The Importance of Data on People’s Career Credentials to Labor Markets’ Efficiency
The data on people’s career credentials: employment history, education, experiences, skills, assessments, compensation, assignments, trainings, certifications etc, is key to sustaining labor markets’ efficiencies and has enormous potential upside for individuals and businesses. As Artificial Intelligence solutions in HR develop, we expect to see even more incredible and new opportunities to leverage career data, further increasing its importance and value.
Currently, individuals share their resume with prospective employers when they apply for a job. In the best case, they update their LinkedIn profile on major career events, so we get a snapshot every now and then of what’s going on. If we were to have a continuously updated picture of the individual’s career records, the result would be a movie rather than a set of discrete, static pictures. As a result, we could begin to see a broader context and trends over time. Data could be used in a richer and more meaningful way than today, guiding people as they develop and manage their careers.
In 1978, Jac Fitz-Enz, Ph.D. published “The Measurement Imperative” proposing a radical idea. In it, he proposed that human resource activities, and their impact on the bottom line, could be measured. This article triggered debate and interest by scholars and spurred more research into measuring HR. Dr. Fitz-Enz’s work literally initiated the beginning of data capturing and benchmarking key HR activities, such as retention, staffing, compensation, and competency development. Yet, it was quickly then found that benchmarks alone could provide limited actionable insight, and only provide a momentary comparison of a company’s human resource activities to others.
A more advanced and comprehensive use of metrics was later discovered in 2002 by the Oakland Athletics baseball team. Their general manager Billy Beane was able to assess players’ value and employ sabermetrics (player data based on extensive analysis of baseball) in the selection of players. Billy realized that players with strong sabermetrics correlated better to winning games versus players who were strong in traditional metrics, like batting average. Sabermetrics also enabled the coach to form a winning team utilizing data versus expensive rookie recruiting. With Oakland Athletics’ limited $41 million budget, significantly less than competitors with larger budgets, he was able to create a winning team – the result was phenomenal.
Based on Oakland Athletics success in sourcing winning athletes at less cost, in 2003 Michael Lewis developed a path-breaking strategy on metrics-based selection models, known as the Moneyball concept¹. And later in 2009, global leader Google started Project Oxygen to identify the attributes of effective managers.
Google’s Project Oxygen became globally renown, when in 2011, Google shared the results, highlighting data-based findings about the perfect manager. Soon thereafter, there were a series of research publications, which highlighted the benefits of using analytics in workforce management. Amongst them, a study by Patrick and Auke² generating 20 articles alone on the different aspects of workforce analytics. What evolved at this stage, based on Project Oxygen and subsequent research, was a dynamic shift from traditional metrics-based HR measurements to predictive analysis, which was a futuristic development.
It is clear that the potential upside is enormous. Most companies’ largest spend is on their people, and much of this enormous expense is driven by management decisions that are made by gut feeling and individual heuristics.
With the help of technology, organizations and individuals are now able to make informed decisions and more importantly, transform daily recruiting, assessment, onboarding and management practices in the labor market.