Data Science, more than talking robots

Dan Watkins
Sep 23, 2019 · 2 min read
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The expectation that AI applications should all be glamorous can cause people to miss the huge impact that Data Science is having now (on companies that embrace it).

The popular press tends to use the terms AI and Data Science interchangeably (and most lean toward using “AI” because it sounds sexier). Fair enough. An article in the popular press is MUCH less likely to be read if it says that a company used “Data Science to reduce inventory in a factory by 5%.” That title simply doesn’t create subconscious associations with human-like robotic intelligence (think HAL, The Terminator, Agent Smith, Wall-E). A more likely title these days is “AI runs the Factory of the Future” (which, when I googled it, actually turned out to be a pretty good discussion of how Data Science will impact factory productivity).

Maybe some current applications of data science are less exciting than self-driving cars, AI automated factories, or software that can write bestselling novels — all things that once were sci-fi but will someday become commonplace. Even so, Data Science is currently giving the early adopters a competitive advantage (and making them a lot of money).

Research published the Harvard Business Review (entitled “Most of AI’s Business Uses will be in Two Areas,”) shows huge value creation potential in two fairly unglamorous categories:

(1) In Marketing and sales, AI can create up to $2.6 trillion of value. For instance, using customer data to personalize promotions.

(2) In Supply chain management and manufacturing, AI can create up to $2 trillion in value. For example, predictive maintenance or inventory management (via improved demand prediction)

These two areas account for two-thirds of their predicted potential AI value creation. Again, AI as used to describe these applications, is what many would call Data Science.

We are a few years away from human-like AI robots having an impact, but that doesn’t mean that you can sit on the sidelines and not use the new Data Science tools/capabilities/technology. The CEO that fails to move now, when their competitors are getting more efficient and gaining revenue using Data Science, is going to fall behind and may not be around to use those AI robots.

Mercury Data Science

AI to solve the world’s life sciences challenges

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