What’s with all this bold talk from millennials? Don’t they know to keep hush about their outlandish opinions? In a recent article from Lisa Earle McLeod the author submits an open letter (closer to a manifesto) that explains why millennials have the opinions they have.
She has two key points. First, employers are tolerating poor performers, and those poor performers drag everyone else down, including highly-motivated millennials. It’s not so much that millennials are unreasonably ambitious and over-eager, it is that their enthusiasm is the correct attitude and lower-functioning colleagues should not be setting the pace. Fair ball.
Secondly, we must give our work purpose. Organizations that have “a purpose bigger than money” have better business results. This purpose-driven organization is reminiscent of Simon Sinek’s Power of Why although McLeod’s critique is closer to a sense of Noble Purpose amongst the sales team, a major concern of hers.
This focus on enthusiastic front-line staff is consistent with other critiques. Josh Bersin notes that many organizations are flipping their hierarchy to place priority on engaged employees first, who then attract and retain customers who, in turn, keep the profits alive. If it works, go for it.
Can big data reduce crime? Yes it can. This is a great TED Talk by Anne Milgram about using analytics to improve the criminal justice system. The talk from October 2013 describes how Milgram successfully attempted to “moneyball” policing and the work of judges in her role as attorney general of New Jersey. Hers is a great story, and has many features in common with the Moneyball book and movie.
The speaker describes how she built a team, created raw data, analyzed it, and produced simple and meaningful tools. Her most impressive outcome is a risk assessment tool that helps judges identify the likelihood a defendant will re-offend, not show up in court, or commit a violent act. She and her team have successfully reduced crime.
Baseball players and police officers alike have a culture of bravado and confidence which may be critical when handling conflict, intimidation, and credibility. Yet what police officers and baseball players often need is a safe space to question their assumptions, assess whether they could do better, and decide that they will do better. These types of vulnerable moments don’t play out well when a player is at bat, or when an officer is handling complaints from the perpetrators.
In Milgram’s talk, where others see cool math tricks, I see a change in mindset and demeanor. The speaker expresses curiosity about the information, enthusiasm for unexpected findings, modesty about baseline effectiveness, a lack of blame, and a can-do attitude about trying to do more and do better.
It’s a great metaphor for business. In those workplaces where managers fiercely claw their way to the top, there may be a reduced willingness to talk about shortcomings in a manner that requires trust and collaboration. Yet making exceptional decisions require that leaders choose an entirely different mood and posture while they explore an uncharted area, allow information to out-rank instinct, and aspire to a more subtle kind of greatness. Put posture aside, and just do good work. The way things are changing, those are the only kinds of people who will stay on top.
Apostrophe Absent. Photo courtesy of Michael Derr.
Are you compatible with your organizational culture? I sure hope not. You need the freedom to break from the pack in order to pass along new information and adapt to disruptive change.
In the 2011 book Connected by Nicholas Christakis and James Fowler, the authors describe the way opinions and behaviors spread through social networks. They describe a Three Degrees of Influence rule: we influence and are influenced by people three degrees removed from us, most of whom we do not even know.
You might know one hundred people, but those people may know another one hundred people (each), and so on. This could result in a million people crowd-sourcing shared opinions. You would pick up many opinions from this extended network. The reverse is true as well. You could spontaneously assert that we should have all better table manners, and a million people might change their behaviors. Or maybe they’ll just talk about having better manners.
The implication is that you do not entirely experience independent thought. You might control what time you arrive at work, what garments to wear to the office, and how you respond emotionally to what your manager just said. But the allocation of housework in your household, the social norms in appropriate dress, and the organizational culture of two-way conversation could all be things that have significant third-party influence. You’re not exactly an autonomous hero in the workplace; you are a team-player in an environment where culture runs deep.
This critique has been revisited in a recent book review in which Yuval Harari summarizes The Knowledge Illusion by Steven Sloman and Philip Fernbach. Sloman and Ferbach posit that individual thinking is a myth, and that we actually think in groups. With modern civilization we have come to rely increasingly on the expertise of others. This crowd-think has mostly been good for us, but it also has a downside. People “…lock themselves inside an echo chamber of like-minded friends and self-confirming newsfeeds, where their beliefs are constantly reinforced and seldom challenged.”
Group loyalty and pride in our presumed intelligence causes us to stick to the normal way of doing things. This is a challenge to those of us who produce or consume new information. New information and new ideas disrupt stable group environments. If we are trying to change the workplace so that things are done differently, we must exchange discomforting opinions. We must propose ideas that will be rejected. We must try things out that won’t work.
Wot You Lookin’ At. Photo courtesy of Kate Russel.
How do you really know if someone is trying to fool you? Sometimes it’s easy. You know the kid took the cookie. You know the employee wasn’t sick. You know corporate is just cutting costs. But big data makes it harder to know what to believe. The raw data takes hours to read and is in a specialization that is not your area. Everyone who works with the data is beholden to an interest. And what if that cool thing the data scientists have figured out his how to scam you as a target? Thankfully, there is help.
A recent article from the New Yorker advises on How to Call B.S. on Big Data. It’s a great summary of a course at the University of Washington which became available in January 2017. In the spirit of public education, you can access a large amount of the materials in the course’s web site with videos, tools, and case studies.
There are some simple protective measures that are known to those in the number-crunching fields. Watch out for unfair comparisons; remember that correlation doesn’t imply causation; and beware of the hubris of those making bold claims. On average you need to keep information in context and ask for a plausible theory about why a fact would be true. The plausible theory is your hypothesis, and the scientific method is to test the hypothesis. No plausible theory; no science.
Amongst the precautions is that data taken from the general public will often re-create prejudice. I see this all the time when looking at inequalities in women’s promotions and salaries. Superficially it does appear that many women are self-selecting into less onerous careers. Deeper into the analysis, you tend to find that women do more than their share of the caring (in all of its forms) and that it’s a pervasive imposition, a subtle stereotype, and just about everyone is causing this to happen.
I’m glad to know that there is a growing desire among non-quants to consume information in a more sophisticated manner. For my own work, this doesn’t worry me. I’m honest and my motives are transparent [about stuart] If anything, I’m intimidated by the volume of work ahead of me. I used to have a clear sense of the amount of work required to seek the truth in the data and share what I found with a sincere audience. Since the rise of fake news and the increasing complexities of social media, a Pandora’s Box has been opened people like me are obliged to investigate ten times as many topics. We may be asked to fact-check nonsensical statements, defend controversial findings that were created in a neutral setting, or spend excess hours establishing credibility.
The most unsettling concept raised in the article is the BS Asymmetry Principle coined by Alberto Brandolini: the amount of energy needed to refute BS is an order of magnitude bigger than that needed to produce it. And so begins the new hybrid skill set of doing good math, and then talking about it properly.
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.”
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.
Stuart Murray Speaks 