Billions of bytes of new pieces of data are uploaded on the internet every single day. And to manage, interpret, and analyze this data, we need data scientists.
However, a human alone can’t gatekeep a trend or website with all this ever-growing data. This is where automation and artificial intelligence come into play. While data scientists are quintessential for the sustenance of a proper data structure and utilization, artificial intelligence has made these processes much faster and more efficient.
This raises the million-dollar question: Can artificial intelligence replace data scientists?
If you look at it, a large chunk of the work associated with data science is automated. So, is it not possible that in the upcoming years, the scope of artificial intelligence will widen and take over the jobs of data scientists?
Experts in the data science field do not see this happening anytime soon. According to them, artificial intelligence is not independently functional enough yet to handle all the tasks in the data science world. So, if you ask, “Can data science replace data scientists with artificial intelligence,” the answer in the current scenario is no.
But what do data scientists offer that artificial intelligence cannot? And is artificial intelligence really an adversary to data scientists today? Read on to find the answers to these intriguing questions.
Job Roles Of Data Scientists And Artificial Intelligence In Data Science
The job role of a data scientist roughly covers all functions of the data science realm. From data collection and management to data visualization and interpretation, data scientists handle and manipulate information crucial to the company, business, and industry on an everyday basis. However, if you have to categorize the nature of their work, you will get two broad aspects:
● First is data engineering, where individuals are responsible for collecting data from varied and viable sources. They also handle cleaning of the data to remove irrelevant pieces and assort a set of relevant information to be forwarded for analysis and interpretation. The secure storage of data and managing its exchange between different departments within and outside of the company also falls under this category.
● The second role is that of a data scientist, where individuals work on the collected, cleaned, and assorted data. This aspect involves the major consequential tasks, including spotting trends, interpreting, manipulating, and visualizing data, and conveying it in a comprehensive and actionable form. Data scientists are also involved in studying business problems and finding solutions through studying data sets, machine learning modelling, and other ways.
There are some of the roles mentioned above that artificial intelligence can handle. Many of the roles of a data engineer can be automated to ensure accuracy and efficiency. However, there are some responsibilities beyond the scope of artificial intelligence that require human input.
Given below are some roles in the data science world that can be automated:
● Collection of data from a list of reliable and pre-approved sources
● Cleaning of data by identifying the important pieces
● Data assortment, secure storage, and maintenance
● Detecting trends and small patterns in a given data set
By automating these responsibilities, you can reduce a significant workload on the data scientists and engage them in more demanding tasks.
Listed below are some roles that require to be handled by data scientists:
● Data optimization and support
● Working on non-SQL pipelines
● Managing the core structure of data
Therefore, artificial intelligence can take over some of the tasks in the data science realm, but the core responsibilities require the experience and skills of a data scientist.
Why Can’t Data Science Replace Data Scientists With Artificial Intelligence?
We established earlier in the article that data scientists are necessary to maintain and utilize the sizeable amount of data available at our disposal. But how can we tell so certainly? Well, certain qualities and values are lacking in artificial intelligence that are necessary when working in the data science realm.
Listed below are some of the reasons why artificial intelligence cannot replace data scientists.
Humans are more adaptable and versatile.
Data science is a world of new challenges. Every day, you are faced with a new problem, a new trend, a new requirement, and so on. Adaptability and versatility are the two core values you require to deal with this scenario.
Artificial intelligence works based on data fed into it and the training it has received. However, it is impossible to train and prepare this technology for every possible scenario in this unpredictable field. There will be certain business problems that artificial intelligence is not trained to address, and the work output in this case can raise complications.
Humans, on the other hand, are versatile. They learn with every experience and use their intellect and learnings in the problem presented to them.
Therefore, even in the face of new unpredictable scenarios, humans are more likely to come up with a plausible and effective solution than artificial intelligence.
Humans are observant and intuitive.
As mentioned earlier, artificial intelligence functions based on its training. It does only what it is told. However, in real-life scenarios, there are unpredictable situations that require observation and intuition.
Suppose the data set to be studied is incomplete. When given the command, artificial intelligence will deliver the results based on this incomplete input. However, humans are intuitive and observant and, therefore, can pick up on the oddity.
The same goes for problem-solving tasks. Humans can interpret a given data set and provide insights and suggestions based on the objective. In automated cases, however, this might not be possible.
Humans have soft skills along with technical ones.
Data science is a field that requires technical excellence. However, that is not all there is to data science. Soft skills like communication, attention to detail, empathy, and working with the consumers in mind are required for successful data utilization.
Data scientists can ascertain consumer mood by studying the trends and can look for possible solutions to improve the scenario. The empathic human nature allows them to come up with consumer-centred strategies and ideas.
While emotional intelligence is being developed in AI, it is still far from what comes naturally to humans.
Is Artificial Intelligence An Adversary Of Data Scientists?
Artificial intelligence is viewed as a threat to the living and earning of data scientists and for acceptable reasons. But after going through why data science can’t replace data scientists, we know that the job roles of data scientists are safe.
However, we cannot deny the utility of artificial intelligence in the data science realm and how it can make certain processes much easier. So, if this is the case, is artificial intelligence really an adversary?
Not if you employ it wisely. Here are some ways you can use artificial intelligence as your partner or assistant in the data science career field:
● Use AI or automation to handle mundane tasks like data collection, cleaning, and assortment.
● AI can help you analyze large pieces of raw data and spot small patterns in a considerably short time
● You can automate the mundane steps of machine learning modelling to save time
● AI can help you interpret a set of data and give suggestions about its visualization and presentation.
The Outlook
In the earlier sections, we established why we can’t data science replace data scientists with artificial intelligence. But while human intuition, empathy, and analytical skills are the backbones of the data science realm, you cannot deny the accuracy and efficiency of artificial intelligence.
You can use this technology to your advantage to cut off your workload and put your attention to more important and demanding tasks. This will not only make things easier for you but also improve the quality and efficiency of your work.
