Why Analytics Careers Are Growing So Quickly (and What It Means for the Future of Work)

If you’ve been looking into careers in analytics, you’ve probably noticed the same pattern: the field seems to be expanding everywhere at once.

Not just in tech, but in healthcare. In finance. In retail. In logistics. In manufacturing. In government. In sports. In higher education.

Those who enter analytics will be joining a field expected to grow over the next decade, with the World Economic Forum projecting that data scientist positions will experience 34% growth and operations research analysts 21% from 2024 to 2034.

That raises an important question for anyone considering the field: What is fueling demand for analytics professionals across so many industries?

The answer starts with a basic reality. Nearly every organization now has more data than it used to, but not every organization knows what to do with it. That gap is helping drive demand for professionals who can turn raw information into decisions, forecasts, efficiencies, and competitive advantage.

As a result, analytics is no longer confined to narrow specialties. It is becoming part of how organizations operate across sectors.

Why analytics is no longer just a tech-industry function

Data is no longer concentrated in a few digital-first companies. It is everywhere, with organizations using it to forecast outcomes, justify decisions, and generate new opportunities.

Joe Reilly, MPS in Analytics program director at Northeastern University, makes the point that analysts and data professionals are not “just at the Googles and Facebooks of the world.” They are also at startups, traditional businesses, and organizations in all kinds of sectors that want to become more data-driven.

He adds that there is still “a big demand for those types of people who can get insights and value out of data sets.”

Gail Fitzgerald, senior director of marketing, recruiting, and digital strategies for Northeastern’s Khoury College of Computer Sciences, describes the hiring picture in similar terms. She says Northeastern is “seeing every industry represented,” including across:

• Finance
• Pharma
• Hospitals
• Retail
• Sports betting
• Supply Chain/Logistics

Organizations of every size want to be more evidence-based, more efficient, and more predictive. But wanting that is not the same as knowing how to do it well.

What business forces are driving analytics job growth?

In practical terms, three business forces are helping push demand upward.

Digital expansion

More systems, platforms, transactions, and touchpoints mean more information flowing through organizations every day. That increases pressure to turn data into results, whether it be forecasting demand or identifying inefficiencies at a faster rate.

Data complexity

Reilly points out that businesses are not only dealing with structured data. They are also dealing with unstructured data such as images, text, reviews, and other forms of information that are easier to collect than they are to interpret. He notes that this is “not a problem that somebody who has a little bit of Excel experience can tackle.”

Business pressure

Leaders are under growing pressure to forecast more accurately, optimize resources, justify spending, reduce risk, and act faster—making analytics central to decision-making.

Additionally, organizations leverage data analysis across multiple functions.Some organizations need analysts focused on operations. Others need people doing predictive modeling, experimentation, business intelligence, or market and customer analysis. The functions vary, but the underlying need is similar: turn data into better action.

The World Economic Forum’s Future of Jobs Report 2025 reinforces that trajectory. It identifies big data specialists, AI and machine learning specialists, and related technology roles among the fastest-growing jobs, and says analytical thinking remains the most sought-after core skill among employers, with seven in 10 companies considering it essential in 2025.

How AI is accelerating demand for analytics talent

At first glance, the rise of AI might seem like a reason to doubt the future of analytics careers. If tools can summarize data, suggest SQL queries, and help generate dashboards, why would demand keep rising?

In reality, AI may be one reason demand continues to grow.

Reilly says many analytics functions are now “a lot more democratized,” which means more people can build dashboards or write queries than before. But he also warns that “just because you can do it doesn’t mean that you have the domain expertise to actually do it well.”

AI can expand what organizations are able to attempt. It can help produce more outputs, more quickly, and put more data in front of more teams. But that often creates more need for people who can check quality, evaluate results, connect data sources, and translate outputs into decisions—raising the value of good analysts.

That is especially true when organizations want to use AI responsibly. Fitzgerald argues that the human role still includes “the translational piece of things,” “the quality control piece of things,” and “the validation piece of things”—in other words, deciding whether a model or output actually answers the right question, uses the right data, and presents the results in a way that makes sense for the people making decisions.

What the future of work looks like in analytics

The future of work in analytics is likely to be more cross-functional, more embedded, and more decision-oriented.

• Cross-functional means analytics professionals increasingly work with operations, product, finance, marketing, engineering, leadership, and client-facing teams rather than staying in isolated reporting functions.
• Embedded means analytics is becoming part of everyday workflows rather than something used occasionally for a special project.
• Decision-oriented means the job is not only to describe what happened. It is increasingly to help teams understand what may happen next, what matters most, and what action should follow.

Fitzgerald frames that future in business terms. Companies, she says, need people who can help determine “what is the next product,” “what should we be thinking about going forward,” and how to model possible outcomes in a way that helps organizations make smarter decisions and “add to the business bottom line.”

That future also makes analytics attractive to different kinds of learners. Some want to build a career in the field directly. Others want to bring stronger analytics capability back into an industry they already know.

Fitzgerald notes that many students are layering data and analytics skills onto backgrounds in fields like biology, chemistry, or business so they can be more effective where they already work.

Why analytics appeals to career advancers, changers, and fresh grads

One reason analytics remains a durable field is that it supports more than one kind of career move.

• For career advancers, analytics can help move someone from execution into decision support, forecasting, or strategy.
• For career changers, it can provide a path into a field with visible demand across many sectors.
• For new graduates, it can offer entry into roles that build both technical and business fluency early.

That flexibility is part of what makes analytics appealing to different kinds of learners. Northeastern’s programs, for example, are designed to help students analyze, interpret, visualize, and transform data into strategic decisions through hands-on experience and industry-standard tools.

Where a graduate degree fits into this picture

A growing field does not mean every path into it looks the same. That’s where graduate education can matter.

The value of a graduate degree in analytics is not just about learning tools. It’s also about building applied experience, gaining clearer role direction, and developing evidence that you can use analytics in realistic settings.

Reilly encourages students to start with actual job postings: Identify the skills a target role requires, then figure out how to build and demonstrate those skills. Northeastern’s programs emphasize experiential learning and applied focus, with students leaving not just with coursework but with “a very rich portfolio of slide decks and reports and dashboards and things that you built.”

That matters because analytics doesn’t lead to just one destination.

Northeastern’s portfolio reflects that range:

• Master of Professional Studies in Analytics: For practical, business-oriented analytics and decision support, this program emphasizes applied problem-solving, business intelligence, and portfolio-based work. Students can graduate with a portfolio of work samples and complete projects tied to sponsoring organizations.
• Master of Science in Data Science: For deeper technical and predictive work, this program is designed around modeling, computing, and technical data work.
• Master of Science in Data Analytics Engineering: For systems, optimization, and engineering applications, this program focuses on improving products, processes, and systems through data mining, data management, machine learning, and visualization.
• MS in Applied Quantitative Methods & Social Analysis: For quantitative work in policy, research, and social systems, this program emphasizes quantitative research methods, computational social science, network analysis, and related social-analysis tools.

Turn demand into direction with the right graduate program

So, are analytics careers growing? Yes. But perhaps more importantly, the field is growing in multiple directions at once.

The demand is not limited to one industry, one employer type, or one narrow technical profile. It spans sectors, role types, and levels of specialization. That gives prospective students more than one way to enter, advance, and adapt.

If you’re trying to turn that demand into a clearer next step, Northeastern’s analytics portfolio offers multiple ways to prepare for a field that is changing quickly and still growing in importance.