You Need an Algorithm, Not a Data Scientist

Published January 7, 2015   |   
Kira Radinsky

Mark Twain once said: “The past does not repeat itself, but it rhymes.” Although future events have unique circumstances, they typically follow familiar past patterns. Today, data scientists can predict everything from disease outbreaks to mortality to riots.

It’s no surprise, then, that companies trying to hear the rhymes and see the patterns in their sales conversions are trying to manually analyze their own data, hire the best data scientists, and train their managers to be more quantitative.

However, this people-centric, high-touch approach is not scalable. Markets are too dynamic, and some of the changes too imperceptible, to be realistically captured by humans.

Consider a company that is selling electronic devices. Let’s say that historically they have been selling well to companies that value their fast delivery and the quality of their product. As time passes, the competition grows and a global trend for green products arises. The profile of the company’s perfect customer slowly shifts and could go unnoticed by manually examining the market. However, those small shifts are identifiable by algorithms that continuously monitor the historical sales cycle of the company, cross-referencing it with external sources, like social media posts and newspaper articles discussing these trends, and finding correlations with the propensity to buy. Due to the size of this information base and its unstructured nature, monitoring all those delicate changes in real time becomes an almost impossible task for a human analyst.

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