What is Data Analytics?

Data analytics is a fast-moving field that considers sets of information to help leaders develop informed decisions and strategies in all types of organizations. It's used in every industry, from finance and healthcare to retail and hospitality.

What is Data Analytics in Simple Terms?

Data analytics focuses on collecting, inspecting, cleaning, summarizing and interpreting collections of related information.

Organizations are gathering, analyzing and leveraging more data than ever before to ensure that their decisions are data-driven, said Dr. Susan McKenzie, a senior associate dean overseeing data analytics programs at Southern New Hampshire University (SNHU).

As the ability to gather information — and lots of it — advances, data analytics becomes increasingly valuable in the workplace. The increase in information leads to big data, which refers to the tremendous quantity of data accumulated.

“The era of big data has arrived and changed the role of analytics in every aspect of our lives,” McKenzie said. "This creates the need to (expand) traditional data-handling tools and storage to process and store the data based on volume, speed, structure, accuracy and value.”

The adoption of artificial intelligence (AI) is also speeding up technical tasks within data analytics, McKenzie noted.

"This means analysts spend less time preparing data and more time interpreting results, evaluating generated insights, communicating recommendations, and business impact," she said.

As the ways that information is gathered, managed and processed rapidly change, companies in most fields need employees who are up to date with the most current methods of handling these numbers and have an eye for future needs.

What Are the 4 Types of Analytics?

The four types of analytics are descriptive, predictive, diagnostic and prescriptive, according to McKenzie. Each serves a different purpose:

Since all four types of analytics have varying goals and outcomes, analysts must approach them with different tools and mindsets.

What is an Example of Data Analytics?

While data analytics supports business operations daily, the COVID-19 pandemic is a widely recognizable example of how data informs decisions, according to McKenzie.

Analysts in the public health sector monitored how fast trained professionals at the local, state and national levels could administer vaccines to the public and the number of vaccinations produced, to inform and adjust timelines.

“Somebody had to interpret all of that information, so they were tracking the data coming in: who's coming in, how many are coming in, what are the side effects,” McKenzie said. “There's all that variety of information that needed to be processed to make a decision, and in this case, this decision had an impact ... on a large volume of people.”

Their findings also helped leaders develop re-opening plans and offered the general public insight into the local, regional, national and global impact of this disease.

What is the Role of Data Analytics?

Data analytics plays an informative role in all industries today, contributing to effective business decisions and operations. As a data analyst, your primary challenge is not just making sense of numbers but figuring out ways to analyze and use the quantitative and qualitative information as fast as it's collected.

“The goal of data analytics is to discover information to support decision-making, ultimately to make informed conclusions,” McKenzie said.

Analysts also act as the liaison between the data itself and business leaders, interpreting what certain trends and patterns mean for the organization and then communicating it in a way that leaders and other decision-makers can understand.

Often, analysts need to explain data to people who do not share the same mathematical background, making strong communication skills a must.

“It's about taking the data you have … transform, cleanse, clarify and convert the data into something that's understandable," McKenzie said. Typically, the ability to construct visuals such as charts and tables can be helpful for the average person to process the information, she noted.

Synthesizing rapidly accumulating data can give companies a leg up against their competitors. They can reap the benefits of data analytics when they use it to make better and smarter decisions.

“The power of deep data analysis can transform an industry overnight,” McKenzie said.

Will AI Replace Data Analysts?

There's no indication that artificial intelligence will replace data analysts.

"While AI can generate answers, organizations still require people who can ask the right questions, identify reliable data and evaluate AI outputs," McKenzie said. "It is key to have individuals who can understand the business implications, communicate recommendations and make ethical decisions."

That said, there's no question that AI is rapidly changing the field. It can generate visuals and dashboards, automate reports and more to assist data professionals, McKenzie said.

The U.S. Bureau of Labor Statistics (BLS) contends that the data science profession is seeing a boost in demand due to increasing use of information technology, including AI. Between 2024 and 2034, the employment of data scientists is projected to increase 33.5% — a rate approximately 11 times faster than the expected growth for all occupations.

McKenzie noted that good data professionals now must have AI literacy and data governance awareness, in addition to the more traditional skills, like analytics, communication and business understanding.

Human judgment, strategic thinking, strategy, context and decision support are going to become increasingly valuable, too, in this evolving field, she said.

To meet the changing needs of the data workforce, some schools are adapting their degree programs to introduce students to new technologies and analytics environments.

"With a future focus, students learn AI-assisted analytics workflows, prompt engineering, ethical AI use and how to evaluate AI-generated outputs to work in an enterprise analytics practice," McKenzie said.

Is Data Analytics a Good Career?

The increased importance of data analytics lends itself to greater demand for industry professionals who possess quantitative and qualitative data analysis skills and can efficiently manage information.

“Every job, every company, every role played in a company has to deal with data in some way, shape or form, and we're in desperate need of people who understand how to clean, classify and transform the data into meaningful information," McKenzie said.

Analyst positions, such as market research analysts and operations analysts, are on the rise.* BLS reported that jobs for market research analysts are projected to increase by 7% through 2034, and operations analysts are predicted to grow by 21% in the same timeframe.*

BLS also reports that mathematicians and statisticians, who made a median annual salary of $104,350 in 2024, are expected to increase by 8% during the same period.*

While specific job titles can vary by industry, additional roles someone working in data analytics might hold include:

• Data analyst
• Data specialist
• Data manager
• Data analytics engineer
• Business analyst

Read more: How to Become a Data Analyst

So, Should I Get a Degree in Data Analytics?

With information playing such an essential role in business practices, having the skills and knowledge to understand and use potentially massive amounts of data will help you succeed in a career in this field.

This is where a degree might prove helpful.

Undergraduate Data Analytics Degree

Data analytics is very much a mathematical world. McKenzie said a foundation in statistics and data modeling tools is important for this line of work, both of which are components of data analytics programs.

“Depending on your interest and commitment to the field, an associate in data analytics or a bachelor’s in data analytics is a good place to start," McKenzie said.

Some schools offer an accelerated pathway between a bachelor's and master's data analytics programs, too, which can save you time and money if you know you want to earn both degrees.

If you're new to the field, you might find yourself in an entry-level data processing role. That's what Charles Lacerte '26 was doing when he decided to transfer community college credits to his "hometown" school — SNHU — and earn a bachelor's in data analytics.

It was the nuts and bolts of his program that made a difference. "I've learned more effective math, like getting an understanding on statistical tests using code like Python and R," he said. These skills, he said, helped reinforce the need to build convincing data-driven arguments.

Lacerte, an investment service specialist, said his degree helped him gain the confidence to look beyond his role, at more advanced data analytics positions in his industry and beyond.

Graduate Data Analytics Degree and Beyond

A master's in data analytics might be a strategic next step if you're already working with data in your current position or are looking to move your career into this field.

While some roles, like a market research analyst and operations research analyst roles, typically require a bachelor's degree for entry, BLS reported that some employers prefer applicants to have a master's degree. Other positions, such as that of a mathematician and statistician may require a master's degree for entry, according to BLS.

Since the field is constantly advancing, pursuing microcredentials, such as certifications and badges, can help you stay relevant. At SNHU, you could earn a Data Literacy Practitioner badge at the end of a 6-week project-based professional development course.

That's what Maria Santini '25MPH did after earning a Master of Public Health degree at SNHU. While working in a new clinical research position, she realized she needed to upskill in data cleaning, management and communication.

So, she continued her education, completing the professional certificate, "Data Literacy: Exploration to Storytelling."

"I took this class because I felt I needed a better understanding of data literacy principles to apply to my new job," she said. "In my previous roles, I used spreadsheets for basic functions — in my new role, there is a significant amount of data management, and the skills class has helped to support this new workflow for me."

When you choose to pursue a data analytics education, you aren't limited to positions with 'analytics' in the job title. Related fields, such as data science, data mining and data architecture, use similar strategies that could benefit from a deeper understanding of data analytics.

So, if a career handling, analyzing and interpreting data interests you, consider your goals and determine what skills and education will be required.

Rebecca LeBoeuf Blanchette '18 '22G is a writer at Southern New Hampshire University, where she fulfills her love of learning daily through conversations with professionals across a range of fields. She earned her Bachelor of Arts in Communication with a minor in Professional Writing at SNHU’s campus in Manchester, New Hampshire, and followed her love of storytelling into the online Master of Arts in English and Creative Writing at SNHU. Connect with her on LinkedIn.