How do you see it: a fruitful intersection or two one-way streets?Many technologies navigating the big data recruiting space are still brand-new, and one simple reality has left some to focus on uncertainties rather than the promise of cutting-edge candidate insights: there are still technical and philosophical questions that need to be answered about the methods and analysis. “Talent Analytics” (big data’s role in Human Resources and recruitment practices) is a concept, and novel methodology, that may inspire trepidation and skepticism—leaving people to question its ethics, insights, and future utility.
Joyce Lain Kennedy, in “Big Data: Recruiting future or fad?”, presents two distinct viewpoints on the issue, while offering her own doubts as to whether there is a scientific foundation to the process. Kennedy highlights the opinions of Dr. Wendell Williams, managing director of Scientific Selection, as an umbrella assessment of skeptics and critics.
Williams’s main point is that big data metrics are about numbers and not about (the “little understood”) human performance on the job. The concern is that the data will only reflect past performance—having little relation to how an employee acquires knowledge, thinks critically, makes decisions, and communicates effectively within a new company. In short, he argues that the data has the potential to confuse things and, more importantly, hiring managers don’t have a way to gain direct correlations between specific skill-sets and accomplishments. “Absent the full story, big data will just be another short-term HR fad.”
While it’s not easy to navigate all of the hype surrounding big data, it’s important to note again that big data in recruiting is just getting off the ground—and perhaps our focus should shift toward all the new questions that are being answered every day.
Josh Bersin optimistically highlights the rise of big data recruiting in his Forbes.com article “Big Data in Human Resources: Talent Analytics Comes of Age.”
According to Bersin, the information and technology research firm Gartner predicts that the market for big data analytics can “generate $3.7 Trillion in products and services and generate 4.4 million new jobs by 2015.” Confident that this will fuel big data in HR, Bersin boldly states that organizations have a limited understanding of what drives performance; thus many hiring decisions are based on gut feel.
Pushing for a more scientific approach to understanding decades of information gathered from HR, employee, and performance data, Bersin does acknowledge that recruiters and managers don’t have a way to use this data.
Don Peck wrote an extensive piece for TheAtlantic.com entitled “They’re watching you at Work” that beautifully maps out the historical trajectory of hiring techniques—providing a coherent, detailed, outlook on the new developments in big data recruiting. Sharply noting that a science-based process could limit past biases in the hiring process, Peck addresses concerns over established hiring practices: “A mountain of scholarly literature has shown that the intuitive way we now judge professional potential is rife with snap judgments and hidden biases.”
Studies have repeatedly shown that physical attraction, and other physical attributes, correlates to personal career success. Taller men and women tend to get hired and promoted more easily, and women may receive unfair advantages based upon how attractive they are. Other well known biases orbit issues like age, race, gender, and sex. Workers that are too young may be hindered by assumptions that insinuate they lack a strong work ethic, focus, and coherent conceptual skills. If getting a bad rap for being a Millennial isn’t enough, older workers are often overlooked because of misconceptions centered on their supposed inability to adapt to change. And even though the Civil Rights Act of 1964 was geared toward stopping companies’ discriminatory hiring practices, Peck highlights a 2004 study called “Are Emily and Greg More Employable than Lakisha and Jamal?” The study, unsurprisingly, found that resumes with black-sounding names got fewer callbacks for interviews.
Many of these biases are rather salient, including the social phenomena of recruiters hiring people who share their interests, experiences, and personal traits. It shouldn’t come as a surprise that employment actions like “rubber stamping” have created static workforces of people that are too similar minded. A key example of the problems inherent in this practice is seen when a university hires professors and administration staff based on their stance regarding a string of ideologies or political leanings. It’s not hard to see how this is a significant problem: universities are supposed to house free-thought and critical thinking, but when you clone departments, critical dialogues that lead to novel (and universal) insights diminish.
A close cousin of biases in this context involves good old-fashioned misconceptions. Some hiring managers place so much emphasis on unfounded metrics that they are tempted to skip the interview process and hire people solely on their performance on tests, or the reputation of the school they attended. Is this fair to do? Should you be judged based solely on how well you do on a series of psychological and cognitive tests? Also, what’s the value of placing an emphasis upon what school you attended, and the GPA you achieved?
Aline Learner addresses some of these misconceptions, and she came to some surprising insights in her article “Lessons from a year’s worth of hiring data.” Learner, like Bersin, both come to a consensus concerning some of the “folk wisdom” surrounding hiring.
What gives insight into success:
- Less typos, errors, or grammatical mistakes on one’s resume
- Ability to craft success with only vague instruction
- Having worked at a top-notch company, with demonstrated accomplishments
- Experience with planning, time management, and multitasking
What doesn’t seem to matter:
- Where the candidate went to school
- What the candidates’ GPA was
- The quality of the candidates’ references
- Listing side projects on one’s resume (looking good on paper)
Some of this may come as a surprise, yet it’s important to note that these misconceptions aren’t slowly being dismissed. For example, Kaiser Permanente still runs ads claiming that their doctors are the best in the industry because they come from top medical schools in the United States. Is it fair to assume that if a doctor is from Stanford, John Hopkins, or Harvard he or she will have more passion for their work, and treat you better as a human and a patient?
Peck goes one step further—“Given this sort of clubby, insular thinking, it should come as no surprise that the prevailing system of hiring and management in this country involves a level of dysfunction that should be inconceivable in an economy as sophisticated as ours.”
Entelo represents an effort to reinvent the recruiting landscape through objective analysis. This sophistication highlighted by Peck, and the challenging dysfunction gap, is prompting people to find inventive solutions to problems in the hiring process. Fostering the fundamental shifts in data trends, and utilizing the aggregation of sources, will make recruiting more proactive, objective, and ethical. (For more insight into Entelo’s discourse and view on ethics, relative to the hiring process, check out our Recruiter Academy Course on Ethics).
So how can big data and talent analytics bypass and erase some of our biases and misconceptions? Can it help us move beyond our strongly held beliefs, reinvent or shed longstanding practices, while at the same time convincing skeptics that the hiring process will be fairer for everyone? Peck seems to think so:
When I began my reporting for this story, I was worried that people analytics, if it worked at all, would only widen the divergent arcs of our professional lives, further gilding the path of the meritocratic elite from cradle to grave, and shutting out some workers more definitively. But I now believe the opposite is likely to happen, and that we’re headed toward a labor market that’s fairer to people at every stage of their careers.
The ambitious solutions we are after at Entelo directly challenge Dr. Wendall Williams’s claim that the metrics gathered will give little insight into employee performance. Simply put, the digital signals are intriguing and play into a long term goal of using the information people are placing on sites like GitHub, Dribbble, Quora, and Stack Overflow to overcome matching challenges. This isn’t meant to replace human judgment in the workplace, or the interview process, but to act as a supplement to a hiring manager’s experiential knowledge and methodologies (it may also give recruiters more free time to work on other projects).
Data-driven sourcing, through the “whole web” approach, has led Entelo to find quality candidates by looking at their social footprints. Many social media sites have millions of users, and the information they supply will transform how we find top talent. “10 Reasons Why People Choose Entelo ” provides a broad overview of how Entelo navigates social sourcing
A prime example of an approach that disrupts recruiting strategies, and limits biases, can be seen in how analytic measurements are used form a site like GitHub. If you’re looking to find great software developers, regardless of the size of your company, the site is perfect for surfacing technical talent because GitHub users create profiles full of meaningful data.
Overall, the importance of resumes will decrease for certain skilled workers as tangible and testable data becomes more prevalent across a variety of social networks. GitHub is only one example relative to computer programmers, but there are others like Quora, StackOverflow, Dribbble, and Behance. For more insights into how these data sources work, check out our post “The Modern Resume.”
By using these sources, particularly those that function as hubs for learning and knowledge transmission, big data recruiting software can make it easier for recruiters who are scaling companies to discover candidates who’d be a great fit.
There are still many questions left to be answered. Will the software and methods have utility for working professionals in all careers or fields? Will a candidate survive the job hunt without social media? What are the limitations of using these sites as a recruiting tool?
A main consensus linking Peck and Bersin at this moment in time is their acknowledgment that people analytics won’t exclude candidates—it will actually expose more and more candidates to relevant opportunities. Candidates can leverage the diversity of social media networks to share their skills and gain exposure to more opportunities. Peck put it in simple terms: “People analytics will unquestionably provide many workers with more options and more power.” For the time being many skeptics will have to think in broader terms to truly understand the potential of big data recruiting.
