Data Democracy, For Better or Worse

Vote Here. By Andrew DuPont
Vote Here. Photo courtesy of Andrew DuPont

Do you wish that there was more equality in our access to information?  I do.  In the past (i.e. a few decades ago) it used to be far more common for information to be more tightly-held by those with power.  However, major employers are pushing data downward into the hands of more people within their organization.

Here is an interesting article about data democratization, a buzzword that warrants some clarity.  Author Bernard Marr, in his July 2017 article in Forbes, describes data democratization through general themes.  An organization’s internal data is no longer “owned” by the Information Technology department, rather the data is put into the hands of diverse users.  Everyone has access to the data and there are no gatekeepers creating an access bottleneck.  People from varied ranks and diverse professional backgrounds can use the data to advance their goals.  There are down-sides, including redundant efforts by distributed users, concerns about data security, the fact that some data still exists in silos, and misunderstandings by those who don’t deal with the data every day.

It’s important to take this phenomenon seriously as a trend that is building steam, and which is probably here to stay.

In my opinion, the word “democracy” is problematic.  For example only those with digital literacy who are inside the organization can take full advantage.  Those with more power can use the new information more significantly to their advantage.  There also tends to be a winner-takes-all outcome, where the person with the best information and the most sophisticated ability to use it tends to come out ahead.

While you might think that these phenomena imply data is undemocratic, guess again.  Electoral democracy, although pure in spirit, tends only to involve between one-half and three-quarters of voters who cast a ballot.  Those who are powerful (i.e. business owners and property owners) have a strange ability to get more out of elected governments than others.  And those who are the best at politics will tend to win all of the power, leaving others in the dust.  Much like parliamentary democracy, data democracy works best for those who have the upper hand.  In both cases, the system is a pseudo-democracy of established interests choosing amongst themselves who they will share power with.  I think that’s called aristocracy.

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.

Share Your Data, Share Your Power

Sharing, by Andrii Zymohliad
Sharing.  Courtesy of Andrii Zymohliad.

I looked up the phrase “information is power” to figure out where it came from.  It turns out, it comes from everywhere.  The phrase is actually part of several longer and more complex quotes.  It is a call to freedom, such as Kofi Annan’s statement that “Education is the premise of progress, in every society, in every family.”  By contrast, the control of information has a coercive impact which we must overcome.  Robin Morgan says “the secreting or hoarding of knowledge or information may be an act of tyranny camouflaged as humility.”  Both themes ring true with workplace analytics.  First you need to take full advantage of information for your own empowerment.  Second, you are obliged to use that power justly.  The smart money is on the sharing of information, and the sharing of power, in a high-functioning modern workplace.

Data Turns People Into Money

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.

Long Service, Secret Edge

Lockers, attributed to flattop341.

After many years in one job, little things can become easy.  Amongst the things that give you an advantage are small tips from colleagues about the way things really work.  For instance, I once worked in an office adjacent to a swimming pool.  At lunch, I would swim at the pool, and return to work relaxed and productive.  At this pool, there were coin-operated lockers that cost fifty cents.

I would frequently run into colleagues at the pool during lunch hour.  On one occasion, a long-service colleague attempted to use the same locker as me, locker #51.  He said “oh you go ahead and use the free locker.”  Free locker?  “Yes, the coin slot is broken but the lock works, so there’s no charge.”

This colleague showed me how.  Put your clothes in, close the door, skip the step where you put money in, then turn the key and pull it from the keyhole.  Lo and behold, there was a locked locker and a key in hand.  He handed me the key and said, “enjoy.”

In most workplaces, there are small advantages everywhere, just like locker #51.  An undiscovered staircase, prior versions of the report you’re working on, and contacts who can answer your question in one minute.  These are not always things that you find on the web site or in training materials.  These tips are not a result of having rank, data access, or an advanced degree.  They are just little tips that favor those who have been around for a few years and listened to their peers.

There are many twists and turns in our careers, things that make us energetic or complacent or curious or mad.  In the middle of these many changes, things get a little easier every year.  If we leave, we will lose this advantage.  I think it’s a secret reason why people stick around.

Can you think of the last time you found a locker #51 in your workplace?  What are some of the tips or tricks that you have learned from others?

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?