Data scientists won’t be replaced by robots anytime soon.

Everything today is data-driven. We’re collecting and processing more data than ever. Data scientists are responsible for a major part of this task and this has driven up the demand for them all over the world.

Yet, on the other hand, companies are developing artificial intelligence and machine learning at an increasing pace and there is a lot of discussion about whether these technologies will be able to replicate the role of data scientists. But is it really possible?

It might be difficult to predict the full extent to which these technologies will be able to influence the role of data scientists. It’s only recently that AI started gaining traction in big industries and there’s still a long way to go. Also, it takes more than facial-recognition and robots that tell you your best bet in tomorrow’s football game to solve the complex issues faced by modern businesses. Therefore, the “human-effect” of data scientists is still crucial for any business.

Yet, with the rapid development of data-related technologies, many are questioning whether they’ll replace humans. But for now, they can mostly only compliment the work of data scientists and do not have the ability to create meaningful outcomes on their own. Here are some reasons why.

1. Machines can’t give the “human touch”

Whatever said and done, organizations still need human judgment to think beyond sequences and algorithms to make reasonable decisions. The complex process of converting raw data into meaningful information can’t be managed by machines alone just yet. This is because these technologies still have little to no capabilities to take external factors and complexities into consideration when providing an outcome. This means that although AI and machine learning can help to understand trends, they don’t have the capacity to explain the importance of those trends to the organization and the impact of those trends to the relationships the organization has. So as of now, human input is essential to interpret data processed by software.

Developers have been working on overcoming this issue for a long time as this is one of the key reasons why AI technologies cannot operate independently. Furthermore, with the increase in demand for data-processing in various industries, data scientists won’t be able to keep up with the demand. Therefore, technical alternatives are essential for the progress of the data industry.

2. The need to adapt

Although there are numerous tools and technologies that now simply the process of collecting and cleaning data, uncovering relevant and actionable insights requires time and expertise. Today, there is an increasing demand for qualified data scientists, as artificial intelligence has created a need for professionals who can truly utilize technology and turn it into something useful. The demand for data scientists has skyrocketed because they are so hard to retain.

Although it may seem that technology poses a threat to data science jobs, it is more likely that it will act as an incredibly smart assistant to data scientists, that allow them to run complex data scenarios never seen before. As the role of the data scientist continues to evolve, technology and artificial intelligence will allow data scientists to focus their attention in other more creative and innovative roles that do not even exist yet.

3. Advances in technology and artificial intelligence are increasing the demand for talent

The reality over the last few years has been that the astronomical advances in AI and technology have only created an unprecedented demand for new talent, which has created a large gap between the demand and supply of data scientists. Numerous reports predict, that by 2025, there would be almost 9 million new jobs in data science, robot monitoring, automation specialization and content curation. More than ever, we now need workers who are able to master technology before we can even think about how they can be replaced.

The number of data scientists been produced are increasing steadily, yet it is only the most recent data professional and graduates that receive the correct training in machine learning technologies and advanced AI. This has meant, an even increased demand for data scientists who are able to understand and utilize AI and machine learning tools effectively, and at Octopus BI we are very confident that this trend will not change in the near future!

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