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Even the most complex enterprise needs metrics and analytics to stay on course and compete in the global marketplace. Of all the parts of a company that can be optimized with analytics, the most valuable asset is the workforce itself.
Gathering good data to describe and define employee performance with HR metrics and analytics isn't as simple as deriving metrics from warehousing or marketing campaign performance data. Descriptive analytics can expose subtle traits in employees and define their patterns of success or failure, and predictive analytics can help managers choose the right employees for upcoming team projects.
But it's all for naught if the data is bad.
Objective vs. subjective measures
When it comes to human performance, having a handle on not only what is being measured but how can make a huge difference in the ultimate value of the metric. Management appraisal of employee performance, for example, is necessarily subjective, and that's a good thing -- it's where thoughtful opinion should prevail. But too often, the performance data gathered via management evaluation is both static and linear; it only captures what's happening at the moment. Giving an employee a score of seven out of 10 for performance says almost nothing about the employee and is of little value to an organization.
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Adding another dimension to these subjective evaluations greatly enhances their usefulness for HR metrics and analytics. If a manager adds a rating for employee potential to the score for employee performance that forms a unified two-axis metric, then the value of the metric in analytics is greatly multiplied. An employee with low performance but high potential is remediated differently than an employee with high performance but low potential: The former needs mentoring, while the latter needs training. And the application of analytics to these metrics can result in more focused, effective mentoring and training programs for the entire workforce. Moreover, HR can potentially identify an employee who is not doing well in a current job but may flourish in a different position.
HR predictive analytics for employee retention
Nine percent of HR analytics is dedicated to predictive modeling, which more than doubled in two years and is used largely to help determine why high-performing employees leave their company.
Traditionally, quantitative measures have held sway over the evaluation of employee effectiveness. Salespeople live and die by their numbers, as do assembly line workers and delivery people. But traditional metrics offer no analytical insight into why raw productivity data might be rising or falling, and that's the information the enterprise truly needs.
Descriptive analytics can make a difference by enhancing an otherwise static metric. Imagine a sales team exceeding quotas for most of the year, then experiencing a collective drop-off in numbers. Upper management may speak to the sales manager and get a top-down view of the team's performance lapse, but HR metrics and analytics can open windows into how an individual's performance is affecting group performance.
Retaining top employees
In an increasingly migratory business ecosphere, people move from one company to another far more frequently than in past decades. Keeping employees happy is more important than ever. And when an employee decides to move on, money is seldom the most important factor. Deciding factors can include upward mobility, benefits, education and training support, a family-friendly environment and a positive corporate culture.
It's often combinations of these factors that keep employees stable and satisfied. Upward mobility and training programs are likely to be shared values for some employees, while a good health plan and a family-friendly atmosphere may appeal to others. Digging into the data and using HR metrics and analytics to detect and resolve these patterns of employee values leads to more comprehensive HR initiatives to retain employees.
Additionally, isolating these factors can help HR create more reliable hiring practices. While there's certainly merit in following industry recommendations on selecting new employees, it's even more effective to analyze in-house employee data to establish what work-related experiences and personal traits make for a good fit. Relying on dynamics that have proved to be successful in the workplace is usually a better bet than spinning the roulette wheel on a prospect sporting a well-written résumé.
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