Why do so many companies still struggle to build a smooth-running pipeline from data to insights? They invest in heavily hyped machine-learning algorithms to analyze data and make business predictions.
But then, inevitably, they realize that algorithms aren’t magic: If they’re fed junk data, their insights won’t be stellar. So they employ data scientists who spend 90% of their time washing and folding in a data-cleaning laundromat, leaving just 10% of their time to do the job for which they were hired. What’s also flawed about this process is that companies only get excited about machine learning for end-of-the-line algorithms.
Author: R. Danes
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