Data science automation is huge right now.

And it is only getting bigger.

In that context, here are a few questions that should be answered:

Will data science be automated? Will it ever become completely automated? Will data scientists become obsolete and get replaced by the AI machines?

Well, that is exactly what you are going to learn in this article.

I argue that data science work will not be automated, and Data Scientists will not be unemployed anytime soon, because:

  • Machines are not smart enough to handle the data preparation process
  • AI needs human guidance to derive insights from raw data
  • Bots can manage simple and repeatable tasks, but more challenging tasks are still a problem
  • Innovation in automated data science drives demand for data scientists who can handle advanced tasks
  • Higher-level jobs are created faster than the workforce is trained

Let’s get down to the details!

Five Reasons AI Will Not Replace Data Scientists

1. Data Preparation Process Is Challenging

Many individuals who are all in with the idea that data scientists will be automated and unemployed soon underestimate the complexities of the data preparation process.

To automate anything, you need to feed “smart” data to the machine. By smart, I mean that this data should be somehow structured and collected with a plan in mind in the first place.

Just imagine:

You need to implement a predictive solution for commercial loan evaluation.

As a data scientist, you will have to research the ins and outs of the industry. And only then will you come up with some sort of a plan on how to collect and analyze data that will be used to implement the solution.

While you may argue that banks will provide engineers will all the data they need, this cannot be farther from the truth.

In reality, it is data scientists who are responsible for searching all the data for the model. They need to figure out important variables, discover patterns, and analyze key predictors to determine a good vs. bad commercial loan.

Do you really think that AI can do all of that?

I don’t think so.

That being said, even the smartest machine learning systems work with what you tell it to work with. Likewise, it will optimize data using representative training data set that you have prepared.

There is no magic black box where you input any data to instantly get a perfectly working model.

2. AI Cannot Gain Key Business Insights From Raw Data

Companies have never collected more data to facilitate problem-solving and enhance decision-making, but their ability to dig insights from it is highly reliant on data scientists.

Though companies have managed to automate big data collection, cleansing, structuring, and analysis (to some extent), AI and ML have a long way to go.

Specifically, automated machine learning systems still need a human layer on top to derive valuable business insights from raw data.

Why?

Machines cannot judge what organizations need and what they do not as humans would.

While they are good at “uncovering” trends and patterns, machines do not actually understand what this or that trend means in the real-world context and, most importantly, how they can impact business performance. They can “see” the ties and dependencies between different operations, but cannot analyze how they can really or potentially benefit the organization.

In other words, machines cannot interpret data and its moving parts in a meaningful manner. We still need data scientists to do that.

Note: Lower-level data wrangling tasks are getting automated, though. AI will be capable of basic data interpretation and visualization in the coming years. The data scientist’s role would be to add meaning to the data and to develop scripts that enable automation of these tasks in the first place.

3. Bots Can Only Automate Lower-Level Tasks

According to Gartner, more than 40% of data science tasks will be automated by 2020.

And while this can be true percentwise, realistically AI can only replace data scientists when it comes to lower-level tasks, like data cleansing, ingesting, visualization, delivery, and model fitting.

Now, you might be wondering:

How will this influence the industry? Will automation leave any data scientists unemployed?

Well, it is clear that many simple tasks, which might otherwise be carried out by beginner data scientists will be handled by artificial intelligence soon.

So the answer to the latter is positive:

Some data scientists will lose jobs!

However, automated machine learning systems cannot handle complex, problem-solving, and theoretical tasks that involve critical thinking and interpretation of results. Data scientists will have to evolve with the field and prioritize higher value tasks.

You won’t automate yourself out of a job, you’ll be freed up to do higher value tasks!
— Michael London (@Mlondon83)

4. Innovation in Data Science Automation Needs Talent

Ironically, the advance of automated data science only increases demand for data scientists.

As long as enterprises become technology- and data-driven, they need more professionals who understand AI, machine learning, and big data. They need someone to help them not only automate but also support their existing data-powered initiatives. They need experienced data scientists to uncover and analyze insights in a continuous manner.

And yet, the demand increases disproportionately.

While simple activities are completely automated or carried out with the assistance of intelligent bots, data scientists who can handle creative and innovative tasks in complex scenarios are and will be hard to come by.

5. New Higher-Level Jobs Are Created Fast

Data science follows suit of programming.

That is, the more complex its moving parts become, the more higher-level jobs are created around the industry.

In the coming years, data science will become the lifeblood of most technology-driven businesses. It will not be “the next big thing,” but a required commodity.

According to Forrester, automation will replace 16% of US jobs by 2025. However, it will also generate around 9 million higher-level jobs in technology-driven industries, including data science.

In other words:

While others may lose their “repeatable” jobs, more data scientists will be needed to support automated tasks and fill out higher-level positions that require knowledge of AI and machine learning.

The industry lacks workforce; it cannot afford losing data scientists, AI professionals, and machine learning experts.

Which Data Science Functions Will Be Automated?

As of now, we are almost used to the fact that some blue-collar and white-collar jobs will be automated soon.

Fortunately, “data scientist” is not on the list of jobs that will be replaced by robots by 2025.

And yet, some low-level, predictable, and repetitive data science functions will become obsolete. Just to name a few:

  • Data Cleansing
  • Data Integration
  • Model Building
  • Model Fitting
  • Data Delivery
  • Data Ingesting
  • Data Visualization

However, even some aspects of these functions are out of robots reach if they entail creativity, curiosity, and critical thinking. Machines do not think in the conventional sense, and that leaves a gap for the simplest of data science tasks.

Conclusion

The data scientist is the most needed job in the USA. It tops the Glassdoor list of “50 Best Jobs in America,” with a median salary of $110k and over 4,5k job openings.

So will data science be automated?

For sure, someday it will. However, only mundane and repeatable tasks are under threat in the near future.

The best parts of a data scientist’s job — research, development, critical thinking, decision-making, and creative problem-solving — is and will be left for humans.

AI-powered machines and intelligent bots no matter how “smart” they are will only complement data scientists and allow them to do more with less. Data scientists will have more time to advance their skills and practice synthetic thinking, which machines are not capable of.

In other words, data science automation is real, yet the future of data scientists has never been brighter. They have everything to thrive, and they will.

What do you think about the prospects of data science and the future of data scientists? Please share your thoughts in the comments section!