Is Data Analytics a Good Career?

Yes, data analytics is a very good career. Simply put, there has never been a better time to be a data professional. About 2.5 quintillion bytes of data are created every day—and that pace is only quickening. That explosion of data is driving the industry that leverages it; as organizations’ data collection grows in scope and sophistication, it’s inevitable that they’ll want to make use of that data, and Data Analysts are at the forefront of this trend.

Fittingly, high demand for Data Analysts correlates to an increase in salary—many Data Analysts’ salaries sit quite comfortably above the $70,000 line, even in junior positions, with senior and highly specialized positions typically reaching over $100,000.

Besides the high demand and commensurate salary, Data Analysts have the opportunity to work collaboratively and contribute to the decision-making process at the highest level, which can also translate into an opportunity to move into more managerial positions. Many Data Analysts also enjoy an ability to travel and work remotely or relocate easily, even internationally. Whether the nature of the work itself is a good fit depends entirely on the individual, but the salary, perks, and job security are considerable.

The job outlook for Data Analysts is very positive as data analytics is in great demand. In 2017, IBM predicted that the number of jobs for data professionals in the U.S. alone would surge another 364,000 (to 2,720,000) before the end of 2020. Other sources confirm the trend of companies making big investments in big data; according to a recent study by Dresner Advisory Services, big data adoption in enterprise businesses surged from 17 percent in 2015 to 59 percent in 2018.

It’s not just tech companies jumping on board, either. There are opportunities to apply data analysis skills across a wide range of industries. The Dresner study found that adoption of big-data analytics was highest in telecommunications (95 percent adoption), insurance (83 percent), and advertising (77 percent), followed by financial services (71 percent), healthcare (64 percent), and technology (58 percent), and was most often applied to research and development (75 percent) and operations (63 percent).

As adoption of data analytics grows, so does the range of its applications—in fact, entire industries are on the brink of total transformation by big data. A recent McKinsey report forecasted the ways digital analytics will change marketing—with the promise of data-activated, one-to-one marketing interactions—as well as operations and manufacturing. Still more industries have yet to fully tap this potential. Another McKinsey study, for example, found that if the U.S. healthcare industry were to use big data to improve its efficiency and quality, the sector could create more than $300 billion in value, and a large retailer using big data to its fullest potential could increase its operating margin by more than 60 percent. In other words, we don’t expect this growth to slow down anytime soon.

The career path for a Data Analyst depends on the industry you’re working in. Someone looking to become a Data Analyst can typically enter the field and qualify for entry-level Data Analyst jobs straight out of school or a certificate course — possibly with a Bachelor’s degree in statistics, mathematics, or computer science. Some people also transition into data analysis from a related field like business, economics, or even the social sciences, typically by upgrading their skills mid-career through a data analytics course.

Data Analyst is also an excellent entry point into the typically more advanced world of data science. A Data Scientist tends to be a more senior position than a Data Analyst, as it is usually more strategic in nature, requiring a more highly developed set of technical skills, including experience with various programming languages, statistical tools, and predictive analytics models.

To land a job in data, aspiring Data Scientists and Data Analysts typically start by learning a language like R or SQL. From there, they have to learn how to build databases, perform basic analysis, and generate visualizations using programs like Tableau. Not every Data Analyst will need to know how to do all of these things but you should be able to perform all these tasks if you hope to progress in your career.

Depending on the sector and the type of work you’re doing, you may choose to learn Python or R, become a pro at data cleaning, or concentrate on building complex statistical models.

You may also learn a bit of everything, which can help you take on a leadership position and progress toward a Senior Data Analyst title. With broad and deep enough experience, a Senior Data Analyst is can take on a leadership role overseeing a team of other Data Analysts. With additional skills training, Data Analysts can also in a strong position to move into a Data Scientist job or other more senior data analytics jobs.

There are three main subfields of jobs in data analytics — Data Analyst, Data Scientist, and Data Engineer — and they are all job titles in themselves, you can also think about them as the three main categories that most data jobs fall into. And there are many permutations of these positions, most of which constitute either an evolution of one of these roles (for example, the advancement from Data Engineer to Data Architect) or a specialization within them, often based on sector (such as the specialization from Data Analyst to Business Intelligence Analyst).

Let’s take a closer look at some common data jobs along the Data Analyst career path:

• Data Analyst
• Business Analyst
• Systems Analyst
• Research Analyst
• Operations Analyst
• Marketing Analyst
• Researcher
• Statistician
• Data Scientist
• Data Engineer