Data science has seen enormous growth in recent years due to the amount of data that is being collected by organizations. Ever since the word was coined in 2011, the data science domain has exploded in nine years. However, the increase in data collection is not the only reason why data science democratized over the years. The availability of computational resources was as vital as the data itself. But, as data became ubiquitous, companies struggle to process different types of information they gather. Therefore, this led to siloed data — information being stored on databases while waiting to be analyzed — within organizations. Consequently, companies are looking for automation tools that can streamline the data science initiative in organizations. This brings us to the question: will data science be ever automated? Or can AI take over data science?
Need For Automation In Data Science
Undoubtedly, the amount of data that organizations are gathering will take years for humans to analyze. And by the time they mold the data, will it be worth to find insights into the information that was generated a few years ago? In the ever-changing world, users’ behavior is continuously changing, and thereby historical data might not be useful in every use case. For instance, people used to watch longer videos; however, today, the rise of TikTok popularity tells a different story; users are consuming short videos more often. While historical data will still be relevant, but there is a need for quickly processing information to make business decisions and gain a competitive edge.
Rise Of Automation Solutions
As IoT is expected to increase in the coming years, data will only get more ubiquitous. Consequently, firms are moving towards streaming analytics — real-time insights — for gaining insights into data immediately after it gets generated. However, this requires a robust data pipeline for simplifying data analysis, which is near impossible as information comes from different sources. Nevertheless, companies are trying to streamline the process on a case-by-case basis.
Another solution that is showing the promise is AutoML — automatic machine learning. AutoML solutions are focused on automating the machine learning model selection for data scientists. Usually, professionals waste time determining which ML model brings more value to businesses. Numerous companies, such as DataRobot, H2O.ai, among others, offer superior AutoML solutions for doing all the heavy lifting for organizations. Besides, these AutlML tools can be leveraged by non-experts to build ML models and make informed decisions.
Why Data Science Cannot Be Completely Automated
Data scientists require many skills like data intuition that cannot be integrated into AutoML tools. Besides, professionals spend 80 percent of their time finding, cleaning, and organizing data. Although a tedious task, data wrangling eats up most of the time. To carry out such tasks, data scientists need skills like identifying the data source, understanding the structure of the website to crawl the data, and more. Unfortunately, automation tools can never perform those cumbersome activities. However, this doesn’t mean that machines cannot automate other tasks. AutoML and streaming analytics tools are helping companies to simplify the data science workflows, but a plethora of data science techniques are still untouchable by these solutions.
If you are not on Mars, you must be aware of the fact that artificial intelligence doesn’t have common sense. This makes AI models ineffectual in replicating humans in a wide range of tasks. Since the data science landscape must further evolve and accomplish common sense, we are poised to witness several changes in the way we process data. For one, the self-driving car could not deliver on the hype it created and need to be revamped the way it operates. Therefore, development in the data science space is going to increase further, which would be difficult for automation solutions to catch up with the market. However, if the landscape gets matured enough to enable AI agents to replicate human-like behaviors, one can then argue that data science might be automated.