activities or skills of each worker, and rely on rules-based information to make decisions about compensation, rewards, promotions or succession planning. And while they often will have the necessary data available to them in some form or another, HR managers likely won’t have a user friendly tool to analyze the data and intelligently make use of it. From the employee perspective, most workers have an unclear idea where to start when it comes to identifying and leveraging learning materials which will most help advance their careers. But, by pairing crowdsourced data with systems that can learn from the information, managers have visibility into the strengths and weaknesses of all employees, in addition to the ability to create a customized learning program to develop individual success and anticipate the needs of the organization.
Across the global talent management landscape, HR and learning managers can take people development to the next level by applying machine learning principles to crowdsourced data. Once they do this, areas such as succession planning and employee development will never be the same again.
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