What Is Data Analytics?

Data analytics is a technology field that helps individuals and organizations make sense of the data that analysts collect, organize, and analyze. This career requires technical expertise to analyze and visualize data to help improve company decision-making and performance.

Want to learn more about data analytics and how to work in this growing field? This guide has it all.

Data Analytics Definition and Overview

In its simplest form, data analytics involves looking at data to find answers to specific questions. Businesses use data analytics to help them figure out how to optimize their productivity, processes, performance, and profits.

“Data analytics involves preparing, cleansing, analyzing, and presenting data — it could be big data, streaming data, little/high variety data, etc. — in a way that answers the ‘big’ or ‘key’ questions asked by business stakeholders,” says Chris Mattmann, chief technology and innovation officer at NASA Jet Propulsion Laboratory and an adjunct research professor who teaches data science and big data at the University of Southern California.

So, what is big data analytics? The key difference is that data analytics primarily involves structured (quantitative) data, while big data analytics may use raw and unstructured (qualitative) data. This makes big data both larger and more complex to analyze.

Data Analytics Industries

A career in data analytics can create a pathway into many different industries. In fact, virtually any business, industry, or organization with data can benefit from leveraging data analytics to understand how to optimize its business. Data analysts have become increasingly valued across diverse industries, as they play a vital role in driving organizational change.

“Skills like those developed by data analysts are highly sought after in all industries,” says Theresa Kushner, head of the North American Innovation Center at NTT Data Services, who has more than 25 years of experience in data analytics. “As more and more organizations turn to data analytics and data science to change the way they operate and help them grow and thrive, data analysts are entering the folds of every industry, from finance to healthcare, government to manufacturing, education to entertainment — and collaborating across departments like marketing, finance, and HR.” 

Aswini Thota, a lead data scientist at Bose Corporation with over 12 years of experience in data analytics, agrees that the need for data analysts is ubiquitous. “Data analysts are not just limited to tech or product companies,” Thota says. “In fact, any medium to the large-scale company that uses enterprise resource planning systems to organize their operations hires data analysts.”

Not every organization aligns data analysts in the same way, however. According to Thota, some businesses place all of their data teams under one center of excellence. Others prefer to organize by business functions, such as having a business analyst forecast budgets or solve larger business problems.

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Some industries that use data analytics include:

• Automotive
• Aerospace 
• Commercial sector
• Energy
• Entertainment
• Finance
• Government
• Health care
• Manufacturing
• Marketing
• Oil and gas
• Technology

How to Get Into a Data Analytics Career

The Bureau of Labor Statistics reports that between 2022 and 2032, employment growth for operations research analysts, a job category that includes data analysts, should increase by 23%, emphasizing the high demand for these professionals. But how do you get started? A combination of education, training, and skills development can help you break into the field.

Education and Training

Graduating with a degree in a quantitative area or STEM (science, technology, engineering, or math) is the most straightforward way to get into data analytics. A bachelor’s degree in computer science, business analytics, mathematics, economics, or statistics will enable you to learn core technical skills, such as SQL, Python, statistics, and business intelligence.

However, you don’t need a Ph.D. in data science or even a related bachelor’s degree to become a data analyst — though these certainly can help. As an alternative to formal schooling, you can build your foundational knowledge to gain these skills in a more hands-on and experiential setting through online bootcamps, certifications, virtual job simulations, courses, or internships. 

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For example, conducting data analysis using real data and open-source code can prepare you for a career in data analytics. “Have real examples that you’ve solved and questions answered,” Mattmann says. “Not the solve-in-a-day ones — instead use the ones that take four to six weeks and have some ‘meat’ that you can show the results of to a hiring manager or recruiter.” 

Skills Development

Along with building your technical chops, you should also expand your soft skills. Kushner lists critical thinking, curiosity, and creativity as important soft skills that can complement your technical skills to offer more advanced and unique insight into data analytics. 

Prospective data analysts should also develop a online portfolio of their work to demonstrate their competence and passion in the field. Being inquisitive and asking questions about confidence in data can also help you land a job in data analytics. “A lot of debate majors are great future analysts and data scientists,” Mattmann says.

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Networking

Networking is another powerful tool that can facilitate landing a job in data analytics — for example, through LinkedIn. “Talk about your work, your GitHub, your posts,” Mattmann says. “Show real examples of datasets and problems you’ve solved. Get involved in the open-source community and different projects. Do tutorials in machine learning and data science. Get certifications online. This is extremely valuable — sometimes as valuable as a four-year degree.”

Kushner agrees that you should start building contacts in the fields you’d like to work in so you have a chance at landing an entry-level position, at the very least. From there, you can work your way up. 

Pros and Cons of Working in Data Analytics

Why choose data analytics as a career path? As with any field, it’s essential to be aware of the potential positives and negatives of working in data analytics. Here are some of the key pros and cons, according to industry experts.

Pros

Professional Satisfaction

If you enjoy solving puzzles and interesting challenges, then data analytics may be an excellent fit for you. This career involves constantly looking at problems, analyzing the data, and identifying solutions, which can be especially rewarding when the business takes recommendations from the data analytics team seriously and acts upon them efficiently. “Seeing an analysis accepted by the business and put into action is the greatest reward a data analyst can receive,” Kushner says. 

Also, because data is at the epicenter of innovation for most enterprises, that makes data analysts who can tame and decipher big data crucial members of any organization. “Data analysts play a vital role in the decision-making process within companies and organizations,” Thota notes. “Business leaders use the insights and information gleaned from this data to make strategic investments, target customers, assess risks, and allocate capital.”

In many instances, the insights from data analysis can directly impact employees’ lives and the company’s bottom line. You might need to mine for insights on critical questions such as:

• How can we improve our diversity hiring? 
• What are the key traits of our happy customers?
• How many units can our newly launched product sell?

“The realization that your insights informed your company’s critical decisions can give you immense professional satisfaction,” Thota says.

High Visibility

People who like having their work spotlighted may enjoy this career. Data analysts often need to present their findings to executives and other high-level decision-makers and build highly scalable systems. Both of these responsibilities increase the visibility of the data analyst’s role. 

“Data analysts inform all aspects of the business, from identifying potential customers and designing marketing campaigns to improving customer satisfaction,” Thota explains. “Business leaders and executives are increasingly interested in data and its potential to drive business growth.” 

In addition to making presentations to leaders, data analysts are also responsible for enterprise-wide business intelligence systems. “Once a solution is finalized, data analysts leverage business intelligence tools to develop visualizations and reports that can provide a point-in-time view of a business function or an initiative,” Thota says. “The systems that data analysts build are often deemed as the single source of truth and are used by many business users across the enterprise.” 

Career Mobility

Working in data analytics is cross-functional by nature since these professionals need proficiency with many tools, such as programming languages, databases, data visualization, cloud computing, and statistics. 

To work effectively, data analytics professionals possess a wide range of practical skills that are in high demand. And because the skills are easily transferable from industry to industry, data analysts have impressive career mobility. 

“Data analysts develop strong domain knowledge as they are expected to have a deep understanding of business operations and internal processes,” Thota says. “This business acumen is highly desired in different roles such as management consulting, business development, marketing, etc.”

Cons 

It Can Be Hard to Feel ‘Done’

Mattmann points out that it can be very difficult in data analytics to collect “ground truth” or know what “done” means. “Some industries are constantly in exploratory analysis mode, but never in product mode,” Mattmann explains. “So you can feel stuck in the research world, and not the delivering world.”

A possible drawback of working in data analytics is as simple as businesses ignoring recommendations and never seeing the value they can provide, explains Kushner. “For some data analysts, being stuck in a never-ending analytics loop signals ‘analysis paralysis,’ which is basically like death to a good data analyst,” she says.

Need for Constant Learning

Since data analysts must balance technical expertise and business acumen, they need to possess a deep understanding of the business they’re supporting and the technical tools and methods used to collect and analyze data. This means thinking on your feet and shifting gears quickly and flexibly as required. 

“While understanding new business and the corresponding systems can be fun at first, the need for constant learning and staying on top of the procedural changes in an organization can be a little intimidating,” Thota says.

Tight Deadlines

Most organizations operate in a constantly changing environment and must make tactical decisions to respond to evolving market conditions. As a result, it’s typical in data analytics careers to find yourself managing “fire drills,” according to Thota. “To make these tactical decisions, leaders often rely on data analysts to provide them with rapid insights,” he says. “This could lead to a high workload and tight deadlines for data analysts.” Interested in exploring other data careers? Learn about the data engineering field and what it takes to become a data engineer.

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