The emergence of automation has posed a concern for data scientists--will it eventually replace them?
But there is no cause to worry as the eventuality will never happen. Instead, automation will help data scientists to improve the results they derive from analyzing vast amounts of data.
When it comes to the marriage between automation and data science, you can regard the former is actually a mechanism to efficiently execute the tedious activities associated with the latter. With automated tools, data scientists can become smarter and powerful, enabling them to create more value.
So, let us see how automation is a boon for data scientists:
There are many aspects of data analysis that are time-consuming and simultaneously retard the project speed. Like compiling, cleaning, and formatting data, associated tasks often consume more time that can extend the project completion time. You can do away with the time consumption by investing in automation that bears the capability to make your data science projects more productive and time-bound.
It is wrong to believe that automation will eventually take away data scientist jobs. Automation will only help them to perform tasks more efficiently. What will remain is that companies will need to hire data scientists even with the predominance of automation.
What is more, automated tools will enable them to spend more time on tasks that derive more value.
With automation, data scientists can do away with simple tasks, like putting the data in the right format, focusing on tasks like constructing algorithms that help create more value.
It will help if you bear in mind that smart technological tools cannot replace human intelligence and experience. And, the tools may not even be able to detect errors that cause unreliable results.
What automation does is that it performs repetitive tasks that do not require human intelligence, which frees people to use their skills in creative ways to benefit the business.
It has become a common trend of many data science projects to meet failure eventually. The failure happens due to a slew of factors, ranging from inaccurate data to blunt human skills. It is also where automation can help by giving data scientists appropriate and efficient resources to succeed. As a case in point, automation can help data scientists test hypotheses faster to discard the incorrect hypotheses more accurately. As such, it speeds up the project.
The bottom line is that automation tools enable data scientists to work more efficiently by freeing them from repetitive tasks. When data scientists are free from executing the repetitive tasks, they can focus solely on actuating the corrective steps when the project shows signs of failure.
With the implementation of automated tools, you can speed up your data science project and also get better results.
You can regard it as a word of caution that a data science algorithm can only be as smart as the human who builds it.
If you fall to the temptation of letting automated tools do most of the work, then be aware as the approach might lead to errors. And to do away with the possibility of committing mistakes, some experts recommend using augmented intelligence, which is a combination of artificial intelligence with human knowledge.
The recent history of data science automation shows that it derived significant benefits for the businesses that implemented it. One such benefit is enabling data scientists to work from anywhere. Say, if a data scientist uses an automation-as-a-service tool to cut down on manual tasks, he can do it through the cloud from anywhere.
No automated tool can replace the human ability to understand a business problem. Apart from deciding on the right algorithms, a data scientist also has to write the script, interpret as well as address the business problem correctly, and interpret the results correctly. It implies that data scientists play an indispensable role in a project that no automated tool can execute.
Whether automated tools will replace data science professionals will elicit an answer that will be in the negative. And the main reason is that there can be no tool that can replace human experts. However, such tools can make people more productive.
Automation can impact data science in the following major ways:
In the traditional data science processes, you tend to follow what is called “waterfall” approaches that involve heavily time-consuming efforts, such as data cleansing, ETL, and feature engineering. Automation tools can execute these tasks, so you can use the time to explore high impact use cases.
There can be numerous potential analytics use cases in big companies. Automation tools enable the company to use people of different skill sets to work on parts data science projects. And this allows the expert data science professionals to focus on high-value-creation use cases.
The growing popularity of data science rests on the fact that it can provide a high return on investment across multiple industries and use cases. Data science predominately helps businesses predict new target customers, measure product demand, or detect product failures.
Despite the many benefits of data science and its remarkable potential to positively impact the decision-making process, businesses have struggled to extract value from data science projects.
You can extract significant value from data science initiatives by implementing an automation tool that will speed up the projects and deliver productivity and efficiency.