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!”

First Do Your Homework, Then You Can Play Ball

Posted on by Stuart Murray
Shane Battier. Courtesy of Keith Allison.
Shane Battier. Courtesy of Keith Allison.

What impact does analytics have on teamwork?  Plenty, it turns out.

I happened upon an interesting blog post by Thomas Marsden of Saberr, a team-development firm.  Marsden was fascinated by a session with Shane Battier at the Wharton People Analytics Conference.  Battier is a basketball player with many distinctions.

One distinction is that Battier won a team-player award because he served as his team’s data translator.  Players on his basketball team were handed massive statistics packs about the opposing team’s behavior.  He actually read them, a behavior that was rare.  I often wonder what happens when I produce a ream of analysis and send it off to a client.  Sometimes (but not always) someone comes back to me with tough questions, follow-up inquiries, and demands for deeper dives.  In those cases I have struck upon an expert consumer.  They are like wine experts, indie rock snobs, or film buffs.  They don’t produce the product; they just really know how to consume it.

Battier is an expert consumer.  He did his homework and made interpretations in order to play better.  Other players had not done this, so he would help teammates and “drip feed information at the right moment through the game.”  He was acting as the intermediary between the statistical analysts and the front-line players.  This is a key bridging link between two cliques.  In network theory the person who causes information to jump from one crowd to the next becomes a go-to person for both cliques.

For some people, they see the shots being made.  For me, one of the greatest games on earth is watching the information pass from one person to the next.  There is a bounce, a spin, a clever move, a change of play.  I watch big people, breaking a sweat, moving my data across the court.  And when they score, it’s fist-pumps and high-fives.  Good game.

Data Turns People Into Money

Posted on by Stuart Murray
chinese-penny-attributable-to-marhawkman-some-rights-reserved
Chinese Penny.  Attributed to Marhawkman.

I often think of human resource metrics as the center of a Chinese penny, like the one in the picture above.  There are unknowns at the middle of every problem.  When you resolve an unknown, it’s as if you have punched a little hollow square in a metal disk, and turned it into currency.

While people-culture can be the main driver of business success, this is the thing you can only ponder once you get past some obstacles.  Math is an obstacle upon which you can stumble time and time again.  The puzzles include things such as benefits cost, engagement scores, or performance ratings.

Yet it is not the obsession about the math puzzles in HR that cause success.  It is the ability to move beyond.  If you figure out how to get benefits costs under control, find the source of lagging engagement, or make the switch to performance conversations, you will probably do so because you took proper care of the math, not because you ignored it or obsessed about it.

You need to change the shape of the problem, so that you are talking about people without frustration and confusion.  It is common for someone to start with a basic numbers question that is bothering them. The moment that question is resolved they can move on to a totally different topic.  That new topic can be one question beyond the one they just asked.  However, it’s a shift.

When math is the problem in the middle of things, and you eliminate that problem, you move on to the currency of human resources.  And that currency is people.