The sheer amount of data available to firms—and the tools used to analyze this information—has expanded tremendously in the past decade. The rise of data-driven strategies has been accompanied by an onslaught of data skill-related hires—and the more robust a data team is, the greater returns for the company.
Despite this momentum, data teams aren’t immune to the effects of economic anxieties. Last year saw widespread layoffs at tech companies which have continued into the new year, with firms like Microsoft, Meta, Amazon, and Twitter cutting thousands of jobs. These layoffs have darkened the mood of the industry and have left some tech workers—including data scientists—weary about potential new levels of scrutiny as business leaders look for more ways to cut costs.
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Still, there’s robust demand among students who are eager to break into this field. The schools on Fortune’s ranking of the best online master’s degree programs in data science saw 20% enrollment growth between the 2020-2021 and 2021-2022 academic years.
What changes are on the horizon this year? Fortune spoke with Jason Davis, CEO and co-founder of Simon Data, about the outlook for the field of data science in 2023, how data teams can maximize return on investment (ROI) during uncertain times, and how prospective data scientists can differentiate themselves. For the past two decades, Davis has been leading data teams and developing technology that allow companies access to valuable data-derived insights.
Here is what Davis predicts for data science in 2023 and beyond.
Data scientists should expect to become more specialized in the future
Whether you’re just considering a career in data science or have already been working in data roles for several years, you must be willing to evolve along with the field. As the job shifts away from a generalist approach and technological capacity ramps up, Davis predicts that data scientists will need to specialize into the following three categories: business and market analysts, artificial intelligence (AI) and machine learning technology, and infrastructure and data cleansing.
Business and market analysts
Davis foresees business and market analysts bridging the gap between business and data units. As data and marketing tools become more widely adopted, people on marketing teams will be empowered to become more analytical in their work. Essentially, some responsibilities that are currently reserved for data teams will move to business teams.
“Technology is sort-of enabling folks who have a degree of technicality to go a step more technical,” Davis says. “Whenever you can get an army of people to be more analytical and be more data-driven, it’s incredibly powerful.”
AI and machine learning technology
Another career path within data science that’s likely to emerge, according to Davis, will be a specialization in AI and machine learning technology.
“Programs like ChatGPT is going to create a feeding frenzy for anyone competent around building neural networks and doing hardcore AI research and machine learning engineering,” Davis says. “Folks who have years and decades of experience will be a very, very hot commodity.”
Infrastructure and data cleansing
The final data scientist role Davis expects to emerge will be filled by people who are building the infrastructure and cleaning the data—a major part of the demand for data scientists that likely isn’t going away anytime soon.
“There’s an adage in data science that 90% of data science is data cleaning and I think there’s been a bit of a renaissance this year around data quality,” Davis says. “Now we are asking: With these amazing data capabilities, how do you really build the right processes, teams, and technologies in place to make sure they do it as cleanly as possible?”
There are, of course, skills that all three types of data scientists should possess to maximize their efficiency and success.
In data science, the majority of failures don’t happen because a particular problem is too hard to solve, Davis points out. Rather, the problem is that oftentimes a data scientist was focused on the wrong problem. That’s why effective data science requires collaboration with business teams, effective communication, and solving the right problem.
“I think the issue with data scientists isn’t that they aren’t credibly brilliant and smart and motivated individuals,” Davis says. “But at a management and strategy level, they’re just not deployed properly.”
Data scientists must keep up with technology and focus on real-world applications
One way data scientists can increase their value, to employers and more broadly, is by keeping up with technology. Davis has watched firsthand the transformative changes that have overtaken the industry. He earned a bachelor’s degree in computer science from Cornell University, then went on to a stint as a search quality engineer at Google before earning a Ph.D. in machine learning, data mining, and statistics from the University of Texas at Austin. In 2008, Davis founded Adtuitive, a retail product advertising platform, and in the following year his company was acquired by Etsy. As the director of search and data at Etsy, Davis led the teams tasked with building out the company’s data infrastructure. In 2013, Davis founded Simon Data and the customer data platform has been on the market since 2015.
As Davis has experienced, data teams are tasked not only with maintaining and building data infrastructures but as technology develops, they must also keep their skills and tools up to date. As cloud data warehouses and other tools evolve, Davis says a lot of data teams aren’t equipped to use the latest technology.
“Application tiers cannot keep up and I think in some sense, we are seeing the field of data science reflect this,” he says. “There’s all this development we’re seeing around cloud-enabled data infrastructure and business teams are still completely starved.”
One way to combat this trend is for data scientists to begin to specialize in a particular domain. Until now, the term “data scientist” has been very broadly used. A data scientist could be someone running day-to-day analytics or they could be constructing deep learning models.
As data’s influence grows and technology advances, specific roles on data teams will be needed to maximize efficiency. Economic uncertainty will enhance scrutiny and therefore cause leaders to encourage more specificity and ROI from their data teams.
An online master’s degree in data science, individual courses, or data bootcamps are also all viable ways for data scientists to upgrade their skill sets and hone in on a specialization.
“Today, many data scientists and data science teams are too disconnected from core business outcomes, focused on interesting experiments instead of programs that deliver measurable revenue,” Davis says. “Even with the relative scarcity of talent, the economic need to show results will evolve roles to be more revenue-based.”
What’s to come for data teams
Less than a month into a new year, and 2023 is already shaping up to be difficult for tech workers. More than 220 tech companies have announced layoffs totaling 68,000-plus workers, according to data compiled by Layoffs.fyi, and there are concerns about a potential recession this year. Those dynamics could create higher levels of scrutiny at companies more broadly, and an emphasis on the economics with specific units.
What’s more, weakness in financial markets is creating financial pressures around how businesses spend money—including who they hire and who they fire—and necessitating an understanding of their ROI, Davis says.
But not all is doom-and-gloom in this industry. The number of data scientist roles is projected to grow 36% between 2021 and 2031, making it one of the fastest-growing occupations in the U.S. And the continued buzz around ChatGPT may create additional demand for people with AI and machine learning expertise.
What’s more, the data space is also undergoing tremendous change as there is an explosion in first-party data for marketers. Google plans to phase out the use of third-party cookies on Chrome to increase user privacy and there’s been a rapid expansion of cloud computing in Big Tech. Companies like Snowflake provide a slew of data-related services to organizations including data warehousing, data engineering, and analysis.
“Data infrastructure is booming—Snowflake’s market cap is bigger than Salesforce and Adobe’s marketing technologies combined,” Davis says. “A lot of these other areas of the data space are certainly not struggling, but they aren’t on the same growth trajectory as a lot of core data infrastructure.”
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