First Do Your Homework, Then You Can Play Ball

Posted on by Stuart Murray
Shane Battier. Courtesy of Keith Allison.
Shane Battier. Courtesy of Keith Allison.

What impact does analytics have on teamwork?  Plenty, it turns out.

I happened upon an interesting blog post by Thomas Marsden of Saberr, a team-development firm.  Marsden was fascinated by a session with Shane Battier at the Wharton People Analytics Conference.  Battier is a basketball player with many distinctions.

One distinction is that Battier won a team-player award because he served as his team’s data translator.  Players on his basketball team were handed massive statistics packs about the opposing team’s behavior.  He actually read them, a behavior that was rare.  I often wonder what happens when I produce a ream of analysis and send it off to a client.  Sometimes (but not always) someone comes back to me with tough questions, follow-up inquiries, and demands for deeper dives.  In those cases I have struck upon an expert consumer.  They are like wine experts, indie rock snobs, or film buffs.  They don’t produce the product; they just really know how to consume it.

Battier is an expert consumer.  He did his homework and made interpretations in order to play better.  Other players had not done this, so he would help teammates and “drip feed information at the right moment through the game.”  He was acting as the intermediary between the statistical analysts and the front-line players.  This is a key bridging link between two cliques.  In network theory the person who causes information to jump from one crowd to the next becomes a go-to person for both cliques.

For some people, they see the shots being made.  For me, one of the greatest games on earth is watching the information pass from one person to the next.  There is a bounce, a spin, a clever move, a change of play.  I watch big people, breaking a sweat, moving my data across the court.  And when they score, it’s fist-pumps and high-fives.  Good game.

Data Turns People Into Money

Posted on by Stuart Murray
chinese-penny-attributable-to-marhawkman-some-rights-reserved
Chinese Penny.  Attributed to Marhawkman.

I often think of human resource metrics as the center of a Chinese penny, like the one in the picture above.  There are unknowns at the middle of every problem.  When you resolve an unknown, it’s as if you have punched a little hollow square in a metal disk, and turned it into currency.

While people-culture can be the main driver of business success, this is the thing you can only ponder once you get past some obstacles.  Math is an obstacle upon which you can stumble time and time again.  The puzzles include things such as benefits cost, engagement scores, or performance ratings.

Yet it is not the obsession about the math puzzles in HR that cause success.  It is the ability to move beyond.  If you figure out how to get benefits costs under control, find the source of lagging engagement, or make the switch to performance conversations, you will probably do so because you took proper care of the math, not because you ignored it or obsessed about it.

You need to change the shape of the problem, so that you are talking about people without frustration and confusion.  It is common for someone to start with a basic numbers question that is bothering them. The moment that question is resolved they can move on to a totally different topic.  That new topic can be one question beyond the one they just asked.  However, it’s a shift.

When math is the problem in the middle of things, and you eliminate that problem, you move on to the currency of human resources.  And that currency is people.

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.