Introduction

In every industry we need to be able to ask, “What is the performance of X and how do we measure it?” For example, in terms of education we need to be able to ask the question, “What is the performance of a good school supposed to be and how do we measure it?” and “What is the performance of a good teacher supposed to be and how do we measure it?” In the Information Technology industry we need to be able to ask “What should the performance of technology be?” and “What should the performance of a programmer be and how should we measure it?” In the field of sports we need to be able to ask both, “What should the performance of a player be?” and “What should the performance of a good team be?” There is a variety of names for metrics across the market: metrics, statistics, indicators, criteria, and so on. All of these names amount to a numerical way of judging performance. This is indeed what Hume’s sentiment comes to; without metrics we can say very little with certainty.

The main reason we need metrics is that we can’t know how to improve if we don’t have a benchmark of how we are doing today. However, we also need to be able to chart how we have been doing over the last few days, months, and years. Therefore, our metrics should be saved so that we can access them and conduct research with them in the future. By and large our metrics need to use industry-wide standards so that we can compare one firm or individual’s performance with another person or firm’s performance in that industry. And indeed, these last two points are two of Geri Stengel’s Ten Tips for Measuring and Improving Performance, “Compare Yourself to the Competition” and “Conduct Research” (Stengel 2003). However, there is no way to compare your firm with other firms or to conduct research without reliable metrics. This chapter will discuss metrics as they are used in (1) baseball, (2) the government, (3) information technology, (4) education, and (5) crime. Using these examples will help us to get a little clearer on why metrics are so important and the differences between the right and wrong metrics.

Ultimately the goal of this chapter will be to use the metrics of other industries to find best practices for generating HR metrics. This will help ensure that human resources metrics truly serve the industry. In 1978 Jac Fitz-enz proposed that “human resources activities and their impact on the bottom line could—and should—be measured.” He was the first to argue for the relevance of HR Metrics because human resources have “a real impact on the bottom line.” (Caudron 2004) At the time, this idea seemed pretty crazy, whereas now it is a commonplace.

HR metrics allow us to talk about human resources with others in our department, in our firms, and outside our firms. As Jamie Barber writes, “Perhaps the most crucial advantage of a sound HR metrics program is that it enables HR to converse with senior management in the language of business. Operational decisions taken by HR are then based on cold, hard facts rather than gut feeling, the figures being used to back up business cases and requests for resource.” (Barber 2004) However, now that HR metrics have come of age it is high time that we try to understand what differentiates good and bad metrics. After describing the performance metrics used in a variety of industries, I will conclude with a list of 10 lessons that should be adhered to when generating performance metrics.