Happy National Spreadsheet Day

Greetings everyone.  Happy national spreadsheet day!  If you want to know more about the invention of the electronic spreadsheet, peek at my earlier blog post on the topic.

Making Beautiful Art With Excel

Augutso Tokumori. Photo courtesy of tenjishituT6.
Augutso Tokumori. Photo courtesy of tenjishituT6.

Incidentally, there’s a gentleman named Tatsuo Horiuchi who makes fine art using the Microsoft Excel software.  It’s gorgeous, check it out.  In addition to his artistic creativity, I give him bonus points for choosing Excel because it’s less expensive that other applications.

An Ode to the Number Pad

Number Pad. By Tony Cuozzo
Number Pad. By Tony Cuozzo.

Everyone who doesn’t use their number pad is taking orders from someone who does.  Just placing your middle finger on that nub on the number-five key will increase your professional drive.  If you’re right handed, you’ll see that the thumb on your right hand is hovering over the arrow keys, allowing you to easily navigate your territory on a spreadsheet. Your pinkie rests on top of the enter key; moving onward after entering some numbers is effortless.

If you aren’t using the number pad as a course of habit, try a little data entry.  Maybe at home you can key-in a column of questionable expenses that you saw on your bank statement.  Or maybe there’s something from a web site or a PDF that isn’t cutting-and-pasting so easily.  Just find a good excuse to do ten minutes of data entry.

If it’s your first time using the number key, you’ll notice that your fingers will start to remember where things are.  Your speed will pick up, your accuracy will improve.  Even better, you’ll learn your own margin of error, which gives you the ability to control trade-offs between speed and quality.

With your hand sitting on the home row, everything you need is in reach.  At least, every number you need is in reach.  By contrast, it is the use of words that takes extra effort.

Missteps Make for Better Analysis

Oops. By Malcolm Slaney
Oops.  Courtesy of Malcolm Slaney.

A major voice in people analytics just advocated for the professionalization of my field.  An April 27, 2017 blog post by Max Blumberg and Mark Lawrence suggests that workforce analytics regulate itself under a professional association.  The authors have a good point.  The explosion of alleged experts in my field is making things really confusing for lay audiences.  We have no idea if someone claiming to have expertise is truly knowledgeable.  There is a gold-rush mentality in workforce analytics, and we can barely distinguish those on the cutting edge from the outright con-artists.  Bad experiences and false starts are causing skepticism.

I agree with this assessment of the current state of affairs.  I decline the vast majority of conferences, webinars, and software on offer.  Being strong at workforce analytics turns on having daily exposure to the data itself.  I have yet to hear a provider offer something more interesting than that thing we just figured out last week, by ourselves, with in-house staff using excel.

However, I have to disagree with the proposal that the field should be regulated.  You see, the main opportunity is to democratize the skill set and bolster the overall number of people who read the data and create simple calculations.  If you can get one-tenth of a human resources team to tool-up with a small amount of learning and experimentation with the data, that’s a huge boost in organizational capacity.  There is one specialist for every five or 10 people in the earliest steps of the learning curve.  Tinkerers and new entrants are half of the equation, and sometimes they are the most important half.

There is another problem.  We don’t yet know what excellence in workforce analytics looks like.  Sure, getting the attention of the c-suite, saving money, having clean data, and making your findings presentable are really obvious signs that you know this stuff.  But mysteries abound.  The information is disruptive to those with power, so how shall we deal with the office politics?  The data improves every day, so how do we maintain composure while discussing last-year’s erroneous data.  We’re supposed to align to strategy, but strategy and leadership change is constant.  And how are we to negotiate the boundaries between the professions when accounting has their own cost model, and marketing researchers are experts in employee surveys?

The mystery, confusion, emotional drama, flashes of growth and pride all bring the field to life.  Workforce analytics is a mosh pit.  Our outputs are a meal thrown together from what is leftover in the fridge.  Our first attempt at everything looks like a Pinterest fail.

Let’s keep it messy.  We’re more honest that way.  Besides, we work harder when we’re having fun.

Modern Samurai Wield Swords of Data

Samurai, by Johan Sinso
Samurai.  Courtesy of Johan Sinso.

Data analytics will be regarded as an increasingly important skill for professionals in the modern workplace.  These comments come from Eric Schmidt, executive chairman of Google’s parent company Alphabet, and Jonathan Rosenberg, adviser to Google’s CEO Larry page.  They identify that jobs using analytic skills are expected to grow by 30% over the next seven years.

The executives name some known areas of higher-level skills such as Excel, SQL and Tableau, as well as a grasp of calculus.  However, they also noted that “an understanding of how to approach big data would still be very helpful in finding a job.”  My experience has been that just within Excel a couple of basic cross-tabular tables allow you to pinpoint key findings.  It’s like the high-and-low when you’re creating an outfit or decorating a room.  You need one or two blockbuster items to make things excellent, but other things can be pulled from Ikea or a thrift store.

Eric Schmidt poses the metaphor that “Data is the sword of the 21st century, those who wield it well, the samurai.”

Tiny Portraits of Big Data

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?