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.

Millennials: a Shiny Face on All Behaviour

Posted on by Stuart Murray
Untitled Photo Courtesy of Bina. (=)
Untitled photo courtesy of Bina.

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.

New Technology Not Entirely Helpful

Posted on by Stuart Murray
smartphones. by Sam Churchill
Smartphones.  Photo courtesy of Sam Churchill.

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.