Workforce Metrics for Human Services Organizations

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Another year is winding down. It’s a time full of reflection and anticipation for the future. And for human services organizations, this means taking a close look at your workforce metrics and evaluating how to move forward in a new year.

Workforce Metrics to Consider

There are countless types of metrics you can look at to gain a better understanding of your workforce – so long as you have the data available to analyze. But knowing where to begin and what information you need requires first understanding what metrics you want to analyze and working backwards from there. For human services organizations, there are numerous workforce dimensions to consider, cross-reference, and analyze. For example:

  • Recruiting metrics, such as time to hire, time to fill, and number of applicants per position
  • Retention metrics, such as turnover rates, retention rate per manager, and voluntary versus involuntary turnover
  • Productivity metrics, such as employee and team performance, absenteeism, and amount of overtime
  • Financial metrics, such as program/department costs, program efficiency, daily lost revenue of open positions

Gathering the Data You Need

It’s one thing to understand the types of workforce metrics that can give you a picture of your workforce, and another thing to find the data, compile it, translate the information into meaningful insights, and make decisions based on it. Given the importance of this data, finding ways to make this process simple should be a top priority for organizations that want to ensure their continued success. Unfortunately, the reality is that most organizations struggle in this area and typically don’t feel they have the time or resources to straighten things out here. (So if you’re one of those organizations, you’re certainly not alone!)

However, if you’re going to make an effort to gather your data – however challenging that may be – and want to draw meaningful conclusions from this information, it’s important to recognize that data is useless if …. read more


SOURCE : Originally seen on datis

January 5, 2015
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