Data AnalyticsBusiness Intelligence

Is AI Going to Replace Data Scientists?

AI has the potential to replace data scientists, but is it going to?

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TLDR: No.

 

Artificial Intelligence is changing our lives more and more day by day. At this point, it is. "intelligent" enough, that people start wondering whether it will replace humans or not. Whether it can do it, depends on many different factors, and of course, on the kind of job, some jobs being easier to replace than others.

At first, we thought that AI was going to replace the easiest jobs, the manual, repetitive, or simple tasks, but as it turned out, it's going to replace jobs that require creativity faster, like designers, writers, movie makers, etc.

 

Is AI Going to Replace Data Scientists?

InsightBase is an AI-driven analytics and Business Intelligence service, and we see how AI is impacting data scientists every day. We believe that AI will not replace data scientists any time soon, and here's why.

 

Data is spread across multiple systems.

Having access to all the data is a prerequisite for analyzing it. We live in a data-rich world, but not in a data-organized world. Companies use a lot of different tools, and most of them are hosting the data in their own systems, which more often than not, are closed, and accessing the data is sometimes simply impossible. Part of the data scientists' jobs is to actually unify the data into a single system in order to analyze it more easily. The AI does not have easy access to all those systems, and so can't perform the same tasks as a data scientist.

 

Data is messy.

Not only is it not organized well, but it's also messy. It can be corrupt - not contain all the information, or it could have missing bits or unnecessary information. Another part of a data scientist's job is to clean up the data and remove the noise, for better analysis. AI could possibly help here, but not without human interaction, because it doesn't know what's right and what's wrong, which information is important, and which information can be ignored.

 

Data is complex.

Understanding the data is a major requirement for analyzing it. AI systems can make some sense of some data, but their ability to do it fades down as the complexity grows. Complex systems require a deep understanding of the business, data collection methods, system architecture, etc.,We don't have yet an advanced enough AI to make sense of it.

 

How AI can help data scientists.

We think that while AI can't replace data scientists, it can at least double their productivity. If you're a data scientist, keep reading to know how AI can help data scientists, and how you can use it in your daily tasks.

 

AI can help you with data architecture.

Since the AI is pretty creative, you can get some interesting insights and ideas on how to organize the data, including building the underlying architecture for the data. It can suggest tools, tactics, or services that you can leverage for this, and it's pretty good at doing it.

 

AI can help you with data analysis.

At InsightBase, an AI-driven analysis and Business Intelligence tool, we use AI to help everyone easily analyze their data. Data scientists can use AI to "chat with their data". They can ask a wide range of questions, and get answers in just a few seconds. This is however not enough always, since the AI can sometimes return bad results, and this is why we're giving our users the flexibility to edit the queries provided by the AI so that they can get the results they need.

We also use AI to make sense of the database schema, understand what it is about, analyze the data, as well as create the right visualizations for user queries. If you don't want to use InsightBase, then you can do the same tasks, but not as easy, by breaking them down and feeding them into ChatGPT or other LLMs.

 

AI can help you translate natural language to code or SQL queries.

This is a very popular use case for AI and it's used quite often by software developers, and it can be used by data scientists as well.

 

Conclusion

We believe that AI won't replace data scientists, instead, it will help them increase their productivity by helping with data analysis, data architecture, code generation, etc.