The changing face of data science: how the ‘most attractive job of the 21 st century’ has evolved
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Looking back and looking forward
Jonas Tillström works at SAS partner Sigma Technology Insight Solutions in Gothenburg as Business Area Manager for Analytics and AI. Sigma Technology Insight Solutions is part of the Sigma Technology Group which is a privately-owned global technology consulting company with operations in Sweden, Hungary, China, Norway, Germany, Kosovo and Ukraine, and global delivery to Europe, the USA, and China. Sigma Technology Group offers cutting-edge expertise in AI, analytics, software development, product information, embedded systems design & development, and digital solutions with expert consultants, offshore delivery, and development teams. Jonas has worked in analytics for 30 years now and has spent most of his working life using SAS. He saw early on that analytics was very much a growth area, but some of the changes have surprised even him.
“There are huge differences between now and when I started in analytics. Back then, there was a focus on what had happened, and on reporting results. It was all about keeping an eye on the figures. Now, though, and especially in the last five years, AI has really exploded, and it’s much more forward-looking. There are so many possible uses, and we’re looking at automating all kinds of different functions and services, right up to autonomous and self-driving cars. I think AI has given us all the ability to dream big.”
My experience is that the growing analytical maturity that we are seeing in many companies means that the market has become much more aware of what it wants. Jonas agrees with this.
“You can very much see how the tools that exist today have evolved. When I started, you had to sit and code things yourself, and it was difficult because customers didn't really know what they wanted. Awareness is now at a completely different level. For most companies, analysis is a normal and natural part of their business, and it’s applied to anything from finance to production processes. The market has definitely both matured and grown.”
Data science in mainstream consciousness
Alongside this change in the market itself, we have also seen changes in the way in which people talk about analytics and data science. It is now 22 years since Thomas Davenport and D. J. Patil described being a data scientist as the sexiest job of the 21st century in an article in Harvard Business Review. Jonas’ long experience in the field leads him to suggest that the job of data scientist emerged a few years before that: he suggests probably in the late noughties. However, he adds that he thinks the role has changed significantly in the last 20 years.
“Since the pandemic, I think much of the work that was originally done by data scientists has become more accessible to others, thanks to new tools. People without deep data science skills can use these tools to solve many of the tasks that would once have fallen to data scientists. My feeling is that the data scientist role has therefore become even more specialized.”
Intelligent automation for the data science process
Jonas suggests that in many cases, data scientists are now often employed by large companies. They tend to use more specialized technology, especially more advanced machine learning algorithms. However, they also need a better understanding of business analytics. He puts this down to the ability to automate many basic analytical tasks but is clear that there is a long-term role for data science.
“I can’t see data scientists becoming redundant or disappearing. I think their focus will be even more advanced and in-depth analysis, such as deep data mining, and other specialized tasks.”
In the last six to eight years, we have seen the emergence of many specialized roles around handling and managing data, including business analysts, data engineers, and machine learning engineers. However, these roles have not necessarily replaced data scientists. Jonas believes that they, too, have evolved—and will continue to do so.
“I see data engineers, for example, as a slightly more general role. They can be involved in anything from compiling and washing data to building machine learning models. I think data scientists are very much digging into the data to try to find the connections. The key now is that they are only called in when companies have very specific needs, rather than for every question that needs analytics—and that’s only going to become more important now that everyone wants to use AI.”
If you want to know more about how Sigma works with data and SAS Software I encourage you to check out this website
https://sigmatechnology.com/service/data-analytics-and-ai/
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