#TechnicalVirtuosity: The Player is the Special Sauce
Once upon a time, a few clicks back into my youth from now, I fancied myself a fairly decent piano player. That illusion came to an abrupt demise when I met Fred Johnson. On the surface, Fred was as milquetoast-Midwestern as they come. You might have expected hay to fly out of his mouth when he spoke. But, that assessment would have been seriously flawed, as I soon learned. It turns outs that Fred was blessed with perfect pitch, had any number of the very long and complex Rachmaninoff and Prokofiev concertos perfectly lodged in memory, and the speed of someone afflicted with the gift of 25 fingers – and all by the 9th grade. He was a quintessential virtuoso as far as I was concerned. What Fred produced at the same piano and with a quick glance at the same sheet music as I had been laboring over for weeks were two entirely different definitions of music. In short, I would need to discover my own virtuosity away from the ivories from then on out.
Anyway, if you are experiencing a déjà vu moment with thoughts of Jim Collins’ 2001 bestseller “Good to Great” where the message was to get the right people in the right seats on the proverbial “bus,” then you would be simpatico with where I am headed with the following thoughts:
In a prior post, entitled “#TechnologyVolatility” (October 27, 2015), we introduced the rather propellerhead-ish concept that the standard deviation of technology spending per employee (TPE) over time – in this case, the 10 years from 2005 to 2014 – yields “TPE dispersion”. To simplify this mouthful, we call this number T-VOL?. It’s a techno-operational analytic that we believe provides insights into the functioning of an enterprise (or business unit, since the analysis has “fractal” qualities).
Not to go too much further into the weeds on this, but on the back of that post a client asked about the relationship between T-VOL? and revenue per employee (RPE). Great question - and we thought it was well worth the exploration. In the exhibit below, revenue and technology spending represent the highest correlation (0.92) among the key variables - which is slightly higher than the correlation between headcount and revenue (0.85). As we stretch out the correlation matrix with more variables (like headcount level) and turn those variables into ratios (like TPE and RPE) and then further into derivations of ratios over time (like T-VOL? and other standard deviation measures) the correlation weakens, and therefore, the power of our T-VOL? analytic as an influencer of the level of RPE seems to weaken too. But, all that said, there are reasons why such a correlation (currently 0.28) may never get very much higher – even with a larger sample size. It is an explanation that takes us full circle back to Fred and why the player is the special sauce.
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Paul Rowady is the Director of Research for the Alphacution Research Conservatory, a research and strategic advisory group focused on financial services technology, analytics and data. He has over 25 years of senior-level research, risk, technology, capital markets and proprietary trading experience. Follow @alphacution.