Then he laughed into my eyes. Photo courtesy of Jose Pesavento.
Too much knowledge can turn you into an idiot. The curse of knowledge is that problem where experts in a field are unable to explain their great knowledge to a lay audience, because they can’t bring it down to earth. The speaker might have good information about the base knowledge of their audience, but they just don’t “get” that their audience hasn’t taken the introductory course in their subject area. It’s odd that someone can be highly esteemed for their knowledge, yet get short-tempered with the very people who hold them in high regard. I think this is why it’s so hard for experts in two different fields to communicate with one another. There is a special skill set in talking to intelligent people who don’t understand what it is that you do.
Is there a simple and straightforward approach to applying human resource analytics to emerging issues? Yes, there is. Two authors have identified a few basic tips that make or break the creation of a meaningful analytics team. First, you must get analysts whose subject matter is human beings. And second, you must use a consulting approach when deciding how to meet business needs.
I would like to endorse the opinions of Alec Levenson and Alexis Fink in their article Winning the HR Analytics Arms Race from April 2017. Their portrayal of what is happening behinds the scenes in human resource analytics very closely matches my own experience of what is really going on.
The article flags that human resource executives are putting top priority on leveraging their data, but they put a low priority on predictive analytics. Just about every other priority area in the top-six are things that could be enhanced by people analytics, like succession planning, workforce planning, and diversity. But notably, executives have determined they are absolutely not ready to enter the ethereal world of predictions. The field is not fully developed and the data set is not yet mature.
The article describes the large number of people who can do clever analytics in different fields, such as engineers, accountants, and rocket scientists. But there’s a problem. Some of these people have never studied employees and their motivations. The authors favor industrial psychologists. My personal experience is that a blend of multiple social sciences is good, as long as everyone has an analytic bent. However, if someone spent the last decade crunching numbers in a field which does not consider the human soul, they’re factually a novice.
The authors also pan the tendency for analysts to try to make something with the data they have available. I ran through this loop in my first four months on the job. The readily-available data is stuff that the payroll and statutory compliance teams needed to get their own work done. But sometimes the most interesting information is stuff that is hard to get. Sometimes you need to create new information from scratch. And often you need to choose a higher standard of data quality.
Levenson and Fink favor a consulting approach, although they don’t call it that. The analyst must meet with the client and figure out what problems keep them up at night. The client won’t name data, they will name a nuanced person-puzzle for which analytics might be finessed into a useful tool. But the solution is several steps in. You have to start with the consulting question. And indeed, this is what business partners do. It is what business analysts do. Face it, if you cared about people, how could you not ask what’s important in the mind of your client?
A major voice in people analytics just advocated for the professionalization of my field. An April 27, 2017 blog post by Max Blumberg and Mark Lawrence suggests that workforce analytics regulate itself under a professional association. The authors have a good point. The explosion of alleged experts in my field is making things really confusing for lay audiences. We have no idea if someone claiming to have expertise is truly knowledgeable. There is a gold-rush mentality in workforce analytics, and we can barely distinguish those on the cutting edge from the outright con-artists. Bad experiences and false starts are causing skepticism.
I agree with this assessment of the current state of affairs. I decline the vast majority of conferences, webinars, and software on offer. Being strong at workforce analytics turns on having daily exposure to the data itself. I have yet to hear a provider offer something more interesting than that thing we just figured out last week, by ourselves, with in-house staff using excel.
However, I have to disagree with the proposal that the field should be regulated. You see, the main opportunity is to democratize the skill set and bolster the overall number of people who read the data and create simple calculations. If you can get one-tenth of a human resources team to tool-up with a small amount of learning and experimentation with the data, that’s a huge boost in organizational capacity. There is one specialist for every five or 10 people in the earliest steps of the learning curve. Tinkerers and new entrants are half of the equation, and sometimes they are the most important half.
There is another problem. We don’t yet know what excellence in workforce analytics looks like. Sure, getting the attention of the c-suite, saving money, having clean data, and making your findings presentable are really obvious signs that you know this stuff. But mysteries abound. The information is disruptive to those with power, so how shall we deal with the office politics? The data improves every day, so how do we maintain composure while discussing last-year’s erroneous data. We’re supposed to align to strategy, but strategy and leadership change is constant. And how are we to negotiate the boundaries between the professions when accounting has their own cost model, and marketing researchers are experts in employee surveys?
The mystery, confusion, emotional drama, flashes of growth and pride all bring the field to life. Workforce analytics is a mosh pit. Our outputs are a meal thrown together from what is leftover in the fridge. Our first attempt at everything looks like a Pinterest fail.
Let’s keep it messy. We’re more honest that way. Besides, we work harder when we’re having fun.
How much can we talk about people without talking about people data? Not very much, it appears. Those dealing with employees of all types must know more about their hearts and souls than ever before. And if you make one false move with a data point, your most brilliant philosophical insights can be taken sideways.
In December 2016, author Simon Sinek was interviewed on Inside Quest on the topic of Millennials. I am a big fan of Sinek, having changed my approach to work based on his influential TED talk on how to Start With Why. The Inside Quest interview (20 minutes long) is also great because it covers many key topics.
Sinek posted a follow-up video days later to clarify much of what he had to say. There was a dramatic change in body language. In the first video he seemed calm and knowledgeable. However, in the follow-up video (from what appears to be his dining-room) he is a little sheepish, making clarifications, imploring people to keep the conversation alive with constructive criticism. The first interview had gone a tad viral and he got a lot of feedback.
During the Inside Quest interview he made piercing social criticism and attributed a lot of what was happening in society to the experience and context of millennials. In what should be described as “a good problem to have,” he understated the importance of his critique. You see, the things he said were true for many of us regardless of generation.
His critique? We must learn to wait. We must put time and years into our greatest accomplishments. We are lonely because we are embarrassed to talk about our disappointments and frustrations. We need to talk through our difficulties. We must aspire to engage in sincere conversations. We must help others. Look up from your phone and be human.
In my opinion these are all massive issues for workplace culture. Managers are struggling to learn how to compel their staff to work hard without being coercive or demeaning. Everyone who takes benefits costs seriously is now hyper-sensitive to whether employees can talk openly about mental health and wellbeing. Executives worried about people quitting are stumbling onto growing evidence that people want to thrive and grow. And still, the dream persists that we can all succeed.
I think that these topics entered the mainstream concurrent with the rise of the millennial workforce, not necessarily because of them. The analytics that identify turnover trends happened largely because of emerging technology; the de-stigmatization of mental illness was popularized by baby-boomer medical professionals; smart phones have been improving for decades; and teachers have been pushing anti-bullying efforts for some time. These things came sharply into focus when millennials first started to speak their minds in the workplace.
Based on his dining-room talk, it appears that Sinek’s feedback came from many non-millennials who want in on the broader discussion. This is important from a social perspective. But the social perspective is the flip-side of a data issue. That is because he got tripped up by a data-labelling error. You see, he casually referred to millennials has having been born approximately 1984 and after. He didn’t specify a 20-year generational cohort. He left it open-ended, like there was an unlimited supply of this generation being born every day. This is problematic because we need good definitions to determine if there are clear differences between clear categories. If the definition is muddy, then the identification of differences will be muddy as well.
I have had the pleasure of working with clearly defined data where I described millennials as those born from 1976 to 1995. By getting specific about date of birth, you will find that each year you look at the data the findings can shift. Age and generation are not the same things, and if you look at the two separately you might find, for example, that millennials as a generation do not have different quit rates. Or you might find that concerns about career advancement are widespread (more on that in a future post).
For me this is an excellent example of how workplace analytics and workplace culture are never that far from one another. To love humans is to wish the very best for them and their data.
Josh Bersin of Bersin Deloitte is expressing some skepticism about whether new technologies are actually delivering productivity improvements. Allah D. Wright contributed this article for shrm.org describing Bersin’s comments at a speech in Hyderabad, India on April 20, 2017. Bersin spoke of the long-term impacts of technology on economic transformation. He noted that historically technology has had very large impacts over time. However, there are some downsides of new technology. The average US worker spends 25% of their time reading or answering email. The average mobile phone user checks their device 150 times per day. Work is getting harder, with 40% of the US population believing that it is impossible to balance work and life. Bersin asserts that it is not HR’s job to cause technology to succeed, but rather to pay attention to the way technology changes the way we work.
In my opinion, the major shift in the past decade has been the flood of incoming information. The new emerging skill is the careful determination of what incoming information is useful. Decision-making about which information is valuable needs to be diffused, or employees will simply be flooded. This means that the new skill sets will be the assessment of information for relevance, taking pauses to reflect between waves of new developments, and the more cautious and deliberate composition of our own outgoing communications. After all, if you’re just passing along high-volume spam and memes, you may be replaceable by artificial intelligence.
Managers and human resource professionals are supposed to have non-discriminatory hiring practices. Yet we are only in the early days of seeing job applicants neutrally. There are several new (and not-so-new) methods for considering applicants fairly. There is also the possibility of using good math to prove and reduce bias.
Canada’s federal public service announced on April 20, 2017 that it is starting a pilot project to recruit job applicants on a name-blind basis. The minister responsible said “research has shown that English-speaking employers are 40 per cent more likely to pick candidates with an English or anglicized name…” At the end of the pilot they will analyze the two sets of candidate shortlists, both name-blind and traditional-method. The results of the experiment will be ready in October, for possible roll-out to the entire public service.
What is worth noting is that the Canadian government is running a formal experiment for a limited time. This raises hope that the eventual course of action will be determined by evidence, not speculation. They will measure the discrimination before attempting to remedy it, which could bolster support. The approach also implies the pilot has permission to fail. After all, they might find something totally different from what they expected. But that kind of thing that happens when you care about science.
Of course this pilot addresses only one part of the discrimination puzzle. I would speculate that résumés that still indicate the year and city in which a degree is attained will tip-off employers about age and ethnicity. An obvious next phase of analysis is to block-out the graduation date and the name of the University. After all, you only need to know if they finished their degree, plus the degree’s level and academic major, and a broad sense of the school ranking (i.e. top-100, top-400).
Job applications also reveal writing style, which should be good. But there are differences between the sexes in the use of words. In the book The Secret Life of Pronouns by James W. Pennebaker the author reveals the findings of high-volume statistical analyses revealing (amongst other things) that men make bold pronouncements without referring to themselves in first-person. Women, by contrast, attribute their story to themselves, which is more clear, social, and modest. I personally think that confidence, and willingness to boast, are unreliable indicators of competence.
In classical music, blind auditions are now commonly used to select new hires onto symphony orchestras. They’ve been doing this for years. The musicians submit recordings of their auditions and provide live performances behind a physical screen. I have heard that judges gossip “you can tell” if the candidate is a man yet when the winner steps out from behind the screen it is often a woman. In this not-so-new paper from 2000, authors Claudio Goldin and Cecilia Rouse conducted an analysis of 7,065 individuals and 588 audition-rounds to see what impact blind auditions had. They identified that the blind auditions work.
When you’re fighting the man, words are important. When you’re putting change into effect, math is importanter.
Stuart Murray Speaks 