You’re Smarter Than You Were an Hour Ago

Posted on by Stuart Murray
Rear-view mirror of Zion Mountains, by daveynin
Rear-view mirror of Zion Mountains, by daveynin.

One of the greatest adventures in uncovering new information is the clash between our new ways of thinking and the opinions we had moments prior.  Our brains play tricks on us, through cognitive fallacies, when dealing with disruptive evidence.

One such fallacy is called “hindsight bias,” a kind of knew-it-all-along effect.  I have given complex and novel findings to clients who quickly proclaim that the information is basic and obvious in some way.  Sometimes it is basic and obvious, but quite often they had opposite views minutes earlier.  Learning and research can be thankless because it is so common for smart people to quickly absorb new information.  They don’t recall being ignorant.  If they do remember being ignorant, they’re not tempted to draw attention to it.

Those who neglect to pursue new knowledge and feed their curiosity become less savvy over time.  The times change, people change, and evidence shifts.  People who figured out the ways of the world many years ago start to lose their grip.  They have dubious clothing, haircuts, and social views.  They overlook emerging evidence.

Real smarts are not really about having a vault of information; it is the act of striving to explore.  I watch new data make its way through the organization, with little or no attribution to me or the original source.  Things quickly become known.  The culture becomes smarter.  It is quietly satisfying.

Tiny Portraits of Big Data

Posted on by Stuart Murray
les-demoiselles-davignon-pablo-picasso-photo-in-public-domain
Les Demoiselles D’Avignon, by Pablo Picasso.

In August of 2016, I visited the Museum of Modern Art in New York City.  As often happens at museums, I developed a new perspective.  I didn’t really understand cubism until I had the audio headset on, and took a close look at the art.  A main concept is that the things we see in real life are a series of smaller pictures that we bring together in our minds.  To portray this in art, the early cubists created larger paintings that were a series of smaller images put side-by-side.  The edges of the smaller pictures squared-off like a full painting.  The smaller pictures don’t flow together into something “pretty.”  Rather, the somewhat nonsensical image is jarring you with the idea that you do indeed perceive the world as a random bundling of small images.

This is not dissimilar to producing human resources metrics in Excel.  The high point for our clients is clean charts that get to the point, which tell a story and cause better decision-making.   For lay audiences, the goal is fewer and cleaner ideas, somewhat pretty, like impressionist or realist art.  However, for the analyst, inside each of the “cells” in Excel, we create stand-alone calculations that are complex and beautiful creations in their own right.  We bundle together thousands of cells with formulas that slightly differ from one row to the next.  They are clever little formulas using commands such as VLOOKUP and SUMIFS.  Sometimes the formulas are complex and interesting, sometimes not.  But every cell has its own story.

I realized that my entire analytic career is built around a cubist perspective of formulas, creating a final canvas that is a fusion of a large number of small ideas.  Some people see a page full of numbers but, for me, it all looks like Demoiselles d’Avignon (shown above).  I didn’t invent this concept – that happened long ago – but I do get to apply the idea to practical effect.  I have the pleasure of taking the concept out of fine art and applying it to the realm of workforce analytics.

What does this mean for you?  If you’re just getting comfortable with formulas, you are allowed to just create one small cell with a simple statement.  Then make a few more. Add a little more complexity.  Then you can stop.  Or build on it over time.

However, if nobody thinks your analysis looks pretty, don’t worry, this isn’t Hollywood.  If nobody wants to buy it, forget about it, you’re busy.  And sure, your colleagues could have made it themselves.

But they didn’t, did they?

Disrupt an MBA Class by Inventing Spreadsheets

Posted on by Stuart Murray
idea-cropped-by-katie-tegtmeyer
Idea.  By Katie Tegtmeyer.  Cropped.

This is a great TED talk by Dan Bricklin, the inventor of the electronic spreadsheet.  The application he created – VisiCalc – is the original precursor to Excel, one of the easiest to use number-crunching tools when doing workforce analysis and just about everything else.  He tells a great story of the disruption that his new tool caused its first audience, the MBA class he attended at Harvard, and the first conference where he revealed it.

This is a story of invention, so watch for key words like error, daydream, magic, imagine, prototype, feedback, find solutions, new capabilities, and marketing.  This is a story of iterative design, where they figured out what they were creating part-way through the exploration.

Become the Boss of Your Data

Posted on by Stuart Murray
close-up-courtesy-of-photosteve-101
Close Up.  Courtesy of photosteve101.

May I let you in on a secret?  There’s a memo going around the “in” crowd, and I think you might be in.  It’s about highlighters.  Highlighters are magical weapons.  But it’s not the highlighter itself that is magical.  It’s just a couple of plastic tubes, a felt tip, and some see-through ink.  What’s special is the way it is used.

At some point you will enter a meeting to discuss human resource metrics.  You will be handed a printed page full of numbers.  For those just getting started with this skill, there may be a flood of emotion.  These pages may look like a blur, like scrambled eggs, or a junkyard, or an ancient text in Linear B.  You recall vivid memories of that schoolteacher who didn’t tell you that you could become good at math.  Maybe you made a big mistake with math one time (don’t worry, everyone did).

But you have a secret weapon.  You have a highlighter.  Take a deep breath, maybe two.  Now, un-sheath your highlighter, and put the cap on the opposite end, to keep it all together.  Make clean, swift movements, like you do this every day.

You are hereby granted permission to mark the page with highlighter.  It’s funny, right?  You weren’t sure if that was okay.  So just go for it.  Maybe test the pen in the corner or something.  There, now you’re ready.  You are the boss of this piece of paper in your hand.

Now look in the bottom right-hand corner of the page.  It’s usually some kind of total.  Highlight the total.  Say the number out loud.  Look at the title in the upper-left of the page.  Does the number you highlighted reflect the title of the page?  It should.  If it doesn’t, then someone other than you is confused.

“This number, what does it mean?”  Just keep it simple.  Don’t apologize.  You see, you are the client.  You are allowed to ask questions.  And this pristine piece of paper with the fancy characters… has just been marked up by you, deciding for yourself what is clear, what is interesting.  Listen to the answer you get.  Make the math people use their words.  Don’t worry, they’re happy you are engaging.

Now, you should have a dry feeling in your mouth.  You’re not nervous.  You’re hungry.   Spend the next few minutes in silence, marking the page.  Find the biggest number on the page.  Then the smallest.  Find things that don’t make sense.  Find things that aren’t what you expected.  Just briefly, consider a new way of thinking.  Catch a typo, and be nice about it.

Now talk about what you found.  Compare notes with others.  You’ll probably get it half-right.

And that’s it.  You’re done with first steps.  But just remember, you can’t do it if you aren’t using your highlighter.  Because a highlighter is a magical weapon that defeats intimidation.

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.