The advent of artificial intelligence, automation, and bots has lead us to think about whether data science is automated? Or, is automated data science going to eliminate the need for data scientists? The experts and observers claim that data science automation is possible, and we will observe automation in the coming few years. However, automated data science and data visualization make it seamlessly easy for executives and experts to access what they need without human input.
All the talk about automated data science has made some data scientists and would-be data scientists concerned. It has given them reasons to believe that the demand for data scientists is likely to go down in the future. However, data scientists should embrace the fact that they are not replaceable by machines. They are the x-factor that gives them irreplaceable power for the following few reasons.
1. Automation is just for speeding things up
Automation of data science is just a “time-saving benefit that data scientists embrace because they seemingly enjoy thinking more than tedium ” said Alexander Gray, the vice president of artificial intelligence at IBM Research. That means automation helps us do tedious activities, and it cannot replace human beings. In other words, automated data science empowers data scientists to do their jobs more effectively and efficiently. Instead of replacing them, automated data science tools help data scientists do a better and faster job.
2. Humans are required to fix automated errors
Automated tools cannot realize that they might not work. Indeed, the models, pipeline, data links are all coded in by people. It is yet another reason why humans are irreplaceable. Automation enables data scientists to do things faster and better. Yet, at the same time, they can propagate human errors if poor programming is underneath your automated tools. The need for data scientists is always there to maintain control over activities and to verify the correctness of automation. In a nutshell, data scientists are integral to the overall process, especially for maintenance and improvements.
3. Human judgment is paramount
Automated data science does not present only the technical problems that data scientists have to fix. Apart from the best underlying programming, algorithms, and scriptwriting, data scientists also have to address the business problems correctly – that involves selecting the correct data sources and interpreting the results appropriately. Michael Li, a data scientist and the founder of The Data Incubator, very aptly summarizes,
“Real-world data are notoriously dirty, and many assumptions have to be made to bridge the gap between the data we have and the business or policy questions we are seeking to address. These assumptions are highly dependent on real-world knowledge and business context.”
This business understanding varies case by case or industry by industry. Therefore you will always need the human aspect to interpret, present and drive insights into your project.
Yes, data science can be automated with the courtesy of artificial intelligence, smart bots, and other modern technologies. However, data scientists are indispensable for the overall process and will never become redundant in the future. The automation of data science complements the job of a data scientist. Data scientists are the ones creating those automation tools, after all.
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