Orwellian technology, capable of monitoring your every message and conversation, may be coming to your office soon.
In keeping with the management adage, “What you can’t measure, you can’t manage,” new employee monitoring methods called talent analytics (or workforce analytics) are hitting the corporate market.
From small startups to global giants such as IBM, tech vendors are offering employers the promise of quantitative, data-driven precision in determining who is a high performer and who is a slacker.
Much like conventional labor management systems that track employee attendance, performance-review ratings, and salary levels, this new generation of systems seems destined for widespread adoption — at least by managers at the big, deep-pocketed companies that are drawn by the prospect of replacing subjective human judgment with hard data and computer algorithms in hiring decisions and performance appraisals.
Perhaps more disconcerting for workers, the emergence of these tools comes at a time when employers are embracing the concept of “employee engagement,” a new-age metric of not just competency and professionalism, but also “of commitment, passion, and loyalty a worker has toward their work and company. The more engaged an employee is, the more work they’ll put forth.”
It’s too early to tell whether this state of workplace surveillance will become the new normal; WhoWhatWhy will follow up to hear from workers, employers, and unions on its impact on job satisfaction, employee morale, and staff productivity.
Every Move You Make
In its annual survey of employee surveillance practices at large firms, the American Management Association said 78 percent of major employers monitor employees’ use of email, internet, or phone, up from only 35 percent back in 1997.
Typically, employee surveillance takes the form of monitoring email to block malware, protecting sensitive information, and screening for inappropriate content. At some secure faculties — such as medical centers and defense manufacturing plants — employees are required to wear chip-embedded security badges so that managers can track their every move.
A new generation of employee surveillance systems is taking monitoring to a new level. Billed as a “productivity tool,” one new system takes screenshots of freelancers every 10 minutes to score their “focus” and “intensity” of effort, to ensure these remote workers are not slacking off.
Rise of the Algorithmic Overlords?
Analytics are being used to remove the risk of subjectivity and hidden (human) bias in determining whom to hire, promote, reassign, or let go — as well as predicting which high performers are most likely to jump ship.
As global competition forces businesses to squeeze out every bit of productivity and efficiency, the endgame of this new “science of HR” is to find objective, quantitative metrics that reliably predict desired business outcomes, in part based on employee attributes and behaviors.
At Google, the mantra is that all HR decisions should be based on analytics, such as determining the most valid criteria to screen resumes to identify qualified candidates, and the right number, length, and structure of job interviews to identify quality hires.
In the near future, one can imagine employers using Natural Language Processing (NLP) to mine employee communications for tone and content, and using network analysis to measure their ability to build relationships, collaborate with others, and work as a team.
As assessing job performance has become more quantitative and data-driven, some experts worry whether the metrics used are valid measures of a worker’s value to the organization. And with the continued growth of outsourcing and automation, gaining such insight into the attributes, behaviors, and actions of one’s own employees may become irrelevant.
But many HR managers see this new generation of tools and technologies — and the quantitative data they generate (however valid or representative they actually are) — as their ticket to “get a seat at the table” at the highest levels of senior leadership.
About the Author: Dr. O’Connor is head of the Data Science and Information Systems degree program at the City University of New York’s School of Professional Studies.
Related front page panorama photo credit: Adapted by WhoWhatWhy from image by Joe Loong / Flickr.