This is Why Data Science Will Never Become Obsolete

Data science has taken over the world in this century by gradually becoming a part of every industry. However, some very rare people are unaware of it. And while the advancement proceeds to take place, some experts raise the query about its longevity. More exactly, they are suspecting if data science is just a short-term fad that will become obsolete after 2-5 years. 

With the speed of technological advancement in this modernized society, many people are inquiring, “Will data science become obsolete?” 

No, Data science will not become obsolete. Data science is one of the rapidly growing industries and it has facilitated AI and ML in many ways. It is a hotchpotch of several tools, algorithms, and machine learning principles to unearth unknown structures from the raw data, which in turn, helps with developing more intelligent systems.

Data Science is the secret sauce at this moment. All the notions that you look at in Hollywood sci-fi pictures can veer around into existence by Data Science. Data Science is the prospect of Artificial Intelligence. 

“A data scientist is someone who can obtain, scrub, explore, model, and interpret data, blending hacking, statistics, and machine learning. Data scientists not only are adept at working with data, but appreciate data itself as a first-class product.” 

– Hillary Mason

This quote by Hillary Mason implies that the functions of data scientists cannot be performed by these man-made machines. This is a clear justification that data science is to grow in the future and it will never become obsolete. 

Thus, it is very crucial to comprehend the trends, and reasons why data science will not become obsolete and instead add significance to your enterprise. 

Reasons Why Data Science will Never Become Obsolete 

“The data scientist was called, only half-jokingly, ‘a caped superhero.’” 

-Ben Rooney

Data science technology is on peek right now. And it is only getting tremendous with time. With that in mind, we argue data science will never become obsolete and data scientists will not be unemployed any time soon for the reasons: 

  • Machines are not clever enough to deal with the data preparation procedure.
  • AI requires human advice to proceed with insights from raw data.
  • Bots can organize easy and repeatable duties, but more demanding and tough duties are still a problem.
  • The invention in automated data science drive command for data scientists who can deal with advanced duties.
  • Higher-level employment is built quicker than the workforce is equipped.

Let’s get into the details of reasons. 

6 Reasons Why Data Science Will Never Become Obsolete

data science will never become obsolete

1. Challenging Data Preparation Process  

Most people who are assuming that data scientists will be automated and unemployed in the upcoming age underestimate the intricacies and complexities of the data preparation process.

To automate something, you are required to enter the “smart” data into the machine. By smart, I imply that this data is in some way structured and compiled with an agenda in mind in the first place. 


You need to enforce a predictive remedy for commercial loan tryouts.

As a data scientist, you will have to do an exploration of the ins and outs of the sector. And merely then will you come up with some kind of agenda on how to compile and assess data that will employ to execute the treatment.

While you may protest that banks will offer engineers with all the data they need, this cannot be distant from the fact. In truth, it is data scientists who are accountable for surveying all the data for the prototype. They want to comprehend significant variables, discover structures and evaluate key visionaries to deduce a nice vs. worse commercial loan.

Do you believe AI can perform all that alone? 

I don’t. 

With that said, even the most creative AI and ML systems function by what the owner tells them to work with. Similarly, it will optimize data employing a suggestive training data set that is prepared by you. This depicts that data scientists are required by the systems no matter how advanced they get. There is no magic black box that will instantly get a perfectly working model for your project. 

2. AI cannot Attain Key Business Insights from Raw Data 

Firms have never compiled additional data to enable problem-solving and improve decision-making, but their proficiency to explore visions and conceptions from it is highly reliant on data scientists.

Though firms have operated to automate vast data assortment, cleansing, structuring, and inspection (to some extent), AI and ML have a lengthy period to go.

If we speak specifically, automated machine learning systems still require a human covering on the crown to develop valuable business insights from raw data.


Machines cannot make outcomes that institutions need and what they do not need as humans would.

While they are useful at “uncovering” fads and structures, machines do not certainly comprehend what this or that trend means in the real-world context and, most significantly, how they can affect enterprise execution. They can “see” the links and reliance between several procedures, but cannot judge how they can truly or potentially profit the institution.

In other terms, machines cannot understand data and its changing positions in a significant way. We still require data scientists to accomplish that.


Lower-level data battling assignments are getting automated, though. AI will be eligible for basic data understanding and visualization in the upcoming years. The data scientist’s part would be to add value to the data and to expand writings that facilitate the automation of these assignments in the first position.

Highly Recommended Read: Will AI Replace Data Science?

3. Bots Can Only Automate Lower-Level Tasks

It was expected that more than 40% of data science duties will be automated by 2020 and it happened. 

And while this was only valid percent-wise, realistically what happened is that AI can only restore data scientists when the lower-level duties or jobs come off, such as data cleansing, ingesting, visualization, delivery, and model fitting.

Now, you must be wondering: 

How will this impact the enterprise? Will such automation by AI and ML systems will leave any data scientist unemployed?

Well, many susceptible tasks, which might contrarily be carried out by amateur data scientists will be dealt with by artificial intelligence soon. 

So the reply to the latter is simple:

Some data scientists will miss jobs!

Regardless, automated machine learning or artificial intelligent systems cannot manage complex, problem-solving, and speculative tasks that encompass critical thinking and understanding of outcomes. Data scientists will have to thrive with the field and prioritize greater value tasks.

“You won’t automate yourself out of a job, you’ll be freed up to do higher-value tasks!”

— Michael London 

4. Creation and Innovation in Data Science Needs Talent 

“Data scientists are statisticians because being a statistician is awesome and anyone who does cool things with data is a statistician.” 

– Robert Rodriguez

Ironically, the development of automated data science only heightens the need for data scientists.

As long as businesses become technology and data-driven, they require more specialists who comprehend AI, machine learning, and big data. They want somebody to assist them in not only automating but also supporting their present data-powered endeavors. They require trained data scientists to unearth and analyze insights in a consecutive manner.

And also, the appetite rises disproportionately.

While easy actions are entirely automated or accomplished with the employment of competent bots, data scientists who can deal with inventive and ingenious tasks in arduous systems are and will be difficult to come by.

5. Exotic Higher Level Jobs Are Created Fast 

Data science obeys the set of programming.

That is, the more complicated its striking parts become, the more higher-level employment is built around the enterprise.

In the upcoming years, data science will come to be the lifeblood of supreme technology-driven enterprises. It won’t be “the second big thing,” but a vital product.

According to Forrester, automation will take place in 16% of US employment by 2025. Nevertheless, it will also produce around 9 million higher-level employments in technology-driven industries, encompassing data science.

In simpler terms: 

While others may miss their “repeatable” employment, more data scientists will be required to support automated jobs and complement higher-level positions that need proficiency in AI and machine learning.

The enterprise lacks a workforce; it cannot afford to miss data scientists, AI experts, and machine learning specialists.

6. To improve performance

What if the algorithm undertaking isn’t decent enough? What if enactment in the real world is worse than what is expected from your initial trials? What if performance damages with time?

Example questions:

  • Is that for the reason that it is a problem with our data (is it our aspects? our tags? 
  • Is it restricted to just limited specimens? Worse data hygiene? 
  • Probably there’s just not sufficient warning in the data?
  • Overfitting, or underfitting?
  • Is it something odd about the specific algorithm being employed by the black box commoditized employment? 
  • What if you call the service oppositely, or attempt an adversary service?
  • Can we envision what’s going on?
  • How does it conduct with several ways of synthetic data?
  • How have the data or the difficulty altered over time?
  • Could you expand accomplishments from your aspect? 
  • What if you alter the data you provide (e.g. preprocess it oppositely, or only provide in the high-quality samples/features)? 
  • What if you had more coaching data? 
  • Can you enhance the data with synthetic data?

All these answers are not understood by machines. Only your data scientists can assist you to answer these questions and many more questions like these. 

This is to confess that you require at least a few data science experts to select and distinguish the right difficulties and to make sure that the stuff is going well. And you will require much more data science expertise if you wish to comprehend why things aren’t acting and working and to enhance them.

Putting simply, you still need data scientists now and in the future too. For this reason, that machines cannot work like humans as at the end of the day, they are just machines. 

Data Science Statistics

“Statistics are ubiquitous in life, and so should be statistical reasoning.” 

-Alan Blinder

Data scientist has been considered as one of the trendiest jobs of this century. Wil MP Van der Aalst writes in his Conference paper named “Data Scientist: The Engineer of the Future” that survival is not possible without exploiting available data intelligently

You see, how important it is to have data scientists. Other than that, if we look into the present covid situation, we might think that these crises may affect data scientists’ jobs. However, that’s not the case here. According to a recent study by staffing firm Burtch Works, we found out that data scientists’ jobs are immune to Covid 19. With that, we also found that data science has outperformed LinkedIn’s Emerging Jobs Report in 2020. 

A poll conducted by KDnuggets asked when will the tasks that are being done by humans at present be automated. 50% of people voted for 10 years or less while 18.8% of people voted for never. And, we think that 18.8% of people are sensible enough to vote for never because we think the same. It will never be automated, completely. 

Other than this poll, all the above researches indicate that data science will never get obsolete, at least, not soon.

Trends of Data Science 

Biggest data science trends you should know

Here are some of the fastest-growing trends that will impact our everyday life and the work of data scientists too. The E-book named “9 Mega Technology Trends: And How They Are Reshaping Our World” carries these data science trends that we are encountering and will encounter soon. 

Trend 1: The boosting datafication of our lives, and how we’re all leaving our (digital) mark

15 million texts were sent per minute in 2017. That’s just one way we flee digital breadcrumbs as we continue our daily activities. In accordance to the Pew Research Center, 77% of Americans possess a smartphone, and every time you utilize it to get from Point A to Point B, glance at your social media accounts, send a text message or browse the day’s headlines you build more data. When you buy online, set a lunch or dinner reservation or delivery order, and even alleviate tension at home binge-watching the Netflix shows or study from your tablet, you proceed to data-of your life. It is expected, we’ll have more data than this in the coming years that will also increase the need for data scientists. 

Trend 2: The Internet of Things (IoT) implies daily devices are evolving smarter

Stuff rather than humans are producing data thanks to the Internet of Things (IoT), all the smart devices and appliances that transmit with one another. These days, nearly anything can be brought about “smart” from refrigerators to your Fitbit to the thermostat in your home. The manner they evolve smart is that they can interact with each other and determine a course of action without any human intervention. Intel predicted there will be 200 billion devices connected to the internet by 2020 and we guess it exceeded this figure because of the covid situation. In the future, we will have more devices that produce data and more data scientists that will process and examine the data directly.

Trend 3: The exponential development in computing power is fuelling tremendous technological improvements

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” 

– By Geoffrey Moore

The enormous expansion in computing power we encountered in current years has made it probable to give rise to the tremendous growth in data we compile, store and analyze. We are now eligible to refine unstructured data, and we employ the cloud to catalog data and conduct computing duties using software and applications that are outside of our computers. We’re near than ever to building a commercially attainable quantum computer that will be exponentially more influential than today’s computers. Future will see an even tremendous boost in computing power and more firms will survive their computing limitation by deploying cloud solutions and employing more data scientists.

To manage such data of all consumers, the need for data science has increased.

Which Data Science Functions Will Be Automated?

At present, we are nearly employed to the reality that some blue-collar and white-collar employment will be automated shorty.

Fortunately, “data scientist” is NOT on the schedule of jobs that will be displaced by robots by 2025.

But ultimately, some low-level, stable, and redundant data science processes will become obsolete. Naming a few of them in the next list: 

  • Data Cleansing
  • Data Integration
  • Model Building
  • Model Fitting
  • Data Delivery
  • Data Ingesting
  • Data Visualization

Nevertheless, even some facets of these processes are out of reach if they encompass inventiveness, novelty, and critical thinking. Machines do not speculate in the human sense, and that vacates a void for the reasonableness of data science chores. 

The Bottom Line 

The data scientist is the most essential and required job in the USA. It was crowned in the Glassdoor catalog of “50 Best Jobs in America,” with a basic salary of $110k and over 4.5k job vacancies.

So will data science become obsolete?

Now we can say, “Yes, some aspects of it, for sure! Someday it will.” We’re saying for the reason that is mentioned above that only mundane and redundant tasks are at risk to become obsolete in the near future.

The best fractions of a data scientist’s job are research, development, critical thinking, decision-making, and creative problem-solving which is and will never become obsolete and will always be left for humans.

AI-powered machines and creative bots no matter how smart or competent they are will merely complement data scientists and enable them to do further with less. Data scientists will have extra time to improve their skills and exercise imaginary thinking, which AI and ML machines are not qualified for.

In simple words, data science automation is substantial, yet the prospect of data scientists has never been brighter and clearer. They have everything to prosper, and they surely will.

“Autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.” 

-Neil Raden

Further Questions about Data Science’s Future 

Will data scientists be replaced soon? 

The quick answer is no. Particular facets of low-level data science tasks can and must be automated. However, human intelligence is significant to the data science field, even though machine learning can assist, it can’t entirely take over. 

Is data science a dead-end job? 

Yes, Data science can be a dead-end career. Even if many of the fields derived from it are getting new popularity, like Artificial Intelligence and all other marketing publicity that goes with it, the career is mostly good for early-term learners only. Data Analysis, Machine Learning, and Deep Learning are the top specializations now.

What is better: AI or data science?

The equipment included in Data Science is a lot more than the ones employed in AI. The reason for this is that data science encompasses numerous measures for evaluating data and developing insights from it. Data Science is all about discovering hidden structures in the data. AI is about imparting independence to the data model.

Do data scientists work from home?  

The jobs of data scientists are among the prime jobs that can be performed remotely, and the field is expected to grow by 16% by 2028. If you finalize a data science Bootcamp online, you have a competitive advantage with your sharpened skillset. 



Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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