Data Analyst Job Outlook

If you’re considering becoming a Data Analyst, you likely have a lot of questions: about work-life balance, about the daily tasks Data Analysts complete, and the job market and career outlook for this profession. This article will take a closer look at the current state of data analytics, as well as the job outlook for those who are interested in breaking into this in-demand field.

The Current State of Data Analytics

In 2019, the global data analytics market was worth $23 billion. This number is projected to increase to $133 billion by 2026. In 2022, more than half of all businesses around the globe view data analytics as a core component of their operations. With the ever-increasing amount of data being created, the need for qualified Data Analysts to analyze it is at an all-time high and will likely continue to increase in the coming years.

In most corporations, the job of a Data Analyst is growing more complex, as new modeling and prescriptive analytic techniques are becoming more mainstream for analysis. The integration of machine learning provides Data Analysts with helpful ways to automate and streamline tasks, but also means that those working with big data must wear many hats to provide their company with the most meaningful insights from the data.

Cloud computing, along with mobile data traffic and AI technologies, is part of a rapid expansion not only of the volume of data that has to be stored and processed but also its complexity. As new technologies are adopted across industries, Data Analysts with a background in machine learning will play an even more integral role in daily operations. There will likely be a corresponding surge in the need for data-driven jobs in fields such as FinTech, retail, social commerce, and cryptocurrency.

Job Prospects for Data Analysts

Many exciting and high-paying career opportunities are currently available for aspiring Data Analysts, as well as those already working in this field who are interested in changing industry focus. So far in 2022, the number-one-ranked job in America is Information Security Analyst. Market Research Analysts, Operations Research Analyst, and Management Analysts are all ranked in the top 20. The Global Big Data Analytics Market is expected to be worth $105 billion by 2027, which reflects a more than 12% growth from 2019 to 2027. Now is a great time to enter the field of data analytics.

Typical Pay for Data Analysts

As of December 2021, the median pay in the U.S. for a Data Analyst was $69K, according to Glassdoor. However, this number varies depending on which source is consulted. For example, Salary.com puts this number between $70K and $89K, whereas LinkedIn approximates the average salary to be $90K. The Bureau of Labor and Statistics places it at $86K, and consulting firm Robert Half lists Data Analysts’ average salary as $106K. Although these estimates differ significantly, the good news for aspiring Data Analysts is that each of these estimates is much higher than the average salary for all jobs in America, which was listed at $56K in 2021.

Depending on the industry, pay rates can vary significantly. For example, in 2021, some of the highest-paying jobs in data analytics offered salaries nearly double this national average. Quantitative Analysts made about $121K, Data Architects earned $133K, and Database Administrators could expect a yearly salary of $140K.

Many factors affect salary rates for Data Analysts, such as:

• Experience: Similar to any other profession, years of training/background working with coding languages and software, as well as work experience, are factored into salary rates for Data Analysts.
• Supply & demand: Data analytics careers that are in-demand, such as FinTech or Cybersecurity, tend to pay employees more.
• Company size: Generally speaking, larger companies pay their Data Analysts higher salaries than smaller organizations because they have a larger budget for working with data. However, it's important to note that working for a large company can be a more demanding and fast-paced environment than it would be in a smaller company.
• Location: The location of a company is a contributing factor to pay rate. For example, companies located in major cities or tech hubs often offer more competitive pay rates for employees. In these places, however, the cost of living tends to be higher.

Job Outlook for Data Analysts

The Bureau of Labor and Statistics expects the number of hired Data Analysts to grow by 25% during the decade from 2020 to 2030. This represents a much sharper increase than the average for other professions. During this decade, it’s projected that there will be more than 10,000 openings for qualified Data Analysts. While some of these spots will likely be for candidates to fill new roles informed by AI technology and machine learning, others will be openings created by workers who transfer to other careers or retire from their current roles.

Metropolitan cities are projected to be where most data analytics job openings will be in the next few years. Cities such as Chicago, Los Angeles, Dallas-Fort Worth, Washington, D.C., and New York City are just a few hubs where job prospects are expected to be quite good for qualified Data Analysts. Because many companies are still struggling to find qualified applicants to fill openings due to the talent shortage, job prospects are expected to continue to be strong in data analytics throughout the next decade.

The Future of Data Analytics

A major transformation is underway in the field of big data analytics. Many of the current trends spring from the convergence of a variety of transformative technologies, such as machine learning, AI, natural language processing, IoT, and cloud-based data sources.

Here are a few predictions for what’s to come for the rapidly evolving data analytics field:

• Continuous Intelligence (CI): This new technology incorporates real-time analytics into business operations and data processing. It evaluates new information against historical patterns in order to recommend actions. The real-time insights help with strategic planning initiatives.
• Explainable AI: This type of AI can explain the benefits and drawbacks to a given model, how it is expected to perform in a given situation, and the potential for bias. This tool allows organizations to pinpoint instances in which decisions are based on bad information, and can provide an understanding of the path that was taken by a system in order for it to arrive at a specific decision.
• Machine learning: One of the most powerful forms of machine learning is deep learning, which helps an individual or team create a neural network, or complex mathematical structure that’s built on massive data stores. It is able to learn from a data structure, which allows it to detect anomalies and offer predictions.
• IoT: Internet of Things (IoT) data analytics enables users to analyze large volumes of data that are generated by connected devices. It affords organizations many benefits, such as engaging additional customers, empowering employees, and optimizing operations.
• Data visualizations: Traditional dashboards are being replaced with self-service business intelligence tools, which have improved capabilities that help end-users create stories from data. This movement of transforming dense reports into engaging visuals helps viewers to focus on outcomes, thus impacting product sales and revenue. The future of data visualization is likely to incorporate more graphs, charts, and heatmaps, all of which can help to frame insights in a manner that can connect with the audience’s emotions.
• Augmented analytics: This process involves automating insights using machine learning and natural language processing. It provides solutions that can help organizations handle complex datasets at scale, provide more universal access to insights, and engage workers at all levels with the data.

Looking back on the past seventy-plus years of how humans have worked with data, it’s evident that the field of data analytics is in a state of perpetual transformation. New technological advances, along with the incorporation of cutting-edge technologies, continue to propel this field into the future.

Start Learning Data Analytics with Hands-On Classes

If you’re interested in starting to learn about big data, or wish to update your skill-set, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Microsoft Power BI, Excel, Python, and Machine Learning, among others skills necessary for analyzing data.

Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 110 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.

For those searching for a data analytics class nearby, Noble’s data analytics Classes Near Me tool provides an easy way to locate and browse the 400-plus data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to nine months and cost $119-$60,229.