“Ideas are easy. Execution is everything in Measure what Matters.”
―In John Doerr’s “Measure what Matters”
John Doerr, who founded the Objective Key Results, a performance management method widely used in Silicon Valley, said that ideas were just the start. Ideas, no matter how brilliant they are, are difficult to execute to the commercialization stage.
Apple paved the way for the fourth industrial revolution with ideas to use finger touch to control devices. If the idea had been suggested by an individual, the outcome might have been very different. The idea could be commercialized due to Apple’s years of accumulated technology and knowledge. Most great ideas, unlike those that fade away in the initial stage, are born from knowledge fusion. Perhaps ideas are more of outcomes rather than starts. This shows why it is just as important to reinforce capabilities, just as encouraging good ideas.
Computer (coding) and mathematical skills are key in the data era. Many view coding skills as vital, but mathematical skills are viewed as owned by a limited few. However, math is key in the world of data. Matrix, differential, probability, and other mathematical concepts form the basis of Artificial Intelligence.
How mathematical concepts are put into use is important in economic fields. Thus we need to redesign our math curriculums at school, reducing the curriculums to function, geometry, matrix, probability, for better efficiency. We also need to put more focus on explaining basic concepts rather than in complex formula. The government encourages start-ups as means to overcome youth unemployment, but less than 30% of new businesses make it past five years. I had pursued studies of liberal arts, giving up on mathematical ambitions during school, but recently taken on AI studies because I was drawn into its appeal. I strongly encourage young entrepreneurs to take early interest in mathematical studies which will help them understand and utilize AI skills needed for the future.