Since the rise of internet-powered devices and the sudden boost of data generation experienced in the past decade, data science became quite trendy.
Data science gave companies a whole new vision on how they could process the data and revolutionize their processes; consequently, companies are after data scientists more than they have ever been. Therefore, data scientists were tagged as “the sexiest job of 21st century.”
However, when something experiences its highs, the lows are bound to come along soon enough; and data science is no different. With all the hype created around it, people have started fearing that it won’t be long before this data science bubble eventually bursts. So, will data science die?
The short answer is No; Data science will not die. Data scientists’ demand will reduce due to AI and Machine learning tools’ rapid growth, but these professionals will still be key in handling such tools.
Nowadays, AI is on the rise, and with new automated machine learning tools coming out every day, such as the likes of AutoML by Google, one cannot be sure how long data science will last. Students also cannot help but dwell upon the confusion of taking data science as their majors – given the cloudy future it holds.
So, in this article, I’ll be answering all your questions regarding the future of data science and whether it will eventually die and be replaced by automation or not.
However, before we start, let’s have a brief overview of data science, so you don’t remain clueless while we’re talking about its different aspects.
Data Science is a multi-disciplinary field that houses several domains under its hood. From data analytics to machine learning algorithms, it has a pivotal role in every field where big data is involved. Data science uses complex statistical and mathematical knowledge to solve problems using available data, no matter what form the data is in.
Data science’s primary aim is to find useful, actionable insight from data and use it to solve real-world problems; the very reason it’s a tremendous asset to companies in the 21st century.
Will Data Science Die?
There’s no denying the fact that data science has a vital role to play when it comes to huge organizations. Data science is everywhere, from acquiring data in various forms to using techniques to fetch insight from this data. However, will it be the same in the coming years as well? Given the rise of automated tools, will data science eventually die?
There sure has been a significant drop in the demand of data scientists in recent times, and the hype is not as much as it was promised in the previous decade but let me make it clear that data science is here to stay. As much as automated tools are coming to light and showing potential, data scientists will always be needed to tackle new problems that come along with these tools.
So, while data scientists’ demand will take a hit due to trivial tasks such as data collection and ETL processes being automated, data scientists will still be needed to look after the new complexities and scenarios generated by these tools.
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Why do people think Data Science Will Die?
While we’ve established the fact that automation could not possibly be a threat to data science even in the long term, there are other reasons for this fear as well. Let’s ignore the fact, for instance, that companies are automating data science; where else is the notion about the end of data science coming from? Let’s see.
Hype Cycle
There’s a very popular theory about the technological hype-cycle that tells us the stages a new technology goes through after its innovation. As seen in the diagram above, whenever a new technology lands, it’s received with a lot of enthusiasm, and people go nuts over it, with investors putting in vast sums of money and media further hyping the situation.
That is exactly what we experienced with data science in the last decade, specifically the 2010s.
However, once the peak is touched, the expectations start to fall down as people start to realize it’s not all roses out there. This goes on till the point when the sentiment of disillusionment is seen. Eventually, people start coming with ideas and products, and the expectations again begin to go up, at a much slower rate, though, and this is when the technology is normalized.
However, the problem with data science is that we don’t even know what stage we’re at. Indeed, we’re past the peak of inflated expectations, but it’s been quite some time that we’ve realized data science is not some super magic tool that can solve all our problems.
Project Failures
Even though data science has revolutionized the business industry in the past decade, we have also seen millions of dollars going to waste in the form of failed data science projects. An interesting fact by Gartner is that only 15% of the data science project get completed, which is quite surprising.
According to VentureBeat AI, “87% of data science projects never make it into production”.
All these resources being drained have been pissing investors off lately, and most people think that data science was a mere buzzword, and it doesn’t bear as much fruit as theory suggests. This has had a huge impact on the use of data science in the industry and is among the biggest reasons people have been considering data science gone for good for a long time now.
If you want to know more about why such a whopping amount of data science projects fail, you can read my article about that here .
Companies Failing to Achieve the ROI
The idea of how smoothly data science integrates with business operations, giving a boost to customer satisfaction and revenue, has been pretty catchy since its dawn. However, this also resulted in companies wrongly perceiving that they could introduce data science in any department, and it will provide them the ROI they need.
However, it’s not what reality looks like. Data science projects are generally quite expensive, and implementing them to solve problems that don’t have enough potential for revenue generation isn’t always a good idea. Nevertheless, many companies learned this the hard way.
There have been countless instances of so-called successful data science projects that didn’t end up producing the required ROI. Instead, the companies got demotivated about data science inclusion in different departments, inflicting a massive loss to the data science sector.
How Data Science is Evolving
The future of data science is not what it seemed to be ten years ago. The field has evolved to a point where automated tools are now doing a considerable ratio of the tasks previously thought to be fulfilled by data scientists.
Data science will not be limited to data management and developing custom ML solutions. As AI starts to kick off with automating solutions previously done by data scientists, there will be a huge paradigm shift. Data scientists will further need to enhance their skills and get domain-knowledge within their pockets as well. This is what the future holds!
It’s only through enough domain knowledge that data scientists could provide value to their companies. Without fully diving into the problem domain, they’re no better than the AI that can develop tailor-made ML solutions.
Having said that, data scientists also need to get comfortable with the newer big data and automation tools available out there. Not only would these tools make them more effective, but they’d also get more efficient in their work. This way, they can achieve more while costing less, leading to data science projects getting more feasible.
Wrap Up
Even though statistics and its use in business decision-making have been around since the 16th and 17th centuries, it was only when the computational capabilities experienced a drastic increase that data science started to boom. Ever since it has been a constant charm to businesses, but lately, there has been much talk that it won’t be long before it starts dying.
Throughout the article, we have seen reasons why this fear is being implanted into people’s minds. Moreover, we also saw why it’s no more than a myth and that data science is here to stay no matter how advanced automation gets.
However, data scientists will surely need to adapt themselves to the changing environments. With being more knowledgeable of the domain, they’re working on topping the list. Also, they need to be aware of organizations’ goals and processes to mend their data science solutions to the organizations’ specific needs.
