Mopping up

Scenario planning is a critical way to anticipate and adapt to (and sometimes take advantage of) the profound changes being faced in the technology, consumer, and labor markets.  Even where you are forced to respond to the external environment, you can still position yourself to be agile enough to respond quickly and effectively.  Perhaps that's why this quote appeals to us so much!


Three Questions to ask with Workforce Analytics

Three Questions to ask with Workforce Analytics

Beechcraft 1900D Cockpit

Beechcraft 1900D Cockpit

I had the pleasure of travelling around the spectacular Bay of Plenty region in New Zealand last week, developing 2024 workforce strategy with some incredible people.  The hop from Auckland to Rotorua was on a Beechcraft 1900D, and I was fortunate enough to be in 1F - the seat that gives you the best of the cockpit, which gave me pause for thought about workforce analytics dashboard design.  Although I usually advocate for a "less is more" approach to analytics in dashboards, the dashboard of an aircraft is fascinating - every gauge, every dial serves a purpose and provides real-time, actionable data.  Many business analytics dashboards don't exhibit these properties.  A good rule of thumb when determining whether workforce analytics should be reported is to ask these three questions:

  • What?: What information is this display giving us?  Does it make sense, and can its purpose be clearly communicated?
  • So What?: Why does this information matter - what is it's connection to organisational strategy?  What's the context as to why we are reporting this?; and
  • Now What?: Is the data actionable, and if so, how?

If you're unable to answer these three questions, it may be time to take your dashboard back to the drawing board.

Mars One

Mars One

Mars One is an initiative to create a human settlement on Mars, with the first crew expected to depart earth in 2024.  Although I can't imagine applying to go on a one-way mission to Mars, I'm fascinated by the concept and intrigued by the process.

Needless to say, taking a job like this is no small commitment - you are literally giving your life to your job, and the first crew of four's commitment and preparation will determine the success of a program to colonise another planet. (No pressure).

What's particularly interesting to me is the selection criteria - although there are some age requirements and no doubt health requirements, the five key criteria are Resiliency, Adaptability, Curiosity, Ability to Trust, and Creativity / Resourcefulness.  Technical skills can be taught, even on a scientific mission - but the right persona is critical.  We see this playing out in all different industries, all round the world - what differentiates a competent member of the workforce from an exception performer is rarely a technical skill - but we typically source for and develop on technical skill alone.

What are the competencies, skills, and personas that separate high performers from everyone else in your workforce?  Knowing that, what are you doing to build HR initiatives to stack the deck towards high performance?

Medians in Workforce Analytics - when Average is not Typical

Recently on Quora, I was asked to answer a question about why organisations base pay on benchmarked Median salaries, rather than Averages.  I thought I'd repeat that answer here, with the help of a comic strip.

There are three main statistical measures of central tendency, or what is "typical" - Mean (aka Average), Median, and Mode.  There are also, according to Mark Twain, three kinds of lies - "Lies, Damned Lies, and Statistics".


Let's address the three measures of central tendency first:

  • The Mean is what you get when you add up all of the values, and divide the total by the number of values.  This is usually referred to at the average.  In the salary example, this is what you'd get if you added up everyone's salaries, and then divided it by the number of employees.
  • The Median is when you rank all of the values in ascending or descending order, and pick the middle one.  In the salary example, you'd pick the salary where there were an equal number of higher and lower salaries - the salary right at the middle of all salaries.
  • The Mode is when you pick the most commonly occurring value.  For salary, this would typically happen where you have a lot of people set pay grades, and one of those pay grades has more people on it than any other.

Any list of values that has a logical lower limit and no logical upper limit should be reported as a Median, because a high value in the list will skew the results - and the measure is subject to fluctuations that no longer represent what is typical.  If the CEO gets a payrise, the mean will increase, but the median won't change.  A median, therefore, is a more typical measure of what's typical in this case.

Depending on your data, different measures can tell you a different  story - and sometimes these stories are misleading.  That's what happens  when you report Salaries, Length of Service, or any other metric where there is a logical lower limit (usually zero) and no logical upper limit.  In those cases, averages are - well, pretty average.  This is also why House Prices use median: it's more representative of what's "typical", and less prone to wild fluctuations than the average.

Which one are you?

A great image found at New Old Stock, that (perhaps inadvertently) is an interesting take on team dynamics.  Which one of these people are you?

Image Source:

Image Source: