Organizations are increasingly relying on data and analytics to inform critical talent decisions.
By Marta Chmielowicz
Necessity breeds innovation, and as the COVID-19 pandemic disrupted the business world, companies responded with new ways of working. According to Mercer’s Global Talent Trends 2020-2021 report, executives met the challenges of 2020 by doubling down on strategic partnerships (40%), using more variable talent pools (39%), and investing in automation (34%).
Data science has become a critical element of the talent strategy, with organizations leveraging analytics, artificial intelligence (AI), and machine learning to enable decision-making. In fact, 61% of executives say that of any current talent trends, using talent analytics has delivered the biggest impact to their business.
COVID-19 drove a data explosion, with companies collecting data on remote working, location, and health. The Mercer research shows that HR has quadrupled its use of predictive analytics from 10% in 2016 to 39% last year. Now, the challenge is using that data to inform forward-looking insights—and HR leaders seem to be stepping up, with more than half reporting that they are able to answer executives’ top talent questions, up from a third in 2019.
However, the way that companies are relying on analytics is changing. Before the pandemic, HR was making headway on operationally-oriented analytics, such as data related to recruitment, selection, or identifying flight risks. These have become less of a priority, with employers instead choosing to focus on strategic analytics that inform foundational decisions around workforce management, such as the reliance on homegrown talent versus hiring from outside.
The fastest-growing analytics include:
- finding pay equities by gender and race/ethnicity (52%);
- identifying why one team is performing well and another is struggling (52%);
- learning how total rewards programs are being utilized by employees (46%);
- leveraging data to better manage healthcare spend (41%); and
- correcting inequities and preventing them from reemerging (12%).
Organizations are also harnessing workforce science for hiring, identifying high potentials, and making promotion decisions. Technical skills assessments are the most prevalent (98%), followed by situational judgment tests (75%), role play-based assessments (73%), and realistic job previews with digital personas (72%). In general, organizations tend to use psychometric tests for external hires, culture assessments for internal promotions, and technical assessments to identify high potentials. However, many continue to rely on experience (87%), interviews (75%), and education (63%) to hire talent, with only 27% using online assessments and only 11% using online simulations.
With the increase in volume and types of data collected comes a greater responsibility to use data safely. To date, more than a quarter (29%) of organizations have revised their policies on access to employee data and developed ethical practice guidelines. Organizations are also taking care to ensure that their AI solutions don’t perpetuate inequities, with 69% reporting confidence in their ability to intervene when technologies find unethical patterns and 67% feeling confident that they can ensure their AI technology doesn’t institutionalize bias.
As organizations combine business data with human intuition to identify and solve business problems, they will need to update guidelines and assessments to ensure their technologies are delivering fair and equitable outcomes.