You Can Herd Cattle, But Not the B.S.

Posted on by Stuart Murray
Wot You Lookin' At, by Kate Russel
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.

Why So Complicated? Go Simple!

Posted on by Stuart Murray
Simplicity, Child Stares at Art (Stuart)
Photo by author, all rights reserved.

The world is complicated.  Do corporate leaders think this is a good thing? No, they do not.  There is an emerging effort to put a greater priority on keeping things simple, while human resources leaders get their people to adapt to changes in the nature of work.

In June of 2014, Josh Bersin developed a fresh opinion that simplicity is the next big thing.  (You’ll need to click past the Forbes pop-up screen, but then you’re in)  Large global corporations were at the time expanding their business, organizing mergers and restructuring efforts, and putting talent management ahead of cost reduction.  There was also a pre-existing struggle to redesign performance management, reduce workload for overwhelmed employees, and create a stronger and more integrated workplace culture.

Bersin notes “We have inadvertently become far too enamored with our technology, mobile phones, social networks, photos, video sharing tools, and all the various competency models, frameworks, process diagrams, and workflows we design in HR.”  Indeed.

By contrast, some organizations have put a lot of effort into simplifying their approach.  This could include reducing the number of competencies they encourage from seven to four, or reducing the performance management process to three simple steps, or creating apps that attach to their HRIS where the apps accomplish just one thing.  Solutions become smaller items that almost belong on Etsy.

For those of you who have written a one-sentence email to an executive it is obvious that keeping it simple is more work, not less.  Designing simple things for a lay audience also requires a special perspective and a devotion to good design.  Yet concentrating effort and attention to just one thing has obvious payoffs in focus and effectiveness.  I don’t have data.  In this case, you can go on instinct.

I’ll Show You My Salary if You Show Me Yours

Posted on by Stuart Murray
Secrets. By Salvatore Barbera
Secrets.  By Salvatore Barbera

Human resources departments and those who handle their data are expected to guard the best secrets.  But one of the biggest secrets is ironically an anti-secret.  Did you know you’re allowed to talk openly about your own pay?  Don’t tell HR.  It’s embarrassing (for them).

This article in Atlantic.com by Jonathan Timm from July 2014 draws attention to the dubious practice of pay secrecy.  I’m not talking about the employer’s obligation to keep your pay information confidential.  Rather it’s an article about employees being obliged to keep their pay a secret from one another.  These obligations are referred to as “gag rules.”

For the uninitiated, there is no meaningful moral obligation for employees to refrain to talking about their salary with each other.  On the contrary, in the United States there are regulations that protect employees’ rights to discuss working conditions with one another.  It’s on the edges of the legislation that allows employees to collectively discuss their lot in life, bargain for improvements, and possibly unionize.

In that context the moral judgement should be obvious.  Those handling the file at human resources desks are not allowed to advance anti-union behavior, and as professionals they should always advise against such policies.

The article describes personal experiences of people struggling with these fake rules.  What is notable is how people presume these gag rules are legitimate, employers and employees alike.   Gag rules create a sense of guilt about whether we should put ourselves ahead of the employer.  They make us self-consciousness about whether we’re being greedy.  We’re embarrassed to talk about whether we’re losers for being the lowest paid person.  Raising the topic with colleagues is “akin to asking about their sex life.”

These emotions are powerful stuff.  But then, that’s how bullying is done, isn’t it?

Above and beyond beef-and-taters union issues, gag rules are also wrapped up in discriminatory pay practices.  That is, it is easier to under-pay women and visible minorities or play favorites if employees don’t talk about their pay.  A woman named Lilly Ledbetter complied with the gag rule at Goodyear for nearly three decades and ultimately found out she was under-paid.  Ms. Ledbetter sued and lost because she did not complain about being under-paid within the first 180 days of her first paycheck.

Ironically, employers share pay information with each other all the time.  They’re called compensation surveys.  They happen on an annual basis (if not monthly), and they are delivered through specialized consulting services.  The work is done under careful checks and balances that ensure data privacy and keep the whole process fair and legal.  Those who have worked on such surveys are proud of their work.  I used to do compensation surveys myself, and I was good at it.

One of the reasons why compensation professionals love doing this work is because it helps make pay fair and equitable.  Looking down from the ivory tower, human resources people know that perceived unfairness in pay creates discord.  So “good” employers put some work into getting it right, behind the scenes, in a kind of lab environment where social justice is organized by experts.  But really we’re just trying to stay one step ahead of the riff-raff.

Let’s face it, employees and the social justice movements they created are the rightful owner of this dialogue.  Gag rules and compensation surveys are just the cultural appropriation of working class politics.

Are You Blinded By Your Own Smarts?

Posted on by Stuart Murray
Then he laughed into my eyes. By Josh Pesavento
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.

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.”

Piecing Together Human Resource Analytics

Posted on by Stuart Murray

Untitled Photo by Kevin Hale

Untitled, by Kevin Hale.

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?