Although data science has been around for quite some time, it did not hit the peak of its existence until approximately two decades ago.
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Ever since then, it remains one of the most productive, most useful, and most advanced resources that people from all professional backgrounds can rely on. To get a sense of just how widespread it is, consider the fact that it is easier to name the markets that do not use data science than it is to list the ones that do.
As with every contemporary resource, a lot of specialists are curious about the longevity of data science. After all, many experts thought that DVD rentals would be a timeless solution to live television. When online streaming was invented, however, DVD rentals became obsolete, and what appeared to be a seemingly unbeatable solution lost the battle to a more innovative alternative. Is it fair to expect that data science will face the same destiny? In other words, will it exist once someone invents something new? Will data science continue to exist in the future?
The History of Data Science
According to a timeline provided by Forbes Magazine, the term “Big Data” was first used in 1997. Michael Cox and David Ellsworth are credited with inventing this name when they published their paper on “Application-Controlled Demand Paging for Out-Of-Core Visualization.”
But big data, and data science, are far older than that. According to the same timeline from Forbes, the first attempts to understand the growth of data date back to 1941 when the term “information explosion” was first used.
In the 1940s, data scientists attempted to describe the growth of data in terms of the number of volumes in collegiate libraries. Fremont Rider, the librarian at Wesleyan University, estimated that the size of library collections was doubling about every 16 years. He then posited in 1944 that by 2040 the Yale Library would have 200 million volumes and a staff of more than 6,000 people to catalog it all.
In the 1960s, scholars noted that the rate of growth for scholarly journals was exponential, not linear. To handle all this new information, other scholars suggested – for the first time – that storage requirements for data should be kept to a minimum.
Throughout the 1970s and 1980s, the rate of creation of new data continued to increase. In fact, researchers noted that society was moving toward a stage in which more detailed and segmented information was becoming more important than mass-produced information that was available up to that point.
As a result, governments and organizations around the world started to measure information and attempted to quantify just how much information was available. It became clear that the sheer volume of information that was available was far more than people could ever possibly intake.
By the 1990s, there was a more concerted shift to not only storage of all this data, but to create methods by which the data could be monitored, sorted, and analyzed. In 1996, digital storage was introduced, which revolutionized big data and helped launch data science as a much more prominent field.
Once the 2000s got underway, scholars began to marvel at the amount of data being created each year. For example, a 2003 study revealed that in 1999, about 250 megabytes of data was produced for every man, woman, and child on the planet or about 1.5 exabytes of information. By 2012, that number was 2837 exabytes.
The amount of information created each year is continuing to increase as well. In fact, by 2024, it is estimated that the world will create 149 zettabytes of information.
The Evolution of Data Science
In reviewing the history of this field, you can easily see how it has evolved over the years.
At first, data analyses were done via print mediums. Then the move to computers came. As time went on, data scientists used programs like Excel to dive deeper into data (and do so more quickly) than ever before.
Today, programs like SQL, Python, SAS, MATLAB, Tableau, Apache Spark, and Big ML are just a few of the most-used programs to organize and analyze data.
The point is that in the 1940s, early data scientists couldn’t have imagined the data analysis tools we would have available today. The same holds true for contemporary data scientists – the future undoubtedly holds new technologies that will make the practice of data science even more efficient, manageable, and informative.
In other words, data science in the future might not look exactly as it does today, but as it grows and changes, its influence on businesses, organizations, and governments will remain very strong.
The Future of Data Science
While the resources involved in data science have certainly improved over the years, the overall presence and purpose of data science remain the same. Thus, besides changes pertaining to the techniques of delivery, it is fair to expect that this multidisciplinary field will continue to exist well into the future.
After all, big data has developed more than data scientists could have predicted. Even though many experts claim that the state-of-the-art resources that currently exist are the peak of innovation, further advancements will most assuredly prove them wrong, just as they did in years past when new and innovative ways to manage data emerged.
Consequently, while it is impossible to predict what exactly will come next, it is safe to say that data science tools will only get better. The question is, what does the future of data science look like?
Let’s take a closer look at a few predictions about the future of data science.
Data Science Will Become More and More Specialized
One thing is for sure – data science is becoming a highly segmented field and it will continue to segment more in the future.
What’s driving this segmentation is the need for companies and organizations to use data that helps them innovate and grow. This necessitates having data scientists with sector-specific skills.
So, while a decade ago it might have sufficed for a data scientist to specialize in artificial intelligence, in the future, it will be necessary for data scientists to segment further – into machine learning or parallel computing, for example.
Not only will the future of data science see this increasing segmentation, but it will also see new career fields emerge. These jobs are going to be very highly specialized – even more than they are today. This should fuel continued growth of the larger field of data science and result in even more data science jobs in the future.
Speaking of Job Growth…
One of the primary reasons why we can expect data science to continue to be prominent in the future is the explosive job growth that is expected.
For example, the Bureau of Labor Statistics (BLS) estimates that jobs for computer and information systems managers will grow by 10 percent through the end of the 2020s, a rate that is much faster than normal. Additionally, the BLS estimates that careers in the computer and information sciences field will grow even faster – 15 percent through 2029.
When broken down into data science and mathematical science employment, the news is even better. The BLS expects a growth rate of 31 percent through the end of the decade.
Because of this explosive job growth, data science faces a problem – more demand than there is supply of workers. This is a particular issue because of the aforementioned need for data scientists to be highly specialized. Many companies have a need for skilled data scientists, but simply can’t find qualified workers who have the specialties that are needed.
So, not only are data science jobs growing, but they are growing fast. This has been the case for several years and will continue to be the case throughout the 2020s and well beyond. One only needs to look at how much data goes without analysis to see the importance of this field. According to a 2018 report, a full 65 percent of businesses couldn’t categorize or analyze all the data they have stored. As the amount of data that is created and stored increases, the need for more data scientists will also increase. In other words, it’s safe to say that data science is safe as a lucrative field in the future.
Artificial Intelligence Will Continue to Grow in Significance
Yet another reason that the future looks bright for data science is the emergence and growth of machine learning and artificial intelligence.
Broadly, artificial intelligence has become one of the most useful tools for data scientists. From medicine to business, retail to research, and many points in between, artificial intelligence is providing scientists with the analytical tools required to make the most of the available data.
Specifically, artificial intelligence has taken on the task of sorting and analyzing breathtaking amounts of data – far more than humans could ever possibly analyze. And as the rate of data creation continues to increase, AI tools like machine learning and deep learning will become ever more important.
That’s because machine learning can improve its performance as time goes by. This can be done without the need to follow a specific set of programming instructions. In other words, machines can go beyond simple automation and farm data that gives data scientists much greater insights. This deeper level of insight can then fuel more innovation – and more rapid innovation – in any number of businesses and industries.
Data Analytics Will Boom Across Industries
As noted earlier, data science is a field that spans industries of all sizes and types. And while data analytics might seem like the best fit for financial services or retail, the need for data scientists in the future will be seen across industries.
For example, data analytics is currently being used in the research sector to find cures for diseases. As another example, farmers can use data analytics to plan how and when their crops are planted to generate more efficient growth. Data analytics is even used in the non-profit sector to assist in planning more efficient operations to minimize costs and maximize how far donated dollars are able to stretch.
And these kinds of applications occur far and wide. Data scientists use data analytics to improve outcomes, streamline operations, fuel innovations, and increase revenue (among many other things). This application of data science across the board is also fueling data-driven decision making from the top levels of corporations to mom and pop shops on Main Street – and there is no slowing down in sight.
In fact, it can be assumed that data analytics will become an even more important part of daily life in businesses and industries in the future. As new, more innovative data analytics techniques are developed, the usefulness and accessibility of data science to an even greater number of people will grow.
Data Literacy Will Become Ever More Important in the Future
Given the continued growth of data science in the future, it holds that data literacy will become a highly important skill.
What’s necessary to note is that data literacy won’t just be a skill that’s needed for data scientists. Instead, data literacy will become part of the daily vernacular for workers in all fields and even for students, too.
Think of it like this – 30 years ago, only a small portion of American households had a computer. Today, an enormous majority of Americans carry around smartphones that have more computing power than the computers so few Americans had in the 1990s.
So, as we’ve had to acquire “smartphone literacy” over the years, so too will we need to develop the ability to understand how to analyze data and use it to inform decisions. Much like good interpersonal communication skills, leadership skills, and computer literacy are important for job applicants today, data literacy skills will become a requirement for job applicants in the future.
Data Science is Here to Stay
Based on the track record of data science, there is nothing to suggest that it won’t continue to be a major player in our world for the foreseeable future. The future of data science looks promising.
Whether you intend to enter data science as a career or are simply interested in how data science has and continues to evolve, the prospect of what it is to become in the future is quite exciting. The possibilities of what artificial intelligence, deep learning, machine learning, and other fields can do for data science make the next few years, and decades, something to watch with a keen eye.
But as we’ve discussed here, whatever happens, data science isn’t going anywhere. It will continue to grow and evolve and is sure to become an even more prominent part of our lives in the future.
Updated April 2021
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