Millennials Saying Aloud What Others Are Thinking

Posted on by Stuart Murray
laughs. Courtesy of Marc Kjerland
laughs.  Courtesy of Marc Kjerland.

The real reasons millennials are described as different is that people are jealous of their courage and freedom.  I can prove it.

There is an interesting report available online at the University of British Columbia (UBC).  In their 2014-15 Benchmark Report to the Board of Governors, UBC Human Resources developed insights about staff turnover that were new at the time.  In particular, they identified that staff turnover was mostly about career advancement.

One of the things this report straightens-out is turnover amongst new people and young people.  The challenge is that there is a large overlap.  A lot of the new employee are young, and vice versa.  To untangle these two populations the report shows a simple 2×2 diagram with subtotals and labels on the outside edge of the grid.  The results on pages 8-9 of the report look like this:

1-3 Years in Job 4+ Years in Job Total (All Lengths of Service)

Age 34 & Under

13.8%

11.8%

13.5%

Age 35 & Over

6.1%

3.1%

4.4%

Total (All Ages)

 9.7% 4.1%

7.3%

It takes a minute to get used to it, so look at it carefully.  Look at the (vertical) columns for years of service, and compare the percentages side-by-side between the 1-3 Years and 4+ Years length of service categories.  For younger staff (the top row) there’s only a 2% spread by years of service, and for older staff (one row down) the spread is 3%.  The total at the bottom shows that new staff quit at a rate that is 5.6% higher for all ages combined.  But that difference is skewed by a large number of younger-and-newer people in the upper-left corner.  When we look at it carefully, there is a very small difference in turnover according to length of service.

Then look horizontally at the rows.  Those age 34 and under have a quit rate of 13.5% in total, and in this case the number is relatively similar by years of service (13.8% for new people and 11.8% for those with longer service).  One row down, you see that those age 35 and over have a quit rate of 4.4% in total, and once again it’s relatively similar by age category.

This means the important information is the totals by age category.  Those aged 35 and over have a turnover rate of 4.4%, while those who are younger have a turnover rate of 13.5%, nine percentage points higher.  Simply put, younger people have a high quit rate.  This phenomenon is not unique to UBC, as the external benchmark provider had similar findings.

Why are young people quitting?  The report looks to three additional data sources and finds that young people largely resign from their jobs for reasons of career advancement.

However, it’s not entirely accurate to say that young people resign because of career advancement.  The problem is that everyone is concerned about career advancement, and it is a major workplace frustration.  What makes those under 35 different is that they are getting frustrated about career advancement and then quitting.  Think about the different home lives of those over the age of 35.  There are things that keep older people in place.  There is home ownership, mortgage payments, the obligation to support kids, spouses who have a job in the same city, and the commitment to their current profession.

It turns out that millennials did not have career expectations that were different from that of others.  They were just more likely to express their opinions in a display of freedom.  Millennials are the gregarious friend at the pub who says out loud what everyone else is thinking.  You can’t really scold them when you’re jealous they have the guts to tell it like it is.

To top it all off, Generation X and Baby Boomers behaved in a similar manner similar when they were that age.  A project by a small team of statistics students identified that it’s a person’s age and not their generation that drives turnover behavior.  As Neil Young puts it, “old man take a look at my life, I’m a lot like you.”

How Many Math Professions? Let Me Count the Ways

Posted on by Stuart Murray
super-geek-nasa-pocket-protector-by-david-orban.jpg
Super geek NASA pocket protector.  By David Orban

Which profession should you go to when seeking answers to a numbers puzzle?  A true professional advances expertise in the area in which they are knowledgeable.  By default this means that you must not advance expertise in an area where others know best.  Understanding the boundary between what you know and what you don’t is critical.  You make yourself stronger by knowing which profession to seek out.  The following list provides examples of professionals who might (or might not) be able to help you, depending on your challenge.

Mathematician.  Those who have done proper degrees in mathematics work in abstract mathematics or applied mathematics.  Abstract mathematics will be familiar to those who learned concepts in high school that you have never applied since.  In my case, trigonometry.  Abstract math is required when creating models for applied mathematics, the latter of which solves real-world problems in many fields.

Statistician.  These are people who have master’s degrees or doctorates on the applied side of mathematics.  They work with large amounts of data solving real-world problems.  In my dealings with statisticians, they are all about the statistical model; figuring out whether it works, is compatible with the data set, is compatible with the software they are using… and whether the client’s question has been answered.  My impression is that statisticians are far more concerned about happy customers than mathematicians are.

Economist.  Economists are in the social sciences and they are cousins to sociologists, psychologists, and a few other fields.  Economics grapples with the social problem of finite resources in a context of infinite demand.  Economists can work on public policy in areas such as central banking, trade regulation, or in a think-tank.  They also work in business using data and models to help the business be more effective.  They differ from statisticians in that they match their models to economic theory, not mathematical theory.  In public debate in Canada there is a presumption that economic thought is about being politically right-wing; this presumption does not exist in other countries or even within the field itself.

Math Teacher.  We need to single-out math teachers because there are a lot of them.  They are also the single biggest driver of the public’s ability to deal with numbers.  If you did well in high school math you are allowed to say you are good at math.  If you say you are bad at math, everyone knows you had an unpleasant encounter with a math teacher who had an off-day.

Business Analyst.  According to their professional association these people “…identify and articulate the need for change in how organizations work, and …facilitate that change.”  This is great, because it’s problem-solving broadly defined and does not identify their data medium.  My experience with Business Analysts is that they’re at the forward edge of re-engineering initiatives, and they function best when they are part of a multi-functional team.  They could be accountants but they’re further ahead if they borrow from every business discipline, including process engineering, human resources, information technology, marketing, and finance.  They’re the Holmes on Homes of strategy and organizational design.  Without the tattoos.

Workforce Analyst.  As I mentioned, Business Analysts work best when they borrow from a variety of fields.  In human resources, they need business analysts who are able to borrow ideas from every specialization within human resources.  This can include recruiting, employment equity, compensation, industrial psychology, health & safety, or industrial relations.  Human resources data is immersed in the human element, entwined in statutory regulation, hyper-sensitive to collective agreements and union politics, and is exposed to a unique source of theory and evidence.

Institutional Analyst.  This is the field that studies how formal institutions behave according to empirical rules and theoretical rules.  There are two Nobel laureates who have influenced this field and the famed sociologist Max Weber influenced it through his work on bureaucracy.  Institutional Analysis is at the threshold between sociology and economics.  This is a big deal because the two crowds often don’t get along, because of a tweed vs. navy blue dynamic that is completely un-related to the facts at hand.

Actuary.  This is a profession that measures and manages risk and uncertainty.  A lot of actuaries work on pensions and insurance, because they calculate with some accuracy the likelihood that your house will be robbed, that you will crash your car, or when you will die.  Actuaries have degrees in actuarial science, a specialization in mathematics.  A lot of them work for consulting firms providing services to the back-office of major corporations.  As such, you won’t meet them in your daily working life until you bump into them at a party, at which point they will never talk about the math.  It’s like they’re secret agents or something.  They calculate danger.

Accountant.  This is one of the most long-established number-crunching fields, and makes up a large fraction of people who work with numbers on a daily basis.  These people measure and report on financial information that helps others make decisions on investment, taxes, and cost-control.  They are typically not boring people.

Financial Adviser.  Financial advisers provide financial services to clients in the investment sector.  They can help you figure out what insurance to buy, where to invest your savings, how to navigate rules on taxes, and how to interpret research and current events as they relate to your personal finances.  Notably, the Wikipedia page on this profession spends two-thirds of its space describing the way the field is regulated.  The problem is that they cannot predict the future even though you will ask them to, they sometimes get commissions for investment products they invite you to buy, and there are abundant one-sided horror-stories about poor advice.  Yet they are extremely helpful because they can steer you away from obvious mistakes.  Just remember: they, like you, are always working for the person who pays their salary.

Demographer.  Demographics is the statistical study of populations – their size, distribution, and characteristics such as education and ethnicity.  You have probably heard of Thomas Robert Malthus, who described how exponential population growth would guarantee famine and poverty (he was partially correct).  Several workforce characteristics can be categorized by demographic traits, which is dicey because often the real driver of differences is the individual people, not their categories.  Demographers run your national census, making the field controversial.  In the middle ages, Christian thinkers opposed demography, including critics such as William of Conches, Bartholomew of Lucca, and Stephen Harper.

Too Much Choice Jams Your Style

Posted on by Stuart Murray
Tea and Breakfast
Tea and Breakfast.  Courtesy of Britishfoodie.

Employers are becoming increasingly frustrated that they can’t find perfect job candidates.  And they can’t get perfect information prior to decision-making.  Yet there is an abundance of people and information.  What’s up?

The Paradox of Choice is a book and a TED talk by Barry Schwartz that describes the downside of having too much choice.  Researchers found that consumers presented with more choices in the purchase of jam reduced the likelihood they would buy any jam.  The more mutual funds an employee could choose for their pension plan, the lower the rate of participation in the plan itself.  In these abundant environments after we make a choice we end up less satisfied with our decisions.  It’s too easy to imagine a world where we could have done better.  It makes us miserable.

Schwartz recommends that we consider lowering our standards.  The concept of “sufficing” is key; that we should make choices that are good enough to meet our needs.  If you later discover you could have done better, don’t worry about it.

This attitude is critical to workforce analytics.  Trying to get that one quick hit of novel information should be enough for now.  Just keep the dream alive that you can make progress every day.  Become a little smarter, make a slight improvement, do a fist-pump, and then move on.  Lower your standards, cover more ground, and always move forward.

Missteps Make for Better Analysis

Posted on by Stuart Murray
Oops. By Malcolm Slaney
Oops.  Courtesy of Malcolm Slaney.

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.

Share Your Data, Share Your Power

Posted on by Stuart Murray
Sharing, by Andrii Zymohliad
Sharing.  Courtesy of Andrii Zymohliad.

I looked up the phrase “information is power” to figure out where it came from.  It turns out, it comes from everywhere.  The phrase is actually part of several longer and more complex quotes.  It is a call to freedom, such as Kofi Annan’s statement that “Education is the premise of progress, in every society, in every family.”  By contrast, the control of information has a coercive impact which we must overcome.  Robin Morgan says “the secreting or hoarding of knowledge or information may be an act of tyranny camouflaged as humility.”  Both themes ring true with workplace analytics.  First you need to take full advantage of information for your own empowerment.  Second, you are obliged to use that power justly.  The smart money is on the sharing of information, and the sharing of power, in a high-functioning modern workplace.

Barbie Provokes Equality (By Accident)

Posted on by Stuart Murray
Teen Talk Barbie 1991, Attributed to Freddycat1
Teen Talk Barbie 1991.  Courtesy of Freddycat1.

It’s important in the modern workplace to know that there used to be a pervasive stereotype that women were bad at math.  It’s relevant to all of us trying to advance math in human resources.  We have the dual obstacle of getting good math across to clients, while also getting past unfair judgments directed at women who have perfectly good numbers in their hands.

This is a brief inter-generational memo which will be perceived differently depending on when you were born.  In 1992, Mattel produced the toy Teen Talk Barbie.  Amongst the 270 possible phrases the dolls would utter, 1.5% of dolls would say the phrase “Math class is tough.”

The doll was decried by the National Council of Teachers of Mathematics for discouraging women from studying math and science.  It was also referenced when the American Association of University Women criticized the relatively poor education that women were getting in math.  Mattel apologized for the mistake and announced that new dolls would not utter the phrase, and anyone who owned such a doll would be offered an exchange.

I don’t know the full history of women in math, but I do know enough to assert that Teen Talk Barbie was a critical incident.  Mattel did us all a favor by screwing up in exactly the right way, obliging many people to snap out of it, encouraging more women to become great at math, and doubling our talent pool of qualified applicants for math-intensive positions.

What fascinates me the most about this incident, is that people born after 1980 show no outward assumptions that women are bad at math.  For those of us who grew up with this assumption, we were repeatedly corrected that the stereotype was wrong, often by living-out an experience where women excelled.  The younger half of the workforce appears to be advancing their careers in blissful ignorance of this archaic stereotype.

The historic stereotype is important within human resources.  Human resources has historically been bad at math and is also a field with a large representation of women.  Quantitative work is becoming increasingly important within human resources, and human resources is obliged to influence business peers who take math very seriously.  As human resources becomes more sophisticated and makes its way to the big-kids table of decision makers, women who are good at math will speak their minds… as did Teen Talk Barbie.  Shortly after the debacle, the Barbie Liberation Organization swapped voice boxes between the Barbies and talking G.I. Joe action figures.  The liberated Barbies had access to the phrases “Eat lead, Cobra!” and “Vengeance is mine!”