Since 1949, Automatic Data Processing, Inc. (ADP) has developed a global reputation for payroll processing products and services. For a number of years, the company had the highest rating given (AAA credit rating) from both Moody’s and Standard & Poor’s. ADP has changed in several ways since its inception. ADP now provides various products and services that connect the HRM function to company strategy by using data analytics. In recent years, the company made headlines for developing an algorithm that predicts employees’ risk of quitting based on several factors, such as employees’ commuting distance and their income relative to neighbors’
incomes.
Given the general decrease in the U.S. unemployment rate since 2009, there is a greater likelihood of increased turnover, as evidence indicates that individuals who are thinking about quitting will be more likely to do so when unemployment rates are lower. The United States has seen an uptick in turnover since 2012 as the unemployment rate has fallen. Because turnover —especially among high-performing employees—can be costly for organizations in terms of lost productivity and increased selection and training costs, adopting ADP’s new algorithm as part of their HRM software packages may help organizations’ bottom lines and help them reduce turnover.

There are legal and ethical implications associated with using employee data of this kind. The kind of personal data used in the algorithm, such as commute length and neighborhood income, could be representatives for other factors, such as socioeconomic status, race, and ethnicity.

Case Discussion Questions 

1. Do you think it is a good idea to gather non-work-related information on employees such as the ADP algorithm does? Why or why not?
2. Do you think that turnover is necessarily a “bad” thing? Defend your answer.
3. What are some ways that data and data management help organizations to be more effective?