Pattern Recognition
On a calendar that I have, there is a “quote” in Gujarati. Translated (loosely), it says,
“There have been learned persons in the past.There are learned persons in the present.There will be learned persons in the future too.They all followed the same path.”
So, obviously, there is a definite “pattern”.
We could substitute “learned persons” by “successful executives”.
Then, we need to define “successful” and “path”.
In the professional world of executives, broadly speaking, “success” is synonymous with:
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Salary-level
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Designation-level
Higher the salary and/or higher the designation-level, the more “successful” is the executive.
This is the universal “Yardstick/Benchmark” for measuring “success”.
Of course, there are two important “modifiers/qualifiers” of this yardstick, viz.:
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Age of executive (whose “success” is being measured)
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Company of executive
This is obvious as you can see from the following examples:
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An executive is drawing a salary of Rs. 20 lakh at the age of 50, whereas another executive is drawing the same salary at the age of 30!
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We would say that (obviously) the younger of the two is more successful than the older.
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An executive is “Vice President” in a small (Rs. 50 cr. turnover) company, whereas another is “Vice President” in a Rs. 500 cr. company (even if both are of same age).
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Obviously, the Vice President of the bigger company is MORE SUCCESSFUL of the two!
So, to compute the “SUCCESS INDEX” of any given executive, we would need to plot the following graphs, for 100,000 (– or even a million) executives:
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A “scattergram” & a “line of best fit” will emerge.
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We could break up this overall total population into sub-populations of:→ Each Industry→ Each Function(Apple-for-Apple comparisons).
We have data for the above & it should not be too difficult to plot this graph.
Next, we need to plot:
(Same remarks as above for “apple-for-apple” comparisons).
Here we have “Annual Salary” data of only those executives who have filled in our Online WEBFORM (resume). We could plot a graph for them, but…
These webforms will give you “Annual Salary” details (of different executives) for different “years”.
For example:
Salary in the year
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1997–98
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98–99
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99–00
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00–01(Rs. Lakhs)
So, you will have a separate graph for each “year”.
They would take this trouble only if we promise (– and live up to our promise) of sending them graphs, showing where exactly they stand in comparison with overall population (– or his industry or his function population).
Once we have data about 100,000 executives, we are in a position to PREDICT!
We can say,
“If you are drawing this salary today (i.e., at your current age), you would most probably draw XYZ salary when you are of a certain age.”(Of course, everything else remaining same.)
OR
“If, at the age of 30, your current designation-level is MANAGER, you can (probabilistically) expect to be designated GENERAL MANAGER when you are 45.”(Once again, everything else remaining same.)
Remember:
One does not have to be superstitious or believe in astrology, to get attracted to reading his horoscope in newspapers daily (since mostly it is so vaguely “reassuring”!).
You once said, www.salary.com has such a feature. If we can automate the content-compilation, we too can manipulate/process such “content” without human intervention.
(Signed)
[Two graphs sketched]
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Graph 1: Designation Level vs Age (with Success Index Line at 100 = Median, and bands at 120, 110, 90, 80).
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Graph 2: Salary vs Age (with Success Index Line at 100 = Median, and bands at 130, 120, 110, 90, 80).
Depending upon the “spread” of “scatter” at each age-level, it should be possible to develop a set of LINES OF BEST FIT.
Mean/Median line (?) will represent 100% SUCCESS INDEX.
Then at each age-level, we can plot frequency distribution & compute σ (Std. deviation).
Then:
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Central Line = 100% Success Index
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+1σ = 110%
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+2σ = 120%
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+3σ = 130%
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–1σ = 90%
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–2σ = 80%
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–3σ = 70%
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