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People Analytics - Result Indicators and Casual Indicators

Actualizado: 2 nov 2022

Advocating the need to align HR activities with corporate activities and strategies, Brian E. Becker, in The HR Scorecard (2001), differentiates two types of ”classic” economic indicators that he applies in his HR analysis framework:

a. The result indicators (called ”laggard indicators”) are the ones that measure performance. Here we line up the business profits.

b. Leading indicators are used to measure the factors that generate results. Happiness, motivation, and commitment go here.

Value creation in the company is measured with the same result indicators that are handled and understood in finance: profitability, liquidity, growth, and risk. They are the indicators that correspond to the creation of value.

Value-generating factors are measured with causal indicators, such as competencies, motivation, and happiness. They are the indicators of the humanist vision of human resources.

In a way, this environment of causal indicators and result indicators follows the same logic as the basic procedures of analytics in which we handle predictor variables (also called ”independent variables”), and result variables (also called ”dependent variables”).

In conclusion, these two types of indicators, result and causal, are perfectly valid for us to measure the synthesis that we propose for employee motivation-happiness and

shareholder value.

To break it down,

”For a company to thrive (result indicator), skilled, and motivated employees (generating factors) are essential.”

Ref: People Analytics - Data and Text Analytics for Human Resources - Eduardo Valencia. Data Analytics Manager - Analytics to Grow.

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