Once upon a time hiring the right employee was relatively easy and inexpensive. A job application and quick interview was all that was needed. Life was simpler. Attitudes were relatively predictable. Replacements were a dime a dozen. A hiring mistake was easily absorbed into the cost of doing business.
If management hopes to get back in the game, then big data offers hope to those companies who seek better hiring and retention solutions. Unfortunately most organizations are lucky if they collect even a little data. And then it typically just gets buried in a cabinet or on a manager's hard drive.
For starters, most companies are still using the interview and reference checks as the holy grail of predictive science. It's like trying to douse a burning building with a garden hose. It's filled with the best of intentions, but soon hiring and retention will become crisis management—and interviews provide little information to help HR leaders navigate the talent management chaos.
Below are three major weaknesses of interviews, and how big data can address the information gap when it comes to hiring:
1. Our Brains Are Biased
At its very core interviews are loaded with bias. I love this quote in a recent Fast Company article:
"...everyone [must] get past the idea that only blatant racists, misogynists, and homophobes are biased. If you have a brain, you are biased. End of story."
Even if a manager is a skilled interviewer, he or she is susceptible to personal bias about everything from gender to skin color to age to hair style, tattoos, and just about anything you can see or hear. That's just human nature. Most of us try our best to block out bias but it's impossible. (If you would like to "test" your bias, try this.)
2. There's No Golden Standard
The criteria for the manager's hiring success is rarely tracked. That's because few people interview exactly the same. Even when the same questions are asked, our personal styles skew how we ask the questions and hear the responses. If by chance all those factors can be neutralized, one big gap still exists.
It is almost impossible to track what makes one candidate more successful than the next based on only the interview. The interview is just too subjective—most companies have no clue how or why a manager selects one candidate and rejects another when the interview is the primary selection tool.
Hiring then becomes a crapshoot: Each recruiter and hiring manager has a different baseline; each hiring event recreates the wheel. With the lack of a "magic formula," it is nearly impossible to replicate success.
3. Candidates Aren't Caught Off Guard
On top of manager bias and personal style, we add the candidate to the interview equation. It's not out of the ordinary to assume he has practiced and polished his interview performance with a lifetime supply of career coaches, his resume is impeccably reviewed and his cover letter is ghostwritten. Oftentimes, the hour-long interview will be the best performance a manager ever sees!
The time a candidate invests in preparing for the interview is typically far more than most managers imagine. For many managers, the interview is just another meeting or disruption in his chaotic schedule. Their goal is to get through it as quickly as possible and move on to more important things—like budget or putting out the next fire—but for candidates, this is a make-or-break moment they have rehearsed in their heads countless times.
How Big Data Can Beat the Antiquated Interview
Big data offers some relief, filling in holes of information and neutralizing personal biases.
Pre-employment testing, where large databases of candidate information are analyzed, is one way big data can ease the hiring process. Patterns and trends from thousands of people are analyzed and sophisticated algorithms sift through combinations of data for predictive trends that an individual couldn't catch. Pre-employment tests add objective and predictive metrics to the sea of subjectivity upon which managers now base hiring decisions.
With access to big data available to almost anyone who wants it, the playing field for talent is almost level. Managers can figure out what makes top performers tick, why mediocre performers miss expectations, where the good talent is hiding, and how to attract the best employees.
Finding where future talent is hiding and how to attract and retain them is an endless journey. But you have to start somewhere. And you have to start right now.
Photo: Creative Commons by Luke Chesser
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How to Create a Data-Driven Workplace
While employees may be a company's greatest asset, it is data in the hands of these employees that can truly make a difference. Workforce data and people analytics can help employees make informed decisions, develop strong strategic initiatives and achieve measurable goals. From increasing leadership effectiveness to analyzing employee performance to developing training for increased customer loyalty, the impact of a data-driven culture runs far and wide. But creating such a culture is no easy feat: it not only requires building an internal analytics team, but also getting buy-in from employees across your organization. For many organizations, simply getting off the ground with an in-house analytics team is intimidating, much less forming a culture around big data. We talked with Teri Schmidt, manager of the Assessment, Measurement & Evaluation (AME) team at JetBlue, about how her team has created a ripple effect of evidence-based decision-making from within. Here, her best advice for other organizations interested in putting analytics at the center of their culture and strategy. Start Small You have to crawl before you walk (or run) with analytics. When you're just beginning a data program, focus on a single metric or goal that can prompt a larger conversation about the impact of analytics, Schmidt advises. "If you can come up with even one metric that people can understand, talk about and utilize, that will have a huge impact," she says. "As people begin to see value in using data, you can continuously improve your metrics, gain more access to data, and get people interested in collecting new data." Lead by Example After you've prompted interest in analytics, continue growing the conversation by sharing anecdotes and case studies with the company. If people see the result of a data-driven decision, they are more likely to trust in the potential of evidence-based decision making themselves. "We started our conversations in a quarterly meeting where people shared success stories about where they had utilized data," Schmidt says. "Getting people talking can help get the ball rolling and take you to a point where you can build and improve." Offer Learning Opportunities The real key to instilling a culture around data, however, is to create opportunities for people to engage with analytics themselves. While conversation and success stories generate intrigue, the next step is dedicating resources to transform intrigue into action. For JetBlue, that meant creating a mentorship program for other employees to learn from AME team members. The AME certification is a performance-based program that educates employees organization-wide about applying analytics, teaching effective data collection, analysis, visualization, communication and use in performance improvement. Participants apply the processes they learn to a real-life project, Schmidt explains. Along the way, AME mentors teach participants how to involve stakeholders in data collection and how to communicate their results effectively. The cumulation of these efforts is company-wide engagement in data analysis. In addition to making smarter and better informed choices, JetBlue employees are now proactively seeking out opportunities to use data to improve the company. "A big part of what we've seen — beyond making improvements that I don't think would've been made without the evidence provided through data — is a big growth of culture where people look to find and use data when they have a decision to make," Schmidt says. "They get excited [when they're able] to say they improved a certain program because they had the data to back it up." Photo: Creative Commons
Data or Intuition: When Should HR Rely on the Numbers?
Do you operate on gut feelings a lot? You know, when you interview someone for a job and within an instant you know this person is right—or wrong—for the job? What about when you're making a big decision about a reorganization, or implementing perks that you just know employees will love? Many HR professionals make all kinds of decisions without looking at the numbers—sometimes it works out and sometimes it doesn't. But the flip side is also true: Sometimes you look at the numbers, make your decision accordingly and things go poorly. So, how do you know when it makes sense to use data and when to trust your gut? The Myth of Homo Economicus Have you ever heard of Homo Economicus? You may not know the term, but you probably know the principle. In economics, Homor Economicus is the assumption that everyone is a rational actor and will choose the option that will maximize their benefit in any situation. While people do tend to do what they think will make them happiest, sometimes their analysis of the situation is way off the mark. And other times, they discount what will really work. For instance, you may want to hire someone with whom you just "click." Except that situation can result in hiring someone who is great for a weekend road trip, but not the best at maximizing sales. Don't use your gut when hiring. Instead, channel your inner Homo Economicus and use the information you know about your business and the information you know about the candidates in order to make a rational decision. Yes, personality can be one of the items on your checklist, but using objective data allows you to avoid making an irrational—and sometimes illegal—decision. Our "guts" tend to favor people who look and sound like ourselves, which means your gut is going to tell you to hire people of the same race, gender and national origin as yourself. Before you start the interviews, make a list of the qualifications that are critical to the success of the position, and then match up the candidates to that list. Using your data can save you a lot of headaches in the future. But What About Perks? I received a question from a reader recently, who asked, "Why do team building events always involve pulling people up to the stage? I hate that." Well, he's not alone. You know who loves that type of stuff? The people who plan team building events. A team building activity is supposed to be fun—it's a perk. The same goes for other office perks you may offer—from Yoga classes to discounts at local restaurants, these things are supposed to make your employees happy. So, why shouldn't you sit in your office and brainstorm ideas for perks? Because you might like certain things, but it doesn't mean your employees will like them. Just like the team-building exercises that only the group leader enjoys, bringing a Yoga instructor on site may satisfy your desires, but not the rest of the office's. Instead, use data to determine what perks and programs will benefit your employees. Send out a survey. Ask for feedback. Use the results to decide where to allot the money for such programs. The results may surprise you—or they may be exactly what you thought. Time to Fire When you fire someone for gross misconduct—like stealing or swearing at customers—that's an easy decision, even if actually carrying out the termination is difficult. But what about when you need to do multiple layoffs for business reasons? That's when your data needs to come out in full force. Hopefully, you've done performance appraisals over the years, and can easily rank your employees. Why is this important? Well, naturally, when you have to do a layoff, you want to keep your best employees if at all possible. This ensures that you are keeping the consistent high performers, not just whoever you happen to like best today. It also protects you from legal challenges, since you'll be able to demonstrate your methodology for selecting people for termination. In addition to looking at individuals, you'll need data to look at which positions really add the most value to the organization. Don't eliminate a position just because it commands a high salary, if that position is also the one bringing in the money. Paycheck Boosts and Bonuses It should be pretty clear that unless you do a flat cost of living increase where every employee gets the same boost (or the same percentage boost) in salary, you need to look at the data before telling employees their increases. Why? Because you can have something called "disparate impact" which means that even though you didn't intentionally discriminate, one group got treated better than another. You want to double check that you didn't inadvertently give higher raises to women than men, for example. You also want to make sure that your raises make sense overall. Bottom line: In nearly any decision, data is your friend. While it's important to keep the "human" in human resources, it doesn't hurt to balance your decision with some solid numbers. You can always look at the data and make a decision to go against it, but at least you'll know that it's your gut and not the numbers that you're following. Photo: Shutterstock
How Big Data Is Closing the Information Gap on Salary
Traditionally, employers have been on one end of the job search, with endless information about their prospects, and hopeful job seekers have been on the other, with limited knowledge of the companies, cultures and careers they're pursuing. But today, that norm is shifting. With the advent of the internet and social media, the process of finding and accepting a job is becoming ever more transparent. Glassdoor provides insider knowledge on company culture, leadership and development, while similar site Fairygodboss crowdsources information on workplace culture and policies specifically for women. Now, a company called Paysa is working to further the job search transparency—particularly when it comes to salary, an area steeped in taboo and infamous for bias I caught up with Paysa co-founder Chris Bolte to learn more about why he started the company, how it works and why it's important to be informed about salary in a competitive job market. By the Numbers Paysa leverages both massive data sets—more than 90 million employee profiles and upwards of 35 million salary data points–and artificial intelligence to provide personalized career insights and guidance on market salary. An understanding of market value provides candidates with more confidence heading into interviews, and strong reference points when negotiating salary. This technology can let them know before accepting an interview, if the salary is within their range. "Our purpose is very straightforward," says Bolte, who co-founded Paysa in 2015. "We help employees maximize their value and by extension, their salary, throughout their career. It's all about making the process easier, and arming people with the data they need to make better decisions." More than Salary Paysa's personalization technology enables people to explore what their salary would be in specific roles, companies and geographies. But that's not all it predicts. The technology can also determine the likelihood that a person will get the job based on their education and skills, the companies where they have worked, the experience they've gained and their various titles and jobs—ultimately helping people narrow their job hunt and find a good fit. "We give people an accurate valuation of their professional worth," says Bolte, "We can point them to related skills that will increase their value and we can connect them to new opportunities relevant to their specific situation." Transparency Is a Win-Win While Paysa is ultimately for job seekers, the platform helps streamline the process for employers as well, ensuring everyone's interests line up to avoid disconnects later in the interview process. "Our goal is transparency—to make data about companies, salaries, jobs, skills and promotion paths available for everyone," says Bolte. He sees Paysa as a tool both employers and employees can use to identify potential matches, agree on a competitive and fair salary, and ultimately ensure a good fit. While employees may shy away from such a direct conversation around salary and opportunity, Bolte sees these conversations as necessary for organizations who want to stay competitive in today's market: "In the end, we believe this will help companies fill open headcount faster, reduce attrition among existing employees, drive greater job satisfaction and productivity for employees and improve ROI on companies' investment in their people." Photo: Twenty20