History and Value of Analytics (Measurement)

Figure 1: Analytics Value Contribution

Figure 1: Analytics Value Contribution

Most of us have lived through more than a few fades in our personal as well as professional lives. There seems to be a new “hot” idea or method for leading, managing, or improving every year and they just keep coming. One might argue that new approaches have become a market unto themselves instead of real tool for productive change. Moreover, we live in a time when knowing and using the latest and greatest is a sign of prestige than effectiveness. Although HR analytics might appear to be a fade on the surface, it creates too much value for organizational and human capital management to be the “next big thing.”
This post and others that follow will explore the value of analytics by examining each of the evolutionary steps it has followed. Like most methods of analysis, the value of the actual knowledge gained increases as tool sophistication grows. Figure 1 captures the increasing sophistication and value of analytics as you view from left to right. Measures give us the basis for comparing to standards or benchmarks and provide the first level of value due to our ability to compare our experience to others. Multiple measures drawn from inputs, processes, outcomes grant us the ability to compare movement and relationships. The use of dashboards assists in this effort and provides an easily accessible visualization method as we increase our analytical sophistication. Measuring leads to associating. Once we know what goes together, we can better measure how the movement in one factor relates to the movement in another. These patterns assist with determining causation or factors that serve as prerequisites to change. From causation, we enhance our ability for prediction and take more control of our future.


The current analytics revolution started with establishing the value of measurement through data collection. Once measurement gained the recognition as a necessary element of effective human resource management practice, descriptive methods came more into use. HR functions initiated more surveys, collected data on core processes, and sought to develop metrics to measure outcomes. In 1984, Jac Fitz-enz published How to Measure Human Resources Management and changed the way we looked at human resource outcomes and transitioned data from “nice to have” to “need to know” in most large organizational human resource functions. His work profoundly influenced our understanding of not just the importance of human capital, but also how it could be measured like other operational areas and the value the function provides. In his second edition in 1995, Fitz-enz identified the most critical measures for assessing effectiveness as:

Revenue per Employee
Expense per Employee
Compensation as a Percentage of Revenue
Compensation as a Percentage of Expense
Benefit Cost as a Percentage of Revenue
Benefit Cost as a Percentage of Expense
Benefit Cost as a Percentage of Compensation
Retiree Benefit Cost per Retiree
Retiree Benefit Cost as a Percentage of Expense
Hires as a Percentage of Total Employees
Cost of Hire
Time to Fill Jobs
Time to Start Jobs
HR Department Expense as a Percentage of Company Expense
HR Headcount Ratio—HR Employees: Company Employees
HR Department Expense per Company Employee
Supervisory Compensation Percentage
Workers’ Compensation Cost as a Percentage of Expense
Workers’ Compensation Cost per Employee
Workers’ Compensation Cost per Claim
Absence Rate
Involuntary Separation
Voluntary Separation
Voluntary Separation by Length of Service
Ratio of Offers Made to Acceptances

SHRM provides a nice webpage with many of these metrics as well as the method of calculation (http://www.shrm.org/templatestools/samples/metrics/pages/default.aspx).

The real value of measurement is summarized well by Jac Fitz-enz from A New Vision for Human Resources: “To move to the center of the organization, HR must be able to talk in quantitative, objective terms. Organizations are managed by data. Unquestionably, at times, managers make decisions based on emotions as fact. Nevertheless, day-to-day operations are discussed, planned and evaluated in hard data terms.”

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