A common thread in technology is the impact that progress will have on how we work, and more specifically how progress leads jobs to be replaced by machines. This discussion is particularly curious in data science because the practice of data science is at once both an intellectual challenge and an example of lots of tedious work.
In practice, as with many examples of automation technologies, this isn’t what Emcien customers experience. Instead what we see is that the nature of work changes dramatically, but the jobs remain. In fact, what happens is the acceleration of data discovery and prediction tasks create additional opportunities for new analyses faster than workers can act on initial results.
When manufacturers of complex configurable machines see how much more they can learn about the buying patterns of their customers, their efforts shift from digging through data to using these new discoveries to make the greatest impact in their business.
When customers first implemented automated sales analytics into their online recommendation systems, they simply replaced the default product associations with algorithmic recommendations that increase sales while vendors can focus on improving their product offerings.
In large scale network analysis we see even greater opportunities. Not only are new discoveries made more quickly, but the number of people who become capable of discovering new insights about their network all create more value than before, and new opportunities for more impactful and important work.
In each of these instances and across very different industries, automating some of the most time consuming and difficult parts of data discovery and analytics doesn’t eliminate work, but creates more opportunities for success.