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  • Writer's pictureAndrée Laforge

Understanding the difference between HR performance indicators and predictive analytics.

First of all, both are essential to the organization but let’s try to define them better.

The purpose of HR metrics is to provide information about employees, hires, departures, applicants, internal and external movements, etc. Every organization needs HR metrics, and this is the first step to get started in HR analytics. However, HR metrics rarely leave the HR fold.


Predictive analytics, on the other hand, gives business leaders and the entire organization the information needed to make informed decisions.


Report versus forecast

HR indicators show the management team what has occurred as a result of decisions they have made in the past. HR indicators are based on past data. You need them not only to be able to demonstrate what has happened, but also as a basis for decision making.


In contrast, predictive analysis looks to the future, projecting the future impacts of different business decisions. Predictive analysis uses the same data from the past and projects possible future situations. However, it is only a forecast. Predictive analytics does not tell us precisely what will happen in the future. Statistical modeling and machine learning identify patterns and trends that can be used to calculate the probability of future outcomes.

For example, you notice an increase in departures in your organization. You anticipate that your turnover rate is increasing. You calculate it and then determine where the turnover is excessive. By comparing your turnover rate across departments, job categories, ages, or seniority groups, you will be able to confirm whether you have a turnover problem or not (it’s even better if you can compare your turnover rate to market data). Identifying the turnover problem is the first step and it is the HR performance indicators (in this case turnover rate) that allow you to come up to that conclusion.


That's all well and good but identifying a problem doesn't necessarily fix it (but it's the start). We need to be able to determine who is at risk of leaving our organization in the next year and more importantly, why they are at risk. This is where good predictive models come in. A good risk of departure model will help you understand the "why" and the "who". The predictive model will help you make the right decisions to minimize exit risk in your organization.


HR metrics and predictive analytics are two different concepts. While the two are closely linked, they are not interchangeable. Understanding the difference between the two is the first step. Producing performance indicators and predictive models will make HR data critical to the decision-making in your organization.


Use the HR indicators to formulate a baseline and identify your issues. The predictive models can then be used to improve the future of your organization. You will become an invaluable source of information, helping your organization make better business decisions.

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