Forecasting Talent Challenges

Workforce had a brief interview with Gail Fosler, the new president of the Conference Board.  While it's brief, I particularly liked the way Gail described the changing need for workforce planning:

[Employers have] been accustomed to the U.S. labor force as being a very rich pool. They can dip their ladle in as they wish and out come exactly the sets of people they want and need. This seems like a no-brainer, but people have [simply been] filling positions rather than charting over a five-year or longer period what people they are going to need, where are they going to need them and what kinds of skills they are going need.

It's another way to express exactly what a client of ours said to us (in a little frustration) recently:  "management wants to trust the math, and think that the math will take care of it - but it needs to be bigger than that".

Both of these points get to a very important matter - many employers are simply looking to calculate how many of the exact same positions they want to fill with a plan for dipping into the labor pool and fishing out the people....a replication of the past or at best a slight variation.

We certainly believe that it's bigger than that!

Dumb Business Moves of 2007 - the Talent Segmentation Example

In Fortune's 101 Dumbest Moments of Business, we are reminded of Circuit City's classic moment in talent segmentation, which scores them #70 in the "dumb" countdown:

In a cost-cutting move, Circuit City lays off all sales associates paid 51 cents or more per hour above an "established pay range" - essentially firing 3,400 of its top performers in one fell swoop. Over the next eight months Circuit City's share price drops by almost 70%.

When we're doing workforce planning, and we are segmenting our workforce....we really do need to ask questions of data, and be sure that we understand what the real story behind it is if we act.  No doubt the people who did the Circuit City layoff made a really impressive case for cost savings, including lots of consideration of how many people would not be affected (although clearly no though about how the segments they were assessing were connected to the business strategy!).  If you see a trend (like certain people earning more than others), ask "why?".  And when you are making a decision based on "efficiencies" you might have found in the data, explore the ramifications of the decision you're making, ensuring that you look for the answer the "Devil's Advocate" might give you.  The numbers can tell many, many stories.

Still, looking back over the the 101 Dumbest Moments in Business for 2007 was a good giggle.

"Prickly" Issues in Data

Another great post from Jon Ingham drawing attention to Edward De Bono's quote about finding issues in our data:

"I was in the desert and our guide was explaining the spikes on the local cacti. 'It's so they don't get eaten,' he said. 'No, it's not,' I said. 'The spikes help keep the air around the plant still, to minimise evaporation.' Everyone thinks it's to stop animals eating them. They looked at the data and came up with the idea. And that became the received wisdom. In fact, it's the wrong idea. Many organizations believe that if they collect enough data in their computers that will set their strategy for them. In fact, unless you see the data in different ways, you will be stuck with the same old notions."

It's a great analogy, but there are several ways that myths and data cross each other's paths, including:

1. Like the cactus example, when we look only at the data and assume the wrong "truth" from what we see. It's a tough one to solve, but we need to be careful not to assume that the most obvious issue really IS the issue. Asking people to "see the data in different ways" is tough - but worth it. Some techniques include scenario planning and backcasting ("what ways might we have got here"), but I think that what we really need is a generous pinch of devil's advocacy - as workforce planners we think of alternate reasons, and ask "why" and "why not" about them to the business
2. The flip side of this problem - when we assume we know "the truth" and have never validated it with our data. This is like a workforce urban legend, where somehow the group have come to believe that a situation is true, and never "asked the question of the data". These workforce legends are not always explicit, but when they are it pays to question them and check the data! It also pays to provide the business with more than just a simple set of figures - well thought out presentation of information can help us to bring these assumptions out into the open and stimulate good dialog about them....occasionally even leading to them being debunked!

There are others - any that you are experiencing?