Data Science Automation is The Dream, Job Openings Are The Reality

Data Science Automation is The Dream, Job Openings Are The Reality

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It’s unsurprising that a field becoming as ubiquitous as quickly as data science has already reached a point where start-ups and tech giants are looking into the future of automating the process as if to feed the Ouroboros of information collection and aggregation into itself. That dream is still a few fair years away, but there’s never a bad time to prepare for market shifts of the future.

The news comes in the form of a press release regarding the firm DataRobot, a start-up that recently finished another round of fundraising, taking the company’s starting bankroll up to an impressive $225 million. Investors expect the firm to focus on automating the field of data science, but the timetable for such an endeavour could be lengthy.

Their proposed system aims to almost entirely automate the work performed by data scientists, but the feasibility of such an approach is left up in the air. DataRobot has been operating since 2012 and is still in the process of securing funding while actual working data engineering projects have been off of the ground for decades. Industry analysts have described data science as “the new Latin” for college students looking for a specialised degree that requires extensive schooling.

It is almost certain that many of the more generalised aspects of data collection will be fully automated, almost to the point where those with experience in the field may be able to avoid the lowest level of interaction that data science requires. At the same time, putting insights to good use without decades of information to draw from requires the specialisation of someone with a Masters in data science or a similar level of education and familiarity with the field. It’s not just a question of computer knowledge, but rather an issue that requires human insight into problems that a machine cannot yet grasp.

Businesses are quickly catching on to the importance of such specialised knowledge. Investing in analysts, engineers and other professionals is becoming the next big step in securing potential customers and filing away erroneous services that could be replaced with better earners and stronger sellers. Crunching the numbers and honing a service can be just as valuable as filling out an employee roster with another batch of hires without the jump in expenses that move requires.

The Drum expands on critical moments in consumer interaction with a business and its offerings, pointing out that we can no longer rely on the simple cycle of a customer seeing something they want and purchasing it in a local storefront. Instead, the vast majority of interaction comes in the form of following up an ad or searching for a product in a moment of need through a digital storefront or internet search. These interactions are almost entirely digital and staying on top of them through decades-old methods are quickly becoming unreliable if not impossible.

Even as our interactions become more complex, the field is becoming easier to interpret and understand, which might settle certain fears regarding the complexity of the field and the knowledge it requires. Allowing those without extensive data science education to quickly interpret and put information to use is intentionally a focus of the very same field. After all, data that is only good to a small handful of people is far less valuable than an easily understood set of data points.

In the future, one can expect fields beyond that of data science to fall under the umbrella of pure automation while human intervention shifts to the upkeep and further development of AI algorithms. We’re likely decades away from seeing machine learning take over a field as integral to AI’s existence. In the meantime, data science is a field that promises to grow while offering opportunities to develop the technologies that power the future and there’s something uniquely satisfying about pushing us towards our best and brightest years.