…It’s People

At the risk of spoiling the classic Charlton Heston film Soylent Green, and the punchline for my article this month, I offer this observation: advanced analytics, machine learning, and big data offer amazing insights but making use of those insights requires people. A skeptic may offer the observation that this conclusion is self-serving and biased, I am after all one of the people trained and experienced in this field. My proposition is that a company looking to grow and either remain or become a market leader will invest not only in the capital but also in the human side of the equation.

Intuition

I believe in data; decisions I make are largely made by categorizing, evaluating, and placing a numeric value on base components of the problem. With this backdrop, I suggest an important component of the evaluation process is a ‘gut-feeling,’ or more properly put, intuition. A business executive focused solely on numbers misses this, or worse, can be mislead. The growing dataset and ease of accessing has only deepened the age old problem of decision paralysis. Decision makers waiting for information to be perfect, models to be flawless, or timing to be ideal all to often wait too long. Decisions must be made, and the data will rarely be perfect when it is time. Intuition fills the gap between what is known and what is uncertain, and this intuition is a human trait.

Machine learning as a field is making incredible strides and advanced analytics tools are able to fill in many of the unknowns in properly developed models, yet there remains a place for people. A model, it has been posited, that truly accounts for every variable, does not have any unknowns and therefore is not in need of any “intuition” to fill in gaps. This model would perfectly predict outcomes and make correct decisions. Such a model, however; does not exist. More variables, and more chance, being involved in an equation equals more unknown and an increased importance of human intelligence.

Models and data continue to evolve. IBM’s innovations with their Watson platform are incredible. Machine learning efforts continue to evolve and advance. It may well be that we reach a day when a person is not a necessary component of the equation, but I believe that day is still in the distant future. In the land, labor, and capital equation of productivity, labor (people) will continue to be the element most necessary to achieving success.

 

 

 

 

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