In Douglas Adams' The Hitchhiker's Guide to the Galaxy, the world's greatest computer was asked for an answer to the ultimate question of "Life, the universe and everything."
After millions of years of number crunching, the computer majestically proclaimed the answer to be... 42. It was not the edifying conclusion the audience had been waiting millennia for, but, as the great computer pointed out, that was because they had asked the wrong question: "Life, the Universe and Everything" just wasn't specific enough.
It's a fitting lesson for HR people beginning their workforce analytics journey: ask the right questions. The accuracy of the data, the quality of the analytics, the figures you come up with — everything is irrelevant if you're not asking the right questions.
Think About the Bigger Picture
One of the problems with the type of questions HR professionals typically ask is the narrow focus — the question will address an HR concern, without considering how it impacts the business as a whole.
So, while it may be useful to keep an eye on absence, diversity or engagement metrics, this transactional information will likely not get your CEO's pulse racing.
What executives want to know is how these metrics affect productivity or profitability; they want to know whether there are particular areas of the business where these rates are higher, and why. Above all, they want information and insight into what changes they need to make to change future outcomes, not data about what happened in the past.
But Get Specific
While you need the put your questions in the context of larger business goals, you also need to be detailed about the question itself. If your analytics questions are as vague as "Life, the universe and everything," then the answers will be vague too. It will simply be a fishing trip – you might be lucky and catch something tasty or you might come up with nothing.
So what is the right question? Clearly, that will vary between companies, but the key is for the question to be plugged directly into the matrix of the business. HR can't work in a vacuum, it needs to understand where the business pain points are, to appreciate both the outside market pressures and the inside forces impacting its line managers and leaders.
HR is not short on data — though some areas may fall short on quality — so, you should be able to dig up some interesting revelations with this sweeping approach.
Make a Group Effort
HR doesn't need to work alone on developing the right questions. By working directly with other business leaders, you can work out the answers together in order to make a real difference in business performance.
For example, if you have an issue with high staff turnover, then look beyond the figures to find out why people are leaving. Is there a particular division or location where churn rates are higher? Can you talk to those managers? Or perhaps churn rates are higher among women than men? Look through the exit interview data, and take stock of the gender ratio in management. Is there a high churn rate in an area of business that requires highly prized, in-depth knowledge of the business? It's possible that the people in this department don't understand how much they are valued at the company.
Asking the right question is a great start on the quest for business insight. But whatever the outcome of the analysis, it's also vital that HR maintains and presents the information in a business-friendly and business-relevant manner.
Photo: Creative Commons
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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