The Leading Indicators, A Short History of the Numbers That rule Our World, by Zachary Karabell, Simon and Schuster 2014

The numbers that we grew up with and lived with month to quarter to year at work aren’t, says Karabell, what we thought they were.  Hmmm.  Does that mean that leading indicators such as unemployment rate and GDP (Gross Domestic Product) are now suspect, no longer absolute but “variable numbers” subject to political or social “recalculation?”  In this provocative trip through the numbers that rule, the author answers, “Yes, they ain’t what they seem and they don’t mean what they say.”  Humph, now why would that be another new surprise?

In fact, as Karabell lists the key leading indicators – GDP, unemployment, inflation, trade, consumer sentiment and spending, stock market shifts, and housing – he shows that they truly measure only what they were designed to measure at the time they were invented Back in the Day. 

What is so intriguing to the Mill Girl, however, is the idea that these particular measures were designed to drive and measure industrial strength in the mid-twentieth century and before.  But just as purchasing morphed to global supply network creation and management – a la Apple, for instance – Karabell’s approved list of twenty-first century leading indicators – the right ones – are shockingly different and probably are driving different behaviors.

Take the unemployment indicator, for instance.  Karabell argues that before the Great Depression unemployment was not measured.  But the one-size-fits-all number, whether it is 6 or 7 or 20%, doesn’t tell the full story.  If you are a college-educated woman, the unemployment rate is currently less than 4%.  BUT, if you are an African-American male with a high school education, it is close to 20%.  Learning how to slice and dice these previously one-size leading indicators, therefore, is what Big Data Analytics (and politics) is all about – elections and money. 

So what are the new relevant and accurate leading indicators?  They are not, he argues, a few critical averages.  But instead, they may become more personal meters of success or health, like Bhutan’s Gross National Happiness.  But more specific to our industrial interests, the author suggests that companies develop their own proprietary indicators, rather than following a government big indicator, or even a vast industry measure.  If Apple has created and tracks its innovation patterns for Millennial female Caucasian early adopters, wow, that would be a very meaningful number and the channels that provide the intelligence would be priceless.  Or how about a company striving to beat down its 3D competitors?  Would it not be useful to look beyond current corporate sales to particular sector-driven activities for 3D – medical adoptions by big institutions for osteopathic reconstructions, vs. medical research, for instance?  The data is out there, it’s the channels and interpretations that are now key.

Mill Girl Verdict:  Very exciting, revolutionary, provocative read, especially if you are looking to replace decades old MBO numbers, or create your own portfolio.