Are HR Measures Really Too Soft and Subjective?

By David C. Forman

In his new book, Fearless HR — Driving Business Results, argues that HR cannot move forward until it directly addresses the historical perceptions that have constrained its standing in organizations. He identifies five specific biases, and one is that “HR Measures are Too Soft and Subjective.”

He examines this view in terms of the latest research and leading practices, and his insights follow:

1. Human capital analytics work just fine

There are many examples of human capital analytics leading to better business decisions from organizations such as Google, P&G, Deloitte, IBM, Best Buy and Microsoft.

There is nothing wrong with HR metrics; they may not be perfect, but they are not too soft or subjective. They may not be as precise as, for example, the types of tolerances gained from Six Sigma projects, but that is because more complex interactions are, in fact, harder to measure.

It is also an oversimplification to think that human capital metrics are less rigorous than financial measures. When you talk to financial professionals, they know that their measures are rarely nice, neat, orderly and objective. Financial measures such as revenue and profit attempt to approximate an anticipated reality, but these can (and often do) change rapidly.

Human Resources, even though late to the use of analytics, needs to become more data-driven and evidenced-based; and we have the tools, measures and examples to make this happen now. As HR technology continues to advance — it is a $7 billion industry — the access to integrated data and its subsequent use in improving the business will only increase.

2. The commitment to take action

There should be an unspoken principle that accompanies the effort and resources it takes to analyze workforce data: It is not enough to gather data and find patterns, it should lead to action.

This principle gets back to the two ultimate purposes for analyzing metrics in the first place: Analytics should yield intelligence to make better decisions regarding,

Leveraging success; or,
Mitigating risk.

A great example of a research project leading to action is Google’s Project Oxygen. While this study answered important questions about the qualities of effective managers at Google, its real impact came as the findings were incorporated into ongoing processes, communications, training, recognitions, surveys and the culture of Google.

Project Oxygen was not just a research study; it made a significant difference to the organization.

3. Focus on the consequential few

It is easy to get overcome with data.

To some extent this is natural, especially as a more deductive approach to data gathering is used. In this situation, you mine through a lot of data to find the truly meaningful relationships; but then you focus on these critical few as opposed to the inconsequential many.

Data paralysis can easily happen if you look at too much data. It’s too confusing, there is too much to do, and the really important opportunities get lost.

It is also easy to become enamored of all that is being measured, regardless of its importance to measure. One senior HR leader talked about this phenomenon when she said: “We measure what we value, not value what we measure.”

A good rule of thumb followed in many talent scorecards is to monitor about 5 to 7 measures at any one time. This number coincides with the capacity of short term memory to process information (which is often placed at 7 items, plus or minus 2). This guideline keeps the analytics process manageable, and again emphasizes the importance of prioritization.

4. Don’t let perfect be the enemy of the good

As Big Data comes to HR, there will be greater sophistication in human capital analytics.

This is inevitable and welcomed; but it can also lead to ignoring findings that are simple and small. Small data can be as powerful and meaningful as Big Data.

Do not fall into the trap that attaches value only to advanced studies that employ sophisticated statistical techniques. Simple is good.