That’s been a trend for many large companies that have had to become more reliant on digital transformation and data to prove themselves during the COVID-19 pandemic. 

As a result of our new tech-first, virtual-focused world, all companies have oceans of data, and they need skilled people to make sense of that. That has, in turn, driven up the salaries of data scientists, who earn up to $190,000 salaries, according to Glassdoor.

Given the nature of the pandemic, more companies found they needed to perform analysis on data they currently collected—along with new data needs that crop up—to prove to investors that they were staying above water and that consumer engagement still existed in a virtual world, Guy Gomis, a senior vice president, partner, and data and analytics practice leader for recruiting firm Brainworks, explains to Fortune. 

“These companies are going to have to fill those needs,” Gomis says. “Whether you’re with a private company or public company, the investors are going to ask you, ‘Are you becoming data-centric?’ And if you’re not, you’re going to lose your job as a CEO.”

Because companies have had to make such drastic shifts to perform more data-centric work, demand for skilled analysts and data scientists has skyrocketed. In short, data science degrees pay off since the skillset is in such high demand—and will be for the next 20 years to 30 years, Gomis says. 

Data scientists who earn a master’s degree or Ph.D. earn about $100,000 right after graduation, according to the U.S. Bureau of Labor Statistics. Education technology company Skillsoft released a report in November 2021 showing that data scientists with only an undergraduate degree on average earn $81,789, while data scientists with a master’s or doctorate degree earn $95,604—that’s a 17% difference.

Why do data scientists make so much money?

Simply put: “It’s the combination of rapidly growing demand and very small supply,” Michael Yoo, Skillsoft’s general manager of technology and developer portfolio, tells Fortune. 

The demand for data science workers has “exploded” during the past five to seven years, Yoo says, as businesses have started to go through digital transformation—and have needed to understand patterns in their “oceans of data.”

“Companies are looking for patterns in how customers behave, how their sales channels sell, how their products and services are used, and how they perform versus their competitors,” Yoo says. “They are looking for ways to operate more effectively and efficiently using data and AI [artificial intelligence] and machine learning. All of this means nearly every company on the planet needs data scientists and data analysts—it’s no longer just the province of what we think of as ‘traditional’ technology providers.”

Companies will shell out for data scientists because they have a proven return on investment, according to Harnham, a company that specializes in data and analytics recruitment.

“Companies know that if they are to hire someone with data and analytics skill sets, they are going to see a positive impact on the company’s bottom line,” a company spokesperson tells Fortune. 

Plus, from the supply side, there just aren’t many data scientists out there since data scientists traditionally have an advanced degree. In fact, Gomis argues that it’s pretty much a necessity for successful data scientists to have earned a master’s degree or other advanced degree. 

“On the data science side, it’s almost required to have a master’s or Ph.D. just because you get into methodology on how to build algorithms,” he says. “It’s, ‘What is the science behind it? Can you explain it?’ And it’s kind of hard for someone with a bachelor’s degree to do that.”

What successful data science candidates need to have

Strong technical skills are certainly a must, but to really stand out as a candidate, business acumen and communication skills are also important, data science recruiters agree. 

“Companies are looking for hybrid candidates that can do it all,” according to Harnham. “They want a person who is able to understand the technical side of the job, such as the coding languages, but they also want someone who isn’t nervous about speaking openly with stakeholders in a way that is digestible.”

In order to accomplish this, Gomis argues that both early education and higher education institutions need to invest more in getting students interested in STEM-related fields to start developing skills earlier on.

“If someone is strong technically and has strong communication skills, they can do whatever they want, frankly, for the next 30 years,” Gomis says. “They’re going to call the shots.”