Now that we’ve spent several weeks in the Metrics and Analytics Series defining what analytics are and discussing several real world applications, I want to spend this week reviewing and defining metrics and some common mistakes when using them.
Let’s start out by defining what metrics are according to Jac Fitz-Enz in his book The New HR Analytics. According to Fitz-Enz, metrics “are numbers that indicate how well a unit or an organization is performing in a specific function.” Rather than relying on anecdotal evidence, metrics provide context to where we can analyze performance much more accurately.
These metrics come in the form of percentages, ratios, complex formulas, or incremental differences and can be either individual or aggregated. We can also track them over time in order to show trends.
The data for the metrics are found in both internal and external sources. Internally, the data are payroll, employee surveys, Enterprise Resource Planning (ERP) systems, HR functions, marketing data, sales data, and financial statements. Externally, the data are common benchmarks in your industry, national and local labor market trends, salary surveys, actions from competitors, workforce demographics, and government reports.
In addition, the data collected is both quantitative and qualitative.
Quantitative data are the numbers. Examples of quantitative data are retention rate, overtime, training & development hours per employee which can be found in the company’s employee time tracking software. Some other examples are tracking the number of daily employee-client interactions, the number of units an employee produces, etc. These data can be used to rank employees to award bonuses, raises, and promotions for those who excel as well as offer additional training and coaching or discipline employees who are falling short. You can learn more on reputable career coaching at https://juliehancoaching.com/.
Qualitative data are actions and behaviors that are observed. There are no numbers involved which means the data are subjective. Often these data are collected via employee surveys, interviews, and observations. Examples of qualitative data are why employees stay or leave an organization, the value of teamwork in an organization, the effectiveness of how a supervisor manages her direct reports, etc.
It’s also very important to avoid making certain common mistakes when working with metrics. HR Professionals need to show that we understand what we are talking about and how we analyze and report them as they contribute to the improving the business. I have summarized the following common mistakes from the The New HR Analytics.
- Confusing Data with Information – Don’t bury yourself in data thinking you will find some valuable information simply from gathering it. You will need to know what you will do with it once you have it and don’t forget the importance of protecting it with good data protection services at the same time, browse this site for more information.
- Valuing Inside Versus Outside Data – As Fitz-Enz says “…no one in the organization cares what is happening with the human resource function. All they want to know is what value HR is generating for the company.” Don’t get hung up on the HR activities, instead focus on the employee activity and how it impacts the organization.
- Generating Irrelevant Data – Metrics must be able to effectively answer relevant business questions so the focus must be to collect and report only data that is important to the business. Learn and understand what metrics are important to your organization.
- Measuring Activity Versus Impact – Don’t collect and report data that doesn’t show some positive or negative effect. Just reporting costs, quantities, or time cycles without describing their effects on the business is ineffective and a waste of everybody’s time.
- Relying on Gross Numbers – Try to avoid averages as they mask the effects on the business. Analyze the mean, median, mode, and the percentile in order to determine if the data points are spread out in a wide range or are bunched up around the middle.
- Not Telling the Story – After collecting and analyzing the data, make sure to tell the story of what happened, why it happened, when it happened, where it happened, how it happened, and to whom it happened. Never report something if it doesn’t tell a story.
- Analysis Stagnation – What are the implications of the data and what are you going to do with it? If the data and the story are compelling enough, determine how to get management with a document database to take action in order to solve the identified problem or exploit the identified opportunity.
I encourage you to take another look at these common mistakes and do what you can to avoid them. As I mentioned, HR Professionals must, in order to be take seriously, be able to understand what we are talking about and how to effectively analyze and report our data in order to positively impact the business of our organization.
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