The New HR Analytics
Predicting the Economic Value of Your Company's Human Capital Investments
Author:Dr. Jac Fitz-enz
Pub Date: May 2010
Print Edition: $21.95
Print ISBN: 9780814438848
Page Count: 368
Format: Paper or Softback
e-Book ISBN: 9780814416440
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Why Human Capital Measurement Matters and How to Avoid Common Metric Mistakes
If you don’t measure it, you don’t know what is actually happening. If you don’t understand it, you can’t control it. If you can’t control it, you can’t improve it. For these reasons, plus the fact that people are one of the largest organizational expenses, human capital measurement is critical to business success.
“Your business decisions should be based on empirical data,” says Jac Fitz-Enz, world-renowned expert on human capital strategic analysis and measurement. “Analytics are bias free and credible. The numbers require little translation; they speak for themselves.” Yet, as he acknowledges, “Simply collecting a mass of data not only has no value, it can lead to frustration and poor decision making.” In THE NEW HR ANALYTICS (AMACOM May 2010) Fitz-Enz offers tips on avoiding the seven most common metrics mistakes:
1.Confusing data with information. We bury ourselves in data, with the erroneous assumption that we know something. Uninterrupted data collection is a worthless, make-work, dust-gathering expense. The basic question is: What will you do with the data once you have it?
2.Valuing inside versus outside data. No one in the organization cares about the HR function. All people want to know is: what value is HR generating for the company? Report on human capital, the employee activity, rather than on human resources, the department activity.
3.Generating irrelevant data. Presenting metrics on topics of no importance is useless. Metrics must answer relevant business questions. Focus on collecting and reporting only important business data. Otherwise, you’ll just pile up a data dump.
4.Measuring activity versus impact. Reporting costs, time, cycles, and quantities without describing their effects is pointless. When considering such “intermediate metrics,” ask yourself: What difference does it make? What is the result? If the answer is nothing, why report it?
5.Relying on gross numbers. Averages mask effects. If you reduce a large number of outcomes to an average, you have no profile of the phenomenon. What are the mean, the median, the mode, and the percentiles? Are all data points bunched around the middle, or are they spread across a wide range? Average cost and average turnover are meaningless.
6.Not telling the story. We can gather a mass of data and display it in colorful charts, graphs, and tables, but in the end, does it tell a story of what happened, why, when, where, how, and to whom? To have meaning and value, data must be turned into business intelligence.
7.Getting stuck in analysis stagnation. Data must have a purpose and application. How can you use it to spur action on the part of some employee group or to solve a problem?
Adapted from THE NEW HR ANALYTICS: Predicting the Economic Value of Your Company’s Human Capital Investments by Jac Fitz-enz (AMACOM May 2010).
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