The Soil Cultivation Metaphor

Posted on by Stuart Murray
hand-1-by-david-pacey
Hand 1.  Attributed to David Pacey.

One of the greatest pleasures of home ownership is the opportunity to work in the garden.  Gardening is fulfilling for several reasons.  The accomplishment is satisfying and tangible, unlike a lot of office work.  Gardening is great physical exercise, involving a range of low-impact and core-intensive body movements.  Gardeners get time outdoors, bolstering vitamin D intake and exposure to fresh air.  By handling soil, our body develops a resistance to the germs and bacteria, or so rumor has it.  The work is solitary and meditative, improving mindfulness.  The home-grown and fresh-picked produce tastes better and is higher in nutrients.  Gardening is a kind of cure-all for wellbeing.

Soil cultivation is extremely similar to the practice of cleaning a data set.

My first round of soil cultivation was a patch of land that previously had a garden shed sitting on top of it.  There was no organic matter in the soil at all; just a dense patch of dust.  The soil required a major intervention to become useful.  This is also true about a new data set.  It’s great to have new data, but I just know it’s going to require a lot of attention before I can use it properly.  The data will lack clear labels, there will be columns or fields that are useless in some way, and some data points need to be converted.  Sometimes it simply arrives in the wrong format, such as on paper or ascii, or built around a different software environment.  The certainty that I must put work into it in order to get something back, turns this into “real” work.

With data I typically find that some fields are all wrong, and I need to track down or create “lookup” tables that convert the raw data into something that I know is accurate.  I don’t like throwing out data.  I prefer to just keep the dirty data on the left hand side of a spreadsheet, and to the right of a thick, vertical line, create a modified column or field.  The raw data is black, and the modified data is has color, so I know what I’m working with.  I also give the modified data more explicit labels, in succinct but plain English.  Many dubious data fields are suddenly rendered “accurate” by a good label.

As with my fully-remediated soil, my fully-amended data set means that I am ready-to-roll.  I can work with a converted and color-coded batch of cultivated data, culled of garbage and meaningless fields, and turned into something useful.

I know that some people think of a garden as a place where plants grow.  And some people think of data as something that is capable of producing analytic insights.  In both cases, there is something deeply human about taking a mess – soil or data – and turning it into something more.  We advance civilization one step at a time, one cubic foot at a time, one data point at a time.  Sometimes we just need to break a sweat, get some sun, work our bodies, and build immunity.

Millennial Turnover Similar to Prior Generations

Posted on by Stuart Murray
hipster-attributed-to-rodger-evans
Hipster.  Attributed to Rodger Evans.

It is my pleasure to draw your attention to a great paper produced by three students at the University of British Columbia.  Grace Hsu, Geoff Roeder, and Andrew Lee produced a paper for their Statistics 450 course with Dr. Gabriela Cohen Freue which was put in for a student research contest.  The paper, Analysis of Factors Affecting Resignations of University Employees won an honourable mention for the contest.

The paper identifies that “Millennials do not exhibit a practically significant different length of employment compared to other generational groups.”  That is, that although those born after 1975 have a high quit rate right now, they are passing through a high-turnover age group.  Prior generations that passed through the 25-34 year old age group in years past, themselves had high quit rates.

Getting more to the point… “This finding disrupts stereotyped representations of generational factors in the workforce and suggests that younger employees resigning sooner can be better explained as a feature of their age rather than their generational group.” My guess is that age 25-34 is when people figure out their career, partners, and housing, with some things changing a few times before getting stable.

Working with twenty years of data covering 7000 staff who quit, their data model chose “years of service” as the variable that would be explained by other data points.  If we could predict the number of years a new hire would stay, this might be something an employer could improve.  That is, assuming it was not illegal to pre-judge.  Thankfully, their findings suggest we should not pre-judge.

Years of service prior to quitting averaged 1.2 to 1.9 years for 25-to-34 year olds, and 4.3 to 5.5 years amongst 35-to-44 year olds.  There were small differences between generations, but not in a manner that strengthened a stereotype.  For example Generation X quit more quickly when they were younger, but stuck around for longer once they were 35-44.  Baby Boomers were not always big on job loyalty, being the quickest to quit in the 35-to-44 age bracket.

One more thing… men and women do not have a big difference in their length of service.  When sizing-up job candidates for staying power, it is not just unfair and illegal to favour men; it is wrong on the facts.  Keep that in your back pocket next time you help with hiring.

Human Resources is Bad at Math

Posted on by Stuart Murray

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The world of business is full of smart people, like you, who are up for a challenge.  If you like business and you are good at math, you will typically go into marketing, accounting, or finance.  However, if you aren’t as great at math… you might have chosen Human Resources by default.  It’s a good decision for an individual, leading with your strengths and finding a place where your skills are valued.

The problem is, you might be surrounded by peers who did the same thing.  It’s a prisoner’s dilemma.  Yes, you will be better off choosing human resources, unless everyone else like you makes the same decision.  It gives the field a bad name, having an entire cadre of people with the same mortal flaw—they are bad at math.

To the rescue, a small number of people who are good at the math show up to offer some help.  They might be in compensation, HRIS, pensions, or from the surveys side of communications.  There would also be one or two lawyers who had to be in the upper-tenth of math skills just to get into law school.  But there’s a problem: they are all really busy.  They’re busy because everyone needs their help.  They are less likely to get fired.  They are at risk of being unpopular because they are disruptive and not like the others.  They have given themselves permission to act like themselves.

Wouldn’t it be great if you became one of these people?  Wouldn’t it be better to have more of these people around?  Maybe everyone in human resources can be busy, have job security, and advance controversial opinions.  Math makes us smarter.  To be smart and ambitious is to choose to apply math, and other skills as well, a little bit better every day.  There are special opportunities for those who have positioned themselves at the bottleneck.

At some point in a large organization, there is a wake-up call to mathify human resources.  Maybe there is a mistake.  Maybe you see the competition.  Maybe the people in the other strategic pillars have an important meeting without you, or have better boasting rights.  But at some point, things change.  They have to change.

Why?  Because math. And, oh yeah, data. Because data.

I hope to bring these two things together, in equal measure, in my new blog. I hope you’ll join me for the journey…