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6.8: Key Metrics

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  • Learning Outcomes

    • Highlight key people analytics metrics

    Photograph of a woman taking notes in a notebook. She is sitting in front of a laptop, which has various charts on it, as well as the heading "Company Growth." Two other people sit in the background of the photo.Metrics and accountability are critical to performance. However, metrics are simply data, which has no intrinsic value. The value of data is that it provides a basis for analysis and, more specifically, insight. The same is true of data that’s used to conduct people analytics. The 20 data points that Nielsen’s used to address its turnover issue were just pieces of information until analysis identified factors that strongly influenced retention.

    Data has meaning only if it can provide actionable insight into human behavior, culture or operational effectiveness. Used in combination, metrics and analytics can make sense of data and put it in perspective, allowing management to assess the situation and take action that improves business outcomes.

    To understand the connection between HR metrics and people analytics, let’s consider the example illustrated by people analytics expert Erik van Vulpen below. Van Vulpen notes that the key distinction between metrics and people analytics is that “metrics don’t say anything about a cause, they just measure the difference between numbers.” In contrast, people analytics makes the connection between people drivers and business outcomes. That is, people analytics determines not only why something is happening but also quantifies the impact. The so what? People analytics allows management to move from opinion to insight, as Figure 1 illustrates.

    Digagram of five items that flow from one into the next. Item 1: A lot of people are ill this month. Item 2: Data indicates absence levels at 12%. Item 3: Evaluating data against company averages shows significance. Item 4: How does our company compare regionally? Item 5: Act on findings to reduce risk.

    Let’s walk through the above scenario:

    1. In this example, we start with the opinion that “a lot of people are ill this month.” This opinion may or may not be accurate.
    2. To determine whether this opinion is a fact, we would refer to the data, which indicates that absence levels for the month are 12%. However, one data point doesn’t tell us whether this is a relatively high or low percentage.
    3. To evaluate the data, we need a point of reference or norm. If the company average is 8.5% and the national average is 4%, we know the data is abnormally high and there’s a potential problem. This is where metrics can add value/perspective. For example, calculating the cost of lost productivity due to absence will quantify the issue. The calculation for cost of lost productivity is absence x number of employees x average labor cost. If the organization has 100,000 employees and an average annual labor cost of $50,000, the cost of absence is .12 x 100,000 x ($50,000/12) = $50,000,000. That’s a startling monthly number and clearly a question/issue worth resolving.
    4. Applying analytics helps identify causes. Let’s say the number of employees reporting flu-like symptoms has increased significantly. How does that compare with regional or country data?
    5. The final step is insight or, more specifically, acting on the insight. Given that the cost of flu-related illness poses a significant financial and operational risk, the company should consider ways to reduce that risk. For example, the company might consider sponsoring flu vaccinations or developing a contingency plan that involves tapping former employees or the alternative workforce.
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    • Key Metrics. Authored by: Nina Burokas. Provided by: Lumen Learning. License: CC BY: Attribution
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