June 2017

Issue link:

Contents of this Issue


Page 52 of 89

F or decades, mobility professionals have understood the value of the mobile workforce, and that the people we move typically represent a company's "special forces"—the executives, technical experts, critical recruits, and high potentials who drive global expansion and profitability. But when it comes to proving a return on the mobility investment, a formula has been as elusive as the Holy Grail. That is, until now. Over the past several years, tremendous strides have been made in big data and workforce analytics, with more and more companies now able to quantify the relationships that demonstrate why "mobility matters." As it relates to mobility, big data comes in two categories: descriptive and predictive. Descriptive analytics focuses on what is happening—counts, rates, volume, costs, and demographics (age, tenure, gender). Predictive analytics looks for infer - ences about cause-and-effect relationships and pro- vides insights into the "why." For example, why do international assignees have a higher turnover rate, or what drives leadership readiness? The latter requires statistical modeling to produce meaningful or actionable insights. DETERMINE THE IMPACT Experts agree that the most important first step is to determine the impact of the analytics you are striv- ing to create. In other words, what are the critical questions we need to answer with data, and what decisions will be made based on these answers? When I (Amy) joined Whirlpool Corporation in 2014, the company's talent objectives were to "grow Whirlpool's current/future global leadership capability via an aggressive plan to increase global Improve your mobility program, align with talent management, and deliver greater value to the business By Amy Parrent, GMS-T, and Ellie Sullivan, SCRP, SGMS-T THE MOBILE WORKFORCE ANALYTICS:

Articles in this issue

Archives of this issue

view archives of Mobility - June 2017