Do math people make good entrepreneurs

Question: Should engineers, mathematicians, and science people automatically make good entrepreneurs.


MAYBE... if they use their analytical skills properly.


Wipebook whiteboard notebook for figuring things out 

A few old school references

There are a few interesting old school books out there that substantiate the above.


First, there's Michael E. Gerber's 1986 bestseller, The E-Myth: Why Most Businesses Don't Work and What to Do About It.


As this Forbes article tells us, Gerber challenges the "entrepreneurial myth." The myth refers to the mistaken belief that most people starting their own businesses have solid entrepreneurial skills. WHICH IS absolutely false.


Many businesses are started by "technicians" who are good at something, but they have little or no business skills and/or analytical skills.


For example a skilled trades-person like a plumber may start a business because they feel that they are VERY GOOD at what they do and FEEL that they can take their business to a whole new level.


However, at that end of the day, in most instances like this, even though these individuals can make a very nice living at their entrepreneurial endeavours, they ultimately FAIL at making their business scale and grow because they lack the proper tools and mindset.


They fail to make their business a MACHINE that can function without them.


INSTEAD: they tend to work harder and harder as opposed to smarter and smarter.


Second old school reference: The Goal, a 1984 management-oriented novel by Eliyahu M. Goldratt, is another book that college students still use when they're studying constraints and business best practices.


The novel provides readers with case studies that focus on and eliminate factors that cause business bottle necks. 


Another reason why businesses fail: MOST entrepreneurs fail to implement systems and processes that get rid of system bottle necks. 


Both books should be part of any entrepreneur's repertoire.


Entrepreneurs: How can Wipebook's whiteboard notebooks and dry erase products help?


Being a math person has both pros and cons when it comes to business pedigree though


The pros: As I alluded to above both books establish the premise that building a business is like building a machine. A scalable, sustainable business essentially comprises operable sub-systems and processes that you can plug people and/or automated machines or computers into.


Math people are good at grasping this concept because the truth is, engineers and math people really enjoy making processes work and making them work efficiently.


They absolutely LOVE finding order in chaos.


That’s how our brains work. (I say our because I am one of these guys.)


Building business processes involves hypothesizing, measuring, and testing. And in addition to understanding how their products REALLY function, engineers and math people tend to do these things really well in comparison to other entrepreneurs from other backgrounds.


The cons: Since math people are naturally comfortable with NUMBERS the downside is: sometimes they yearn for too much data. AND THE RESULT IS: ANALYSIS PARALYSIS.


Analysis: How can Wipebook's whiteboard notebooks and dry erase products help?

Analysis paralysis

It's a math persons Achilles heel. Don't misunderstand me -- there's nothing wrong with data. But engineers and math people have a tendency to crave a lot of data before making a decision.


A business is like a train. And at the end of the day the train keeps moving.


Analysis paralysis could seriously hinder business growth because the people in charge spend too much time over analyzing data and ultimately at the end of the day fail to MAKE any decision, let alone the wrong one.


Analysis: How can Wipebook's whiteboard notebooks and dry erase products help?


So what's the answer?

Yes math people can make awesome business people because they have an analytical mindset and love implementing measurable processes and continuously improving them.


But the down side is math people can sometimes get lost in the data which prevents them from making decisions fast enough to keep the machine growing.


Just sayin'...


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