The Line Between Commercial and Industrial Data Science

It’s no secret that investing in data can lead to major benefits for organizations. Not only is data vital to success, companies that utilize insight-driven practices are twice as likely to be market leaders within their industries.

When you combine that perspective with the fact that upward of 80% of all collected data goes unused, the possibilities really begin to present themselves. But data science practices are vastly different when you compare industrial and commercial organizations. For each, data sets take on different forms, from the frequency of data inputs to the costs of experiments and models to, most critically, the impact associated with the resultant insights such as the cost of failure.

Author: Mark Bernardo


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